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## org.apache.hadoop.contrib.utils.join
Class DataJoinReducerBase

    java.lang.Object
      org.apache.hadoop.contrib.utils.join.JobBase
          org.apache.hadoop.contrib.utils.join.DataJoinReducerBase

All Implemented Interfaces:
  Closeable, JobConfigurable, Mapper, Reducer

    public abstract class DataJoinReducerBase

extends JobBase

This abstract class serves as the base class for the reducer class of a data join job. The reduce function will first group the values according to their input tags, and then compute the cross product of over the groups. For each tuple in the cross product, it calls the following method, which is expected to be implemented in a subclass. protected abstract TaggedMapOutput combine(Object[] tags, Object[] values); The above method is expected to produce one output value from an array of records of different sources. The user code can also perform filtering here. It can return null if it decides to the records do not meet certain conditions.

Field Summary
`protected long` | `collected`
`protected JobConf` | `job`
`protected long` | `largestNumOfValues`
`static Text` | `NUM_OF_VALUES_FIELD`
`protected long` | `numOfValues`
`protected Reporter` | `reporter`
`static Text` | `SOURCE_TAGS_FIELD`

Fields inherited from class org.apache.hadoop.contrib.utils.join.JobBase
`LOG`

Constructor Summary
`DataJoinReducerBase()`

Method Summary
`void` | `close()`
`protected void` | `collect(Object key, TaggedMapOutput aRecord, OutputCollector output, Reporter reporter)` The subclass can overwrite this method to perform additional filtering and/or other processing logic before a value is collected.
`protected abstract TaggedMapOutput` | `combine(Object[] tags, Object[] values)`
`void` | `configure(JobConf job)` Initializes a new instance from a `JobConf`.
`protected ResetableIterator` | `createResetableIterator()` The subclass can provide a different implementation on ResetableIterator.
`void` | `map(Object arg0, Object arg1, OutputCollector arg2, Reporter arg3)` Maps a single input key/value pair into an intermediate key/value pair.
`void` | `reduce(Object key, Iterator values, OutputCollector output, Reporter reporter)` Reduces values for a given key.

Methods inherited from class org.apache.hadoop.contrib.utils.join.JobBase
`addDoubleValue, addLongValue, getDoubleValue, getLongValue, getReport, report, setDoubleValue, setLongValue`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### reporter

    protected Reporter reporter

### largestNumOfValues

    protected long largestNumOfValues

### numOfValues

    protected long numOfValues

### collected

    protected long collected

### job

    protected JobConf job

### SOURCE_TAGS_FIELD

    public static Text SOURCE_TAGS_FIELD

### NUM_OF_VALUES_FIELD

    public static Text NUM_OF_VALUES_FIELD

Constructor Detail

### DataJoinReducerBase

    public DataJoinReducerBase()

Method Detail

### close

    public void close()
               throws IOException

  Throws:
    `IOException`

### configure

    public void configure(JobConf job)

  Description copied from class: `JobBase`
  Initializes a new instance from a `JobConf`.

  Specified by:
    `configure` in interface `JobConfigurable`
  Overrides:
    `configure` in class `JobBase`
  Parameters:
    `job` - the configuration

### createResetableIterator

    protected ResetableIterator createResetableIterator()

  The subclass can provide a different implementation on ResetableIterator. This is necessary if the number of values in a reduce call is very high. The default provided here uses ArrayListBackedIterator

  Returns:
    an Object of ResetableIterator.

### reduce

    public void reduce(Object key,
                       Iterator values,
                       OutputCollector output,
                       Reporter reporter)
                throws IOException

  Description copied from interface: `Reducer`
  Reduces values for a given key.

  The framework calls this method for each `<key, (list of values)>` pair in the grouped inputs. Output values must be of the same type as input values. Input keys must not be altered. The framework will reuse the key and value objects that are passed into the reduce, therefore the application should clone the objects they want to keep a copy of. In many cases, all values are combined into zero or one value.

  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 key.
    `values` - the list of values to reduce.
    `output` - to collect keys and combined values.
    `reporter` - facility to report progress.
  Throws:
    `IOException`

### collect

    protected void collect(Object key,
                           TaggedMapOutput aRecord,
                           OutputCollector output,
                           Reporter reporter)
                    throws IOException

  The subclass can overwrite this method to perform additional filtering and/or other processing logic before a value is collected.

  Parameters:
    `key` -
    `aRecord` -
    `output` -
    `reporter` -
  Throws:
    `IOException`

### combine

    protected abstract TaggedMapOutput combine(Object[] tags,
                                               Object[] values)

  Parameters:
    `tags` - a list of source tags
    `values` - a value per source
  Returns:
    combined value derived from values of the sources

### map

    public void map(Object arg0,
                    Object arg1,
                    OutputCollector arg2,
                    Reporter arg3)
             throws IOException

  Description copied from interface: `Mapper`
  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:
    `arg0` - the input key.
    `arg1` - the input value.
    `arg2` - collects mapped keys and values.
    `arg3` - facility to report progress.
  Throws:
    `IOException`

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