Halyard

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What Halyard is

Halyard is a free, open-source RDF store for large semantic data models and Linked Data snapshots. It stores datasets in Apache HBase tables, supports named graphs, and answers queries using SPARQL 1.1. Multiple repositories can point to one shared dataset. Written in Java and built on Eclipse RDF4J and Apache HBase, Halyard provides MapReduce bulk loading from HDFS, along with tools for exporting data, running SPARQL updates, calculating statistics, and deleting triples or named graphs. Bulk Load accepts RDF formats including Turtle, RDF/XML, N-Triples, TriG, N-Quads, and JSON-LD. Queries can be handled through RDF4J Console, Workbench, or Server endpoint APIs; a separate command-line application can launch a SPARQL endpoint. An optional Elasticsearch index adds text search over dataset literals. Deployment is documented for Amazon Elastic MapReduce or a Hadoop cluster with an HBase client. Hadoop and HBase are not bundled, and the getting-started requirements include Hadoop 2.5.1 or later, HBase 1.1.2 or later, and Java 8 Runtime.

Who it suits

Halyard suits teams managing large RDF datasets that need named graphs, bulk data operations, and SPARQL 1.1 queries. It is a fit for environments prepared to provide Hadoop and HBase.

What it does well

Free and open source.
Supports SPARQL 1.1 and named graphs.
Bulk-loads multiple RDF formats with MapReduce.
Offers query access through RDF4J tools and APIs.
Can calculate and export dataset statistics.

What to know first

Hadoop and HBase are not bundled.
Requires Hadoop 2.5.1 or later and HBase 1.1.2 or later.
External SPARQL endpoint service types are not recognized.

Limits

The runtime requires Apache Hadoop 2.5.1 or later, Apache HBase 1.1.2 or later and Java 8 Runtime; Hadoop and HBase components must be supplied separately. Halyard Summary is experimental, and federated queries do not recognize external SPARQL endpoint service types.

Getting started

Visit the Halyard website for documentation and deployment instructions.
Choose a documented deployment on Amazon Elastic MapReduce or an Apache Hadoop cluster with an Apache HBase client.
Provide Apache Hadoop 2.5.1 or later, Apache HBase 1.1.2 or later, and Java 8 Runtime.
Configure the RDF4J tools to access a local or remote repository endpoint.
On a secured cluster, authenticate with kinit credentials before using RDF4J Console.

Verdict

Halyard brings RDF storage, querying, and bulk dataset management together around Hadoop and HBase. Check its runtime requirements and federation limit against your environment before choosing it.

Questions

Is Halyard free?

Yes. It is free and open source, and there is no free trial.

Which RDF formats does it support?

Listed formats include N-Triples, RDF/XML, Turtle, N3, RDF/JSON, TriG, N-Quads, BinaryRDF, TriX and JSON-LD.

How can I query a Halyard dataset?

SPARQL access is available through RDF4J Console, RDF4J Workbench and RDF4J Server SPARQL endpoint REST APIs.

What does Halyard need to run?

The getting-started requirements are Apache Hadoop 2.5.1 or later, Apache HBase 1.1.2 or later and Java 8 Runtime. Hadoop and HBase are not bundled.

Does it support federated queries?

Federated queries can span Halyard datasets using SPARQL SERVICE, but external SPARQL endpoint service types are not recognized.

Who created Halyard?

The home page lists Adam Sotona as the author contact.

Facts from Halyard's own pages

Bulk data tools: Its command-line and MapReduce tools support bulk loading and exporting RDF data, dataset statistics, SPARQL updates, and deletion of large sets of triples or named graphs. · merck.github.io, 8 Oct 2026
Bulk loading: Halyard Bulk Load uses MapReduce to load RDF data from HDFS into HBase, and the documented input formats include Turtle, RDF/XML, N-Triples, TriG, N-Quads, and JSON-LD. · merck.github.io, 8 Oct 2026
Data deletion behavior: Deleting a repository configuration does not delete its Halyard dataset, while clearing the repository or deleting statements affects the shared data for all users. · merck.github.io, 8 Oct 2026
Data management: Command-line tools support SPARQL Update, parallel bulk updates, export, and bulk deletion of triples or named graphs. · merck.github.io, 8 Oct 2026
Data storage: Halyard datasets are stored as HBase tables, and multiple repositories can point to one shared dataset. · merck.github.io, 8 Oct 2026
Deployment: The maker documents deployment on Amazon Elastic MapReduce or on an Apache Hadoop cluster node configured with an Apache HBase client. · merck.github.io, 8 Oct 2026
Endpoint: Halyard Endpoint is a command-line application for launching a SPARQL endpoint to serve queries. · merck.github.io, 8 Oct 2026
Experimental feature: Halyard Summary is described as an experimental MapReduce application that calculates an approximate dataset summary and exports it to a file. · merck.github.io, 8 Oct 2026
Federation limit: Federated queries can span Halyard datasets using SPARQL SERVICE, but external SPARQL endpoint service types are not recognized. · merck.github.io, 8 Oct 2026
Implementation: Halyard is written in Java and is based on Eclipse RDF4J and Apache HBase. · merck.github.io, 8 Oct 2026
Not bundled: Apache Hadoop and Apache HBase components are not bundled with Halyard and must be available in the runtime environment. · merck.github.io, 8 Oct 2026
Product: Halyard is a horizontally scalable RDF store with named graph support, designed for very large semantic data models and SPARQL 1.1 queries over Linked Data snapshots. · merck.github.io, 8 Oct 2026
Purpose: Halyard is a horizontally scalable RDF store with named graph support for storing and integrating large semantic data models and querying linked data snapshots with SPARQL 1.1. · merck.github.io, 8 Oct 2026
Querying: The included RDF4J Console can evaluate SPARQL queries and connect to local or remote repository endpoints. · merck.github.io, 8 Oct 2026
Query interface: Halyard supports SPARQL access through RDF4J Console, RDF4J Workbench, and RDF4J Server SPARQL endpoint REST APIs. · merck.github.io, 8 Oct 2026
RDF formats: Bulk Load accepts RDF4J RIO-supported formats including N-Triples, RDF/XML, Turtle, TriG, N-Quads, and JSON-LD, with Hadoop-supported compression codecs. · merck.github.io, 8 Oct 2026
Runtime requirements: The getting-started page lists Apache Hadoop 2.5.1 or later, Apache HBase 1.1.2 or later, and Java 8 Runtime as requirements. · merck.github.io, 8 Oct 2026
Search integration: An optional supplementary Elasticsearch index can index dataset literals to provide more advanced text search features. · merck.github.io, 8 Oct 2026
Security behavior: On a secured cluster, the RDF4J Console instructions say to authenticate with kinit credentials before use. · merck.github.io, 8 Oct 2026
Statistics: Halyard Stats calculates dataset statistics and stores them in a named graph or exports them to a file. · merck.github.io, 8 Oct 2026
Storage modes: For Amazon EMR deployments, HBase can use HDFS or S3 storage. · merck.github.io, 8 Oct 2026
Support: The home page links to documentation, open GitHub issues, a discussion group, and lists Adam Sotona as the author contact. · merck.github.io, 8 Oct 2026
Technology: Halyard is written in Java and built on Eclipse RDF4J and Apache HBase. · merck.github.io, 8 Oct 2026

Where Halyard stands on Freedom251

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