Stardog is the world’s leading Knowledge Graph platform for the Enterprise Stardog makes it fast and easy to turn enterprise data into knowledge.

What’s New in Stardog
• ✓ MongoDB Virtual Graph support

• ✓ Apache Hive Virtual Graph support

• ✓ GraphQL Introspection

• ✓ Named Entity Recognition and Linking in BITES

• ✓ XGBoost for Machine Learning

• ✓ Kerberos Authentication

• ✓ Automatic Data Mappings for RDBMS

• ✓ S3 Backups

Check out the Quick Start Guide to get Stardog installed and running in five easy steps.

# Introduction

Stardog 6.1.0 (16 Jan 2019) supports the RDF graph data model; SPARQL query language; property graph model and Gremlin graph traversal language; OWL 2 and user-defined rules for inference and data analytics; virtual graphs; geospatial query answering; and programmatic interaction via several languages and network interfaces.

Stardog is made by hand with skill, taste, and a point of view by people who care. 🌟🐶

Download Stardog to get started. The Stardog support forum is the place to report bugs, ask questions, etc.

# Enterprise Support

## Real-time Support

Get access to the core Stardog development team in real-time via voice or chat. Let us help you get the most from Stardog, 24/7. Our core team has more semantic graph application and tool development experience than any other team on the planet. Other vendors shunt you off to inexperienced level one techs. We’ve never done that and never will.

## Private Maven Repositories

See Using Maven for details; this includes a per-customer, private Maven repository, CDN-powered, for 24/7 builds, updates, and feature releases.

We’re also tying Maven and Docker together, providing private Docker repositories for customers, which allows us to build out clusters, custom configurations, best practices, and devops tips-and-tricks into custom Docker images…​so that you don’t have to.

## Private Docker Repositories

Docker-based deliverables not only shortens your development and devops cycles but keeps you up-to-date with the latest-greatest versions of Stardog, including security fixes, performance hot fixes, and deployment best practices and configurations.

## Priority Bug Fixes

With Maven and Docker in place, we’ve got a software delivery mechanism ready to push priority bug fixes into your enterprise as soon as they’re ready. We’ve averaged one Stardog release every two weeks since 2012. Enterprise Premium Support customers can now take advantage of our development pace in a controlled fashion.

## Priority Feature Releases

We hate holding new features in a feature branch, especially for mundane business reasons; we want to release new stuff as soon as possible to our customers. With Enterprise Premium Support, we can maintain a disruptive pace of innovation without disrupting you.

# Quick Start Guide

## Requirements

It just doesn’t get any easier than this: Stardog runs on Java 8. Stardog runs best on, but does not require, a 64-bit JVM that supports sun.misc.Unsafe.

## Insecurity

We optimize Stardog out-of-the-box for ease and simplicity. You should take additional steps to secure it before production deployment. It’s easy and it’s smart, so just do it. In case that’s not blunt enough:

 Note Stardog ships with an insecure but usable default setting: the super user is admin and the admin password is "admin". This is fine until it isn’t, at which point you should read the Security section.

If you are upgrading to Stardog 6 from any previous version, please see Migrating to Stardog 6 for details about auto-migrating on-disk indexes.

## Package Managers

As of version 5.0.6 Stardog is available in rpm and deb packages and apt and yum repositories have been setup to make installation easier.

### Debian Based Systems

To install Stardog using apt-get run the following commands:

curl http://packages.stardog.com/stardog.gpg.pub | apt-key add
echo "deb http://packages.stardog.com/deb/ stable main" >> /etc/apt/sources.list
apt-get update
apt-get install -y stardog[=<version>]

This will first add the Stardog gpg key to the systems and then fetch and install the latest Stardog deb package.

### RPM Based Systems

To install Stardog using yum run the following commands:

curl http://packages.stardog.com/rpms/stardog.repo > /etc/yum.repos.d/stardog.repo
yum install -y stardog[-<version>]
##### Amazon EC2

Certain Amazon EC2 instances do not let you redirect output into /etc/yum.repos.d as specified above. On such instances you can install Stardog like so:

sudo yum-config-manager --add-repo http://packages.stardog.com/rpms/stardog.repo
sudo yum-config-manager --enable stardog
yum install -y stardog[-<version>]

### Package Layout

The packages require that OpenJDK 8 and all of its dependencies are installed on the system. The package managers will install them if they are not already there. Stardog is then configured to start on boot via systemd and thus it can be controlled by the systemctl tool as shown below:

systemctl start stardog
systemctl restart stardog
systemctl stop stardog

To customize the environment in which stardog is run the file /etc/stardog.env.sh can be altered with key value pairs, for example:

export STARDOG_HOME=/var/opt/stardog
export STARDOG_SERVER_JAVA_ARGS="-Xmx8g -Xms8g -XX:MaxDirectMemorySize=2g"

## Linux and OSX

First, tell Stardog where its home directory (where databases and other files will be stored) is:

$export STARDOG_HOME=/data/stardog Note: If using a package manager this line is added to the /etc/stardog.env.sh file. If you’re using some weird Unix shell that doesn’t create environment variables in this way, adjust accordingly. If STARDOG_HOME isn’t defined, Stardog will use the Java user.dir property value.  Note You should consider the upgrade process when setting STARDOG_HOME for production or other serious usage. In particular, you probably don’t want to set the directory where you install Stardog as STARDOG_HOME as that makes upgrading less easy. Set STARDOG_HOME to some other location. Second, copy the stardog-license-key.bin into the right place: $ cp stardog-license-key.bin $STARDOG_HOME Of course stardog-license-key.bin has to be readable by the Stardog process. Stardog won’t run without a valid stardog-license-key.bin in STARDOG_HOME. Third, optionally, place the bin folder of the Stardog install on your PATH so the stardog and stardog-admin scripts can be used regardless of current working directory $ export PATH="$PATH:/opt/my-stardog-install/bin" Fourth, start the Stardog server. By default the server will HTTP on port 5820. $ stardog-admin server start

Fifth, create a database with an input file:

$stardog-admin db create -n myDB /path/to/some/data.ttl Sixth, query the database: $ stardog query myDB "SELECT DISTINCT ?s WHERE { ?s ?p ?o } LIMIT 10"

You can use Stardog Studio to search or query the new database you created by connecting to the http://localhost:5820 endpoint.

## Windows

Windows…​really? Okay, but don’t blame us if this hurts…​The following steps are carried out using the Windows command prompt which you can find under   Programs  Accessories  Command Prompts or   Run  cmd.

First, tell Stardog where its home directory (where databases and other files will be stored) is:

> SET STARDOG_HOME=C:\data\stardog

Second, copy the stardog-license-key.bin into the right place:

> COPY /B stardog-license-key.bin %STARDOG_HOME%

The /B is required to perform a binary copy or the license file may get corrupted. Of course stardog-license-key.bin has to be readable by the Stardog process. Finally, Stardog won’t run without a valid stardog-license-key.bin in STARDOG_HOME.

Third, optionally, place the bin folder of the Stardog install on your PATH so the stardog.bat and stardog-admin.bat scripts can be used regardless of current working directory

> SET PATH=%PATH%;C:\stardog-5.2.2\bin

Fourth, start the Stardog server. By default the server will expose HTTP on port 5820.

> stardog-admin.bat server start

This will start the server in the current command prompt, you should leave this window open and open a new command prompt window to continue.

Fifth, create a database with some input file:

> stardog-admin.bat db create -n myDB C:\path\to\some\data.ttl

Sixth, query the database:

> stardog.bat query myDB "SELECT DISTINCT ?s WHERE { ?s ?p ?o } LIMIT 10"

You can use Stardog Studio to search or query the new database you created by connecting to the http://localhost:5820 endpoint.

## Stardog Sandbox

If you’re interested to play with, learn, and discover the joys of Stardog without installing it, then maybe Stardog Sandbox is for you. It’s a free, hosted environment that lets you run Stardog Studio in your browser and interact with Stardog running in the Cloud. No installation, no maintenance, just powerful Knowledge Graph joy at your fingertips.

### Environment

Stardog Sandbox is a limited resource environment, so please be mindful of this as you’re using the software. As of Sandbox 0.1, the VM includes 2 cores and 1G RAM and a small, 64G SSD drive. Also note, that this is a single instance, not an HA cluster. The Sandbox should work on all modern browsers.

If you need access to a more powerful machine, or the HA cluster, the same software running in the Sandbox is available for download where you can install and test within your own environment. You can also email us at inquiries@stardog.com for more information.

### Features

Stardog Sandbox is mostly equivalent to the full version of Stardog. However, there are a few differences, most of which we’ll be removing in the coming months. The following features of Stardog are not currently available in the Sandbox:

• Virtual Graphs

• BITES, our unstructured data unification and processing engine

• Machine Learning

• ICV

• No user-defined Lucene analyzers

### Restrictions

In addition to these differences, there are a couple other restrictions worth mentioning:

• No access to security system, i.e., no creating users, updating permission

• You are limited to 10M triples in the database

• No access via HTTP, e.g. via stardog,stardog-admin, or programmatically

### What’s Next?

We hope you enjoy using the Stardog Sandbox! We understand there are some features that are present in the real platform that are not yet available in the Sandbox. We’re working hard to bring more features and more demo datasets to Sandbox. We look to remove the aforementioned restrictions and disabled features in the months ahead. In addition to those changes, we will also add:

• Structured & semi-structured external data sources for use with Virtual Graphs

• Larger machines

• New demo datasets

• Remote machine access

• SALE: Stardog Active Learning Environment

If there’s anything that you’d like to see added to Sandbox, please drop us a line at inquiries@stardog.com

In this chapter we describe the administration of Stardog Server and Stardog databases, including command-line programs, configuration options, etc.

Security is an important part of Stardog administration; it’s discussed separately (Security).

## Command Line Interface

Stardog’s command-line interface (CLI) comes in two parts:

1. stardog-admin: administrative client

2. stardog: a user’s client

The admin and user’s tools operate on local or remote databases using HTTP protocol. These CLI tools are Unix-only, are self-documenting, and the help output of these tools is their canonical documentation.[1]

### Help

To use the Stardog CLI tools, you can start by asking them to display help:

stardog help

Or:

$stardog-admin help These work too: $ stardog
$stardog-admin ### Security Considerations We divide administrative functionality into two CLI programs for reasons of security: stardog-admin will need, in production environments, to have considerably tighter access restrictions than stardog.  Caution For usability, Stardog provides a default user "admin" and password "admin" in stardog-admin commands if no user or password are given. This is insecure; before any serious use of Stardog is contemplated, read the Security section at least twice, and then—​minimally—​change the administrative password to something we haven’t published on the interwebs! ### Command Groups The CLI tools use "command groups" to make CLI subcommands easier to find. To print help for a particular command group, just ask for help: $ stardog help [command_group_name]

The command groups and their subcommands:

• query: search, execute, explain, status;

• reasoning: explain, consistency;

• server: start, stop;

• user: add, drop, edit, grant, list, permission, revoke, passwd;

• role: add, drop, grant, list, permission, revoke;

• db: backup, copy, create, drop, migrate, optimize, list, online, offline, repair, restore, status;

• virtual: add, import, list, mappings, options, remove.

 Note See the man pages for the canonical list of commands.

The main help command for either CLI tool will print a listing of the command groups:

usage: stardog <command> [ <args> ]

The most commonly used stardog commands are:
data        Commands which can modify or dump the contents of a database
help        Display help information
icv         Commands for working with Stardog Integrity Constraint support
namespace   Commands which work with the namespaces defined for a database
query       Commands which query a Stardog database
reasoning   Commands which use the reasoning capabilities of a Stardog database

See 'stardog help' for more information on a specific command.

To get more information about a particular command, simply issue the help command for it including its command group:

$stardog help query execute Finally, everything here about command groups, commands, and online help works for stardog-admin, too: $ stardog reasoning consistency -u myUsername -p myPassword -r myDB

$stardog-admin db migrate -u myUsername -p myPassword myDb ### Autocomplete Stardog also supports CLI autocomplete via bash autocompletion. To install autocomplete for bash shell, you’ll first want to make sure bash completion is installed: #### Homebrew To install: $ brew install bash-completion

To enable, edit .bash\_profile:

if [ -f brew --prefix/etc/bash_completion ]; then
. brew --prefix/etc/bash_completion
fi

#### MacPorts

First, you really should be using Homebrew…​ya heard?

If not, then:

$sudo yum install bash-completion #### All Platforms Now put the Stardog autocomplete script—stardog-completion.sh—into your bash\_completion.d directory, typically one of /etc/bash_completion.d, /usr/local/etc/bash_completion.d or ~/bash_completion.d. Alternately you can put it anywhere you want, but tell .bash_profile about it: source ~/.stardog-completion.sh ### How to Make a Connection String You need to know how to make a connection string to talk to a Stardog database. A connection string may consist solely of the database name in cases where 1. Stardog is listening on the standard port 5820; and 2. the command is invoked on the same machine where the server is running. In other cases, a "fully qualified" connection string, as described below, is required. Further, the connection string is now assumed to be the first argument of any command that requires a connection string. Some CLI subcommands require a Stardog connection string as an argument to identify the server and database upon which operations are to be performed. Connection strings are URLs and may either be local to the machine where the CLI is run or they may be on some other remote machine. Stardog connection strings use the http:// protocol scheme. ### Example Connection Strings To make a connection string, you need to know the machine name and the port Stardog Server is running on and the name of the database: {scheme}{machineName}:{port}/{databaseName};{connectionOptions} Here are some example connection strings: http://server/billion-triples-punk http://localhost:5000/myDatabase http://169.175.100.5:1111/myOtherDatabase;reasoning=true Using the default port for Stardog’s use of HTTP protocol simplifies connection strings. connectionOptions are a series of ; delimited key-value pairs which themselves are = delimited. Key names must be lowercase and their values are case-sensitive. ## Server Admin Stardog Server supports all the administrative functions over the HTTP protocol. ### Upgrading Stardog Server The process of installation is pretty simple; see the Quick Start Guide for details. But how do we easily upgrade between versions? The key is judicious use of STARDOG_HOME. Best practice is to keep installation directories for different versions separate and use a STARDOG_HOME in another location for storing databases.[2] Once you set your STARDOG_HOME environment variable to point to this directory, you can simply stop the old version and start the new version without copying or moving any files. You can also specify the home directory using the --home argument when starting the server. ### Server Security See the Security section for information about Stardog’s security system, secure deployment patterns, and more. ### Configuring Stardog Server  Note The properties described in this section control the behavior of the Stardog Server; to set properties or other metadata on individual Stardog databases, see Database Admin. Stardog Server’s behavior can be configured via the JVM arg stardog.home, which sets Stardog Home, overriding the value of STARDOG_HOME set as an environment variable. Stardog Server’s behavior can also be configured via a stardog.properties—which is a Java Properties file—file in STARDOG_HOME. To change the behavior of a running Stardog Server, it is necessary to restart it. #### Configuring Temporary ("Scratch") Space Stardog uses the value of the JVM argument java.io.tmpdir to write temporary files for many different operations. If you want to configure temp space to use a particular disk volume or partition, use the java.io.tmpdir JVM argument on Stardog startup. Bad (or, at least, weird) things are guaranteed to happen if this part of the filesystem runs out of (or even low on) free disk space. Stardog will delete temporary files when they’re no longer needed. But Stardog admins should configure their monitoring systems to make sure that free disk space is always available, both on java.io.tmpdir and on the disk volume that hosts STARDOG_HOME.[3] #### Stardog Configuration The following twiddly knobs for Stardog Server are available in stardog.properties:[4] 1. query.all.graphs: Controls what data Stardog Server evaluates queries against; if true, it will query over the default graph and the union of all named graphs; if false (the default), it will query only over the default graph. 2. query.pp.contexts: Controls how property paths interact with named graphs in the data. When set to true and the property path pattern is in the default scope (i.e. not inside a graph keyword), Stardog will check that paths do not span multiple named graphs (per 18.1.7). For this to affect query results either there should be multiple FROM clauses or query.all.graphs must be also set to true. 3. query.timeout: Sets the upper bound for query execution time that’s inherited by all databases unless explicitly overriden. See Managing Query Performance section below for details. 4. logging.[access,audit].[enabled,type,file]: Controls whether and how Stardog logs server events; described in detail below. 5. logging.slow_query.enabled, logging.slow_query.time, logging.slow_query.type: The three slow query logging options are used in the following way. To enable logging of slow queries, set enabled to true. To define what counts as a "slow" query, set time to a time duration value (positive integer plus "h", "m", "s", or "ms" for hours, minutes, seconds, or milliseconds respectively). To set the type of logging, set type to text (the default) or binary. A logging.slow_query.time that exceeds the value of query.timeout will result in empty log entries.** 6. http.max.request.parameters: Default is 1024; any value smaller than Integer.MAX_VALUE may be provided. Useful if you have lots of named graphs and are at risk of blowing out the value of http.max.request.parameters. 7. database.connection.timeout: The amount of time a connection to the database can be open, but inactive, before being automatically closed to reclaim the resources. The timeout values specified in the property file should be a positive integer followed by either letter h (for hours), letter m (for minutes), letter s (for seconds), or letters ms (for milliseconds). Example intervals: 1h for 1 hour, 5m for 5 minutes, 90s for 90 seconds, 500ms for 500 milliseconds. Default value is 1h. NOTE: setting a short timeout can have adverse results, especially if updates are being performed without commit changes to the server, closing the connection prematurely while using it. 8. password.length.min: Sets the password policy for the minimum length of user passwords, the value can’t be lower than password.length.min or greater than password.length.max. Default: 4. 9. password.length.max: Sets the password policy for the maximum length of user passwords. Default: 1024. 10. password.regex: Sets the password policy of accepted chars in user passwords, via a Java regular expression. Default: [\w@#$%!&]+

11. security.named.graphs: Sets named graph security on globally. Default: false.

12. spatial.use.jts: Enabled support for JTS in the geospatial module. Default: false

### Starting & Stopping the Server

 Note Unlike the other stardog-admin subcommands, starting the server may only be run locally, i.e., on the same machine the Stardog Server is will run on.

The simplest way to start the server—running on the default port, detaching to run as a daemon, and writing stardog.log to the current working directory— is

$stardog-admin server start To specify parameters: $ stardog-admin server start --require-ssl --port=8080

The port can be specified using the property --port.

To shut the server down:

$stardog-admin server stop If you started Stardog on a port other than the default, or want to shut down a remote server, you can simply use the --server option to specify the location of the server to shutdown. By default Stardog will bind it’s server to 0.0.0.0. You can specify a different network interface for Stardog to bind to using the --bind property of server start.  Note As of Stardog 6, the web console is deprecated. It will still be available but will be unsupported. We encourage you to use Stardog Studio instead. As a result, the web console is disabled by default when running a Stardog server. To enable it, pass the --web-console flag into your stardog-admin server start command. ### Server Monitoring Stardog provides server monitoring via the Metrics library. In addition to providing some basic JVM information, Stardog also exports information about the Stardog DBMS configuration as well as stats for all databases within the system, such as the total number of open connections, size, and average query time. #### Accessing Monitoring Information Monitoring information is available via the Java API, the HTTP API, the CLI or (if configured) the JMX interface. Performing a GET on /admin/status which will return a JSON object containing the information available the server and all the databases. The endpoint DB/status will return the monitoring information about the database status. The stardog-admin server status command will print a subset of this information on the console. #### Configuring JMX Monitoring By default, JMX monitoring is not enabled. You can enable it by setting metrics.reporter=jmx in the stardog.properties file. Then, you can simply use a tool like VisualVM or JConsole to attach to the process running the JVM, or connect directly to the JMX server. If you want to connect to the JMX server remotely you need to set metrics.jmx.remote.access=true in stardog.properties. Stardog will bind an RMI server for remote access on port 5833. If you want to change this port Stardog binds the remote server to, you can set the property metrics.jmx.port in stardog.properties. Finally, if you wish to disable monitoring completely, set metrics.enabled to false in stardog.properties. ### Locking Stardog Home Stardog Server will lock STARDOG_HOME when it starts to prevent synchronization errors and other nasties if you start more than one Stardog Server with the same STARDOG_HOME. If you need to run more than one Stardog Server instance, choose a different STARDOG_HOME or pass a different value to --home. ### Access & Audit Logging See the exemplar stardog.properties file for a complete discussion of how access and audit logging work in Stardog Server. Audit logging is a superset of the events in access logging. Access logging covers the most often required logging events; you should consider enabling audit logging if you really need to log every server event. Logging generally doesn’t have much impact on performance; but the safest way to insure that impact is negligible is to log to a separate disk (or to a centralized logging server, etc.). The important configuration choices are whether logs should be binary or plain text (both based on ProtocolBuffer message formats); the type of logging (audit or access); the logging location (which may be "off disk" or even "off machine") Logging to a centralized logging facility requires a Java plugin that implements the Stardog Server logging interface; see Java Programming for more information; and the log rotation policy (file size or time). Slow query logging is also available. See the Managing Running Queries section below. ## Database Admin Stardog is a multi-tenancy system and will happily give access to many, physically distinct databases. ### Configuring a Database To administer a Stardog database, some config options must be set at creation time; others may be changed subsequently and some may never be changed. All config options have sensible defaults (except for the database name), so you don’t have to twiddle any of the knobs till you really need to. To configure a database, use the metadata-get and metadata-set CLI commands. See Man Pages for the details. ### Configuration Options 1. Table of Configuration Options Option Mutable Default API database.archetypes Yes DatabaseOptions.ARCHETYPES The name of one or more database archetypes. database.connection.timeout Yes 1h DatabaseOptions.CONNECTION_TIMEOUT Same as database.connection.timeout described in Stardog Configuration but applies only to one database. database.name No DatabaseOptions.NAME A database name, the legal value of which is given by the regular expression [A-Za-z]{1}[A-Za-z0-9_-]. database.namespaces Yes rdf, rdfs, xsd, owl, stardog DatabaseOptions.NAMESPACES Sets the default namespaces for new databases. database.online No true DatabaseOptions.ONLINE The status of the database: online or offline. It may be set so that the database is created initially in online or offline status; subsequently, it can’t be set directly but only by using the relevant admin commands. docs.default.rdf.extractors Yes tika BitesOptions.DOCS_DEFAULT_RDF_EXTRACTORS Comma-separated list of names of RDF extractors to use when processing documents when no RDF extractor names are given. docs.default.text.extractors Yes tika BitesOptions.DOCS_DEFAULT_TEXT_EXTRACTORS Comma-separated list of names of text extractors to use when processing documents when no text extractor names are given. docs.filesystem.uri Yes file:/// BitesOptions.DOCS_FILESYSTEM_URI A URI indicating which FileSystem provider to use for document storage. In addition to local storage (file:///), documents can be stored on Amazon S3 (s3:///) or document storage can be disabled altogether (none). docs.opennlp.models.path Yes BitesOptions.DOCS_OPENNLP_MODELS_PATH The directory where OpenNLP models are located. See Entity Extraction and Linking for details. docs.path Yes docs/ BitesOptions.DOCS_PATH The path under which documents will be stored. A relative path is relative to the database directory. S3 storage should specify an absolute path with the bucket name as the first part of the path. docs.s3.protocol Yes https BitesOptions.DOCS_S3_PROTOCOL Protocol used when storing on S3 (and compatible) stores. Can be set to http to disable TLS/SSL. icv.active.graphs No default ICVOptions.ICV_ACTIVE_GRAPHS Specifies which part of the database, in terms of named graphs, is checked with IC validation. Set to tag:stardog:api:context:all to validate all the named graphs in the database; otherwise, the legal value of icv.active.graphs is a comma-separated list of named graph identifiers. icv.consistency.automatic Yes false ICVOptions.ICV_CONSISTENCY_AUTOMATIC Enables automatic ICV consistency check as part of transactions. icv.enabled Yes false ICVOptions.ICV_ENABLED Determines whether ICV is active for the database; if true, all database mutations are subject to IC validation (i.e., "guard mode"). icv.reasoning.enabled Yes false ICVOptions.ICV_REASONING_ENABLED Determines if reasoning is used during IC validation. index.differential.enable.limit Yes 500,000 IndexOptions.DIFF_INDEX_MIN_LIMIT Sets the minimum size of the Stardog database before differential indexes are used. The legal value is an integer. index.differential.merge.limit Yes 20,000 IndexOptions.DIFF_INDEX_MAX_LIMIT Sets the size in number of RDF triples before the differential indexes are merged to the main indexes. The legal value is an integer. index.literals.canonical No true IndexOptions.CANONICAL_LITERALS Enables RDF literal canonicalization. index.named.graphs No true IndexOptions.INDEX_NAMED_GRAPHS Enables optimized index support for named graphs; speeds SPARQL query evaluation with named graphs at the cost of some overhead for database loading and index maintenance. index.statistics.update.automatic Yes true IndexOptions.AUTO_STATS_UPDATE Determines whether statistics are maintained automatically. index.statistics.update.min.size Yes 10000 IndexOptions.STATS_UPDATE_DB_MIN_SIZE Minimum number of triples that should be in the database for statistics to be updated automatically. index.statistics.update.ratio Yes 0.1 IndexOptions.STATS_UPDATE_RATIO Ratio of updated triples to the number of triples in the database that triggers the automatic statistics computation in a background thread. This option has no effect if index.statistics.update.automatic is off or the index size is less than index.statistics.update.min.size. index.statistics.update.blocking.ratio Yes 0.0 IndexOptions.STATS_UPDATE_BLOCKING_RAITO Similar to index.statistics.update.ratio but once the updates go over this limit statistics computation will be performed synchronously within the transaction instead of a background thread. Setting this option to a non-positive number ({@code ⇐ 0}) will disable blocking updates. preserve.bnode.ids No true DatabaseOptions.PRESERVE_BNODE_IDS Determines how the Stardog parser handles bnode identifiers that may be present in RDF input. If this property is enabled (i.e., TRUE), parsing and data loading performance are improved; but the other effect is that if distinct input files use (randomly or intentionally) the same bnode identifier, that bnode will point to one and the same node in the database. If you have input files that use explicit bnode identifiers, and more than one of those files may use the same bnode identifiers, and you don’t want those bnodes to be smushed into a single node in the database, then this configuration option should be disabled (set to FALSE). query.all.graphs Yes false DatabaseOptions.QUERY_ALL_GRAPHS Determines what data the database evaluates queries against; if true, it will query over the default graph and the union of all named graphs; if false (the default), it will query only over the default graph. This database option overrides any global server settings. query.describe.strategy Yes default DatabaseOptions.QUERY_DESCRIBE_STRATEGY Option to set the default DESCRIBE query strategy for the database query.pp.contexts Yes false DatabaseOptions.PROPERTY_PATH_CONTEXTS Determines how property paths interact with named graphs in the data. When set to true and the property path pattern is in the default scope (i.e. not inside a graph keyword), Stardog will check that paths do not span multiple named graphs (per 18.1.7). For this to affect query results either there should be multiple FROM clauses or query.all.graphs must be also set to true. This database option overrides any global server settings. query.plan.reuse Yes Always DatabaseOptions.QUERY_PLAN_REUSE Option for configuring how Stardog will reuse query plans. Stardog answers queries by first generating an execution plan. Generating an optimal query plan is hard and time-consuming so these plans are cached and reused for structurally equivalent queries; i.e. queries such that one can be transformed into another by replacing constants. This option determines the conditions under which a cached plan will be reused. See QueryPlanReuse for the available values. query.timeout Yes DatabaseOptions.QUERY_TIMEOUT Determines max execution time for query evaluation. reasoning.consistency.automatic Yes false ReasoningOptions.CONSISTENCY_AUTOMATIC Enables automatic consistency checking with respect to a transaction. reasoning.punning.enabled Yes false ReasoningOptions.PUNNING_ENABLED Enables punning. reasoning.schema.graphs Yes * ReasoningOptions.SCHEMA_GRAPHS Determines which, if any, named graph or graphs contains the "TBox", i.e., the schema part of the data. The legal value is a comma-separated list of named graph identifiers, including (optionally) the special names, tag:stardog:api:context:default and tag:stardog:api:context:all, which represent the default graph and the union of all named graphs and the default graph, respectively. In the context of database configurations only, Stardog will recognize default and * as short forms of those URIs, respectively. reasoning.type Yes SL Specifies the reasoning type associated with the database; legal values are SL, RL, QL, EL, DL, RDFS, and NONE. reasoning.approximate Yes false ReasoningOptions.APPROXIMATE Enables approximate reasoning. With this flag enabled Stardog will approximate an axiom that is outside the profile Stardog supports and normally ignored. For example, an equivalent class axiom might be split into two subclass axioms and only one subclass axiom is used. reasoning.sameas Yes OFF ReasoningOptions.EQUALITY_REASONING Option to enable owl:sameAs reasoning. When this option is set to ON reflexive, symmetric, and transitive closure of the owl:sameAs triples in the database is computed. When it is set to FULL, owl:sameAs inferences are computed based on the schema axioms such as functional properties. search.enabled Yes false SearchOptions.SEARCHABLE Enables semantic search for the database. search.wildcard.search.enabled Yes false SearchOptions.LEADING_WILDCARD_SEARCH_ENABLED Enable support in Lucene for searches with leading wildcards. search.default.limit Yes -1 SearchOptions.SEARCH_DEFAULT_LIMIT Specify the default limit on the number of results returned from a full-text search (-1 returns all results) search.reindex.tx Yes true SearchOptions.SEARCH_REINDEX_IN_TX If false, literals added during a transaction are not automatically indexed; users need to optimize the database in order to make them available for search. spatial.enabled Yes false GeospatialOptions.SPATIAL_ENABLED Enables geospatial search for the database. spatial.result.limit Yes 10000 GeospatialOptions.SPATIAL_RESULT_LIMIT Specify the default limit on the number of results returned from a geospatial query (-1 returns all results) spatial.precision No 11 GeospatialOptions.SPATIAL_PRECISION Specifies the precision used for the indexing of geospatial data. The smaller the value, the less precision. strict.parsing No true DatabaseOptions.STRICT_PARSING Controls whether Stardog parses RDF strictly (true, the default) or loosely (false) transaction.isolation Yes SNAPSHOT DatabaseOptions.TRANSACTION_ISOLATION Configures isolation level for transactions; legal values are SNAPSHOT and SERIALIZABLE. transaction.logging Yes false DatabaseOptions.TRANSACTION_LOGGING Enables logged transactions. Logged transactions are activated by default in Cluster mode. transaction.logging.rotation.size Yes 524288000 DatabaseOptions.TRANSACTION_LOGGING_ROTATION_SIZE When transaction.logging is true, it determines the size (in bytes) at which the transaction log will be rotated. Default is 500 MB. transaction.logging.rotation.remove Yes true DatabaseOptions.TRANSACTION_LOGGING_ROTATION_REMOVE When transaction.logging is true, it determines that old log files will be deleted after rotation. Default is true. #### A Note About Database Status A database must be set to offline status before most configuration parameters may be changed. Hence, the normal routine is to set the database offline, change the parameters, and then set the database to online. All of these operations may be done programmatically from CLI tools, such that they can be scripted in advance to minimize downtime. In a future version, we will allow some properties to be set while the database remains online. ### Managing Database Status Databases are either online or offline; this allows database maintenance to be decoupled from server maintenance. #### Online and Offline Databases are put online or offline synchronously: these operations block until other database activity is completed or terminated. See stardog-admin help db for details. #### Examples To set a database from offline to online: $ stardog-admin db offline myDatabase

To set the database online:

$stardog-admin db online myDatabase If Stardog Server is shutdown while a database is offline, the database will be offline when the server restarts. ### Creating a Database Stardog databases may be created locally or remotely; but performance is better if data files don’t have to be transferred over a network during creation and initial loading. See the section below about loading compressed data. All data files, indexes, and server metadata for the new database will be stored in Stardog Home. Stardog won’t create a database with the same name as an existing database. Stardog database names must conform to the regular expression, [A-Za-z]{1}[A-Za-z0-9_-].  Note There are four reserved words that may not be used for the names of Stardog databases: system, admin, and docs. Minimally, the only thing you must know to create a Stardog database is a database name; alternately, you may customize some other database parameters and options depending on anticipated workloads, data modeling, and other factors. See stardog-admin help db create for all the details including examples. ### Database Archetypes Stardog database archetypes are a new feature in 2.0. A database archetype is a named, vendor-defined or user-defined bundle of data and functionality to be applied at database-creation time. Archetypes are primarily for supporting data standards or tool chain configurations in a simple way. For example, the SKOS standard from W3C defines an OWL vocabulary for building taxonomies, thesauruses, etc. SKOS is made up by a vocabulary, some constraints, some kinds of reasoning, and (typically) some SPARQL queries. If you are developing an app that uses SKOS, without Stardog’s SKOS archetype, you are responsible for assembling all that SKOS stuff yourself. Which is tedious, error-prone, and unrewarding—​even when it’s done right the first time. Rather than putting that burden on Stardog users, we’ve created database archetypes as a mechanism to collect these "bundles of stuff" which, as a developer, you can then simply attach to a particular database. The last point to make is that archetypes are composable: you can mix-and-match them at database creation time as needed. Stardog supports two database archetypes out-of-the-box: PROV and SKOS. #### SKOS Archetype The SKOS archetype is for databases that will contain SKOS data, and includes the SKOS schema, SKOS constraints using Stardog’s Integrity Constraint Validation, and some namespace-prefix bindings. #### PROV Archetype The PROV archetype is for databases that will contain PROV data, and includes the SKOS schema, SKOS constraints using Stardog’s Integrity Constraint Validation, and some namespace-prefix bindings. Archetypes are composable, so you can use more of them and they are intended to be used alongside your domain data, which may include as many other schemas, ontologies, etc. as are required. #### User-defined Archetypes Please see the Stardog Examples repository on Github for an example that shows how to create your own Stardog archetype. ### Database Creation Templates As a boon to the overworked admin or devops peeps, Stardog Server supports database creation templates: you can pass a Java Properties file with config values set and with the values (typically just the database name) that are unique to a specific database passed in CLI parameters. #### Examples To create a new database with the default options by simply providing a name and a set of initial datasets to load: $ stardog-admin db create -n myDb input.ttl another_file.rdf moredata.rdf.gz

Datasets can be loaded later as well. To create (in this case, an empty) database from a template file:

$stardog-admin db create -c database.properties At a minimum, the configuration file must have a value for database.name option. If you only want to change only a few configuration options you can directly give the values for these options in the CLI args as follows: $ stardog-admin db create -n db -o icv.enabled=true icv.reasoning.enabled=true -- input.ttl

“--” is used in this case when “-o” is the last option to delimit the value for “-o” from the files to be bulk loaded.

Please refer to the CLI help for more details of the db create command.

### Database Create Options

2. Table of Options for Stardog’s create command
Name Description Arg values Default

--name, -n

Required, the name of the database to create

--copy-server-side,

Flag to specify whether bulk loaded files should be first copied to the server

false

--type, -t

Specifies the kind of database indexes: memory or disk

M, D

disk

--index-triples-only, -i

Specifies that the database’s indexes should be optimized for RDF triples only

false

### Repairing a Database

If an I/O error or an index exception occurs while querying a DB, the DB may be corrupted and repaired with the repair command. If the errors occur during executing admin commands, then the system DB may have been corrupted. System database corruptions can also cause other problems including authorization errors.

This command needs exclusive access to your Stardog home directory and therefore requires the Stardog Server not to be running. This also means that the command can only be run on the machine where the Stardog home directory is located, and you will not be able to start the Stardog Server while this command is running.

 Note The repair process can take considerable time for large databases.

If the built-in Stardog system database is corrupted, then you can use the database name system as the repair argument. To repair the database myDB:

$stardog-admin db repair myDB To repair the system database: $ stardog-admin db repair system

### Backing Up and Restoring

Stardog includes both physical and logical backup utilities; logical backups are accomplished using the export CLI command. Physical backups and restores are accomplished using stardog-admin db backup and stardog-admin db restore commands, respectively.

These tools perform physical backups, including database metadata, rather than logical backups via some RDF serialization. They are native Stardog backups and can only be restored with Stardog tools. Backup may be accomplished while a database is online; backup is performed in a read transaction: reads and writes may continue, but writes performed during the backup are not reflected in the backup.

See the man pages for backup and restore for details.

#### Backup

stardog-admin db backup assumes a default location for its output, namely, $STARDOG_HOME/.backup; that default may be overriden by passing a -t or --to argument. Backup sets are stored in the backup directory by database name and then in date-versioned subdirectories for each backup volume. You can use a variety of OS-specific options to do remote backups over some network or data protocol; those options are left as an exercise for the admin. To backup a Stardog database called foobar: $ stardog-admin db backup foobar

To perform a remote backup, for example, pass in a specific directory that may be mounted in the current OS namespace via some network protocol, thus:

$stardog-admin db backup --to /my/network/share/stardog-backups foobar Backups can also be performed directly to S3. To do so use an S3 URL in the following format: s3://[<endpoint hostname>:<endpoint port>]/<bucket name>/<path prefix>?region=<AWS Region>&AWS_ACCESS_KEY_ID=<access key>&AWS_SECRET_ACCESS_KEY=<verySecretKey1> The endpoint hostname and endpoint port values are only used for on-premises S3 clones. To use Amazon S3 those values can be left blank and the URL will have three / before the bucket, eg s3:///mybucket/backup/prefix?region=us-east-1&AWS_ACCESS_KEY_ID=accessKey&AWS_SECRET_ACCESS_KEY=secret A default S3 backup location can also be specified in the stardog.properties file with the key backup.location. #### Restore To restore a Stardog database from a Stardog backup volume, simply pass a fully-qualified path to the volume in question. The location of the backup should be the full path to the backup, not the location of the backup directory as specified in your Stardog configuration. There is no need to specify the name of the database to restore. To restore a database from its backup: $ stardog-admin db restore $STARDOG_HOME/.backups/myDb/2012-06-21 Backups can also be restored directly from S3 by using an S3 URL in the following format: s3://[<endpoint hostname>:<endpoint port>]/<bucket name>/<path prefix>/<database name>?region=<A WS Region>&AWS_ACCESS_KEY_ID=<access key>&AWS_SECRET_ACCESS_KEY=<verySecretKey1> Note: Unlike the backup URL the database name must be specified as the last entry of the path field in the URL. ##### Restore On Startup Stardog can be configured to automatically restore databases from a backup location on startup. For example, when a Stardog cluster node first starts it could pull all of the database data down from an S3 backup before joining the cluster. There are two options that control this behavior. 3. Table of Auto-restore options Option Description backup.autorestore.dbnames A regular expression that matches the names of the databases to automatically restore on startup, eg: .* for every database. backup.autorestore.onfailure A boolean value that determines if all databases which failed to load should be automatically restored from a backup location. ##### One-time Database Migrations for Backup The backup system cannot directly backup databases created in versions before 2.1. These databases must be explicitly migrated to use the new backup system; this is a one-time operation per database and is accomplished by running $ stardog-admin db migrate foobar

to migrate a database called foobar. Again, this is a one-time operation only and all databases created with 2.1 (or later) do not require it.

### Namespace Prefix Bindings

Stardog allows database administrators to persist and manage custom namespace prefix bindings:

1. At database creation time, if data is loaded to the database that has namespace prefixes, then those are persisted for the life of the database. This includes setting the default namespace to the default that appears in the file. Any subsequent queries to the database may simply omit the PREFIX declarations:

$stardog query myDB "select * {?s rdf:type owl:Class}" 2. To add new bindings, use the namespace subcommand in the CLI: $ stardog namespace add myDb --prefix ex --uri 'http://example.org/test#'
3. To change the default binding, use a quote prefix when adding a new one:

$stardog namespace add myDb --prefix "" --uri http://new.default 4. To change an existing binding, delete the existing one and then add a new one: $ stardog namespace remove myDb --prefix ex
5. Finally, to see all the existing namespace prefix bindings:

### Using Integrity Constraint Validation

Stardog supports integrity constraint validation as a data quality mechanism via closed world reasoning. Constraints can be specified in OWL, SWRL, and SPARQL. Please see the Validating Constraints section for more about using ICV in Stardog.

The CLI icv subcommand can be used to add, delete, or drop all constraints from an existing database. It may also be used to validate an existing database with constraints that are passed into the icv subcommand; that is, using different constraints than the ones already associated with the database.

For details of ICV usage, see stardog help icv and stardog-admin help icv. For ICV in transacted mutations of Stardog databases, see the database creation section above.

### Migrating a Database

The migrate subcommand migrates an older Stardog database to the latest version of Stardog. Its only argument is the name of the database to migrate. migrate won’t necessarily work between arbitrary Stardog version, so before upgrading check the release notes for a new version carefully to see whether migration is required or possible.

$stardog-admin db migrate myDatabase will update myDatabase to the latest database format. ### Getting Database Information You can get some information about a database by running the following command: $ stardog-admin metadata get my_db_name

This will return all the metadata stored about the database, including the values of configuration options used for this database instance. If you want to get the value for a specific option then you can run the following command:

$stardog-admin metadata get -o index.named.graphs my_db_name ### Managing Stored Functions Stored functions, available since Stardog 5.1, provide the ability to reuse expressions. This avoids duplication and ensures consistency across instances of the same logic. Stored functions are treated similarly to built-in and user-defined functions in that they can be used in FILTER constraints and BIND assignments in SPARQL queries, path queries and rules. #### Creating and Using Functions Functions are useful to encapsulate computational or business logic for reuse. We can create a new function to compute the permutation using the function add command to stardog-admin on the command line: stardog-admin function add "function permutation(?n, ?r) { factorial(?n) / factorial(?n - ?r) }" We can use this function in a SPARQL query and see that the function is expanded in the query plan: Explaining Query: select * where { ?x :p :q. filter(permutation(?x, 3) > 1) } The Query Plan: Projection(?x) [#1] ─ Filter((factorial(?x) / factorial((?x - "3"^^xsd:integer))) > "1"^^xsd:integer) [#1] ─ Scan[POS](?x, :p, :q) [#1] #### Stored Function Syntax Function definitions provided to the add command must adhere to the following grammar: FUNCTIONS ::= Prolog FUNCTION+ FUNCTION ::= 'function' FUNC_NAME '(' ARGS ')' '{' Expression '}' FUNC_NAME ::= IRI | PNAME | LOCAL_NAME ARGS ::= [Var [',' Var]* ]? Prolog ::= // BASE and PREFIX declarations as defined by SPARQL 1.1 Expression ::= // as defined by SPARQL 1.1 Var ::= // as defined by SPARQL 1.1 We can use IRIs or prefixed names as function names and include several functions in one add call: $ stardog-admin function add "prefix ex: <http://example/> \
function ex:permutation(?n, ?r) { factorial(?n) / factorial(?n - ?r) } \
function <http://example/combination>(?n, ?r) { permutation(?n, ?r) / factorial(?r) }"

Stored 2 functions successfully

The admin commands cover adding, listing and removing functions. Examples of these commands are shown below:

$stardog-admin function list FUNCTION combination(?n,?r) { ((factorial(?n) / factorial((?n - ?r))) / factorial(?r)) } FUNCTION permutation(?n,?r) { (factorial(?n) / factorial((?n - ?r))) } $ stardog-admin function remove permutation
Removed stored function successfully

HTTP APIs are also provided to add, list and remove stored functions:

• GET /admin/functions/stored[/?name={functionName}]

• DELETE /admin/functions/stored[/?name={functionName}]

• POST /admin/functions/stored

The contents of the POST request should be a document containing one or more function definitions using the syntax describes above. The GET request by default returns the definitions for all the functions. If the name parameter is specified a definition for the function with that name is returned. Similarly, the DELETE request deletes all the functions by default or deletes a single function if the name parameter is specified.

Stored functions are persisted in the system database. The system database should be backed up properly to avoid loss of functions.

#### Dependencies Across Stored Functions

Stored functions are compiled at creation time in a way that guarantees they will work indefinitely, even if other functions are removed or changed in ways that would affect them. For this reason, dependent functions need to be reloaded when their dependencies are changed.

### Managing Stored Queries

Stardog 4.2 added the capability to name and store SPARQL queries for future evaluation by referring to the query’s name.

Queries of any type can be stored in Stardog and executed directly by using the name of the stored query. Stored queries can be shared with other users, which gives those users the ability to run those queries provided that they have appropriate permissions for a database.

Stored queries can be managed via CLI, Java API, and HTTP API. The CLI command group is stardog-admin stored. The HTTP API will be detailed below.

#### Storing Queries

Queries can be stored using the stored add admin command and specifying a unique name for the stored query:

$stardog-admin stored add -n types "select distinct ?type {?s a ?type}" If a file is used to specify the query string without an explicit -n/--name option then the name of the query file is used for the stored query: $ stardog-admin stored add listProperties.sparql

Queries can also be stored via HTTP:

POST /admin/queries/stored → application/json

Input JSON example:

{
"name": "types",
"database": "*",
"query": "select distinct ?type {?s a ?type}"
}

By default, stored queries can be executed over any database. But they can be scoped by providing a specific database name with the -d/--database option. Also, by default, only the user who stored the query can access that stored query. Using the --shared flag will allow other users to execute the stored query.

The following example stores a shared query with a custom name that can be executed over only the database myDb:

$stardog-admin stored add --shared -d myDb -n listProperties "select distinct ?p {?s ?p ?o}" The JSON attributes which correspond to --shared and -d are shared and database. Stored query names must be unique for a Stardog instance. Existing stored queries can be replaced using the --overwrite option in the command. #### Running Stored Queries Stored queries can be executed using the regular query execution CLI command by passing the name of the stored query: $ stardog query myDb listProperties

Other commands like query explain also accept stored query names. They can also be passed instead of query string into HTTP API calls.

#### Listing Stored Queries

To see all the stored queries, use the stored list subcommand:

$stardog-admin stored list The results are formatted tabularly: +--------+-----------------------------------------+ | Name | Query String | +--------+-----------------------------------------+ | graphs | SELECT ?graph (count(*) as ?size) | | | FROM NAMED stardog:context:all | | | WHERE { GRAPH ?graph {?s ?p ?o}} | | | GROUP BY ?graph | | | ORDER BY desc(?size) | | people | CONSTRUCT WHERE { | | | ?person a foaf:Person ; | | | ?p ?o | | | } | | types | SELECT DISTINCT ?type ?label | | | WHERE { | | | ?s a ?type . | | | OPTIONAL { ?type rdfs:label ?label } | | | } | +--------+-----------------------------------------+ 3 stored queries Users can only see the queries they’ve stored and the queries stored by other users that have been --shared. The --verbose option will show more details about the stored queries. Stored queries can be obtained via HTTP: GET /admin/queries/stored The results will be returned in JSON, for example: { "queries": [ { "name": "types", "creator": "admin", "database": "*", "query": "select distinct ?type {?s a ?type}", "shared": false } ] } #### Removing Stored Queries Stored queries can be removed using the stored remove command: $ stardog-admin stored remove storedQueryName

If you would like to clear all the stored queries then use the -a/--all option:

$stardog-admin stored remove -a Stored queries can also be removed via HTTP: DELETE /admin/queries/stored/{name} ### Managing Running Queries Stardog includes the capability to manage running queries according to configurable policies set at run-time; this capability includes support for listing running queries; deleting running queries; reading the status of a running query; killing running queries that exceed a time threshold automatically; and logging slow queries for analysis. Stardog is pre-configured with sensible server-wide defaults for query management parameters; these defaults may be overridden or disabled per database, or even per query. #### Configuring Query Management For many uses cases the default configuration will be sufficient. But you may need to tweak the timeout parameter to be longer or shorter, depending on the hardware, data load, queries, throughput, etc. The default configuration has a server-wide query timeout value of query.timeout, which is inherited by all the databases in the server. You can customize the server-wide timeout value and then set per-database custom values, too. Any database without a custom value inherits the server-wide value. To disable query timeout, set query.timeout to 0. If individual queries need to set their own timeout, this can be done (by passing a timeout parameter over HTTP or using the --timeout flag on the CLI), but only if the query.timeout.override.enabled property is set to true for the database (true is the default). #### Listing Queries To see all running queries, use the query list subcommand: $ stardog-admin query list

The results are formatted tabularly:

+----+----------+-------+--------------+
| ID | Database | User  | Elapsed time |
+----+----------+-------+--------------+
| 2  | test     | admin | 00:00:20.165 |
| 3  | test     | admin | 00:00:16.223 |
| 4  | test     | admin | 00:00:08.769 |
+----+----------+-------+--------------+

3 queries running

You can see which user owns the query (superuser’s can see all running queries), as well as the elapsed time and the database against which the query is running. The ID column is the key to deleting queries.

#### Deleting Queries

To delete a running query, simply pass its ID to the query kill subcommand:

$stardog-admin query kill 3 The output confirms the query kill completing successfully: Query 3 killed successfully #### Automatically Killing Queries For production use, especially when a Stardog database is exposed to arbitrary query input, some of which may not execute in an acceptable time, the automatic query killing feature is useful. It will protect a Stardog Server from queries that consume too many resources. Once the execution time of a query exceeds the value of query.timeout, the query will be killed automatically.[5] The client that submitted the query will receive an error message. The value of query.timeout may be overriden by setting a different value (smaller or longer) in database options. To disable, set to query.timeout to 0. The value of query.timeout is a positive integer concatenated with a letter, interpreted as a time duration: 'h' (for hours), 'm' (for minutes), 's' (for seconds), or 'ms' (for milliseconds). For example, '1h' for 1 hour, '5m' for 5 minutes, '90s' for 90 seconds, and '500ms' for 500 milliseconds. The default value of query.timeout is five minutes. #### Query Status To see more detail about query in-flight, use the query status subcommand: $ stardog-admin query status 1

The resulting output includes query metadata, including the query itself:

Username: admin
Database: test
Started : 2013-02-06 09:10:45 AM
Elapsed : 00:01:19.187
Query   :
select ?x ?p ?o1 ?y ?o2
where {
?x ?p ?o1.
?y ?p ?o2.
filter (?o1 > ?o2).
}
order by ?o1
limit 5

#### Slow Query Logging

Stardog does not log slow queries in the default configuration because there isn’t a single value for what counts as a "slow query", which is entirely relative to queries, access patterns, dataset sizes, etc. While slow query logging has minimal overhead, what counts as a slow query in some context may be acceptable in another. See Configuring Stardog Server above for the details.

#### Protocols and Java API

For HTTP protocol support, see Stardog’s Apiary docs.

#### Security and Query Management

The security model for query management is simple: any user can kill any running query submitted by that user, and a superuser can kill any running query. The same general restriction is applied to query status; you cannot see status for a query that you do not own, and a superuser can see the status of every query.

### Managing Query Performance

Stardog answers queries in two major phases: determining the query plan and executing that plan to obtain answers from the data. The former is called query planning (or query optimization) and includes all steps required to select the most efficient way to execute the query. How Stardog evaluates a query can only be understood by analyzing the query plan. Query plan analysis is also the main tool for investigating performance issues as well as addressing them, in particular, by re-formulating the query to make it more amenable to optimization.

#### Query Plan Syntax

We will use the following running example to explain query plans in Stardog.

 SELECT DISTINCT ?person ?name
WHERE {
?article rdf:type bench:Article .
?article dc:creator ?person .
?inproc rdf:type bench:Inproceedings .
?inproc dc:creator ?person .
?person foaf:name ?name
}

This query returns the names of all people who have authored both a journal article and a paper in a conference proceedings. The query plan used by Stardog (in this example, 4.2.2) to evaluate this query is:

 Distinct [#812K]
─ Projection(?person, ?name) [#812K]
─ MergeJoin(?person) [#812K]
+─ MergeJoin(?person) [#391K]
│  +─ Sort(?person) [#391K]
│  │  ─ MergeJoin(?article) [#391K]
│  │     +─ Scan[POSC](?article, rdf:type, bench:Article) [#208K]
│  │     ─ Scan[PSOC](?article, dc:creator, ?person) [#898K]
│  ─ Scan[PSOC](?person, foaf:name, ?name) [#433K]
─ Sort(?person) [#503K]
─ MergeJoin(?inproc) [#503K]
+─ Scan[POSC](?inproc, rdf:type, bench:Inproceedings) [#255K]
─ Scan[PSOC](?inproc, dc:creator, ?person) [#898K]

The plan is arranged in an hierarchical, tree-like structure. The nodes, called operators, represent units of data processing during evaluation. They correspond to evaluations of graphs patterns or solution modifiers as defined in SPARQL 1.1 specification. All operators can be regarded as functions which may take some data as input and produce some data as output. All input and output data is represented as streams of solutions, that is, sets of bindings of the form x → value where x is a variable used in the query and value is some RDF term (IRI, blank node, or literal). Examples of operators include scans, joins, filters, unions, etc.

Numbers in square brackets after each node refer to the estimated cardinality of the node, i.e. how many solutions Stardog expects this operator to produce when the query is evaluated. Statistics-based cardinality estimation in Stardog merits a separate blog post, but here are the key points for the purpose of reading query plans:

1. all estimations are approximate and their accuracy can vary greatly (generally: more precise for bottom nodes, less precise for upper nodes)

2. estimations are only used for selecting the best plan but have no bearing on the actual results of the query

3. in most cases a sub-optimal plan can be explained by inaccurate estimations

#### Stardog Evaluation Model

Stardog generally evaluates query plans according to the bottom-up SPARQL semantics. Leaf nodes are evaluated first and without input, and their results are then sent to their parent nodes up the plan. Typical examples of leaf nodes include scans, i.e. evaluations of triple patterns, evaluations of full-text search predicates, and VALUES operators. They contain all information required to produce output, for example, a triple pattern can be directly evaluated against Stardog indexes. Parent nodes, such as joins, unions, or filters, take solutions as inputs and send their results further towards the root of the tree. The root node in the plan, which is typically one of the solution modifiers, produces the final results of the query which are then encoded and sent to the client.

##### Pipelining And Pipeline Breakers

Stardog implements the Volcano model, in which evaluation is as lazy as possible. Each operator does just enough work to produce the next solution. This is important for performance, especially for queries with a LIMIT clause (of which ASK queries are a special case) and also enables Stardog’s query engine to send the first result(s) as soon as they are available (as opposed to waiting till all results have been computed).

Not all operators can produce output solutions as soon as they get first input solutions from their children nodes. Some need to accumulate intermediate results before sending output. Such operators are called pipeline breakers, and they are often the culprits for performance problems, typically resulting from memory pressure. It is important to be able to spot them in the plan since they can suggest either a way to re-formulate the query to help the planner or a way to make the query more precise by specifying extra constants where they matter.

Here are some important pipeline breakers in the example plan:

• HashJoin algorithms build a hash table for solutions produced by the right operand. Typically all such solutions need to be hashed, either in memory or spilled to disk, before the first output solution is produced by the HashJoin operator.

• Sort: the sort operator builds an intermediate sorted collection of solutions produced by its child node. The main use case for sorting solutions is to prepare data for an operator which can benefit from sorted inputs, such as MergeJoin, Distinct, or GroupBy. All solutions have to be fetched from the child node before the smallest (w.r.t. the sort key) solution can be emitted.

• GroupBy: group-by operators are used for aggregation, e.g. counting or summing results. When evaluating a query like select ?x (count(?y) as ?count) where { …​ } group by ?x Stardog has to scroll through all solutions to compute the count for every ?x key before returning the first result.

Other operators can produce output as soon as they get input:

• MergeJoin: merge join algorithms do a single zig-zag pass over sorted streams of solutions produced by children nodes and output a solution as soon as the join condition is satisfied.

• DirectHashJoin: contrary to the classical hash join algorithm, this operator does not build a hash table. It utilizes Stardog indexes for look-ups which doesn’t require extra data structures. This is only possible when the right operand is sorted by the join key, but the left isn’t, otherwise Stardog would use a merge join.

• Filter: a solution modifier which evaluates the filter condition on each input solution.

• Union: combines streams of children solutions without any extra work, e.g. joining, so there’s no need for intermediate results.

Now, returning to the above query, one can see Sort pipeline breakers in the plan:

Sort(?person) [#391K]
─ MergeJoin(?article) [#391K]
+─ Scan[POSC](?article, rdf:type, bench:Article) [#208K]
─ Scan[PSOC](?article, dc:creator, ?person) [#898K]

This means that all solutions representing the join of ?article rdf:type bench:Article and ?article dc:creator ?person will be put in a sequence ordered by the values of ?person. Stardog expects to sort 391K solutions before they can be further merge-joined with the results of the ?person foaf:name ?name pattern. Alternately the engine may build a hash table instead of sorting solutions; such decisions are made by the optimizer based on a number of factors.

#### Skipping Intermediate Results

One tricky part of understanding Stardog query plans is that evaluation of each operator in the plan is context-sensitive, i.e. it depends on what other nodes are in the same plan, maybe in a different sub-tree. In particular, the cardinality estimations, even if assumed accurate, only specify how many solutions the operator is expected to produce when evaluated as the root node of a plan.

However, as it is joined with other parts of the plan, the results can be different. This is because Stardog employs optimizations to reduce the number of solutions produced by a node by pruning those which are incompatible with other solutions with which they will later be joined.

Consider the following basic graph pattern and the corresponding plan:

?erdoes rdf:type foaf:Person .
?erdoes foaf:name "Paul Erdoes"^^xsd:string .
?document dc:creator ?erdoes .

MergeJoin(?erdoes) [#10]
+─ MergeJoin(?erdoes) [#1]
│  +─ Scan[POSC](?erdoes, rdf:type, foaf:Person) [#433K]
│  ─ Scan[POSC](?erdoes, foaf:name, "Paul Erdoes") [#1]
─ Scan[POSC](?document, dc:creator, ?erdoes) [#898K]

The pattern matches all documents created by a person named Paul Erdoes. Here the second pattern is selective (only one entity is expected to have the name "Paul Erdoes"). This information is propagated to the other two scans in the plan via merge joins, which allows them to skip scanning large parts of data indexes.

In other words, the node Scan[POSC](?erdoes, rdf:type, foaf:Person) [#433K] will not produce all 433K solutions corresponding to all people in the database and, similarly, Scan[POSC](?document, dc:creator, ?erdoes) [#898K] will not go through all 898K document creators.

#### Diagnosing Performance Problems

Performance problems may arise because of two reasons:

1. complexity of the query itself, especially the amount of returned data

2. failure to select a good plan for the query.

It is important to distinguish the two. In the former case the best way forward is to make the patterns in WHERE more selective. In the latter case, i.e. when the query returns some modest number of results but takes an unacceptably long time to do so, one needs to look at the plan, identify the bottlenecks (most often, pipeline breakers), and reformulate the query or report it to us for further analysis.

Here’s an example of a un-selective query:

SELECT DISTINCT ?name1 ?name2
WHERE {
?article1 rdf:type bench:Article .
?article2 rdf:type bench:Article .
?article1 dc:creator ?author1 .
?author1 foaf:name ?name1 .
?article2 dc:creator ?author2 .
?author2 foaf:name ?name2 .
?article1 swrc:journal ?journal .
?article2 swrc:journal ?journal
FILTER (?name1<?name2)
}

The query returns all distinct pairs of authors who published (possibly different) articles in the same journal. It returns more than 18M results from a database of 5M triples. Here’s the plan:

Distinct [#17.7M]
─ Projection(?name1, ?name2) [#17.7M]
─ Filter(?name1 < ?name2) [#17.7M]
─ HashJoin(?journal) [#35.4M]
+─ MergeJoin(?author2) [#391K]
│  +─ Sort(?author2) [#391K]
│  │  ─ NaryJoin(?article2) [#391K]
│  │     +─ Scan[POSC](?article2, rdf:type, bench:Article) [#208K]
│  │     +─ Scan[PSOC](?article2, swrc:journal, ?journal) [#208K]
│  │     ─ Scan[PSOC](?article2, dc:creator, ?author2) [#898K]
│  ─ Scan[PSOC](?author2, foaf:name, ?name2) [#433K]
─ MergeJoin(?author1) [#391K]
+─ Sort(?author1) [#391K]
│  ─ NaryJoin(?article1) [#391K]
│     +─ Scan[POSC](?article1, rdf:type, bench:Article) [#208K]
│     +─ Scan[PSOC](?article1, swrc:journal, ?journal) [#208K]
│     ─ Scan[PSOC](?article1, dc:creator, ?author1) [#898K]
─ Scan[PSOC](?author1, foaf:name, ?name1) [#433K]

This query requires an expensive join on ?journal which is evident from the plan (it’s a hash join in this case). It produces more than 18M results (Stardog expects 17.7M which is pretty accurate here) that need to be filtered and examined for duplicates. Given all this information from the plan, the only reasonable way to address the problem would be to restrict the criteria, e.g. to particular journals, people, time periods, etc.

If a query is well-formulated and selective, but performance is unsatisfactory, one may look closer at the pipeline breakers, e.g. this part of the query plan:

MergeJoin(?person) [#391K]
+─ Sort(?person) [#391K]
|  ─ MergeJoin(?article) [#391K]
|     +─ Scan[POSC](?article, rdf:type, bench:Article) [#208K]
|     ─ Scan[PSOC](?article, dc:creator, ?person) [#898K]
─ Scan[PSOC](?person, foaf:name, ?name) [#433K]

A reasonable thing to do would be to evaluate the join of ?article rdf:type bench:Article and ?article dc:creator ?person separately, i.e. as a separate queries, to see if the estimation of 391K is reasonably accurate and to get an idea about memory pressure. This is a valuable piece of information for a performance problem report, especially when the data cannot be shared with us. Similar analysis can be done for hash joins.

In addition to pipeline breakers, there could be other clear indicators of performance problems. One of them is the presence of LoopJoin nodes in the plan. Stardog implements the nested loop join algorithm which evaluates the join by going through the Cartesian product of its inputs. This is the slowest join algorithm and it is used only as a last resort. It sometimes, but not always, indicates a problem with the query.

Here’s an example:

 SELECT DISTINCT ?person ?name
WHERE {
?article rdf:type bench:Article .
?article dc:creator ?person .
?inproc rdf:type bench:Inproceedings .
?inproc dc:creator ?person2 .
?person foaf:name ?name .
?person2 foaf:name ?name2
FILTER (?name=?name2)
}

The query is similar to an earlier query plan we saw but runs much slower. The plan shows why:

Distinct [#98456.0M]
─ Projection(?person, ?name) [#98456.0M]
─ Filter(?name = ?name2) [#98456.0M]
─ LoopJoin(_) [#196912.1M]
+─ MergeJoin(?person) [#391K]
│  +─ Sort(?person) [#391K]
│  │  ─ MergeJoin(?article) [#391K]
│  │     +─ Scan[POSC](?article, rdf:type, bench:Article) [#208K]
│  │     ─ Scan[PSOC](?article, dc:creator, ?person) [#898K]
│  ─ Scan[PSOC](?person, foaf:name, ?name) [#433K]
─ MergeJoin(?person2) [#503K]
+─ Sort(?person2) [#503K]
│  ─ MergeJoin(?inproc) [#503K]
│     +─ Scan[POSC](?inproc, rdf:type, bench:Inproceedings) [#255K]
│     ─ Scan[PSOC](?inproc, dc:creator, ?person2) [#898K]
─ Scan[PSOC](?person2, foaf:name, ?name2) [#433K]

The loop join near the top of the plan computes the Cartesian product of the arguments which produces almost 200B solutions. This is because there is no shared variable between the parts of the query which correspond to authors of articles and conference proceedings papers, respectively. The filter condition ?name = ?name2 cannot be transformed into an equi-join because the semantics of term equality used in filters is different from the solution compatibility semantics used for checking join conditions.

The difference manifests itself in the presence of numerical literals, e.g. "1"^^xsd:integer = "1.0"^^xsd:float, where they are different RDF terms. However, as long as all names in the data are strings, one can re-formulate this query by renaming ?name2 to ?name which would enable Stardog to use a more efficient join algorithm.

#### Query Plan Operators

The following operators are used in Stardog query plans:

• Scan[Index]: evaluates a triple/quad pattern against Stardog indexes. Indicates the index used, e.g. CSPO or POSC, where S,P,O,C stand for the kind of lexicographic ordering of quads that the index provides. SPOC means that the index is sorted first by *S*ubject, then *P*redicate, *O*bject, and *C*ontext (named graph IRI).

• HashJoin(join key): hash join algorithm, hashes the right operand. Pipeline breaker.

• DirectHashJoin(join key): a hash join algorithm which directly uses indexes for lookups instead of building a hash table. Not a pipeline breaker.

• MergeJoin(join key): merge join algorithm, the fastest option for joining two streams of solutions. Requires both operands be sorted on the join key. Not a pipeline breaker.

• LoopJoin: the nested loops join algorithm, the slowest join option. Not a pipeline breaker.

• Sort(sort key): sorts the argument solutions by the sort key, typically used as a part of a merge join. Pipeline breaker.

• Filter(condition): filters argument solutions according to the condition. Not a pipeline breaker.

• Union: combines streams of argument solutions. If both streams are sorted by the same variable, the result is also sorted by that variable. Not a pipeline breaker.

• PropertyPath: evaluates a property path pattern against Stardog indexes. Not a pipeline breaker.

• GroupBy: groups results of the child operator by values of the group-by expressions (i.e. keys) and aggregates solutions for each key. Pipeline breaker (unless the input is sorted by first key).

• Distinct: removes duplicate solutions from the input. Not a pipeline breaker but accumulates solutions in memory as it runs so the memory pressure increases as the number of unique solutions increases.

• VALUES: produces the inlined results specified in the query. Not a pipeline breaker.

• Search: evaluates a full-text search predicates against the Lucene index within a Stardog database.

• Projection: projects variables as results of a query or a sub-query. Not a pipeline breaker.

• Bind: evaluates expressions on each argument solution and binds their values to (new) variables. Not a pipeline breaker.

• Empty and Singleton: correspond to the empty solution set and a single empty solution, respectively.

• Type: reasoning operator for evaluating patterns of the form ?x rdf:type ?type or :instance rdf:type ?type. Not a pipeline breaker.

• Property: operator for evaluating triple patterns with unbound predicate with reasoning. Not a pipeline breaker.

• Service: SPARQL federation operator which evaluate a pattern against a remote SPARQL endpoint (or a virtual graph).

#### Using Query Hints

Query hints help Stardog generate optimized query plans. They are implemented as SPARQL comments started with the pragma keyword.

The equality.identity hint expects a comma-separated list of variables. It tells Stardog that these variables will be bound to RDF terms (IRIs, bnodes, or literals) for which equality coincides with identity (i.e. any term is equal only to itself). This is not true for literals of certain numerical datatypes [cf. Operator Mapping](https://www.w3.org/TR/sparql11-query/#OperatorMapping). However assuming that the listed variables do not take on values of such datatypes can sometimes lead to faster query plans, for example, because of converting some filters to joins and through value inlining.

 SELECT ?o ?o2 WHERE {
#pragma equality.identity ?o,?o2
:a :p  ?o .
:b :p ?o2 .
}

Sometimes our query planner can produce sub-optimal join orderings. The group.joins hint introduces an explicit scoping mechanism to help with join order optimization. Patterns in the scope of the hint, given by the enclosing {}, will be joined together before being joined with anything else. This way, you can tell the query planner what you think is the optimal way to join variables.

select ?s where {
?s :p ?o1 .
{
#pragma group.joins
#these patterns will be joined first, before being joined with the other pattern
?s :p ?o2 .
?o1 :p ?o3 .
}
}

The push.filters hint controls how the query optimizer pushes filters down the query plan. There are three possible values: default, aggressive, and off. The aggressive option means that the optimizer will push every filter to the deepest operator in the plan which binds variables used in the filter expression. The off option turn the optimization off and each filter will be applied to the top operator in the filter’s graph pattern (in case there’re multiple filters, their order is not specified). Finally, the default option (or absence of the hint) means that the optimizer will decide whether to push each filter down the plan based on various factors, e.g. the filter’s cost, selectivity of the graph pattern, etc.

select ?s where {
#pragma push.filters off
#the filter in the top scope will not be pushed into the union
?s :p ?o1 .
FILTER (?o2 > 10)
{
#pragma push.filters aggressive
#the optimizer will place this filter directly on top of ?s :r ?o3
#and it will be evaluated before the results are joined with ?s :p ?o2
?s :p ?o2 ;
:r ?o3 .
FILTER (?o3 > 1000)
}
UNION
{
#pragma push.filters default
#the optimizer will decide whether to place the filter directly
#on top of ?s :q ?o3 or leave it on top of the join
?s a :Type ;
:q ?o3 .
FILTER (?o3 < 50)
}
}

### ACID Transactions

What follows is specific guidance about Stardog’s transactional semantics and guarantees.[6]

#### Atomicity

Databases may guarantee atomicity—​groups of database actions (i.e., mutations) are irreducible and indivisible: either all the changes happen or none of them happens. Stardog’s transacted writes are atomic. Stardog does not support nested transactions.[7]

#### Consistency

Data stored should be valid according to the data model (in this case, RDF) and to the guarantees offered by the database, as well as to any application-specific integrity constraints that may exist. Stardog’s transactions are guaranteed not to violate integrity constraints during execution. A transaction that would leave a database in an inconsistent or invalid state is aborted.

See the Validating Constraints section for a more detailed consideration of Stardog’s integrity constraint mechanism.

#### Isolation

A Stardog connection will run in READ COMMITTED isolation level if it has not started an explicit transaction and will run in READ COMMITTED SNAPSHOT or SERIALIZABLE isolation level depending on the value of the transaction.isolation. In any of these modes, uncommitted changes will only be visible to the connection that made the changes: no other connection can see those values before they are committed. Thus, "dirty reads" can never occur.

The difference between READ COMMITTED and READ COMMITTED SNAPSHOT isolation levels is that in the former case a connection will see updates committed by another connection immediately, whereas in the latter case a connection will see a transactionally consistent snapshot of the data as it existed at the start of the transaction and will not see any updates.

We illustrate the difference between these two levels with the following example where initially the database has a single triple :x :val 1.

 Time Connection 1 Connection 2 Connection 3 0 SELECT ?val {?x :val ?val} ⇐ 1 SELECT ?val {?x :val ?val} ⇐ 1 SELECT ?val {?x :val ?val} ⇐ 1 1 BEGIN TX 2 INSERT {:x :value 2} DELETE {:x :value ?old} 3 SELECT ?val {?x :val ?val} ⇐ 2 SELECT ?val {?x :val ?val} ⇐ 1 SELECT ?val {?x :val ?val} ⇐ 1 4 BEGIN TX 5 COMMIT 6 SELECT ?val {?x :val ?val} ⇐ 2 SELECT ?val {?x :val ?val} ⇐ 2 SELECT ?val {?x :val ?val} ⇐ 1 8 INSERT {:x :value 3} DELETE {:x :value ?old} 9 COMMIT 10 SELECT ?val {?x :val ?val} ⇐ 3 SELECT ?val {?x :val ?val} ⇐ 3 SELECT ?val {?x :val ?val} ⇐ 3

No locks are taken, or any conflict resolution performed, for concurrent transactions in READ COMMITTED SNAPSHOT isolation level. If there are conflicting changes, the latest commit wins which may yield unexpected results since every transaction reads from a snapshot that was created at the time transaction started.

Consider the following query being executed by two concurrent threads in READ COMMITTED SNAPSHOT isolation level against a database having the triple :counter :val 1 initially:

INSERT { :counter :val ?newValue }
DELETE { :counter :val ?oldValue }
WHERE  { :counter :val ?oldValue
BIND (?oldValue+1 AS ?newValue) }

Since each transaction will read the current value from its snapshot, it is possible that both transactions will read the value 1 and insert the value 2 even though we expect the final value to be 3.

Isolation level SERIALIZABLE can be used to avoid these situations. In SERIALIZABLE mode an exclusive lock needs to be acquired before a transaction begins. This ensures concurrent updates cannot interfere with each other, but as a result update throughput will decrease since only one transaction can run at a time.

#### Durability

By default Stardog’s transacted writes are durable and no other actions are required.

#### Commit Failure Autorecovery

Stardog’s transaction framework is low maintenance; but there are some rare conditions in which manual intervention may be needed.

Stardog’s strategy for recovering automatically from commit failure is as follows:

1. Stardog will roll back the transaction upon a commit failure;

2. Stardog takes the affected database offline for maintenance;[8] then

3. Stardog will begin recovery, bringing the recovered database back online once that task is successful so that operations may resume.

With an appropriate logging configuration for production usage (at least error-level logging), log messages for the preceding recovery operations will occur. If for whatever reason the database fails to be returned automatically to online status, an administrator may use the CLI tools (i.e., stardog-admin db online) to try to online the database.

Stardog tries hard to do bulk loading at database creation time in the most efficient and scalable way possible. But data loading time can vary widely, depending on factors in the data to be loaded, including the number of unique resources, etc. Here are some tuning tips that may work for you:

1. Use the bulk_load memory configuration for loading large databases (see Memory Configuration section).

2. Load compressed data since compression minimizes disk access

3. Use a multicore machine since bulk loading is highly parallelized and indexes are built concurrently

4. Load many files together at creation time since different files will be parsed and processed concurrently improving the load speed

5. Turn off strict parsing (see Configuring a Database for the details).

6. If you are not using named graphs, use triples only indexing.

## Memory Management

As of version 5.0, Stardog by default uses a custom memory management approach to minimize GC activity during query evaluation. All intermediate query results are now managed in native (off-heap) memory which is pre-allocated on server start-up and never returned to the OS until server shutdown. Every query, including SPARQL Update queries with the WHERE clause, gets a chunk of memory from that pre-allocated pool to handle intermediate results and will return it back to the pool when it finishes or gets cancelled. More technical details about this GC-less memory management scheme are available in a recent blog post.

The main goal of this memory management approach is to improve server’s resilience under heavy load. A common problem with JVM applications under load is the notorious Out-Of-Memory (OOM) exceptions which are hard to foresee and impossible to reliably recover from. Also, in the SPARQL world, it is generally difficult to estimate how many intermediate results any particular query will have to process before the query starts (although the selectivity statistics offers great help to this end). As such, the server has to deal with the situation when there is no memory available to continue with the current query. Stardog handles this by placing all intermediate results into custom collections which are tightly integrated with the memory manager. Every collection, e.g. for hashing, sorting, or aggregating binding sets, requests memory blocks from the manager and transparently spills data to disk when such requests are denied.

This helps avoid OOMs at any time during query evaluation since running out of memory only means triggering spilling and the query will continue slower because of additional disk access. This also means Stardog 5.0+ can run harder, e.g. analytic, queries which may exceed the memory capacity on your server. We have also seen performance improvements in specific (but common) scenarios, such as with many concurrent queries, where the GC pressure would considerably slow down the server running on heap. However, everything comes at a price and the custom collections can be slightly slower than those based on JDK collections when the server is under light load, all queries are selective, and there is no GC pressure. For that reason Stardog has a server option memory.management which you can set to JVM in stardog.properties to disable custom memory management and have Stardog run all queries on heap.

The spilling.dir server option specifies the directory which will be used for spilling data in case the server runs out of native memory. It may make sense to set this to another disk to minimize disk contention.

### Memory Configuration

Stardog 5.0 provides a range of configuration options related to memory management. Query engine by default uses the custom memory management approach described above but it is not the only critical Stardog component which may require a large amount of memory. Memory is also consumed aggressively during bulk loading and updates. Stardog defines three standard memory consumption modes to allow users to configure how memory should be distributed based on the usage scenario. The corresponding server property is memory.mode which accepts the following values:

1. default: This is the default option which provides roughly equal amount of memory for queries and updates (including bulk loading). This should be used either when the server is expected to run both read queries and updates in roughly equal proportion or when the expected load is unknown.

2. read_optimized: This option provides more memory to read queries and SPARQL Update queries with the WHERE clause. This minimizes the chance of having to spill data to disk during query execution at the expense of update and bulk loading operations. This option should be used when the transactions will be infrequent or small in size, e.g. up to a thousand triples since such transactions do not use significant amount of memory.

3. write_optimized: This option should be used for optimal loading and update performance. Queries may run slower if there is not enough memory for processing intermediate results. It may be also suitable when the server is doing a lot of updates and some read queries but the latter are selective and are not highly concurrent.

4. bulk_load: This option should be used for bulk loading very large databases (billions of triples) where there is no other workload on the server. When bulk loading is complete, the memory configuration should be changed and the server restarted.

As with any server option the server has to be restarted after the user changes the memory mode. The stardog-admin server status command displays detailed information on memory usage and the current configuration.

## Capacity Planning

The primary system resources used by Stardog are CPU, memory, and disk.[9] Stardog will take advantage of more than one CPU, core, and core-based thread in data loading and in throughput-heavy or multi-user loads. Stardog performance is influenced by the speed of CPUs and cores. But some workloads are bound by main memory or by disk I/O (or both) more than by CPU. Use the fastest CPUs you can afford with the largest secondary caches and the most number of cores and core-based threads of execution, especially in multi-user workloads.

The following subsections provides more detailed guidance for the memory and disk resource requirements of Stardog.

### Memory usage

Stardog uses system memory aggressively and the total system memory available to Stardog is often the most important factor in performance. Stardog uses both JVM memory (heap memory) and also the operating system memory outside the JVM (off heap memory). Having more system memory available is always good; however, increasing JVM memory too close to total system memory is not prudent as it may tend to increase Garbage Collection (GC) time in the JVM.

The following table shows recommended JVM memory and system memory requirements for Stardog.[10]

5. Table of Memory Usage for Capacity Planning
# of Triples JVM Memory Off-heap memory

100 million

3GB

3GB

1 billion

4GB

8GB

10 billion

8GB

64GB

20 billion

16GB

128GB

50 billion

16GB

256GB

Out of the box, Stardog sets the maximum JVM memory to 2GB and off-heap memory to 1GB. These settings work fine for most small databases (up to, say, 100 million triples). As the database size increases, we recommend increasing memory. You can increase the memory for Stardog by setting the system property STARDOG_SERVER_JAVA_ARGS using the standard JVM options. For example, you can set this property to "-Xms4g -Xmx4g -XX:MaxDirectMemorySize=8g" to increase the JVM memory to 4GB and off-heap to 8GB. We recommend setting the minimum heap size (-Xms option) and max heap size (-Xmx option) to the same value.

#### System Memory and JVM Memory

Stardog uses an off-heap, custom memory allocation scheme. Please note that the memory provisioning recommendations above are for two kinds of memory allocations for the JVM in which Stardog will run. The first is for memory that the JVM will manage explicitly (i.e., "JVM memory"); and the second, i.e., "Off-heap memory" is for memory that Stardog will manage explicitly, i.e., off the JVM heap, but for which the JVM should be notified via the MaxDirectMemorySize property. The sum of these two values should be less than 90% of the total memory available to the underlying operating system as requirements dictate.

### Disk usage

Stardog stores data on disk in a compressed format. The disk space needed for a database depends on many factors besides the number of triples, including the number of unique resources and literals in the data, average length of resource identifiers and literals, and how much the data is compressed. The following table shows typical disk space used by a Stardog database.

6. Table of Typical Disk Space Requirements
# of triples Disk space

1 billion

70GB to 100GB

10 billion

700GB to 1TB

These numbers are given for information purposes only; the actual disk usage for a database may be different in practice. Also it is important to realize the amount of disk space needed at creation time for bulk loading data is higher as temporary files will be created. The extra disk space needed at bulk loading time can be 40% to 70% of the final database size.

Disk space used by a database is non-trivially smaller if triples-only indexing is used. Triples-only indexing reduces overall disk space used by 25% on average; however, note the tradeoff: SPARQL queries involving named graphs perform better with quads indexing.

The disk space used by Stardog is additive for more than one database and there is little disk space used other than what is required for the databases. To calculate the total disk space needed for more than one database, one may sum the disk space needed by each database.

## Using Stardog on Windows

Stardog provides batch (.bat) files for use on Windows; they offer roughly the same set of functionality provided by the Bash scripts which are used on Unix-like systems. There are, however, a few small differences between the two. When you start a server with server start on Windows, this does not detach to the background, it will run in the current console.

To shut down the server correctly, you should either issue a server stop command from another window or press Ctrl+C (and then Y when asked to terminate the batch job). Do not under any circumstance close the window without shutting down the server. This will simply kill the process without shutting down Stardog, which may cause your database to be corrupted.

The .bat scripts for Windows support our standard STARDOG_HOME and STARDOG_SERVER_JAVA_ARGS environment variables which can be used to control where Stardog’s database is stored and how much memory is given to Stardog’s JVM when it starts. By default, the script will use the JVM that is available in the directory from which Stardog is run via the JAVA_HOME environment variable. If this is not set, it will simply execute java from within that directory.

### Running Stardog as a Windows Service

You can run Stardog as a Windows Service using the following configuration. Please, note, that the following assumes commands are executed from a Command Prompt with administrative privileges.

#### Installing the Service

Change the directory to the Stardog installation directory:

cd c:\stardog-$VERSION #### Configuring the Service The default settings with which the service will be installed are • 2048 MB of RAM • STARDOG_HOME is the same as the installation directory • the name of the installed service will be "Stardog Service" • Stardog service will write logs to the "logs" directory within the installation directory To change these settings, set appropriate environment variables: • STARDOG_MEMORY: the amount of memory in MB (e.g., set STARDOG_MEMORY=4096) • STARDOG_HOME: the path to STARDOG_HOME (e.g., set STARDOG_HOME=c:\\stardog-home) • STARDOG_SERVICE_DISPLAY_NAME: a different name to be displayed in the list of services (e.g., set STARDOG_SERVICE_DISPLAY_NAME=Stardog Service) • STARDOG_LOG_PATH: a path to a directory where the log files should be written (e.g., set STARDOG_LOG_PATH=c:\\stardog-logs) If you have changed the default administrator password, you also need to change stop-service.bat and specify the new username and password there (by passing -u and -p parameters in the line that invokes stardog-admin server stop). #### Installing Stardog as a Service Run the install-service.bat script. At this point the service has been installed, but it is not running. To run it, see the next section or use any Windows mechanism for controlling the services (e.g., type services.msc on the command line). #### Starting, Stopping, & Changing Service Configuration Once the service has been installed, execute stardog-serverw.exe, which will allow you to configure the service (e.g., set whether the service is started automatically or manually), manually start and stop the service, as well as to configure most of the service parameters. #### Uninstalling the Stardog Service The service can be uninstalled by running uninstall-service.bat script. ## Using Stardog with Kerberos Stardog can be configured to run in both MIT and Active Directory Kerberos environments. In order to do so a keytab file must be properly created. Once the keytab file is acquired the following options can be set in stardog.properties:[11] 1. krb5.keytab: The path to the keytab file for the Stardog server. 2. krb5.principal: The principal of the credential stored in the keytab file. Often this is of the format HTTP/<canonical DNS name of the host>@<REALM>. 3. krb5.admin.principal: The Kerberos principal that will be the default administrator of this service. 4. krb5.debug: A boolean value to enable debug logging in the Java Kerberos libraries. Once Stardog is propery configured for Kerberos Stardog user names should be created that match their associated Kerberos principal names. Authentication will be done based on the Kerberos environment and authorization is done based on the principal names matching Stardog users. # Enterprise Data Unification Stardog is an Enterprise Knowledge Graph platform, which means that it’s also a data unification platform. Enterprises have lots of data and lots of data sources and almost all of them are locked away in IT silos and stovepipes that impede insight, analysis, reporting, compliance, and operations. State of the art IT management tells us to organize data, systems, assets, staffs, schedules, and budgets vertically to mirror lines of business. But increasingly all the internal and external demands on IT are horizontal in nature: the data is organized vertically, but the enterprise increasingly needs to access and understand it horizontally. ## Structured Data Stardog supports a set of techniques for unifying structured enterprise data, chiefly, Virtual Graphs: tabular data declaratively mapped into a Stardog database graph and queries by Stardog in situ, typically using SQL. Stardog rewrites (a portion of) SPARQL queries against the Stardog database into SQL, issues that SQL query to an RDBMS, and translates the SQL results into SPARQL results. Virtual Graphs can be used for mapping any tabular data, e.g. CSV, to RDF. Stardog also supports mapping NoSQL sources such as MongoDB or Cassandra to graphs, and in the future other semi-structured formats including XML, JSON and enterprise directory services. A Virtual Graph has three components: • a unique name • configuration options • data source connection parameters • query and data parameters • mappings for the data A virtual graph’s name must conform to the regular expression [A-Za-z]{1}[A-Za-z0-9_-]. The configuration file includes several parameters, including a JDBC connection. Finally, the mappings define how the tabular data stored in the RDBMS will be represented in RDF. The mappings are defined using the R2RML mapping language, but the simpler Stardog Mapping Syntax and Stardog Mapping Syntax 2 are also supported as alternatives. ### Supported Databases Stardog Virtual Graphs support the following relational database systems: • Apache Hive • Apache/Cloudera Impala • AWS Aurora • IBM DB2 • H2 & Derby • Microsoft SQL Server • MySQL & MariaDB • Oracle • PostgreSQL • Sybase ASE Stardog Virtual Graphs also supports the following NoSQL databases: • Apache Cassandra • Cosmos DB • MongoDB Please inquire if you need support for another. ### Managing Virtual Graphs In order to query a Virtual Graph it must first be registered with Stardog. Adding a new virtual graph is done via the following command: $ stardog-admin virtual add dept.properties dept.ttl

The first argument (dept.properties) is the configuration file for the virtual graph and the second argument ('dept.ttl') is the mappings file. The name of the configuration file is used as the name of the virtual graph, so the above command registers a virtual graph named dept. Configuration files should be in the Java properties file format and provide JDBC data source and virtual graph configuration. The configuration reference is at Virtual Graph Configuration Options. A minimal example looks like this:

jdbc.url=jdbc:mysql://localhost/dept
jdbc.driver=com.mysql.jdbc.Driver
 Note Stardog does not ship with JDBC drivers. You need to manually copy the JAR file containing the driver to the STARDOG/server/dbms/ or STARDOG_EXT directory so that it will be available to the Stardog server. The server needs to be restarted after the JAR is copied.

The credentials for the JDBC connection need to be provided in plain text. An alternative way to provide credentials is to use the password file mechanism. The credentials should be stored in a password file called services.sdpass located in STARDOG_HOME directory. The password file entries are in the format hostname:port:database:username:password so for the above example there should be an entry localhost:*:dept:admin:admin in this file. Then the credentials in the configuration file can be omitted.

The configuration file can also contain a property called base to specify a base URI for resolving relative URIs generated by the mappings (if any). If no value is provided, the base URI will be virtual://myGraph where myGraph is the name of the virtual graph.

The add command by default assumes the mappings are using the Stardog Mapping Syntax. Mappings in standard R2RML syntax can be used by adding the --format r2rml option in the above command. There is also Stardog Mapping Syntax 2 (SMS2), a syntax that better supports hierarchical datasources like JSON and XML. To add a virtual graph using mappings in SMS2 format, add --format sms2 to the command line.

If there are no mappings provided to the add commands then the R2RML direct mapping is used.

Registered virtual graphs can be listed:

$stardog-admin virtual list 1 virtual graphs dept The commands virtual mappings and virtual options can be used to retrieve the mappings and configuration options associated with a virtual graph respectively. Registered virtual graphs can be removed using the virtual remove command. See the Man Pages for the details of these commands. ### Querying Virtual Graphs Querying Virtual Graphs is done by using the GRAPH clause, using a special graph URI in the form virtual://myGraph to query the Virtual Graph named myGraph. The following example shows how to query dept: SELECT * { GRAPH <virtual://dept> { ?person a emp:Employee ; emp:name "SMITH" } } Virtual graphs are defined globally in Stardog Server. Once they are registered with the server they can be accessed via any Stardog database as allowed by the access rules. We can query the local Stardog database and virtual graph’s remote data in a single query. Suppose we have the dept virtual graph, defined as above, that contains employee and department information, and the Stardog database contains data about the interests of people. We can use the following query to combine the information from both sources: SELECT * { GRAPH <virtual://dept> { ?person a emp:Employee ; emp:name "SMITH" } ?person foaf:interest ?interest }  Note Query performance will be best if the GRAPH clause for Virtual Graphs is as selective as possible. ### Virtual Graph Query Syntax Virtual Graph queries are implemented by executing a query against the remote data source. This is a powerful feature and care must be taken to ensure peak performance. SPARQL and SQL don’t have feature parity, especially given the varying capabilities of SQL implementations. Stardog’s query translator supports most of the salient features of SPARQL including: • Arbitrarily nested subqueries (including solution modifiers) • Aggregation • FILTER (including most SPARQL functions) • OPTIONAL, UNION, BIND That said, there are also limitations on translated queries. This includes: • SPARQL MINUS is not currently translated to SQL • Comparisons between objects with different datatypes don’t always follow XML Schema semantics • Named graphs in R2RML are not supported ### Virtual Graph Security Accessing Virtual Graphs can be controlled similar to regular named graphs as explained in the Named Graph Security section. If named graph security is not enabled for a database, all registered Virtual Graphs in the server will be accessible through that database. If named graph security is enabled for a database, then users will be able to query only the Virtual Graphs for which they have been granted access. If the virtual graphs contain any sensitive information, then it is recommended to enable named graph security globally by setting security.named.graphs=true in stardog.properties. Otherwise creating a new database without proper configuration would allow users to access those Virtual Graphs. ### Materializing Virtual Graphs In some cases you need to materialize the information stored in RDBMS directly into RDF. There is a special command virtual import that can be used to import the contents of the RDBMS into Stardog. The command can be used as follows: $ stardog-admin virtual import myDb dept.properties dept.ttl

This command adds all the mapped triples from the RDBMS into the default graph. Similar to virtual add, this command assumes Stardog Mapping Syntax by default and can accept R2RML mappings using the --format r2rml option or Stardog Mapping Syntax 2 mappings using the --format sms2 option.

It is also possible to specify a target named graph by using the -g/--namedGraph option:

$stardog-admin virtual import -g http://example.com/targetGraph myDb dept.properties dept.ttl This virtual import command is equivalent to the following SPARQL update query: ADD <virtual://dept> TO <http://example.com/targetGraph> If the RDBMS contents change over time, and we need to update the materialization results in the future, we can clear the named graph contents and rematerialize again. This can be done by using the --remove-all option in virtual import or with the following SPARQL query: COPY <virtual://dept> TO <http://example.com/targetGraph> Query performance over materialized graphs will be better as the data will be indexed locally by Stardog, but materialization may not be practical in cases where frequency of change is very high. ### CSV as Virtual Graph Mappings can be used to treat CSV files as Virtual Graphs since they represent tabular data similar to RDBMS tables. But unlike RDBMS tables, CSV files are supported only for importing into Stardog. The same import command above can be used to specify a mappings file and an input CSV file: $ stardog-admin virtual import myDB cars.ttl cars.csv

If the input file is using different kind of separators, e.g. tab character, a configuration file can be provided

$stardog-admin virtual import myDB cars.properties cars.ttl cars.tsv The configuration file for CSV files can specify values for the following properties: csv.separator (character for separating fields), csv.quote (character for strings), csv.escape (character for escaping special characters), csv.header (boolean value for specifying whether or not the input file has a header line at the beginning). Note that, whitespace characters in Java properties file need to be escaped so if you want to import tab-separated value files set csv.separator=\t in the configuration file.  Note CSV files are processed on the client side and then sent to the Stardog server. If you run out of memory, you can increase the memory for the client using the STARDOG_JAVA_ARGS environment variable. This environment variable should only be set for the virtual import command to avoid affecting other client command invocations. This can be done like so: $ STARDOG_JAVA_ARGS="-Xmx1g" stardog-admin virtual import myDB cars.properties cars.ttl cars.csv

### Stardog Mapping Syntax

The Stardog Mapping Syntax (SMS) is an alternative way to serialize R2RML mappings that is much simpler to read and write than R2RML. Mappings written in SMS can be converted to R2RML and vice versa. We will use the example database from the R2RML specification to explain SMS. The SQL schema that corresponds to this example is

CREATE TABLE "DEPT" (
"deptno" INTEGER UNIQUE,
"dname" VARCHAR(30),
"loc" VARCHAR(100));
INSERT INTO "DEPT" ("deptno", "dname", "loc")
VALUES (10, 'APPSERVER', 'NEW YORK');

CREATE TABLE "EMP" (
"empno" INTEGER PRIMARY KEY,
"ename" VARCHAR(100),
"job" VARCHAR(30),
"deptno" INTEGER REFERENCES "DEPT" ("deptno"),
"etype" VARCHAR(30));
INSERT INTO "EMP" ("empno", "ename", "job", "deptno", "etype" )
VALUES (7369, 'SMITH', 'CLERK', 10, 'PART_TIME');

Suppose we would like to represent this information in RDF using the same translation for job codes as in the original example:

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix emp: <http://example.com/emp/> .
@prefix dept: <http://example.com/dept/> .

dept:10 a dept:Department ;
dept:location "NEW YORK" ;
dept:deptno "10"^^xsd:integer .

emp:7369 a emp:Employee ;
emp:name "SMITH" ;
emp:role emp:general-office ;
emp:department dept:10 .

SMS looks very similar to the output RDF representation:

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix emp: <http://example.com/emp/> .
@prefix dept: <http://example.com/dept/> .
@prefix sm: <tag:stardog:api:mapping:> .

dept:{"deptno"} a dept:Department ;
dept:location "{\"loc\"}" ;
dept:deptno "{\"deptno\"}"^^xsd:integer ;
sm:map [
sm:table "DEPT" ;
] .

emp:{"empno"} a emp:Employee ;
emp:name "{\"ename\"}" ;
emp:role emp:{ROLE} ;
emp:department dept:{"deptno"} ;
sm:map [
sm:query """
SELECT \"empno\", \"ename\", \"deptno\", (CASE \"job\"
WHEN 'CLERK' THEN 'general-office'
WHEN 'NIGHTGUARD' THEN 'security'
WHEN 'ENGINEER' THEN 'engineering'
END) AS ROLE FROM \"EMP\"
""" ;
] .

SMS is based on Turtle, but it’s not valid Turtle since it uses the URI templates of R2RML—​curly braces can appear in URIs. Other than this difference, we can treat an SMS document as a set of RDF triples. SMS documents use the special namespace tag:stardog:api:mapping: that we will represent with the sm prefix below.

Every subject in the SMS document that has a sm:map property maps a single row from the corresponding table/view to one or more triples. If an existing table/view is being mapped, sm:table is used to refer to the table. Alternatively, a SQL query can be provided inline using the sm:query property.

The values generated will be a URI, blank node, or a literal based on the type of the value used in the mapping. The column names referenced between curly braces will be replaced with the corresponding values from the matching row.

SMS can be translated to the standard R2RML syntax. For completeness, we provide the R2RML mappings corresponding to the above example:

@prefix rr: <http://www.w3.org/ns/r2rml#> .
@prefix emp: <http://example.com/emp#> .
@prefix dept: <http://example.com/dept#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@base <http://example.com/base/> .

<DeptTriplesMap>
a rr:TriplesMap;
rr:logicalTable [ rr:tableName "DEPT" ];
rr:subjectMap [ rr:template "http://data.example.com/dept/{\"deptno\"}" ;
rr:class dept:Department ];
rr:predicateObjectMap [
rr:predicate	  dept:deptno ;
rr:objectMap    [ rr:column "\"deptno\""; rr:datatype xsd:positiveInteger ]
];
rr:predicateObjectMap [
rr:predicate	dept:location ;
rr:objectMap	[ rr:column "\"loc\"" ]
].

<EmpTriplesMap>
a rr:TriplesMap;
rr:logicalTable [ rr:sqlQuery """
SELECT "EMP".*, (CASE "job"
WHEN 'CLERK' THEN 'general-office'
WHEN 'NIGHTGUARD' THEN 'security'
WHEN 'ENGINEER' THEN 'engineering'
END) AS ROLE FROM "EMP"
""" ];
rr:subjectMap [
rr:template "http://data.example.com/employee/{\"empno\"}";
rr:class emp:Employee
];
rr:predicateObjectMap [
rr:predicate		emp:name ;
rr:objectMap    [ rr:column "\"ename\"" ];
];
rr:predicateObjectMap [
rr:predicate emp:role;
rr:objectMap [ rr:template "http://data.example.com/roles/{ROLE}" ];
];
rr:predicateObjectMap [
rr:predicate emp:department;
rr:objectMap [ rr:template "http://example.com/dept/{\"deptno\"}"; ];
].

### Semi-Structured as Virtual Graph

Semi-structured datasources such as NoSQL databases, enterprise directory services, and RESTful web services return data in non-tabular, often hierarchical formats such as JSON and XML. They also often have proprietary APIs and query languages. To address these challenges, Stardog supports a mapping syntax, Stardog Mapping Syntax 2 (SMS2), where source mappings are specified intuitively using native formats. This syntax will be extended to support all structured and semi-structured data sources such as CSV, XML and enterprise directory services.

To add a semi-structured virtual graph, use the virtual add command with SMS2 as the format option:

$stardog-admin virtual add --format SMS2 movies.properties movies.sms The properties file should have a platform-specific connection property (mongodb.uri for MongoDB, cassandra.contact.point for Cassandra) in place of any properties with a jdbc prefix. The mappings file must be in SMS2 syntax for all semi-structured datasources except Cassandra, which supports all three mappings formats. ### Stardog Mapping Syntax 2 Stardog Mapping Syntax 2 (SMS2) is a way to represent virtual graph mappings that is designed to support a broad range of source data formats, including semi-structured formats such as JSON and XML, as well as structured formats like SQL RDBMS. SMS2 is loosely based on the SPARQL CONSTRUCT query. An abbreviated example looks like this: PREFIX : <http://stardog.com/movies/> MAPPING <urn:movies> FROM JSON { "movie":{ "_id":"?movieId", "name":"?name", } } TO { ?movie a :Movie ; :name ?name . } WHERE { BIND (template("http://stardog.com/movies/Title_{movieId}") AS ?movie) } It starts with optional PREFIX declarations, followed by the MAPPING keyword and an optional mapping name. A FROM clause defines the source data and assigns fields to variables. A TO clause expresses how the variables will be used to create new RDF statements using standard Turtle. Lastly a WHERE clause is used to transform data in the process of mapping. To help illustrate SMS2, we’ll use the following JSON for a movie collection from a MongoDB database: { "_id":"unforgiven", "name":"Unforgiven", "datePublished":new Date("1992-08-07T00:00:00.000Z"), "genre":["Drama", "Western"], "boxOffice":101157447, "description":"Retired gunslinger reluctantly takes on one last job.", "director":[ {"director":"clintEastwood", "name":"Clint Eastwood"} ], "actor":[ {"actor":"morganFreeman", "name":"Morgan Freeman"}, {"actor":"clintEastwood", "name":"Clint Eastwood"}, {"actor":"geneHackman", "name":"Gene Hackman"} ] } { "_id":"noWayOut", "name":"No Way Out", "datePublished":new Date("1987-08-14T00:00:00.000Z"), "genre":["Action", "Mystery", "Drama", "Thriller"], "boxOffice":35509515, "description":"A coverup and witchhunt occur after a politician accidentally kills his mistress.", "director":[ {"director":"rogerDonaldson", "name":"Roger Donaldson"} ], "actor":[ {"actor":"geneHackman", "name":"Gene Hackman"}, {"actor":"kevinCostner", "name":"Kevin Costner"} ] } For this example we’ll create mappings that represent the data as this RDF: @prefix : <http://stardog.com/movies/> . :Title_noWayOut a :Movie ; :name "No Way Out" ; :datePublished "1987-08-14"^^xsd:date ; :boxOffice 35509515 ; :description "A coverup and witchhunt occur after a politician accidentally kills his mistress." ; :genre "Action", "Mystery", "Drama", "Thriller" ; :directed :Job_noWayOut_rogerDonaldson ; :actedIn :Job_noWayOut_geneHackman, :Job_noWayOut_kevinCostner . :Title_unforgiven a :Movie ; :name "Unforgiven" ; :datePublished "1992-08-07"^^xsd:date ; :boxOffice 101157447 ; :description "Retired gunslinger reluctantly takes on one last job." ; :genre "Drama", "Western" ; :directed :Job_unforgiven_clintEastwood ; :actedIn :Job_unforgiven_morganFreeman, :Job_unforgiven_clintEastwood, :Job_unforgiven_geneHackman . :Job_noWayOut_rogerDonaldson a :DirectedMovie ; :name "Roger Donaldson" ; :director :Name_rogerDonaldson . :Name_rogerDonaldson a :Person . :Job_unforgiven_clintEastwood a :DirectedMovie ; :name "Clint Eastwood" ; :director :Name_clintEastwood . :Job_unforgiven_clintEastwood a :ActedInMovie ; :name "Clint Eastwood" ; :actor :Name_clintEastwood . :Name_clintEastwood a :Person . :Job_noWayOut_geneHackman a :ActedInMovie ; :name "Gene Hackman" ; :actor :Name_geneHackman . :Job_unforgiven_geneHackman a :ActedInMovie ; :name "Gene Hackman" ; :actor :Name_geneHackman . :Name_geneHackman a :Person . :Job_noWayOut_kevinCostner a :ActedInMovie ; :name "Kevin Costner" ; :actor :Name_kevinCostner . :Name_kevinCostner a :Person . :Job_unforgiven_morganFreeman a :ActedInMovie ; :name "Morgan Freeman" ; :actor :Name_morganFreeman . :Name_morganFreeman a :Person . Notice there are many IRIs that contain both Movie and Person ids. These scoped IRIs are redundant in this dataset but they serve a purpose when working with denormalized datasources, which is common in NoSQL databases like MongoDB. In this dataset, the name of a Person can appear in an actor or director object. The name is repeated for every directing or acting job that Person has had. There’s no guarantee that a Person’s name is constant across all their jobs, either because the field reflects the name the person had at the time of the job, or because of a problem during an update that led to the inconsistency. Without IRIs that scope a Person to a specific Movie, when you query for the Person’s name, the correct response is a record for every Person/name pair, which can be an expensive query. See the blog post Mapping Denormalized Data for more details. The SMS2 for this mapping exercise looks like this: PREFIX : <http://stardog.com/movies/> MAPPING <urn:movies> FROM JSON { "movie":{ "_id":"?movieId", "name":"?name", "datePublished":"?datePublished", "genre":["?genre"], "boxOffice":"?boxOffice", "description":"?description", "director":[ { "director":"?directorId", "name":"?directorName" } ], "actor":[ { "actor":"?actorId", "name":"?actorName" } ] } } TO { ?movie a :Movie ; :name ?name ; :datePublished ?xsdDatePublished ; :genre ?genre ; :boxOffice "?boxOffice"^^xsd:integer ; :description ?description ; :directed ?directedMovie ; :actedIn ?actedInMovie . ?directedMovie a :DirectedMovie ; :director ?director ; :name ?directorName . ?director a :Person . ?actedInMovie a :ActedInMovie ; :actor ?actor ; :name ?actorName . ?actor a :Person . } WHERE { BIND (template("http://stardog.com/movies/Job_{movieId}_{directorId}") AS ?directedMovie) BIND (template("http://stardog.com/movies/Job_{movieId}_{actorId}") AS ?actedInMovie) BIND (template("http://stardog.com/movies/Title_{movieId}") AS ?movie) BIND (template("http://stardog.com/movies/Name_{directorId}") AS ?director) BIND (template("http://stardog.com/movies/Name_{actorId}") AS ?actor) BIND (xsd:date(?datePublished) AS ?xsdDatePublished) } SMS2 consists of five clauses: PROLOGUE, MAPPING, FROM, TO, and WHERE. The PROLOGUE consists of a series of prefix declarations. The MAPPING through WHERE clauses define a mapping and can be repeated, separated by a semicolon. The MAPPING clause consists of the MAPPING keyword followed by an optional IRI for naming the mapping. The FROM clause describes the input. The FROM clause starts with the FROM keyword and is followed by a data format keyword (JSON in this case, but can be JSON, GraphQL or SQL) followed by a definition that describes the structure of the data and assigns fields to variable names. The TO clause defines how the output RDF should look. It is analogous to the CONSTRUCT portion of the SPARQL CONSTRUCT query. It consists of a set of triples where variables can be used as values. The WHERE clause is where transformations can be made to the data during the process of mapping. Each transformation is expressed using a BIND function. The currently supported functions for use within BIND are template and the cast functions (xsd:string, xsd:boolean, xsd:integer, xsd:float, xsd:double, xsd:decimal, xsd:dateTime, xsd:date). Notice there are no platform-specific query elements (such as MongoDB query syntax) present in the mapping, only descriptions of the source and target data schemas and transformations for mapping the relationship between the source and target. The structure of the FROM JSON definition is the source JSON with some changes. 1. There is an outermost key to indicate the name of the collection (movie). 2. Values are replaced by variable names. 3. Arrays contain a single element. 4. Only one document is supplied. Fields are interpreted as strings unless given a specific data type, either by using a cast function in the WHERE clause as illustrated in the example with the datePublished field, or directly in the TO clause as illustrated by the boxOffice field. The FROM GraphQL definition is an alternative format for hierarchical data. It is a Selection Set consisting of Fields, which can be aliased and can contain nested selection sets. By default, each field will be mapped to a variable with the same name as the field. If the field is aliased the alias will serve as the variable name. To identify an array, use an @array directive. The following mapping uses a FROM GraphQL clause to produce the same results as our prior example that used a FROM JSON clause. PREFIX : <http://stardog.com/movies/> MAPPING <urn:movies> FROM GraphQL { movie { movieId: _id name datePublished genre @array boxOffice description director @array { directorId: director directorName: name } actor @array { actorId: actor actorName: name } } } TO { ?movie a :Movie ; :name ?name ; :datePublished ?xsdDatePublished ; :genre ?genre ; :boxOffice "?boxOffice"^^xsd:integer ; :description ?description ; :directed ?directedMovie ; :actedIn ?actedInMovie . ?directedMovie a :DirectedMovie ; :director ?director ; :name ?directorName . ?director a :Person . ?actedInMovie a :ActedInMovie ; :actor ?actor ; :name ?actorName . ?actor a :Person . } WHERE { BIND (template("http://stardog.com/movies/Job_{movieId}_{directorId}") AS ?directedMovie) BIND (template("http://stardog.com/movies/Job_{movieId}_{actorId}") AS ?actedInMovie) BIND (template("http://stardog.com/movies/Title_{movieId}") AS ?movie) BIND (template("http://stardog.com/movies/Name_{directorId}") AS ?director) BIND (template("http://stardog.com/movies/Name_{actorId}") AS ?actor) BIND (xsd:date(?datePublished) AS ?xsdDatePublished) } Note how an array of primitives like genre has the @array directive while an array of objects has the @array directive followed by a selection set. If we wished to map, say, the genre field to a genres variable, we would use an alias, giving this complete line for the genre field: genres: genre @array. The third option for the FROM clause is FROM SQL, which is for RDBMS datasources. It differs from the JSON and GraphQL source template formats in that for SQL we provide a query in place of a data description. Stardog will interrogate the database schema to determine the field names (which will become variable names) to use for mapping. To explain the FROM SQL format, recall the SMS mapping in Stardog Mapping Syntax. @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . @prefix emp: <http://example.com/emp/> . @prefix dept: <http://example.com/dept/> . @prefix sm: <tag:stardog:api:mapping:> . dept:{"deptno"} a dept:Department ; dept:location "{\"loc\"}" ; dept:deptno "{\"deptno\"}"^^xsd:integer ; sm:map [ sm:table "DEPT" ; ] . emp:{"empno"} a emp:Employee ; emp:name "{\"ename\"}" ; emp:role emp:{ROLE} ; emp:department dept:{"deptno"} ; sm:map [ sm:query """ SELECT \"empno\", \"ename\", \"deptno\", (CASE \"job\" WHEN 'CLERK' THEN 'general-office' WHEN 'NIGHTGUARD' THEN 'security' WHEN 'ENGINEER' THEN 'engineering' END) AS ROLE FROM \"EMP\" """ ; ] . The SMS2 equivalent of this mapping looks like this: PREFIX emp: <http://example.com/emp/> PREFIX dept: <http://example.com/dept/> MAPPING <urn:departments> FROM SQL { SELECT * FROM "DEPT" } TO { ?deptIri a dept:Department ; dept:location ?loc ; dept:deptno "{?deptno}"^^xsd:integer . } WHERE { BIND (template("http://example.com/dept/{deptno}") AS ?deptIri) } ; MAPPING <urn:employees> FROM SQL { SELECT \"empno\", \"ename\", \"deptno\", (CASE \"job\" WHEN 'CLERK' THEN 'general-office' WHEN 'NIGHTGUARD' THEN 'security' WHEN 'ENGINEER' THEN 'engineering' END) AS ROLE FROM \"EMP\" } TO { ?empIri a emp:Employee ; emp:name ?ename ; emp:role ?roleIri ; emp:department ?deptIri . } WHERE { BIND (template("http://example.com/emp/{empno}") AS ?empIri) BIND (template("http://example.com/dept/{deptno}") AS ?deptIri) BIND (template("http://example.com/emp/{ROLE}") AS ?roleIri) } Note the use of the semicolon to separate multiple mappings, which were necessary because we needed the two SQL statements. ### MongoDB Considerations To connect to a MongoDB database, the MongoDB client jar must be copied to the Stardog server/dbms folder. The client jar for MongoDB version "x.y.z" can be obtained from http://central.maven.org/maven2/org/mongodb/mongo-java-driver/x.y.z/mongo-java-driver-x.y.z.jar The Stardog server must be restarted after copying the jar file. MongoDB has one Date type. It is stored as a 64-bit integer that represents the number of milliseconds since Jan 1, 1970, Universal Time Coordinated (UTC). This Date type can be mapped to xsd:date, xsd:dateTime or xsd:dateTimeStamp data types. (The xsd:dateTimeStamp data type is the same as xsd:dateTime except instead of having an optional timezone the timezone is required.) When a Date is mapped to either xsd:date or xsd:dateTimeStamp, it will be represented in the UTC timezone. When a Date field is mapped to an xsd:dateTime, the Date will be converted to the local timezone of the Stardog server and the label will include the timezone. ### Cassandra Considerations To create a Cassandra virtual graph, you’ll need to copy the shaded Cassandra client jar into the Stardog server/dbms folder and then restart the server. The client jar for Cassandra version "x.y.z" can be obtained from http://central.maven.org/maven2/com/datastax/cassandra/cassandra-driver-core/x.y.z/cassandra-driver-core-x.y.z-shaded.jar Cassandra is special in the way it attempts to prevent users from distributing queries over a large number of server nodes. If you have experience with CQL queries, you have no doubt seen the ubiquitous error message, Cannot execute this query as it might involve data filtering and thus may have unpredictable performance. If you want to execute this query despite the performance unpredictability, use ALLOW FILTERING. This reflects the Cassandra modeling principle that favors writing the same data to multiple tables (perhaps through the use of Materialized Views), where each table is optimized for answering different queries. In order to support as many queries as possible, we recommend creating mappings to each of these tables and letting Stardog choose which mappings apply for each query. It is possible that no mappings can support a particular query. In such cases, Stardog will write an entry to the log file and return no results. This is the default behavior, which can be changed by setting the cassandra.allow.filtering virtual graph option to true. When set, Stardog will include the ALLOW FILTERING clause at the end of each CQL query. Please note that the use of this option is highly discouraged in large-scale production environments. Cassandra is also special for how SQL-like its query language is (for a NoSQL database). As this is the case, Stardog supports the use of SQL queries in the mappings files for Cassandra virtual graphs. That is, you can use the rr:sqlQuery predicate for R2RML mappings, the sm:query predicate for Stardog Mapping Syntax, or the FROM SQL clause for Stardog Mapping Syntax 2. In all cases, you can supply a SQL query to describe a view to use for a virtual graph mapping, however, the SQL query can only contain operators that are supported in CQL - no joins, subqueries, SQL functions, etc. are allowed. ### Virtual Graph Configuration Options The following table lists the allowed options in the virtual graph configuration file. Additionally, connection pool configuration of the built-in Tomcat connection pool are allowed. The set of allowed properties is listed in the Tomcat JDBC Connection Pool documentation. Any unknown options will be ignored. 7. Table of Virtual Graph Configuration Options Option Default base Base IRI used to resolve relative IRIs from virtual graphs. jdbc.url The URL of the JDBC connection. jdbc.username The username used to make the JDBC connection. jdbc.password The password used to make the JDBC connection. jdbc.driver The driver class name used to make the JDBC connection. csv.separator , A single-character separator used when importing tabular data files. csv.quote " A single character used to used to encapsulate values containing special characters. csv.escape A single character used to escape values containing special characters. csv.header true Should the import process read the header row? When headers are enabled the first row of the input file is used to retrieve the column names and mappings can refer to those column names. (true/false) csv.skip.empty true Should empty values be skipped in the CSV file? If true no triples will be generated for templates that refer to a column with empty value. (true/false) mongodb.uri The URI for the MongoDB connection. Examples: mongodb://localhost/mydb or mongodb+srv://username:password@cluster0-kgprod.company.com/mydb cassandra.contact.point The address of the Cassandra node(s) that the driver uses to discover the cluster topology. Example: cassandra.abc.com cassandra.keyspace The Cassandra keyspace to use for this session cassandra.username The username for the Cassandra cluster cassandra.password The password for the Cassandra cluster cassandra.allow.filtering false Whether to include the ALLOW FILTERING clause at the end of Cassandra CQL queries. Not recommended for production use. percent.encode true Should IRI template strings be percent-encoded to be valid IRIs? (true/false) import.optimize true Should virtual import and ?s ?p ?o queries use the optimized translation? (true/false) parser.sql.quoting If unspecified, R2RML views (using rr:sqlQuery) will be parsed using the DB-native identifier quoting convention. For example, MySQL queries will be parsed treating backtick as the identifier quote character. If set to ANSI, the ANSI SQL convention of treating a double quote as the identifier quote character will be used instead. sql.functions A comma-separated list of SQL function names to register with the parser. If an R2RML view (using rr:sqlQuery) fails to parse, this option can be set to allow use of non-standard functions. sql.schemas A comma-separated list of schemas to append to the schema search path. This option allows R2RML tables and queries to reference tables that are outside of the default schema for the connected user. default.mapping.include.tables A comma-separated list of tables to include when generating default mappings. If blank, mappings will be generated for all tables in the default schema for the connected user, plus any schemas listed in sql.schemas. Cannot be combined with default.mapping.exclude.tables. default.mapping.exclude.tables A comma-separated list of tables to exclude when generating default mappings. Mappings will be generated for all tables in the default schema for the connected user, plus any schemas listed in sql.schemas, except those tables listed in this option. Cannot be combined with default.mapping.include.tables. ## Unstructured Data Unifying unstructured data is, by necessity, a different process from unifying structured or semistructured data. As of 4.2, Stardog includes a document storage subsystem called BITES[12], which provides configurable storage and processing for unifying unstructured data with the Stardog graph. The following figure shows the main BITES components: ### Storage BITES allows storage and retrieval of documents in the form of files. Stardog treats documents as opaque blobs of data; it defers to the extraction process to make sense of individual documents. Document storage is independent of file and data formats. Stardog internally stores documents as files. The location of these files defaults to a subdirectory of STARDOG_HOME but this can be overridden. Documents can be stored on local filesystem, or an abstraction thereof, accessible from the Stardog server or on Amazon S3 by setting the docs.filesystem.uri configuration option. The exact location is given by the docs.path configuration option. ### Structured Data Extraction BITES supports an optional processing stage in which a document is processed to extract an RDF graph to add to the database. BITES has the following built-in RDF extractors: • tika: This extractor is based on Apache Tika, collects metadata about the document and asserts this set of RDF statements to a named graph specific to the document. • text: (Since version 5.3) Adds a RDF statement with the full text extracted from the document. Side-effect of this extractor is that a document’s text will be indexed by the search index twice: one for the document itself, other for the value of this RDF statement. • entities: [Beta] (Since version 5.2) This extractor uses OpenNLP to extract all the mentions of named entities from the document and adds this information to the document named graph. • linker: [Beta] (Since version 5.2) This extractor works just like entities but after it finds a named entity mention in the document it also finds the entity in the database that best matched that mention. • dictionary: [Beta] (Since version 5.2.3) Similar to linker, but using a user-provided dictionary that maps named entity mentions to IRIs. • CoreNLPMentionRDFExtractor, CoreNLPMentionRDFExtractor, and CoreNLPRelationRDFExtractor available through the bites-corenlp repository. See Entity Extraction and Linking section for more details about some of these extractors. ### Text Extraction The document store is fully integrated with Stardog’s Search. As with RDF extraction, text extraction supports arbitrary file formats and pluggable extractors are able to retrieve the textual contents of a document for indexing. Once a document is added to BITES, its contents can be searched in the same way as other literals using the standard textMatch predicate in SPARQL queries. ### Managing Documents CRUD operations on documents can be performed from the command line, Java API or HTTP API. Please refer to the StardocsConnection API for details of using the document store from Java. The following is an example session showing how to manage documents from the command line: # We have a document stored in the file whyfp90.pdf' which we will add to the document store$ ls -al whyfp90.pdf
-rw-r--r-- 1 user user 200007 Aug 30 09:46 whyfp90.pdf

# We add it to the document store and receive the document's IRI as a return value
$bin/stardog doc put myDB whyfp90.pdf Successfully put document in the document store: tag:stardog:api:docs:myDB:whyfp90.pdf # Adding the same document again will delete all previous extraction results and insert new ones. # By setting the correct argument, previous assertions will be kept, and new ones appended.$ bin/stardog doc put myDB —keep-assertions -r text whyfp90.pdf
Successfully put document in the document store: tag:stardog:api:docs:myDB:whyfp90.pdf

# Alternatively, we can add it with a different name. Repeated calls
# will update the document and refresh extraction results
$bin/stardog doc put myDB --name why-functional-programming-matters.pdf whyfp90.pdf Successfully put document in the document store: tag:stardog:api:docs:myDB:why-functional-programming-matters.pdf # We can subsequently retrieve documents and store them locally$ bin/stardog doc get myDB whyfp90.pdf
Wrote document 'whyfp90.pdf' to file 'whyfp90.pdf'

# Local files will not be overwritten
$bin/stardog doc get myDB whyfp90.pdf File 'whyfp90.pdf' already exists. You must remove it or specify a different filename. # How many documents are in the document store?$ bin/stardog doc count myDB
Count: 2 documents

# Removing a document will also clear it's named graph and full-text search index entries
$bin/stardog doc delete myDB whyfp90.pdf Successfully executed deletion. # Re-indexing the docstore allows to apply a different rdf or text extractor # to all the documents, refreshing extraction results$ bin/stardog doc reindex myDB -r entities
"Re-indexed 1 documents"

See the Man Pages for more details about the CLI commands.

### Named Graphs and Document Queries

Documents in BITES are identified by IRI. As shown in the command line examples above, the IRI is returned from a document put call. The IRI is a combination of a prefix, the database name, and the document name. The CLI uses the document name to refer to the documents. The RDF index, and therefore SPARQL queries, use the IRIs to refer to the documents. RDF assertions extracted from a document are placed into a named graph identified by the document’s IRI.

Here we can see the results of querying a document’s named graph when using the default metadata extractor:

$stardog query execute myDB "select ?p ?o { graph <tag:stardog:api:docs:myDB:whyfp90.pdf> { ?s ?p ?o } }" +--------------------------------------------+--------------------------------------+ | p | o | +--------------------------------------------+--------------------------------------+ | rdf:type | http://xmlns.com/foaf/0.1/Document | | rdf:type | tag:stardog:api:docs:Document | | tag:stardog:api:docs:fileSize | 200007 | | http://purl.org/dc/elements/1.1/identifier | "whyfp90.pdf" | | rdfs:label | "whyfp90.pdf" | | http://ns.adobe.com/pdf/1.3/PDFVersion | "1.3" | | http://ns.adobe.com/xap/1.0/CreatorTool | "TeX" | | http://ns.adobe.com/xap/1.0/t/pg/NPages | 23 | | http://purl.org/dc/terms/created | "2006-05-19T13:42:00Z"^^xsd:dateTime | | http://purl.org/dc/elements/1.1/format | "application/pdf; version=1.3" | | http://ns.adobe.com/pdf/1.3/encrypted | "false" | +--------------------------------------------+--------------------------------------+ Query returned 11 results in 00:00:00.045 ### Entity Extraction and Linking BITES, by default, uses the tika RDF extractor that only extracts metadata from documents. Stardog can be configured to use the OpenNLP library to detect named entities mentioned in documents and optionally link those mentions to existing resources in the database. Stardog can also be configured to use Stanford’s CoreNLP library for entity extraction, linking, and relationship extraction. More information in the bites-corenlp repository. The first step to use entity extractors is to identify the set of OpenNLP models that will be used. The following models are always required: • A tokenizer and sentence detector. OpenNLP provides models for several languages (e.g., en-token.bin and en-sent.bin) • At least one name finder model. Stardog supports both dictionary-based and custom trained models. OpenNLP provides models for several types of entities and languages (e.g., en-ner-person.bin). We provide our own name finder models created from Wikipedia and DBPedia, which provide high recall / low precision in identifying Person, Organization, and Location types from English language documents. All this files should be put in the same directory and, after or during database creation time, the configuration option docs.opennlp.models.path should be set to its location. For example, suppose you have a folder /data/stardog/opennlp with files en-token.bin, en-sent.bin, and en-ner-person.bin. The database creation command would be as follows: $ stardog-admin db create -o docs.opennlp.models.path=/data/stardog/opennlp -n movies

For consistency, model filenames should follow specific patterns:

• *-token.bin for tokenizers (e.g., en-token.bin)

• *-sent.bin for sentence detectors (e.g., en-sent.bin)

• *-ner-*.dict for dictionary-based name finders (e.g., dbpedia-en-ner-person.dict)

• *-ner-*.bin for custom trained name finders (e.g., wikipedia-en-ner-organization.bin)

#### Entities

The entities extractor detects the mentions of named entities based on the configured models and creates RDF statements for those entities. When we are putting a document we need to specify that we want to use a non-default extractor. We can use both the tika metadata extractor and the entities extractor at the same time:

$stardog doc put --rdf-extractors tika,entities movies CastAwayReview.pdf The result of entity extraction will be in a named graph where an auto-generated IRI is used for the entity: <tag:stardog:api:docs:movies:CastAwayReview.pdf> { <tag:stardog:api:docs:entity:9ad311b4-ddf8-4da2-a49f-3fa8f79813c2> rdfs:label "Wilson" . <tag:stardog:api:docs:entity:0d25b4ed-9cd4-4e00-ac3d-f984012b67f5> rdfs:label "Tom Hanks" . <tag:stardog:api:docs:entity:e559b828-714f-407d-aa73-7bdc39ee8014> rdfs:label "Robert Zemeckis" . } #### Linker The linker extractor performs the same task as entities but after the entities are extracted it links those entities to the existing resources in the database. Linking is done by matching the mention text with the identifier and labels of existing resources in the database. This extractor requires the search feature to be enabled to find the matching candidates and uses string similarity metrics to choose the best match. The commonly used properties for labels are supported: rdfs:label, foaf:name, dc:title, skos:prefLabel and skos:altLabel. $ stardog doc put --rdf-extractors linker movies CastAwayReview.pdf

The extraction results of linker will be similar to entities, but only contain existing resources for which a link was found. The link is available through the dc:references property.

<tag:stardog:api:docs:movies:CastAwayReview.pdf> {

<tag:stardog:api:docs:entity:0d25b4ed-9cd4-4e00-ac3d-f984012b67f5> rdfs:label "Tom Hanks" ;
<http://purl.org/dc/terms/references> <http://www.imdb.com/name/nm0000158> .

<tag:stardog:api:docs:entity:e559b828-714f-407d-aa73-7bdc39ee8014> rdfs:label "Robert Zemeckis" ;
<http://purl.org/dc/terms/references> <http://www.imdb.com/name/nm0000709> .
}

#### Dictionary

The dictionary extractor fullfills the same purpose as the linker, but instead of heuristically trying to match a mention’s text with existent resources, it uses a user-defined dictionary to perform that task. The dictionary provides a set of mappings between text and IRIs. Each mention found in the document will be searched in the dictionary and, if found, the IRIs will be added as dc:references links.

Dictionaries are .linker files, which need to be available in the docs.opennlp.models.path folder. Stardog provides several dictionaries created from Wikipedia and DBPedia, which allow users to automatically link entity mentions to IRIs in those knowledge bases.

$stardog doc put --rdf-extractors dictionary movies CastAwayReview.pdf When using the dictionary option, all .linker files in the docs.opennlp.models.path folder will be used. The output follows the same syntax as the linker. <tag:stardog:api:docs:movies:CastAwayReview.pdf> { <tag:stardog:api:docs:entity:0d25b4ed-9cd4-4e00-ac3d-f984012b67f5> rdfs:label "Tom Hanks" ; <http://purl.org/dc/terms/references> <http://en.wikipedia.org/wiki/Tom_Hanks> ; <http://purl.org/dc/terms/references> <http://dbpedia.org/resource/Tom_Hanks> . } User-defined dictionaries can be created programmatically. For example, the Java class below will create a dictionary that links every mention of Tom Hanks to two IRIs. import java.io.File; import java.io.IOException; import com.complexible.stardog.docs.nlp.impl.DictionaryLinker; import com.google.common.collect.ImmutableMultimap; import com.stardog.stark.model.IRI; import static ccom.stardog.stark.Values.iri; public class CreateLinker { public static void main(String[] args) throws IOException { ImmutableMultimap<String, IRI> aDictionary = ImmutableMultimap.<String, IRI>builder() .putAll("Tom Hanks", iri("https://en.wikipedia.org/wiki/Tom_Hanks"), iri("http://www.imdb.com/name/nm0000158")) .build(); DictionaryLinker.Linker aLinker = new DictionaryLinker.Linker(aDictionary); aLinker.to(new File("/data/stardog/opennlp/TomHanks.linker")); } } #### SPARQL Both entities, linker, and dictionary extractors are also available as a SPARQL service, which makes them applicable to any data in the graph, whether stored directly in Stardog or accessed remotely on SPARQL endpoints or virtual graphs. prefix docs: <tag:stardog:api:docs:> select * { ?review :content ?text service docs:entityExtractor { [] docs:text ?text ; docs:mention ?mention } } The entities extractor is accessed by using the docs:entityExtractor service, which receives one input argument, docs:text, with the text to be analyzed. The output will be the extracted named entity mentions, bound to the variable given in the docs:mention property. +-----------------------------------------------------------------------------------+------------------+---------------+ | text | mention | review | +-----------------------------------------------------------------------------------+------------------+---------------+ | "Directed by Robert Zemeckis, featuring Tom Hanks and a volleyball called Wilson" | "Robert Zemeckis"| :MovieReview | | "Directed by Robert Zemeckis, featuring Tom Hanks and a volleyball called Wilson" | "Tom Hanks" | :MovieReview | | "Directed by Robert Zemeckis, featuring Tom Hanks and a volleyball called Wilson" | "Wilson" | :MovieReview | +-----------------------------------------------------------------------------------+------------------+---------------+ By adding an extra output variable, docs:entity, the linker extractor will be used instead. prefix docs: <tag:stardog:api:docs:> select * { ?review :content ?text service docs:entityExtractor { [] docs:text ?text ; docs:mention ?mention ; docs:entity ?entity } } +-------------------------+------------------+----------------+---------------+ | text | mention | entity | review | +-------------------------+------------------+----------------+---------------+ | "Directed by Robert..." | "Tom Hanks" | imdb:nm0000158 | :MovieReview | | "Directed by Robert..." | "Robert Zemeckis"| imdb:nm0000709 | :MovieReview | +-------------------------+------------------+----------------+---------------+ The dictionary extractor is called in a similar way to linker, with an extra argument docs:mode set to docs:Dictionary. prefix docs: <tag:stardog:api:docs:> select * { ?review :content ?text service docs:entityExtractor { [] docs:text ?text ; docs:mention ?mention ; docs:entity ?entity ; docs:mode docs:Dictionary } } +-------------------------+------------------+---------------------+---------------+ | text | mention | entity | review | +-------------------------+------------------+---------------------+---------------+ | "Directed by Robert..." | "Tom Hanks" | imdb:nm0000158 | :MovieReview | | "Directed by Robert..." | "Tom Hanks" | wikipedia:Tom_Hanks | :MovieReview | +-------------------------+------------------+---------------------+---------------+ All extractors accept one more output variable, docs:type, which will output the type of entity (e.g., Person, Organization), when available. prefix docs: <tag:stardog:api:docs:> select * { ?review :content ?text service docs:entityExtractor { [] docs:text ?text ; docs:mention ?mention ; docs:entity ?entity ; docs:type ?type } } +-------------------------+------------------+----------------+-----------+---------------+ | text | mention | entity | type | review | +-------------------------+------------------+----------------+-----------+---------------+ | "Directed by Robert..." | "Tom Hanks" | imdb:nm0000158 | :Person | :MovieReview | | "Directed by Robert..." | "Robert Zemeckis"| imdb:nm0000709 | :Person | :MovieReview | +-------------------------+------------------+----------------+-----------+---------------+ ### Custom Extractors The included extractors are intentionally basic, especially when compared to machine learning or text mining algorithms. A custom extractor connects the document store to algorithms tailored specifically to your data. The extractor SPI allows integration of any arbitrary workflow or algorithm from NLP methods like part-of-speech tagging, entity recognition, relationship learning, or sentiment analysis to machine learning models such as document ranking and clustering. Extracted RDF assertions are stored in a named graph specific to the document, allowing provenance tracking and versatile querying. The extractor must implement the RDFExtractor interface. The convenience class TextProvidingRDFExtractor is provided which extracts the text from the document before calling the extractor. Entity linking extractors can be tweaked to specific needs by extending their classes, EntityRDFExtractor and EntityLinkingRDFExtractor. The text extractor SPI gives you the opportunity to support arbitrary document formats. Implementations will be given a raw document and be expected to extract a string of text which will be added to the full-text search index. Text extractors should implement the TextExtractor interface. Custom extractors are registered with the Java ServiceLoader under the RDFExtractor or TextExtractor class names. Custom extractors can be referred to from the command line or APIs by their fully qualified or "simple" class names. For an example of a custom extractor, see our github repository. # High Availability Cluster In this section we explain how to configure, use, and administer Stardog Cluster for uninterrupted operations. Stardog Cluster is a collection of Stardog Server instances running on one or more virtual or physical machines that, from the client’s perspective, behave like a single Stardog Server instance. To fully achieve this effect requires DNS (i.e., with SRV records) and proxy configuration that’s left as an exercise for the user. Of course Stardog Cluster should have some different operational properties, the main one of which is high availability. But from the client’s perspective Stardog Cluster should be indistinguishable from non-clustered Stardog.[13] While Stardog Cluster is primarily geared toward HA, it is also important to remember that it should be tuned for your specific use case. Our detailed blog post discusses a variety of factors that you should consider when deploying Stardog Cluster as well as some adjustments you should make depending on your workload.  Note Stardog Cluster depends on Apache ZooKeeper. High Availability requires at least three Stardog and three ZooKeeper nodes in the Cluster. ZooKeeper works best, with respect to fault resiliency, with an ensemble size that is an odd-number greater than or equal to three: 3, 5, 7, etc.[14] With respect to performance, larger Stardog clusters perform better than smaller ones for reads, while larger cluster sizes perform worse for writes. It is the responsibility of the administrator to find the right balance.  Tip ## Guarantees A cluster is composed of a set of Stardog servers and a ZooKeeper ensemble running together. One of the Stardog servers is the Coordinator and the others are Participants. The Coordinator orchestrates transactions and maintains consistency by expelling any nodes that fail an operation. An expelled node must sync with a current member to rejoin the cluster. In case the Coordinator fails at any point, a new Coordinator will be elected out of the remaining available Participants. Stardog Cluster supports both read (e.g., querying) and write (e.g., adding data) requests. All read and write requests can be handled by any of the nodes in the cluster. When a client commits a transaction (containing a list of write requests), it will be acknowledged by the receiving node only after every non-failing peer node has committed the transaction. If a peer node fails during the process of committing a transaction, it will be expelled from the cluster by the Coordinator and put in a temporary failed state. If the Coordinator fails during the process, the transaction will be aborted. At that point the client can retry the transaction and it should succeed with the new cluster coordinator. Since failed nodes are not used for any subsequent read or write requests, if a commit is acknowledged, then Stardog Cluster guarantees that the data has been accordingly modified at every available node in the cluster. While this approach is less performant with respect to write operations than eventual consistency used by other distributed databases, typically those databases offer a much less expressive data model than Stardog, which makes an eventually consistency model more appropriate for those systems (and less so for Stardog). But since Stardog’s data model is not only richly expressive but rests in part on provably correct semantics, we think that a strong consistency model is worth the cost.[15] ## Single Server Migration It is assumed that Stardog nodes in a Stardog Cluster are always going to be used within a cluster context. Therefore, if you want to migrate from a Stardog instance running in single server mode to running in a cluster, it is advised that you create backups of your current databases and then import them to the cluster in order to be able to provide the guarantees explained above. If you simply add a Stardog instance to cluster that was previously running in single server mode, it will sync to the state of the cluster; local data could be removed when syncing with the cluster state. ## Configuration In this section we will explain how to manually deploy a Stardog Cluster using stardog-admin commands and some additional configuration. If you are deploying your cluster to AWS then you can use the Stardog Graviton that will automate this process. You can use the stardog-admin cluster generate command to bootstrap a cluster configuration and, thus, to ease installation by simply passing a list of hostnames or IP addresses for the cluster’s nodes. $ stardog-admin cluster generate --output-dir /home/stardog 10.0.0.1 10.0.0.2 10.0.0.3

See the man page for the details.

In a production environment we strongly recommend that each ZooKeeper process runs in a different machine and, if possible, that ZooKeeper has a separate drive for its data directory. If you need a larger cluster, adjust accordingly.

In the following example we will set up a cluster with total of 6 nodes. Zookeeper will be deployed on nodes 1-3 whereas Stardog will be deployed on nodes 4-6.

1. Install Stardog 6.1.0 on each machine in the cluster.

 Note The best thing to do here, of course, is to use whatever infrastructure you have in place to automate software installation. Adapting Stardog installation to Chef, Puppet, cfengine, etc. is left as an exercise for the reader.
2. Make sure a valid Stardog license key (whether Developer, Enterprise, or a 30-day eval key) for the size of cluster you’re creating exists and resides in STARDOG_HOME on each node. You must also have a stardog.properties file with the following information for each Stardog node in the cluster:

# Flag to enable the cluster, without this flag set, the rest of the properties have no effect
pack.enabled=true
# this node's IP address (or hostname) where other Stardog nodes are going to connect
# this value is optional but if provided it should be unique for each Stardog node
# the connection string for ZooKeeper where cluster state is stored
pack.zookeeper.address=196.69.68.1:2180,196.69.68.2:2180,196.69.68.3:2180

pack.zookeeper.address is a ZooKeeper connection string where cluster stores its state. pack.node.address is not a required property. The local address of the node, by default, is InetAddress.getLocalhost().getAddress(), which should work for many deployments. However if you’re using an atypical network topology and the default value is not correct, you can provide a value for this property.

3. Create the ZooKeeper configuration for each ZooKeeper node. This config file is just a standard ZooKeeper configuration file and the same config file can be used for all ZooKeeper nodes. The following config file should be sufficient for most cases.

tickTime=3000
# Make sure this directory exists and
# ZK can write and read to and from it.
clientPort=2180
initLimit=5
syncLimit=2
# This is an enumeration of all Zk nodes in
# the cluster and must be identical in
# each node's config.
server.1=196.69.68.1:2888:3888
server.2=196.69.68.2:2888:3888
server.3=196.69.68.3:2888:3888
 Note The clientPort specified in zookeeper.properties and the ports used in pack.cluster.address in stardog.properties must be the same.
4. dataDir is where ZooKeeper persists cluster state and where it writes log information about the cluster.

$mkdir /data/zookeeperdata # on node 1$ mkdir /data/zookeeperdata # on node 2
$mkdir /data/zookeeperdata # on node 3 5. ZooKeeper requires a myid file in the dataDir folder to identify itself, you will create that file as follows for node1, node2, and node3, respectively: $ echo 1 > /data/zookeeperdata/myid # on node 1
$echo 2 > /data/zookeeperdata/myid # on node 2$ echo 3 > /data/zookeeperdata/myid # on node 3

## Installation

In the next few steps you will use the Stardog Admin CLI commands to deploy Stardog Cluster: that is, ZooKeeper and Stardog itself. We’ll also configure HAProxy as an example of how to use Stardog Cluster behind a proxy for load-balancing and fail-over capability. There’s nothing special about HAProxy here; you could implement this proxy functionality in many different ways. For example, Stardog Graviton uses Amazon’s Elastic Load Balancer.

1. Start ZooKeeper instances

First, you need to start ZooKeeper nodes. You can do this using the standard command line tools that come with ZooKeeper. As a convenience, we provide a stardog-admin cluster zkstart subcommand that you can use to start ZooKeeper instances:

$./stardog-admin cluster zkstart --home ~/stardog # on node 1$ ./stardog-admin cluster zkstart --home ~/stardog # on node 2
$./stardog-admin cluster zkstart --home ~/stardog # on node 3 This uses the zookeeper.properties config file in ~/stardog and log its output to ~/stardog/zookeeper.log. If your $STARDOG_HOME is set to ~/stardog, then you don’t need to specify the --home option. For more info about the command:

$./stardog-admin help cluster zkstart 2. Start Stardog instances Once ZooKeeper is started, you can start Stardog instances: $ ./stardog-admin server start --home ~/stardog --port 5821 # on node 4
$./stardog-admin server start --home ~/stardog --port 5821 # on node 5$ ./stardog-admin server start --home ~/stardog --port 5821 # on node 6

Important: When starting Stardog instances for the cluster, unlike single server mode, you need to provide the credentials of a superuser that will be used for securing the data stored in ZooKeeper and for intra-cluster communication. Each node should be started with the same superuser credentials. By default, Stardog comes with a superuser admin that has password "admin" and that is the default credentials used by the above command. For a secure installation of Stardog cluster you should change these credentials by specifying the pack.zookeeper.auth setting in stardog.properties and restart the cluster with new credentials:

pack.zookeeper.auth=username:password

And again, if your $STARDOG_HOME is set to ~/stardog, you don’t need to specify the --home option.  Note Make sure to allocate roughly twice as much heap for Stardog than you would normally do for single-server operation since there can be an additional overhead involved for replication in the cluster. Also, we start Stardog here on the non-default port (5821) so that you can use a proxy or load-balancer in the same machine which can run on the default port (5820), meaning that Stardog clients can act normally (i.e., use the default port, 5820) since they need to interact with HAProxy. 3. Start HAProxy (or equivalent) In most Unix-like systems, HAProxy is available via package managers, e.g. in Debian-based systems: $ sudo apt-get update
$sudo apt-get install haproxy At the time of this writing, this will install HAProxy 1.4. You can refer to the official site to install a later release. Place the following configuration in a file (such as haproxy.cfg) in order to point HAProxy to the Stardog Cluster. You’ll notice that there are two backends specified in the config file: stardog_coordinator and all_stardogs. An ACL is used to route all requests containing transaction in the path to the coordinator. All other traffic is routed via the default backend, which is simply round-robin across all of the Stardog nodes. For some use cases routing transaction-specific operations (e.g. commit) directly to the coordinator performs slightly better. However, round-robin routing across all of the nodes is generally sufficient. global daemon maxconn 256 defaults # you should update these values to something that makes # sense for your use case timeout connect 5s timeout client 1h timeout server 1h mode http # where HAProxy will listen for connections frontend stardog-in option tcpka # keep-alive bind *:5820 # the following lines identify any routes with "transaction" # in the path and send them directly to the coordinator, if # haproxy is unable to determine the coordinator all requests # will fall through and be routed via the default_backend acl transaction_route path_sub -i transaction use_backend stardog_coordinator if transaction_route default_backend all_stardogs # the Stardog coordinator backend stardog_coordinator option tcpka # the following line returns 200 for the coordinator node # and 503 for non-coordinators so traffic is only sent # to the coordinator option httpchk GET /admin/cluster/coordinator # the check interval can be increased or decreased depending # on your requirements and use case, if it is imperative that # traffic be routed to the coordinator as quickly as possible # after the coordinator changes, you may wish to reduce this value default-server inter 5s # replace these IP addresses with the corresponding node address # maxconn value can be upgraded if you expect more concurrent # connections server stardog1 196.69.68.1:5821 maxconn 64 check server stardog2 196.69.68.2:5821 maxconn 64 check server stardog3 196.69.68.3:5821 maxconn 64 check # the Stardog servers backend all_stardogs option tcpka # keep-alive # the following line performs a health check # HAProxy will check that each node accepts connections and # that it's operational within the cluster. Health check # requires that Stardog nodes do not use --no-http option option httpchk GET /admin/healthcheck default-server inter 5s # replace these IP addresses with the corresponding node address # maxconn value can be upgraded if you expect more concurrent # connections server stardog1 196.69.68.1:5821 maxconn 64 check server stardog2 196.69.68.2:5821 maxconn 64 check server stardog3 196.69.68.3:5821 maxconn 64 check If you wish to operate the cluster in HTTP-only mode, you can add the mode http to backend settings. Finally, $ haproxy -f haproxy.cfg

For more info on configuring HAProxy please refer to the official documentation. In production environments we recommend running multiple proxies to avoid single point of failures, and use DNS solutions for fail-over.

Now Stardog Cluster is running on 3 nodes, one each on 3 machines. Since HAProxy was conveniently configured to use port 5820 you can execute standard Stardog CLI commands to the Cluster:

$./stardog-admin db create -n myDb$ ./stardog data add myDb /path/to/my/data
$./stardog query myDb "select * { ?s ?p ?o } limit 5" If your cluster is running on another machine then you will need to provide a fully qualified connection string in the above commands. ## Shutdown In order to shut down the cluster you only need to execute the following command once: $ ./stardog-admin cluster stop

The cluster stop request will cause all available nodes in the cluster to shutdown. If a node was expelled from the cluster due to a failure it would not receive this command and might need to be shutdown manually. In order to shutdown a single node in the cluster use the regular server stop command and be sure to specify the server address:

$./stardog-admin --server http://localhost:5821 server stop If you send the server stop command to the load balancer then a random node selected by the load balancer will shutdown. In addition to the Stardog cluster you still need to shut down the ZooKeeper cluster independently. Refer to the ZooKeeper documentation for details. ## Automated Deployment As of Stardog 5, we support both AWS and Pivotal Cloud Foundry as first-class deployment environments. ### Stardog Graviton Configuring and managing highly available cluster applications can be a complex black art. Graviton is a tool that leverages the power of Amazon Web Services to make launching the Stardog cluster easy. The source code is available as Apache 2.0 licensed code. #### Download • Linux • OSX #### Requirements • A Stardog release zip file (5.0 or later). • A Stardog license. • An AWS account. • terraform 0.8.8. • packer 0.12.3. #### Setup Your Environment In order to use stardog-graviton in its current form the following environment variables must be set. AWS_ACCESS_KEY_ID=<a valid aws access key> AWS_SECRET_ACCESS_KEY=<a valid aws secret key> The account associated with the access tokens must have the ability to create IAM credentials and full EC2 access. Both terraform and packer must be in your system path. The easiest way to launch a cluster is to run stardog-graviton in interactive mode. This will cause the program to ask a series of questions in order to get the needed values to launch a cluster. Here is a sample session: $ stardog-graviton --log-level=DEBUG launch mystardog423
What version of stardog are you launching?: 4.2.3
What is the path to the Stardog release?:
A value must be provided.
What is the path to the Stardog release?: /Users/bresnaha/stardog-4.2.3.zip
There is no base image for version 4.2.3.
- Running packer to build the image...
done
AMI Successfully built: ami-c06246a0
Creating the new deployment mystardog423
Would you like to create an SSH key pair? (yes/no): no
EC2 keyname (default): <aws key name>
Private key path: /path/to/private/key
\ Calling out to terraform to create the volumes...
- Calling out to terraform to stop builder instances...
Successfully created the volumes.
\ Creating the instance VMs......
Successfully created the instance.
Waiting for stardog to come up...
The instance is healthy
\ Opening the firewall......
Successfully opened up the instance.
The instance is healthy
The instance is healthy
Stardog is available here: http://mystardog423sdelb-1763823291.us-west-1.elb.amazonaws.com:5821
ssh is available here: mystardog423belb-124202215.us-west-1.elb.amazonaws.com
Using 3 stardog nodes
10.0.101.189:5821
10.0.100.107:5821
10.0.100.140:5821
Success.

To avoid being asked questions a file named ~/.graviton/default.json can be created. An example can be found in the defaults.json.example file.

All of the components needed to run a Stardog cluster are considered part of a deployment. Every deployment must be given a name that is unique to each cloud account. In the above example the deployment name is mystardog2.

#### Status

Once the image has been successfully launched its health can be monitored with the status command:

$stardog-graviton --log-level=DEBUG status mystardog423 The instance is healthy Stardog is available here: http://mystardog423sdelb-1763823291.us-west-1.elb.amazonaws.com:5821 ssh is available here: mystardog423belb-124202215.us-west-1.elb.amazonaws.com Using 3 stardog nodes 10.0.101.189:5821 10.0.100.107:5821 10.0.100.140:5821 Success. #### Cleanup AWS EC2 charges by the hour for the VMs that Graviton runs thus when the cluster is no longer in use it is important to clean it up with the destroy command. stardog-graviton --log-level=DEBUG destroy mystardog423 This will destroy all volumes and instances associated with this deployment. Do you really want to destroy? (yes/no): yes / Deleting the instance VMs... Successfully destroyed the instance. \ Calling out to terraform to delete the images... Successfully destroyed the volumes. Success. #### More information For more information about Graviton check out the README and the blog post. ### Pivotal Cloud Foundry As of Stardog 5, we’ve added support for Pivotal Cloud Foundry. More docs are available at PCF. Our open source service broker adheres to the Open Service Broker API and thus can be used with Cloud Foundry, Open Shift, and Kubernetes. ## Configuration Issues ### Topologies & Size In the configuration instructions above, we assume a particular Cluster topology, which is to say, for each node n of a cluster, we run Stardog, ZooKeeper, and a load balancer. But this is not the only topology supported by Stardog Cluster. ZooKeeper nodes run independently, so other topologies—​three ZooKeeper servers and five Stardog servers are possible—​you just have to point Stardog to the corresponding ZooKeeper ensemble. To add more Stardog Cluster nodes, simply repeat the steps for Stardog on additional machines. Generally, as mentioned above, Stardog Cluster size should be an odd number greater or equal to 3.  Warning ZooKeeper uses a very write heavy protocol; having Stardog and ZooKeeper both writing to the same disk can yield contention issues, resulting in timeouts at scale. We recommend at a minimum having the two services writing to separate disks to reduce contention or, ideally, have them run on separate nodes entirely. ### Open File Limits If you expect to use Stardog Cluster with heavy concurrent write workloads, then you should probably increase the number of open files that host OS will permit on each Cluster node. You can typically do this on a Linux machine with ulimit -n or some variant thereof. Because nodes communicate between themselves and with ZooKeeper, it’s important to make sure that there are sufficient file handle resources available.[16] ### Connection/Session Timeouts Stardog nodes connect to the ZooKeeper cluster and establishes a session. The session is kept alive by PING requests sent by the client. If the Stardog node does not send these requests to the ZooKeeper server (due to network issues, node failure, etc.) the session will timeout and the Stardog node will get into a suspended state and it will reject any queries or transactions until it can establish the session again. If a Stardog node is overloaded then it might fail to send the PING requests to ZooKeeeper server in a timely manner. This usually happens when Stardog’s memory usage is close to the limit and there are frequent GC pauses. This would cause Stardog nodes to be suspended unnecessarily. In order to prevent this problem make sure Stardog nodes have enough memory allocated and tweak the timeout options. There are two different configuration options that control timeouts for the ZooKeeper server. The pack.connection.timeout option specifies the max time that Stardog waits to establish a connection to ZooKeeper. The pack.session.timeout option specifies the session timeout explained above. You can set these values in stardog.properties as follows: pack.connection.timeout=15s pack.session.timeout=60s Note that, ZooKeeper has limitations about how these values can be set based on the tickTime value specified in the ZooKeeper configuration file. Session timeout needs to be a minimum of 2 times the tickTime and a maximum of 20 times the tickTime. So a session timeout of 60s requires the tickTime to be at least 3s (in ZooKepeer configuration file this value should be entered in milliseconds). If the session timeout is not in the allowed range the ZooKeeper will negotiate a new timeout value and Stardog will print a warning about this in the stardog.log file. ### Client Usage To use Stardog Cluster with standard Stardog clients and CLI tools in the ordinary way--stardog-admin and stardog--you must have Stardog installed locally. With the provided Stardog binaries in the Stardog Cluster distribution you can query the state of Cluster: $ ./stardog-admin --server http://<ipaddress>:5820/ cluster info

where ipaddress is the IP address of any of the nodes in the cluster. This will print the available nodes in the cluster, as well as the roles (participant or coordinator). You can also input the proxy IP address and port to get the same information.

To add or remove data, issue stardog data add or remove commands to any node in the cluster. Queries can be issued to any node in the cluster using the stardog query command. All the stardog-admin features are also available in Cluster, which means you can use any of the commands to create databases, administer users, and the rest of the functionality.

### Adding Nodes to a Cluster

Stardog cluster stores the UUID of the last committed transaction for each database in ZooKeeper. When a new node is joining the cluster it will compare the local transaction ID of each database with the corresponding transaction ID stored in ZooKeeper. If there is a mismatch the node will synchronize the database contents from another node in the cluster. If there are no nodes in the cluster the new node cannot join the cluster and will shut itself down. For this reason, if you are starting a new cluster then you should make sure that the ZooKeeper state is cleared. If you are retaining an existing cluster then new nodes should be started when there is at least one node in the cluster.

If there are active transactions in the cluster joining node will wait for those transactions to finish and then synchronize its databases. More transactions may take place during synchronization and in that case the joining node will continue synchronization and retrieve the data from new transactions. Thus, it will take longer for a node to join the cluster if there are continuous transactions. Note that, the new node will not be available for requests until all the databases are synchronized. The proxy/load-balancer should perform a health check before forwarding the requests to a new node (as shown in the above configuration) so user requests will always be forwarded to available nodes.

The process to upgrade Stardog Cluster is straightfoward; however, there are a few extra steps you should take to ensure the upgrade goes as quickly and smoothly as possible. Before you begin the upgrade, make sure to place the new Stardog binaries on all of the cluster nodes.

Also make sure to note which node is the coordinator since this is the first node that will be started as part of the upgrade. stardog-admin cluster info will show the nodes in the cluster and which one is the coordinator.

Next you should ensure that there are no transactions running, e.g., stardog-admin db status <db name> will show if there are any open transactions for a database. This step is not strictly required, however, it can minimize downtime and streamline the process, allowing the cluster to stop quickly and helping avoid non-coordinator nodes from having to re-sync when they attempt to join the upgraded cluster.

When you are ready to begin the upgrade, you can shutdown the cluster with stardog-admin cluster stop. Once all nodes have stopped, backup the STARDOG_HOME directories on all of the nodes.

With the new version of Stardog, bring the cluster up one node at a time, starting with the previous coordinator. As each node starts make sure that it is able to join the cluster cleanly before moving on to the next node.

# Search

Stardog’s builtin full-text search system indexes data stored in Stardog for information retrieval queries.

## Indexing Strategy

The indexing strategy creates a "search document" per RDF literal. Each document consists of two fields: literal ID and literal value. See User-defined Lucene Analyzer for details on customizing Stardog’s search programmatically.

Full-text support for a database is disabled by default but can be enabled at any time by setting the configuration option search.enabled to true. For example, you can create a database with full-text support as follows:

### HTTP

For HTTP, the reasoning flag is specified with the other HTTP request parameters:

$curl -u admin:admin -X GET "http://localhost:5822/myDB/query?reasoning=true&query=..." ### Reasoning Connection API In order to use the ReasoningConnection API one needs to enable reasoning. See the Java Programming section for details. Currently, the API has two methods: • isConsistent(), which can be used to check if the database is (logically) consistent with respect to the reasoning type. • isSatisfiable(URI theURIClass), which can be used to check if the given class if satisfiable with respect to the database and reasoning type. ## Explaining Reasoning Results Stardog can be used to check if the current database logically entails a set of triples; moreover, Stardog can explain why this is so.[19] An explanation of an inference is the minimum set of statements explicitly stored in the database that, together with the schema and any valid inferences, logically justify the inference. Explanations are useful for understanding data, schema, and their interactions, especially when large number of statements interact with each other to infer new statements. Explanations can be retrieved using the CLI by providing an input file that contains the inferences to be explained: $ stardog reasoning explain myDB inference_to_explain.ttl

The output is displayed in a concise syntax designed to be legible; but it can be rendered in any one of the supported RDF syntaxes if desired. Explanations are also accessible through Stardog’s extended HTTP protocol and in Java. See the examples in the stardog-examples Github repo for more details about retrieving explanations programmatically.

### Proof Trees

Proof trees are a hierarchical presentation of multiple explanations (of inferences) to make data, schemas, and rules more intelligible. Proof trees[20] provide an explanation for an inference or an inconsistency as a hierarchical structure. Nodes in the proof tree may represent an assertion in a Stardog database. Multiple assertion nodes are grouped under an inferred node.

#### Example

For example, if we are explaining the inferred triple :Alice rdf:type :Employee, the root of the proof tree will show that inference:

INFERRED :Alice rdf:type :Employee

The children of an inferred node will provide more explanation for that inference:

INFERRED :Alice rdf:type :Employee
ASSERTED :Manager rdfs:subClassOf :Employee
INFERRED :Alice rdf:type :Manager

The fully expanded proof tree will show the asserted triples and axioms for every inference:

INFERRED :Alice rdf:type :Employee
ASSERTED :Manager rdfs:subClassOf :Employee
INFERRED :Alice rdf:type :Manager
ASSERTED :Alice :supervises :Bob
ASSERTED :supervises rdfs:domain :Manager

The CLI explanation command prints the proof tree using indented text; but, using the SNARL API, it is easy to create a tree widget in a GUI to show the explanation tree, such that users can expand and collapse details in the explanation.

Another feature of proof trees is the ability to merge multiple explanations into a single proof tree with multiple branches when explanations have common statements. Consider the following example database:

#schema
:Manager rdfs:subClassOf :Employee
:ProjectManager rdfs:subClassOf :Manager
:ProjectManager owl:equivalentClass (:manages some :Project)
:supervises rdfs:domain :Manager
:ResearchProject rdfs:subClassOf :Project
:projectID rdfs:domain :Project

#instance data
:Alice :supervises :Bob
:Alice :manages :ProjectX
:ProjectX a :ResearchProject
:ProjectX :projectID "123-45-6789"

In this database, there are three different unique explanations for the inference :Alice rdf:type :Employee:

##### Explanation 1
:Manager rdfs:subClassOf :Employee
:ProjectManager rdfs:subClassOf :Manager
:supervises rdfs:domain :Manager
:Alice :supervises :Bob
##### Explanation 2
:Manager rdfs:subClassOf :Employee
:ProjectManager rdfs:subClassOf :Manager
:ProjectManager owl:equivalentClass (:manages some :Project)
:ResearchProject rdfs:subClassOf :Project
:Alice :manages :ProjectX
:ProjectX a :ResearchProject
##### Explanation 3
:Manager rdfs:subClassOf :Employee
:ProjectManager rdfs:subClassOf :Manager
:ProjectManager owl:equivalentClass (:manages some :Project)
:projectID rdfs:domain :Project
:Alice :manages :ProjectX
:ProjectX :projectID "123-45-6789"

All three explanations have some triples in common; but when explanations are retrieved separately, it is hard to see how these explanations are related. When explanations are merged, we get a single proof tree where alternatives for subtrees of the proof are shown inline. In indented text rendering, the merged tree for the above explanations would look as follows:

INFERRED :Alice a :Employee
ASSERTED :Manager rdfs:subClassOf :Employee
1.1) INFERRED :Alice a :Manager
ASSERTED :supervises rdfs:domain :Manager
ASSERTED :Alice :supervises :Bob
1.2) INFERRED :Alice a :Manager
ASSERTED :ProjectManager rdfs:subClassOf :Manager
INFERRED :Alice a :ProjectManager
ASSERTED :ProjectManager owl:equivalentClass (:manages some :Project)
ASSERTED :Alice :manages :ProjectX
2.1) INFERRED :ProjectX a :Project
ASSERTED :projectID rdfs:domain :Project
ASSERTED :ProjectX :projectID "123-45-6789"
2.2) INFERRED :ProjectX a :Project
ASSERTED :ResearchProject rdfs:subClassOf :Project
ASSERTED :ProjectX a :ResearchProject

In the merged proof tree, alternatives for an explanation are shown with a number id. In the above tree, :Alice a :Manager is the first inference for which we have multiple explanations so it gets the id 1. Then each alternative explanation gets an id appended to this (so explanations 1.1 and 1.2 are both alternative explanations for inference 1). We also have multiple explanations for inference :ProjectX a :Project so its alternatives get ids 2.1 and 2.2.

## User-defined Rule Reasoning

Many reasoning problems may be solved with OWL’s axiom-based approach; but, of course, not all reasoning problems are amenable to this approach. A user-defined rules approach complements the OWL axiom-based approach nicely and increases the expressive power of a reasoning system from the user’s point of view. Many RDF databases support user-defined rules only. Stardog is the only RDF database that comprehensively supports both axioms and rules. Some problems (and some people) are simply a better fit for a rules-based approach to modeling and reasoning than to an axioms-based approach (and, of course, vice versa).

 Note There isn’t a one-size-fits-all answer to the question "rules or axioms or both?" Use the thing that makes the most sense given the task at hand. This is engineering, not religion.

Stardog supports user-defined rule reasoning together with a rich set of built-in functions using the SWRL syntax and builtins library. In order to apply SWRL user-defined rules, you must include the rules as part of the database’s schema: that is, put your rules where your axioms are, i.e., in the schema. Once the rules are part of the schema, they will be used for reasoning automatically when using the SL reasoning type.

Assertions implied by the rules will not be materialized. Instead, rules are used to expand queries just as regular axioms are used.

 Note To trigger rules to fire, execute a relevant query—​simple and easy as the truth.

### Stardog Rules Syntax

Stardog supports two different syntaxes for defining rules. The first is native Stardog Rules syntax and is based on SPARQL, so you can re-use what you already know about SPARQL to write rules. Unless you have specific requirements otherwise, you should use this syntax for user-defined rules in Stardog. The second is the de facto standard RDF/XML syntax for SWRL. It has the advantage of being supported in many tools; but it’s not fun to read or to write. You probably don’t want to use it. Better: don’t use this syntax!

Stardog Rules Syntax is basically SPARQL "basic graph patterns" (BGPs) plus some very explicit new bits (IF-THEN) to denote the head and the body of a rule.[21] You define URI prefixes in the normal way (examples below) and use regular SPARQL variables for rule variables. As you can see, some SPARQL 1.1 syntactic sugar—​property paths, especially, but also bnode syntax—​make complex Stardog Rules concise and elegant.

 Note Starting in Stardog 3.0, it’s legal to use any valid Stardog function in Stardog Rules (see rule limitations below for few exceptions).

#### How to Use Stardog Rules

There are three things to sort out:

1. Where to put these rules?

2. How to represent these rules?

3. What are the gotchas?

First, the rules go into the database, of course. Unless you’ve changed the value of reasoning.schema.graphs option, you can store the rules in any named graph (or the default graph) in the database and you will be fine; that is, just add the rules to the database and it will all work out.[22]

Second, you include the rules directly in a Turtle file loaded into Stardog. Rules can be mixed with triples in the file. Here’s an example:

:c a :Circle ;

IF {
?r a :Rectangle ;
:width ?w ;
:height ?h
BIND (?w * ?h AS ?area)
}
THEN {
?r :area ?area
}

That’s pretty easy. Third, what are the gotchas?

##### Rule Representation Options

Inline rules in Turtle data can be named for later reference and management. We assign an IRI, :FatherRule in this example, to the rule and use it as the subject of other triples:

@prefix : <http://example.org/> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

RULE :FatherRule
IF {
?x a <http://example.org/Male> , <http://example.org/Parent> .
}
THEN {
?x a <http://example.org/Father> .
}

:FatherRule rdfs:comment "This rule defines fathers" ;
a :MyRule .

In addition to the inline Turtle representation of rules, you can represent the rules with specially constructed RDF triples. This is useful for maintaining Turtle compatibility or for use with SPARQL INSERT DATA. This example shows the object of a triple which contains one rule in Stardog Rules syntax embedded as literal.

@prefix rule: <tag:stardog:api:rule:> .

[] a rule:SPARQLRule;
rule:content """
IF {
?r a :Rectangle ;
:width ?w ;
:height ?h
BIND (?w * ?h AS ?area)
}
THEN {
?r :area ?area
}
""".
##### Rule Limitations & Gotchas
1. The RDF serialization of rules in, say, a Turtle file has to use the tag:stardog:api:rule: namespace URI and then whatever prefix, if any, mechanism that’s valid for that serialization. In the examples here, we use Turtle. Hence, we use @prefix, etc.

2. However, the namespace URIs used by the literal embedded rules can be defined in two places: the string that contains the rule—​in the example above, you can see the default namespace is urn:test:--or in the Stardog database in which the rules are stored. Either place will work; if there are conflicts, the "closest definition wins", that is, if foo:Example is defined in both the rule content and in the Stardog database, the definition in the rule content is the one that Stardog will use.

3. Stardog Rule Syntax has the same expressivity of SWRL which means the SPARQL features allowed in rules are limited. Specifically, a triple pattern in a rule should be in one of the following forms:

a) term1 rdf:type class-uri

b) term1 prop-uri term2

where class-uri is a URI referring to a user-defined class and prop-uri is a URI referring to a user-defined property.[23]

Only type of property paths allowed in rules are inverse paths (^p), sequence paths (p1 / p2) and alternative paths (p1 | p2) but these paths should not violate the above conditions. For example, the property path rdf:type/rdfs:label is not valid because according to the SPARQL spec this would mean the object of a rdf:type triple pattern is a variable and not a user-defined class.

Rule body (IF) and only rule body may optionally contain UNION, BIND or FILTER clauses. However, functions EXISTS, NOT EXISTS, or NOW() cannot be used in rules. User-defined functions (UDF) may be used in rules but if the UDF is not a pure function then the results are undefined.

Other SPARQL features are not allowed in rules.

4. Having the same predicate both in the rule body (IF) and the rule head (THEN) are supported in a limited way. Cycles are allowed only if the rule body does not contain type triples or filters and the triples in the rule body are linear (i.e. no cycles in the rule body either).

In other words, a property used in the rule head depends on a property in the rule body and this dependency graph may contain cycles under some limits. One of these is that a rule body should not contain type triples or filters. Tree-like dependencies are always allowed.

Of course the rule body may also contain triple patterns, which constitute a different kind of graph: it should be linear when edge directions are ignored. So no cycles or trees are allowed in this graph pattern. Linear when directions are ignored means that { ?x :p ?y . ?x :p ?z } is linear but { ?x :p ?y . ?x :p ?z . ?x :p ?t } is not because there are three edges for the node represented by ?x.

The reason for these limits boils down to the fact that recursive rules and axioms are rewritten as SPARQL property paths. This is why rule bodies cannot contain anything but property atoms. Cycles are allowed as long as we can express these as a regular grammar. Another way to think about this is that these rules should be as expressive as OWL property chains and the same restrictions defined for property chains apply here, too.

Let’s consider some examples.

These rules are acceptable since no cycles appear in dependencies:

IF
{ ?x :hasFather ?y . ?y :hasBrother ?z }
THEN
{ ?x :hasUncle ?z }
IF
{ ?x :hasUncle ?y . ?y :hasWife ?z }
THEN
{ ?x :hasAuntInLaw ?z }

These rules are not acceptable since there is a cycle:

IF
{ ?x :hasFather ?y . ?y :hasBrother ?z }
THEN
{ ?x :hasUncle ?z }
IF
{ ?x :hasChild ?y . ?y :hasUncle ?z }
THEN
{ ?x :hasBrother ?z }

This kind of cycle is allowed:

IF
{ ?x :hasChild ?y . ?y :hasSibling ?z }
THEN
{ ?x :hasChild ?z }
 Note (3) is a general limitation, not specific to Stardog Rules Syntax: recursion or cycles can occur through multiple rules, or it may occur as a result of interaction of rules with other axioms (or just through axioms alone).

#### Stardog Rules Examples

PREFIX rule: <tag:stardog:api:rule:>
PREFIX : <urn:test:>
PREFIX gr: <http://purl.org/goodrelations/v1#>

:Product1 gr:hasPriceSpecification [ gr:hasCurrencyValue 100.0 ] .
:Product2 gr:hasPriceSpecification [ gr:hasCurrencyValue 500.0 ] .
:Product3 gr:hasPriceSpecification [ gr:hasCurrencyValue 2000.0 ] .

IF {
?offering gr:hasPriceSpecification ?ps .
?ps gr:hasCurrencyValue ?price .
FILTER (?price >= 200.00).
}
THEN {
?offering a :ExpensiveProduct .
}

This example is self-contained: it contains some data (the :Product…​ triples) and a rule. It also demonstrates the use of SPARQL’s FILTER to do numerical (and other) comparisons.

Here’s a more complex example that includes four rules and, again, some data.

PREFIX rule: <tag:stardog:api:rule:>
PREFIX : <urn:test:>

:c a :Circle ;

:t a :Triangle ;
:base 4 ;
:height 10 .

:r a :Rectangle ;
:width 5 ;
:height 8 .

:s a :Rectangle ;
:width 10 ;
:height 10 .

IF {
?r a :Rectangle ;
:width ?w ;
:height ?h
BIND (?w * ?h AS ?area)
}
THEN {
?r :area ?area
}

IF {
?t a :Triangle ;
:base ?b ;
:height ?h
BIND (?b * ?h / 2 AS ?area)
}
THEN {
?t :area ?area
}

IF {
?c a :Circle ;
BIND (math:pi() * math:pow(?r, 2) AS ?area)
}
THEN {
?c :area ?area
}

IF {
?r a :Rectangle ;
:width ?w ;
:height ?h
FILTER (?w = ?h)
}
THEN {
?r a :Square
}

This example also demonstrates how to use SPARQL’s BIND to introduce intermediate variables and do calculations with or to them.

Let’s look at some other rules, but just the rule content this time for concision, to see some use of other SPARQL features.

This rule says that a person between 13 and 19 (inclusive) years of age is a teenager:

PREFIX swrlb: <http://www.w3.org/2003/11/swrlb#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>

IF {
?x a :Person; hasAge ?age.
FILTER (?age >= 13 && ?age <= 19)
}
THEN {
?x a :Teenager.
}

This rule says that a male person with a sibling who is the parent of a female is an "uncle with a niece":

IF {
?x a Person; a :Male; :hasSibling ?y;
?y :isParentOf ?z;
?z a :Female.
}
THEN {
?x a :UncleOfNiece.
}

We can use SPARQL 1.1 property paths (and bnodes for unnecessary variables (that is, ones that aren’t used in the THEN)) to render this rule even more concisely:

IF {
?x a :Person, :Male; :hasSibling/:isParentOf [a :Female]
}
THEN {
?x a :UncleOfNiece.
}

Aside: that’s pure awesome.

And of course a person who’s male and has a niece or nephew is an uncle of his niece(s) and nephew(s):

IF {
?x a :Male; :isSiblingOf/:isParentOf ?z
}
THEN {
?x :isUncleOf ?z.
}

Next rule example: a super user can read all of the things!

IF {
?x a :SuperUser.
?y a :Resource.
?z a <http://www.w3.org/ns/sparql#UUID>.
}
THEN {
?z a :Role.
}

### Supported Built-Ins

Stardog supports a wide variety of functions from SPARQL, XPath, SWRL, and some native Stardog functions, too. All of them may be used in either Stardog Rules syntax or in SWRL syntax. The supported functions are enumerated here.

## Special Predicates

Stardog supports some builtin predicates with special meaning in order to make queries and rules easier to read and write. These special predicates are primarily syntactic sugar for more complex structures.

### Direct/Strict Subclasses, Subproperties, & Direct Types

Besides the standard RDF(S) predicates rdf:type, rdfs:subClassOf and rdfs:subPropertyOf, Stardog supports the following special built-in predicates:

• sp:directType

• sp:directSubClassOf

• sp:strictSubClassOf

• sp:directSubPropertyOf

• sp:strictSubPropertyOf

Where the sp prefix binds to tag:stardog:api:property:. Stardog also recognizes sesame:directType, sesame:directSubClassOf, and sesame:strictSubClassOf predicates where the prefix sesame binds to http://www.openrdf.org/schema/sesame#.

We show what these each of these predicates means by relating them to an equivalent triple pattern; that is, you can just write the predicate rather than the (more unwieldy) triple pattern.

#c1 is a subclass of c2 but not equivalent to c2

:c1 sp:strictSubClassOf :c2      =>       :c1 rdfs:subClassOf :c2 .
FILTER NOT EXISTS {
:c1 owl:equivalentClass :c2 .
}

#c1 is a strict subclass of c2 and there is no c3 between c1 and c2 in
#the strict subclass hierarchy

:c1 sp:directSubClassOf :c2      =>       :c1 sp:strictSubClassOf :c2 .
FILTER NOT EXISTS {
:c1 sp:strictSubClassOf :c3 .
:c3 sp:strictSubClassOf :c2 .
}

#ind is an instance of c1 but not an instance of any strict subclass of c1

:ind sp:directType :c1           =>       :ind rdf:type :c1 .
FILTER NOT EXISTS {
:ind rdf:type :c2 .
:c2 sp:strictSubClassOf :c1 .
}

The predicates sp:directSubPropertyOf and sp:strictSubPropertyOf are defined analogously.

### New Individuals with SWRL

Stardog also supports a special predicate that extends the expressivity of SWRL rules. According to SWRL, you can’t create new individuals (i.e., new instances of classes) in a SWRL rule.

 Note Don’t get hung up by the tech vocabulary here…​"new individual" just means that you can’t have a rule that creates a new instance of some RDF or OWL class as a result of the rule firing.

This restriction is well-motivated; without it, you can easily create rules that do not terminate, that is, never reach a fixed point. Stardog’s user-defined rules weakens this restriction in some crucial aspects, subject to the following restrictions, conditions, and warnings.

 Warning This special predicate is basically a loaded gun with which you may shoot yourselves in the foot if you aren’t very careful.

So despite the general restriction in SWRL, in Stardog we actually can create new individuals with a rule by using the function UUID() as follows:

IF {
?p a :Parent .
BIND (UUID() AS ?parent) .
}
THEN {
?parent a :Person .
}
 Note Alternatively, we can use the predicate http://www.w3.org/ns/sparql#UUID as a unary SWRL built-in.

This rule will create a random URI for each instance of the class :Parent and also assert that each new instance is an instance of :Person--parents are people, too!

#### Remarks

1. The URIs for the generated individuals are meaningless in the sense that they should not be used in further queries; that is to say, these URIs are not guaranteed by Stardog to be stable.

2. Due to normalization, rules with more than one atom in the head are broken up into several rules.

Thus,

IF {
?person a :Person .
BIND (UUID() AS ?parent) .
}
THEN {
?parent a :Parent ;
a :Male .
}

will be normalized into two rules:

IF {
?person a :Person .
BIND (UUID() AS ?parent) .
}
THEN {
?parent a :Parent .
}

IF {
?person a :Person .
BIND (UUID() AS ?parent) .
}
THEN {
?parent a :Male .
}

As a consequence, instead of stating that the new individual is both an instance of :Male and :Parent, we would create two different new individuals and assert that one is male and the other is a parent. If you need to assert various things about the new individual, we recommend the use of extra rules or axioms. In the previous example, we can introduce a new class (:Father) and add the following rule to our schema:

IF {
?person a :Father .
}
THEN {
?parent a :Parent ;
a :Male .
}

And then modify the original rule accordingly:

IF {
?person a :Person .
BIND (UUID() AS ?parent) .
}
THEN {
?parent a :Father .
}

## Query Rewriting

Reasoning in Stardog is based (mostly) on a query rewriting technique: Stardog rewrites the user’s query with respect to any schema or rules, and then executes the resulting expanded query (EQ) against the data in the normal way. This process is completely automated and requires no intervention from the user.

As can be seen in Figure 1, the rewriting process involves five different phases.

2. Figure 2. Query Rewriting

We illustrate the query answering process by means of an example. Consider a Stardog database, MyDB1, containing the following schema:

 :SeniorManager rdfs:subClassOf :manages some :Manager
:manages some :Employee rdfs:subClassOf :Manager
:Manager rdfs:subClassOf :Employee

Which says that a senior manager manages at least one manager, that every person that manages an employee is a manager, and that every manager is also an employee.

Let’s also assume that MyDB1 contains the following data assertions:

:Bill rdf:type :SeniorManager
:Robert rdf:type :Manager
:Ana :manages :Lucy
:Lucy rdf:type :Employee

Finally, let’s say that we want to retrieve the set of all employees. We do this by posing the following query:

SELECT ?employee WHERE { ?employee rdf:type :Employee }

To answer this query, Stardog first rewrites it using the information in the schema. So the original query is rewritten into four queries:

SELECT ?employee WHERE { ?employee rdf:type :Employee }
SELECT ?employee WHERE { ?employee rdf:type :Manager }
SELECT ?employee WHERE { ?employee rdf:type :SeniorManager }
SELECT ?employee WHERE { ?employee :manages ?x. ?x rdf:type :Employee }

Then Stardog executes these queries over the data as if they were written that way to begin with. In fact, Stardog can’t tell that they weren’t. Reasoning in Stardog just is query answering in nearly every case.

The form of the EQ depends on the reasoning type. For OWL 2 QL, every EQ produced by Stardog is guaranteed to be expanded into a set of queries. If the reasoning type is OWL 2 RL or EL, then the EQ may (but may not) include a recursive rule. If a recursive rule is included, Stardog’s answers may be incomplete with respect to the semantics of the reasoning type.

### Why Query Rewriting?

Query rewriting has several advantages over materialization. In materialization, the data gets expanded with respect to the schema, not with respect to any actual query. And it’s the data—​all of the data—​that gets expanded, whether any actual query subsequently requires reasoning or not. The schema is used to generate new triples, typically when data is added or removed from the system. However, materialization introduces several thorny issues:

1. data freshness. Materialization has to be performed every time the data or the schema change. This is particularly unsuitable for applications where the data changes frequently.

2. data size. Depending on the schema, materialization can significantly increase the size of the data, sometimes dramatically so. The cost of this data size blowup may be applied to every query in terms of increased I/O.

3. OWL 2 profile reasoning. Given the fact that QL, RL, and EL are not comparable with respect to expressive power, an application that requires reasoning with more than one profile would need to maintain different corresponding materialized versions of the data.

4. Resources. Depending on the size of the original data and the complexity of the schema, materialization may be computationally expensive. And truth maintenance, which materialization requires, is always computationally expensive.

## Same As Reasoning

Stardog 3.0 adds full support for OWL 2 sameAs reasoning. However, sameAs reasoning works in a different way than the rest of the reasoning mechanism. The sameAs inferences are computed and indexed eagerly so that these materialized inferences can be used directly at query rewriting time. The sameAs index is updated automatically as the database is modified so the difference is not of much direct concern to users.

In order to use sameAs reasoning, the database configuration option reasoning.sameas should be set either at database creation time or at a later time when the database is offline. This can be done through the Web Console or using the command line as follows:

$./stardog-admin db create -o reasoning.sameas=FULL -n myDB There are legal three values for this option: • OFF disables all sameAs inferences, that is, only asserted sameAs triples will be included in query results.[24] • ON computes sameAs inferences using only asserted sameAs triples, considering the reflexivity, symmetry and transitivity of the sameAs relation. • FULL same as ON but also considers OWL functional properties, inverse functional properties, and hasKey axioms while computing sameAs inferences.  Note The way sameAs reasoning works differs from the OWL semantics slightly in the sense that Stardog designates one canonical individual for each sameAs equivalence set and only returns the canonical individual. This avoids the combinatorial explosion in query results while providing the data integration benefits. Let’s see an example showing how sameAs reasoning works. Consider the following database where sameAs reasoning is set to ON: dbpedia:Elvis_Presley dbpedia-owl:birthPlace dbpedia:Mississippi ; owl:sameAs freebase:en.elvis_presley . nyt:presley_elvis_per nyt:associated_article_count 35 ; rdfs:label "Elvis Presley" ; owl:sameAs dbpedia:Elvis_Presley . freebase:en.elvis_presley freebase:common.topic.official_website <http://www.elvis.com/> . Now consider the following query and its results: $ ./stardog query --reasoning elvis 'SELECT * { ?s dbpedia-owl:birthPlace ?o; rdfs:label "Elvis Presley" }'
+-----------------------+---------------------+
|           s           |          o          |
+-----------------------+---------------------+
| nyt:presley_elvis_per | dbpedia:Mississippi |
+-----------------------+---------------------+

Let’s unpack this carefully. There are three things to note.

First, the query returns only one result even though there are three different URIs that denote Elvis Presley. Second, the URI returned is fixed but chosen randomly. Stardog picks one of the URIs as the canonical URI and always returns that and only that canonical URI in the results. If more sameAs triples are added the chosen canonical individual may change. Third, it is important to point out that even though only one URI is returned, the effect of sameAs reasoning is visible in the results since the rdfs:label and dbpedia-owl:birthPlace properties were asserted about different instances (i.e., different URIs).

Now, you might be inclined to write queries such as this to get all the properties for a specific URI:

SELECT * {
nyt:presley_elvis_per owl:sameAs ?elvis .
?elvis ?p ?o
}

However, this is completely unnecessary; rather, you can write the following query and get the same results since sameAs reasoning would automatically merge the results for you. Therefore, the query

SELECT * {
nyt:presley_elvis_per ?p ?o
}

would return these results:

+----------------------------------------+-----------------------+
|                   p                    |           o           |
+----------------------------------------+-----------------------+
| rdfs:label                             | "Elvis Presley"       |
| dbpedia-owl:birthPlace                 | dbpedia:Mississippi   |
| nyt:associated_article_count           | 35                    |
| freebase:common.topic.official_website | http://www.elvis.com/ |
| rdf:type                               | owl:Thing             |
+----------------------------------------+-----------------------+
 Note The URI used in the query does not need to be the same one returned in the results. Thus, the following query would return the exact same results, too:
SELECT * {
dbpedia:Elvis_Presley ?p ?o
}

The only time Stardog will return a non-canonical URI in the query results is when you explicitly query for the sameAs inferences as in this next example:

$./stardog query -r elvis 'SELECT * { freebase:en.elvis_presley owl:sameAs ?elvis }' +---------------------------+ | elvis | +---------------------------+ | dbpedia:Elvis_Presley | | freebase:en.elvis_presley | | nyt:presley_elvis_per | +---------------------------+ In the FULL sameAs reasoning mode, Stardog will also take other OWL axioms into account when computing sameAs inferences. Consider the following example: #Everyone has a unique SSN number :hasSSN a owl:InverseFunctionalProperty , owl:DatatypeProperty . :JohnDoe :hasSSN "123-45-6789" . :JDoe :hasSSN "123-45-6789" . #Nobody can work for more than one company (for the sake of the example) :worksFor a owl:FunctionalProperty , owl:ObjectProperty ; rdfs:domain :Employee ; rdfs:range :Company . :JohnDoe :worksFor :Acme . :JDoe :worksFor :AcmeInc . #For each company, there can only be one employee with the same employee ID :Employee owl:hasKey (:employeeID :worksFor ). :JohnDoe :employeeID "1234-ABC" . :JohnD :employeeID "1234-ABC" ; :worksFor :AcmeInc . :JD :employeeID "5678-XYZ" ; :worksFor :AcmeInc . :John :employeeID "1234-ABC" ; :worksFor :Emca . For this database, with sameAs reasoning set to FULL, we would get the following answers: $ ./stardog query -r acme "SELECT * {?x owl:sameAs ?y}"
+----------+----------+
|    x     |    y     |
+----------+----------+
| :JohnDoe | :JohnD   |
| :JDoe    | :JohnD   |
| :Acme    | :AcmeInc |
+----------+----------+

We can follow the chain of inferences to understand how these results were computed:

1. :JohnDoe owl:sameAs :JohnD can be computed due to the fact that both have the same SSN numbers and hasSSN property is inverse functional.

2. We can infer :Acme owl:sameAs :AcmeInc since :JohnDoe can work for at most one company.

3. :JohnDoe owl:sameAs :JohnD can be inferred using the owl:hasKey definition since both individuals are known to work for the same company and have the same employee ID.

4. No more sameAs inferences can be computed due to the key definition, since other employees either have different IDs or work for other companies.

## Removing Unwanted Inferences

Sometimes reasoning can produce unintended inferences. Perhaps there are modeling errors in the schema or incorrect assertions in the data. After an unintended inference is detected, it might be hard to figure out how to fix it, because there might be multiple different reasons for the inference. The reasoning explain command can be used to see the different explanations and the reasoning undo command can be used to generate a SPARQL update query that will remove the minimum amount of triples necessary to remove the unwanted inference:

$./reasoning undo myDB ":AcmeInc a :Person" ## Performance Hints The query rewriting approach suggests some guidelines for more efficient query answering. ### Hierarchies and Queries Avoid unnecessarily deep class/property hierarchies. If you do not need to model several different types of a given class or property in your schema, then don’t do that! The reason shallow hierarchies are desirable is that the maximal hierarchy depth in the schema partly determines the maximal size of the EQs produced by Stardog. The larger the EQ, the longer it takes to evaluate, generally. For example, suppose our schema contains a very thorough and detailed set of subclasses of the class :Employee: :Manager rdfs:subClassOf :Employee :SeniorManager rdfs:subClassOf :Manager ... :Supervisor rdfs:subClassOf :Employee :DepartmentSupervisor rdfs:subClassOf :Supervisor ... :Secretary rdfs:subClassOf :Employee ... If we wanted to retrieve the set of all employees, Stardog would produce an EQ containing a query of the following form for every subclass :Ci of :Employee: SELECT ?employee WHERE { ?employee rdf:type :Ci } Thus, ask the most specific query sufficient for your use case. Why? More general queries—​that is, queries that contain concepts high up in the class hierarchy defined by the schema—​will typically yield larger EQs. ### Domains and Ranges Specify domain and range of the properties in the schema. These types of axiom can improve query performance significantly. Consider the following query asking for people and the employees they manage: SELECT ?manager ?employee WHERE { ?manager :manages ?employee. ?employee rdf:type :Employee. } We know that this query would cause a large EQ given a deep hierarchy of :Employee subclasses. However, if we added the following single range axiom: :manages rdfs:range :Employee then the EQ would collapse to  SELECT ?manager ?employee WHERE { ?manager :manages ?employee } which is considerably easier to evaluate. ### Very Large Schemas If you are working with a very large schema like SNOMED then there are couple things to note. First of all, Stardog reasoning works by pulling the complete schema into memory. This means you might need to increase the default memory settings for Stardog for a large schema. Stardog performs all schema reasoning upfront and only once but waits until the first reasoning query arrives. With a large schema, this step can be slow but subsequent reasoning queries will be fast. Also note that, Stardog will update schema reasoning results automatically after the database is modified so there will be some processing time spent then. Reasoning with very expressive schemas can be time consuming and use a lot of memory. To get the best performance out of Stardog with large schemas, limit the expressivity of your schema to OWL 2 EL. You can also set the reasoning type of the database to EL and Stardog will automatically filter any axiom outside the EL expressivity. See Reasoning Types for more details on reasoning types. OWL 2 EL allows range declarations for properties and user-defined datatypes but avoiding these two constructs will further improve schema reasoning performance in Stardog. ## Not Seeing Expected Results? Here’s a few things that you might want to consider. ### Are variable types ambiguous? When a SPARQL query gets executed, each variable is bound to a URI, blank node, or to a literal to form a particular result (a collection of these results is a result set). In the context of reasoning, URIs might represent different entities: individuals, classes, properties, etc. According to the relevant standard, every variable in a SPARQL query must bind to at most one of these types of entity. Stardog can often figure out the right entity type from the query itself (e.g., given the triple pattern ?i ?p "a literal", we know ?p is supposed to bind to a data property); however, sometimes this isn’t possible (e.g., ?s ?p ?o). In case the types can’t be determined automatically, Stardog logs a message and evaluates the query by making some assumptions, which may not be what the query writer intended, about the types of variables. You can add one or more type triples to the query to resolve these ambiguities.[25] These "type triples" have the form ?var a TYPE, where TYPE is a URI representing the type of entity to which the variable ?var is supposed to bind: the most common are owl:ObjectProperty or owl:DatatypeProperty; in some cases, you might want owl:NamedIndividual, or owl:Class. For instance, you can use the following query to retrieve all object properties and their characteristics; without the type triple, ?s will bind only to individuals:  SELECT ?o WHERE { ?s rdf:type ?o. ?s a owl:ObjectProperty. }. Since Stardog now knows that ?s should bind to an object property, it can now infer that ?o binds to property characteristics of ?s. ### Is the schema where you think it is? Starting in Stardog 3.0, Stardog will extract the schema from all named graphs and the default graph. If you require that the schema only be extracted from one or more specific named graphs, then you must tell Stardog where to find the schema. See database configuration options for details. You can also use the reasoning schema command to export the contents of the schema to see exactly what is included in the schema that Stardog uses. ### Are you using the right reasoning type? Perhaps some of the modeling constructs (a.k.a. axioms) in your database are being ignored. By default, Stardog uses the SL reasoning type. You can find out which axioms are being ignored by looking at the Stardog log file. ### Are you using DL? Stardog supports full OWL 2 DL reasoning but only for data that fits into main memory. ### Are you using SWRL? SWRL rules—​whether using SWRL syntax or Stardog Rules Syntax—​are only taken into account using the SL reasoning type. ### Do you know what to expect? The OWL 2 primer is a good place to start. ## Known Issues Stardog 6.1.0 does not • Follow ontology owl:imports statements automatically; any imported OWL ontologies that are required must be loaded into a Stardog database in the normal way. • Handle recursive queries. If recursion is necessary to answer the query with respect to the schema, results will be sound (no wrong answers) but potentially incomplete (some correct answers not returned) with respect to the requested reasoning type. ## Terminology This chapter uses the following terms of art. ### Databases A database (DB), a.k.a. ontology, is composed of two different parts: the schema or Terminological Box (TBox) and the data or Assertional Box (ABox). Analogus to relational databases, the TBox can be thought of as the schema, and the ABox as the data. In other words, the TBox is a set of axioms, whereas the ABox is a set of assertions. As we explain in OWL 2 Profiles, the kinds of assertion and axiom that one might use for a particular database are determined by the fragment of OWL 2 to which you’d like to adhere. In general, you should choose the OWL 2 profile that most closely fits the data modeling needs of your application. The most common data assertions are class and property assertions. Class assertions are used to state that a particular individual is an instance of a given class. Property assertions are used to state that two particular individuals (or an individual and a literal) are related via a given property. For example, suppose we have a DB MyDB2 that contains the following data assertions. We use the usual standard prefixes for RDF(S) and OWL. :complexible rdf:type :Company :complexible :maintains :Stardog Which says that :complexible is a company, and that :complexible maintains :Stardog. The most common schema axioms are subclass axioms. Subclass axioms are used to state that every instance of a particular class is also an instance of another class. For example, suppose that MyDB2 contains the following TBox axiom: :Company rdfs:subClassOf :Organization stating that companies are a type of organization. ### Queries When reasoning is enabled, Stardog executes SPARQL queries depending on the type of Basic Graph Patterns they contain. A BGP is said to be an "ABox BGP" if it is of one of the following forms: • term1 rdf:type uri • term1 uri term2 • term1 owl:differentFrom term2 • term1 owl:sameAs term2 A BGP is said to be a TBox BGP if it is of one of the following forms: • term1 rdfs:subClassOf term2 • term1 owl:disjointWith term2 • term1 owl:equivalentClass term2 • term1 rdfs:subPropertyOf term2 • term1 owl:equivalentProperty term2 • term1 owl:inverseOf term2 • term1 owl:propertyDisjointWith term2 • term1 rdfs:domain term2 • term1 rdfs:range term2 A BGP is said to be a Hybrid BGP if it is of one of the following forms: • term1 rdf:type ?var • term1 ?var term2 where term (possibly with subscripts) is either an URI or variable; uri is a URI; and ?var is a variable. When executing a query, ABox BGPs are handled by Stardog. TBox BGPs are executed by Pellet embedded in Stardog. Hybrid BGPs by a combination of both. ### Reasoning Intuitively, reasoning with a DB means to make implicit knowledge explicit. There are two main use cases for reasoning: to infer implicit knowledge and to discover modeling errors. With respect to the first use case, recall that MyDB2 contains the following assertion and axiom:  :complexible rdf:type :Company :Company rdfs:subClassOf :Organization From this DB, we can use Stardog in order to infer that :complexible is an organization: :complexible rdf:type :Organization Using reasoning in order to infer implicit knowledge in the context of an enterprise application can lead to simpler queries. Let us suppose, for example, that MyDB2 contains a complex class hierarchy including several types of organization (including company). Let us further suppose that our application requires to use Stardog in order to get the list of all considered organizations. If Stardog were used with reasoning, then we would need only issue the following simple query: SELECT ?org WHERE { ?org rdf:type :Organization} In contrast, if we were using Stardog with no reasoning, then we would have to issue a more complex query that considers all possible types of organization, thus coupling queries to domain knowledge in a tight way: SELECT ?org WHERE { { ?org rdf:type :Organization } UNION { ?org rdf:type :Company } UNION ... } Which of these queries seems more loosely coupled and more resilient to change? Stardog can also be used in order to discover modeling errors in a DB. The most common modeling errors are unsatisfiable classes and inconsistent DBs. An unsatisfiable class is simply a class that cannot have any instances. Say, for example, that we added the following axioms to MyDB2:  :Company owl:disjointWith :Organization :LLC owl:equivalentClass :Company and :Organization stating that companies cannot be organizations and vice versa, and that an LLC is a company and an organization. The disjointness axiom causes the class :LLC to be unsatisfiable because, for the DB to be free of any logical contradiction, there can be no instances of :LLC. Asserting (or inferring) that an unsatisfiable class has an instance, causes the DB to be inconsistent. In the particular case of MyDB2, we know that :complexible is a company and an organization; therefore, we also know that it is an instance of :LLC, and as :LLC is known to be unsatisfiable, we have that MyDB2 is inconsistent. Using reasoning in order to discover modeling errors in the context of an enterprise application is useful in order to maintain a correct contradiction-free model of the domain. In our example, we discovered that :LLC is unsatisfiable and MyDB2 is inconsistent, which leads us to believe that there is a modeling error in our DB. In this case, it is easy to see that the problem is the disjointness axiom between :Company and :Organization. ### OWL 2 Profiles As explained in the OWL 2 Web Ontology Language Profiles Specification, an OWL 2 profile is a reduced version of OWL 2 that trades some expressive power for efficiency of reasoning. There are three OWL 2 profiles, each of which achieves efficiency differently. • OWL 2 QL is aimed at applications that use very large volumes of instance data, and where query answering is the most important reasoning task. The expressive power of the profile is necessarily limited; however, it includes most of the main features of conceptual models such as UML class diagrams and ER diagrams. • OWL 2 EL is particularly useful in applications employing ontologies that contain very large numbers of properties and classes. This profile captures the expressive power used by many such ontologies and is a subset of OWL 2 for which the basic reasoning problems can be performed in time that is polynomial with respect to the size of the ontology. • OWL 2 RL is aimed at applications that require scalable reasoning without sacrificing too much expressive power. It is designed to accommodate OWL 2 applications that can trade the full expressivity of the language for efficiency, as well as RDF(S) applications that need some added expressivity. Each profile restricts the kinds of axiom and assertion that can be used in a DB. Colloquially, QL is the least expressive of the profiles, followed by RL and EL; however, strictly speaking, no profile is more expressive than any other as they provide incomparable sets of constructs. Stardog supports the three profiles of OWL 2. Notably, since TBox BGPs are handled completely by Pellet, Stardog supports reasoning for the whole of OWL 2 for queries containing TBox BGPs only. # Validating Constraints Stardog Integrity Constraint Validation ("ICV") validates RDF data stored in a Stardog database according to constraints described by users and that make sense for their domain, application, and data. These constraints may be written in SPARQL, OWL, or SWRL. Support for SHACL constraints have been added as of version 6.1. This chapter explains how to use ICV. The use of high-level languages (OWL 2, SWRL, and SPARQL) to validate RDF data using closed world semantics is one of Stardog’s unique capabilities. Using high level languages like OWL, SWRL, and SPARQL as schema or constraint languages for RDF and Linked Data has several advantages: • Unifying the domain model with data quality rules • Aligning the domain model and data quality rules with the integration model and language (i.e., RDF) • Being able to query the domain model, data quality rules, integration model, mapping rules, etc with SPARQL • Being able to use automated reasoning about all of these things to insure logical consistency, explain errors and problems, etc.  Tip See the extended ICV tutorial in the stardog-examples repo on Github and our blog post Data Quality with ICV for more details about using ICV. ## Using ICV from CLI To add constraints to a database: $ stardog-admin icv add myDb constraints.rdf

To drop all constraints from a database:

$stardog-admin icv drop myDb To remove one or more specific constraints from a database: $ stardog-admin icv remove myDb constraints.rdf

To convert new or existing constraints into SPARQL queries for export:

$stardog icv convert myDb constraints.rdf To explain a constraint violation: $ stardog icv explain --contexts http://example.org/context1 http://example.org/context2 -- myDb

To export constraints:

$stardog icv export myDb constraints.rdf To validate a database (or some named graphs) with respect to constraints: $ stardog icv validate --contexts http://example.org/context1 http://example.org/context2 -- myDb

## ICV & OWL 2 Reasoning

An integrity constraint may be satisfied or violated in either of two ways: by an explicit statement in a Stardog database or by a statement that’s been validly inferred by Stardog.

When ICs are being validated the user needs to specify if reasoning will be used or not. So ICV is performed with three inputs:

1. a Stardog database,

2. a set of constraints, and

3. a reasoning flag (which may be, of course, set to false for no reasoning).

This is the case because domain modelers, ontology developers, or integrity constraint authors must consider the interactions between explicit and inferred statements and how these are accounted for in integrity constraints.

## ICV Guard Mode

Stardog will also apply constraints as part of its transactional cycle and fail transactions that violate constraints. We call this "guard mode". It must be enabled explicitly in the database configuration options. Using the command line, these steps are as follows:

$./stardog-admin db offline myDb #take the database offline$ ./stardog-admin metadata set -o icv.enabled=true myDb #enable ICV
$./stardog-admin db online myDb #put the database online Once guard mode is enabled, modifications of the database (via SPARQL Update or any other method), whether adds or deletes, that violate the integrity constraints will cause the transaction to fail. ## Explaining ICV Violations ICV violations can be explained using Stardog’s Proof Trees. The following command will explain the IC violations for constraints stored in the database: $ stardog icv explain --reasoning "myDB"

The command is flexible to change the number of violations displayed, and to explain violations for external constraints by passing the file with constraints as an additional argument:

$stardog icv explain --reasoning --limit 2 "myDB" constraints.ttl ### Security Note  Warning There is a security implication in this design that may not be obvious. Changing the reasoning type associated with a database and integrity constraint validation may have serious security implications with respect to a Stardog database and, thus, may only be performed by a user role with sufficient privileges for that action. ## Repairing ICV Violations Stardog 3.0 adds support for automatic repair of some kinds of integrity violation. This can be accomplished programmatically via API, as well as via CLI using the icv fix subcommand. $ stardog help icv fix

Repair plans are emitted as a sequence of SPARQL Update queries, which means they can be applied to any system that understands SPARQL Update. If you pass --execute the repair plan will be applied immediately.

icv fix will repair violations of all constraints in the database; if you’d prefer to fix the violations for only some constraints, you can pass those constraints as an additional argument. Although a possible (but trivial) fix for any violation is to remove one or more constraints, icv fix does not suggest that kind of repair, even though it may be appropriate in some cases.

## SHACL Constraints (Beta)

As of version 6.1, Stardog supports validation of SHACL constraints. SHACL constraints can be managed like any other constraint Stardog supports and all the existing validation commands work with SHACL constraints.

Normally constraints are stored in the system database and managed with special commands icv add and icv remove. This is still possible with SHACL constraints but if desired SHACL constraints can be loaded into the database along with regular data using data add. Validation results will be the same in both cases.

SHACL support comes with a new validation command that outputs the SHACL validation report:

$stardog icv report myDb ### SHACL Support Limitations Stardog supports all the features in the core SHACL language with the following exceptions: 1. Stardog does not support qualified value shape constraints 2. Stardog supports SPARQL-based constraints but does not support prebinding the $shapesGraph or $currentShape variables in SPARQL 3. Stardog does not support property validators 4. Stardog does not support the Advanced Features or the JavaScript Extensions ## OWL Constraint Examples Stardog ICV has a formal semantics. But let’s just look at some examples instead; these examples use OWL 2 Manchester syntax, and they assume a simple data schema, which is available as an OWL ontology and as a UML diagram. The examples assume that the default namespace is http://example.com/company.owl# and that xsd: is bound to the standard, http://www.w3.org/2001/XMLSchema#. Reference Java code is available for each of the following examples and is also distributed with Stardog. ### Subsumption Constraints This kind of constraint guarantees certain subclass and superclass (i.e., subsumption) relationships exist between instances. #### Managers must be employees. ##### Constraint :Manager rdfs:subClassOf :Employee ##### Database A (invalid) :Alice a :Manager . ##### Database B (valid) :Alice a :Manager , :Employee . This constraint says that if an RDF individual is an instance of Manager, then it must also be an instance of Employee. In A, the only instance of Manager, namely Alice, is not an instance of Employee; therefore, A is invalid. In B, Alice is an instance of Database both Manager and Employee; therefore, B is valid. ### Domain-Range Constraints These constraints control the types of subjects and objects used with a property. #### Only project leaders can be responsible for projects. ##### Constraint :is_responsible_for rdfs:domain :Project_Leader ; rdfs:range :Project . ##### Database A (invalid) :Alice :is_responsible_for :MyProject . :MyProject a :Project . ##### Database B (invalid) :Alice a :Project_Leader ; :is_responsible_for :MyProject . ##### Database C (valid) :Alice a :Project_Leader ; :is_responsible_for :MyProject . :MyProject a :Project . This constraint says that if two RDF instances are related to each other via the property is_responsible_for, then the subject must be an instance of Project_Leader and the object must be an instance of Project. In Database A, there is only one pair of individuals related via is_responsible_for, namely (Alice, MyProject), and MyProject is an instance of Project; but Alice is not an instance of Project_Leader. Therefore, A is invalid. In B, Alice is an instance of Project_Leader, but MyProject is not an instance of Project; therefore, B is not valid. In C, Alice is an instance of Project_Leader, and MyProject is an instance of Project; therefore, C is valid. #### Only employees can have an SSN. ##### Constraint :ssn rdfs:domain :Employee ##### Database A (invalid) :Bob :ssn "123-45-6789" . ##### Database B (valid) :Bob a :Employee ; :ssn "123-45-6789" . This constraint says that if an RDF instance i has a data assertion via the the property SSN, then i must be an instance of Employee. In A, Bob is not an instance of Employee but has SSN; therefore, A is invalid. In B, Bob is an instance of Employee; therefore, B is valid. #### A date of birth must be a date. ##### Constraint :dob rdfs:range xsd:date ##### Database A (invalid) :Bob :dob "1970-01-01" . ##### Database B (valid) :Bob :dob "1970-01-01"^^xsd:date This constraint says that if an RDF instance i is related to a literal l via the data property DOB, then l must have the XML Schema type xsd:date. In A, Bob is related to the untyped literal "1970-01-01" via DOB so A is invalid. In B, the literal "1970-01-01" is properly typed so it’s valid. ### Participation Constraints These constraints control whether or not an RDF instance participates in some specified relationship. #### Each supervisor must supervise at least one employee. ##### Constraint #this constraint is very concise in Terp syntax: #:Supervisor rdfs:subClassOf (:supervises some :Employee) :Supervisor rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :supervises ; owl:someValuesFrom :Employee ] . ##### Database A (valid) :Alice a owl:Thing . ##### Database B (invalid) :Alice a :Supervisor . ##### Database C (invalid) :Alice a :Supervisor ; :supervises :Bob . ##### Database D (valid) :Alice a :Supervisor ; :supervises :Bob . :Bob a :Employee This constraint says that if an RDF instance i is of type Supervisor, then i must be related to an individual j via the property supervises and also that j must be an instance of Employee. In A, Supervisor has no instances; therefore, A is trivially valid. In B, the only instance of Supervisor, namely Alice, is related to no individual; therefore, B is invalid. In C, Alice is related to Bob via supervises, but Bob is not an instance of Employee; therefore, C is invalid. In D, Alice is related to Bob via supervises, and Bob is an instance of Employee; hence, D is valid. #### Each project must have a valid project number. ##### Constraint #Again, this constraint in Terp syntax rocks the hizzous: #:Project rdfs:subClassOf (:number some xsd:integer[>= 0, < 5000]) :Project rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :number ; owl:someValuesFrom [ a rdfs:Datatype ; owl:onDatatype xsd:integer ; owl:withRestrictions ([xsd:minInclusive 0] [ xsd:maxExclusive 5000]) ] ] . ##### Database A (valid) :MyProject a owl:Thing . ##### Database B (invalid) :MyProject a :Project ##### Database C (invalid) :MyProject a :Project ; :number "23" . ##### Database D (invalid) :MyProject a :Project ; :number "6000"^^xsd:integer . ##### Database E (valid) :MyProject a :Project ; :number "23"^^xsd:integer . This constraint says that if an RDF instance i is of type Project, then i must be related via the property number to an integer between 0 and 5000 (inclusive)—that is, projects have project numbers in a certain range. In A, the individual MyProject is not known to be an instance of Project so the constraint does not apply at all and A is valid. In B, MyProject is an instance of Project but doesn’t have any data assertions via number so A is invalid. In C, MyProject does have a data property assertion via number but the literal "23" is untyped—​that is, it’s not an integer—​therefore, C is invalid. In D, MyProject is related to an integer via number but it is out of the range: D is invalid. Finally, in E, MyProject is related to the integer 23 which is in the range of [0,5000] so E is valid. ### Cardinality Constraints These constraints control the number of various relationships or property values. #### Employees must not work on more than 3 projects. ##### Constraint #Constraint in Terp syntax: #:Employee rdfs:subClassOf (:works_on max 3 :Project) :Employee rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :works_on; owl:maxQualifiedCardinality "3"^^xsd:nonNegativeInteger ; owl:onClass :Project ] . ##### Database A (valid) :Bob a owl:Thing. ##### Database B (valid) :Bob a :Employee ; :works_on :MyProject . :MyProject a :Project . ##### Database C (invalid) :Bob a :Employee ; :works_on :MyProject , :MyProjectFoo , :MyProjectBar , :MyProjectBaz . :MyProject a :Project . :MyProjectFoo a :Project . :MyProjectBar a :Project . :MyProjectBaz a :Project . If an RDF instance i is an Employee, then i must not be related via the property works_on to more than 3 instances of Project. In A, Bob is not known to be an instance of Employee so the constraint does not apply and the A is valid. In B, Bob is an instance of Employee but is known to work on only a single project, namely MyProject, so B is valid. In C, Bob is related to 4 instances of Project via works_on.  Note Stardog ICV implements a weak form of the unique name assumption, that is, it assumes that things which have different names are, in fact, different things.[26] Since Stardog ICV uses closed world (instead of open world) semantics,[27] it assumes that the different projects with different names are, in fact, separate projects, which (in this case) violates the constraint and makes C invalid. #### Departments must have at least 2 employees. ##### Constraint #Constraint in Terp syntax: #:Department rdfs:subClassOf (inverse :works_in min 2 :Employee) :Department rdfs:subClassOf [ a owl:Restriction ; owl:onProperty [owl:inverseOf :works_in] ; owl:minQualifiedCardinality "2"^^xsd:nonNegativeInteger ; owl:onClass :Employee ] . ##### Database A (valid) :MyDepartment a owl:NamedIndividual . ##### Database B (invalid) :MyDepartment a :Department . :Bob a :Employee ; :works_in :MyDepartment . ##### Database C (valid) :MyDepartment a :Department . :Alice a :Employee ; :works_in :MyDepartment . :Bob a :Employee ; :works_in :MyDepartment . This constraint says that if an RDF instance i is a Department, then there should exist at least 2 instances j and k of class Employee which are related to i via the property works_in (or, equivalently, i should be related to them via the inverse of works_in). In A, MyDepartment is not known to be an instance of Department so the constraint does not apply. In B, MyDepartment is an instance of Department but only one instance of Employee, namely Bob, is known to work in it, so B is invalid. In C, MyDepartment is related to the individuals Bob and Alice, which are both instances of Employee and (again, due to weak Unique Name Assumption in Stardog ICV), are assumed to be distinct, so C is valid. #### Managers must manage exactly 1 department. ##### Constraint #Constraint in Terp syntax: #:Manager rdfs:subClassOf (:manages exactly 1 :Department) :Manager rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :manages ; owl:qualifiedCardinality "1"^^xsd:nonNegativeInteger ; owl:onClass :Department ] . ##### Database A (valid)  Individual: Isabella ##### Database B (invalid) :Isabella a :Manager . ##### Database C (invalid) :Isabella a :Manager ; :manages :MyDepartment . ##### Database D (valid) :Isabella a :Manager ; :manages :MyDepartment . :MyDepartment a :Department . ##### Database E (invalid) :Isabella a :Manager ; :manages :MyDepartment , :MyDepartment1 . :MyDepartment a :Department . :MyDepartment1 a :Department . This constraint says that if an RDF instance i is a Manager, then it must be related to exactly 1 instance of Department via the property manages. In A, the individual Isabella is not known to be an instance of Manager so the constraint does not apply and A is valid. In B, Isabella is an instance of Manager but is not related to any instances of Department, so B is invalid. In C, Isabella is related to the individual MyDepartment via the property manages but MyDepartment is not known to be an instance of Department, so C is invalid. In D, Isabella is related to exactly one instance of Department, namely MyDepartment, so D is valid. Finally, in E, Isabella is related to two (assumed to be) distinct (again, because of weak UNA) instances of Department, namely MyDepartment and MyDepartment1, so E is invalid. #### Entities may have no more than one name. ##### Constraint :name a owl:FunctionalProperty . ##### Database A (valid) :MyDepartment a owl:Thing . ##### Database B (valid) :MyDepartment :name "Human Resources" . ##### Database C (invalid) :MyDepartment :name "Human Resources" , "Legal" . This constraint says that no RDF instance i can have more than one assertion via the data property name. In A, MyDepartment does not have any data property assertions so A is valid. In B, MyDepartment has a single assertion via name, so the ontology is also valid. In C, MyDepartment is related to 2 literals, namely "Human Resources" and "Legal", via name, so C is invalid. ### Property Constraints These constraints control how instances are related to one another via properties. #### The manager of a department must work in that department. ##### Constraint :manages rdfs:subPropertyOf :works_in . ##### Database A (invalid) :Bob :manages :MyDepartment ##### Database B (valid) :Bob :works_in :MyDepartment ; :manages :MyDepartment . This constraint says that if an RDF instance i is related to j via the property manages, then i must also be related to j via the property works_in. In A, Bob is related to MyDepartment via manages, but not via works_in, so A is invalid. In B, Bob is related to MyDepartment via both manages and works_in, so B is valid. #### Department managers must supervise all the department’s employees. ##### Constraint :is_supervisor_of owl:propertyChainAxiom (:manages [owl:inverseOf :works_in]) . ##### Database A (invalid) :Jose :manages :MyDepartment ; :is_supervisor_of :Maria . :Maria :works_in :MyDepartment . :Diego :works_in :MyDepartment . ##### Database B (valid) :Jose :manages :MyDepartment ; :is_supervisor_of :Maria , :Diego . :Maria :works_in :MyDepartment . :Diego :works_in :MyDepartment . This constraint says that if an RDF instance i is related to j via the property manages and k is related to j via the property works_in, then i must be related to k via the property is_supervisor_of. In A, Jose is related to MyDepartment via manages, Diego is related to MyDepartment via works_in, but Jose is not related to Diego via any property, so A is invalid. In B, Jose is related to Maria and Diego--who are both related to MyDepartment by way of works_in--via the property is_supervisor_of, so B is valid. ### Complex Constraints Constrains may be arbitrarily complex and include many conditions. #### Employee Constraints Each employee works on at least one project, or supervises at least one employee that works on at least one project, or manages at least one department. ##### Constraint #Constraint in Terp syntax: #how are you not loving Terp by now?! #:Employee rdfs:subClassOf (:works_on some (:Project or #(:supervises some (:Employee and (:works_on some :Project))) or (:manages some :Department))) :Employee rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :works_on ; owl:someValuesFrom [ owl:unionOf (:Project [ a owl:Restriction ; owl:onProperty :supervises ; owl:someValuesFrom [ owl:intersectionOf (:Employee [ a owl:Restriction ; owl:onProperty :works_on ; owl:someValuesFrom :Project ]) ] ] [ a owl:Restriction ; owl:onProperty :manages ; owl:someValuesFrom :Department ]) ] ] . ##### Database A (invalid) :Esteban a :Employee . ##### Database B (invalid) :Esteban a :Employee ; :supervises :Lucinda . :Lucinda a :Employee . ##### Database C (valid) :Esteban a :Employee ; :supervises :Lucinda . :Lucinda a :Employee ; :works_on :MyProject . :MyProject a :Project . ##### Database D (valid) :Esteban a :Employee ; :manages :MyDepartment . :MyDepartment a :Department . ##### Database E (valid) :Esteban a :Employee ; :manages :MyDepartment ; :works_on :MyProject . :MyDepartment a :Department . :MyProject a :Project . This constraint says that if an individual i is an instance of Employee, then at least one of three conditions must be met: • it is related to an instance of Project via the property works_on • it is related to an instance j via the property supervises and j is an instance of Employee and is also related to some instance of Project via the property works_on • it is related to an instance of Department via the property manages. A and B are invalid because none of the conditions are met. C meets the second condition: Esteban (who is an Employee) is related to Lucinda via the property supervises whereas Lucinda is both an Employee and related to MyProject, which is a Project, via the property works_on. D meets the third condition: Esteban is related to an instance of Department, namely MyDepartment, via the property manages. Finally, E meets the first and the third conditions because in addition to managing a department Esteban is also related an instance of Project, namely MyProject, via the property works_on. #### Employees and US government funding Only employees who are American citizens can work on a project that receives funds from a US government agency. ##### Constraint #Constraint in Terp syntax: #:Project and (:receives_funds_from some :US_Government_Agency)) rdfs:subClassOf #(inverse :works_on only (:Employee and (:nationality value "US"))) [ owl:intersectionOf (:Project [ a owl:Restriction ; owl:onProperty :receives_funds_from ; owl:someValuesFrom :US_Government_Agency ]) . ] rdfs:subClassOf [ a owl:Restriction ; owl:onProperty [owl:inverseOf :works_on] ; owl:allValuesFrom [ owl:intersectionOf (:Employee [ a owl:Restriction ; owl:hasValue "US" ; owl:onProperty :nationality ]) ] ] . ##### Database A (valid) :MyProject a :Project ; :receives_funds_from :NASA . :NASA a :US_Government_Agency ##### Database B (invalid) :MyProject a :Project ; :receives_funds_from :NASA . :NASA a :US_Government_Agency . :Andy a :Employee ; :works_on :MyProject . ##### Database C (valid) :MyProject a :Project ; :receives_funds_from :NASA . :NASA a :US_Government_Agency . :Andy a :Employee ; :works_on :MyProject ; :nationality "US" . ##### Database D (invalid) :MyProject a :Project ; :receives_funds_from :NASA . :NASA a :US_Government_Agency . :Andy a :Employee ; :works_on :MyProject ; :nationality "US" . :Heidi a :Supervisor ; :works_on :MyProject ; :nationality "US" . ##### Database E (valid) :MyProject a :Project ; :receives_funds_from :NASA . :NASA a :US_Government_Agency . :Andy a :Employee ; :works_on :MyProject ; :nationality "US" . :Heidi a :Supervisor ; :works_on :MyProject ; :nationality "US" . :Supervisor rdfs:subClassOf :Employee . SubClassOf: Employee This constraint says that if an individual i is an instance of Project and is related to an instance of US_Government_Agency via the property receives_funds_from, then any individual j which is related to i via the property works_on must satisfy two conditions: • it must be an instance of Employee • it must not be related to any literal other than "US" via the data property nationality. A is valid because there is no individual related to MyProject via works_on, so the constraint is trivially satisfied. B is invalid since Andy is related to MyProject via works_on, MyProject is an instance of Project and is related to an instance of US_Government_Agency, that is, NASA, via receives_funds_from, but Andy does not have any data property assertions. C is valid because both conditions are met. D is not valid because Heidi violated the first condition: she is related to MyProject via works_on but is not known to be an instance of Employee. Finally, this is fixed in E—​by way of a handy OWL axiom—​which states that every instance of Supervisor is an instance of Employee, so Heidi is inferred to be an instance of Employee and, consequently, E is valid.[28] If you made it this far, you deserve a drink! ### Constraints Formats In addition to OWL, ICV constraints can be expressed in SPARQL and Stardog Rules. In both cases, the constraints define queries and rules to find violations. These constraints can be added individually, or defined together in a file as shown below: @prefix rule: <tag:stardog:api:rule:> . @prefix icv: <tag:stardog:api:icv:> . # Rule Constraint [] a rule:SPARQLRule ; rule:content """ prefix : <http://example.org/> IF { ?x a :Employee . } THEN { ?x :employeeNum ?number . } """ . # SPARQL Constraint [] a icv:Constraint ; icv:query """ prefix : <http://example.org/> select * { ?x a :Employee . FILTER NOT EXISTS { ?x :employeeNum ?number . } } """ . ## Using ICV Programmatically Here we describe how to use Stardog ICV via the SNARL APIs. For more information on using SNARL in general, please refer to the chapter on Java Programming. There is command-line interface support for many of the operations necessary to using ICV with a Stardog database; please see Administering Stardog for details. To use ICV in Stardog, one must: 1. create some constraints 2. associate those constraints with a Stardog database ### Creating Constraints Constraints can be created using the ConstraintFactory which provides methods for creating integrity constraints. ConstraintFactory expects your constraints, if they are defined as OWL axioms, as RDF triples (or graph). To aid in authoring constraints in OWL, ExpressionFactory is provided for building the RDF equivalent of the OWL axioms of your constraint. You can also write your constraints in OWL in your favorite editor and load them into the database from your OWL file. We recommend defining your constraints as OWL axioms, but you are free to define them using SPARQL SELECT queries. If you choose to define a constraint using a SPARQL SELECT query, please keep in mind that if your query returns results, those are interpreted as the violations of the integrity constraint. An example of creating a simple constraint using ExpressionFactory: IRI Product = Values.iri("urn:Product"); IRI Manufacturer = Values.iri("urn:Manufacturer"); IRI manufacturedBy = Values.iri("urn:manufacturedBy"); // we want to say that a product should be manufactured by a Manufacturer: Constraint aConstraint = ConstraintFactory.constraint(subClassOf(Product, some(manufacturedBy, Manufacturer))); ### Adding Constraints to Stardog The ICVConnection interface provides programmatic access to the ICV support in Stardog. It provides support for adding, removing and clearing integrity constraints in your database as well as methods for checking whether or not the data is valid; and when it’s not, retrieving the list of violations. This example shows how to add an integrity constraint to a Stardog database. // We'll start out by creating a validator from our SNARL Connection ICVConnection aValidator = aConn.as(ICVConnection.class); // add add a constraint, which must be done in a transaction. aValidator.addConstraint(aConstraint); Here we show how to add a set of constraints as defined in a local OWL ontology. // We'll start out by creating a validator from our SNARL Connection ICVConnection aValidator = aConn.as(ICVConnection.class); // add add a constraint aValidator.addConstraints() .format(RDFFormat.RDFXML) .file(Paths.get("myConstraints.owl")); ### IC Validation Checking whether or not the contents of a database are valid is easy. Once you have an ICVConnection you can simply call its isValid() method which will return whether or not the contents of the database are valid with respect to the constraints associated with that database. Similarly, you can provide some constraints to the isValid() method to see if the data in the database is invalid for those specific constraints; which can be a subset of the constraints associated with the database, or they can be new constraints you are working on. If the data is invalid for some constraints—either the explicit constraints in your database or a new set of constraints you have authored—you can get some information about what the violation was from the SNARL IC Connection. ICVConnection.getViolationBindings() will return the constraints which are violated, and for each constraint, you can get the violations as the set of bindings that satisfied the constraint query. You can turn the bindings into the individuals which are in the violation using ICV.asIndividuals(). ### ICV and Transactions In addition to using the ICConnection as a data oracle to tell whether or not your data is valid with respect to some constraints, you can also use Stardog’s ICV support to protect your database from invalid data by using ICV as a guard within transactions. When guard mode for ICV is enabled in Stardog, each commit is inspected to ensure that the contents of the database are valid for the set of constraints that have been associated with it. Should someone attempt to commit data which violates one or more of the constraints defined for the database, the commit will fail and the data will not be added/removed from your database. By default, reasoning is not used when you enable guard mode, however you are free to specify any of the reasoning types supported by Stardog when enabling guard mode. If you have provided a specific reasoning type for guard mode it will be used during validation of the integrity constraints. This means you can author your constraints with the expectation of inference results satisfying a constraint. try (AdminConnection dbms = AdminConnectionConfiguration.toEmbeddedServer().credentials("admin", "admin").connect()) { dbms.disk("icvWithGuard") // disk db named 'icvWithGuard' .set(ICVOptions.ICV_ENABLED, true) // enable icv guard mode .set(ICVOptions.ICV_REASONING_ENABLED, true) // specify that guard mode should use reasoning .create(new File("data/sp2b_10k.n3")); // create the db, bulk loading the file(s) to start } This illustrates how to create a persistent disk database with ICV guard mode and reasoning enabled. Guard mode can also be enabled when the database is created on the CLI. ## Terminology This chapter may make more sense if you read this section on terminology a few times. ### ICV, Integrity Constraint Validation The process of checking whether some Stardog database is valid with respect to some integrity constraints. The result of ICV is a boolean value (true if valid, false if invalid) and, optionally, an explanation of constraint violations. ### Schema, TBox A schema (or "terminology box" a.k.a., TBox) is a set of statements that define the relationships between data elements, including property and class names, their relationships, etc. In practical terms, schema statements for a Stardog database are RDF Schema and OWL 2 terms, axioms, and definitions. ### Data, ABox All of the triples in a Stardog database that aren’t part of the schema are part of the data (or "assertional box" a.k.a. ABox). ### Integrity Constraint A declarative expression of some rule or constraint which data must conform to in order to be valid. Integrity Constraints are typically domain and application specific. They can be expressed in OWL 2 (any legal syntax), SWRL rules, or (a restricted form of) SPARQL queries. ### Constraints Constraints that have been associated with a Stardog database and which are used to validate the data it contains. Each Stardog may optionally have one and only one set of constraints associated with it. ### Closed World Assumption, Closed World Reasoning Stardog ICV assumes a closed world with respect to data and constraints: that is, it assumes that all relevant data is known to it and included in a database to be validated. It interprets the meaning of Integrity Constraints in light of this assumption; if a constraint says a value must be present, the absence of that value is interpreted as a constraint violation and, hence, as invalid data. ### Open World Assumption, Open World Reasoning A legal OWL 2 inference may violate or satisfy an Integrity Constraint in Stardog. In other words, you get to have your cake (OWL as a constraint language) and eat it, too (OWL as modeling or inference language). This means that constraints are applied to a Stardog database with respect to an OWL 2 profile. ### Monotonicity OWL is a monotonic language: that means you can never add anything to a Stardog database that causes there to be fewer legal inferences. Or, put another way, the only way to decrease the number of legal inferences is to delete something. Monotonicity interacts with ICV in the following ways: 1. Adding data to or removing it from a Stardog database may make it invalid. 2. Adding schema statements to or removing them from a Stardog database may make it invalid. 3. Adding new constraints to a Stardog database may make it invalid. 4. Deleting constraints from a Stardog database cannot make it invalid. # GraphQL Queries  Note Stardog Web Console does not support executing GraphQL queries. ## Introduction Stardog supports querying data stored (or mapped) in a Stardog database using GraphQL queries. You can load data into Stardog as usual and execute GraphQL queries without creating a GraphQL schema. You can also associate one or more GraphQL schemas with a database and execute GraphQL queries against one of those schemas. The following table shows the correspondence between RDF concepts and GraphQL:  RDF GraphQL Node Object Class Type Property Field Literal Scalar Execution of GraphQL queries in Stardog does not follow the procedural rules defined in the GraphQL spec. Instead Stardog translates GraphQL queries to SPARQL and then SPARQL results to GraphQL results based on the correspondences shown in the preceding table. Each RDF node represents a GraphQL object. Properties of the node are the fields of the object with the exception of rdf:type property which represents the type of the object. Literals in RDF are mapped to GraphQL scalars.  RDF GraphQL xsd:integer IntValue xsd:float FloatValue xsd:string StringValue xsd:boolean BooleanValue UNDEF NullValue IRI EnumValue In the following sections we will use a slightly modified version of the canonical GraphQL Star Wars example to explain how GraphQL queries work in Stardog. The following graph shows the core elements of the dataset and links between those nodes: 3. Subset of the Star Wars Graph The full dataset in Turtle format is available in the examples repo. ## Executing GraphQL Queries GraphQL queries can be run via the CLI, the Java API or the HTTP API. The GraphQL command can be executed by providing a query string: $ stardog graphql starwars "{ Human { name }}"

or a file containing the query:

$stardog graphql starwars query.file The --reasoning flag can be used with the CLI command to enable reasoning. The HTTP command can be used to execute GraphQL queries. The endpoint for GraphQL queries is http://HOST:PORT/{db}/graphql. The following command uses curl to execute a GraphQL query: $ curl -G -vsku admin:admin --data-urlencode query="{ Human { name }}" localhost:5820/starwars/graphql

Reasoning can be enabled by setting a special variable @reasoning in the GraphQL query variables.

Any standard GraphQL client, like GraphiQL, can be used with Stardog:

 Note Stardog by default uses HTTP basic access authentication. In order to use GraphiQL with Stardog you either need to start the Stardog server with --disable-security option so it won’t require credentials or set the HTTP header Authorization in the request. If the default credentials admin/admin are being used in non-production settings, the HTTP header Authorization may be set to the value Basic YWRtaW46YWRtaW4= in the GraphiQL UI. The curl example above can be used to see the correct value of the header for your credentials.

## Fields and Selection Sets

A top-level element in GraphQL by default represents a type and will return all the nodes with that type. The fields in the query will return matching properties:

 Query Result { Human { name } } { "data" : [ { "name" : "Luke Skywalker" }, { "name" : "Han Solo" }, { "name" : "Leia Organa" }, { "name" : "Darth Vader" }, { "name" : "Wilhuff Tarkin" } ] }

Each field in the query is treated as a required property of the node (unless an @optional directive is used) so any node without corresponding properties will not appear in the results:

 Query Result { Human { name homePlanet } } { "data" : [ { "name" : "Luke Skywalker", "homePlanet" : "Tatooine" }, { "name" : "Leia Organa", "homePlanet" : "Alderaan" }, { "name" : "Darth Vader", "homePlanet" : "Tatooine" } ] }

If a node in the graph has multiple properties, then in the query results those results will be returned as an array:

 Query Result { Droid { name friends } } { "data" : [ { "name" : "C-3PO", "friends" : [ "luke", "han", "leia", "artoo" ] }, { "name" : "R2-D2", "friends" : [ "luke", "han", "leia" ] } ] }

Also note that Stardog does not enforce the GraphQL requirement that leaf fields must be scalars. In the previous example friends of a droid are objects but the query does not provide any fields. In those cases, the identifier of the node will be returned as a string.

## Arguments

In GraphQL fields are, conceptually, functions which return values and may accept arguments that alter their behavior. Arguments have no predefined semantics but the typical usage is for defining lookup values for fields. Stardog adopts this usage and treats arguments as filters for the query. The following query return only the node whose id field is 1000:

 Query Result { Human(id: 1000) { id name homePlanet } } { "data": { "name": "Luke Skywalker", "id": 1000, "homePlanet": "Tatooine" } }

Arrays can be used to specify multiple values for a field in which case nodes matching any field will be returned:

 Query Result { Human(id: [1000, 1003]) { id name homePlanet } } { "data": [ { "name": "Luke Skywalker", "id": 1000, "homePlanet": "Tatooine" }, { "name": "Leia Organa", "id": 1003, "homePlanet": "Alderaan" } ] }

## Reasoning

GraphQL queries by default only return results based on explicit nodes and edges in the graph. Reasoning may be enabled in the usual ways to run queries with inferred nodes and edges, e.g. to perform type inheritance. In the example graph, Human and Droid are defined as subclasses of the Character class. The following query will return no results without reasoning but when reasoning is enabled Character will act like a GraphQL interface and the query will return both humans and droids:

 Query Result { Character { name } } { "data" : [ { "name" : "Luke Skywalker" }, { "name" : "Han Solo" }, { "name" : "Leia Organa" }, { "name" : "C-3PO" }, { "name" : "R2-D2" }, { "name" : "Darth Vader" }, { "name" : "Wilhuff Tarkin" } ] } Query Variables { "@reasoning": true }

## Fragments

Stardog supports GraphQL fragments (both inline or via fragment definitions). This query shows how fragments can be combined with reasoning to select different fields for subtypes:

 Query Result { Character { name ... on Human { friends } ... on Droid { primaryFunction } } } { "data" : [ { "name" : "Luke Skywalker", "friends" : [ "threepio", "artoo", "han", "leia" ] }, { "name" : "Han Solo", "friends" : [ "leia", "artoo", "luke" ] }, { "name" : "Leia Organa", "friends" : [ "threepio", "artoo", "luke", "han" ] }, { "name" : "C-3PO", "primaryFunction" : "Protocol" }, { "name" : "R2-D2", "primaryFunction" : "Astromech" }, { "name" : "Darth Vader", "friends" : "tarkin" }, { "name" : "Wilhuff Tarkin", "friends" : "vader" } ] }

## Aliases

By default, the key in the response object will use the field name queried. However, you can define a different name by specifying an alias. The following query renames both of the fields in the query:

 Query Result { Human { fullName: name bornIn: homePlanet } } { "data": [ { "fullName": "Luke Skywalker", "bornIn": "Tatooine" }, { "fullName": "Leia Organa", "bornIn": "Alderaan" }, { "fullName": "Darth Vader", "bornIn": "Tatooine" } ] }

## Variables

A GraphQL query can be parameterized with variables which must be defined at the top of an operation. Variables are in scope throughout the execution of that operation. A value should be provided for GraphQL variables before execution or an error will occur. The following query will return a single result when executed with the input {"id": 1000}:

 Query Result query getHuman($id: Integer) { Human(id:$id) { id name } } { "data": { "name": "Luke Skywalker", "id": 1000 } } Query Variables { "id": 1000 }

## Ordering Results

The results of GraphQL queries may be randomly ordered. A special argument orderBy can be used at the top level to specify which field to use for ordering the results. The following query uses the values of the name field for ordering the results:

 Query Result { Human(orderBy: name) { name } } { "data": [ { "name": "Darth Vader" }, { "name": "Han Solo" }, { "name": "Leia Organa" }, { "name": "Luke Skywalker" }, { "name": "Wilhuff Tarkin" } ] }

The results are ordered in ascending order by default. We can sort results in descending order as follows:

 Query Result { Human(orderBy: {field: name, desc: true}) { name } } { "data": [ { "name": "Wilhuff Tarkin" }, { "name": "Luke Skywalker" }, { "name": "Leia Organa" }, { "name": "Han Solo" }, { "name": "Darth Vader" } ] }

Multiple ordering criteria can be used:

 Query Result { Human(orderBy: [homePlanet, {field: name, desc: false}]) { name homePlanet @optional } } { "data": [ { "name": "Wilhuff Tarkin" }, { "name": "Han Solo" }, { "name": "Leia Organa", "homePlanet": "Alderaan" }, { "name": "Luke Skywalker", "homePlanet": "Tatooine" }, { "name": "Darth Vader", "homePlanet": "Tatooine" } ] }

We first use the homePlanet field for ordering and the results with no home planet come up first. If two results have the same value for the first order criteria, e.g. Luke Skywalker and Darth Vader, then the second criteria is used for ordering.

## Paging Results

Paging through the GraphQL results is accomplished with first and skip arguments used at the top level. The following query returns the first three results:

 Query Result { Human(orderBy: name, first: 3) { name } } { "data": [ { "name": "Darth Vader" }, { "name": "Han Solo" }, { "name": "Leia Organa" } ] }

The following query skips the first result and returns the next two results:

 Query Result { Human(orderBy: name, skip:1, first: 2) { name } } { "data": [ { "name": "Han Solo" }, { "name": "Leia Organa" } ] }

## Directives

Directives provide a way to describe alternate runtime execution and type validation behavior in GraphQL. The spec defines two built-in directives: @skip and @include. Stardog supports both directives and introduces several others.

### @skip(if: EXPR)

The skip directive includes a field value in the result conditionally. If the provided expression evaluates to true the field will not be included. Stardog allows arbitrary SPARQL expressions to be used as the conditions. Any of the supported SPARQL Query Functions can be used in these expressions. The expression can refer to any field in the same selection set and is not limited to the field directive is attached to. The following query returns the name field only if the name does not start with the letter L:

 Query Result { Human { id name @skip(if: "strstarts($name, 'L')") } } { "data": [ { "id": 1000 }, { "name": "Han Solo", "id": 1002 }, { "id": 1003 }, { "name": "Darth Vader", "id": 1001 }, { "name": "Wilhuff Tarkin", "id": 1004 } ] } ### @include(if: EXPR) The @include directive works negation of the @skip directive; that is, the field is included only if the expression evaluates to true. We can use variables inside the conditions, too. The following example executed with input {"withFriends": false} will not include friends in the results:  Query Result query HumanAndFriends($withFriends: Boolean) { Human @type { name friends @include(if: $withFriends) { name } } } { "data": [ { "name": "Luke Skywalker" }, { "name": "Han Solo" }, { "name": "Leia Organa" }, { "name": "Darth Vader" }, { "name": "Wilhuff Tarkin" } ] } Query Variables { "withFriends": false } ### @filter(if: EXPR) The @filter directive looks similar to @skip and @include but filters the whole object instead of just a single field. In that regard it works more like arguments but arbitrary expressions can be used to select specific nodes. The next query returns all humans whose id is less than 1003:  Query Result { Human { name id @filter(if: "$id < 1003") } } { "data": [ { "name": "Luke Skywalker", "id": 1000 }, { "name": "Han Solo", "id": 1002 }, { "name": "Darth Vader", "id": 1001 } ] }

Unlike the previous two filters it doesn’t matter which field the @filter directive is syntactically adjacent to since it applies to the whole selection set.

### @optional

Stardog treats every field as required by default and will not return any nodes if they don’t have a matching value for the fields in the selection set. The @optional directive can be used to mark a field as optional. The following query returns the home planets for humans if it exists but skips that field if it doesn’t:

 Query Result { Human { name homePlanet @optional } } { "data": [ { "name": "Luke Skywalker", "homePlanet": "Tatooine" }, { "name": "Han Solo" }, { "name": "Leia Organa", "homePlanet": "Alderaan" }, { "name": "Darth Vader", "homePlanet": "Tatooine" }, { "name": "Wilhuff Tarkin" } ] }

### @type

By default every field in the GraphQL query other than the topmost field represents a property in the graph. Sometimes we might want to filter some nodes based on their types; that is, based on the values of the special built-in property rdf:type. Stardog provides a directive as a shortcut for this purpose. The following query returns only the droid friends of humans because the Droid field is marked with the @type directive:

 Query Result { Human { name friends { Droid @type name } } } { "data": [ { "name": "Luke Skywalker", "friends": [ { "name": "R2-D2" }, { "name": "C-3PO" } ] }, { "name": "Han Solo", "friends": { "name": "R2-D2" } }, { "name": "Leia Organa", "friends": [ { "name": "R2-D2" }, { "name": "C-3PO" } ] } ] } }

### @bind(to: EXPR)

Fields bind to properties in the graph but it is also possible to have fields with computed values. When @bind directive is used for a field the value of that field will be compared by evaluating the given SPARQL expression. The following example splits the name field on a space to compute firstName and lastName fields:

 Query Result { Human { name @hide firstName @bind(to: "strbefore($name, ' ')") lastName @bind(to: "strafter($name, ' ')") } } { "data": [ { "firstName": "Luke", "lastName": "Skywalker" }, { "firstName": "Han", "lastName": "Solo" }, { "firstName": "Leia", "lastName": "Organa" }, { "firstName": "Darth", "lastName": "Vader" }, { "firstName": "Wilhuff", "lastName": "Tarkin" } ] }

### @hide

Query results can be flattened using the @hide directive. For example, in our data characters are linked to episode instances that have an index property. The following query retrieves the episode indexes but, by hiding the intermediate episode instances, humans are directly linked to the episode index:

 Query Result { Human { name appearsIn @hide { episodes: index } } } { "data": [ { "name": "Luke Skywalker", "episodes": [4, 5, 6] }, { "name": "Han Solo", "episodes": [4, 5, 6] }, { "name": "Leia Organa", "episodes": [4, 5, 6] }, { "name": "Darth Vader", "episodes": [4, 5, 6] }, { "name": "Wilhuff Tarkin", "episodes": 4 } ] }

## Namespaces

RDF uses IRIs as identifiers whereas in GraphQL we have simple names as identifiers. The examples so far use a single default namespace where names in GraphQL are treated as local names in that namespace. If a Stardog graph uses multiple namespaces, then it is possible to use them in GraphQL queries in several different ways.

If there are stored namespaces in the database then the associated prefixes can be used in the queries. For example, suppose we have the prefix foaf associated with the namespace http://xmlns.com/foaf/0.1/ in the database. In SPARQL the prefixed name foaf:Person would be used for the IRI http://xmlns.com/foaf/0.1/Person. In GraphQL, the : character cannot be used in field names so instead Stardog uses the _ character: the prefixed name here would be foaf_Person. The query using FOAF namespace would look like this:

{
foaf_Person {
foaf_name
foaf_mbox
}
}

If the namespace is not stored in the database an inline prefix definition can be provided with the @prefix directive:

query withPrefixes @prefix(foaf: "http://xmlns.com/foaf/0.1/") {
foaf_Person {
foaf_name
foaf_mbox
}
}
 Note Sometimes field names might use the underscore character and it might not indicate a prefix. To differentiate two cases Stardog looks at the prefix before the underscore and checks if it is defined in the query or if it is stored in the database. In some cases the IRI local name might be using characters like - that is not allowed in GraphQL names. In those cases an alias can be defined to map a field name to an IRI. These aliases are defined in a @config directive at the query level as follows:
query withAliases @config(alias: {myType: "http://example.com/my-type",
myProp: "http://example.com/my-prop"})
{
myType {
myProp
}
}

## Named Graphs

GraphQL queries by default are evaluated over the union of all graphs stored in the Stardog database. It is possible to limit the scope of the query to one or more specific named graphs. Suppose we partition the Star Wars dataset by moving instances of each type to a different named graph using the following SPARQL update query:

DELETE { ?s ?p ?o }
INSERT { GRAPH ?type { ?s ?p ?o } }
WHERE { ?s a ?type ; ?p ?o }

The following queries (with reasoning) will return 5 humans, 2 droids and all 7 characters respectively:

 Query Result query onlyHumanGraph @config(graph: Human) { Character { name } } { "data": [ { "name": "Luke Skywalker" }, { "name": "Han Solo" }, { "name": "Leia Organa" }, { "name": "Darth Vader" }, { "name": "Wilhuff Tarkin" } ] }
 Query Result query onlyDroidGraph @config(graph: Droid) { Character { name } } { "data": [ { "name": "C-3PO" }, { "name": "R2-D2" } ] }
 Query Result query bothGraphs @config(graph: [Human, Droid]) { Character { name } } { "data": [ { "name": "Luke Skywalker" }, { "name": "Han Solo" }, { "name": "Leia Organa" }, { "name": "C-3PO" }, { "name": "R2-D2" }, { "name": "Darth Vader" }, { "name": "Wilhuff Tarkin" } ] }

## GraphQL Schemas

GraphQL is a strongly-typed language where the fields used in a query should conform to the type definitions in a GraphQL schema. By default, Stardog relaxes this restriction and allows queries to be executed without an explicit schema. However, if desired, one or more GraphQL schemas can be added to the database and used during query execution. The benefits of using an explicit schema are as follows:

• Queries will be validated with strict typing

• Default translation of RDF values to GraphQL values can be overridden

• Only the parts of the graph defined in the schema will be exposed to the user

Here is an example schema that can be used with the Star Wars dataset:

schema {
query: QueryType
}

type QueryType {
Character: Character
Human(id: Int, first: Int, skip: Int, orderBy: ID): Human
Droid(id: Int): Droid
}

interface Character {
id: Int!
name: String!
friends(id: Int): [Character]
appearsIn: [Episode]
}

type Human implements Character {
iri: ID!
id: Int!
name: String!
friends(id: Int): [Character]
appearsIn: [Episode]
}

type Droid implements Character {
id: Int!
name: String!
friends(id: Int): [Character]
appearsIn: [Episode]
primaryFunction: String
}

type Episode {
index: Int!
name: String!
}

Each GraphQL schema defines a query type which specifies the top-level field that can be used in a query. In Stardog the query type is simply an enumeration of classes in the database that we want to expose in queries. For example, the schema defines the Episode type but does not list it under QueryType which means you cannot query for episodes directly.

Note that, without a schema each top-level type can have various built-in arguments like first or skip. In this schema we chose to define them for the Human type but not for others. This means a query like { Droid(first: 1) { name } } will be invalid with respect to this schema and rejected even though it is valid if executed without a schema.

This schema can be added to the database by giving it a name:

$stardog graphql schema --add characters starwars characters.graphqls Added schema characters We can then execute the query by specifying the schema name along with the query: $ stardog graphql --schema characters starwars "{ Human { name friends { name } } }"

When a schema is specified for a query it gets added to the query parameters using a special variable named @schema. When using the HTTP API directly this variable can be set to choose the schema for a query by sending the query variable {"schema": "characters" }.

 Query Result { Human(id: 1004) { name friends { name } } } { "data": [ { "name": "Wilhuff Tarkin", "friends": [ { "name": "Darth Vader" } ] } ] } Query Variables { "@schema": "characters" }

Note that the friends field in the result is an array value due to the corresponding definition in the schema. This query executed with a schema would return the single object value for the field.

An important point about schemas is that the types defined in the schema do not filter the query results. For example, we can define a much simpler humans schema against the Star Wars dataset:

schema {
query: QueryType
}

type QueryType {
Human(id: [Int]): Human
}

type Human {
id: Int!
name: String!
friends: [Human]
}

This query allows only Human instances to be queried at the top level and declares that the friend of each Human is also a Human. This schema definition is incompatible with the data since humans have droid friends. Stardog does not check if the schema is correct with respect to the data and will not enforce type restrictions in the results. So if we ask for the friends of a human, then the droids will also be returned in the results:

 Query Result { Human(id: 1000) { name friends { name } } } { "data": [ { "name": "Luke Skywalker", "friends": [ { "name": "Han Solo" }, { "name": "Leia Organa" }, { "name": "C-3PO" }, { "name": "R2-D2" } ] } ] } Query Variables { "@schema": "humans" }

## Introspection

Stardog supports GraphQL introspection which means GraphQL tooling works out of the box with Stardog. Introspection allows schema queries to be discovered, exposed, and executed and to retrieve information about the types and fields defined in a schema. This feature is used in GraphQL tools to support features like autocompletion, query validation, etc.

Stardog supports introspection queries for the GraphQL schemas registered in the system. There is a separate dedicated endpoint for each schema registered in the system in the form http://HOST:PORT/{db}/graphql/{schema}. The introspection queries executed against this endpoint will be answered using the corresponding schema.

Introspection queries are not supported by the default GraphQL endpoint as there is no dedicated schema associated with the default endpoint.

## Implementation

Stardog translates GraphQL queries to SPARQL and SPARQL results to JSON. The CLI command graphql explain can be used to see the generated SPARQL query and the low-level query plan created for the SPARQL query which is useful for debugging correctness and performance issues:

$stardog graphql explain starwars "{ Human(id: 1000) { name knows: friends { name } } }" SPARQL: SELECT * FROM <tag:stardog:api:context:all> { ?0 rdf:type :Human . ?0 :id "1000"^^xsd:integer . ?0 :name ?1 . ?0 :friends ?2 . ?2 :name ?3 . } FIELDS: 0 -> {1=name, 2=knows} 2 -> {3=name} PLAN: prefix : <http://api.stardog.com/> From all Projection(?0, ?1, ?2, ?3) [#3] ─ MergeJoin(?2) [#3] +─ Sort(?2) [#3] │ ─ NaryJoin(?0) [#3] │ +─ Scan[POSC](?0, :id, "1000"^^<http://www.w3.org/2001/XMLSchema#integer>) [#1] │ +─ Scan[POSC](?0, <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>, :Human) [#5] │ +─ Scan[PSOC](?0, :name, ?1) [#10] │ ─ Scan[PSOC](?0, :friends, ?2) [#20] ─ Scan[PSOC](?2, :name, ?3) [#10] The variables in the SPARQL query will be mapped to objects and field values in the JSON results. The binding for variable 0 will be the root object. The FIELDS output show that 0 is linked to 1 via the name field and linked to 2 via the knows field (note that knows is an alias and in the actual query we have the pattern ?0 :friends ?2). The GraphQL query plans can also be retrieved by setting the special query variable @explain to true when executing a query. # Path Queries Stardog extends SPARQL to find paths between nodes in the RDF graph, which we call path queries. They are similar to SPARQL 1.1 property paths which traverse an RDF graph and find pairs of nodes connected via a complex path of edges. But SPARQL property paths only return the start and end nodes of a path and do not allow variables in property path expressions. Stardog path queries return all intermediate nodes on each path—​that is, they return a path from start to end—​and allow arbitrary SPARQL graph patterns to be used in the query.  Tip ## Path Query Syntax We add path queries as a new top-level query form, i.e. separate from SELECT, CONSTRUCT or other query types. The syntax is as follows: PATHS [SHORTEST|ALL] [CYCLIC] [<DATASET>] START ?s [ = <IRI> | <GRAPH PATTERN> ] END ?e [ = <IRI> | <GRAPH PATTERN> ] VIA <GRAPH PATTERN> | <VAR> | <PATH> [MAX LENGTH <int>] [ORDER BY <condition>] [OFFSET <int>] [LIMIT <int>] The graph pattern in the VIA clause must bind both ?s and ?e variables. Next we informally present examples of common path queries and finally the formal Path Query Evaluation Semantics. ## Shortest Paths Suppose we have a simple social network where people are connected via different relationships: 4. Simple Graph If we want to find all the people Alice is connected to and how she is connected to them we can use the following path query: PATHS START ?x = :Alice END ?y VIA ?p We specify a start node for the path query but the end node is unrestricted. So all paths starting from Alice will be returned. Note that we use the shortcut VIA ?p instead of a graph pattern to match each edge in the path. This is a syntactic sugar for VIA { ?s ?p ?e }. Similarly we could use a predicate, e.g. VIA :knows or a property path expression, e.g. VIA :knows | :worksWith. This query is effectively equivalent to the SPARQL property path :Alice :knows+ ?y, but the results will include the nodes in the path(s). The path query results are printed in a tabular format by default: +----------+------------+----------+ | x | p | y | +----------+------------+----------+ | :Alice | :knows | :Bob | | | | | | :Alice | :knows | :Bob | | :Bob | :knows | :David | | | | | | :Alice | :knows | :Bob | | :Bob | :worksWith | :Charlie | | | | | | :Alice | :knows | :Bob | | :Bob | :worksWith | :Charlie | | :Charlie | :parentOf | :Eve | +----------+------------+----------+ Query returned 4 paths in 00:00:00.055 Each row of the result table shows one edge and adjacent edges on a path are printed on subsequent rows of the table. Multiple paths in the results are separated by an empty row. We can change the output format to text which serializes the results in a property graph like syntax: $ stardog query -f text exampleDB "PATHS START ?x = :Alice END ?y VIA ?p"
(:Alice)-[p=:knows]->(:Bob)

(:Alice)-[p=:knows]->(:Bob)-[p=:knows]->(:David)

(:Alice)-[p=:knows]->(:Bob)-[p=:worksWith]->(:Charlie)

(:Alice)-[p=:knows]->(:Bob)-[p=:worksWith]->(:Charlie)-[p=:parentOf]->(:Eve)

Query returned 4 paths in 00:00:00.047

Execution happens by recursively evaluating the graph pattern in the query and replacing the start variable with the binding of the end variable in the previous execution. If the query specifies a start node, that value is used for the first evaluation of the graph pattern. If the query specifies an end node, which our example doesn’t, execution stops when we reach the end node. Only simple cycles, i.e. paths where the start and the end nodes coincide, are allowed in the results.

 Note The Stardog optimizer may choose to traverse paths backwards, i.e. from the end node to the start, for performance reasons but it does not affect the results.

We can specify the end node in the query and restrict the kind of patterns in paths to a specific property as in the next example that queries how Alice is connected to David via knows relationships:

PATHS START ?x = :Alice END ?y = :David VIA :knows

This query would return a single path with two edges:

+--------+--------+
|   x    |   y    |
+--------+--------+
| :Alice | :Bob   |
| :Bob   | :David |
+--------+--------+

## Complex Paths

Graph patterns inside the path queries can be arbitrarily complex. Suppose, we want to find undirected paths between Alice and David in this graph. Then we can make the graph pattern to match both outgoing and incoming edges:

$stardog query exampleDB "PATHS START ?x = :Alice END ?y = :David VIA ^:knows | :knows" +--------+--------+ | x | y | +--------+--------+ | :Alice | :Bob | | :Bob | :David | +--------+--------+ Sometimes a relationship between two nodes might be implicit and there might not be an explicit link between those two nodes in the RDF graph. Consider the following set of triples that show some movies and actors who starred in those movies: :Apollo_13 a :Film ; :starring :Kevin_Bacon , :Gary_Sinise . :Spy_Game a :Film ; :starring :Brad_Pitt , :Robert_Redford . :Sleepers a :Film ; :starring :Kevin_Bacon , :Brad_Pitt . :A_Few_Good_Men a :Film ; :starring :Kevin_Bacon , :Tom_Cruise . :Lions_for_Lambs a :Film ; :starring :Robert_Redford , :Tom_Cruise . :Captain_America a :Film ; :starring :Gary_Sinise , :Robert_Redford . There is an implicit relationship between actors based on the movies they appeared together. We can use a basic graph pattern with multiple triple patterns in the path query to extract this information: PATHS START ?x = :Kevin_Bacon END ?y = :Robert_Redford VIA { ?movie a :Film ; :starring ?x , ?y } This query executed against the above set of triples would return three paths: +--------------+------------------+-----------------+ | x | movie | y | +--------------+------------------+-----------------+ | :Kevin_Bacon | :Apollo_13 | :Gary_Sinise | | :Gary_Sinise | :Captain_America | :Robert_Redford | | | | | | :Kevin_Bacon | :Sleepers | :Brad_Pitt | | :Brad_Pitt | :Spy_Game | :Robert_Redford | | | | | | :Kevin_Bacon | :A_Few_Good_Men | :Tom_Cruise | | :Tom_Cruise | :Lions_for_Lambs | :Robert_Redford | +--------------+------------------+-----------------+ If the movie is irrelevant, then a more concise version can be used: PATHS START ?x = :Kevin_Bacon END ?y = :Robert_Redford VIA ^:starring/:starring ## All Paths Path queries return only shortest paths by default. We can use the ALL keyword in the query to retrieve all paths between two nodes. For example, the query above returned only one path between Alice and David. We can get all paths as follows: $ stardog query exampleDB "PATHS ALL START ?x = :Alice END ?y = :David
VIA { {?x ?p ?y} UNION {?y ?p ?x} }"
+----------+------------+----------+
|    x     |     p      |    y     |
+----------+------------+----------+
| :Alice   | :knows     | :Bob     |
| :Bob     | :knows     | :David   |
|          |            |          |
| :Alice   | :knows     | :Bob     |
| :Bob     | :worksWith | :Charlie |
| :Charlie | :parentOf  | :Eve     |
| :Eve     | :knows     | :David   |
+----------+------------+----------+
 Caution The ALL qualifier can dramatically increase the number of paths so use with caution.

## Cyclic Paths

There’s a special keyword CYCLIC to specifically query for cyclic paths in the data. For example, there might be a dependsOn relationship in the database and we might want to query for cyclic dependencies:

PATHS CYCLIC START ?start END ?end VIA :dependsOn

Again, arbitrary cycles in the paths are not allowed to ensure a finite number of results.

## Limiting Paths

In a highly connected graph the number of possible paths between two nodes can be impractically high. There are two different ways we can limit the results of path queries. The first possibility is to use the LIMIT keyword just like in other query types. We can ask for at most 2 paths starting from Alice as follows:

PATHS START ?x = :Alice END ?y VIA ?p LIMIT 2

This query would return 2 results as expected :

+----------+------------+----------+
|    x     |     p      |    y     |
+----------+------------+----------+
| :Alice   | :knows     | :Bob     |
|          |            |          |
| :Alice   | :knows     | :Bob     |
| :Bob     | :knows     | :David   |
+----------+------------+----------+

Note that, the path from Alice to Charlie is not included in this result even though it is not any longer than the path between Alice and David. This is because with LIMIT the query will stop producing results as soon as the maximum number of paths are returned.

The other alternative for limiting the results is by specifying the maximum length of paths that can be returned. The following query shows how to query for paths thar are at most 2-edge long:

PATHS START ?x = :Alice END ?y VIA ?p MAX LENGTH 2

This time we will get 3 results:

+----------+------------+----------+
|    x     |     p      |    y     |
+----------+------------+----------+
| :Alice   | :knows     | :Bob     |
|          |            |          |
| :Alice   | :knows     | :Bob     |
| :Bob     | :knows     | :David   |
|          |            |          |
| :Alice   | :knows     | :Bob     |
| :Bob     | :worksWith | :Charlie |
+----------+------------+----------+

It is also possible to use both LIMIT and MAX LENGTH keywords in a single query.

## Path Queries With Start and End Patterns

In all examples presented so far the start and end variables were either free variables or bound to a single IRI. This is insufficient for navigating paths which must begin at multiple nodes satisfying certain conditions and terminate at nodes satisfying some other conditions. Assume the movie and actor data above is extended with information about the date of birth of each actor:

:Kevin_Bacon :birthdate "1958-07-08"^^xsd:date

:Gary_Sinise :birthdate "1957-03-17"^^xsd:date

:Robert_Redford :birthdate "1936-08-18"^^xsd:date

:Tom_Cruise :birthdate "1962-07-03"^^xsd:date

Now, having only variables and constants as valid path start and end expressions would make it hard to write a query to find all connections between Kevin Bacon and actors over 80 years old. The following attempt, for example, won’t match any data:

PATHS START ?x = :Kevin_Bacon END ?y VIA {
?movie a :Film ; :starring ?x , ?y .
?y :birthdate ?date .
FILTER (year(?date) - year(now()) >= 80)
}

The problem is that the age filter is applied at each recursive step, i.e. the query is looking for paths where every intermediate actor is over 80, but none of those co-starred with Kevin Bacon (in our toy dataset). Instead we need a query which checks the condition only at candidate end nodes:

PATHS START ?x = :Kevin_Bacon
END ?y { ?y :birthdate ?date .
FILTER (year(?date) - year(now()) >= 80) }
VIA ^:starring/:starring

This query will return the expected results along with the date of birth for end nodes:

+------------------+---------------------+------------------------+
|        x         |          y          |          date          |
+------------------+---------------------+------------------------+
| test:Kevin_Bacon | test:Gary_Sinise    |                        |
| test:Gary_Sinise | test:Robert_Redford | "1936-08-18"^^xsd:date |
|                  |                     |                        |
| test:Kevin_Bacon | test:Brad_Pitt      |                        |
| test:Brad_Pitt   | test:Robert_Redford | "1936-08-18"^^xsd:date |
|                  |                     |                        |
| test:Kevin_Bacon | test:Tom_Cruise     |                        |
| test:Tom_Cruise  | test:Robert_Redford | "1936-08-18"^^xsd:date |
+------------------+---------------------+------------------------+

## Path Queries With Reasoning

As other kinds of queries, path queries can be evaluated with reasoning. If reasoning is enabled, a path query will return paths in the inferred graph, i.e. each edge corresponds to a relationship between the nodes which is inferred from the data based on the schema.

Consider the following example:

:Arlington :partOf :DCArea .

:DCArea :locatedIn :EastCoast .

:EastCoast :partOf :US .

:US :locatedIn :NorthAmerica .

Adding the following rule (or an equivalent OWL sub-property chain axiom) infers :partOf edges based on compositions of :partOf and :locatedIn edges:

IF
{ ?x :partOf ?y . ?y :locatedIn ?z }
THEN
{ ?x :partOf ?z }

Now the following path query will find the inferred path from :Arlington to :NorthAmerica via :DCArea and :US:

PATHS START ?x = :Arlington END ?y = :NorthAmerica VIA {
?x :partOf ?y
}
 Note This feature should be used with care. There may be a lot more paths than one expects. Also keep in mind that some patterns are particularly expensive with reasoning, e.g. triple patterns with the unbound predicate variable or with a variable in the object position of rdf:type.

## Path Query Evaluation Semantics

Given a pair of variable names s and e a path is a sequence of SPARQL solutions S[1], …​, S[n] s.t. S[i](t) = S[i-1](s) for i from 2 to n. We call the S[0](s) and S[n](t) values the start and end nodes of the path, resp. Each solution in the sequence is called an edge.

The evaluation semantics of path queries is based on the following recursive extension of SPARQL solution:

(1) Solution := { (V -> Value)* }    // solution: mapping from variables to values (as in SPARQL)
(2) Value := RDF-Term                // an RDF term is a value (as in SPARQL)
(3) Value := Solution                // a solution is a value (extension)
(4) Value := [ Value* ]              // an array of values is a value (extension)

Informally such extensions allow us to represent each path as a single solution where a distinguished variable (in the sequel called path variable) is mapped to an ordered array of solutions representing edges.

We first consider simple path queries for ALL paths with only variables after the START and END keywords, i.e. queries of the form PQ(s, e, p, P), where s and e are start and end variable names, p is a path variable name, and P is a SPARQL graph pattern. Given a dataset D with the active graph G, abbreviated as D(G), we define eval(PQ(s, e, P), D(G)) as a set of all such (extended) solutions S that:

(1) S(p) is a path Sp[1] ... Sp[n] w.r.t. s and e
(2) Sp(1) is in eval(P, D(G))
(3) Sp[i] is a solution to eval(sub(P, s, Sp[i-1](e), D(G)) for i = 2 ... n
(4) S(s) = Sp[1](s)
(5) S(e) = Sp[n](e)
(6) All terms which s and e bind to in all Sp[i] are unique except that Sp[1](s) could be equal to Sp[n](e)

where sub(P, var, t) is a graph pattern obtained by substituting the variable var by the fixed RDF term t.

Informally conditions (2) and (3) state that each edge in a path is obtained by evaluating the path pattern with the start variable substituted by the end variable value of the previous edge (to ensure connectedness). The conditions (4) and (5) bind the s and e variables in the top level solution.

Next we define the semantics of path queries with start and end patterns:

eval(PQ(s, PS, e, PE, PQ) = Join(PS, Join(PE, eval(PQ(s, e, PQ), DG)))

where PS and PE are start and end graph patterns which must bind s and e variables, respectively. Here Join stands for the standard SPARQL join semantics which does not require extensions since joins are performed on variables s and e which bind to RDF terms only, rather than arrays or solutions (conditions (4) and (5) above ensure that).

Finally we note that path queries with start or end constants are a special case of path queries with the corresponding singleton VALUES patterns, e.g.

PATHS START ?s = :Alice END ?e = :Dave VIA :knows

is a syntactic sugar for

PATHS START ?s { VALUES ?s { :Alice } } END ?e { VALUES ?e { :Dave } } VIA :knows

Keywords SHORTEST (default) and CYCLIC are self-explanatory and place further restrictions on each S(p): the sequence should be the shortest among all results or represent a simple cycle. The solution modifiers ORDER BY, LIMIT, and OFFSET have the exact same semantics as in SPARQL 1.1.

# Geospatial Query

Stardog supports geospatial queries over data encoded using WGS 84 or the OGC’s GeoSPARQL vocabulary. Any RDF data stored in Stardog using one or both of these vocabularies will be automatically indexed for geospatial queries.

 Tip

## Enabling Geospatial Support

To get started using Stardog’s geospatial support, you’ll need to create a database with geospatial support enabled. You can do this by setting the option spatial.enabled to true:

stardog-admin db create -o spatial.enabled=true -n mySpatialDb

Similarly, you can set the option using GeospatialOptions#SPATIAL_ENABLED when creating the database programmatically:

aAdminConnection.disk("mySpatialDb")
.set(GeospatialOptions.SPATIAL_ENABLED, true)
.create()

### Precision & Accuracy

When creating a database with geospatial support, you can specify the precision with which the features are indexed. The database property spatial.precision or programmatically via GeospatialOptions#SPATIAL_PRECISION, which can only be specified when the database is created, can control the index precision. The default value is 11 which yields sub-meter precision; a value of 8 will give a precision +/- 50m. Setting the precision value lower than the default can improve the performance of spatial queries at the cost of accuracy.

## Geospatial Data

The WGS84 or OGC vocabularies can be used to encode geospatial features within your dataset. When data is committed, Stardog will look for these vocabularies and automatically extract all features and insert them into the geospatial index. Here is an example of using WKT to define the location of the White House:

:whiteHouse a geo:Feature ;
rdfs:label "White House" ;
geo:hasGeometry :whiteHouseGeo .

:whiteHouseGeo a geo:Geometry ;
geo:asWKT "Point(-77.03653 38.897676 )"^^geo:wktLiteral .

Note that for WKT formatted points, the location is <long, lat>. The location of the White House can also be encoded using the WGS 84 vocabulary:

:whiteHouse a :Location ;
rdfs:label "White House" ;
wgs:lat "38.897676"^^xsd:float ;
wgs:long "-77.03653"^^xsd:float .

## SPARQL Integration

Once your data has been indexed, you can perform several type of geospatial queries on the data. These are seamlessly integrated into SPARQL so you can query for non-spatial information about features in your dataset alongside the geospatial queries.

The operators supported by Stardog are geof:relate, geof:distance, geof:within, geof:nearby and geof:area. The geof namespace is http://www.opengis.net/def/function/geosparql/.

This query gets all features within 2km of Complexible HQ in DC:

select ?name where {
?loc rdfs:label ?name .
?loc geo:hasGeometry ?feature .
?hq geo:hasGeometry ?hqGeo ; rdfs:label "Complexible Headquarters" .
?feature geof:nearby (?hqGeo 2 <http://qudt.org/vocab/unit#Kilometer>).
}

More query examples can be found on our blog.

### Geospatial Datatypes

The QUDT ontology, namespace http://qudt.org/vocab/unit#, is used to specify units for distances; Kilometer, Meter, Centimeter, MileUSStatute, Yard, Foot, Inch. Additionally, the OGC units vocabulary http://www.opengis.net/def/uom/OGC/1.0/ defines degree, radian and metre.

## Enhanced Polygons

Stardog’s geospatial support covers the use of basic WKT formatted shapes; specifically points and rectangles. However, WKT can encode more complex spatial structures, most notably, polygons.

To enable support for these more complex shapes, download JTS and include the JAR in Stardog’s classpath by placing it into the server/ext folder of the installation (you may need to create this folder) or into the folder specified by the STARDOG_EXT environment variable. Then set spatial.use.jts=true in your stardog.properties file. When you restart Stardog, it will pick up JTS and you’ll be able to use more complex WKT formatted shapes.

# Machine Learning

In this section, you’ll learn how to use Stardog’s machine learning capabilities for the general problem of predictive analytics. We’ll show you how to build a machine learning model and use it for prediction, plus best practices on modelling your data and improving the quality of results.

## Predictive Analytics

Suppose you have data about movies. But that data is incomplete; some movies are missing the genre field. Filling out that missing data is time consuming, and you would like to do it automatically using all the information you already have about the movies. This is where Stardog’s predictive analytics comes into the game. Using the data you have about movies with genre, you can create a machine learning model that will predict the genre for the movies that are missing it. Isn’t that sweet?

Supervised learning is the basis of this capability. You give Stardog some data about the domain you’re interested in, and it will learn a model that can be used to make predictions about properties of that data.

## Learning a Model

First step is learning a model, by defining which data will be used in the learning and the target that we are actually trying to predict.

With Stardog, all this is naturally done via SPARQL. The best way to understand the syntax is through an example. Here, we learn a model to predict the genre of a movie given its director, year, and studio.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:ClassificationModel ;
spa:arguments (?director ?year ?studio) ;
spa:predict ?genre .
}
}
WHERE {
?movie :directedBy ?director ;
:year ?year ;
:studio ?studio ;
:genre ?genre .
}

The WHERE clause selects the data and a special graph, spa:model, is used to specify the parameters of the training. :myModel is the unique identifier given to this model and is composed of 3 mandatory properties.

First, we need to define the type of learning we are performing:

• classification, spa:ClassificationModel, if we are interested in predicting a categorical value that has a limited set of possible values (e.g., genre of a movie)

• regression, spa:RegressionModel, if we predict a numerical value that can naturally have an unlimited set of values (e.g., box office of a movie)

• similarity, spa:SimilarityModel, if we want to predict the degree of similarity between two objects (e.g., most similar movies)

The second property, spa:arguments, defines the variables from the WHERE clause that will be used as features when learning the model. Here is where you define the data that you think will help to predict the third property, given by spa:predict.

In this case, our model will be trained to predict the value of ?genre based on the values of ?director , ?year, and ?studio.

Properly defining this 3 properties is the main task when creating any model. Using more advanced parameters is covered in the Mastering the Machine section.

## Making Predictions

Now that we’ve learned a model, we can move on to more exciting stuff and use it to actually predict things.

prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:arguments (?director ?year ?studio) ;
spa:predict ?predictedGenre .
}

:TheGodfather :directedBy ?director ;
:year ?year ;
:studio ?studio ;
:genre ?originalGenre .
}

We select a movie’s properties and use them as arguments to the model Stardog previously learned. The magic comes with the ?predictedGenre variable; during query execution, its value is not going to come from the data itself (like ?originalGenre), but will instead be predicted by the model, based on the values of the arguments.

The result of the query will look like this:

| director            | year | studio             | originalGenre | predictedGenre |
| ------------------- | ---- | ------------------ | ------------- | -------------- |
| :FrancisFordCoppola | 1972 | :ParamountPictures | Drama         | Drama          |

Our model seems to be predicting correctly the genre for The Godfather. Yee!

### Query Syntax Restrictions

At this point, only basic graph patterns can be used directly inside the prediction query. If more advanced constructs, like OPTIONAL or FILTER, are necessary, that part of the query needs to be in a sub-query, e.g.:

prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:arguments (?director ?year ?studio) ;
spa:predict ?predictedGenre .
}

{
SELECT * WHERE {
?movie  :directedBy ?director ;
:year ?year ;
:genre ?originalGenre .
OPTIONAL { ?movie :studio ?studio }
FILTER (?year > 2000)
}
}
}

## Selecting a Library

For classification and regression, Stardog can use two distinct machine learning libraries under the covers: Vowpal Wabbit (default) and XGBoost. Both support the same set of functionalities, and can be used interchangeably.

At model creation, the desired library can be selected with the spa:library property: spa:VowpalWabbit or spa:XGBoost.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:ClassificationModel ;
spa:library spa:XGBoost ;
spa:arguments (?director ?year ?studio) ;
spa:predict ?genre .
}
}
...

Vowpal Wabbit is recommended for large, sparse, datasets, while XGBoost is known to perform better in domains with numeric values. We recommend testing both libraries, as their strengths are largely dependent on particularities of the data.

 Note Learning models with large datasets might exceed the default max query execution time, especially with XGBoost. In those cases, it is recommended to increase the value for the query.timeout configuration. Increasing the amount of memory available to Stardog might also make the learning faster.

## Assessing Model Quality

### Metrics

We provide some special aggregate operators that help quantify the quality of a model.

For classification and similarity problems, one of the most important measures is accuracy, that is, the frequency that we predict the target variable correctly.

prefix spa: <tag:stardog:api:analytics:>

SELECT (spa:accuracy(?originalGenre, ?predictedGenre) as ?accuracy) WHERE {
graph spa:model {
:myModel  spa:arguments (?director ?year ?studio) ;
spa:predict ?predictedGenre .
}

?movie  :directedBy ?director ;
:year ?year ;
:studio ?studio ;
:genre ?originalGenre .
}
+---------------------+
| accuracy            |
| ------------------- |
| 0.92488254018       |
+---------------------+

For regression, we provide three different measures:

• Mean absolute error, or, on average, how far away is the prediction from the real target number: spa:mae(?originalValue, ?predictedValue)

• Mean square error, on average, how much is the squared difference between prediction and the target number: spa:mse(?originalValue, ?predictedValue)

• Root mean square error, the square root of the mean square error: spa:rmse(?originalValue, ?predictedValue)

### Automatic Evaluation

Classification and regression models are automatically evaluated with the data used in their training. The score and respective metric can be queried from spa:model.

prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:evaluationMetric ?metric ;
spa:evaluationScore ?score .
}
}
+------------------------------------+-------+
|               metric               | score |
+------------------------------------+-------+
| tag:stardog:api:analytics:accuracy | 1.0   |
+------------------------------------+-------+

By default, spa:accuracy is used for classification problems, and spa:mae for regression. This metric can be changed during model learning, by setting the spa:evaluationMetric argument.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:RegressionModel ;
spa:evaluationMetric spa:rmse ;
...
}
}
...

### Cross Validation

The default automatic evaluation technique of measuring the accuracy of the model on the same data as training might be prone to overfitting. The most accurate measure we can have is testing on data that the model has never seen before.

We provide a spa:crossValidation property, which will automatically apply K-Fold cross validation on the training data, with the number of folds given as an argument.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:RegressionModel ;
spa:crossValidation 10 ;
spa:evaluationMetric spa:rmse ;
...
}
}
...
prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:evaluation ?validation ;
spa:evaluationMetric ?metric ;
spa:evaluationScore ?score .
}
}
+-------------+------------------------------------+-------+
| validation  |               metric               | score |
+-------------+------------------------------------+-------+
| "KFold=10"  | tag:stardog:api:analytics:rmse     | 0.812 |
+-------------+------------------------------------+-------+

## Modelling Data

The way you input data into Stardog during model learning is of utmost importance in order to achieve good quality predictions.

### Data Representation

For better results, each individual you are trying to model should be encoded in a single SPARQL result.

For example, suppose you want to add information about actors into the previous model. The query selecting the data would look as follow:

SELECT * WHERE {
?movie :actor ?actor ;
:directedBy ?director ;
:year ?year ;
:studio ?studio ;
:genre ?genre .
}
| movie         | actor         | director            | year | studio             | genre  |
| ------------- | ------------- | ------------------- | ---- | ------------------ | ------ |
| :TheGodfather | :MarlonBrando | :FrancisFordCoppola | 1972 | :ParamountPictures | Drama  |
| :TheGodfather | :AlPacino     | :FrancisFordCoppola | 1972 | :ParamountPictures | Drama  |

Due to the nature of relational query languages like SPARQL, results are returned for all the combinations between the values of the selected variables.

In order to properly model relational domains like this, we introduced a special aggregate operator, set. Used in conjunction with GROUP BY, we can easily model this kind of data as a single result per individual.

prefix spa: <tag:stardog:api:analytics:>

SELECT ?movie (spa:set(?actor) as ?actors) ?director ?studio ?genre WHERE {
?movie :actor ?actor ;
:directedBy ?director ;
:year ?year ;
:studio ?studio ;
:genre ?genre .
}
GROUP BY ?movie ?director ?studio ?genre
| movie         | actors                    | director            | year | studio             | genre  |
| ------------- | ------------------------- | ------------------- | ---- | ------------------ | ------ |
| :TheGodfather | [:MarlonBrando :AlPacino] | :FrancisFordCoppola | 1972 | :ParamountPictures | Drama  |

### Data Types

Carefully modelling your data with the correct datatypes can dramatically increase the quality of your model.

As of 6.1.0, Stardog does special treatment on values of the following types:

• Numbers, such as xsd:int, xsd:short, xsd:byte, xsd:float, and xsd:double, are treated internally as weights and properly model the difference between values

• Strings, xsd:string and rdf:langString, are tokenized and used in a bag-of-words fashion

• Sets, created with the spa:set operator, are interpreted as a bag-of-words of categorical features

• Booleans, xsd:boolean, are modeled as binary features

Everything else is modeled as categorical features.

Setting the correct data type for the target variable, given through spa:predict, is extremely important:

• with regression, make sure values are numeric

• with classification, individuals of the same class should have consistent data types and values

• with similarity, use values that uniquely identify an object, e.g., an IRI

For evertything else, using the datatype that is closer to its original meaning is a good rule of thumb.

## Mastering the Machine

Let’s look at some other issues around the daily care and feeding of predictive analytics and models in Stardog.

### Overwriting Models

By default, you cannot create a new model with the same identifier as an already existent one. If you try to do so, you’ll be greeted with a Model already exists error.

In order to reuse an existent identifier, users can set the spa:overwrite property to True. This will delete the previous model and save the new one in its place.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:RegressionModel ;
spa:overwrite True ;
...
}
}
...

### Deleting Models

Finding good models is an iterative process, and sometimes you’ll want to delete your old---not as awesome and now unnecessary---models. This can be achieved with DELETE DATA and the spa:deleteModel property applied to the model identifier.

prefix spa: <tag:stardog:api:analytics:>

DELETE DATA {
graph spa:model {
[] spa:deleteModel :myModel .
}
}

### Classification and Similarity with Confidence Levels

Sometimes, besides predicting the most probable value for a property, you will be interested to know the confidence of that prediction. By providing the spa:confidence property, you can get confidence levels for all the possible predictions.

prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:arguments (?director ?year ?studio) ;
spa:confidence ?confidence ;
spa:predict ?predictedGenre .
}

:TheGodfather :directedBy ?director ;
:year ?year ;
:studio ?studio .
}
ORDER BY DESC(?confidence)
LIMIT 3
| director            | year | studio             | predictedGenre | confidence     |
| ------------------- | ---- | ------------------ | -------------- | -------------- |
| :FrancisFordCoppola | 1972 | :ParamountPictures | Drama          | 0.649688932    |
| :FrancisFordCoppola | 1972 | :ParamountPictures | Crime          | 0.340013045    |
| :FrancisFordCoppola | 1972 | :ParamountPictures | Sci-fi         | 0.010298023    |

These values can be interpreted as the probability of the given prediction being the correct one and are useful for tasks like ranking and multi-label classification.

### Tweaking Parameters

Both Vowpal Wabbit, XGBoost, and similarity search can be configured with the spa:parameters property.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:ClassificationModel ;
spa:library spa:VowpalWabbit ;
spa:parameters [
spa:learning_rate 0.1 ;
spa:sgd True ;
spa:hash 'all'
] ;
spa:arguments (?director ?year ?studio) ;
spa:predict ?genre .
}
}
...

Parameter names for both libraries are valid properties in the spa prefix, and their values can be set during model creation.

#### Vowpal Wabbit

By default, models are learned with [ spa:loss_function "logistic"; spa:probabilities true; spa:oaa true ] in classification mode, and [ spa:loss_function "squared" ] in regression. Those parameters are overwritten when using the spa:arguments property with regression, and appended in classification.

Check the official documentation for a full list of parameters. Some tips that might help with your choices:

• Use cross-validation when tweaking parameters. Otherwise, make sure your testing set is not biased and represents a true sample of the original data.

• The most important parameter to tweak is the learning rate spa:l. Values between 1 and 0.01 usually give the best results.

• To prevent overfitting, set spa:l1 or spa:l2 parameters, preferably with a very low value (e.g., 0.000001).

• If number of distinct features is large, make sure to increase the number of bits spa:b to a larger value (e.g., 22).

• Each argument given with spa:arguments has its own namespace, identified by its numeric position in the list (starting with 0). For example, to create quadratic features between ?director and ?studio, set spa:q "02".

• If caching is enabled (e.g., with spa:passes), always use the [ spa:k true; spa:cache_file "fname" ] parameters, where fname is a unique filename for that model.

• In regression, the target variable given with spa:predict is internally normalized into the [0-1] range, and denormalized back to its normal range during query execution. For certain problems where numeric arguments have large values, performance might be improved by performing a similar normalization as a pre-processing step.

#### XGBoost

Models are learned with spa:objective "multi:softprob" in classification, and spa:objective "reg:linear" in regression. See this list for a complete set of available parameters.

The underlying algorithm is based on cluster pruning, an approximate search algorithm which groups items based on their similarity in order to speed up query performance.

The minimum number of items per cluster can be configured with the spa:minClusterSize property, which is set to 100 by default.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:SimilarityModel ;
spa:parameters [
spa:minClusterSize 100 ;
] ;
spa:arguments (?director ?year ?studio) ;
spa:predict ?movie .
}
}
...

This number should be increased with datasets containing many near-duplicate items.

During prediction, there are two parameters available:

• spa:limit, which restricts the number of top N items to return; by default, it returns only the top item, or all items if using spa:confidence.

• spa:clusters, which sets the number of similarity clusters used during the search, with a default value of 1. Larger numbers will increase recall, at the expense of slower query time.

For example, the following query will return the top 3 most similar items and their confidence scores, restricting the search to 10 clusters.

prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:parameters [
spa:limit 3 ;
spa:clusters 10 .
] ;
spa:confidence ?confidence ;
spa:arguments (?director ?year ?studio) ;
spa:predict ?similar .
}
}
...

### Hyperparameter Optimization

Finding the best parameters for a model is a time consuming, laborious, process. Stardog helps to ease the pain by performing an exhaustive search through a manually specified subset of parameter values.

prefix spa: <tag:stardog:api:analytics:>

INSERT {
graph spa:model {
:myModel  a spa:ClassificationModel ;
spa:library spa:VowpalWabbit ;
spa:parameters [
spa:learning_rate (0.1 1 10) ;
spa:hash ('all' 'strings')
] ;
spa:arguments (?director ?year ?studio) ;
spa:predict ?genre .
}
}
...

All possible sets of parameter configurations that can be built from the given values (spa:learning_rate 0.1 ; spa:hash 'all', spa:learning_rate 1 ; spa:hash 'all', and so on) will be evaluated. The best configuration will be chosen, and its model saved in the database.

Afterwards, parameters are available for querying, just like any other model metadata.

prefix spa: <tag:stardog:api:analytics:>

SELECT * WHERE {
graph spa:model {
:myModel  spa:parameters [ ?parameter ?value ]
}
}
+-------------------+-------+
|     parameter     | value |
+-------------------+-------+
| spa:hash          | "all" |
| spa:learning_rate | 1     |
+-------------------+-------+

### Native Library Errors

Stardog ships with a pre-compiled version of Vowpal Wabbit (VW) that works out of the box with most MacOSX/Linux 64bit distributions.

If you have a 32 bit operating system, or an older version of Linux, you will be greeted with a Unable to load analytics native library error when trying to create your first model.

Exception in thread "main" java.lang.RuntimeException: Unable to load analytics native library. Please refer to http://www.stardog.com/docs/#_native_library_errors
at vowpalWabbit.learner.VWLearners.initializeVWJni(VWLearners.java:76)
at vowpalWabbit.learner.VWLearners.create(VWLearners.java:44)
...
Caused by: java.lang.RuntimeException: Unable to load vw_jni library for Linux (i386)

In this case, you will need to install VW manually. Fear not! Instructions are easy to follow.

git clone https://github.com/cpdomina/vorpal.git
cd vorpal/build-jni/
./build.sh
sudo cp transient/lib/vw_wrapper/vw_jni.lib /usr/lib/libvw_jni.so

You might need to install some dependencies, namely zlib-devel, automake, libtool, and autoconf.

After this process is finished, restart the Stardog server and everything should work as expected.

# Property Graphs

In addition to RDF, SPARQL, OWL, and SNARL, Stardog supports the non-semantic property graph model, Gremlin graph traversal language, and Apache TinkerPop 3 APIs. For information on how to use the TinkerPop 3, please refer to its documentation. Details about Stardog’s support for TinkerPop 3 Features can be found in Stardog Feature Set.

 Note Stardog 6.1.0 supports TinkerPop 3.0.2-incubating.

## Motivation & Implementation

Stardog’s implementation of TinkerPop 3 is based ultimately on a (seamless and opaque) translation to and from RDF, in which Stardog persists all vertices, edges and properties. In order to support edge properties in the RDF model, Stardog includes a reification function which allows statement identifiers to be used as the subject of an RDF quad; this extends the RDF Quad model used in Stardog to have a notion of virtual "quints".

Having virtual quints in Stardog lets us manipulate existing RDF content as a property graph; but, most importantly, it lets us use Stardog capabilities (reasoning, ICV, etc) with property graphs. Reification extends existing Stardog graph database and let users add edge properties if required via the TinkerPop 3 or even SPARQL.

Okay, so why add property graph support to Stardog? A few reasons:

1. sometimes you need to traverse, rather than query, a graph

2. sometimes you need to traverse a semantic graph

### Example

Loading the TinkerGraph Modern graph via TinkerPop 3 (using Gremlin Console), using the described Graph Configuration:

Graph graph = StardogGraphFactory.open(...)  (1)
graph.io(graphml()).readGraph('data/tinkerpop-modern.xml')  (2)
1. Get the Graph from the StardogGraphFactory

2. Load the graph tinkerpop-modern included in Gremlin Console distribution at data/tinkerpop-modern.xml.

That produces the following internal representation in Stardog:

@prefix : <http://api.stardog.com/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix stardog: <tag:stardog:api:> .
@prefix tp: <https://www.tinkerpop.com/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

tp:software-bd37dbca-b8d5-4989-abb7-58b82e14411e tp:name "ripple" ;
rdfs:label "software" ;
tp:lang "java" .

tp:person-5c8001e5-2bfa-4816-9a76-c69d77e19b62 tp:name "josh" ;
tp:age "32"^^xsd:int ;
rdfs:label "person" ;
tp:created-aac3d5b1-e5ff-460f-aaf3-267b3d11b710 tp:software-5964a0af-1bb4-4469-b362-6b7db5e617e2 ;
tp:created-33504b11-6393-4ab8-a762-280bf5914e0b tp:software-bd37dbca-b8d5-4989-abb7-58b82e14411e .

tp:software-5964a0af-1bb4-4469-b362-6b7db5e617e2 tp:name "lop" ;
rdfs:label "software" ;
tp:lang "java" .

<tag:stardog:api:id:21115cc5c645c6d35b7571acb4ff3756> rdfs:label "created" ;
tp:weight 4.0E-1 .

<tag:stardog:api:id:83775dcbec39e9ef064557f9e7fdee47> rdfs:label "created" ;
tp:weight 1.0E0 .

tp:person-c56860db-505d-4805-abd0-eecd7698149a tp:name "peter" ;
tp:age "35"^^xsd:int ;
rdfs:label "person" ;
tp:created-6d0837a6-c891-4419-b320-80136651a645 tp:software-5964a0af-1bb4-4469-b362-6b7db5e617e2 .

<tag:stardog:api:id:b751315c74567c781eb95dba9be6ba94> rdfs:label "created" ;
tp:weight 2.0E-1 .

tp:age "27"^^xsd:int ;
rdfs:label "person" .

tp:age "29"^^xsd:int ;
rdfs:label "person" ;
tp:knows-463a4473-6a1f-46df-a893-de0f4236ee83 tp:person-344dd010-dfc4-4356-aca8-f580c07f52d7 ;
tp:knows-22569d6b-b7bb-47c5-b0c4-71a2c78ef900 tp:person-5c8001e5-2bfa-4816-9a76-c69d77e19b62 .

<tag:stardog:api:id:b46d0155b1c5558598e8c3425432ab81> rdfs:label "created" ;
tp:weight 4.0E-1 .

<tag:stardog:api:id:19f7ef4ca398cf79850e3b6463f776bd> rdfs:label "knows" ;
tp:weight 5.0E-1 .

<tag:stardog:api:id:f1ed3591ab20391a1b169d85dc74be5a> rdfs:label "knows" ;
tp:weight 1.0E0 .
 Warning This translation between RDF and property graph models is transparent to the user. It just works. But, of course, since in the end it’s just RDF, you can always query or interact with it as RDF directly using SPARQL, Jena, Sesame, or SNARL code, etc. However, the mapping between Property Graphs and RDF is not considered part of Stardog’s contract so it may change without notice. You’ve been warned!

Getting properties for in-edges for a vertex from the previous graph, using the TinkerPop 3 API:

g = graph.traversal()                        (1)
g.V('https://www.tinkerpop.com/software-5964a0af-1bb4-4469-b362-6b7db5e617e2').inE().properties()  (2)
g.V().has('name','lop').inE().properties()  (3)
1. Get a traversal that can be reused

2. Get a vertex using its IRI Id and list in-edge properties

3. Get a vertex filtering by name and list in-edge properties

### Integration with SPARQL

Access to the reification function is available via SPARQL in order to be able to query edge properties created via the TinkerPop 3 API, e.g. query to find the first 10 edge properties, excluding the label:

select ?srcName ?edgeLabel ?destName ?edgeProp ?val where {
?src ?pred ?dest .
?src tp:name ?srcName .
?dest tp:name ?destName .
BIND(stardog:identifier(?src, ?pred, ?dest) as ?edgeId) . (1)
?edgeId rdfs:label ?edgeLabel .
?edgeId ?edgeProp ?val .
FILTER (?edgeProp != rdfs:label) .
} limit 10
1. Using the stardog:identifier() (aka "reification") function.

### Database Configuration

Any Stardog database should work out-of-the-box with the Stardog TinkerPop 3 implementation, but given that Stardog enables by default RDF literal canonicalization, some property value types may not be as expected when fetching them from the TinkerPop 3 graph. To allow for better compatibility between TinkerPop 3 and Stardog, the setting index.literals.canonical must be disabled in the database at creation time, using the following command:

$stardog-admin db create -o index.literals.canonical=false -n <dbname> ### Graph Configuration In order to create TinkerPop 3 graphs, a configuration object must be created to set up the graph. The TinkerPop 3 implementation for Stardog contains a tool for creating this configuration easily, supporting many of the features available in Stardog, such as reasoning and named-graphs. The StardogGraphConfiguration, is available via the API or the Gremlin Console in Groovy. gremlin> graphConf = StardogGraphConfiguration.builder() ... gremlin> graphConf.connectionString("http://localhost:5820/mygraph").credentials("admin", "admin") ... gremlin> graphConf.baseIRI("http://tinkerpop.incubator.apache.org/").reasoning(false) ==>gremlin.graph=tag:stardog:api:context:default stardog.computer.cache_size=5000 stardog.label_iri=http://www.w3.org/2000/01/rdf-schema#label stardog.connection=http://localhost:5820/mygraph stardog.user=admin stardog.password=admin stardog.base_iri=http://tinkerpop.incubator.apache.org/ stardog.reasoning_enabled=false gremlin> graph = StardogGraphFactory.open(graphConf.build()) ==>cachedstardoggraph[cachedstardoggraph] ## Stardog & Gremlin Console Stardog’s TinkerPop 3 implementation includes a plugin for Gremlin Console. ### Installation The following steps describe how to install the Stardog plugin into the Gremlin console: 1. Create stardog-gremlin/plugin directory within the ext/ directory in the Gremlin console directory. ~/gremlin-console/ext/$ mkdir -p stardog-gremlin/plugin
1. Flat-copy all Stardog client jar files to the directory created in the previous step.

~/gremlin-console/ext/stardog-gremlin/plugin$find stardog/client -iname '*.jar' -exec cp \{\} . \; 1. Make sure the jar file stardog-gremlin-X.X.X.jar is contained in the stardog-gremlin/plugin directory along with all other Stardog jars; copy the jar if it doesn’t exist. 2. Start the Gremlin Console and make sure the complexible.stardog plugin has been loaded. ~/gremlin-console$ bin/gremlin.sh
\,,,/
(o o)
-----oOOo-(3)-oOOo-----
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
plugin activated: tinkerpop.tinkergraph
gremlin> :plugin list
==>tinkerpop.server[active]
==>tinkerpop.gephi
==>tinkerpop.utilities[active]
==>tinkerpop.sugar
==>complexible.stardog
==>tinkerpop.tinkergraph[active]
1. Activate the complexible.stardog plugin in Gremlin Console

gremlin> :plugin use complexible.stardog
==>complexible.stardog activated
1. You’re done installing the stardog-gremlin plugin for Gremlin Console. Now you can create a StardogGraph and start exploring the TinkerPop 3 API with Stardog.

### Using a Stardog Graph

The following describes the process to create a StardogGraph and explore data in Stardog using the TinkerPop 3 API via the Gremlin Console.

The only requirement is that you have an existent database in Stardog as directed in Database Configuration, which could be in-memory or disk based. Assuming you already installed the Stardog plugin for the Gremlin Console and it is active, start the Gremlin Console.

gremlin-console$bin/gremlin.sh In the Gremlin Console, create the configuration settings for opening the StardogGraph. Assuming the Stardog server is running in localhost:5820, the user is admin and password admin. gremlin> graphConf = StardogGraphConfiguration.builder() ... gremlin> graphConf.connectionString("http://localhost:5820/mygraph").credentials("admin", "admin").baseIRI("http://tinkerpop.incubator.apache.org/") ==>gremlin.graph=tag:stardog:api:context:default stardog.computer.cache_size=5000 stardog.label_iri=http://www.w3.org/2000/01/rdf-schema#label stardog.connection=http://localhost:5820/mygraph stardog.user=admin stardog.password=admin stardog.base_iri=http://tinkerpop.incubator.apache.org/ gremlin> graph = StardogGraphFactory.open(graphConf.build()) ==>cachedstardoggraph[cachedstardoggraph] #### Named Graphs The previous commands will create a Graph within the default graph of the Stardog database mygraph. A database can contain multiple graphs, which would be the equivalent to named-graphs in Stardog. To create a StardogGraph over a specific named-graph, just set the named-graph URI in the Graph Configuration for the StardogGraph to create: gremlin> graphConf.namedGraph("tag:graph1") ==>gremlin.graph=tag:stardog:api:context:default ... stardog.named_graph=tag:graph1 ...  Note by default, the property gremlin.graph is set to the default graph in a Stardog database; setting the stardog.named_graph configuration option will override the graph option. ## Stardog & Gremlin Server The TinkerPop 3 implementation for Stardog includes a plugin for Gremlin Server. ### Installation The following steps describe how to install the Stardog plugin into the gremlin server: 1. Create stardog-gremlin/plugin directory within the ext/ directory in the gremlin server directory. ~/gremlin-server/ext/$ mkdir -p stardog-gremlin/plugin
1. Flat-copy all Stardog client jar files to the directory created in the previous step.

~/gremlin-server/ext/stardog-gremlin/plugin$find ~/stardog/client -iname '*.jar' -exec cp \{\} . \; 1. Make sure the jar file stardog-gremlin-X.X.X.jar is contained in the stardog-gremlin/plugin directory along with all other Stardog jars; copy the jar if it doesn’t exist. ### Configure Stardog Graphs To setup a graph for use with the Gremlin Server you need to create a configuration file in conf/ with the Stardog graph properties. The following example file, stardoggraph-mygraph.properties, contains the required properties to use a Stardog graph, described in Graph Configuration: # Properties for creating a StardogGraph in Gremlin Server gremlin.graph=com.complexible.stardog.gremlin.structure.StardogGraph stardog.connection=http://localhost:5820/mygraph stardog.user=admin stardog.password=admin stardog.named_graph=tag:stardog:api:graph:default stardog.reasoning_enabled=false In the previous example, gremlin.graph defines the TinkerPop Class implementation to use, in this case is the StardogGraph. The property gremlin.stardog.named_graph is required when configuring a graph in Gremlin Server, if the graph is contained in the Stardog DB’s default graph, the value to use is: tag:stardog:api:graph:default as shown in the example; if other named-graph is used, just set the value to the named-graph’s URI. The rest of the properties are just connection settings to the Stardog server. Now you need to point to the Stardog graph properties file from the server configuration file, conf/gremlin-server.yaml, and enable the Stardog plugin. the following are the relevant parts of the configuration file that need to be set: graphs: { graph: conf/stardoggraph-mygraph.properties (1) } plugins: - complexible.stardog (2) ... 1. set the stardog graph properties 2. enable the stardog gremlin plugin ### Running the Gremlin Server Having a Stardog server running, at this point you’re ready to start the Gremlin Server. ~/gremlin-server$ bin/gremlin-server.sh

You should see that the Gremlin Server creates an instance of the StardogGraph, named graph, based on the properties file configured.

[INFO] Graphs - Graph [graph] was successfully configured via [conf/stardoggraph-mygraph.properties].

# Security

Stardog’s security model is based on standard role-based access control: users have permissions over resources during sessions; permissions can be grouped into roles; and roles can be assigned to users.

Stardog uses Apache Shiro for authentication, authorization, and session management and jBCrypt for password hashing.

## Resources

A resource is some Stardog entity or service to which access is controlled. Resources are identified by their type and their name. A particular resource is denoted as type_prefix:name. The valid resource types with their prefixes are shown below.

8. Table of System Resources
Resource Prefix Description

User

user

A user (e.g., user:admin)

Role

role

A role assigned to a user (role:reader)

Database

db

A database (db:myDB)

Named Graph

named-graph

A named graph (graph subset) (named-graph:myDb\named-graph-id)

metadata

Metadata of a database (metadata:myDB)

admin

Database admin tasks (e.g., admin:myDB)

Integrity Constraints

icv-constraints

Integrity constraints associated with a database (e.g., icv-constraints:myDB)

## Permissions

Permissions are composed of a permission subject, an action, and a permission object, which is interpreted as the subject resource can perform the specified action over the object resource.

Permission subjects can be of type user or role only. Permission objects can be of any valid type.

 Note write permission in Stardog refers to graph contents, including mutative operations performed via SPARQL Update (i.e., INSERT, DELETE, etc.). The other permissions, i.e., create and delete, apply to resources of the system itself, i.e., users, databases, database metadata, etc.

Valid actions include the following:

read

write

Permits changing the resource properties

create

Permits creating new resources

delete

Permits deleting a resource

grant

Permits granting permissions over a resource

revoke

Permits revoking permissions over a resource

execute

Permits executing administration actions over a database

all

Special action type that permits all previous actions over a resource

### Wildcards

Stardog understands the use of wildcards to represent sets of resources. A wildcard is denoted with the character *. Wildcards can be used to create complex permissions; for instance, we can give a user the ability to create any database by granting it a create permission over db:*. Similarly, wildcards can be used in order to revoke multiple permissions simultaneously.

### Superusers

It is possible at user-creation time to specify that a given user is a superuser. Being a superuser is equivalent to having been granted an all permission over every resource, i.e., *:*. Therefore, as expected, superusers are allowed to perform any valid action over any existing (or future) resource.

### Database Owner Default Permissions

When a user creates a resource, it is automatically granted delete, write, read, grant, and revoke permissions over the new resource. If the new resource is a database, then the user is additionally granted write, read, grant, and revoke permissions over icv-constraints:theDatabase and execute permission over admin:theDatabase. These latter two permissions give the owner of the database the ability to administer the ICV constraints for the database and to administer the database itself.

## Default Security Configuration

 Warning Out of the box, the Stardog security setup is minimal and insecure: user:admin with password set to "admin" is a superuser; user:anonymous with password "anonymous" has the "reader" role; role:reader allows read of any resource.

Do not deploy Stardog in production or in hostile environments with the default security settings.

To setup the constraints used to validate passwords when adding new users, configure the following settings in the stardog.properties configuration file.

• password.length.min: Sets the password policy for the minimum length of user passwords, the value can’t be less than 1 or greater than password.length.max. Default: 4.

• password.length.max: Sets the password policy for the maximum length of user passwords, the value can’t be greater than 1024 or less than 1. Default: 20.

### Authenticated User Cache

Stardog includes a time constrained cache with a configurable time for eviction, default to 24 hours. To disable the cache, the eviction time must be set to 0ms.

### Authorization

The LDAP server is used for authentication only. Permissions and roles are assigned in Stardog.

#### Stale Permissions/Roles

Permissions and roles in Stardog might refer to users that no long exist, i.e., those that were deleted from the LDAP server. This is safe as these users will not be able to authenticate (see above). It is possible to configure Stardog to periodically clean up the list of permissions and roles according to the latest users in the LDAP server. In order to do this, we pass a Quartz cron expression using the ldap.consistency.scheduler.expression property:

## Execute the consistency cleanup at 6pm every day
ldap.consistency.scheduler.expression = 0 0 18 * * ?

## Managing Stardog Securely

Stardog resources can be managed securely by using the tools included in the admin CLI or by programming against Stardog APIs. In this section we describe the permissions required to manage various Stardog resources either by CLI or API.

### Users

Create a user

create permission over user:*. Only superusers can create other superusers.

Delete a user

delete permission over the user.

Enable/Disable a user

User must be a superuser.

User must be a superuser or user must be trying to change its own password.

Check if a user is a superuser

read permission over the user or user must be trying to get its own info.

Check if a user is enabled

read permission over the user or user must be trying to get its own info.

List users

Superusers can see all users. Other users can see only users over which they have a permission.

### Roles

Create a role

create permission over role:*.

Delete a role

delete permission over the role.

Assign a role to a user

grant permission over the role and user must have all the permissions associated to the role.

Unassign a role from a user

revoke permission over the role and user must have all the permissions associated to the role.

List roles

Superusers can see all roles. Other users can see only roles they have been assigned or over which they have a permission.

### Databases

Create a database

create permission over db:*.

Delete a database

delete permission over db:theDatabase.

Add/Remove integrity constraints to a database

write permission over icv-constraints:theDatabase.

Verify a database is valid

read permission over icv-constraints:theDatabase.

Online/Offline a database

execute permission over admin:theDatabase.

Migrate a database

execute permission over admin:theDatabase.

Optimize a database

execute permission over admin:theDatabase.

List databases

Superusers can see all databases. Regular users can see only databases over which they have a permission.

### Permissions

Grant a permission

grant permission over the permission object and user must have the permission that it is trying to grant.

Revoke a permission from a user or role over an object resource

revoke permission over the permission object and user must have the permission that it is trying to revoke.

List user permissions

User must be a superuser or user must be trying to get its own info.

List role permissions

User must be a superuser or user must have been assigned the role.

## Deploying Stardog Securely

To ensure that Stardog’s RBAC access control implementation will be effective, all non-administrator access to Stardog databases should occur over network (i.e., non-native) database connections.[29]

To ensure the confidentiality of user authentication credentials when using remote connections, the Stardog server should only accept connections that are encrypted with SSL.

### Configuring Stardog to use SSL

Stardog HTTP server includes native support for SSL. To enable Stardog to optionally support SSL connections, just pass --enable-ssl to the server start command. If you want to require the server to use SSL only, that is, to reject any non-SSL connections, then use --require-ssl.

When starting from the command line, Stardog will use the standard Java properties for specifying keystore information:

• javax.net.ssl.keyStorePassword (the password)

• javax.net.ssl.keyStore (location of the keystore)

• javax.net.ssl.keyStoreType (type of keystore, defaults to JKS)

These properties are checked first in stardog.properties; then in JVM args passed in from the command line, e.g. -Djavax.net.ssl.keyStorePassword=mypwd. If you’re creating a Server programmatically via ServerBuilder, you can specify values for these properties using the appropriate ServerOptions when creating the server. These values will override anything specified in stardog.properties or via normal JVM args.

### Configuring Stardog Client to use SSL

Stardog HTTP client supports SSL when the https: scheme is used in the database connection string. For example, the following invocation of the Stardog command line utility will initiate an SSL connection to a remote database:

$stardog query https://stardog.example.org/sp2b_10k "ask { ?s ?p ?o }" If the client is unable to authenticate to the server, then the connection will fail and an error message like the following will be generated. Error during connect. Cause was SSLPeerUnverifiedException: peer not authenticated The most common cause of this error is that the server presented a certificate that was not issued by an authority that the client trusts. The Stardog client uses standard Java security components to access a store of trusted certificates. By default, it trusts a list of certificates installed with the Java runtime environment, but it can be configured to use a custom trust store.[30] The client can be directed to use a specific Java KeyStore file as a trust store by setting the javax.net.ssl.trustStore system property. To address the authentication error above, that trust store should contain the issuer of the server’s certificate. Standard Java tools can create such a file. The following invocation of the keytool utility creates a new trust store named my-truststore.jks and initializes it with the certificate in my-trusted-server.crt. The tool will prompt for a passphrase to associate with the trust store. This is not used to encrypt its contents, but can be used to ensure its integrity.[31] $ keytool -importcert  -keystore my-truststore.jks -alias stardog-server -file my-trusted-server.crt

The following Stardog command line invocation uses the newly created truststore.

$STARDOG_SERVER_JAVA_ARGS="-Djavax.net.ssl.trustStore=my-truststore.jks"$ stardog query https://stardog.example.org/sp2b_10k "ask { ?s ?p ?o }"

For custom Java applications that use the Stardog client, the system property can be set programmatically or when the JVM is initialized.

The most common deployment approach requiring a custom trust store is when a self-signed certificate is presented by the Stardog server. For connections to succeed, the Stardog client must trust the self-signed certificate. To accomplish this with the examples given above, the self-signed certificate should be in the my-trusted-server.crt file in the keytool invocation.

A client may also fail to authenticate to the server if the hostname in the Stardog database connection string does not match a name contained in the server certificate.[32]

This will cause an error message like the following

Error during connect.  Cause was SSLException: hostname in certificate didn't match

The client does not support connecting when there’s a mismatch; therefore, the only workarounds are to replace the server’s certificate or modify the connection string to use an alias for the same server that matches the certificate.

# Programming Stardog

You can program Stardog in Java, over HTTP, JavaScript, Clojure, Groovy, Spring, and .Net.

## Sample Code

There’s a Github repo of example Java code that you can fork and use as the starting point for your Stardog projects. Feel free to add new examples using pull requests in Github.

# Java Programming

In the Network Programming section, we look at how to interact with Stardog over a network via HTTP. In this chapter we describe how to program Stardog from Java using SNARL Stardog Native API for the RDF Language, Sesame, and Jena. We prefer SNARL to Sesame to Jena and recommend them—​all other things being equal—​in that order.

If you’re a Spring developer, you might want to read Spring Programming or if you prefer a ORM-style approach, you might want to checkout Empire, an implementation of JPA for RDF that works with Stardog.

## Examples

The best way to learn to program Stardog with Java is to study the examples:

We offer some commentary on the interesting parts of these examples below.

AdminConnection provides simple programmatic access to all administrative functions available in Stardog.

#### Creating a Database

You can create an empty database with default configuration options in one line of code:

try (AdminConnection aAdminConnection = AdminConnectionConfiguration.toEmbeddedServer().credentials("admin", "admin").connect()) {
}
 Warning It’s crucially important to always clean up connections to the database by calling AdminConnection#close(). Using try-with-resources where possible is a good practice.

The newDatabase function returns a DatabaseBuilder object which you can use to configure the options of the database you’d like to create. The create function takes the list of files to bulk load into the database when you create it and returns a valid ConnectionConfiguration which can be used to create new Connections to your database.

try (AdminConnection aAdminConnection = AdminConnectionConfiguration.toEmbeddedServer().credentials("admin", "admin").connect()) {
.set(SearchOptions.SEARCHABLE, true)
.create();
}

This illustrates how to create a temporary memory database named test which supports full text search via [Searching].

try (AdminConnection dbms = AdminConnectionConfiguration.toEmbeddedServer().credentials("admin", "admin").connect()) {
aAdminConnection.newDatabase("icvWithGuard")            // disk db named 'icvWithGuard'
.set(ICVOptions.ICV_ENABLED, true)    // enable icv guard mode
.set(ICVOptions.ICV_REASONING_ENABLED, true)    // specify that guard mode should use reasoning
}

This illustrates how to create a persistent disk database with ICV guard mode and reasoning enabled. For more information on what the available options for set are and what they mean, see the Database Admin section. Also note, Stardog database administration can be performed from the CLI.

### Creating a Connection String

As you can see, the ConnectionConfiguration in com.complexible.stardog.api package class is where the initial action takes place:

Connection aConn = ConnectionConfiguration
.to("exampleDB")                      // the name of the db to connect to
.connect();

The to method takes a Database Name as a string; and then connect connects to the database using all specified properties on the configuration. This class and its constructor methods are used for all of Stardog’s Java APIs: SNARL native Stardog API, Sesame, Jena, as well as HTTP. In the latter cases, you must also call server and pass it a valid URL to the Stardog server using HTTP.

Without the call to server, ConnectionConfiguration will attempt to connect to a local, embedded version of the Stardog server. The Connection still operates in the standard client-server mode, the only difference is that the server is running in the same JVM as your application.

 Note Whether using SNARL, Sesame, or Jena, most, if not all, Stardog Java code will use ConnectionConfiguration to get a handle on a Stardog database—​whether embedded or remote—​and, after getting that handle, can use the appropriate API.

### Managing Security

We discuss the security system in Stardog in Security. When logged into the Stardog DBMS you can access all security related features detailed in the security section using any of the core security interfaces for managing users, roles, and permissions.

### Using SNARL

In examples 1 and 4 above, you can see how to use SNARL in Java to interact with Stardog. The SNARL API will give the best performance overall and is the native Stardog API. It uses some Sesame domain classes but is otherwise a clean-sheet API and implementation.

The SNARL API is fluent with the aim of making code written for Stardog easier to write and easier to maintain. Most objects are easily re-used to make basic tasks with SNARL as simple as possible. We are always interested in feedback on the API, so if you have suggestions or comments, please send them to the mailing list.

Let’s take a closer look at some of the interesting parts of SNARL.

aConn.begin();

.io()
.file(Paths.get("data/test.ttl"));

Collection<Statement> aGraph = Collections.singleton(
Values.statement(Values.iri("urn:subj"),
Values.iri("urn:pred"),
Values.iri("urn:obj")));

Resource aContext = Values.iri("urn:test:context");

aConn.commit();

You must always enclose changes to a database within a transaction begin and commit or rollback. Changes are local until the transaction is committed or until you try and perform a query operation to inspect the state of the database within the transaction.

By default, RDF added will go into the default context unless specified otherwise. As shown, you can use Adder directly to add statements and graphs to the database; and if you want to add data from a file or input stream, you use the io, format, and stream chain of method invocations.

See the SNARL API Javadocs for all the gory details.

### Removing Data

// first start a transaction
aConn.begin();

aConn.remove()
.io()
.file(Paths.get("data/remove_data.nt"));

// and commit the change
aConn.commit();

Let’s look at removing data via SNARL; in the example above, you can see that file or stream-based removal is symmetric to file or stream-based addition, i.e., calling remove in an io chain with a file or stream call. See the SNARL API docs for more details about finer-grained deletes, etc.

### Parameterized SPARQL Queries

// A SNARL connection provides parameterized queries which you can use to easily
// build and execute SPARQL queries against the database.  First, let's create a
// simple query that will get all of the statements in the database.
SelectQuery aQuery = aConn.select("select * where { ?s ?p ?o }");

// But getting *all* the statements is kind of silly, so let's actually specify a limit, we only want 10 results.
aQuery.limit(10);

// We can go ahead and execute this query which will give us a result set.  Once we have our result set, we can do
// something interesting with the results.
// NOTE: We use try-with-resources here to ensure that our results sets are always closed.
try(SelectQueryResult aResult = aQuery.execute()) {
System.out.println("The first ten results...");

QueryResultWriters.write(aResult, System.out, TextTableQueryResultWriter.FORMAT);
}

// Query objects are easily parameterized; so we can bind the "s" variable in the previous query with a specific value.
// Queries should be managed via the parameterized methods, rather than created by concatenating strings together,
// because that is not only more readable, it helps avoid SPARQL injection attacks.
IRI aIRI = Values.iri("http://localhost/publications/articles/Journal1/1940/Article1");
aQuery.parameter("s", aIRI);

// Now that we've bound 's' to a specific value, we're not going to pull down the entire database with our query
// so we can go head and remove the limit and get all the results.
aQuery.limit(SelectQuery.NO_LIMIT);

// We've made our modifications, so we can re-run the query to get a new result set and see the difference in the results.
try(SelectQueryResult aResult = aQuery.execute()) {
System.out.println("\nNow a particular slice...");

QueryResultWriters.write(aResult, System.out, TextTableQueryResultWriter.FORMAT);
}

SNARL also lets us parameterize SPARQL queries. We can make a Query object by passing a SPARQL query in the constructor. Simple. Obvious.

Next, let’s set a limit for the results: aQuery.limit10; or if we want no limit, aQuery.limitQuery.NO_LIMIT. By default, there is no limit imposed on the query object; we’ll use whatever is specified in the query. But you can use limit to override any limit specified in the query, however specifying NO_LIMIT will not remove a limit specified in a query, it will only remove any limit override you’ve specified, restoring the state to the default of using whatever is in the query.

We can execute that query with executeSelect and iterate over the results. We can also rebind the "?s" variable easily: aQuery.parameter"s", aURI, which will work for all instances of "?s" in any BGP in the query, and you can specify null to remove the binding.

Query objects are re-usable, so you can create one from your original query string and alter bindings, limit, and offset in any way you see fit and re-execute the query to get the updated results.

We strongly recommend the use of SNARL’s parameterized queries over concatenating strings together in order to build your SPARQL query. This latter approach opens up the possibility for SPARQL injection attacks unless you are very careful in scrubbing your input.[33]

### Getter Interface

aConn.get()
.subject(aURI)
.statements()
.forEach(System.out::println);

// Getter objects are parameterizable just like Query, so you can easily modify and re-use them to change
// what slice of the database you'll retrieve.
Getter aGetter = aConn.get();

// We created a new Getter, if we iterated over its results now, we'd iterate over the whole database; not ideal.  So
// we will bind the predicate to rdf:type and now if we call any of the iteration methods on the Getter we'd only
// pull back statements whose predicate is rdf:type
aGetter.predicate(RDF.TYPE);

// We can also bind the subject and get a specific type statement, in this case, we'll get all the type triples
// for *this* individual.  In our example, that'll be a single triple.
aGetter.subject(aURI);

System.out.println("\nJust a single statement now...");

aGetter.statements()
.forEach(System.out::println);

// Getter objects are stateful, so we can remove the filter on the predicate position by setting it back to null.
aGetter.predicate(null);

// Subject is still bound to the value of aURI so we can use the graph method of Getter to get a graph of all
// the triples where aURI is the subject, effectively performing a basic describe query.
Stream<Statement> aStatements = aGetter.statements();

System.out.println("\nFinally, the same results as earlier, but as a graph...");

RDFWriters.write(System.out, RDFFormats.TURTLE, aStatements.collect(Collectors.toList()));

SNARL also supports some sugar for the classic statement-level getSPO--scars, anyone?--interactions. We ask in the first line of the snippet above for an iterator over the Stardog connection, based on aURI in the subject position. Then a while-loop, as one might expect…​You can also parameterize Getters by binding different positions of the Getter which acts like a kind of RDF statement filter—​and then iterating as usual.

 Note the aIter.close which is important for Stardog databases to avoid memory leaks. If you need to materialize the iterator as a graph, you can do that by calling graph.

The snippet doesn’t show object or context parameters on a Getter, but those work, too, in the obvious way.

### Reasoning

Stardog supports query-time reasoning using a query rewriting technique. In short, when reasoning is requested, a query is automatically rewritten to n queries, which are then executed. As we discuss below in Connection Pooling, reasoning is enabled at the Connection layer and then any queries executed over that connection are executed with reasoning enabled; you don’t need to do anything up front when you create your database if you want to use reasoning.

ReasoningConnection aReasoningConn = ConnectionConfiguration
.to("reasoningExampleTest")
.reasoning(true)
.connect()
.as(ReasoningConnection.class);

In this code example, you can see that it’s trivial to enable reasoning for a Connection: simply call reasoning with true passed in.

### Search

Stardog’s search system can be used from Java. The fluent Java API for searching in SNARL looks a lot like the other search interfaces: We create a Searcher instance with a fluent constructor: limit sets a limit on the results; query contains the search query, and threshold sets a minimum threshold for the results.

// Let's create a Searcher that we can use to run some full text searches over the database.
// Here we will specify that we only want results over a score of 0.5, and no more than 50 results
// for things that match the search term mac.  Stardog's full text search is backed by [Lucene](http://lucene.apache.org)
// so you can use the full Lucene search syntax in your queries.
Searcher aSearch = aSearchConn.search()
.limit(50)
.query("mac")
.threshold(0.5);

// We can run the search and then iterate over the results
SearchResults aSearchResults = aSearch.search();

try (CloseableIterator<SearchResult> resultIt = aSearchResults.iterator()) {
System.out.println("\nAPI results: ");
while (resultIt.hasNext()) {
SearchResult aHit = resultIt.next();

System.out.println(aHit.getHit() + " with a score of: " + aHit.getScore());
}
}

// The Searcher can be re-used if we want to find the next set of results.  We already found the
// first fifty, so lets grab the next page.
aSearch.offset(50);

aSearchResults = aSearch.search();

Then we call the search method of our Searcher instance and iterate over the results i.e., SearchResults. Last, we can use offset on an existing Searcher to grab another page of results.

Stardog also supports performing searches over the full-text index within a SPARQL query via the LARQ SPARQL syntax. This provides a powerful mechanism for querying both your RDF index and full-text index at the same time while also giving you a more performant option to the SPARQL regex filter.

### User-defined Lucene Analyzer

Stardog’s Semantic Search capability uses Lucene’s default text analyzer, which may not be ideal for your data or application. You can implement a custom analyzer that Stardog will use by implementing org.apache.lucene.analysis.Analyzer. That lets you customize Stardog to support different natural languages, domain-specific stop word lists, etc.

See Custom Analyzers in the stardog-examples Github repo for a complete description of the API, registry, sample code, etc.

### User-defined Functions and Aggregates

Stardog may be extended via Function and Aggregate extensibility APIs, which are fully documented, including sample code, in the stardog-examples Github repo section about function extensibility.

In short you can extend Stardog’s SPARQL query evaluation with custom functions and aggregates easily. Function extensibility corresponds to built-in expressions used in FILTER, BIND and SELECT expressions, as well as aggregate operators in a SPARQL query like COUNT or SAMPLE.

### SNARL Connection Views

SNARL Connections support obtaining a specified type of Connection. This lets you extend and enhance the features available to a Connection while maintaining the standard, simple Connection API. The Connection as method takes as a parameter the interface, which must be a sub-type of a Connection, that you would like to use. as will either return the Connection as the view you’ve specified, or it will throw an exception if the view could not be obtained for some reason.

An example of obtaining an instance of a SearchConnection to use Stardog’s full-text search support would look like this:

SearchConnection aSearchConn = aConn.as(SearchConnection.class);

## Using Sesame

Stardog supports the Sesame API; thus, for the most part, using Stardog and Sesame is not much different from using Sesame with other RDF databases. There are, however, at least two differences worth pointing out.

### Wrapping connections with StardogRepository

// Create a Sesame Repository from a Stardog ConnectionConfiguration.  The configuration will be used
// when creating new RepositoryConnections
Repository aRepo = new StardogRepository(ConnectionConfiguration
.to("testSesame")

// init the repo
aRepo.initialize();

// now you can use it like a normal Sesame Repository
RepositoryConnection aRepoConn = aRepo.getConnection();

// always best to turn off auto commit
aRepoConn.setAutoCommit(false);

As you can see from the code snippet, once you’ve created a ConnectionConfiguration with all the details for connecting to a Stardog database, you can wrap that in a StardogRepository which is a Stardog-specific implementation of the Sesame Repository interface. At this point, you can use the resulting Repository like any other Sesame Repository implementation. Each time you call Repository.getConnection, your original ConnectionConfiguration will be used to spawn a new connection to the database.

### Autocommit

Stardog’s RepositoryConnection implementation will, by default, disable autoCommit status. When enabled, every single statement added or deleted via the Connection will incur the cost of a transaction, which is too heavyweight for most use cases. You can enable autoCommit and it will work as expected; but we recommend leaving it disabled.

## Using RDF4J

Stardog also supports RDF4J, the follow-up to Sesame. Its use is nearly identical to the Stardog Sesame API, mostly with package name updates.

### Wrapping connections with StardogRepository

The RDF4J API uses com.complexible.stardog.rdf4j.StardogRepository, which works the same way as the Sesame StardogRepository mentioned above. Its constructor will take either a ConnectionConfiguration like Sesame’s or a Connection String.

### Autocommit

The major difference between the RDF4J and Sesame APIs is that the RDF4J one will leave the autoCommit mode ON by default, instead of disabling it. This is because as of RDF4J’s 2.7.0 release, they have deprecated the setAutoCommit method in favor of assuming it to be always on unless begin()/commit() are used, which we still VERY highly recommend.

## Using Jena

Stardog supports Jena via a Sesame-Jena bridge, so it’s got more overhead than Sesame or SNARL. YMMV. There are two points in the Jena example to emphasize.

### Init in Jena

// obtain a Jena model for the specified stardog database connection.  Just creating an in-memory
// database; this is roughly equivalent to ModelFactory.createDefaultModel.
Model aModel = SDJenaFactory.createModel(aConn);

The initialization in Jena is a bit different from either SNARL or Sesame; you can get a Jena Model instance by passing the Connection instance returned by ConnectionConfiguration to the Stardog factory, SDJenaFactory.

// start a transaction before adding the data.  This is not required,
// but it is faster to group the entire add into a single transaction rather
// than rely on the auto commit of the underlying stardog connection.
aModel.begin();

// read data into the model.  note, this will add statement at a time.
// Bulk loading needs to be performed directly with the BulkUpdateHandler provided
// by the underlying graph, or by reading in files in RDF/XML format, which uses the
// bulk loader natively.  Alternatively, you can load data into the Stardog
// database using SNARL, or via the command line client.

// done!
aModel.commit();

Jena also wants to add data to a Model one statement at a time, which can be less than ideal. To work around this restriction, we recommend adding data to a Model in a single Stardog transaction, which is initiated with aModel.begin. Then to read data into the model, we recommend using RDF/XML, since that triggers the BulkUpdateHandler in Jena or grab a BulkUpdateHandler directly from the underlying Jena graph.

The other options include using the Stardog CLI client to bulk load a Stardog database or to use SNARL for loading and then switch to Jena for other operations, processing, query, etc.

## Client-Server Stardog

Using Stardog from Java in either embedded or client-server mode is very similar--the only visible difference is the use of url in a ConnectionConfiguration: when it’s present, we’re in client-server model; else, we’re in embedded mode.

That’s a good and a bad thing: it’s good because the code is symmetric and uniform. It’s bad because it can make reasoning about performance difficult, i.e., it’s not entirely clear in client-server mode which operations trigger or don’t trigger a round trip with the server and, thus, which may be more expensive than they are in embedded mode.

In client-server mode, everything triggers a round trip with these exceptions:

• closing a connection outside a transaction

• any parameterizations or other of a query or getter instance

• any database state mutations in a transaction that don’t need to be immediately visible to the transaction; that is, changes are sent to the server only when they are required, on commit, or on any query or read operation that needs to have the accurate up-to-date state of the data within the transaction.

Stardog generally tries to be as lazy as possible; but in client-server mode, since state is maintained on the client, there are fewer chances to be lazy and more interactions with the server.

## Connection Pooling

Stardog supports connection pools for SNARL Connection objects for efficiency and programmer sanity. Here’s how they work:

// We need a configuration object for our connections, this is all the information about
// the database that we want to connect to.
ConnectionConfiguration aConnConfig = ConnectionConfiguration
.to("testConnectionPool")

// We want to create a pool over these objects.  See the javadoc for ConnectionPoolConfig for
ConnectionPoolConfig aConfig = ConnectionPoolConfig
.using(aConnConfig)                // use my connection configuration to spawn new connections
.minPool(10)                    // the number of objects to start my pool with
.maxPool(1000)                    // the maximum number of objects that can be in the pool (leased or idle)
.expiration(1, TimeUnit.HOURS)            // Connections can expire after being idle for 1 hr.
.blockAtCapacity(1, TimeUnit.MINUTES);		// I want obtain to block for at most 1 min while trying to obtain a connection.

// now i can create my actual connection pool
ConnectionPool aPool = aConfig.create();

// if I want a connection object...
Connection aConn = aPool.obtain();

// now I can feel free to use the connection object as usual...

// and when I'm done with it, instead of closing the connection, I want to return it to the pool instead.
aPool.release(aConn);

// and when I'm done with the pool, shut it down!
aPool.shutdown();

Per standard practice, we first initialize security and grab a connection, in this case to the testConnectionPool database. Then we setup a ConnectionPoolConfig, using its fluent API, which establishes the parameters of the pool:

 using Sets which ConnectionConfiguration we want to pool; this is what is used to actually create the connections. minPool, maxPool Establishes min and max pooled objects; max pooled objects includes both leased and idled objects. expiration Sets the idle life of objects; in this case, the pool reclaims objects idled for 1 hour. blockAtCapacity Sets the max time in minutes that we’ll block waiting for an object when there aren’t any idle ones in the pool.

Whew! Next we can create the pool using this ConnectionPoolConfig thing.

Finally, we call obtain on the ConnectionPool when we need a new one. And when we’re done with it, we return it to the pool so it can be re-used, by calling release. When we’re done, we shutdown the pool.

Since reasoning in Stardog is enabled per Connection, you can create two pools: one with reasoning connections, one with non-reasoning connections; and then use the one you need to have reasoning per query; never pay for more than you need.

## API Deprecation

Methods and classes in SNARL API that are marked with the com.google.common.annotations.Beta are subject to change or removal in any release. We are using this annotation to denote new or experimental features, the behavior or signature of which may change significantly before it’s out of "beta".

We will otherwise attempt to keep the public APIs as stable as possible, and methods will be marked with the standard @Deprecated annotation for a least one full revision cycle before their removal from the SNARL API. See Compatibility Policies for more information about API stability.

Anything marked @VisibleForTesting is just that, visible as a consequence of test case requirements; don’t write any important code that depends on functions with this annotation.

## Using Maven

As of Stardog 3.0, we support Maven for both client and server JARs. The following table summarizes the type of dependencies that you will have to include in your project, depending on whether the project is a Stardog client, or server, or both. Additionally, you can also include the Jena or Sesame bindings if you would like to use them in your project. The Stardog dependency list below follows the Gradle convention and is of the form: groupId:artifactId:VERSION. Versions 3.0 and higher are supported.

 Type Stardog Dependency Type client com.complexible.stardog:client-http:VERSION pom server com.complexible.stardog:server:VERSION pom rdf4j com.complexible.stardog.rdf4j:stardog-rdf4j:VERSION jar sesame com.complexible.stardog.sesame:stardog-sesame-core:VERSION jar jena com.complexible.stardog.jena:stardog-jena:VERSION jar gremlin com.complexible.stardog.gremlin:stardog-gremlin:VERSION jar

You can see an example of their usage in our examples repository on Github.

 Warning If you’re using Maven as your build tool, then client-http and server dependencies require that you specify the packaging type as POM (pom):
<dependency>
<groupId>com.complexible.stardog</groupId>
<artifactId>client-http</artifactId>
<version>$VERSION</version> <type>pom</type> (1) </dependency> 1. The dependency type must be set to pom. Note: Though Gradle may still work without doing this, it is still best practice to specify the dependency type there as well: compile "com.complexible.stardog:client-http:${VERSION}@pom"

### Public Maven Repo

The public Maven repository for the current Stardog release is https://maven.stardog.com. To get started, you need to add the following endpoint to your preferred build system, e.g. in your Gradle build script:

repositories {
maven {
url "https://maven.stardog.com"
}
}

Similarly, if you’re using Maven you’ll need to add the following to your Maven pom.xml:

<repositories>
<repository>
<id>stardog-public</id>
<url>https://maven.stardog.com</url>
</repository>
</repositories>

### Private Maven Repo

Customer Access

This feature or service is available to Stardog customers. For information about licensing, please /contact[contact] us.

For access to nightly builds, priority bug fixes, priority feature access, hot fixes, etc. Enterprise Premium Support customers have access to their own private Maven repository that is linked to our internal development repository. We provide a private repository which you can either proxy from your preferred Maven repository manager—​e.g. Artifactory or Nexus—​or add the private endpoint to your build script.

### Connecting to Your Private Maven Repo

Similar to our public Maven repo, we will provide you with a private URL and credentials to your private repo, which you will refer to in your Gradle build script like this:

repositories {
maven {
url $yourPrivateUrl credentials { username$yourUsername
password $yourPassword } } } Or if you’re using Maven, add the following to your pom.xml: <repositories> <repository> <id>stardog-private</id> <url>$yourPrivateUrl</url>
</repository>
</repositories>

Then in your ~/.m2/settings.xml add:

<settings>
<servers>
<server>
<id>stardog-private</id>
<username>$yourUsername</username> <password>$yourPassword</password>
</server>
</servers>
</settings>

# Network Programming

In the Java Programming section, we consider interacting with Stardog programmatically from a Java program. In this section we consider interacting with Stardog over HTTP. In some use cases or deployment scenarios, it may be necessary to interact with or control Stardog remotely over an IP-based network.

Stardog supports SPARQL 1.0 HTTP Protocol; the SPARQL 1.1 Graph Store HTTP Protocol; the Stardog HTTP Protocol; and SNARL, an RPC-style protocol based on Google Protocol Buffers.

## SPARQL Protocol

Stardog supports the standard SPARQL Protocol HTTP bindings, as well as additional functionality via HTTP. Stardog also supports SPARQL 1.1’s Service Description format. See the spec if you want details.

### Stardog HTTP Protocol

The Stardog HTTP Protocol supports SPARQL Protocol 1.1 and additional resource representations and capabilities. The Stardog HTTP API v4 is also available on Apiary: http://docs.stardog.apiary.io/. The Stardog Linked Data API (aka "Annex") is also documented on Apiary: http://docs.annex.apiary.io/.

#### Generating URLs

If you are running the HTTP server at

http://localhost:12345/

To form the URI of a particular Stardog Database, the Database Short Name is the first URL path segment appended to the deployment URI. For example, for the Database called cytwombly, deployed in the above example HTTP server, the Database Network Name might be

http://localhost:12345/cytwombly

All the resources related to this database are identified by URL path segments relative to the Database Network Name; hence:

http://localhost:12345/cytwombly/size

In what follows, we use URI Template notation to parameterize the actual request URLs, thus: /{db}/size.

We also abuse notation to show the permissible HTTP request types and default MIME types in the following way: REQ | REQ /resource/identifier → mime_type | mime_type. In a few cases, we use void as short hand for the case where there is a response code but the response body may be empty.

### HTTP Headers: Content-Type & Accept

All HTTP requests that are mutative (add or remove) must include a valid Content-Type header set to the MIME type of the request body, where "valid" is a valid MIME type for N-Triples, Trig, Trix, Turtle, NQuads, JSON-LD, or RDF/XML:

 RDF/XML application/rdf+xml Turtle application/x-turtle or text/turtle N-Triples application/n-triples TriG application/trig TriX application/trix N-Quads application/n-quads JSON-LD application/ld+json

SPARQL CONSTRUCT queries must also include a Accept header set to one of these RDF serialization types.

When issuing a SELECT query the Accept header should be set to one of the valid MIME types for SELECT results:

 SPARQL XML Results Format application/sparql-results+xml SPARQL JSON Results Format application/sparql-results+json SPARQL Boolean Results text/boolean SPARQL Binary Results application/x-binary-rdf-results-table

### Response Codes

Stardog uses the following HTTP response codes:

 200 Operation has succeeded. 202 Operation was received successfully and will be processed shortly. 400 Indicates parse errors or that the transaction identifier specified for an operation is invalid or does not correspond to a known transaction. 401 Request is unauthorized. 403 User attempting to perform the operation does not exist, their username or password is invalid, or they do not have the proper credentials to perform the action. 404 A resource involved in the request—​for example the database or transaction—​does not exist. 409 A conflict for some database operations; for example, creating a database that already exists. 500 A unspecified failure in some internal operation…​Call your office, Senator!

There are also Stardog-specific error codes in the SD-Error-Code header in the response from the server. These can be used to further clarify the reason for the failure on the server, especially in cases where it could be ambiguous. For example, if you received a 404 from the server trying to commit a transaction denoted by the path /myDb/transaction/commit/293845klf9f934…​it’s probably not clear what is missing: it’s either the transaction or the database. In this case, the value of the SD-Error-Code header will clarify.

The enumeration of SD-Error-Code values and their meanings are as follows:

 0 Authentication error 1 Authorization error 2 Query evaluation error 3 Query contained parse errors 4 Query is unknown 5 Transaction not found 6 Database not found 7 Database already exists 8 Database name is invalid 9 Resource (user, role, etc) already exists 10 Invalid connection parameter(s) 11 Invalid database state for the request 12 Resource in use 13 Resource not found 14 Operation not supported by the server 15 Password specified in the request was invalid

In cases of error, the message body of the result will include any error information provided by the server to indicate the cause of the error.

## Stardog Resources

To interact with Stardog over HTTP, use the following resource representations, HTTP response codes, and resource identifiers.

### A Stardog Database

GET /{db} → void

Returns a representation of the database. As of Stardog 6.1.0, this is merely a placeholder; in a later release, this resource will serve the web console where the database can be interacted with in a browser.

### Database Size

GET /{db}/size → text/plain

Returns the number of RDF triples in the database.

### Query Evaluation

GET | POST /{db}/query

The SPARQL endpoint for the database. The valid Accept types are listed above in the HTTP Headers section.

To issue SPARQL queries with reasoning over HTTP, see Using Reasoning.

### SPARQL update

GET | POST /{db}/update → text/boolean

The SPARQL endpoint for updating the database with SPARQL Update. The valid Accept types are application/sparql-update or application/x-www-form-urlencoded. Response is the result of the update operation as text, eg true or false.

### Query Plan

GET | POST /{db}/explain → text/plain

Returns the explanation for the execution of a query, i.e., a query plan. All the same arguments as for Query Evaluation are legal here; but the only MIME type for the Query Plan resource is text/plain.

### Transaction Begin

POST /{db}/transaction/begin → text/plain

Returns a transaction identifier resource as text/plain, which is likely to be deprecated in a future release in favor of a hypertext format. POST to begin a transaction accepts neither body nor arguments.

#### Transaction Security Considerations

 Warning Stardog’s implementation of transactions with HTTP is vulnerable to man-in-the-middle attacks, which could be used to violate Stardog’s isolation guarantee (among other nasty side effects).

Stardog’s transaction identifiers are 64-bit GUIDs and, thus, pretty hard to guess; but if you can grab a response in-flight, you can steal the transaction identifier if basic access auth or RFC 2069 digest auth is in use. You’ve been warned.

In a future release, Stardog will use RFC 2617 HTTP Digest Authentication, which is less vulnerable to various attacks and will never ask a client to use a different authentication type, which should lessen the likelihood of MitM attacks for properly restricted Stardog clients—​that is, a Stardog client that treats any request by a proxy server or origin server (i.e., Stardog) to use basic access auth or RFC 2069 digest auth as a MitM attack. See RFC 2617 for more information.

### Transaction Commit

POST /{db}/transaction/commit/{txId} → void | text/plain

Returns a representation of the committed transaction; 200 means the commit was successful. Otherwise a 500 error indicates the commit failed and the text returned in the result is the failure message.

As you might expect, failed commits exit cleanly, rolling back any changes that were made to the database.

### Transaction Rollback

POST /{db}/transaction/rollback/{txId} → void | text/plain

Returns a representation of the transaction after it’s been rolled back. 200 means the rollback was successful, otherwise 500 indicates the rollback failed and the text returned in the result is the failure message.

### Querying (Transactionally)

GET | POST /{db}/{txId}/query

Returns a representation of a query executed within the txId transaction. Queries within transactions will be slower as extra processing is required to make the changes visible to the query. Again, the valid Accept types are listed above in the HTTP Headers section.

GET | POST /{db}/{txId}/update → text/boolean

The SPARQL endpoint for updating the database with SPARQL Update. Update queries are executed within the specified transaction txId and are not atomic operations as with the normal SPARQL update endpoint. The updates are executed when the transaction is committed like any other change. The valid Accept types are application/sparql-update or application/x-www-form-urlencoded. Response is the result of the update operation as text, eg true or false.

POST /{db}/{txId}/add → void | text/plain

Returns a representation of data added to the database of the specified transaction. Accepts an optional parameter, graph-uri, which specifies the named graph the data should be added to. If a named graph is not specified, the data is added to the default (i.e., unnamed) context. The response codes are 200 for success and 500 for failure.

### Deleting Data (Transactionally)

POST /{db}/{txId}/remove → void | text/plain

Returns a representation of data removed from the database within the specified transaction. Also accepts graph-uri with the analogous meaning as above--Adding Data (Transactionally). Response codes are also the same.

### Clear Database

POST /{db}/{txId}/clear → void | text/plain

Removes all data from the database within the context of the transaction. 200 indicates success; 500 indicates an error. Also takes an optional parameter, graph-uri, which removes data from a named graph. To clear only the default graph, pass DEFAULT as the value of graph-uri.

### Export Database

GET /{db}/export → RDF

Exports the default graph in the database in Turtle format. Also takes an optional parameter, graph-uri, which selects a named graph to export. The valid Accept types are the ones defined above in HTTP Headers for RDF Formats.

### Explanation of Inferences

POST /{db}/reasoning/explain → RDF
POST /{db}/reasoning/{txId}/explain → RDF

Returns the explanation of the axiom which is in the body of the POST request. The request takes the axioms in any supported RDF format and returns the explanation for why that axiom was inferred as Turtle.

### Explanation of Inconsistency

GET | POST /{db}/reasoning/explain/inconsistency → RDF

If the database is logically inconsistent, this returns an explanation for the inconsistency.

### Consistency

GET | POST /{db}/reasoning/consistency → text/boolean

Returns whether or not the database is consistent w.r.t to the TBox.

### Listing Integrity Constraints

GET /{db}/icv → RDF

Returns the integrity constraints for the specified database serialized in any supported RDF format.

POST /{db}/icv/add

Accepts a set of valid Integrity constraints serialized in any RDF format supported by Stardog and adds them to the database in an atomic action. 200 return code indicates the constraints were added successfully, 500 indicates that the constraints were not valid or unable to be added.

### Removing Integrity Constraints

POST /{db}/icv/remove

Accepts a set of valid Integrity constraints serialized in any RDF format supported by Stardog and removes them from the database in a single atomic action. 200 indicates the constraints were successfully remove; 500 indicates an error.

### Clearing Integrity Constraints

POST /{db}/icv/clear

Drops all integrity constraints for a database. 200 indicates all constraints were successfully dropped; 500 indicates an error.

### Converting Constraints to SPARQL Queries

POST /{db}/icv/convert

The body of the POST is a single integrity constraint, serialized in any supported RDF format, with Content-type set appropriately. Returns either a text/plain result containing a single SPARQL query; or it returns 400 if more than one constraint was included in the input.

### Execute GraphQL Query

GET | POST /{db}/graphql → application/json

Executes a GraphQL query. The GET request accepts a query variable which should be a GraphQL query and an optional variables property that is a JSON document for representing input variable bindings. The body of the POST request should be a JSON document with query and (optionally) variables fields. Reasoning can be enabled by setting the @reasoning variable to true in the variables. A schema for the query can be used by setting the @schema variable to the name of the schema.

PUT /{db}/graphql/schemas/{schema}

Adds a GraphQL schema to the database. The name of the schema is specified by the path variable schema.

### Getting GraphQL Schemas

GET /{db}/graphql/schemas/{schema} → application/graphql

Returns the contents of the specified GraphQL schema. The name of the schema is specified by the path variable schema. The response is the GraphQL schema document.

### Removing GraphQL Schemas

DELETE /{db}/graphql/schemas/{schema}

Removes a GraphQL schema from the database. The name of the schema is specified by the path variable schema.

### Removing All GraphQL Schemas

DELETE /{db}/graphql/schemas

Removes all GraphQL schemas from the database.

### List GraphQL Schemas

GET /{db}/graphql/schemas → application/json

Lists all the GraphQL schemas in the database. The response is a JSON document where the schemas field is a list of schema names.

To administer Stardog over HTTP, use the following resource representations, HTTP response codes, and resource identifiers.

### List databases

GET /admin/databases → application/json

Lists all the databases available.

Output JSON example:

{ "databases" : ["testdb", "exampledb"] }

### Copy a database

PUT /admin/databases/{db}/copy?to={db_copy}

Copies a database db to another specified db_copy.

### Create a new database

POST /admin/databases

Creates a new database; expects a multipart request with a JSON specifying database name, options and filenames followed by (optional) file contents as a multipart POST request.

Expected input (application/json):

{
"dbname" : "testDb",
"options" : {
"icv.active.graphs" : "http://graph, http://another",
"search.enabled" : true,
...
},
"files" : [{ "filename":"fileX.ttl", "context":"some:context" }, ...]
}

### Drop an existing database

DELETE /admin/databases/{db}

Drops an existing database db and all the information that it contains. Goodbye Callahan!

### Repair a database

PUT /admin/databases/{db}/repair

Repairs a corrupted Stardog database. This command needs a running Stardog server and the database to be offline.

### Backup a database

PUT /admin/databases/{db}/backup[?to={backup_location}]

Creates a backup of a database. A backup is a physical copy of the database and preserves database metadata in addition to the database contents. By default, backups are stored in the '.backup' directory in your Stardog home (or the 'backup.dir' property specified in your 'stardog.configuration') but the to parameter can be used to specify a different location.

### Restore a database from a backup

PUT /admin/databases?from={backup_location}[&name={new_name}][&force={true|false}]

Restores a database from its backup. The location of the backup should be the full path to the backup on the server side. If you wish to restore the backup to a different database, a new name can be provided. A backup will not be restored over an existing database of the same name; the force flag should be used to overwrite the database.

### Optimize a database

PUT /admin/databases/{db}/optimize

Optimizes a database for query answering after a database has been heavily modified.

### Sets an existing database online.

PUT /admin/databases/{db}/online

Request message to set an existing database online.

### Sets an existing database offline.

PUT /admin/databases/{db}/offline

Request message to set an existing database offline; receives optionally a JSON input to specify a timeout for the offline operation. When not specified, defaults to 3 minutes as the timeout; the timeout should be provided in milliseconds. The timeout is the amount of time the database will wait for existing connections to complete before going offline. This will allow open transaction to commit/rollback, open queries to complete, etc. After the timeout has expired, all remaining open connections are closed and the database goes offline.

Optional input (application/json):

{ "timeout" : timeout_in_ms}

### Set option values to an existing database.

POST /admin/databases/{kb}/options

Set options in the database passed through a JSON object specification, i.e. JSON Request for option values. Database options can be found here.

Expected input (application/json):

{
"database.name" : "DB_NAME",
"icv.enabled" : true | false,
"search.enabled" : true | false,
...
}

### Get option values of an existing database.

PUT /admin/databases/{kb}/options → application/json

Retrieves a set of options passed via a JSON object. The JSON input has empty values for each key, but will be filled with the option values in the database in the output.

Expected input:

{
"database.name" : ...,
"icv.enabled" : ...,
"search.enabled" : ...,
...
}

Output JSON example:

{
"database.name" : "testdb",
"icv.enabled" : true,
"search.enabled" : true,
...
}

### Add a new user to the system.

POST /admin/users

Adds a new user to the system; allows a configuration option for superuser as a JSON object. Superuser configuration is set as default to false. The password must be provided for the user.

Expected input:

{
"superuser" : true | false
}

PUT /admin/users/{user}/pwd

Expected input:

{"password" : "xxxxx"}

### Check if user is enabled.

GET /admin/users/{user}/enabled → application/json

Verifies if user is enabled in the system.

Output JSON example:

{
"enabled": true
}

### Check if user is superuser.

GET /admin/users/{user}/superuser → application/json

Verifies if the user is a superuser:

{
"superuser": true
}

### Listing users.

GET /admin/users → application/json

Retrieves a list of users.

Output JSON example:

{
}

### Listing user roles.

GET /admin/users/{user}/roles → application/json

Retrieves the list of the roles assigned to user.

Output JSON example:

{
}

### Deleting users.

DELETE /admin/users/{user}

Removes a user from the system.

### Enabling users.

PUT /admin/users/{user}/enabled

Enables a user in the system; expects a JSON object in the following format:

{
"enabled" : true
}

### Setting user roles.

PUT /admin/users/{user}/roles

Sets roles for a given user; expects a JSON object specifying the roles for the user in the following format:

{
}

POST /admin/roles

Adds the new role to the system.

Expected input:

{
"rolename" : ""
}

### Listing roles.

GET /admin/roles → application/json

Retrieves the list of roles registered in the system.

Output JSON example:

{
}

### Listing users with a specified role.

GET /admin/roles/{role}/users → application/json

Retrieves users that have the role assigned.

Output JSON example:

{
"users": ["anonymous"]
}

### Deleting roles.

DELETE /admin/roles/{role}?force={force}

Deletes an existing role from the system; the force parameter is a boolean flag which indicates if the delete call for the role must be forced.

### Assigning permissions to roles.

PUT /admin/permissions/role/{role}

Creates a new permission for a given role over a specified resource; expects input JSON Object in the following format:

{
"action" : "read" | "write" | "create" | "delete" | "revoke" | "execute" | "grant" | "*",
"resource_type" : "user" | "role" | "db" | "named-graph" | "metadata" | "admin" | "icv-constraints" | "*",
"resource" : ""
}

### Assigning permissions to users.

PUT /admin/permissions/user/{user}

Creates a new permission for a given user over a specified resource; expects input JSON Object in the following format:

{
"action" : "read" | "write" | "create" | "delete" | "revoke" | "execute" | "grant" | "*",
"resource_type" : "user" | "role" | "db" | "named-graph" | "metadata" | "admin" | "icv-constraints" | "*",
"resource" : ""
}

### Deleting permissions from roles.

POST /admin/permissions/role/{role}/delete

Deletes a permission for a given role over a specified resource; expects input JSON Object in the following format:

{
"action" : "read" | "write" | "create" | "delete" | "revoke" | "execute" | "grant" | "*",
"resource_type" : "user" | "role" | "db" | "named-graph" | "metadata" | "admin" | "icv-constraints" | "*",
"resource" : ""
}

### Deleting permissions from users.

POST /admin/permissions/user/{user}/delete

Deletes a permission for a given user over a specified resource; expects input JSON Object in the following format:

{
"action" : "read" | "write" | "create" | "delete" | "revoke" | "execute" | "grant" | "*",
"resource_type" : "user" | "role" | "db" | "named-graph" | "metadata" | "admin" | "icv-constraints" | "*",
"resource" : ""
}

### Listing role permissions.

GET /admin/permissions/role/{role} → application/json

Retrieves permissions assigned to the role.

Output JSON example:

{
}

### Listing user permissions.

GET /admin/permissions/user/{user} → application/json

Retrieves permissions assigned to the user.

Output JSON example:

{
}

### Listing user effective permissions.

GET /admin/permissions/effective/user/{user} → application/json

Retrieves effective permissions assigned to the user.

Output JSON example:

{
"permissions": ["stardog:*"]
}

### Get nodes in cluster

GET /admin/cluster

Retrieves the list of nodes in the cluster; the elected cluster coordinator is the first element in the array. This route is only available when Stardog is running within a cluster setup.

Output JSON example:

{
"nodes": [
"192.168.69.1:5820 (Available)",
"192.168.69.2:5820 (Available)",
"192.168.69.3:5820 (Available)"
]
}

### Shutdown server.

POST /admin/shutdown

Shuts down the Stardog Server. If successful, returns a 202 to indicate that the request was received and that the server will be shut down shortly.

GET | POST /{db}/vcs/query

Issue a query over the version history metadata using SPARQL. Method has the same arguments and outputs as the normal query method of a database.

### Versioned Commit

POST /{db}/vcs/{tid}/commit_msg

Input example:

This is the commit message

Accepts a commit message in the body of the request and performs a VCS commit of the specified transaction

### Create Tag

POST /{db}/vcs/tags/create

Input example:

"f09c0e02350627480839da4661b8e9cbd70f6372", "This is the commit message"

Create a tag from the given revision id with the specified commit message.

### Delete Tag

POST /{db}/vcs/tags/delete

Input example:

"f09c0e02350627480839da4661b8e9cbd70f6372"

Delete the tag with the given revision.

### Revert to Tag

POST /{db}/vcs/revert

Input example:

"f09c0e02350627480839da4661b8e9cbd70f6372", "893220fba7910792084dd85207db94292886c4d7", "This is the revert message"

Perform a revert of a revision to the specified revision with the given commit message.

# Extending Stardog

In this chapter we discuss the various ways you can extend the Stardog Knowledge Graph Platform. Stardog’s extension mechanisms utilize the JDK Service Loader to load new services at runtime and make them available to the various parts of the system.

To register an extension, a file should be placed in META-INF/services whose name is the fully-qualified of the extension type. This must be included in the jar file with the compile source of the extension. The jar then should be placed somewhere in Stardog’s classpath, usually either server/ext or a folder specified by the environment variable STARDOG_EXT. Stardog will pick up the implementations on startup by using the JDK ServiceLoader framework.

## HTTP Server

Your extension to the server should extend com.stardog.http.server.undertow.HttpService. You’ll need a service definition in META-INF/services called com.stardog.http.server.undertow.HttpService, which contains the fully-qualified class name(s) of your extension(s). That service definition should be included in your jar file and dropped into the classpath. On (re)start, Stardog will find the service and auto load it into the server.

An HttpService uses a subset of the JAX-RS specification for route definition. All HttpServices are scanned when the server starts and the routes are extracted, compiled into lambdas to avoid the overhead of java.lang.reflect and passed into the server for route handling.

For example, if you wanted to define a route that will accept only POST requests whose body is JSON and produces binary output, it would look like this:

@POST
@Path("/path/to/my/service")
@Consumes("application/json")
@Produces("application/octet-stream")
public void myMethod(final HttpServerExchange theExchange) {
// implementation goes here
}

There’s a couple things to note here. First, the path of the route is partially defined by the @Path annotation. That value is post-fixed to any root @Path specified on the service itself. That complete path is normally with respect to the root of the server. However, services can have sub-services, in which case it would be relative to the parent service, or you can simply override this altogether by overriding the routes method. Similarily, you can override the routes method if you would like to define child services for the route. Here’s how the transaction service mounts the SPARQL protocol and SPARQL Update protocol as child services:

public Iterable<Route> routes(@Nonnull final HttpPath thePath) {
List<HttpService> aServices = Lists.newArrayList(
new SPARQLProtocol(mKernel),
new SPARQLUpdate(mKernel)
);

final HttpPath aRoot = thePath.var(VAR_TX);

return () -> Iterators.concat(aServices.stream()
.flatMap(aService -> Streams.stream(aService.routes(aRoot)))
.iterator(),
HttpService.super.routes(thePath).iterator());
}

Further, note that the route method takes a single parameter: HttpServerExchange. This is the raw Undertow HttpServerExchange. No attempt is made to parse out arguments such as javax.ws.rs.QueryParam, that is left to the implementor. The only part of the exchange that is parsed is any path variables. If your path is /myservice/{db}/myaction then db is a variable and will match whatever is included in that segment of the path. This is included in HttpServerExchange#getQueryParameters.

An HttpService should either have a no-argument constructor, or a constructor that accepts a single argument of type HttpServiceLoader.ServerContext. The ServerContext argument will contain information about the server: initialization options such as port, or whether security is disabled, as well as a reference to Stardog itself in the event your service needs a handle to Stardog. If your service is extending Stardog in some way, for convenience, you can extend from KernelHttpService.

The implementation of the route itself should conform to Undertow’s guide.

Here’s the implementation of user list:

@GET
@Produces("application/json")
public void listUsers(final HttpServerExchange theExchange) {
JsonArray aUsers = new JsonArray();

mKernel.get().getUserManager().getAllUsers().stream()
.map(JsonPrimitive::new)

JsonObject aObj = new JsonObject();

final String aJSON = aObj.toString();

HttpServerExchanges.Responses.contentType(theExchange, "application/json");
HttpServerExchanges.Responses.contentLength(theExchange, aJSON.length());

theExchange.getResponseSender().send(aJSON);
}

## Query Functions

The Stardog com.complexible.stardog.plan.filter.functions.Function interface is the extension point for section 17.6 (Extensible Value Testing) of the SPARQL spec.

Function corresponds to built-in expressions used in FILTER, BIND and SELECT expressions, as well as aggregate operators in a SPARQL query. Examples include && and || and functions defined in the SPARQL spec like sameTerm, str, and now.

### Implementing Custom Functions

The starting point for implementing your own custom function is to extend AbstractFunction. This class provides much of the basic scaffolding for implementing a new Function from scratch.

If your new function falls into one of the existing categories, it should implement the appropriate marker interface:

• com.complexible.stardog.plan.filter.functions.cast.CastFunction

• com.complexible.stardog.plan.filter.functions.datetime.DateTimeFunction

• com.complexible.stardog.plan.filter.functions.hash.HashFunction

• com.complexible.stardog.plan.filter.functions.numeric.MathFunction

• com.complexible.stardog.plan.filter.functions.rdfterm.RDFTermFunction

• com.complexible.stardog.plan.filter.functions.string.StringFunction

If not, then it must implement com.complexible.stardog.plan.filter.functions.UserDefinedFunction. Extending one of these marker interfaces is required for the Function to be traverseable via the visitor pattern.

A zero-argument constructor must be provided which delegates some initialization to super, providing first the int number of required arguments followed by one or more URIs which identify the function. Any these URIs can be used to identify the function in a SPARQL query. The URIs are typed as String but should be valid URIs.

For functions which take a range of arguments, for example a minimum of 2, but no more than 4 values, a Range can be used as the first parameter passed to super rather than an int.

Function extends from Copyable, therefore implementations should also provide a "copy constructor" which can be called from the copy method:

private MyFunc(final MyFunc theFunc) {
super(myFunc);
// make copies of any local data structures
}

@Override
public MyFunc copy() {
return new MyFunc(this);
}

Evaluating the function is handled by Value internalEvaluate(final Value…​) The parameters of this method correspond to the arguments passed into the function; it’s the values of the variables for each solution of the query. Here we can perform whatever actions are required for our function. AbstractFunction will have already taken care of validating that we’re getting the correct number of arguments to the function, but we still have to validate the input. AbstractFunction provides some convenience methods to this end, for example assertURI and assertNumericLiteral for requiring that inputs are either a valid URI, or a literal with a numeric datatype respectively.

Errors that occur in the evaluation of the function should throw a com.complexible.stardog.plan.filter.ExpressionEvaluationException; this corresponds to the ValueError concept defined in the SPARQL specification.

### Registering Custom Functions

Create a file called com.complexible.stardog.plan.filter.functions.Function in the META-INF/services directory with the name of your custom Function class.

### Using Custom Functions

Functions are identified by their URI; you can reference them in a query using their fully-qualified URI, or specify prefixes for the namespaces and utilize only the qname. For this example, if the namespace tag:stardog:api: is associated with the prefix stardog and within that namespace we have our function myFunc we can invoke it from a SPARQL query as: bind(stardog:myFunc(?var) as ?tc)

## Custom Aggregates

While the SPARQL specification has an extension point for value testing and allows for custom functions in FILTER/BIND/SELECT expressions, there is no similar mechanism for aggregates. The space of aggregates is closed by definition, all legal aggregates are enumerated in the spec itself.

However, as with custom functions, there are many use cases for creating and using custom aggregate functions. Stardog provides a mechanism for creating and using custom aggregates without requiring custom SPARQL syntax.

### Implementing Custom Aggregates

To implement a custom aggregate, you should extend AbstractAggregate.

The rules regarding constructor, "copy constructor" and the copy method for Function apply to Aggregate as well.

Two methods must be implemented for custom aggregates, Value _getValue() throws ExpressionEvaluationException and void aggregate(final Value theValue, final long theMultiplicity) throws ExpressionEvaluationException. _getValue returns the computed aggregate value while aggregate adds a Value to the current running aggregation. In terms of the COUNT aggregate, aggregate would increment the counter and _getValue would return the final count.

The multiplicity argument to aggregate corresponds to the fact that intermediate solution sets have a multiplicity associated with them. It’s most often 1, but joins and choice of the indexes used for the scans internally can affect this. Rather than repeating the solution N times, we associate a multiplicity of N with the solution. Again, in terms of COUNT, this would mean that rather than incrementing the count by 1, it would be incremented by the multiplicity.

### Registering Custom Aggregates

Aggregates such as COUNT or SAMPLE are implementations of Function in the same way sameTerm or str are and are registered with Stardog in the exact same manner.

### Using Custom Aggregates

You can use your custom aggregates just like any other aggregate function. Assuming we have a custom aggregate gmean defined in the tag:stardog:api: namespace, we can refer to it within a query as such:

PREFIX : <http://www.example.org>
PREFIX stardog: <tag:stardog:api:>

SELECT (stardog:gmean(?O) AS ?C)
WHERE { ?S ?P ?O }

## Database Archetypes

The Stardog database archetypes provide a simple way to associate one or more ontologies and optionally a set of constraints with a database. Stardog provides two built-in database archetypes out-of-the-box: PROV and SKOS.

### Running FOAF Example

This example shows a user-define archetype for FOAF.

First build the jar file for this example using gradle:

$./gradlew jar Copy the jar file to your Stardog installation directory and (re)start the server: $ cp examples/foaf/build/libs/foaf-*.jar $STARDOG/server/dbms/$ $STARDOG/bin/stardog-admin server start Create a new database using the FOAF archetype: $ $STARDOG/bin/stardog-admin db create -o database.archetypes="foaf" -n foafDB That’s it. Even though you created a database without any data you will see that there is a default namespace, ontology and constraints associated with this database: $ bin/stardog namespace list foafDB
+---------+---------------------------------------------+
| Prefix  |                  Namespace                  |
+---------+---------------------------------------------+
| foaf    | http://xmlns.com/foaf/0.1/                  |
| owl     | http://www.w3.org/2002/07/owl#              |
| rdf     | http://www.w3.org/1999/02/22-rdf-syntax-ns# |
| rdfs    | http://www.w3.org/2000/01/rdf-schema#       |
| stardog | tag:stardog:api:                            |
| xsd     | http://www.w3.org/2001/XMLSchema#           |
+---------+---------------------------------------------+
$bin/stardog reasoning schema foafDB foaf:publications a owl:ObjectProperty foaf:jabberID a owl:InverseFunctionalProperty foaf:jabberID a owl:DatatypeProperty foaf:interest rdfs:domain foaf:Agent foaf:workInfoHomepage a owl:ObjectProperty foaf:schoolHomepage rdfs:range foaf:Document foaf:status a owl:DatatypeProperty foaf:currentProject rdfs:domain foaf:Person ...$ bin/stardog icv export foafDB
AxiomConstraint{foaf:isPrimaryTopicOf a owl:InverseFunctionalProperty}

### Registering Archetypes

User-defined archetypes are loaded to Stardog through JDK ServiceLoader framework. Create a file called com.complexible.stardog.db.DatabaseArchetype in the META-INF/services directory. The contents of this file should be all of the fully-qualified class names for your custom archetypes.

## Search Analyzers

By default, the full-text index in Stardog uses Lucene’s StandardAnalyzer.

However, any class implementing org.apache.lucene.analysis.Analyzer can be used in place of the default analyzer. To specify a different Analyzer a service named com.complexible.stardog.search.AnalyzerFactory should be registered. AnalyzerFactory returns the desired Analyzer implementation to be used when creating the Lucene index from the RDF contained in the database.

This is an example of an AnalyzerFactory which uses the built-in Lucene analyzer for the French language:

public final class FrenchAnalyzerFactory implements AnalyzerFactory {

/**
* {@inheritDoc}
*/
@Override
public Analyzer get() {
return new FrenchAnalyzer(Version.LUCENE_47);
}
}

Any of the common Lucene analyzers can be used as well as any custom implementation of Analyzer. In the latter case, be sure your implementation is in Stardog’s class path.

Create a file called com.complexible.stardog.search.AnalyzerFactory in the META-INF/services directory. The contents of this file should be the fully-qualified class name of your AnalyzerFactory.

Note, as of Stardog 3.0, only one AnalyzerFactory can be registered at a time, attempts to register more than one will yield errors on startup.

# JavaScript Programming

Source code and documentation for stardog.js are available available on Github and npm.

## stardog.js

This framework wraps all the functionality of a client for the Stardog DBMS and provides access to a full set of functions such as executing SPARQL Queries, administration tasks on Stardog, and the use of the Reasoning API.

The implementation uses the HTTP protocol, since most of Stardog functionality is available using this protocol. For more information, see Network Programming.

The framework is currently supported for node.js and the browser, including test cases for both environments. You’ll need npm to run the test cases and install the dependencies.

# Clojure Programming

The stardog-clj source code is available as Apache 2.0 licensed code.

## Installation

Stardog-clj is available from Clojars. To use, just include the following dependency:

[stardog-clj "2.2.2"]

Starting with Stardog 2.2.2, the stardog-clj version always matches the latest release of Stardog.

## Overview

Stardog-clj provides a set of functions as API wrappers to the native SNARL API. These functions provide the basis for working with Stardog, starting with connection management, connection pooling, and the core parts of the API, such as executing a SPARQL query or adding and removing RDF from the Stardog database. Over time, other parts of the Stardog API will be appropriately wrapped with Clojure functions and idiomatic Clojure data structures.

Stardog-clj provides the following features:

1. Specification based descriptions for connections, and corresponding "connection" and "with-connection-pool" functions and macros

2. Functions for query, ask, graph, and update to execute SELECT, ASK, CONSTRUCT, and SPARQL Update queries respectively

3. Functions for insert and remove, for orchestrating the Adder and Remover APIs in SNARL

4. Macros for resource handling, including with-connection-tx, with-connection-pool, and with-transaction

5. Support for programming Stardog applications with either the connection pool or direct handling of the connection

6. Idiomatic clojure handling of data structures, with converters that can be passed to query functions

The API with source docs can be found in the stardog.core and stardog.values namespaces.

## API Overview

The API provides a natural progression of functions for interacting with Stardog

(create-db-spec "testdb" "snarl://localhost:5820/" "admin" "admin" "none")

This creates a connection space for use in connect or make-datasource with the potential parameters:

{:url "snarl://localhost:5820/" :db "testdb" :pass "admin" :user "admin" :max-idle 100 :max-pool 200 :min-pool 10 :reasoning false}

Create a single Connection using the database spec. Can be used with with-open, with-transaction, and with-connection-tx macros.

(connect db-spec)

Creates a data source, i.e. ConnectionPool, using the database spec. Best used within the with-connection-pool macro.

(make-datasource db-spec)

Executes the body with a transaction on each of the connections. Or establishes a connection and a transaction to execute the body within.

(with-transaction [connection...] body)
(with-connection-tx binding-forms body)

Evaluates body in the context of an active connection obtained from the connection pool.

(with-connection-pool [con pool] .. con, body ..)

## Examples

Here are some examples of using stardog-clj

### Create a connection and run a query

=> (use 'stardog.core)
=> (def c (connect {:db "testdb" :url "snarl://localhost"}))
=> (def results (query c "select ?n { .... }"))
=> (take 5 results)
({:n #<StardogURI http://example.org/math#2>} {:n #<StardogURI http://example.org/math#3>} {:n #<StardogURI http://example.org/math#5>} {:n #<StardogURI http://example.org/math#7>} {:n #<StardogURI http://example.org/math#11>})

=> (def string-results (query c "select ?n { .... }" {:converter str}))
=> (take 5 string-results)
({:n "http://example.org/math#2"} {:n "http://example.org/math#3"} {:n "http://example.org/math#5"} {:n "http://example.org/math#7"} {:n "http://example.org/math#11"})

### Insert data

(let [c (connect test-db-spec)]
(with-transaction [c]
(insert! c ["urn:test" "urn:test:clj:prop2" "Hello World"])
(insert! c ["urn:test" "urn:test:clj:prop2" "Hello World2"]))

### Run a query with a connection pool

myapp.core=> (use 'stardog.core)
nil
#'myapp.core/db-spec
myapp.core=> (def ds (make-datasource db-spec))
myapp.core=> (with-connection-pool [conn ds]
#_=>   (query conn "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 2"))
({:s #<URI urn:test1>, :p #<URI urn:test:predicate>, :o "hello world"} {:s #<URI urn:test1>, :p #<URI urn:test:predicate>, :o "hello world2"})

### SPARQL Update

;; First, add a triple
;; Then run an udpate query, which is its own transaction
(with-open [c (connect test-db-spec)]
(with-transaction [c]
(insert! c ["urn:testUpdate:a1" "urn:testUpdate:b" "aloha world"]))
(update c "DELETE { ?a ?b \"aloha world\" } INSERT { ?a ?b \"shalom world\" } WHERE { ?a ?b \"aloha world\"  }"
{:parameters {"?a" "urn:testUpdate:a1" "?b" "urn:testUpdate:b"}})
(ask c "ask { ?s ?p \"shalom world\" }") => truthy)

### Graph function for Construct queries

;; Graph results converted into Clojure data using the values methods
(with-open [c (connect test-db-spec)]
(let [g (graph c "CONSTRUCT { <urn:test> ?p ?o } WHERE { <urn:test> ?p ?o } ")]
g) => (list [(as-uri "urn:test") (as-uri "urn:test:clj:prop3") "Hello World"]))

;; Ask returns a Boolean
(with-open [c (connect test-db-spec)]
(ask c "ask { ?s <http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#teacherOf> ?o }")) => truthy)

# .Net Programming

In the Network Programming section, we looked at how to interact with Stardog over a network via HTTP and SNARL protocols. In this chapter we describe how to program Stardog from .Net using http://www.dotnetrdf.org.

 Note .dotNetRDF is an open source library developed and supported by third parties; questions or issues with the .Net API should be directed to http://www.dotnetrdf.org.

You should also be aware that dotNetRDF uses the HTTP API for all communication with Stardog so you must enable the HTTP server to use Stardog from .Net. It’s enabled by default so most users should not need to do anything to fulfill this requirement.

## dotNetRDF Documentation

See the documentation for using dotNetRDF with Stardog.

# Spring Programming

The Spring for Stardog source code is available on Github. Binary releases are available on the Github release page.

As of 2.1.3, Stardog-Spring and Stardog-Spring-Batch can both be retrieved from Maven central:

• com.complexible.stardog:stardog-spring:2.1.3

• com.complexible.stardog:stardog-spring-batch:2.1.3

The corresponding Stardog Spring version will match the Stardog release, e.g. stardog-spring-2.2.2 for Stardog 2.2.2.

## Overview

Spring for Stardog makes it possible to rapidly build Stardog-backed applications with the Spring Framework. As with many other parts of Spring, Stardog’s Spring integration uses the template design pattern for abstracting standard boilerplate away from application developers.

Stardog Spring can be included via Maven with com.complexible.stardog:stardog-spring:version and com.complexible.stardog:stardog-spring-batch for Spring Batch support. Both of these dependencies require the public Stardog repository to be included in your build script, and the Stardog Spring packages installed in Maven. Embedded server is still supported, but via providing an implementatino of the Provider interface. This enables users of the embedded server to have full control over how to use the embedded server.

At the lowest level, Spring for Stardog includes

1. DataSouce and DataSourceFactoryBean for managing Stardog connections

2. SnarlTemplate for transaction- and connection-pool safe Stardog programming

3. DataImporter for easy bootstrapping of input data into Stardog

In addition to the core capabilities, Spring for Stardog also integrates with the Spring Batch framework. Spring Batch enables complex batch processing jobs to be created to accomplish tasks such as ETL or legacy data migration. The standard ItemReader and ItemWriter interfaces are implemented with a separate callback writing records using the SNARL Adder API.

## Basic Spring

There are three Beans to add to a Spring application context:

• DataSourceFactoryBean: com.complexible.stardog.ext.spring.DataSourceFactoryBean

• SnarlTemplate: com.complexible.stardog.ext.spring.SnarlTemplate

• DataImporter: com.complexible.stardog.ext.spring.DataImporter

DataSourceFactoryBean is a Spring FactoryBean that configures and produces a DataSource. All of the Stardog ConnectionConfiguration and ConnectionPoolConfig methods are also property names of the DataSourceFactoryBean--for example, "to", "url", "createIfNotPresent". If you are interested in running an embedded server, use the Provider interface and inject it into the DataSourceFactoryBean. Note: all of the server jars must be added to your classpath for using the embedded server.

javax.sql.DataSource, that can be used to retrieve a Connection from the ConnectionPool. This additional abstraction serves as place to add Spring-specific capabilities (e.g. spring-tx support in the future) without directly requiring Spring in Stardog.

SnarlTemplate provides a template abstraction over much of Stardog’s native API, SNARL, and follows the same approach of other Spring template, i.e., JdbcTemplate, JmsTemplate, and so on.

Spring for Stardog also comes with convenience mappers, for automatically mapping result set bindings into common data types. The SimpleRowMapper projects the BindingSet as a List> and a SingleMapper that accepts a constructor parameter for binding a single parameter for a single result set.

The key methods on SnarlTemplate include the following:

query(String sparqlQuery, Map args, RowMapper)

query() executes the SELECT query with provided argument list, and invokes the mapper for result rows.

doWithAdder(AdderCallback)

doWithAdder() is a transaction- and connection-pool safe adder call.

doWithGetter(String subject, String predicate, GetterCallback)

doWithGetter() is the connection pool boilerplate method for the Getter interface, including the programmatic filters.

doWithRemover(RemoverCallback)

doWithRemover() As above, the remover method that is transaction and pool safe.

execute(ConnectionCallback)

execute() lets you work with a connection directly; again, transaction and pool safe.

construct(String constructSparql, Map args, GraphMapper)

construct() executes a SPARQL CONSTRUCT query with provided argument list, and invokes the GraphMapper for the result set.

DataImporter is a new class that automates the loading of RDF files into Stardog at initialization time.

It uses the Spring Resource API, so files can be loaded anywhere that is resolvable by the Resource API: classpath, file, url, etc. It has a single load method for further run-time loading and can load a list of files at initialization time. The list assumes a uniform set of file formats, so if there are many different types of files to load with different RDF formats, there would be different DataImporter beans configured in Spring.

## Spring Batch

In addition to the base DataSource and SnarlTemplate, Spring Batch support adds the following:

• SnarlItemReader: com.complexible.stardog.ext.spring.batch.SnarlItemReader

• SnarlItemWriter: com.complexible.stardog.ext.spring.batch.SnarlItemWriter

• BatchAdderCallback: com.complexible.stardog.ext.spring.batch.BatchAdderCallback

# Groovy Programming

Groovy is an agile and dynamic programming language for the JVM, making popular programming features such as closures available to Java developers. Stardog’s Groovy support makes life easier for developers who need to work with RDF, SPARQL, and OWL by way of Stardog.

The Groovy for Stardog source code is available on Github.

Binary releases are available on the Github release page and via Maven central as of version 2.1.3 and beyond using the following dependency declaration (Gradle style) com.complexible.stardog:stardog-groovy:2.1.3.

As of version 2.1.3, Stardog-Groovy can be included via "com.complexible.stardog:stardog-groovy:2.1.3" from Maven central.

 Note You must include our public repository in your build script to get the Stardog client dependencies into your local repository.

Using the embedded server with Stardog Groovy is not supported in 2.1.2, due to conflicts of the asm library for various third party dependencies. If you wish to use the embedded server with similar convenience APIs, please try Stardog with Spring. Also 2.1.3 and beyond of Stardog-Groovy no longer requires the use of the Spring framework.

The Stardog-Groovy version always matches the Stardog release, e.g. for Stardog 2.2.2 use stardog-groovy-2.2.2.

## Overview

Groovy for Stardog provides a set of Groovy API wrappers for developers to build applications with Stardog and take advantage of native Groovy features. For example, you can create a Stardog connection pool in a single line, much like Groovy SQL support. In Groovy for Stardog, queries can be iterated over using closures and transaction safe closures can be executed over a connection.

For the first release, Groovy for Stardog includes com.complexible.stardog.ext.groovy.Stardog with the following methods:

1. Stardog(map) constructor for managing Stardog connection pools

2. each(String, Closure) for executing a closure over a query’s results, including projecting SPARQL result variables into the closure.

3. query(String, Closure) for executing a closure over a query’s results, passing the BindingSet to the closure

4. insert(List) for inserting a list of vars as a triple, or a list of list of triples for insertion

5. remove(List) for removing a triple from the database

6. withConnection for executing a closure with a transaction safe instance of Connection

## Examples

Here are some examples of the more interesting parts of Stardog Groovy.

### Create a Connection

def stardog = new Stardog([url: "snarl://localhost:5820/", to:"testdb", username:"admin", password:"admin"])
stardog.query("select ?x ?y ?z WHERE { ?x ?y ?z } LIMIT 2", { println it } )
// in this case, it is a BindingSet, ie TupleQueryResult.next() called until exhausted and closure executed

### SPARQL Vars Projected into Groovy Closures

// there is also a projection of the results into the closure's binding
// if x, y, or z are not populated in the answer, then they are still valid binidng but are null
stardog.each("select ?x ?y ?z WHERE { ?x ?y ?z } LIMIT 2", {
println x
println y
println z // may be a LiteralImpl, so you get full access to manipulate Value objects
}
)

// insert and remove
stardog.insert([["urn:test3", "urn:test:predicate", "hello world"],
["urn:test4", "urn:test:predicate", "hello world2"]])
stardog.remove(["urn:test3", "urn:test:predicate", "hello world"])
stardog.remove(["urn:test4", "urn:test:predicate", "hello world2"])

### withConnection Closure

// withConnection, tx safe
stardog.withConnection { con ->
def queryString = """
SELECT ?s ?p ?o
{
?s ?p ?o
}
"""
TupleQueryResult result = null;
try {
Query query = con.query(queryString);
result = query.executeSelect();
while (result.hasNext()) {
println result.next();
}

result.close();

} catch (Exception e) {
println "Caught exception ${e}" } } ### SPARQL Update Support // Accepts the SPARQL Update queries stardog.update("DELETE { ?a ?b \"hello world2\" } INSERT { ?a ?b \"aloha world2\" } WHERE { ?a ?b \"hello world2\" }") def list = [] stardog.query("SELECT ?x ?y ?z WHERE { ?x ?y \"aloha world2\" } LIMIT 2", { list << it } ) assertTrue(list.size == 1) # Migration Guide Please be aware of the following incompatible changes in major releases of Stardog and plan migrations accordingly. ## Migrating to Stardog 7 beta Stardog 7 introduces a new storage engine and snapshot isolation for concurrent transactions. This section provides an overview of those changes and how they affect users and programs written against previous versions.  Warning As a beta release, Stardog 7 beta is not suitable for mission-critical data or any data that you cannot afford to lose. Please make sure you have backups for any valuable data you use with the beta release. The versioning functionality is not available in Stardog 7 beta so you should not upgrade if you rely on that feature Stardog 7 introduces a completely new disk index format and databases created by previous versions of Stardog must be migrated in order to work with Stardog 7. There is a dedicated CLI command for migrating the contents of an existing Stardog home directory (i.e., all of the databases in a multi-tenant system).  Note The following instructions are for migrating all the databases in an existing STARDOG_HOME directory. Instead of migrating all the databases you can start with a new empty home directory and restore select databases using backups created by Stardog versions 4 or 5. If you use the following instructions with very large databases then you should increase the memory settings by setting the environment variable STARDOG_SERVER_JAVA_ARGS. ### Migrating Single-Server Stardog The steps for a single server migration: • Stop the existing Stardog server; do not start Stardog 7 or have either server running • Create a new empty Stardog home folder (we’ll call it NEW_HOME) • Copy your license file to NEW_HOME • Unzip the Stardog 7 distribution • In Stardog 7 distribution, run the following command: $ stardog-admin server migrate OLD_HOME NEW_HOME

The command will migrate the contents of the each database along with the system database that contains users, roles, permissions, and other metadata. Progress for the migration will be printed to STDOUT and can take a significant amount of time if you have large databases. The stardog.properties (if it exists) file will not be copied automatically. See the next section for changes to the configuration options.

### Migrating Docker-hosted Stardog

The migration process for Stardog running in Docker is effectively the same with a couple of Docker-specific differences.

• Create a new directory on the Docker host machine (we’ll call it NEW_HOME).

• Copy your license file to NEW_HOME

• Run the Stardog 7 Docker container in the following way, which will bring you to a command prompt within the container:

$docker run -v <path to NEW_HOME>:/var/opt/stardog -v <path to OLD_HOME>:/old_stardog \ --entrypoint /bin/bash -it complexible-eps-docker.jfrog.io/stardog:6.0.0-alpha • Run the Stardog 7 migration tool in the following way: $ /opt/stardog/bin/stardog-admin server migrate /old_stardog /var/opt/stardog

### Migrating Stardog Cluster

The migration steps for the cluster:

• Stop all of the cluster nodes, but not the ZK cluster

• Follow the above steps for single server migration on any one cluster node

• Run the command stardog-admin zk clear

• Start the node where migration completed with Stardog 7

• On the other cluster nodes, create empty home folders

• Start another node, wait for the node to join the cluster, and then repeat for each cluster node

### Disk Usage and Layout

The layout of data in Stardog 7 home directory is different than in all previous versions. Previously the data stored in a database was stored under a directory with the name of the database. In Stardog 7 the data for all databases is stored in a directory named data in the home directory. The database directories still exist but they contain only index metadata along with search and spatial index if those features are enabled.

The disk usage requirements for Stardog 7 are higher than Stardog 6. The actual difference will depend on the characteristics of your data, but you should expect to see 20% to 30% increase in disk usage. Similar to Stardog 6, the disk usage of bulk loaded databases, e.g. when data is loaded by the stardog-admin db create command, will be lower than the disk usage when the same data is added incrementally, that is, in smaller transactions over time.

### Memory Databases

Stardog 7 no longer supports in-memory databases. If keeping all data in memory is desired, we recommend placing the home directory on a RAM disk and create databases in the usual way.

### Memory Configuration

Stardog 7 uses RocksDb as the storage engine which is written in C++ and integrated as a native library. Unlike Java controlling the memory used by native code is quite challenging. The intended behavior for Stardog 7 is for users to provide limits for the Java heap memory (-Xmx option) and the off-heap memory (-XX:MaxDirectMemorySize option) just as in Stardog 6. Stardog 7 will internally partition the off-heap memory limit into Java off-heap usage and native memory usage.

Under heavy write loads it is likely that the native memory usage of Stardog 7 beta can go higher than the limits specified by the user. For continuous write use cases the total memory limit (sum of heap and non-heap memory) should be set lower than the total available memory in the system to avoid running out of memory. The final release of Stardog 7 will address this issue and have stricter controls for memory usage.

### Database Optimization & Compaction

Similar to Stardog 6, Stardog 7 performance degrades over time as the database is updated with transactions. The disk usage will continue to increase and data deleted by transactions will not be removed from disk. The existing db optimize command can be used to perform index compaction on disk to improve the performance of reads and writes. The optimize command now provides additional options for the administrators to instruct which exact optimization steps to perform. Please see the CLI help for details.

### Database Configuration

Most server and database options and their semantics are unchanged in Stardog 7, with the following exceptions:

• Options starting with index.differential.. Stardog 7 has a new mechanism which replaces the previous implementation of differential index.

• transaction.isolation needs to be set to SERIALIZABLE for ICV guard mode in order to ensure data integrity w.r.t. the constraints.

### Snapshot Isolation

Stardog 7 uses a multi-versioned concurrency control (MVCC) model providing lock-free transactions with snapshot isolation guarantees. Stardog 6 provided a weaker snapshot isolation mechanism that required writers to acquire locks that sometimes blocked other transactions for a very long time, which is no longer the case. As a result, the performance of concurrent updates is greatly improved in Stardog 7, especially in the cluster setting.

There are two different modes for the MVCC transactions based on how conflicting changes made by two concurrent transactions will be handled by setting the transaction.write.conflict.strategy option.

#### Last Commit Wins

This is the default behavior (transaction.write.conflict.strategy=last_commit_wins) where the change made by the last committed transaction will be accepted. If two concurrent transactions try to add or remove the same quad the change made by the transaction last committed will be accepted while the other change is silently ignored. This is similar to Stardog 6 behavior which uses locks to achieve the same effect in a less efficient way.

This option provides the best write throughput performance but it also means Stardog cannot maintain the aggregate indexes it otherwise uses for statistics and answering some queries. For this reason, the option index.aggregate is set to off in this mode.

This also means Stardog cannot track the exact size of the database without introducing additional overhead. In this mode, when you ask for the size of the database using the data size CLI command or Connection.size() API call you will get an approximate number. For example, if you add a quad that already exists in the database it might be double counted. Stardog will periodically update this number to be accurate but the accuracy is not guaranteed in general. The option to retrieve the exact size of the database is provided both in the CLI and the Java API but it will require scanning the contents of whole database which might be slow for large databases.

#### Abort on Conflict

In this mode (transaction.write.conflict.strategy=abort_on_conflict), if two concurrent transactions try to add or remove the same quad, one of the transactions will be aborted with a transaction conflict. The client then should decide if conflicted transactions should be retried or aborted. This check introduces additional overhead for write transactions but makes it possible to maintain additional indexes and provide exact size information by setting the option index.aggregate to on.

## Migrating to Stardog 6

There are two major changes to take account of.

First, the primary incompatible change in Stardog 6 is a new core API, called Stark, which replaces Sesame as the core API around graph concepts. Additional information about that change is detailed below.

Second, as of Stardog 6, the web console is DEPRECATED. It is still available, but it is NOT supported. We encourage you to use Stardog Studio instead. As a result, Web Console is disabled by default when running a Stardog server. To enable it, pass the --web-console flag into your stardog-admin server start command.

### STARK API

The first thing you might notice is some different naming conventions than traditional Java libraries. Most notably, the Java Bean-style conventions of get and set prefixes are abandoned in favor of shorter, more concise method names. Similarly, you’ll notice exceptions are not post fixed with Exception, and are instead MalformedQuery or InvalidRDF. We don’t think the Exception postfix adds anything; it’s clear from usage that it’s an Exception, no need to add noise to the name.

Additionally, you will not find null returned by any method in Stark. If it’s the case that there is no return value, you get an Optional instead of null. This includes the optional context of a Statement; instead of using null to denote the default context, there’s a specific constant to indicate this, namely Values#DEFAULT_GRAPH and utility methods on Values for checking if a Value or Statement corresponds to the default graph. If you’re using an IDE that will leverage the JSR-305 annotations, @Nullable and @Nonnull, we’ve used these throughout the interface to document the behavior and you should see warnings if you’re mis-using the API.

There’s no longer a Graph class, so for cases where it’s appropriate to return a collection of Statement, such as the result of parsing a file, we’re simply using Set<Statement>. If you need to select subsets of Statement objects, such as all of the rdf:type assertions, there are utility methods provided from Graphs and Statements, or you can simply get a Stream from the Set and do the filting like you would with any other Collection.

Many of the core APIs have been cleaned up from their original counterparts. For example, Literal was trimmed down to just the basics, and if you need to get its value as a different type, like an int, there are static methods available from the Literal class.

In addition to the changes already mentioned, one thing to look out for is Value#stringValue on the older, Sesame based API. It returned the label of a Literal, the ID of a BNode and an IRI as a String. Generally, the correct replacement this behavior is Literal#str, but in some usages, using toString is sufficient. Value#toString in STARK returns the complete value of the Value object, eg, for a Literal it includes the lang/datatype, whereas stringValue did not.

This is a list of commonly used classes from the previous API, and their new counterparts:

10. New Classes
Sesame Stark

ModelIO

RdfWriters, RdfParsers

TupleQueryResult

SelectQueryResult

Graph

java.util.Set

QueryResultIO

QueryResultWriters, QueryResultParsers

RDFFormat

RDFFormats

### Predictive Analytics Vocabulary

The IRIs used to assess the quality of machine learning models have been renamed as follows:

11. Predictive Analytics Vocabulary
Stardog 5 Stardog 6

spa:validation

spa:evaluation

spa:validationMetric

spa:evaluationMetric

spa:validationScore

spa:evaluationScore

See the examples in Automatic Evaluation section about the usage of these terms.

## Migrating to Stardog 5

Stardog 5 introduces significant changes; this section provides an overview of those changes and how they affect users.

### Disk Indexes

Stardog 5 does not change the format of disk indexes but uses new algorithms and data structures for computing an storing statistics for improved query planning. Migration of statistics is performed automatically the first time Stardog is started even if server start is executed without the --upgrade option. This migration might take a while based on your databases size and a progress of the update will be printed on the console.

The new statistics is not backward compatible and old versions of Stardog cannot be started with the same home directory after statistics has been migrated. If you want to revert back to an older version of Stardog you should manually delete all the statistics.* directories and Stardog 4 will recompute the statistics on start up. Again, this might take considerable time for large databases. If you want to switch between Stardog 4 and Stardog 5 quickly you should have two copies of your home directory.

### Network Protocol

SNARL protocol was deprecated in Stardog 4 and is completely removed in Stardog 5. Note that, this does not affect the SNARL Java API which continues to be the preferred API to work with Stardog 5. If you have been using SNARL protocol, i.e. connection strings that begin with snarl:// (or snarls://), then you should change your connection strings to begin with http:// (or https://). If you are using the SNARL Java API you might need to update your library dependencies to use the HTTP client dependency. See the Using Maven section for details.

### Embedded Mode

Stardog 5 no longer requires a running server for use of Stardog in an embedded manner. To use an embedded version of Stardog, you simply start Stardog:

Stardog aStardog = Stardog.builder().create();
try {
// use stardog
}
finally {
aStardog.shutdown();
}

Then use the existing SNARL API methods for connecting to an embedded server. See our examples for a complete demonstration of using Stardog.

# Understanding Stardog

Background information on performance, testing, terminology, known issues, compatibility policies, etc.

## FAQ

### How do I report a bug? What information should I include?

Question

Something isn’t working and I don’t know what to do…​

A bug report seems prudent in this case; customers should use their dedicated support channel. Others may use the support forum. You should include, at a minimum:

1. which release and version of Stardog you are using 4.x? 5.x? Community? Enterprise?

2. which JVM you are using

3. anything from stardog.log (in STARDOG_HOME) that seems relevant

### Why can’t I load Dbpedia (or other RDF) data?

Question

I get a parsing error when loading Dbpedia or some other RDF. What can I do?

First, it’s not a bad thing to expect data providers to publish valid data. Second, it is, apparently, a very naive thing to expect data providers to publish valid data…​

Stardog supports a loose parsing mode which will ignore certain kinds of data invalidity and may allow you to load invalid data. See strict.parsing in Configuration Options.

### Why doesn’t search work?

Question

I created a database but search doesn’t work.

Search is disabled by default; you can enable it at database creation time, or at any subsequent time, using the Web Console or by using metadata set CLI. It can be enabled using db create too.

### Why don’t my queries work?!

Question

I’ve got some named graphs and blah blah my queries don’t work blah blah.

Queries with FROM NAMED with a named graph that is not in Stardog will not cause Stardog to download the data from an arbitrary HTTP URL and include it in the query. Stardog will only evaluate queries over data that has been loaded into it.

SPARQL queries without a context or named graph are executed against the default, unnamed graph. In Stardog, the default graph is not the union of all the named graphs and the default graph. This behavior is configurable via the query.all.graphs configuration parameter.

### Why is Stardog Cluster acting weird or running slowly?

Question

Should I put Stardog HA and Zookeeper on the same hard drives?

Never do this! Zookeeper is disk-intensive and displays bad I/O contention with Stardog query evaluation. Running both Zk and Stardog on the same disks will result in bad performance and, in some cases, intermittent failures.

### SPARQL 1.1

Question

Does Stardog support SPARQL 1.1?

Yes.

Question

Stardog slows down or deadlocks?! I don’t understand why, I’m just trying to send some queries and do something with the results…​in a tight inner loop of doom!

Make sure you are closing result sets (TupleQueryResult and GraphQueryResult; or the Jena equivalents) when you are done with them. These hold open resources both on the client and on the server and failing to close them when you are done will cause files, streams, lions, tigers, and bears to be held open. If you do that enough, then you’ll eventually exhaust all of the resources in their respective pools, which can cause slowness or, in some cases, deadlocks waiting for resources to be returned.

Similarly close your connections when you are done with them. Failing to close Connections, Iterations, QueryResults, and other closeable objects will lead to undesirable behavior.

### Update Performance

Question

I’m adding one triple at a time, in a tight loop, to Stardog; is this the ideal strategy with respect to performance?

The answer is "not really"…​Update performance is best if there are fewer transactions that modify larger number of triples. If you are using the Stardog Java API, the client will buffer changes in large transactions and flush the buffer periodically so you don’t need to worry about memory problems. If you need transactions with small number of triples then you may need to experiment to find the sweet spot with respect to your data, database size, the size of the differential index, and update frequency.

### Public Endpoint

Question

I want to use Stardog to serve a public SPARQL endpoint; is there some way I can do this without publishing user account information?

We don’t necessarily recommend this, but it’s possible. Simply pass --disable-security to stardog-admin when you start the Stardog Server. This completely disables security in Stardog which will let users access the SPARQL endpoint, and all other functionality, without needing authorization.

Question

I’m trying to create a database and bulk load files from my machine to the server and it’s not working, the files don’t seem to load, what gives?

Stardog does not transfer files during database creation to the server, sending big files over a network kind of defeats the purpose of blazing fast bulk loading. If you want to bulk load files from your machine to a remote server, copy them to the server and bulk load them.

### Canonicalized Literals

Question

Why doesn’t my literal look the same as I when I added it to Stardog?

Stardog performs literal canonicalization by default. This can be turned off by setting index.literals.canonical to false. See Configuration Options for the details.

### Cluster Isn’t Working

Question

I’ve setup Stardog Cluster, but it isn’t working and I have NoRouteToHostException exceptions all over my Zookeeper log.

Typically—​but especially on Red Hat Linux and its variants—​this means that iptables is blocking one, some, or all of the ports that the Cluster is trying to use. You can disable iptables or, better yet, configure it to unblock the ports Cluster is using.

### Client Connection Isn’t Working

Question

I’m getting a ServiceConfigurationError saying that SNARLDriver could not be instantiated.

### Logging

Question

Why doesn’t Stardog implement our (byzantine and proprietary!) corporate logging scheme?

Stardog 4 will log to $STARDOG_HOME/stardog.log by default, but you can use a log4j 2 config file in $STARDOG_HOME so that Stardog will log wherever & however you want.

Question

How can I load data from a compressed format that Stardog doesn’t support without decompressing the file?

Stardog supports several compression formats by default (zip, gzip, bzip2) so files compressed with those formats can be passed as input directly without decompression. Files compressed with other formats can also be loaded to Stardog by decompressing them on-the-fly using named pipes in Unix-like systems. The following example shows using a named pipe where the decompressed data is sent directly to Stardog without being writing to disk.

$mkfifo some-data.rdf$ xz -dc some-data.rdf.xz > some-data.rdf &
$stardog-admin db create -n test some-data.rdf ### SNARL Protocol Question I’m using Stardog and I’m seeing messages about SNARL deprecation, what’s that about? Answer As of Stardog 4.2, the SNARL protocol is no longer the default protocol for Stardog and is deprecated. Support for the SNARL protocol will be removed in the Stardog 5 release. The HTTP protocol is now the default protocol and is recommended for all users. If you’re seeing the deprecation warnings it’s because you’re explicitly using the SNARL protocol somewhere in your application. All you have to do to migrate these usages is change snarl/snarls to http/https and you’re done. You can no longer disable support for HTTP when starting the server; the --no-http option does noting, and the SNARL protocol is not enabled by default anymore and has to be explicitly enabled by using --snarl. Please note, the SNARL protocol has been deprecated. The SNARL API is still supported and the recommended Java API for Stardog. ### Working with RDF Files Question I have some RDF files that I need to process without loading into Stardog. What can I do? Answer As of Stardog 5.0, Stardog provides some CLI commands that work directly over files. These commands exist under the stardog file command. For example, you can use the file cat command to concatenate multiple RDF files into a single file and file split command to split a single RDF file into multiple RDF files. These commands are similar to their *nix counterparts but can handle RDF formats and perform compression/decompression on-the-fly. There is also the file obfuscate command that can create an obfuscated version of the input RDF files similar to data obfuscate command. ### Virtual Graph JDBC Driver Requirements Question What JDBC driver do I need for a virtual graph connection? Answer Virtual graph connections require a JDBC driver supporting JDBC 4.1 / Java 7. For Oracle, this means ojdbc7.jar or later. ### Virtual Graph Identifier Quoting Question How do I quote field and table names in mappings and when should I do it? Answer Interpretation of identifiers follows that of the database system backing the virtual graph. For example, Oracle, interprets nonquoted identifiers as uppercase. PostgreSQL interprets unquoted identifiers as lowercase. In general, if you need to quote the identifier in a query, then you should quote it in a mapping. Quoting is done using the native quoting character of the database. This means double quote for Oracle, PostgreSQL and other SQL standard-compatible systems. MySQL uses a backquote and SQL Server uses square brackets. This setting can be overridden by adding parser.sql.quoting=ANSI to your virtual graph properties file. This will allow the use of double quotes to quote identifiers. This is commonly done to write mappings using the R2RML convention of using double quotes and supporting mappings generated by other systems. ### Virtual Graph Table not Found Question Why am I getting an error when I try to create a virtual graph? Unable to parse logical table : From line 1, column 15 to line 1, column 18: Object 'SOME_TABLE' not found Answer The virtual graph subsystem maintains a set of metadata including a list of tables and the types of their fields. If a table is not found, it’s likely that it either needs to be quoted or the schema needs to be added to the search path by adding sql.schemas to your virtual graph properties file. This setting enables Stardog to see the table metadata in the named schemas. The table/query still needs to be qualified with the schema name when referring to it. ## Benchmark Results Live, dynamically updated performance data from BSBM, SP2B, LUBM benchmarks against the latest Stardog release. ## Compatibility Policies The Stardog 5.x release ("Stardog" for short) is a major milestone in the development of the system. Stardog is a stable platform for the growth of projects and programs written for Stardog. Stardog provides (and defines) several user-visible things: 1. SNARL API 2. Stardog HTTP Protocol 3. a command-line interface It is intended that programs—as well as SPARQL queries—written to Stardog APIs, protocols, and interfaces will continue to run correctly, unchanged, over the lifetime of Stardog. That is, over all releases identified by version 5.x.y. At some indefinite point, Stardog 6.x will be released; but, until that time, and likely even after it, Stardog programs that work today should continue to work even as future releases of Stardog occur. APIs, protocols, and interfaces may grow, acquiring new parts and features, but not in a way that breaks existing Stardog programs. ### Expectations Although we expect that nearly all Stardog programs will maintain this compatibility over time, it is impossible to guarantee that no future change will break any program. This document sets expectations for the compatibility of Stardog programs in the future. The main, foreseeable reasons for which this compatibility may be broken in the future include: 1. Security: We reserve the right to break compatibility if doing so is required to address a security problem in Stardog. 2. Unspecified behavior: Programs that depend on unspecified[34] behaviors may not work in the future if those behaviors are modified. 3. 3rd Party Specification Errors: It may become necessary to break compatibility of Stardog programs in order to address problems in some 3rd party specification. 4. Bugs: It will not always be possible to fix bugs found in Stardog—​or in its 3rd party dependencies—​while also preserving compatibility. With that proviso, we will endeavor to only break compatibility when repairing critical bugs. It is always possible that the performance of a Stardog program may be (adversely) affected by changes in the implementation of Stardog. No guarantee can be made about the performance of a given program between releases, except to say that our expectation is that performance will generally trend in the appropriate direction. ### Data Migration & Safety We expect that data safety will always be given greater weight than any other consideration. But since Stardog stores a user’s data differently from the form in which data is input to Stardog, we may from time to time change the way it is stored such that explicit data migration will be necessary. Stardog provides for two data migration strategies: 1. Command-line migration tool(s) 2. Dump and reload We expect that explicit migrations may be required from time to time between different releases of Stardog. We will endeavor to minimize the need for such migrations. We will only require the "dump and reload" strategy between major releases of Stardog (that is, from 1.x to 2.x, etc.), unless that strategy of migration is required to repair a security or other data safety bug. ## Known Issues The known issues in Stardog 6.1.0: 1. Our implementation of CONSTRUCT slightly deviates from the SPARQL 1.1 specification: it does not implicitly DISTINCT query results; rather, it implicitly applies REDUCED semantics to CONSTRUCT query results.[35] 2. Asking for all individuals with reasoning via the query {?s a owl:Thing} might also retrieve some classes and properties. WILLFIX 3. Schema queries do not bind graph variables. 4. Dropping a database deletes all of the data files in Stardog Home associated with that database. If you want to keep the data files and remove the database from the system catalog, then you need to manually copy these files to another location before dropping the database. 5. If relative URIs exist in the data files passed to create, add, or remove commands, then they will be resolved using the constant base URI http://api.stardog.com/ if, but only if, the format of the file allows base URIs. Turtle and RDF/XML formats allows base URIs but N-Triples format doesn’t allow base URIs and relative URIs in N-Triples data will cause errors. 6. Queries with FROM NAMED with a named graph that is not in Stardog will not cause Stardog to download the data from an arbitrary HTTP URL and include it in the query. 7. SPARQL queries without a context or named graph are executed against the default, unnamed graph. In Stardog, the default graph is not the union of all the named graphs and the default graph. Note: this behavior is configurable via the query.all.graphs configuration parameter. 8. RDF literals are limited to 8MB (after compression) in Stardog. Input data with literals larger than 8MB (after compression) will raise an exception. ## Glossary In the Stardog documentation, the following terms have a specific technical meaning.  Stardog Database Management System, aka Stardog Server An instance of Stardog; only one Stardog Server may run per JVM. A computer may run multiple Stardog Servers by running one per multiple JVMs. Stardog Home, aka STARDOG_HOME A directory in a filesystem in which Stardog stores files and other information; established either in a Stardog configuration file or by environment variable. Only one Stardog Server may run simultaneously from a STARDOG_HOME. Stardog Network Home A URL (HTTP or SNARL) which identifies a Stardog Server running on the network. Database A Stardog database is a graph of RDF data under management of a Stardog Server. It may contain zero or more RDF Named Graphs. A Stardog Server may manage more than one Database; there is no hard limit, and the practical limit is disk space. Database Short Name, aka Database Name An identifier used to name a database, provided as input when a database is created. Database Network Name A Database Short Name is part of the URI of a Database addressed over some network protocol. Index The unit of persistence for a Database. We sometimes (sloppily) use Database and Index interchangeably in the manual. Memory Database A Database may be stored in-memory or on disk; a Memory Database is read entirely into system memory but can be (optionally) persisted to disk. Disk Database A Disk Database is only paged into system memory as needed and is persisted using one or more indexes. Connection String An identifier (a restricted subset of legal URLs, actually) that is used to connect to a Stardog database to send queries or perform other operations. Named Graph A Named Graph is an explicitly named unit of data within a Database. Named Graphs are queries explicitly by specifying them in SPARQL queries. There is no practical limit on the number of Named Graphs in a Database. Default Graph The Default Graph in a Database is the context into which RDF triples are stored when a Named Graph is not explicitly specified. A SPARQL query executed by Stardog that does not contain any Named Graph statements is executed against the data in the Default Graph only. Security Realm A Security Realm defines the users and their permissions for each Database in an Stardog Server. There is only one Security Realm per Stardog Server. # Appendix Just move it to the Appendix for a great good! ## Man Pages ## SPARQL Query Functions Stardog supports all of the functions in SPARQL, as well as some others from XPath and SWRL. Any of these functions can be used in queries or rules. Function names don’t require namespace prefixes in general unless ambiguity is present. XPath functions take precedence when resolving functions without namespace prefixes. Some functions appear in multiple namespaces, but all of the namespaces will work:  Prefix Namespace stardog tag:stardog:api:functions: fn http://www.w3.org/2005/xpath-functions# math http://www.w3.org/2005/xpath-functions/math# swrlb http://www.w3.org/2003/11/swrlb# leviathan http://www.dotnetrdf.org/leviathan# afn http://jena.hpl.hp.com/ARQ/function# The function names and URIs supported by Stardog are included below. Some of these functions exist in SPARQL natively, which just means they can be used without an explicit namespace. Some of the functions have a URI that can be used but they are also overloaded arithmetic operators. For example, if you want to add two day time durations you can simply use the expression ?duration1 + ?duration2 instead of swrlb:addDayTimeDurations(?duration1 + ?duration2).  Function name Recognized URIs and Symbols abs acos addDayTimeDurations +, swrlb:addDayTimeDurations addDayTimeDurationToDate +, swrlb:addDayTimeDurationToDate addDayTimeDurationToDateTime +, swrlb:addDayTimeDurationToDateTime addDayTimeDurationToTime +, swrlb:addDayTimeDurationToTime addYearMonthDurations +, swrlb:addYearMonthDurations addYearMonthDurationToDate +, swrlb:addYearMonthDurationToDate addYearMonthDurationToDateTime +, swrlb:addYearMonthDurationToDateTime asin atan math:atan bnode BNODE boolean xsd:boolean bound BOUND cartesian leviathan:cartesian ceil coalesce COALESCE concat contains containsIgnoreCase swrlb:containsIgnoreCase cos cosec leviathan:cosec cosec-1 leviathan:cosec-1 cosh stardog:cosh cotan leviathan:cotan cotan-1 leviathan:cotan-1 cube leviathan:cube datatype Datatype date swrlb:date dateTime xsd:dateTime day dayTimeDuration swrlb:dayTimeDuration decimal xsd:decimal divideDayTimeDuration /, swrlb:divideDayTimeDuration divideYearMonthDuration /, swrlb:divideYearMonthDuration double xsd:double e encode_for_uri factorial leviathan:factorial float xsd:float floor gmean tag:stardog:api:gmean hours identifier tag:stardog:api:identifier if IF integer xsd:integer iri isbnode IsBNode isiri isliteral IsLiteral isnumeric IsNumeric isresource IsResource lang Lang langmatches LangMatches lcase localname stardog:localname, afn:localname log log10 max md5 min minutes mod swrlb:mod month multiplyDayTimeDuration *, swrlb:multiplyDayTimeDuration multiplyYearMonthDuration *, swrlb:multiplyYearMonthDuration namespace stardog:namespace, afn:namespace normalizeSpace now NOW numeric-add fn:numeric-add numeric-divide numeric-integer-divide numeric-multiply fn:numeric-multiply numeric-round-half-to-even numeric-subtract numeric-unary-minus numeric-unary-plus pi pow pythagoras leviathan:pythagoras rand reciprocal leviathan:reciprocal regex replace root leviathan:root round sameTerm sameTerm sec leviathan:sec sec-1 leviathan:sec-1 seconds sha1 SHA1 sha256 sha384 SHA384 sha512 SHA512 sin sinh stardog:sinh sq leviathan:sq sqrt str Str strafter strbefore strdt STRDT strends string xsd:string stringEqualIgnoreCase swrlb:stringEqualIgnoreCase strlang STRLANG strlen strstarts struuid STRUUID substr subtractDates -, swrlb:subtractDates subtractDayTimeDurationFromDate -, swrlb:subtractDayTimeDurationFromDate subtractDayTimeDurationFromDateTime -, swrlb:subtractDayTimeDurationFromDateTime subtractDayTimeDurationFromTime -, swrlb:subtractDayTimeDurationFromTime subtractDayTimeDurations -, swrlb:subtractDayTimeDurations subtractTimes -, swrlb:subtractTimes subtractYearMonthDurationFromDate -, swrlb:subtractYearMonthDurationFromDate subtractYearMonthDurationFromDateTime -, swrlb:subtractYearMonthDurationFromDateTime subtractYearMonthDurations -, swrlb:subtractYearMonthDurations tan tanh stardog:tanh ten leviathan:ten time swrlb:time timezone toDegrees stardog:toDegrees, leviathan:radians-to-degrees toRadians stardog:toRadians, leviathan:degrees-to-radians translate tz TZ ucase uuid UUID year yearMonthDuration swrlb:yearMonthDuration ## Milestones This timeline describes major features and other notable changes to Stardog starting at 1.0; it will be updated for each notable new release. For a complete list of changes, including notable bug fixes, see the release notes.  4.1 Multi-coordinator Cluster Cluster stability & performance improvements Query graph & revision history simultaneously New, faster SPARQL parser 4.0 TinkerPop3 and property graphs virtual graphs RDF 1.1 Java 8 Geospatial query answering 3.1 Named Graph security BOSH-based cluster management tool proper logshipping in the Cluster 3.0 Equality reasoning via hybrid materialization Improved incremental write performance HA Cluster production ready Integrity constraint violation repair plans Improved query performance 2.2.1 Stardog HA Cluster (beta) 2.2 Support for RDF versioning Admin support for Web Console 2.1 Database repair, backup & restore utilities Improved query scalability by flowing intermediate results off-heap or onto disk; requires a JDK that supports sun.misc.Unsafe Performance: significant improvement in performance of bulk loading and total scalability of a database Generation of multiple proofs for inferences & inconsistencies; proofs for integrity constraint violations Reduced memory footprint of queries while being executed 2.0 SPARQL 1.1 Update: the most requested feature ever! Stardog Web Console: a Stardog Web app for managing Stardog Databases; includes Linked Data Server, etc. JMX monitoring: includes graphical monitoring via Web Console HTTP & SNARL servers unified into a single server (default port 5820) Database Archetypes for PROV, SKOS; extensible for user-defined ontologies, schemas, etc. Stardog Rules Syntax: new syntax for user-defined rules Performance improvements for SPARQL query evaluation Hierarchical explanations of inferences using proof trees SL reasoning profile Client and server dependencies cleanly separated Evaluation of non-recursive datalog queries to improve reasoning performance 1.2 Query management: slow query log, kill-able queries, etc. new CLI new transaction layer SPARQL Service Description new security layer Query rewrite cache Removed Stardog Shell 1.1.2 New optimizer for subqueries 1.1 SPARQL 1.1 Query Transitive reasoning User-defined rules in SWRL new SWRL builtins and syntactic sugar for schema-queries Improved performance of reasoning queries involving rdf:type Improved performance of search indexing Deprecated Stardog Shell 1.0.4 Convert ICVs to SPARQL queries in the CLI or Java API Running as a Windows Service Parametric queries in CLI 1.0.2 Stardog Community edition introduced ICV in SNARL and HTTP HTTP Admin protocol extensions SPARQL 1.1 Graph Store Protocol 1.0.1 Self-hosting Stardog documentation Prefix mappings per database Access and audit logging 1.0 Execute DESCRIBE queries against multiple resources Database consistency checking from CLI Inference explanations from CLI ## Previous Versions of Docs  Stardog Version HTML PDF 6.1.0 ✓ ✓ 4.2 ✓ ✓ 4.1.3 ✓ ✓ 4.1.2 ✓ ✓ 4.1.1 ✓ ✓ 4.1 ✓ ✓ 4.0.5 ✓ ✓ 4.0.3 ✓ ✓ 4.0.2 ✓ ✓ 4.0.1 ✓ ✓ 4.0 ✓ ✓ 1. In other words, if there is a conflict between this documentation and the output of the CLI tools' help command, the CLI output is correct. 2. We’re big fans of /opt/stardog/{$version} and setting STARDOG_HOME to /var/stardog but YMMV.
3. This is equally true, when using Stardog HA Cluster, of Zookeeper’s access to free disk space. Bad things happen to the Stardog Cluster if Zookeeper cannot write to disk.
4. For more details about configuring these values, see https://github.com/Complexible/stardog-examples/blob/master/config/stardog.properties.
5. However, there may be some delay since Stardog only periodically checks the query.timeout value against internal query evaluation timers.
6. A good general purpose discussion of these issues in context of J2EE is this beginner’s guide.
7. As discussed in SPARQL Update, since Update queries are implicitly atomic transactional operations, which means you shouldn’t issue an Update query within an open transaction.
8. The probability of recovering from a catastrophic transaction failure is inversely proportional to the number of subsequent write attempts; hence, Stardog offlines the database to prevent subsequent write attempts and to increase the likelihood of recovery.
9. Stardog also uses file handles and sockets, but we don’t discuss those here.
10. These are conservative values and are dataset specific. Your data may require less memory…​or more!
11. For more details about configuring these values, see https://github.com/Complexible/stardog-examples/blob/master/config/stardog.properties.
12. Blob Indexing and Text Enrichment with Semantics
13. "Client" here means the client of Stardog APIs.
14. "Because ZooKeeper requires a majority, it is best to use an odd number of machines. For example, with four machines ZooKeeper can only handle the failure of a single machine; if two machines fail, the remaining two machines do not constitute a majority. However, with five machines ZooKeeper can handle the failure of two machines." See Zk Admin for more.
15. Based on customer feedback we may relax these consistency guarantees in some future release. Please get in touch if you think an eventually consistent approach is more appropriate for your use of Stardog.
16. This point is especially true of Cluster but may be relevant for some workloads on a single Stardog database, that is, non-Cluster configuration, too.
17. You only pay for the reasoning that you use; no more and no less. Eager materialization is mostly a great strategy for hard disk manufacturers.
18. Sometimes called a "TBox".
19. Find another database, any other database anywhere, that can do that! We’ll wait…​
20. Triggered using the --format tree option of the reasoning explain CLI command.
21. Quick refresher: the IF clause defines the conditions to match in the data; if they match, then the contents of the THEN clause "fire", that is, they are inferred and, thus, available for other queries, rules, or axioms, etc.
22. Of course if you’ve tweaked reasoning.schema.graphs, then you should put the rules into the named graph(s) that are specified in that configuration parameter.
23. Built-in URIs such as rdfs:subClassOf or owl:TransitiveProperty are not allowed in rules
24. This is effectively the only setting for Stardog prior to 3.0.
25. These are harmless and won’t otherwise affect query evaluation; they can also be added to the data, instead of to queries, if that fits your use case better.
26. The standard inference semantics of OWL 2 do not adopt the unique name assumption because, in information integration scenarios, things often have more than one name but that doesn’t mean they are different things. For example, when several databases or other data sources all contain some partial information about, say, an employee, but they each name or identify the employee in different ways. OWL 2 won’t assume these are different employees just because there are several names.
27. Strictly speaking, this is a bit misleading. Stardog ICV uses both open and closed world semantics: since inferences can violate or satisfy constraints, and Stardog uses open world semantics to calculate inferences, then the ICV process is compatible with open world reasoning, to which it then applies a form of closed world validation, as described in this chapter.
28. This is a good example of open world and closed world reasoning interacting for the win.
29. In other words, embedded Stardog access is inherently insecure and should be used accordingly.
30. The Stardog client uses an X509TrustManager. The details of how a trust store is selected to initialize the trust manager are http://docs.oracle.com/javase/6/docs/technotes/guides/security/jsse/JSSERefGuide.html#X509TrustManager.
31. See the javax.net.ssl.trustStorePassword system property docs: http://docs.oracle.com/javase/6/docs/technotes/guides/security/jsse/JSSERefGuide.html#X509TrustManager.
32. The matching algorithm used is described—​http://hc.apache.org/httpcomponents-client-ga/tutorial/html/connmgmt.html-- in the Apache docs about BrowserCompatHostnameVerifier`.
33. You won’t be careful enough.
34. The relevant specs include the Stardog-specific specifications documented on this site, but also W3C (and other) specifications of various languages, including SPARQL, RDF, RDFS, OWL 2, HTTP, Google Protocol Buffers, as well as others.
35. Strictly speaking, this is a Sesame parser deviation from the SPARQL 1.1 spec with which we happen to agree.