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[SPARK-14267] [SQL] [PYSPARK] execute multiple Python UDFs within single batch #12057

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[SPARK-14267] [SQL] [PYSPARK] execute multiple Python UDFs within single batch #12057

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f6b7373
multiple Python UDFs in single batch
Mar 30, 2016
8e6e5bc
fast path for single UDF
Mar 30, 2016
8dc1adf
improve performance, address comments
Mar 31, 2016
dd71ba9
be safe
Mar 31, 2016
8597bba
fix udt with udf
Mar 31, 2016
72a5ec0
avoid unnecessary conversion
Mar 31, 2016
876f9f9
udpate error message
Mar 31, 2016
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fast path for single UDF
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Davies Liu committed Mar 30, 2016
commit 8e6e5bc623adfa77e25ebf02712b10ca1a8a7a60
1 change: 1 addition & 0 deletions python/pyspark/sql/tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -315,6 +315,7 @@ def test_chained_udf(self):
self.assertEqual(row[0], 6)

def test_multiple_udfs(self):
self.sqlCtx.registerFunction("double", lambda x: x * 2, IntegerType())
[row] = self.sqlCtx.sql("SELECT double(1), double(2)").collect()
self.assertEqual(tuple(row), (2, 4))
[row] = self.sqlCtx.sql("SELECT double(double(1)), double(double(2) + 2)").collect()
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3 changes: 2 additions & 1 deletion python/pyspark/worker.py
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Expand Up @@ -91,8 +91,9 @@ def read_udfs(pickleSer, infile):
if num_udfs == 1:
udf = udfs[0][2]

# fast path for single UDF
def mapper(args):
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@JoshRosen JoshRosen Mar 30, 2016

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I bet you could even do mapper = udf if you wanted to.

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@davies davies Mar 31, 2016

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We can't, input of mapper is a tuple, but udf is not

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@JoshRosen JoshRosen Mar 31, 2016

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Ah, got it. Makes sense.

return (udf(*args),)
return udf(*args)
else:
def mapper(args):
return tuple(udf(*args[start:end]) for start, end, udf in udfs)
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16 changes: 13 additions & 3 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchPythonEvaluation.scala
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Expand Up @@ -71,7 +71,6 @@ case class BatchPythonEvaluation(udfs: Seq[PythonUDF], output: Seq[Attribute], c

val (pyFuncs, children) = udfs.map(collectFunctions).unzip
val numArgs = children.map(_.length)
val resultType = StructType(udfs.map(u => StructField("", u.dataType, u.nullable)))

val pickle = new Pickler
// flatten all the arguments
Expand All @@ -97,15 +96,26 @@ case class BatchPythonEvaluation(udfs: Seq[PythonUDF], output: Seq[Attribute], c
.compute(inputIterator, context.partitionId(), context)

val unpickle = new Unpickler
val row = new GenericMutableRow(1)
val mutableRow = new GenericMutableRow(1)
val joined = new JoinedRow
val resultType = if (udfs.length == 1) {
udfs.head.dataType
} else {
StructType(udfs.map(u => StructField("", u.dataType, u.nullable)))
}
val resultProj = UnsafeProjection.create(output, output)

outputIterator.flatMap { pickedResult =>
val unpickledBatch = unpickle.loads(pickedResult)
unpickledBatch.asInstanceOf[java.util.ArrayList[Any]].asScala
}.map { result =>
val row = EvaluatePython.fromJava(result, resultType).asInstanceOf[InternalRow]
val row = if (udfs.length == 1) {
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@JoshRosen JoshRosen Mar 30, 2016

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Rather than evaluating this if condition for every row, could we lift this out of the map and perform it once while building the RDD DAG? i.e. assign the result of line 108 to a variable and have the if be the last return value of this block?

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@JoshRosen JoshRosen Mar 30, 2016

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If you do this, you could reduce the scope of the mutableRow created up on line 99, too.

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@davies davies Mar 31, 2016

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Comparing evaluate Python UDF, I think this does not matter, JIT compiler could predict this branch pretty easy.

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@JoshRosen JoshRosen Mar 31, 2016

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Fair enough.

// fast path for single UDF
mutableRow(0) = EvaluatePython.fromJava(result, resultType)
mutableRow
} else {
EvaluatePython.fromJava(result, resultType).asInstanceOf[InternalRow]
}
resultProj(joined(queue.poll(), row))
}
}
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