[GitHub] [spark] icexelloss commented on a change in pull request #24981: [WIP][SPARK-27463][PYTHON] Support Dataframe Cogroup via Pandas UDFs- Arrow Stream Impl

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icexelloss commented on a change in pull request #24981: [WIP][SPARK-27463][PYTHON] Support Dataframe Cogroup via Pandas UDFs- Arrow Stream Impl
URL: https://github.com/apache/spark/pull/24981#discussion_r299703366
 
 

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 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/python/InterleavedArrowPythonRunner.scala
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+/*
+ * 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._
+import java.net._
+
+import org.apache.arrow.vector.VectorSchemaRoot
+import org.apache.arrow.vector.ipc.ArrowStreamWriter
+
+import org.apache.spark._
+import org.apache.spark.api.python._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.arrow.ArrowWriter
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+
+class InterleavedArrowPythonRunner(
+                         funcs: Seq[ChainedPythonFunctions],
+                         evalType: Int,
+                         argOffsets: Array[Array[Int]],
+                         leftSchema: StructType,
+                         rightSchema: StructType,
+                         timeZoneId: String,
+                         conf: Map[String, String])
+  extends BaseArrowPythonRunner[(Iterator[InternalRow], Iterator[InternalRow])](
+    funcs, evalType, argOffsets) {
+
+  protected def newWriterThread(
+        env: SparkEnv,
+        worker: Socket,
+        inputIterator: Iterator[(Iterator[InternalRow], Iterator[InternalRow])],
+        partitionIndex: Int,
+        context: TaskContext): WriterThread = {
+    new WriterThread(env, worker, inputIterator, partitionIndex, context) {
+
+      protected override def writeCommand(dataOut: DataOutputStream): Unit = {
+
+        // Write config for the worker as a number of key -> value pairs of strings
+        dataOut.writeInt(conf.size)
+        for ((k, v) <- conf) {
+          PythonRDD.writeUTF(k, dataOut)
+          PythonRDD.writeUTF(v, dataOut)
+        }
+
+        PythonUDFRunner.writeUDFs(dataOut, funcs, argOffsets)
+      }
+
+      protected override def writeIteratorToStream(dataOut: DataOutputStream): Unit = {
+        while (inputIterator.hasNext) {
+          dataOut.writeInt(SpecialLengths.START_ARROW_STREAM)
+          val (nextLeft, nextRight) = inputIterator.next()
+          writeGroup(nextLeft, leftSchema, dataOut)
 
 Review comment:
   e.g. comparing the performance with:
   * Each group has 1 row
   * Each group has 10 row
   * Each group has 100 row
   ...
   * The dataframe contains a single group..
   
   @BryanCutler @HyukjinKwon WDYT?

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