SparQL endpoints, microservices, and federated queries. How are they related?


In the era of interconnected systems and vast amounts of data, the ability to query, manipulate, and integrate information effectively is crucial. Three concepts that play a significant role in modern data-driven applications are federated queries, SPARQL endpoints, and microservices. These concepts allow developers to build powerful, flexible, and scalable systems that can handle complex data requirements and integration tasks.

Federated queries enable querying multiple data sources simultaneously, providing a mechanism for integrating and aggregating data from different sources. SPARQL endpoints are web services that allow remote access to RDF data using the SPARQL query language, making semantic data accessible to a wide range of applications and services. Microservices architecture is an approach to software development that breaks down applications into small, independently-deployable services, each responsible for a specific functionality, ensuring modularity and scalability.

These concepts are important because they address the challenges of handling diverse data sources, integrating heterogeneous information, and building scalable systems in a world where data is continuously growing in volume and complexity. By understanding and leveraging federated queries, SPARQL endpoints, and microservices, developers can create applications that not only process and analyze data effectively but also adapt and scale to accommodate evolving requirements and technologies.

SparQL endpoints

A SPARQL endpoint is a web service that enables users to run SPARQL queries against an RDF store or triple store. It allows users to query and manipulate RDF data over HTTP or HTTPS, making it accessible to a wide range of applications and services. SPARQL endpoints follow the SPARQL Protocol and RDF Query Language (SPARQL) standards defined by the World Wide Web Consortium (W3C).

The main features of a SPARQL endpoint include:

  1. Query execution: It allows clients to execute SPARQL queries, such as SELECT, CONSTRUCT, ASK, and DESCRIBE, to retrieve or manipulate RDF data.
  2. Data updates: Some SPARQL endpoints support the SPARQL 1.1 Update language, which allows clients to modify RDF data by adding, deleting, or updating triples.
  3. Remote access: SPARQL endpoints are accessible over the web, enabling clients to query and update RDF data remotely.
  4. Standardized protocol: SPARQL endpoints follow the W3C standards for SPARQL, ensuring compatibility and interoperability between different RDF stores and applications.
  5. Content negotiation: SPARQL endpoints often support content negotiation, allowing clients to request the query results in various formats, such as XML, JSON, CSV, or TSV.

To use a SPARQL endpoint, you typically send an HTTP request with a SPARQL query embedded in the request’s parameters. The endpoint then processes the query, accesses the RDF store, and returns the results in the specified format. Many public datasets and knowledge graphs, such as DBpedia, Wikidata, and the Linked Open Data cloud, provide SPARQL endpoints to facilitate easy access and querying of their RDF data.

Federated Queries. Combining SparQL endpoints together

You can combine SPARQL endpoints together using a technique called “federated querying.” Federated querying allows you to query multiple SPARQL endpoints in a single query, enabling you to retrieve and combine data from different RDF stores. SPARQL 1.1 introduced the SERVICE keyword to support federated querying.

Here’s a simple example to illustrate how to combine two SPARQL endpoints using federated querying:

Suppose you want to get information about a person’s birthplace from DBpedia and their occupation from Wikidata. You can create a federated query like this:

PREFIX dbo: <>
PREFIX dbp: <>
PREFIX dbr: <>
PREFIX wd: <>
PREFIX wdt: <>
PREFIX rdfs: <>

SELECT ?person ?birthPlace ?occupation
  ?person dbo:birthPlace ?birthPlace .
  SERVICE <> {
    ?person wdt:P106 ?occupationId .
    ?occupationId rdfs:label ?occupation .
    FILTER (LANG(?occupation) = "en")
  VALUES ?person { dbr:Albert_Einstein }

In this query, the information about the person’s birthplace is retrieved from the local RDF store (e.g., DBpedia), while the occupation is retrieved from the remote SPARQL endpoint at Wikidata using the SERVICE keyword. The query returns a combined result, including the person’s birthplace and occupation.

Keep in mind that federated querying can be slower than querying a single endpoint because it involves communication between multiple remote endpoints. Additionally, not all SPARQL endpoints support federated querying. It’s essential to consider these factors when designing your application or system.

SparQL endpoint as a microservice

A SPARQL endpoint can be implemented as a microservice. In a microservices architecture, an application is divided into a collection of small, loosely-coupled, and independently-deployable services. Each microservice is responsible for a specific functionality or domain within the application. A SPARQL endpoint microservice would be responsible for handling SPARQL queries and providing access to the RDF store or triplestore.

To implement a SPARQL endpoint as a microservice, follow these steps:

  1. Define the microservice: Identify the specific functionality you want the SPARQL endpoint microservice to handle, such as executing SPARQL queries and providing access to RDF data.
  2. Choose a query engine: Select a suitable RDF store or triple store that supports SPARQL querying, such as Virtuoso, Apache Jena, or RDF4J. This query engine will be used by the SPARQL endpoint microservice to process queries and interact with the RDF store.
  3. Implement the API: Design and implement a RESTful API or a GraphQL API for the SPARQL endpoint microservice. This API should expose methods for executing SPARQL queries and returning the results in various formats, such as JSON, XML, CSV, or TSV.
  4. Containerize the microservice: Package the SPARQL endpoint microservice and its dependencies into a container, such as a Docker container. This makes it easy to deploy, scale, and manage the microservice.
  5. Deploy the microservice: Deploy the SPARQL endpoint microservice to a suitable hosting environment, such as a Kubernetes cluster or a serverless computing platform. Ensure that the microservice is accessible over the network, so other microservices and applications can interact with it.
  6. Secure the microservice: Implement proper authentication and authorization mechanisms to ensure that only authorized users and applications can access the SPARQL endpoint microservice.
  7. Monitor and log: Set up monitoring and logging to track the performance and health of the SPARQL endpoint microservice.

By implementing a SPARQL endpoint as a microservice, you can take advantage of the flexibility, scalability, and modularity offered by the microservices architecture while providing access to semantic data querying and manipulation using SPARQL.


In conclusion, SPARQL endpoints and microservices can be effectively combined to build powerful, flexible, and scalable systems. SPARQL endpoints enable users to query and manipulate RDF data remotely over HTTP or HTTPS, providing easy access to semantic data. Microservices architecture, on the other hand, allows applications to be divided into small, independently-deployable services, each responsible for specific functionality.

Federated querying allows for combining multiple SPARQL endpoints in a single query, enabling data retrieval and integration from different RDF stores. Implementing a SPARQL endpoint as a microservice can bring the benefits of both paradigms, offering a modular system that leverages the power of semantic data querying.

When designing and implementing systems that combine SPARQL endpoints and microservices, it is essential to consider factors such as performance, security, monitoring, and logging to ensure a robust and efficient system. By following best practices and recommended techniques, developers can create applications that take full advantage of both SPARQL querying and microservices architecture.