Graph Query Languages - An Introduction

A graph query language is a specialised language designed to interact with and retrieve data from graph databases or graph-like data structures. It provides a means to express queries that traverse and explore the relationships and connections within the graph data.

Graph query languages are tailored to work with graph-oriented data models, where entities (nodes) are connected by relationships (edges). These languages allow users to specify patterns, conditions, and constraints on the data, enabling them to retrieve specific information and navigate the graph structure.

Graph query languages enable users to express complex queries that go beyond simple data retrieval. They allow for traversing and exploring relationships, performing aggregations and calculations, filtering data based on specific criteria, and handling optional or conditional patterns. These languages provide a way to interact with the graph data model and harness the power of graph databases or graph-like structures.

Graph query languages are a vital component in graph databases and graph-based technologies, facilitating efficient and expressive querying of graph data. By leveraging a graph query language, users can uncover valuable insights, discover patterns, and gain a deeper understanding of the relationships and connections within their data. The three most popular graph query languages are Cypher, Gremlin and SPARQL, an overview of these is provided below.


Cypher is a graph query language specifically designed for querying and manipulating data in graph databases. Neo4j initially developed it, but other graph database vendors and frameworks have since adopted it.

Key features of Cypher include:

  1. Pattern Matching: Cypher uses a pattern-matching syntax to express graph patterns. It allows users to specify nodes, relationships, and properties as patterns, enabling them to retrieve specific structures and relationships within the graph. The pattern-matching syntax resembles ASCII art, making it intuitive and easy to read.
  2. Graph Traversal: Cypher supports graph traversal operations to navigate through the graph. Users can specify paths and patterns to traverse relationships and explore connected nodes. Traversal operations can include filtering, sorting, and aggregating data during the traversal process.
  3. Declarative Syntax: Cypher has a declarative syntax, allowing users to specify what they want to retrieve from the graph rather than how to retrieve it. This makes Cypher expressive and readable, particularly for complex graph queries.
  4. Extensibility: Cypher provides extensibility through user-defined functions and procedures. Users can define custom functions and procedures to perform calculations, transformations, and other operations on the graph data during query execution.
  5. Integration with SQL: Cypher has integration capabilities with SQL through the Cypher for Apache Calcite project. This integration enables users to combine Cypher queries with traditional SQL queries, allowing for seamless interaction with both relational and graph data.
  6. Adoption by Graph Database Systems: While Cypher was initially developed for Neo4j, it has gained wider adoption and is supported by other graph database systems such as AgensGraph, Memgraph, and SAP HANA. This makes Cypher a versatile and portable query language within the graph database ecosystem.

Cypher provides a high-level, expressive, and intuitive way to work with graph data. Its focus on pattern matching and graph traversal allows users to formulate complex queries to retrieve specific information and relationships within the graph. The language's popularity and adoption have contributed to the growth of the graph database ecosystem, and it has become one of the main graph query languages in use today.



Gremlin is a query language and traversal language for graph databases, developed as part of Apache TinkerPop™. It provides a standardised way to interact with graph data by allowing users to traverse, query, and manipulate the graph's nodes, edges, and properties.

Key features of the Gremlin query language include:

  1. Graph Traversal: Gremlin focuses on graph traversal, allowing users to explore the graph by following relationships and navigating through the nodes and edges. It provides a set of step-by-step traversal operators that enable users to move between vertices, traverse edges, filter results, and perform computations.
  2. Declarative Syntax: Gremlin has a declarative syntax that allows users to specify what they want to retrieve from the graph rather than how to retrieve it. Users can describe the desired patterns and conditions in a readable and expressive manner, making it easier to formulate complex graph queries.
  3. Graph-Based Operations: Gremlin provides a rich set of graph-based operations, such as filtering, mapping, sorting, aggregating, and joining. These operations allow users to perform various calculations, aggregations, and transformations on the graph data during traversal.
  4. Step Modifiers: Gremlin supports step modifiers that can be applied to traversal steps. Step modifiers provide additional functionalities and allow users to refine their queries by applying conditions, limiting results, or adjusting the traversal behaviour.
  5. Language Flexibility: Gremlin is a flexible language that can be used with multiple graph databases and frameworks. It is not tied to a specific database system but is designed to work with any graph database that supports the Gremlin language.
  6. Language Ecosystem: Gremlin has a rich ecosystem of libraries, tools, and frameworks that support its use. Apache TinkerPop™, an open-source graph computing framework, includes Gremlin as its default query language. Various Gremlin-based implementations and connectors are also available for different graph database systems.

Gremlin allows users to express complex graph traversals and queries concisely and readably. Its focus on graph traversal and the ability to perform operations on the graph structure make it a powerful language for querying and analysing graph data.



SPARQL is a query language specifically designed for querying data represented in the Resource Description Framework (RDF) format. RDF is a standardised data model for representing knowledge on the web, and SPARQL provides a means to retrieve and manipulate RDF data.

SPARQL allows users to express complex queries to extract specific information from RDF graphs. It provides a rich set of capabilities for querying and manipulating graph data, enabling users to explore relationships and patterns within the data. With SPARQL, you can query not only individual triples but also traverse the graph by following edges and querying related entities.

Here are some key features and concepts of SPARQL:

  1. Pattern Matching: SPARQL queries are expressed as graph patterns, which specify a set of triples that should match in the RDF graph. These patterns can include variables, constants, and predicates, allowing for flexible querying and pattern matching.
  2. Triple-based Queries: SPARQL queries typically involve matching and retrieving triples from the RDF graph. Queries can specify conditions on subjects, predicates, objects, or any combination of these components.
  3. Graph Pattern Matching: SPARQL allows the specification of more complex graph patterns by combining multiple triples using logical operators such as AND, OR, and NOT. This enables users to express complex conditions and constraints on the data.
  4. Variable Binding: SPARQL supports the binding of variables, which allows you to retrieve specific information from the RDF graph and use it in subsequent parts of the query. Variables are denoted with a leading "?" or "$" symbol.
  5. Filtering and Expressions: SPARQL provides various operators and functions to filter and manipulate data during query execution. These expressions enable you to perform operations such as comparison, arithmetic, string manipulation, and more.
  6. Joins and Optional Patterns: SPARQL supports joining multiple graph patterns and specifying optional patterns. This allows you to express queries that retrieve data from different parts of the RDF graph and handle missing or optional information.
  7. Aggregation and Grouping: SPARQL provides aggregation functions (e.g., COUNT, SUM, AVG) and grouping capabilities. This allows you to perform calculations and summary operations on groups of data in the RDF graph.
  8. Result Formatting: SPARQL allows you to control the format and presentation of query results. You can specify whether you want the results in XML, JSON, or other formats, making integrating with other systems and applications easy.

SPARQL is a powerful and expressive language for querying RDF data. It is widely used in the semantic web community and plays a vital role in exploring and extracting knowledge from RDF graphs, supporting tasks such as data integration, semantic search, and reasoning.


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Other Graph Query Languages

In addition to Cypher, Gremlin, and SPARQL, several other graph query languages are used in various graph database systems and graph processing frameworks. Here are a few notable examples:

  1. GraphQL: GraphQL is a query language and runtime for APIs, but it can also be used to query graph data structures. GraphQL supports querying relationships and nested data structures, making it suitable for querying graph-like data. It allows clients to specify the shape and structure of the data required, enabling efficient and precise data fetching.
  2. SQL-Graph Extensions: Many relational database systems have incorporated graph querying capabilities through extensions to SQL. These extensions, such as SQL/PGQL for Oracle, SQL/Graph for SQL Server, and SQL/Graph for PostgreSQL, introduce graph-specific syntax and operators to query graph-related data stored in tables and relationships.
  3. AQL (ArangoDB Query Language): AQL is the query language used by ArangoDB, a multi-model database that supports graph, document, and key-value data models. AQL provides graph traversal and querying capabilities to work with the graph database features of ArangoDB.
  4. GSQL: GSQL is a query language specifically designed for TigerGraph, a graph database platform. GSQL allows users to define graph schemas, perform graph traversal and pattern matching, and implement complex graph algorithms within the TigerGraph database.
  5. Datalog: Datalog is a declarative logic programming language often used for querying and manipulating graph-like structures. It provides a rule-based syntax for expressing queries, with support for recursive rules and pattern matching. Datalog is commonly used in graph processing frameworks like Apache Giraph and Apache Flink.

It's worth noting that some graph databases or graph processing frameworks may offer their own custom query languages or query APIs. These languages are often tailored to the specific features and capabilities of the respective graph technology.

Each graph query language has its own syntax, features, and strengths, allowing users to work with graph data in different ways. The choice of the graph query language depends on the specific graph database or graph processing framework being used, as well as the user's or development team's requirements and preferences.



GQL (Graph Query Language) is formally known as ISO/IEC 9075-15:2019, and it specifies a standard query language for graph databases.

The ISO/IEC 9075-15:2019 standard defines the syntax, semantics, and features of GQL for querying graph data. It provides a standardised approach to querying graph databases, ensuring interoperability and compatibility across different graph database systems.

The GQL standard aims to facilitate the exchange of graph data and queries between different graph database implementations and tools. It promotes consistency and allows developers and users to work with graph data using a common query language regardless of the specific graph database system they are using.

By adhering to the GQL standard, graph database vendors can ensure compatibility with other GQL-compliant systems and provide a consistent querying experience for their users. It helps foster interoperability and enables the development of tools, frameworks, and applications that work seamlessly with different GQL-supporting graph databases.

ISO standards are essential in promoting standardisation and best practices in various domains. The ISO/IEC 9075-15:2019 standard for GQL is one such effort to establish a common query language for graph databases, promoting consistency and interoperability within the graph database ecosystem.

Standardisation of Graph Query Languages

While GQL is an ISO standard, it is not the only initiative in this space. There have been other attempts to standardise graph query languages and related technologies. Here are a few other notable attempts to standardise graph-related technologies:

  1. Property Graph Schema (PGS): The Property Graph Schema is an open standard developed by the Linked Data Benchmark Council (LDBC). PGS provides a standard way to describe the structure and semantics of graph data, promoting interoperability and data exchange between graph databases. It aims to define a common schema for property graphs, including node types, edge types, and their properties.
  2. W3C RDF and SPARQL: The World Wide Web Consortium (W3C) has developed several standards for the Semantic Web, including the Resource Description Framework (RDF) and the SPARQL query language, as described earlier. These standards promote interoperability and enable data exchange and integration in the Semantic Web domain.
  3. Apache TinkerPop: Apache TinkerPop is an open-source graph computing framework that aims to provide a standard for graph traversal and query languages. TinkerPop enables interoperability between different graph databases and graph processing systems by providing a common API and query language. TinkerPop includes the Gremlin query language described earlier.
  4. SQL Graph Extensions: Several relational database systems have introduced graph extensions to SQL to support querying and manipulating graph-like data. These extensions, such as SQL/PGQL, SQL/Graph, and SQL/Graph for different database systems, provide a standardised way to query graph data within the SQL framework.
  5. OpenCypher: The OpenCypher initiative involves collaboration among various graph database vendors, developers, and researchers to collectively evolve and advance the Cypher language. By establishing OpenCypher as a standardised industry language, it enables users to write queries that can be executed on different graph database systems, fostering interoperability and making it easier to work with graph data regardless of the underlying platform.

These initiatives and standards focus on different aspects of graph-related technologies, including data modelling, query languages, schema definition, and interoperability. They aim to establish common practices, promote interoperability, and enable the exchange and integration of graph data across different systems and platforms.

While GQL is an ISO standard and is considered a significant step towards standardisation, it's important to note that the graph database landscape is diverse, and different graph databases and technologies may have their own query languages and approaches.

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The Benefits of Standardisation of Graph Query Languages

Standardisation offers numerous benefits across various domains and industries. Here are some key advantages of standardisation:

  1. Interoperability: Standards promote interoperability by providing a common framework and language that allows different systems, products, or services to work together seamlessly. When multiple entities adhere to the same standards, exchanging data, integrating systems, and achieving compatibility becomes easier.
  2. Compatibility and Integration: Standards ensure compatibility between components, technologies, or systems. They enable smooth integration by defining common interfaces, protocols, or formats that facilitate communication and data exchange. This simplifies the process of connecting disparate systems and promotes the development of ecosystems that can interoperate effectively.
  3. Quality and Reliability: Standards often include best practices, guidelines, and quality requirements that help improve the quality and reliability of products, services, or processes. By following established standards, organisations can enhance their offerings, streamline operations, and meet customer expectations more effectively.
  4. Efficiency and Cost Reduction: Standards can increase efficiency by eliminating redundancies, streamlining processes, and reducing complexities. They provide a common framework for implementation, reducing the need for custom solutions and enabling organisations to leverage existing tools, technologies, or infrastructures. This can result in cost savings and improved resource allocation.
  5. Safety and Security: Standards often incorporate safety and security considerations, helping to protect individuals, assets, and information. By adhering to relevant standards, organisations can enhance the safety and security of their operations. They establish risk assessment, mitigation, and compliance guidelines, reducing the likelihood of accidents, vulnerabilities, or data breaches.
  6. Market Access and Global Trade: Standards play a crucial role in facilitating market access and international trade. Compliance with recognised standards can help organisations meet regulatory requirements, gain market acceptance, and enter new markets. Standards also foster trust and confidence among customers and trading partners, facilitating the exchange of goods, services, and knowledge across borders.
  7. Innovation and Collaboration: Standards encourage innovation and collaboration by providing a common reference point and a shared understanding. They enable organisations to build upon existing knowledge, technologies, or solutions, fostering an environment of continuous improvement and collective problem-solving. Standards can also stimulate competition and drive innovation by setting benchmarks and encouraging new approaches.
  8. Portability and Interoperability: When a graph query language is standardised, it becomes easier for companies to switch between different graph database vendors that support the same language. By adhering to the standard language, companies can write queries and develop applications that are not tied to any specific vendor's proprietary query language. This portability allows companies to transition their graph data and applications to alternative vendors without the need for significant code rewriting or query translation.
  9. Ecosystem and Vendor Competition: Standardisation of a graph query language fosters a vibrant ecosystem of tools, libraries, and frameworks that support the language. This ecosystem encourages multiple vendors to adopt and support the standardised language, creating competition among them. As a result, companies have more options, and vendors are incentivised to offer competitive pricing, improved services, and innovative features to attract and retain customers.
  10. Skill Portability: When a graph query language is standardised and widely adopted, it promotes skill portability among developers and data professionals. Employees with knowledge and experience in the standardised query language can easily transition between different graph database vendors that support the language. This reduces the dependency on specific vendor-specific query languages and ensures a broader talent pool for companies.
  11. Reduced Learning Curve and Development Effort: Standardised graph query languages provide consistent syntax, semantics, and features across different graph database vendors. This reduces the learning curve and development effort required when switching vendors or adopting new graph databases. Developers familiar with the standardised language can quickly adapt and leverage their existing knowledge, resulting in cost and time savings for companies.
  12. Innovation and Third-Party Integrations: Standardisation of a graph query language encourages the development of third-party tools, libraries, and integrations that support the language. These offerings enhance the ecosystem and provide companies with a broader range of options for analytics, visualisation, data integration, and other graph-related functionalities. The availability of diverse tools and integrations reduces the risk of being tied to a specific vendor's proprietary solutions, giving companies more flexibility and choice.



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