AngersZhuuuu commented on a change in pull request #29085:
URL: https://github.com/apache/spark/pull/29085#discussion_r459182038
##########
File path:
sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveScriptTransformationExec.scala
##########
@@ -54,87 +52,110 @@ case class HiveScriptTransformationExec(
script: String,
output: Seq[Attribute],
child: SparkPlan,
- ioschema: HiveScriptIOSchema)
- extends BaseScriptTransformationExec {
+ ioschema: ScriptTransformationIOSchema)
+ extends BaseScriptTransformationExec with HiveInspectors {
- override def processIterator(
- inputIterator: Iterator[InternalRow],
- hadoopConf: Configuration): Iterator[InternalRow] = {
- val cmd = List("/bin/bash", "-c", script)
- val builder = new ProcessBuilder(cmd.asJava)
-
- val proc = builder.start()
- val inputStream = proc.getInputStream
- val outputStream = proc.getOutputStream
- val errorStream = proc.getErrorStream
-
- // In order to avoid deadlocks, we need to consume the error output of the
child process.
- // To avoid issues caused by large error output, we use a circular buffer
to limit the amount
- // of error output that we retain. See SPARK-7862 for more discussion of
the deadlock / hang
- // that motivates this.
- val stderrBuffer = new CircularBuffer(2048)
- new RedirectThread(
- errorStream,
- stderrBuffer,
- "Thread-ScriptTransformation-STDERR-Consumer").start()
+ private def initInputSerDe(
+ input: Seq[Expression]): Option[(AbstractSerDe, StructObjectInspector)]
= {
+ ioschema.inputSerdeClass.map { serdeClass =>
+ val (columns, columnTypes) = parseAttrs(input)
+ val serde = initSerDe(serdeClass, columns, columnTypes,
ioschema.inputSerdeProps)
+ val fieldObjectInspectors = columnTypes.map(toInspector)
+ val objectInspector = ObjectInspectorFactory
+ .getStandardStructObjectInspector(columns.asJava,
fieldObjectInspectors.asJava)
+ (serde, objectInspector)
+ }
+ }
- val outputProjection = new InterpretedProjection(input, child.output)
+ private def initOutputSerDe(
+ output: Seq[Attribute]): Option[(AbstractSerDe, StructObjectInspector)]
= {
+ ioschema.outputSerdeClass.map { serdeClass =>
+ val (columns, columnTypes) = parseAttrs(output)
+ val serde = initSerDe(serdeClass, columns, columnTypes,
ioschema.outputSerdeProps)
+ val structObjectInspector =
serde.getObjectInspector().asInstanceOf[StructObjectInspector]
+ (serde, structObjectInspector)
+ }
+ }
- // This nullability is a performance optimization in order to avoid an
Option.foreach() call
- // inside of a loop
- @Nullable val (inputSerde, inputSoi) =
ioschema.initInputSerDe(input).getOrElse((null, null))
+ private def parseAttrs(attrs: Seq[Expression]): (Seq[String], Seq[DataType])
= {
+ val columns = attrs.zipWithIndex.map(e => s"${e._1.prettyName}_${e._2}")
+ val columnTypes = attrs.map(_.dataType)
+ (columns, columnTypes)
+ }
- // This new thread will consume the ScriptTransformation's input rows and
write them to the
- // external process. That process's output will be read by this current
thread.
- val writerThread = new HiveScriptTransformationWriterThread(
- inputIterator.map(outputProjection),
- input.map(_.dataType),
- inputSerde,
- inputSoi,
- ioschema,
- outputStream,
- proc,
- stderrBuffer,
- TaskContext.get(),
- hadoopConf
- )
+ private def initSerDe(
Review comment:
> Sorry for the confusion, but, on second thought, its better to pull
out hive-serde related functions from `HiveScriptTransformationExec` then
create a companion object having them for readability
[maropu@972775b](https://github.com/maropu/spark/commit/972775b821406d81d3c1ba1c718de3037a0ca068).
WDTY?
Agree, make ScripTransformExec only handle data process.
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