public interface Reducer<K2,V2,K3,V3>
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:
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.
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.
SecondarySortIf 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.
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("mapred.task.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);
}
}
}
Mapper, Partitioner, Reporter, MapReduceBase
void reduce(K2 key,
Iterator<V2> values,
OutputCollector<K3,V3> output,
Reporter reporter)
throws IOException
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).
key - the key.
values - the list of values to reduce.
output - to collect keys and combined values.
reporter - facility to report progress.
IOException
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