spark InputFormatInfo 源码

spark InputFormatInfo 代码

文件路径：/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.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.scheduler

import scala.collection.JavaConverters._
import scala.collection.immutable.Set
import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.mapred.{FileInputFormat, JobConf}
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.util.ReflectionUtils

import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.internal.Logging

/**
 * :: DeveloperApi ::
 * Parses and holds information about inputFormat (and files) specified as a parameter.
 */
@DeveloperApi
class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_],
    val path: String) extends Logging {

  var mapreduceInputFormat: Boolean = false
  var mapredInputFormat: Boolean = false

  validate()

  override def toString: String = {
    "InputFormatInfo " + super.toString + " .. inputFormatClazz " + inputFormatClazz + ", " +
      "path : " + path
  }

  override def hashCode(): Int = {
    var hashCode = inputFormatClazz.hashCode
    hashCode = hashCode * 31 + path.hashCode
    hashCode
  }

  // Since we are not doing canonicalization of path, this can be wrong : like relative vs
  // absolute path .. which is fine, this is best case effort to remove duplicates - right ?
  override def equals(other: Any): Boolean = other match {
    case that: InputFormatInfo =>
      // not checking config - that should be fine, right ?
      this.inputFormatClazz == that.inputFormatClazz &&
        this.path == that.path
    case _ => false
  }

  private def validate(): Unit = {
    logDebug("validate InputFormatInfo : " + inputFormatClazz + ", path  " + path)

    try {
      if (classOf[org.apache.hadoop.mapreduce.InputFormat[_, _]].isAssignableFrom(
        inputFormatClazz)) {
        logDebug("inputformat is from mapreduce package")
        mapreduceInputFormat = true
      }
      else if (classOf[org.apache.hadoop.mapred.InputFormat[_, _]].isAssignableFrom(
        inputFormatClazz)) {
        logDebug("inputformat is from mapred package")
        mapredInputFormat = true
      }
      else {
        throw new IllegalArgumentException("Specified inputformat " + inputFormatClazz +
          " is NOT a supported input format ? does not implement either of the supported hadoop " +
            "api's")
      }
    }
    catch {
      case e: ClassNotFoundException =>
        throw new IllegalArgumentException("Specified inputformat " + inputFormatClazz +
          " cannot be found ?", e)
    }
  }


  // This method does not expect failures, since validate has already passed ...
  private def prefLocsFromMapreduceInputFormat(): Set[SplitInfo] = {
    val conf = new JobConf(configuration)
    SparkHadoopUtil.get.addCredentials(conf)
    FileInputFormat.setInputPaths(conf, path)

    val instance: org.apache.hadoop.mapreduce.InputFormat[_, _] =
      ReflectionUtils.newInstance(inputFormatClazz, conf).asInstanceOf[
        org.apache.hadoop.mapreduce.InputFormat[_, _]]
    val job = Job.getInstance(conf)

    val retval = new ArrayBuffer[SplitInfo]()
    val list = instance.getSplits(job)
    for (split <- list.asScala) {
      retval ++= SplitInfo.toSplitInfo(inputFormatClazz, path, split)
    }

    retval.toSet
  }

  // This method does not expect failures, since validate has already passed ...
  private def prefLocsFromMapredInputFormat(): Set[SplitInfo] = {
    val jobConf = new JobConf(configuration)
    SparkHadoopUtil.get.addCredentials(jobConf)
    FileInputFormat.setInputPaths(jobConf, path)

    val instance: org.apache.hadoop.mapred.InputFormat[_, _] =
      ReflectionUtils.newInstance(inputFormatClazz, jobConf).asInstanceOf[
        org.apache.hadoop.mapred.InputFormat[_, _]]

    val retval = new ArrayBuffer[SplitInfo]()
    instance.getSplits(jobConf, jobConf.getNumMapTasks()).foreach(
        elem => retval ++= SplitInfo.toSplitInfo(inputFormatClazz, path, elem)
    )

    retval.toSet
   }

  private def findPreferredLocations(): Set[SplitInfo] = {
    logDebug("mapreduceInputFormat : " + mapreduceInputFormat + ", mapredInputFormat : " +
      mapredInputFormat + ", inputFormatClazz : " + inputFormatClazz)
    if (mapreduceInputFormat) {
      prefLocsFromMapreduceInputFormat()
    }
    else {
      assert(mapredInputFormat)
      prefLocsFromMapredInputFormat()
    }
  }
}




object InputFormatInfo {
  /**
    Computes the preferred locations based on input(s) and returned a location to block map.
    Typical use of this method for allocation would follow some algo like this:

    a) For each host, count number of splits hosted on that host.
    b) Decrement the currently allocated containers on that host.
    c) Compute rack info for each host and update rack to count map based on (b).
    d) Allocate nodes based on (c)
    e) On the allocation result, ensure that we don't allocate "too many" jobs on a single node
       (even if data locality on that is very high) : this is to prevent fragility of job if a
       single (or small set of) hosts go down.

    go to (a) until required nodes are allocated.

    If a node 'dies', follow same procedure.

    PS: I know the wording here is weird, hopefully it makes some sense !
  */
  def computePreferredLocations(formats: Seq[InputFormatInfo]): Map[String, Set[SplitInfo]] = {

    val nodeToSplit = new HashMap[String, HashSet[SplitInfo]]
    for (inputSplit <- formats) {
      val splits = inputSplit.findPreferredLocations()

      for (split <- splits) {
        val location = split.hostLocation
        val set = nodeToSplit.getOrElseUpdate(location, new HashSet[SplitInfo])
        set += split
      }
    }

    nodeToSplit.mapValues(_.toSet).toMap
  }
}

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