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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/06/09 15:58:36 UTC
[GitHub] [spark] maropu commented on a change in pull request #27983: [SPARK-31936][SQL]Implement ScriptTransformation in sql/core
maropu commented on a change in pull request #27983:
URL: https://github.com/apache/spark/pull/27983#discussion_r437476009
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/script/ScriptTransformationExec.scala
##########
@@ -0,0 +1,239 @@
+/*
+ * 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.script
+
+import java.io._
+import java.nio.charset.StandardCharsets
+import java.sql.Date
+
+import scala.collection.JavaConverters._
+import scala.util.control.NonFatal
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.TaskContext
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
+import org.apache.spark.sql.catalyst.util.{DateFormatter, DateTimeUtils, TimestampFormatter}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils.SQLTimestamp
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.types.{DataType, DateType, TimestampType}
+import org.apache.spark.util.{CircularBuffer, RedirectThread}
+
+/**
+ * Transforms the input by forking and running the specified script.
+ *
+ * @param input the set of expression that should be passed to the script.
+ * @param script the command that should be executed.
+ * @param output the attributes that are produced by the script.
+ */
+case class ScriptTransformationExec(
+ input: Seq[Expression],
+ script: String,
+ output: Seq[Attribute],
+ child: SparkPlan,
+ ioschema: ScriptTransformIOSchema)
+ extends ScriptTransformBase {
+
+ override def producedAttributes: AttributeSet = outputSet -- inputSet
+
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ 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()
+
+ val outputProjection = new InterpretedProjection(input, child.output)
+
+ // 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 ScriptTransformationWriterThread(
+ inputIterator.map(outputProjection),
+ input.map(_.dataType),
+ ioschema,
+ outputStream,
+ proc,
+ stderrBuffer,
+ TaskContext.get(),
+ hadoopConf
+ )
+
+ val reader = new BufferedReader(new InputStreamReader(inputStream, StandardCharsets.UTF_8))
+ val outputIterator: Iterator[InternalRow] = new Iterator[InternalRow] {
+ var curLine: String = null
+ val mutableRow = new SpecificInternalRow(output.map(_.dataType))
+
+ override def hasNext: Boolean = {
+ try {
+ if (curLine == null) {
+ curLine = reader.readLine()
+ if (curLine == null) {
+ checkFailureAndPropagate(writerThread, null, proc, stderrBuffer)
+ return false
+ }
+ }
+ true
+ } catch {
+ case NonFatal(e) =>
+ // If this exception is due to abrupt / unclean termination of `proc`,
+ // then detect it and propagate a better exception message for end users
+ checkFailureAndPropagate(writerThread, e, proc, stderrBuffer)
+
+ throw e
+ }
+ }
+
+ override def next(): InternalRow = {
+ if (!hasNext) {
+ throw new NoSuchElementException
+ }
+ val prevLine = curLine
+ curLine = reader.readLine()
+ if (!ioschema.isSchemaLess) {
+ new GenericInternalRow(
+ prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+ .map(CatalystTypeConverters.convertToCatalyst))
+ } else {
+ new GenericInternalRow(
+ prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2)
+ .map(CatalystTypeConverters.convertToCatalyst))
+ }
+ }
+ }
+
+ writerThread.start()
+
+ outputIterator
+ }
+}
+
+private class ScriptTransformationWriterThread(
+ iter: Iterator[InternalRow],
+ inputSchema: Seq[DataType],
+ ioschema: ScriptTransformIOSchema,
+ outputStream: OutputStream,
+ proc: Process,
+ stderrBuffer: CircularBuffer,
+ taskContext: TaskContext,
+ conf: Configuration)
+ extends ScriptTransformationWriterThreadBase(
+ iter,
+ inputSchema,
+ outputStream,
+ proc,
+ stderrBuffer,
+ taskContext,
+ conf) {
+
+ setDaemon(true)
+
+ protected val lineDelimiter = ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+ protected val fieldDelimiter = ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD")
+
+ override def processRows(): Unit = {
+ val len = inputSchema.length
+ iter.foreach { row =>
+ val data = if (len == 0) {
+ ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+ } else {
+ val sb = new StringBuilder
+ sb.append(row.get(0, inputSchema(0)))
+ var i = 1
+ while (i < len) {
+ sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+ val columnType = inputSchema(i)
+ val fieldValue = row.get(i, columnType)
+ val fieldStringValue = columnType match {
+ case _: DateType =>
+ val dateFormatter = DateFormatter(DateTimeUtils.defaultTimeZone.toZoneId)
+ dateFormatter.format(fieldValue.asInstanceOf[Int])
+ case _: TimestampType =>
+ DateTimeUtils.timestampToString(
+ TimestampFormatter.getFractionFormatter(DateTimeUtils.defaultTimeZone.toZoneId),
+ fieldValue.asInstanceOf[SQLTimestamp])
Review comment:
Two questions I have here;
- Why do we need the special handling for date/timestamp here? It seems the original one (w/ the null serde case) simply output as strings: https://github.com/apache/spark/blob/6befb2d8bdc5743d0333f4839cf301af165582ce/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformationExec.scala#L289-L306
- Do you have any plan to support a custom serde in the Spark-native implementation?
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/script/ScriptTransformationExec.scala
##########
@@ -0,0 +1,239 @@
+/*
+ * 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.script
+
+import java.io._
+import java.nio.charset.StandardCharsets
+import java.sql.Date
+
+import scala.collection.JavaConverters._
+import scala.util.control.NonFatal
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.TaskContext
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
+import org.apache.spark.sql.catalyst.util.{DateFormatter, DateTimeUtils, TimestampFormatter}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils.SQLTimestamp
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.types.{DataType, DateType, TimestampType}
+import org.apache.spark.util.{CircularBuffer, RedirectThread}
+
+/**
+ * Transforms the input by forking and running the specified script.
+ *
+ * @param input the set of expression that should be passed to the script.
+ * @param script the command that should be executed.
+ * @param output the attributes that are produced by the script.
+ */
+case class ScriptTransformationExec(
+ input: Seq[Expression],
+ script: String,
+ output: Seq[Attribute],
+ child: SparkPlan,
+ ioschema: ScriptTransformIOSchema)
+ extends ScriptTransformBase {
+
+ override def producedAttributes: AttributeSet = outputSet -- inputSet
+
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ 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()
+
+ val outputProjection = new InterpretedProjection(input, child.output)
+
+ // 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 ScriptTransformationWriterThread(
+ inputIterator.map(outputProjection),
+ input.map(_.dataType),
+ ioschema,
+ outputStream,
+ proc,
+ stderrBuffer,
+ TaskContext.get(),
+ hadoopConf
+ )
+
+ val reader = new BufferedReader(new InputStreamReader(inputStream, StandardCharsets.UTF_8))
+ val outputIterator: Iterator[InternalRow] = new Iterator[InternalRow] {
+ var curLine: String = null
+ val mutableRow = new SpecificInternalRow(output.map(_.dataType))
+
+ override def hasNext: Boolean = {
+ try {
+ if (curLine == null) {
+ curLine = reader.readLine()
+ if (curLine == null) {
+ checkFailureAndPropagate(writerThread, null, proc, stderrBuffer)
+ return false
+ }
+ }
+ true
+ } catch {
+ case NonFatal(e) =>
+ // If this exception is due to abrupt / unclean termination of `proc`,
+ // then detect it and propagate a better exception message for end users
+ checkFailureAndPropagate(writerThread, e, proc, stderrBuffer)
+
+ throw e
+ }
+ }
+
+ override def next(): InternalRow = {
+ if (!hasNext) {
+ throw new NoSuchElementException
+ }
+ val prevLine = curLine
+ curLine = reader.readLine()
+ if (!ioschema.isSchemaLess) {
+ new GenericInternalRow(
+ prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+ .map(CatalystTypeConverters.convertToCatalyst))
+ } else {
+ new GenericInternalRow(
+ prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2)
+ .map(CatalystTypeConverters.convertToCatalyst))
+ }
+ }
+ }
+
+ writerThread.start()
+
+ outputIterator
+ }
+}
+
+private class ScriptTransformationWriterThread(
+ iter: Iterator[InternalRow],
+ inputSchema: Seq[DataType],
+ ioschema: ScriptTransformIOSchema,
+ outputStream: OutputStream,
+ proc: Process,
+ stderrBuffer: CircularBuffer,
+ taskContext: TaskContext,
+ conf: Configuration)
+ extends ScriptTransformationWriterThreadBase(
+ iter,
+ inputSchema,
+ outputStream,
+ proc,
+ stderrBuffer,
+ taskContext,
+ conf) {
+
+ setDaemon(true)
+
+ protected val lineDelimiter = ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+ protected val fieldDelimiter = ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD")
+
+ override def processRows(): Unit = {
+ val len = inputSchema.length
+ iter.foreach { row =>
+ val data = if (len == 0) {
+ ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+ } else {
+ val sb = new StringBuilder
+ sb.append(row.get(0, inputSchema(0)))
+ var i = 1
+ while (i < len) {
+ sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+ val columnType = inputSchema(i)
+ val fieldValue = row.get(i, columnType)
+ val fieldStringValue = columnType match {
+ case _: DateType =>
+ val dateFormatter = DateFormatter(DateTimeUtils.defaultTimeZone.toZoneId)
+ dateFormatter.format(fieldValue.asInstanceOf[Int])
+ case _: TimestampType =>
+ DateTimeUtils.timestampToString(
+ TimestampFormatter.getFractionFormatter(DateTimeUtils.defaultTimeZone.toZoneId),
+ fieldValue.asInstanceOf[SQLTimestamp])
Review comment:
Two questions I have here;
- Why do we need the special handling for date/timestamp here? It seems the original one (w/ the null serde case) simply outputs rows as strings though: https://github.com/apache/spark/blob/6befb2d8bdc5743d0333f4839cf301af165582ce/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformationExec.scala#L289-L306
- Do you have any plan to support a custom serde in the Spark-native implementation?
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