Question
· Oct 11

How do I analyze and tune parallel query execution in IRIS SQL engine for complex joins?

#InterSystems IRIS

I have large joins involving millions of rows. How can I profile and tune the SQL engine’s parallel execution? Are there EXPLAIN plan features to inspect threading and task distribution?

Discussion (1)2
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DC AI Bot · Oct 11

To analyze and tune parallel query execution in the InterSystems IRIS SQL engine for complex joins, you need to focus on the available profiling tools, query processing configurations, and tuning utilities provided by the platform.

Analyzing Parallel Execution

  1. EXPLAIN and Show Plan: Use EXPLAIN or Show Plan to interpret and view detailed execution plans of your queries. This will highlight if and how parallelism is being utilized, including subplans for tasks distributed across threads. These tools enable you to understand the choices made by the optimizer and adjust accordingly [1].

  2. SQL Process View: The "SQL Activity" view in the System Management Portal lists currently running SQL statements. You can drill down to see query plans and diagnose performance issues, particularly for long-running queries. This feature simplifies identifying concurrency bottlenecks [2].

  3. Query Statistics: The SQL Performance Analysis Toolkit allows you to gather detailed runtime statistics, such as execution count, time, and average rows processed, to analyze query behavior systematically [3][1].

Tuning Parallel Execution

  1. Enable Parallel Query Processing:

    • System-wide Parallel Processing: Configure this via the Management Portal (System Administration → Configuration → SQL and Object Settings) or programmatically using $SYSTEM.SQL.Util.SetOption("AutoParallel", 1). When enabled, the SQL engine automatically evaluates which queries benefit from parallelism [4][5].
    • Query-specific Parallel Processing: Use the %PARALLEL keyword within the FROM clause of specific queries to suggest parallel execution for selective workloads [5].

  2. Optimize Query Plans with Hints:

    • The %STARTTABLE and %PARALLEL keywords provide explicit directions to the query optimizer to utilize specific tables or parallel processing strategies for joins in complex queries [6][5].
    • Ensure indices are on optimal columns to avoid unnecessary temporary files and improve join efficiency [1].

  3. Shared Memory Configuration:

    • Parallel queries use the generic memory heap (gmheap) for inter-process communication. Ensure sufficient gmheap size to accommodate high concurrency scenarios, especially with large data sets. Monitor and adjust gmheap as necessary through the Management Portal [4].

Additional Considerations

  1. Tune Tables: Use the TUNE TABLE command to gather or refresh table statistics for accurate query plan generation based on current data distribution [3].

  2. Query Complexity: While parallelism benefits aggregate queries (e.g., SUM, AVG, GROUP BY), it may not be effective for queries with correlated subqueries or process-specific functions. Analyze query patterns carefully [4][5].

  3. Diagnostic Notes in Plans: In newer IRIS releases, query plans provide notes identifying predicates used for selectivity estimation and specific runtime-related performance decisions [2].

By leveraging tools like Show Plan, configuring parallel options, and aligning your schema design with system capabilities, you can successfully profile and optimize parallel query execution for large-scale joins.

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