[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r138858332 --- Diff: python/pyspark/serializers.py --- @@ -199,6 +211,33 @@ def __repr__(self): return "ArrowSerializer" +class ArrowPandasSerializer(ArrowSerializer): + +def __init__(self): +super(ArrowPandasSerializer, self).__init__() + +def dumps(self, series): +""" +Make an ArrowRecordBatch from a Pandas Series and serialize +""" +import pyarrow as pa --- End diff -- Should we catch `ImportError`? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/18659#discussion_r138855947
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala
---
@@ -82,7 +82,6 @@ private[sql] object ArrowConverters {
val root = VectorSchemaRoot.create(arrowSchema, allocator)
val arrowWriter = ArrowWriter.create(root)
-
--- End diff --
(Looks unrelated change)
---
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[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user BryanCutler commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r138447017 --- Diff: python/pyspark/sql/functions.py --- @@ -2112,7 +2113,7 @@ def wrapper(*args): @since(1.3) -def udf(f=None, returnType=StringType()): +def udf(f=None, returnType=StringType(), vectorized=False): --- End diff -- It seems like the consensus is for `pandas_udf` and I'm fine with that too. I'll make that change and the others brought up here. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r138273961 --- Diff: python/pyspark/sql/functions.py --- @@ -2112,7 +2113,7 @@ def wrapper(*args): @since(1.3) -def udf(f=None, returnType=StringType()): +def udf(f=None, returnType=StringType(), vectorized=False): --- End diff -- and also `**kwargs` to bring the size information --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r138273859 --- Diff: python/pyspark/sql/functions.py --- @@ -2112,7 +2113,7 @@ def wrapper(*args): @since(1.3) -def udf(f=None, returnType=StringType()): +def udf(f=None, returnType=StringType(), vectorized=False): --- End diff -- as we discussed in the email, we should also accept data type of string format. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r138273168 --- Diff: python/pyspark/sql/functions.py --- @@ -2112,7 +2113,7 @@ def wrapper(*args): @since(1.3) -def udf(f=None, returnType=StringType()): +def udf(f=None, returnType=StringType(), vectorized=False): --- End diff -- I think `@pandas_udf(DoubleType())` is better than `@udf(DoubleType(), vectorized=True)`, which is more concise. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r138272979 --- Diff: python/pyspark/serializers.py --- @@ -81,6 +81,12 @@ class SpecialLengths(object): NULL = -5 +class PythonEvalType(object): +NON_UDF = 0 +SQL_BATCHED_UDF = 1 +SQL_ARROW_UDF = 2 --- End diff -- ditto --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/18659#discussion_r138272886
--- Diff: core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
---
@@ -83,10 +83,23 @@ private[spark] case class PythonFunction(
*/
private[spark] case class ChainedPythonFunctions(funcs:
Seq[PythonFunction])
+/**
+ * Enumerate the type of command that will be sent to the Python worker
+ */
+private[spark] object PythonEvalType {
+ val NON_UDF = 0
+ val SQL_BATCHED_UDF = 1
+ val SQL_ARROW_UDF = 2
--- End diff --
the new udf parameter is pandas `Series`, I think it's more accurate to
call it `SQL_PANDAS_UDF`.
---
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[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18659#discussion_r137412019
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowEvalPythonExec.scala
---
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.python
+
+import java.io.File
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.api.python.{ChainedPythonFunctions,
PythonEvalType, PythonRunner}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.arrow.{ArrowConverters, ArrowPayload}
+import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.util.Utils
+
+
+/**
+ * A physical plan that evaluates a [[PythonUDF]],
+ */
+case class ArrowEvalPythonExec(udfs: Seq[PythonUDF], output:
Seq[Attribute], child: SparkPlan)
+ extends SparkPlan {
+
+ def children: Seq[SparkPlan] = child :: Nil
+
+ override def producedAttributes: AttributeSet =
AttributeSet(output.drop(child.output.length))
+
+ private def collectFunctions(udf: PythonUDF): (ChainedPythonFunctions,
Seq[Expression]) = {
+udf.children match {
+ case Seq(u: PythonUDF) =>
+val (chained, children) = collectFunctions(u)
+(ChainedPythonFunctions(chained.funcs ++ Seq(udf.func)), children)
+ case children =>
+// There should not be any other UDFs, or the children can't be
evaluated directly.
+assert(children.forall(_.find(_.isInstanceOf[PythonUDF]).isEmpty))
+(ChainedPythonFunctions(Seq(udf.func)), udf.children)
+}
+ }
+
+ protected override def doExecute(): RDD[InternalRow] = {
+val inputRDD = child.execute().map(_.copy())
+val bufferSize = inputRDD.conf.getInt("spark.buffer.size", 65536)
+val reuseWorker =
inputRDD.conf.getBoolean("spark.python.worker.reuse", defaultValue = true)
+
+inputRDD.mapPartitions { iter =>
+
+ // The queue used to buffer input rows so we can drain it to
+ // combine input with output from Python.
+ val queue = HybridRowQueue(TaskContext.get().taskMemoryManager(),
+new File(Utils.getLocalDir(SparkEnv.get.conf)),
child.output.length)
+ TaskContext.get().addTaskCompletionListener({ ctx =>
+queue.close()
+ })
+
+ val (pyFuncs, inputs) = udfs.map(collectFunctions).unzip
+
+ // flatten all the arguments
+ val allInputs = new ArrayBuffer[Expression]
+ val dataTypes = new ArrayBuffer[DataType]
+ val argOffsets = inputs.map { input =>
+input.map { e =>
+ if (allInputs.exists(_.semanticEquals(e))) {
+allInputs.indexWhere(_.semanticEquals(e))
+ } else {
+allInputs += e
+dataTypes += e.dataType
+allInputs.length - 1
+ }
+}.toArray
+ }.toArray
+ val projection = newMutableProjection(allInputs, child.output)
+ val schema = StructType(dataTypes.zipWithIndex.map { case (dt, i) =>
+StructField(s"_$i", dt)
+ })
+
+ // Input iterator to Python: input rows are grouped so we send them
in batches to Python.
+ // For each row, add it to the queue.
+ val projectedRowIter = iter.map { inputRow =>
+queue.add(inputRow.asInstanceOf[UnsafeRow])
+projection(inputRow)
+ }
+
+ val context = TaskContext.get()
+
+ val inputIterator = ArrowConverters.toPayloadIterator(
+ projectedRowIter, schema, conf.arrowMaxRecordsPerBatch, context).
+map(_.asPythonSerializable)
+
+ val schemaOut =
St
[GitHub] spark pull request #18659: [SPARK-21190][PYSPARK][WIP] Simple Python Vectori...
Github user BryanCutler commented on a diff in the pull request: https://github.com/apache/spark/pull/18659#discussion_r137402913 --- Diff: python/pyspark/sql/functions.py --- @@ -2112,7 +2113,7 @@ def wrapper(*args): @since(1.3) -def udf(f=None, returnType=StringType()): +def udf(f=None, returnType=StringType(), vectorized=False): --- End diff -- @felixcheung does this fit your idea for a more generic decorator? Not exclusively labeled as `pandas_udf`, just enable vectorization with a flag, e.g. `@udf(DoubleType(), vectorized=True)` --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
