Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/15551#discussion_r84196777
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriteOutput.scala
---
@@ -0,0 +1,514 @@
+/*
+ * 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.datasources
+
+import java.util.{Date, UUID}
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter,
FileOutputFormat}
+import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+
+import org.apache.spark._
+import org.apache.spark.internal.Logging
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
+import org.apache.spark.sql.{Dataset, SparkSession}
+import org.apache.spark.sql.catalyst.catalog.BucketSpec
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.{SQLExecution,
UnsafeKVExternalSorter}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField,
StructType}
+import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter
+
+
+/**
+ * A helper object for writing data out to an existing partition.
+ */
+object WriteOutput extends Logging {
+
+ /** A shared job description for all the write tasks. */
+ private class WriteJobDescription(
+ val serializableHadoopConf: SerializableConfiguration,
+ val outputWriterFactory: OutputWriterFactory,
+ val allColumns: Seq[Attribute],
+ val partitionColumns: Seq[Attribute],
+ val nonPartitionColumns: Seq[Attribute],
+ val bucketSpec: Option[BucketSpec],
+ val isAppend: Boolean,
+ val path: String,
+ val outputFormatClass: Class[_ <: OutputFormat[_, _]])
+ extends Serializable {
+
+ assert(allColumns.toSet == (partitionColumns ++
nonPartitionColumns).toSet)
+ }
+
+ /**
+ * Basic work flow of this command is:
+ * 1. Driver side setup, including output committer initialization and
data source specific
+ * preparation work for the write job to be issued.
+ * 2. Issues a write job consists of one or more executor side tasks,
each of which writes all
+ * rows within an RDD partition.
+ * 3. If no exception is thrown in a task, commits that task, otherwise
aborts that task; If any
+ * exception is thrown during task commitment, also aborts that task.
+ * 4. If all tasks are committed, commit the job, otherwise aborts the
job; If any exception is
+ * thrown during job commitment, also aborts the job.
+ */
+ def write(
+ sparkSession: SparkSession,
+ plan: LogicalPlan,
+ fileFormat: FileFormat,
+ outputPath: Path,
+ hadoopConf: Configuration,
+ partitionColumns: Seq[Attribute],
+ bucketSpec: Option[BucketSpec],
+ refreshFunction: () => Unit,
+ options: Map[String, String],
+ isAppend: Boolean): Unit = {
+
+ val job = Job.getInstance(hadoopConf)
+ job.setOutputKeyClass(classOf[Void])
+ job.setOutputValueClass(classOf[InternalRow])
+ FileOutputFormat.setOutputPath(job, outputPath)
+
+ val partitionSet = AttributeSet(partitionColumns)
+ val dataColumns = plan.output.filterNot(partitionSet.contains)
+ val queryExecution = Dataset.ofRows(sparkSession, plan).queryExecution
+
+ // Note: prepareWrite has side effect. It sets "job".
+ val outputWriterFactory =
+ fileFormat.prepareWrite(sparkSession, job, options,
dataColumns.toStructType)
+
+ val description = new WriteJobDescription(
+ serializableHadoopConf = new
SerializableConfiguration(job.getConfiguration),
+ outputWriterFactory = outputWriterFactory,
+ allColumns = plan.output,
+ partitionColumns = partitionColumns,
+ nonPartitionColumns = dataColumns,
+ bucketSpec = bucketSpec,
+ isAppend = isAppend,
+ path = outputPath.toString,
+ outputFormatClass = job.getOutputFormatClass)
+
+ SQLExecution.withNewExecutionId(sparkSession, queryExecution) {
+ // This call shouldn't be put into the `try` block below because it
only initializes and
+ // prepares the job, any exception thrown from here shouldn't cause
abortJob() to be called.
+ val committer = setupDriverCommitter(job, outputPath.toString,
isAppend)
+
+ try {
+ sparkSession.sparkContext.runJob(queryExecution.toRdd,
+ (taskContext: TaskContext, iter: Iterator[InternalRow]) => {
+ executeTask(
+ description = description,
+ sparkStageId = taskContext.stageId(),
+ sparkPartitionId = taskContext.partitionId(),
+ sparkAttemptNumber = taskContext.attemptNumber(),
+ iterator = iter)
+ })
+
+ committer.commitJob(job)
+ logInfo(s"Job ${job.getJobID} committed.")
+
+ refreshFunction()
+ } catch { case cause: Throwable =>
+ logError(s"Aborting job ${job.getJobID}.", cause)
+ committer.abortJob(job, JobStatus.State.FAILED)
+
+ throw new SparkException("Job aborted.", cause)
+ }
+ }
+ }
+
+ /**
+ * Writes data out in a single Spark task.
+ */
+ def executeTask(
+ description: WriteJobDescription,
+ sparkStageId: Int,
+ sparkPartitionId: Int,
+ sparkAttemptNumber: Int,
+ iterator: Iterator[InternalRow]): Unit = {
+
+ // Set up the attempt context required to use in the output committers.
+ val taskAttemptContext: TaskAttemptContext = {
+ val jobId = SparkHadoopWriter.createJobID(new Date, sparkStageId)
+ val taskId = new TaskID(jobId, TaskType.MAP, sparkPartitionId)
+ val taskAttemptId = new TaskAttemptID(taskId, sparkAttemptNumber)
+
+ // Set up the configuration object
+ val hadoopConf = description.serializableHadoopConf.value
+ hadoopConf.set("mapred.job.id", jobId.toString)
+ hadoopConf.set("mapred.tip.id", taskAttemptId.getTaskID.toString)
+ hadoopConf.set("mapred.task.id", taskAttemptId.toString)
+ hadoopConf.setBoolean("mapred.task.is.map", true)
+ hadoopConf.setInt("mapred.task.partition", 0)
+
+ new TaskAttemptContextImpl(hadoopConf, taskAttemptId)
+ }
+
+ val committer = newOutputCommitter(
+ description.outputFormatClass, taskAttemptContext, description.path,
description.isAppend)
+ committer.setupTask(taskAttemptContext)
+
+ val jobId = taskAttemptContext.getJobID
+ val taskId = taskAttemptContext.getTaskAttemptID.getTaskID
+
+ val writeTask =
+ if (description.partitionColumns.isEmpty &&
description.bucketSpec.isEmpty) {
+ new SingleDirectoryWriteTask(description, taskAttemptContext,
committer)
+ } else {
+ new DynamicPartitionWriteTask(description, taskAttemptContext,
committer)
+ }
+
+ try {
+ Utils.tryWithSafeFinallyAndFailureCallbacks(block = {
+ // Execute the task to write rows out
+ writeTask.execute(iterator)
+ writeTask.releaseResources()
+
+ // Commit the task
+ SparkHadoopMapRedUtil.commitTask(committer, taskAttemptContext,
jobId.getId, taskId.getId)
+ })(catchBlock = {
+ // If there is an error, release resource and then abort the task
+ try {
--- End diff --
No because we can't abort the task if there is no error (which would happen
if we put it in finally).
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