When to Use GraphQL to Accelerate API Delivery
New ISO Announcement: Graph Query Language (GQL)
Well, this wrecked my weekend, but it’s super exciting and I wanted to make sure I had this in my folks’ hands this AM. Feel free to fwd/share as you see fit. Apologies but I cannot forward the reference PDF as it is watermarked to me from ISO.
Cheers,
Dave
Dave Mayer
Vice President | Program Director, Gartner Data and Analytics Research Board
Gartner
6312 S. Fiddlers Green Circle Suite 300E
Greenwood Village, CO 80111
Direct: +01 720 490 1205 | Office: +01 720 550 4300
david.mayer@gartner.com
Registration is open for our Chicago flagship meeting June 3-5, 2024!
Schedule me at https://calendly.com/davemayer
From: David Mayer
Sent: Monday, April 22, 2024 8:08 AM
Subject: New ISO Announcement: Graph Query Language (GQL)
Good morning, DARB family!
I wanted to take a moment of your time this morning to acquaint you with an important development in our field.
Recently, the International Standards Organization finalized its guidance on ISO/IEC 39075, a standard detailing the new Graph Query Language (GQL). This is a landmark publication with significant potential to significantly accelerate innovation in data management and analytics. GQL, by design, harnesses the power of graph databases to manage complex and connected data structures, representing a significant shift from traditional SQL databases in terms of flexibility and performance in specific scenarios.
The full 610-page reference publication can be found here, but to ensure you have all the basics in hand, to follow is a quick summary of ISO39075:
Key Capabilities and Innovations of GQL
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Property Graph Model. GQL focuses on the Property Graph model, which structures data into nodes and edges, each with associated labels and properties. This model is inherently more flexible than the conventional Relational Database Framework (RDF), supporting complex data relationships and providing intuitive query capabilities that mimic natural data relationships more closely .
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Enhanced Data Interactions. The GQL ability to use path pattern matching for data queries simplifies and enhances the efficiency of data interaction. Unlike the rigid structure of SQL joins, path pattern matching in GQL allows for more flexible and performance-optimized queries, particularly useful in traversing relationships in datasets like social networks, supply chains, or complex system architectures .
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Transactional and Declarative Query Language. GQL is not just about reading data; it’s fully transactional, allowing for modifications, updates, and management of data within a graph database. It takes inspiration from SQL to provide a familiar yet enhanced experience for database professionals. This approach ensures that GQL can handle both analytical and operational workloads effectively, making it suitable for real-time applications .
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Standardization Across Platforms. The development of GQL as a standardized language aims to provide uniformity across various graph database implementations. This standardization will likely lead to wider adoption and portability of applications across different platforms, reducing vendor lock-in and fostering innovation through a common development standard.
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Scalability and Security Models. GQL integrates robust scalability and security features, ensuring that it can support large-scale enterprise applications. The language facilitates complex queries and transactions while providing mechanisms to secure access and maintain data integrity across distributed environments.
Industry Reaction
GQL has already garnered attention particularly from the database and developer communities due to its innovative approach to handling graph data. Support for GQL has been strong thus far among national standards bodies and industry leaders. For instance, companies like Neo4j have been vocal proponents, noting that GQL combines the strengths of existing graph query languages and brings a standardized approach that could help unify various graph database technologies. This is seen as a pivotal move to enhance the efficiency and capabilities of graph databases in handling complex data structures more natively and intuitively. Further, GQL’s development process has involved broad community input and collaboration, including public forums, discussions, and workshops that have helped shape the language’s specifications and ensure its alignment with industry needs and existing technologies. The community engagement has also included systematic analysis of existing languages to avoid redundancy and ensure that GQL can effectively address current and emerging data management challenges.
Synergy with SQL
GQL is designed to interact with SQL, particularly through the integration of property graph features within SQL environments. This interaction is part of a broader effort to ensure that GQL not only stands alone as a powerful graph query language but also complements existing SQL capabilities by allowing users to employ graph data models alongside traditional relational databases. GQL and SQL/PGQ (Property Graph Queries), which is part of the SQL standard, share a significant amount of syntax and functionality. The development of GQL has considered the existing features of SQL/PGQ to ensure compatibility and interoperability between the two. This allows users to perform graph queries directly within SQL databases, effectively blending graph querying capabilities with the extensive support system and established use cases of SQL. Moreover, GQL aims to be a superset of SQL/PGQ, which means it includes all capabilities of SQL/PGQ and extends them with additional features specific to more complex graph structures and queries. This relationship ensures that GQL can serve as a bridge between graph databases and relational databases, facilitating a more integrated approach to data management across different database systems. Just as SQL standardized how relational databases are queried, GQL aims to unify and enhance the querying of property graph databases. This standardization is crucial, as it allows for greater interoperability among different systems and simplifies the learning curve for developers by providing a consistent querying language.
Strategic Benefits for Enterprises
Implementing GQL has the potential to transform how organizations manage and leverage their data. With its emphasis on relationships and connections, GQL appears capable of empowering a developer community that can unlock new insights in data sets where connections play a critical role, such as in fraud detection, recommendation systems, and network analysis. Additionally, the standardization and flexibility of GQL foster a conducive environment for innovation, allowing businesses to **adapt quickly to cha
nging market dynamics and technology advancements**.
As our coverage of GQL evolves, we will be sure to keep you apprised of Gartner advisory guidance and best practices regarding including GQL in your development environment for data and analytics.
Best,
Dave
Dave Mayer
Vice President | Program Director, Gartner Data and Analytics Research Board
Gartner
6312 S. Fiddlers Green Circle Suite 300E
Greenwood Village, CO 80111
Direct: +01 720 490 1205 | Office: +01 720 550 4300
david.mayer@gartner.com
Registration is open for our Chicago flagship meeting June 3-5, 2024!
[[Schedule me at https://calendly.com/davemayer