hadoop PipesMapRunner 源码

  • 2022-10-20
  • 浏览 (141)

haddop PipesMapRunner 代码

文件路径:/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/pipes/PipesMapRunner.java

/**
 * 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.hadoop.mapred.pipes;

import java.io.IOException;

import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapRunner;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SkipBadRecords;
import org.apache.hadoop.mapreduce.MRJobConfig;

/**
 * An adaptor to run a C++ mapper.
 */
class PipesMapRunner<K1 extends WritableComparable, V1 extends Writable,
    K2 extends WritableComparable, V2 extends Writable>
    extends MapRunner<K1, V1, K2, V2> {
  private JobConf job;

  /**
   * Get the new configuration.
   * @param job the job's configuration
   */
  public void configure(JobConf job) {
    this.job = job;
    //disable the auto increment of the counter. For pipes, no of processed 
    //records could be different(equal or less) than the no of records input.
    SkipBadRecords.setAutoIncrMapperProcCount(job, false);
  }

  /**
   * Run the map task.
   * @param input the set of inputs
   * @param output the object to collect the outputs of the map
   * @param reporter the object to update with status
   */
  @SuppressWarnings("unchecked")
  public void run(RecordReader<K1, V1> input, OutputCollector<K2, V2> output,
                  Reporter reporter) throws IOException {
    Application<K1, V1, K2, V2> application = null;
    try {
      RecordReader<FloatWritable, NullWritable> fakeInput = 
        (!Submitter.getIsJavaRecordReader(job) && 
         !Submitter.getIsJavaMapper(job)) ? 
	  (RecordReader<FloatWritable, NullWritable>) input : null;
      application = new Application<K1, V1, K2, V2>(job, fakeInput, output, 
                                                    reporter,
          (Class<? extends K2>) job.getOutputKeyClass(), 
          (Class<? extends V2>) job.getOutputValueClass());
    } catch (InterruptedException ie) {
      throw new RuntimeException("interrupted", ie);
    }
    DownwardProtocol<K1, V1> downlink = application.getDownlink();
    boolean isJavaInput = Submitter.getIsJavaRecordReader(job);
    downlink.runMap(reporter.getInputSplit(), 
                    job.getNumReduceTasks(), isJavaInput);
    boolean skipping = job.getBoolean(MRJobConfig.SKIP_RECORDS, false);
    try {
      if (isJavaInput) {
        // allocate key & value instances that are re-used for all entries
        K1 key = input.createKey();
        V1 value = input.createValue();
        downlink.setInputTypes(key.getClass().getName(),
                               value.getClass().getName());
        
        while (input.next(key, value)) {
          // map pair to output
          downlink.mapItem(key, value);
          if(skipping) {
            //flush the streams on every record input if running in skip mode
            //so that we don't buffer other records surrounding a bad record.
            downlink.flush();
          }
        }
        downlink.endOfInput();
      }
      application.waitForFinish();
    } catch (Throwable t) {
      application.abort(t);
    } finally {
      application.cleanup();
    }
  }
  
}

相关信息

hadoop 源码目录

相关文章

hadoop Application 源码

hadoop BinaryProtocol 源码

hadoop DownwardProtocol 源码

hadoop OutputHandler 源码

hadoop PipesNonJavaInputFormat 源码

hadoop PipesPartitioner 源码

hadoop PipesReducer 源码

hadoop Submitter 源码

hadoop UpwardProtocol 源码

0  赞