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## org.apache.hadoop.mapred
Interface Mapper<K1,V1,K2,V2>

All Superinterfaces:
  Closeable, JobConfigurable

All Known Subinterfaces:
  ILocalAnalysis<K,V>

All Known Implementing Classes:
  ChainMapper, DataJoinMapperBase, DataJoinReducerBase, DelegatingMapper, DistributedPentomino.PentMap, FieldSelectionMapReduce, IdentityLocalAnalysis, IdentityMapper, IndexUpdateMapper, InverseMapper, JobBase, LineDocLocalAnalysis, MultiFileWordCount.MapClass, PiEstimator.PiMapper, PipeMapper, RegexMapper, SleepJob, TeraGen.SortGenMapper, TokenCountMapper, ValueAggregatorCombiner, ValueAggregatorJobBase, ValueAggregatorMapper, ValueAggregatorReducer

    public interface Mapper<K1,V1,K2,V2>

extends JobConfigurable, Closeable

Maps input key/value pairs to a set of intermediate key/value pairs.

Maps are the individual tasks which transform input records into a intermediate records. The transformed intermediate records need not be of the same type as the input records. A given input pair may map to zero or many output pairs.

The Hadoop Map-Reduce framework spawns one map task for each `InputSplit` generated by the `InputFormat` for the job. `Mapper` implementations can access the `JobConf` for the job via the `JobConfigurable.configure(JobConf)` and initialize themselves. Similarly they can use the `Closeable.close()` method for de-initialization.

The framework then calls `map(Object, Object, OutputCollector, Reporter)` for each key/value pair in the `InputSplit` for that task.

All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to a `Reducer` to determine the final output. Users can control the grouping by specifying a `Comparator` via `JobConf.setOutputKeyComparatorClass(Class)`.

The grouped `Mapper` outputs are partitioned per `Reducer`. Users can control which keys (and hence records) go to which `Reducer` by implementing a custom `Partitioner`.

Users can optionally specify a `combiner`, via `JobConf.setCombinerClass(Class)`, to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the `Mapper` to the `Reducer`.

The intermediate, grouped outputs are always stored in `SequenceFile`s. Applications can specify if and how the intermediate outputs are to be compressed and which `CompressionCodec`s are to be used via the `JobConf`.

If the job has zero reduces then the output of the `Mapper` is directly written to the `FileSystem` without grouping by keys.

Example:

>          public class MyMapper<K extends WritableComparable, V extends Writable>
>          extends MapReduceBase implements Mapper<K, V, K, V> {

>            static enum MyCounters { NUM_RECORDS }

>            private String mapTaskId;
>            private String inputFile;
>            private int noRecords = 0;

>            public void configure(JobConf job) {
>              mapTaskId = job.get("mapred.task.id");
>              inputFile = job.get("map.input.file");
>            }

>            public void map(K key, V val,
>                            OutputCollector<K, V> output, Reporter reporter)
>            throws IOException {
>              // Process the <key, value> pair (assume this takes a while)
>              // ...
>              // ...

>              // Let the framework know that we are alive, and kicking!
>              // reporter.progress();

>              // Process some more
>              // ...
>              // ...

>              // Increment the no. of <key, value> pairs processed
>              ++noRecords;

>              // Increment counters
>              reporter.incrCounter(NUM_RECORDS, 1);

>              // Every 100 records update application-level status
>              if ((noRecords%100) == 0) {
>                reporter.setStatus(mapTaskId + " processed " + noRecords +
>                                   " from input-file: " + inputFile);
>              }

>              // Output the result
>              output.collect(key, val);
>            }
>          }

Applications may write a custom `MapRunnable` to exert greater control on map processing e.g. multi-threaded `Mapper`s etc.

See Also:
  `JobConf`, `InputFormat`, `Partitioner`, `Reducer`, `MapReduceBase`, `MapRunnable`, `SequenceFile`

Method Summary
`void` | `map(K1 key, V1 value, OutputCollector<K2,V2> output, Reporter reporter)` Maps a single input key/value pair into an intermediate key/value pair.

Methods inherited from interface org.apache.hadoop.mapred.JobConfigurable
`configure`

Methods inherited from interface java.io.Closeable
`close`

Method Detail

### map

    void map(K1 key,
             V1 value,
             OutputCollector<K2,V2> output,
             Reporter reporter)
             throws IOException

  Maps a single input key/value pair into an intermediate key/value pair.

  Output pairs need not be of the same types as input pairs. A given input pair may map to zero or many output pairs. Output pairs are collected with calls to `OutputCollector.collect(Object,Object)`.

  Applications can use the `Reporter` provided to report progress or just indicate that they are alive. In scenarios where the application takes an insignificant amount of time to process individual key/value pairs, this is crucial since the framework might assume that the task has timed-out and kill that task. The other way of avoiding this is to set mapred.task.timeout to a high-enough value (or even zero for no time-outs).

  Parameters:
    `key` - the input key.
    `value` - the input value.
    `output` - collects mapped keys and values.
    `reporter` - facility to report progress.
  Throws:
    `IOException`

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