US Patent Application 17822429. INSTRUCTION LEVEL PARALLELISM IN A DECLARATIVE GRAPH QUERY LANGUAGE simplified abstract

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INSTRUCTION LEVEL PARALLELISM IN A DECLARATIVE GRAPH QUERY LANGUAGE

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Anders Tungeland Gjerdrum of Tromso (NO)

Tor Kreutzer of Tromso (NO)

Jan-Ove Almli Karlberg of Tromso (NO)

INSTRUCTION LEVEL PARALLELISM IN A DECLARATIVE GRAPH QUERY LANGUAGE - A simplified explanation of the abstract

This abstract first appeared for US patent application 17822429 titled 'INSTRUCTION LEVEL PARALLELISM IN A DECLARATIVE GRAPH QUERY LANGUAGE

Simplified Explanation

- The patent application describes example solutions for executing a query in a declarative graph query language. - The solutions involve receiving a query for data in a database and determining if the query can be executed in parallel. - The determination is based on whether there is a pattern in the query and if the data in the database make it suitable for parallel execution. - If the query is suitable for parallel execution, the solutions inject fork and join operations into a query plan. - The query is then executed according to the query plan. - Some examples also consider the efficiency of executing the query in parallel and only execute resource-efficient queries in parallel.


Original Abstract Submitted

Example solutions for executing a query in a declarative graph query language include receiving the query for data in a database and determining if one or both of i) a pattern in the query, and ii) the data in the database render the query suitable for being executed, at least in part, in parallel. If either condition indicates that the query is suitable for being executed, at least in part, in parallel, one or more fork operations and join operations are injected into a query plan, and the query is executed according to the query plan. Some examples further determine whether executing the query in parallel is computing resource-efficient, and only executes computing resource-efficient queries in parallel.