#gql

Chirps tagged with #gql

Microsoft Fabric Graph has added support for additional #GQL features with richer path semantics, traversal controls, and composable query constructs for analyzing complex graph structures. Variable-length and unbounded paths can be combined with modes such as WALK, TRAIL, SIMPLE, ACYCLIC, and DIFFERENT EDGES to control whether vertices or edges may be revisited during traversal. ANY SHORTEST provides shortest-path matching, while functions such as NODES, EDGES, INNER_NODES, SOURCE, DESTINATION, and ELEMENT_ID expose individual components and identifiers of matched paths. Query composition is enhanced through NEXT, EXISTS, CALL, OPTIONAL CALL, and UNION DISTINCT, enabling multi-stage pattern matching, conditional execution, subqueries, and deduplicated result sets. Aggregation is more expressive through filtered and nested aggregate expressions, alongside COLLECT_ONE, COLLECT_LIST, and COLLECT_ELEMENTS for constructing scalar and collection-based results from graph matches. The language also supports dynamic nested values and explicit type conversion with CAST, together with functions such as RANGE and POWER for generating sequences and performing numerical operations within queries.

https://learn.microsoft.com/en-us/fabric/graph/gql-language-guide

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learn.microsoft.com

GQL Language Guide for graph in Microsoft Fabric - Microsoft Fabric

Learn how to write GQL queries for graph in Microsoft Fabric, including pattern matching, filtering, aggregation, sorting, and subqueries with examples.

froGQL is a lightweight, open-source graph database that implements the ISO #GQL standard while keeping the graph, schema, and indexes in a single file. It speeds up complex graph queries using worst-case optimal joins and can use static type checking to reject impossible queries before reading the graph, avoiding potentially expensive computation

https://sferrada.com/talk/frogql-worst-case-optimal-joins-and-type-driven-optimization-for-lightweight-gql/

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sferrada.com

froGQL: Worst-Case Optimal Joins and Type-Driven Optimization for Lightweight GQL | Sebastián Ferrada's Academic Site

We present froGQL, an open-source graph database that keeps a property graph, its schema, and its indexes in a single file and runs inside the host application, in the manner of SQLite, while implementing the ISO GQL standard. froGQL makes two contributions. First, its engine evaluates multi-pattern queries with Leapfrog Triejoin, a worst-case optimal algorithm whose cost is bounded by the largest result the query could produce, applied directly over a sorted-adjacency layout so that no intermediate results are built. On queries that start from one node identified by an indexed property and then follow several edges, froGQL is the fastest of the three systems we measure on the LDBC Social Network Benchmark: 13 ms on IC2 against 19 ms for Kùzu and 21 ms for GraphQLite, and 1.1 ms on IC11 against Kùzu's 4.8 ms. Resolving the equality filter on the starting node before the search begins earns that margin: without it, IC11 takes 4.6 s. Second, a static type system rejects queries that cannot return a result before any part of the graph is read. The check costs 0.1 ms on a four-hop query the engine would otherwise spend 132 s enumerating, and stays below 1% of query time in 26 of the 36 valid cases measured; it does not depend on froGQL's runtime and can be placed in front of any conforming GQL engine. froGQL is MIT-licensed and available at https://github.com/pleiad/frogql, with packages for Rust, Python, Node.js, and the browser.

SurrealDB 3.3.0 lets any #Postgres client connect and query the database, delivers results row by row as they are generated, and dramatically speeds up graph traversals and filtered searches by combining indexes as bitmaps to minimize unnecessary record reads. It also adds locked reads, per-session permission contexts, JWT audience validation, S3/GCS/Azure cloud storage support for file buckets, and Chinese, Japanese, Korean word segmentation for full-text search, alongside automatic in-place data migrations from 3.2 without requiring manual exports or imports. The release includes hundreds of performance tweaks and bug fixes, and makes the ISO #GQL (Cypher Query Language) functionality available by default without needing an experimental flag enabled.

https://surrealdb.com/releases/3.3

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surrealdb.com

Release 3.3 | SurrealDB

SurrealDB 3.3.0 lets any Postgres client query SurrealDB, streams results as they are produced, and makes graph traversals and filtered searches much faster.…