hadoop InputFormat 源码

  • 2022-10-20
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haddop InputFormat 代码


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 *     http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.hadoop.mapred;

import java.io.IOException;

import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.fs.FileSystem;

 * <code>InputFormat</code> describes the input-specification for a 
 * Map-Reduce job. 
 * <p>The Map-Reduce framework relies on the <code>InputFormat</code> of the
 * job to:<p>
 * <ol>
 *   <li>
 *   Validate the input-specification of the job. 
 *   <li>
 *   Split-up the input file(s) into logical {@link InputSplit}s, each of 
 *   which is then assigned to an individual {@link Mapper}.
 *   </li>
 *   <li>
 *   Provide the {@link RecordReader} implementation to be used to glean
 *   input records from the logical <code>InputSplit</code> for processing by 
 *   the {@link Mapper}.
 *   </li>
 * </ol>
 * <p>The default behavior of file-based {@link InputFormat}s, typically 
 * sub-classes of {@link FileInputFormat}, is to split the 
 * input into <i>logical</i> {@link InputSplit}s based on the total size, in 
 * bytes, of the input files. However, the {@link FileSystem} blocksize of  
 * the input files is treated as an upper bound for input splits. A lower bound 
 * on the split size can be set via 
 * <a href="{@docRoot}/../hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml#mapreduce.input.fileinputformat.split.minsize">
 * mapreduce.input.fileinputformat.split.minsize</a>.</p>
 * <p>Clearly, logical splits based on input-size is insufficient for many 
 * applications since record boundaries are to be respected. In such cases, the
 * application has to also implement a {@link RecordReader} on whom lies the
 * responsibilty to respect record-boundaries and present a record-oriented
 * view of the logical <code>InputSplit</code> to the individual task.
 * @see InputSplit
 * @see RecordReader
 * @see JobClient
 * @see FileInputFormat
public interface InputFormat<K, V> {

   * Logically split the set of input files for the job.  
   * <p>Each {@link InputSplit} is then assigned to an individual {@link Mapper}
   * for processing.</p>
   * <p><i>Note</i>: The split is a <i>logical</i> split of the inputs and the
   * input files are not physically split into chunks. For e.g. a split could
   * be <i>&lt;input-file-path, start, offset&gt;</i> tuple.
   * @param job job configuration.
   * @param numSplits the desired number of splits, a hint.
   * @return an array of {@link InputSplit}s for the job.
  InputSplit[] getSplits(JobConf job, int numSplits) throws IOException;

   * Get the {@link RecordReader} for the given {@link InputSplit}.
   * <p>It is the responsibility of the <code>RecordReader</code> to respect
   * record boundaries while processing the logical split to present a 
   * record-oriented view to the individual task.</p>
   * @param split the {@link InputSplit}
   * @param job the job that this split belongs to
   * @return a {@link RecordReader}
  RecordReader<K, V> getRecordReader(InputSplit split,
                                     JobConf job, 
                                     Reporter reporter) throws IOException;


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