AngersZhuuuu commented on a change in pull request #29085:
URL: https://github.com/apache/spark/pull/29085#discussion_r456751726
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/BaseScriptTransformationExec.scala
##########
@@ -56,10 +65,85 @@ trait BaseScriptTransformationExec extends UnaryExecNode {
}
}
- def processIterator(
+ protected def initProc: (OutputStream, Process, InputStream, CircularBuffer)
= {
+ 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,
+ s"Thread-${this.getClass.getSimpleName}-STDERR-Consumer").start()
+ (outputStream, proc, inputStream, stderrBuffer)
+ }
+
+ protected def processIterator(
inputIterator: Iterator[InternalRow],
hadoopConf: Configuration): Iterator[InternalRow]
+ protected def createOutputIteratorWithoutSerde(
+ writerThread: BaseScriptTransformationWriterThread,
+ inputStream: InputStream,
+ proc: Process,
+ stderrBuffer: CircularBuffer): Iterator[InternalRow] = {
+ new Iterator[InternalRow] {
+ var curLine: String = null
+ val reader = new BufferedReader(new InputStreamReader(inputStream,
StandardCharsets.UTF_8))
+
+ val processRowWithoutSerde = if (!ioschema.schemaLess) {
+ prevLine: String =>
+ new GenericInternalRow(
+
prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+ .zip(fieldWriters)
+ .map { case (data, writer) => writer(data) })
+ } else {
+ prevLine: String =>
+ new GenericInternalRow(
+
prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2)
+ .map(CatalystTypeConverters.convertToCatalyst))
+ }
+
Review comment:
@maropu Here I change for support schema less mode.
In test case I choose not to use sql since hive serde can't support
schemaless mode well in spark's way.
```
[info] - SPARK-25990: TRANSFORM should handle schema less correctly ***
FAILED *** (360 milliseconds)
[info] Results do not match for Spark plan:
[info] HiveScriptTransformation [a#86, b#87, c#88, d#89, e#90], python
/Users/angerszhu/Documents/project/AngersZhu/spark/sql/core/target/test-classes/test_script.py,
[key#96, value#97],
ScriptTransformationIOSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim,
)),List((field.delim,
)),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),true)
[info] +- Project [_1#75 AS a#86, _2#76 AS b#87, _3#77 AS c#88, _4#78 AS
d#89, _5#79 AS e#90]
[info] +- LocalTableScan [_1#75, _2#76, _3#77, _4#78, _5#79]
[info]
[info]
[info] == Results ==
[info] !== Expected Answer - 3 == ==
Actual Answer - 3 ==
[info] ![1,1 1.0 1.000000000000000000 1969-12-31
16:00:00.001] [1,1]
[info] ![2,2 2.0 2.000000000000000000 1969-12-31
16:00:00.002] [2,2]
[info] ![3,3 3.0 3.000000000000000000 1969-12-31
16:00:00.003] [3,3] (SparkPlanTest.scala:96)
[info] org.scalatest.exceptions.TestFailedException:
[info] at
org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
[info] at
org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
[info] at
org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
[i
```
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