Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/5526#discussion_r29814711
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala ---
@@ -41,3 +55,359 @@ private[sql] case class InsertIntoDataSource(
Seq.empty[Row]
}
}
+
+private[sql] case class InsertIntoFSBasedRelation(
+ @transient relation: FSBasedRelation,
+ @transient query: LogicalPlan,
+ partitionColumns: Array[String],
+ mode: SaveMode)
+ extends RunnableCommand {
+
+ override def run(sqlContext: SQLContext): Seq[Row] = {
+ require(
+ relation.paths.length == 1,
+ s"Cannot write to multiple destinations:
${relation.paths.mkString(",")}")
+
+ val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
+ val outputPath = new Path(relation.paths.head)
+ val fs = outputPath.getFileSystem(hadoopConf)
+ val qualifiedOutputPath = fs.makeQualified(outputPath)
+
+ val doInsertion = (mode, fs.exists(qualifiedOutputPath)) match {
+ case (SaveMode.ErrorIfExists, true) =>
+ sys.error(s"path $qualifiedOutputPath already exists.")
+ case (SaveMode.Overwrite, true) =>
+ fs.delete(qualifiedOutputPath, true)
+ true
+ case (SaveMode.Append, _) | (SaveMode.Overwrite, _) |
(SaveMode.ErrorIfExists, false) =>
+ true
+ case (SaveMode.Ignore, exists) =>
+ !exists
+ }
+
+ if (doInsertion) {
+ val job = Job.getInstance(hadoopConf)
+ job.setOutputKeyClass(classOf[Void])
+ job.setOutputValueClass(classOf[Row])
+ FileOutputFormat.setOutputPath(job, qualifiedOutputPath)
+
+ val df = sqlContext.createDataFrame(
+ DataFrame(sqlContext, query).queryExecution.toRdd,
+ relation.schema,
+ needsConversion = false)
+
+ if (partitionColumns.isEmpty) {
+ insert(new DefaultWriterContainer(relation, job), df)
+ } else {
+ val writerContainer = new DynamicPartitionWriterContainer(
+ relation, job, partitionColumns, "__HIVE_DEFAULT_PARTITION__")
+ insertWithDynamicPartitions(writerContainer, df, partitionColumns)
+ }
+ }
+
+ Seq.empty[Row]
+ }
+
+ private def insert(writerContainer: BaseWriterContainer, df: DataFrame):
Unit = {
+ try {
+ writerContainer.driverSideSetup()
+
df.sqlContext.sparkContext.runJob(df.queryExecution.executedPlan.execute(),
writeRows _)
+ writerContainer.commitJob()
+ relation.refresh()
+ } catch { case cause: Throwable =>
+ writerContainer.abortJob()
+ throw new SparkException("Job aborted.", cause)
+ }
+
+ def writeRows(taskContext: TaskContext, iterator: Iterator[Row]): Unit
= {
+ writerContainer.executorSideSetup(taskContext)
+
+ try {
+ while (iterator.hasNext) {
+ val row = iterator.next()
+ writerContainer.outputWriterForRow(row).write(row)
+ }
+ writerContainer.commitTask()
+ } catch { case cause: Throwable =>
+ writerContainer.abortTask()
+ throw new SparkException("Task failed while writing rows.", cause)
+ }
+ }
+ }
+
+ private def insertWithDynamicPartitions(
+ writerContainer: BaseWriterContainer,
+ df: DataFrame,
+ partitionColumns: Array[String]): Unit = {
+
+ require(
+ df.schema == relation.schema,
+ s"""DataFrame must have the same schema as the relation to which is
inserted.
+ |DataFrame schema: ${df.schema}
+ |Relation schema: ${relation.schema}
+ """.stripMargin)
+
+ val sqlContext = df.sqlContext
+
+ val (partitionRDD, dataRDD) = {
+ val fieldNames = relation.schema.fieldNames
+ val dataCols = fieldNames.filterNot(partitionColumns.contains)
+ val df = sqlContext.createDataFrame(
+ DataFrame(sqlContext, query).queryExecution.toRdd,
+ relation.schema,
+ needsConversion = false)
+
+ val partitionColumnsInSpec =
relation.partitionSpec.partitionColumns.map(_.name)
+ require(
+ partitionColumnsInSpec.sameElements(partitionColumns),
+ s"""Partition columns mismatch.
+ |Expected: ${partitionColumnsInSpec.mkString(", ")}
+ |Actual: ${partitionColumns.mkString(", ")}
+ """.stripMargin)
+
+ val partitionDF = df.select(partitionColumns.head,
partitionColumns.tail: _*)
+ val dataDF = df.select(dataCols.head, dataCols.tail: _*)
+
+ partitionDF.queryExecution.executedPlan.execute() ->
+ dataDF.queryExecution.executedPlan.execute()
+ }
+
+ try {
+ writerContainer.driverSideSetup()
+ sqlContext.sparkContext.runJob(partitionRDD.zip(dataRDD), writeRows
_)
--- End diff --
Maybe it is better to use a projection to extract values of those partition
columns?
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