Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19147#discussion_r137707828
--- 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 --
Yes, it is a lot of duplicated code from `PythonRunner` that could be
refactored. I'm guessing you did not use the existing code because of the
Arrow stream format? While I would love to start using that in Spark, I think
it would be better to do this at a later time when the required code could be
refactored and the Arrow stream format could replace where we currently use the
file format.
Also, the good part about using the iterator based file format is each
iteration can allow Python to communicate back an error code and exit
gracefully. In my own tests with the streaming format if an error occurred
after the stream had started, Spark could lock up in a waiting state. These
are the reasons I did not use the streaming format in my implementation. Would
this `VectorizedPythonRunner` be able to handle these types of errors?
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
