Reducer (Apache Hadoop Main 3.4.1 API) (original) (raw)

Reduces a set of intermediate values which share a key to a smaller set of values.

The number of Reducers for the job is set by the user via JobConf.setNumReduceTasks(int). Reducer implementations can access the JobConf for the job via the JobConfigurable.configure(JobConf) method and initialize themselves. Similarly they can use the Closeable.close() method for de-initialization.

Reducer has 3 primary phases:

1. Shuffle
   Reducer is input the grouped output of a Mapper. In the phase the framework, for each Reducer, fetches the relevant partition of the output of all the Mappers, via HTTP.
2. Sort
   The framework groups Reducer inputs by keys (since different Mappers may have output the same key) in this stage.
   The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged.
   SecondarySort
   If equivalence rules for keys while grouping the intermediates are different from those for grouping keys before reduction, then one may specify a Comparator via JobConf.setOutputValueGroupingComparator(Class).Since JobConf.setOutputKeyComparatorClass(Class) can be used to control how intermediate keys are grouped, these can be used in conjunction to simulate secondary sort on values.
   For example, say that you want to find duplicate web pages and tag them all with the url of the "best" known example. You would set up the job like:
   • Map Input Key: url
   • Map Input Value: document
   • Map Output Key: document checksum, url pagerank
   • Map Output Value: url
   • Partitioner: by checksum
   • OutputKeyComparator: by checksum and then decreasing pagerank
   • OutputValueGroupingComparator: by checksum
3. Reduce
   In this phase the reduce(Object, Iterator, OutputCollector, Reporter) method is called for each <key, (list of values)> pair in the grouped inputs.
   The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(Object, Object).

The output of the Reducer is not re-sorted.

Example:

public class MyReducer<K extends WritableComparable, V extends Writable> 
extends MapReduceBase implements Reducer<K, V, K, V> {

  static enum MyCounters { NUM_RECORDS }
   
  private String reduceTaskId;
  private int noKeys = 0;
  
  public void configure(JobConf job) {
    reduceTaskId = job.get(JobContext.TASK_ATTEMPT_ID);
  }
  
  public void reduce(K key, Iterator<V> values,
                     OutputCollector<K, V> output, 
                     Reporter reporter)
  throws IOException {
  
    // Process
    int noValues = 0;
    while (values.hasNext()) {
      V value = values.next();
      
      // Increment the no. of values for this key
      ++noValues;
      
      // Process the <key, value> pair (assume this takes a while)
      // ...
      // ...
      
      // Let the framework know that we are alive, and kicking!
      if ((noValues%10) == 0) {
        reporter.progress();
      }
    
      // Process some more
      // ...
      // ...
      
      // Output the <key, value> 
      output.collect(key, value);
    }
    
    // Increment the no. of <key, list of values> pairs processed
    ++noKeys;
    
    // Increment counters
    reporter.incrCounter(NUM_RECORDS, 1);
    
    // Every 100 keys update application-level status
    if ((noKeys%100) == 0) {
      reporter.setStatus(reduceTaskId + " processed " + noKeys);
    }
  }
}