spark BaseScriptTransformationExec 源码

spark BaseScriptTransformationExec 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/execution/BaseScriptTransformationExec.scala

/*
 * 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

import java.io.{BufferedReader, File, InputStream, InputStreamReader, OutputStream}
import java.nio.charset.StandardCharsets
import java.util.concurrent.TimeUnit

import scala.collection.JavaConverters._
import scala.util.control.NonFatal

import org.apache.hadoop.conf.Configuration

import org.apache.spark.{SparkFiles, TaskContext}
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Cast, Expression, GenericInternalRow, JsonToStructs, Literal, StructsToJson, UnsafeProjection}
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.{DateTimeUtils, IntervalUtils}
import org.apache.spark.sql.errors.QueryExecutionErrors
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
import org.apache.spark.util.{CircularBuffer, RedirectThread, SerializableConfiguration, Utils}

trait BaseScriptTransformationExec extends UnaryExecNode {
  def script: String
  def output: Seq[Attribute]
  def child: SparkPlan
  def ioschema: ScriptTransformationIOSchema

  protected lazy val inputExpressionsWithoutSerde: Seq[Expression] = {
    child.output.map { in =>
      in.dataType match {
        case _: ArrayType | _: MapType | _: StructType =>
          new StructsToJson(ioschema.inputSerdeProps.toMap, in)
            .withTimeZone(conf.sessionLocalTimeZone)
        case _ => Cast(in, StringType).withTimeZone(conf.sessionLocalTimeZone)
      }
    }
  }

  override def producedAttributes: AttributeSet = outputSet -- inputSet

  override def outputPartitioning: Partitioning = child.outputPartitioning

  override def doExecute(): RDD[InternalRow] = {
    val broadcastedHadoopConf =
      new SerializableConfiguration(session.sessionState.newHadoopConf())

    child.execute().mapPartitions { iter =>
      if (iter.hasNext) {
        val proj = UnsafeProjection.create(schema)
        processIterator(iter, broadcastedHadoopConf.value).map(proj)
      } else {
        // If the input iterator has no rows then do not launch the external script.
        Iterator.empty
      }
    }
  }

  protected def initProc: (OutputStream, Process, InputStream, CircularBuffer) = {
    val cmd = List("/bin/bash", "-c", script)
    val builder = new ProcessBuilder(cmd.asJava)
      .directory(new File(SparkFiles.getRootDirectory()))
    val path = System.getenv("PATH") + File.pathSeparator +
      SparkFiles.getRootDirectory()
    builder.environment().put("PATH", path)

    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 outputRowFormat = ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD")
      val processRowWithoutSerde = if (!ioschema.schemaLess) {
        prevLine: String =>
          new GenericInternalRow(
            prevLine.split(outputRowFormat, -1).padTo(outputFieldWriters.size, null)
              .zip(outputFieldWriters)
              .map { case (data, writer) => writer(data) })
      } else {
        // In schema less mode, hive will choose first two output column as output.
        // If output column size less then 2, it will return NULL for columns with missing values.
        // Here we split row string and choose first 2 values, if values's size less then 2,
        // we pad NULL value until 2 to make behavior same with hive.
        val kvWriter = CatalystTypeConverters.createToCatalystConverter(StringType)
        prevLine: String =>
          new GenericInternalRow(
            prevLine.split(outputRowFormat, -1).slice(0, 2).padTo(2, null)
              .map(kvWriter))
      }

      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()
        processRowWithoutSerde(prevLine)
      }
    }
  }

  protected def checkFailureAndPropagate(
      writerThread: BaseScriptTransformationWriterThread,
      cause: Throwable = null,
      proc: Process,
      stderrBuffer: CircularBuffer): Unit = {
    if (writerThread.exception.isDefined) {
      throw writerThread.exception.get
    }

    // There can be a lag between reader read EOF and the process termination.
    // If the script fails to startup, this kind of error may be missed.
    // So explicitly waiting for the process termination.
    val timeout = conf.getConf(SQLConf.SCRIPT_TRANSFORMATION_EXIT_TIMEOUT)
    val exitRes = proc.waitFor(timeout, TimeUnit.SECONDS)
    if (!exitRes) {
      log.warn(s"Transformation script process exits timeout in $timeout seconds")
    }

    if (!proc.isAlive) {
      val exitCode = proc.exitValue()
      if (exitCode != 0) {
        logError(stderrBuffer.toString) // log the stderr circular buffer
        throw QueryExecutionErrors.subprocessExitedError(exitCode, stderrBuffer, cause)
      }
    }
  }

  private lazy val outputFieldWriters: Seq[String => Any] = output.map { attr =>
    val converter = CatalystTypeConverters.createToCatalystConverter(attr.dataType)
    attr.dataType match {
      case StringType => wrapperConvertException(data => data, converter)
      case BooleanType => wrapperConvertException(data => data.toBoolean, converter)
      case ByteType => wrapperConvertException(data => data.toByte, converter)
      case BinaryType =>
        wrapperConvertException(data => UTF8String.fromString(data).getBytes, converter)
      case IntegerType => wrapperConvertException(data => data.toInt, converter)
      case ShortType => wrapperConvertException(data => data.toShort, converter)
      case LongType => wrapperConvertException(data => data.toLong, converter)
      case FloatType => wrapperConvertException(data => data.toFloat, converter)
      case DoubleType => wrapperConvertException(data => data.toDouble, converter)
      case _: DecimalType => wrapperConvertException(data => BigDecimal(data), converter)
      case DateType if conf.datetimeJava8ApiEnabled =>
        wrapperConvertException(data => DateTimeUtils.stringToDate(UTF8String.fromString(data))
          .map(DateTimeUtils.daysToLocalDate).orNull, converter)
      case DateType =>
        wrapperConvertException(data => DateTimeUtils.stringToDate(UTF8String.fromString(data))
          .map(DateTimeUtils.toJavaDate).orNull, converter)
      case TimestampType if conf.datetimeJava8ApiEnabled =>
        wrapperConvertException(data => DateTimeUtils.stringToTimestamp(
          UTF8String.fromString(data),
          DateTimeUtils.getZoneId(conf.sessionLocalTimeZone))
          .map(DateTimeUtils.microsToInstant).orNull, converter)
      case TimestampType => wrapperConvertException(data => DateTimeUtils.stringToTimestamp(
        UTF8String.fromString(data),
        DateTimeUtils.getZoneId(conf.sessionLocalTimeZone))
        .map(DateTimeUtils.toJavaTimestamp).orNull, converter)
      case TimestampNTZType =>
        wrapperConvertException(data => DateTimeUtils.stringToTimestampWithoutTimeZone(
          UTF8String.fromString(data)).map(DateTimeUtils.microsToLocalDateTime).orNull, converter)
      case CalendarIntervalType => wrapperConvertException(
        data => IntervalUtils.stringToInterval(UTF8String.fromString(data)),
        converter)
      case YearMonthIntervalType(start, end) => wrapperConvertException(
        data => IntervalUtils.monthsToPeriod(
          IntervalUtils.castStringToYMInterval(UTF8String.fromString(data), start, end)),
        converter)
      case DayTimeIntervalType(start, end) => wrapperConvertException(
        data => IntervalUtils.microsToDuration(
          IntervalUtils.castStringToDTInterval(UTF8String.fromString(data), start, end)),
        converter)
      case _: ArrayType | _: MapType | _: StructType =>
        val complexTypeFactory = JsonToStructs(attr.dataType,
          ioschema.outputSerdeProps.toMap, Literal(null), Some(conf.sessionLocalTimeZone))
        wrapperConvertException(data =>
          complexTypeFactory.nullSafeEval(UTF8String.fromString(data)), any => any)
      case udt: UserDefinedType[_] =>
        wrapperConvertException(data => udt.deserialize(data), converter)
      case dt =>
        throw QueryExecutionErrors.outputDataTypeUnsupportedByNodeWithoutSerdeError(nodeName, dt)
    }
  }

  // Keep consistent with Hive `LazySimpleSerde`, when there is a type case error, return null
  private val wrapperConvertException: (String => Any, Any => Any) => String => Any =
    (f: String => Any, converter: Any => Any) =>
      (data: String) => converter {
        if (data == ioschema.outputRowFormatMap("TOK_TABLEROWFORMATNULL")) {
          null
        } else {
          try {
            f(data)
          } catch {
            case NonFatal(_) => null
          }
        }
      }
}

abstract class BaseScriptTransformationWriterThread extends Thread with Logging {

  def iter: Iterator[InternalRow]
  def inputSchema: Seq[DataType]
  def ioSchema: ScriptTransformationIOSchema
  def outputStream: OutputStream
  def proc: Process
  def stderrBuffer: CircularBuffer
  def taskContext: TaskContext
  def conf: Configuration

  setName(s"Thread-${this.getClass.getSimpleName}-Feed")
  setDaemon(true)

  @volatile protected var _exception: Throwable = null

  /** Contains the exception thrown while writing the parent iterator to the external process. */
  def exception: Option[Throwable] = Option(_exception)

  protected def processRows(): Unit

  protected def processRowsWithoutSerde(): Unit = {
    val len = inputSchema.length
    iter.foreach { row =>
      val data = if (len == 0) {
        ioSchema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
      } else {
        val sb = new StringBuilder
        def appendToBuffer(s: AnyRef): Unit = {
          if (s == null) {
            sb.append(ioSchema.inputRowFormatMap("TOK_TABLEROWFORMATNULL"))
          } else {
            sb.append(s)
          }
        }
        appendToBuffer(row.get(0, inputSchema(0)))
        var i = 1
        while (i < len) {
          sb.append(ioSchema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
          appendToBuffer(row.get(i, inputSchema(i)))
          i += 1
        }
        sb.append(ioSchema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
        sb.toString()
      }
      outputStream.write(data.getBytes(StandardCharsets.UTF_8))
    }
  }

  override def run(): Unit = Utils.logUncaughtExceptions {
    TaskContext.setTaskContext(taskContext)

    // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
    // let's use a variable to record whether the `finally` block was hit due to an exception
    var threwException: Boolean = true
    try {
      processRows()
      threwException = false
    } catch {
      // SPARK-25158 Exception should not be thrown again, otherwise it will be captured by
      // SparkUncaughtExceptionHandler, then Executor will exit because of this Uncaught Exception,
      // so pass the exception to `ScriptTransformationExec` is enough.
      case t: Throwable =>
        // An error occurred while writing input, so kill the child process. According to the
        // Javadoc this call will not throw an exception:
        _exception = t
        proc.destroy()
        logError(s"Thread-${this.getClass.getSimpleName}-Feed exit cause by: ", t)
    } finally {
      try {
        Utils.tryLogNonFatalError(outputStream.close())
        if (proc.waitFor() != 0) {
          logError(stderrBuffer.toString) // log the stderr circular buffer
        }
      } catch {
        case NonFatal(exceptionFromFinallyBlock) =>
          if (!threwException) {
            throw exceptionFromFinallyBlock
          } else {
            log.error("Exception in finally block", exceptionFromFinallyBlock)
          }
      }
    }
  }
}

/**
 * The wrapper class of input and output schema properties
 */
case class ScriptTransformationIOSchema(
    inputRowFormat: Seq[(String, String)],
    outputRowFormat: Seq[(String, String)],
    inputSerdeClass: Option[String],
    outputSerdeClass: Option[String],
    inputSerdeProps: Seq[(String, String)],
    outputSerdeProps: Seq[(String, String)],
    recordReaderClass: Option[String],
    recordWriterClass: Option[String],
    schemaLess: Boolean) extends Serializable {
  import ScriptTransformationIOSchema._

  val inputRowFormatMap = inputRowFormat.toMap.withDefault((k) => defaultFormat(k))
  val outputRowFormatMap = outputRowFormat.toMap.withDefault((k) => defaultFormat(k))
}

object ScriptTransformationIOSchema {
  val defaultFormat = Map(
    ("TOK_TABLEROWFORMATFIELD", "\u0001"),
    ("TOK_TABLEROWFORMATLINES", "\n"),
    ("TOK_TABLEROWFORMATNULL" -> "\\N")
  )

  val defaultIOSchema = ScriptTransformationIOSchema(
    inputRowFormat = Seq.empty,
    outputRowFormat = Seq.empty,
    inputSerdeClass = None,
    outputSerdeClass = None,
    inputSerdeProps = Seq.empty,
    outputSerdeProps = Seq.empty,
    recordReaderClass = None,
    recordWriterClass = None,
    schemaLess = false
  )

  def apply(input: ScriptInputOutputSchema): ScriptTransformationIOSchema = {
    ScriptTransformationIOSchema(
      input.inputRowFormat,
      input.outputRowFormat,
      input.inputSerdeClass,
      input.outputSerdeClass,
      input.inputSerdeProps,
      input.outputSerdeProps,
      input.recordReaderClass,
      input.recordWriterClass,
      input.schemaLess)
  }
}

相关信息

spark 源码目录

相关文章

spark AggregatingAccumulator 源码

spark AliasAwareOutputExpression 源码

spark CacheManager 源码

spark CoGroupedIterator 源码

spark CollectMetricsExec 源码

spark Columnar 源码

spark CommandResultExec 源码

spark DataSourceScanExec 源码

spark ExistingRDD 源码

spark ExpandExec 源码