HeartSaVioR commented on code in PR #37893:
URL: https://github.com/apache/spark/pull/37893#discussion_r973864734
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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")
+
+ val MAP_PANDAS_UDF_WITH_STATE_MIN_DATA_COUNT_FOR_SAMPLE =
+
buildConf("spark.sql.execution.applyInPandasWithState.minDataCountForSample")
+ .internal()
+ .doc("When using applyInPandasWithState, specify the minimum number of
records to sample " +
+ "the size of record. The size being retrieved from sampling will be
used to estimate " +
+ "the accumulated size of records. Note that limiting by size does not
work if the " +
+ "number of records are less than the configured value. For such case,
ArrowRecordBatch " +
+ "will only be split for soft timeout.")
+ .version("3.4.0")
+ .intConf
+ .createWithDefault(100)
+
+ val MAP_PANDAS_UDF_WITH_STATE_SOFT_TIMEOUT_PURGE_BATCH =
+
buildConf("spark.sql.execution.applyInPandasWithState.softTimeoutPurgeBatch")
+ .internal()
+ .doc("When using applyInPandasWithState, specify the soft timeout for
purging the " +
+ "ArrowRecordBatch. If batching records exceeds the timeout, Spark will
force splitting " +
+ "the ArrowRecordBatch regardless of estimated size. This config
ensures the receiver " +
+ "of data (both executor and Python worker) to not wait indefinitely
for sender to " +
+ "complete the ArrowRecordBatch, which may hurt both throughput and
latency.")
+ .version("3.4.0")
+ .timeConf(TimeUnit.MILLISECONDS)
+ .createWithDefaultString("100ms")
Review Comment:
This config is to have two different aspects of closing the arrow record
batch, 1) size 2) time on batching.
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