alex-balikov commented on code in PR #37893:
URL: https://github.com/apache/spark/pull/37893#discussion_r974672806
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
##########
@@ -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 =
Review Comment:
I wonder if we really care to have this param. Ultimately if the sizing
estimate works badly, the users can just set a lower value for the batch size
limit. I do not think it is useful to let them tune this parameter.
##########
sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -620,6 +622,35 @@ class RelationalGroupedDataset protected[sql](
Dataset.ofRows(df.sparkSession, plan)
}
+ private[sql] def applyInPandasWithState(
Review Comment:
method level comment
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
##########
@@ -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:
I agree that expressing the limit in terms of bytes is more meaningful that
records. However we estimate the bytes size efficiently. Specifically here I
would rename 'softLimitSizePerBatch' by removing 'soft' - we can clarify in the
comment about that and also including 'Bytes' - 'batchSizeLimitBytes' . Also
wonder if we should have the property specific to applyInPandasWithState or
should we make it general - remove the applyInPandasWithState scoping even if
we do not support this limit initially, seems like generally meaningful and we
can follow up fixing the other places as a bug.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
##########
@@ -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 =
Review Comment:
again, should we really expose this? Lets have a reasonable const value to
start with and not expose a config. It is impossible to understand what this
means unless you intimately know the implementation.
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