yma11 commented on code in PR #5462: URL: https://github.com/apache/incubator-gluten/pull/5462#discussion_r1575695397
########## backends-velox/src/main/scala/org/apache/gluten/execution/python/ColumnarArrowEvalPythonExec.scala: ########## @@ -0,0 +1,365 @@ +/* + * 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.api.python + +import org.apache.gluten.columnarbatch.ColumnarBatches +import org.apache.gluten.extension.GlutenPlan +import org.apache.gluten.memory.arrowalloc.ArrowBufferAllocators +import org.apache.gluten.utils.Iterators +import org.apache.gluten.vectorized.ArrowWritableColumnVector + +import org.apache.spark.{ContextAwareIterator, SparkEnv, TaskContext} +import org.apache.spark.internal.Logging +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.python.{BasePythonRunnerShim, EvalPythonExec, PythonUDFRunner} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{DataType, StructField, StructType} +import org.apache.spark.sql.utils.{SparkArrowUtil, SparkSchemaUtil, SparkVectorUtil} +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector} +import org.apache.spark.util.Utils + +import org.apache.arrow.vector.{VectorLoader, VectorSchemaRoot} +import org.apache.arrow.vector.ipc.{ArrowStreamReader, ArrowStreamWriter} + +import java.io.{DataInputStream, DataOutputStream} +import java.net.Socket +import java.util.concurrent.atomic.AtomicBoolean + +import scala.collection.mutable.ArrayBuffer + +class ColumnarArrowPythonRunner( + funcs: Seq[ChainedPythonFunctions], + evalType: Int, + argOffsets: Array[Array[Int]], + schema: StructType, + timeZoneId: String, + conf: Map[String, String]) + extends BasePythonRunnerShim(funcs, evalType, argOffsets) { + + override val simplifiedTraceback: Boolean = SQLConf.get.pysparkSimplifiedTraceback + + override val bufferSize: Int = SQLConf.get.pandasUDFBufferSize + require( + bufferSize >= 4, + "Pandas execution requires more than 4 bytes. Please set higher buffer. " + + s"Please change '${SQLConf.PANDAS_UDF_BUFFER_SIZE.key}'.") + + protected def newReaderIterator( + stream: DataInputStream, + writerThread: WriterThread, + startTime: Long, + env: SparkEnv, + worker: Socket, + pid: scala.Option[scala.Int], + releasedOrClosed: AtomicBoolean, + context: TaskContext): Iterator[ColumnarBatch] = { + + new ReaderIterator( + stream, + writerThread, + startTime, + env, + worker, + None, + releasedOrClosed, + context) { + private val allocator = ArrowBufferAllocators.contextInstance() + + private var reader: ArrowStreamReader = _ + private var root: VectorSchemaRoot = _ + private var schema: StructType = _ + private var vectors: Array[ColumnVector] = _ + + context.addTaskCompletionListener[Unit] { + _ => + if (reader != null) { + reader.close(false) + } + if (root != null) { + root.close() + } + } + + private var batchLoaded = true + + override protected def read(): ColumnarBatch = { + if (writerThread.exception.isDefined) { + throw writerThread.exception.get + } + try { + if (reader != null && batchLoaded) { + batchLoaded = reader.loadNextBatch() + if (batchLoaded) { + val batch = new ColumnarBatch(vectors) + batch.setNumRows(root.getRowCount) + batch + } else { + reader.close(false) + // Reach end of stream. Call `read()` again to read control data. + read() + } + } else { + stream.readInt() match { + case SpecialLengths.START_ARROW_STREAM => + reader = new ArrowStreamReader(stream, allocator) + root = reader.getVectorSchemaRoot() + schema = SparkArrowUtil.fromArrowSchema(root.getSchema()) + vectors = ArrowWritableColumnVector + .loadColumns(root.getRowCount, root.getFieldVectors) + .toArray[ColumnVector] + read() + case SpecialLengths.TIMING_DATA => + handleTimingData() + read() + case SpecialLengths.PYTHON_EXCEPTION_THROWN => + throw handlePythonException() + case SpecialLengths.END_OF_DATA_SECTION => + handleEndOfDataSection() + null + } + } + } catch handleException Review Comment: `handleException` is implemented in base class for exception processing, refer [code](https://github.com/apache/spark/blob/branch-3.3/core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala#L581-L601). -- This is an automated message from the Apache Git Service. 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