[SPARK-47818][CONNECT][FOLLOW-UP] Introduce plan cache in SparkConnectPlanner to improve performance of Analyze requests - #46098
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| with self.sql_conf({"spark.sql.pyspark.udf.profiler": "perf"}): | ||
| _do_computation(self.spark, action=action) | ||
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| # Without the plan cache, UDF ID will be different for each action |
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also cc @xinrong-meng to check the profiler tests
HyukjinKwon
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Apr 24, 2024
hvanhovell
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Apr 24, 2024
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@xi-db please update the PR. |
# Conflicts: # connector/connect/server/src/main/scala/org/apache/spark/sql/connect/execution/SparkConnectPlanExecution.scala
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Hi @hvanhovell, the CI is green and its ready to merge. |
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Merging. |
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What changes were proposed in this pull request?
In the previous PR, we cache plans of AnalyzePlan requests. We're also enabling it for ExecutePlan in this PR.
Why are the changes needed?
Some operations like spark.sql() issue ExecutePlan requests. By caching them, we can also improve performance if subsequent plans to be analyzed include the plan returned by ExecutePlan as a subtree.
Does this PR introduce any user-facing change?
No.
How was this patch tested?
Existing tests.
Was this patch authored or co-authored using generative AI tooling?
No.