BryanCutler commented on code in PR #36683:
URL: https://github.com/apache/spark/pull/36683#discussion_r889416243
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -596,7 +596,7 @@ def _create_from_pandas_with_arrow(
]
# Slice the DataFrame to be batched
- step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round
int up
+ step = self._jconf.arrowMaxRecordsPerBatch()
Review Comment:
I thought this was to control how many partitions were in the rdd? Each
partition could have multiple batches, and probably should be capped at
`arrowMaxRecordsPerBatch`, but since it was coming from a local Pandas
DataFrame already in memory, that didn't seem to be a big deal.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
##########
@@ -2575,6 +2575,18 @@ object SQLConf {
.booleanConf
.createWithDefault(false)
+ val ARROW_LOCAL_RELATION_THRESHOLD =
+ buildConf("spark.sql.execution.arrow.localRelationThreshold")
+ .doc(
+ "When converting Arrow batches to Spark DataFrame, local collections
are used in the " +
+ "driver side if the byte size of Arrow batches is smaller than this
threshold. " +
+ "Otherwise, the Arrow batches are sent and deserialized to Spark
internal rows " +
+ "in the executors.")
+ .version("3.4.0")
+ .bytesConf(ByteUnit.BYTE)
+ .checkValue(_ >= 0, "This value must be equal to or greater than 0.")
+ .createWithDefaultString("48MB")
Review Comment:
Is this the max size of each batch or all batches together?
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