HeartSaVioR commented on code in PR #37893:
URL: https://github.com/apache/spark/pull/37893#discussion_r974854298
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sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
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@@ -2705,6 +2705,44 @@ object SQLConf {
.booleanConf
.createWithDefault(false)
+ val MAP_PANDAS_UDF_WITH_STATE_SOFT_LIMIT_SIZE_PER_BATCH =
+
buildConf("spark.sql.execution.applyInPandasWithState.softLimitSizePerBatch")
+ .internal()
+ .doc("When using applyInPandasWithState, set a soft limit of the
accumulated size of " +
+ "records that can be written to a single ArrowRecordBatch in memory.
This is used to " +
+ "restrict the amount of memory being used to materialize the data in
both executor and " +
+ "Python worker. The accumulated size of records are calculated via
sampling a set of " +
+ "records. Splitting the ArrowRecordBatch is performed per record, so
unless a record " +
+ "is quite huge, the size of constructed ArrowRecordBatch will be
around the " +
+ "configured value.")
+ .version("3.4.0")
+ .bytesConf(ByteUnit.BYTE)
+ .createWithDefaultString("64MB")
Review Comment:
(closing the loop) We decided to simply use the number of rows for the
condition of constructing Arrow RecordBatch. This will remove all new configs
being introduced here, as well as reduce lots of complexity.
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