Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19147#discussion_r138215300
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/VectorizedPythonRunner.scala
---
@@ -0,0 +1,329 @@
+/*
+ * 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.{BufferedInputStream, BufferedOutputStream,
DataInputStream, DataOutputStream}
+import java.net.Socket
+import java.nio.charset.StandardCharsets
+
+import scala.collection.JavaConverters._
+
+import org.apache.arrow.vector.VectorSchemaRoot
+import org.apache.arrow.vector.stream.{ArrowStreamReader,
ArrowStreamWriter}
+
+import org.apache.spark.{SparkEnv, SparkFiles, TaskContext}
+import org.apache.spark.api.python.{ChainedPythonFunctions,
PythonEvalType, PythonException, PythonRDD, SpecialLengths}
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.arrow.{ArrowUtils, ArrowWriter}
+import org.apache.spark.sql.execution.vectorized.{ArrowColumnVector,
ColumnarBatch, ColumnVector}
+import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
+
+/**
+ * Similar to `PythonRunner`, but exchange data with Python worker via
columnar format.
+ */
+class VectorizedPythonRunner(
+ funcs: Seq[ChainedPythonFunctions],
+ batchSize: Int,
+ bufferSize: Int,
+ reuse_worker: Boolean,
+ argOffsets: Array[Array[Int]]) extends Logging {
+
+ require(funcs.length == argOffsets.length, "argOffsets should have the
same length as funcs")
+
+ // All the Python functions should have the same exec, version and
envvars.
+ private val envVars = funcs.head.funcs.head.envVars
+ private val pythonExec = funcs.head.funcs.head.pythonExec
+ private val pythonVer = funcs.head.funcs.head.pythonVer
+
+ // TODO: support accumulator in multiple UDF
+ private val accumulator = funcs.head.funcs.head.accumulator
+
+ // todo: return column batch?
+ def compute(
--- End diff --
I was referring to the protocol between Scala and Python that is changed
here and could act differently under some circumstances. Here is the behavior
of the `PythonRunner` protocol and `VectorizedPythonRunner` protocol that you
introduce here:
**PythonRunner**
Data blocks are framed by a special length integer. Scala reads each data
block one at a time and checks the length code. If the code is a
`PythonException`, the error is read from Python and a `SparkException` is
thrown with that being the cause.
**VectorizedPythonRunner**
A data stream is opened in Scala with `ArrowStreamReader` and batches are
transferred until `ArrowStreamReader` returns False indicating there is no more
data. Only at this point are the special length codes checked to handle an
error from Python.
This behavior change would probably only cause problems if things are not
working normally. For example, what would happen if `pyarrow` was not
installed on an executor? With `PythonRunner` the ImportError would cause a
`PythonException` to be transferred and thrown in Scala. In
`VectorizedPythonRunner` I believe the `ArrowStreamReader` would try to read
the special length code and then fail somewhere internally to Arrow, not
showing the ImportError.
My point was that this type of behavior change should probably be
implemented in a separate JIRA where we could make sure to handle all of these
cases.
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