public class MapReduceTask<KIn,VIn,KOut,VOut> extends Object
Users should instantiate MapReduceTask with a reference to a cache whose data is used as input for this
task. Infinispan execution environment will migrate and execute instances of provided Mapper
and Reducer
seamlessly across Infinispan nodes.
Unless otherwise specified using onKeys(Object...)
filter all available
key/value pairs of a specified cache will be used as input data for this task.
For example, MapReduceTask that counts number of word occurrences in a particular cache where
keys and values are String instances could be written as follows:
MapReduceTask<String, String, String, Integer> task = new MapReduceTask<String, String, String, Integer>(cache); task.mappedWith(new WordCountMapper()).reducedWith(new WordCountReducer()); Map<String, Integer> results = task.execute();The final result is a map where key is a word and value is a word count for that particular word.
Accompanying Mapper
and Reducer
are defined as follows:
private static class WordCountMapper implements Mapper<String, String, String,Integer> { public void map(String key, String value, Collector<String, Integer> collector) { StringTokenizer tokens = new StringTokenizer(value); while (tokens.hasMoreElements()) { String s = (String) tokens.nextElement(); collector.emit(s, 1); } } } private static class WordCountReducer implements Reducer<String, Integer> { public Integer reduce(String key, Iterator<Integer> iter) { int sum = 0; while (iter.hasNext()) { Integer i = (Integer) iter.next(); sum += i; } return sum; } }
Finally, as of Infinispan 5.2 release, MapReduceTask can also specify a Combiner function. The Combiner is executed on each node after the Mapper and before the global reduce phase. The Combiner receives input from the Mapper's output and the output from the Combiner is then sent to the reducers. It is useful to think of the Combiner as a node local reduce phase before global reduce phase is executed.
Combiners are especially useful when reduce function is both commutative and associative! In such cases we can use the Reducer itself as the Combiner; all one needs to do is to specify the Combiner:
MapReduceTask<String, String, String, Integer> task = new MapReduceTask<String, String, String, Integer>(cache); task.mappedWith(new WordCountMapper()).reducedWith(new WordCountReducer()).combineWith(new WordCountReducer()); Map<String, Integer> results = task.execute();Note that
Mapper
and Reducer
should not be specified as inner classes. Inner classes
declared in non-static contexts contain implicit non-transient references to enclosing class instances,
serializing such an inner class instance will result in serialization of its associated outer class instance as well.
If you are not familiar with concept of map reduce distributed execution model start with Google's MapReduce research paper.
Modifier and Type | Field and Description |
---|---|
protected AdvancedCache<KIn,VIn> |
cache |
protected List<org.infinispan.distexec.mapreduce.MapReduceTask.CancellableTaskPart> |
cancellableTasks |
protected CancellationService |
cancellationService |
protected Reducer<KOut,VOut> |
combiner |
static String |
DEFAULT_TMP_CACHE_CONFIGURATION_NAME |
protected boolean |
distributeReducePhase |
protected Collection<KIn> |
keys |
protected Mapper<KIn,VIn,KOut,VOut> |
mapper |
protected MapReduceManager |
mapReduceManager |
protected Marshaller |
marshaller |
protected Reducer<KOut,VOut> |
reducer |
protected UUID |
taskId |
protected boolean |
useIntermediateSharedCache |
Constructor and Description |
---|
MapReduceTask(Cache<KIn,VIn> masterCacheNode)
Create a new MapReduceTask given a master cache node.
|
MapReduceTask(Cache<KIn,VIn> masterCacheNode,
boolean distributeReducePhase)
Create a new MapReduceTask given a master cache node.
|
MapReduceTask(Cache<KIn,VIn> masterCacheNode,
boolean distributeReducePhase,
boolean useIntermediateSharedCache)
Create a new MapReduceTask given a master cache node.
|
Modifier and Type | Method and Description |
---|---|
protected void |
aggregateReducedResult(Map<KOut,List<VOut>> finalReduced,
Map<KOut,VOut> mapReceived) |
protected Mapper<KIn,VIn,KOut,VOut> |
clone(Mapper<KIn,VIn,KOut,VOut> mapper) |
protected Reducer<KOut,VOut> |
clone(Reducer<KOut,VOut> reducer) |
MapReduceTask<KIn,VIn,KOut,VOut> |
combinedWith(Reducer<KOut,VOut> combiner)
Specifies Combiner to use for this MapReduceTask
|
protected <V> org.infinispan.distexec.mapreduce.MapReduceTask.ReduceTaskPart<V> |
createReducePart(ReduceCommand<KOut,VOut> cmd,
Address target,
String destCacheName) |
protected <V> org.infinispan.distexec.mapreduce.MapReduceTask.MapTaskPart<V> |
createTaskMapPart(MapCombineCommand<KIn,VIn,KOut,VOut> cmd,
Address target,
boolean distributedReduce) |
protected boolean |
distributeReducePhase() |
boolean |
equals(Object obj) |
Map<KOut,VOut> |
execute()
Executes this task across Infinispan cluster nodes.
|
<R> R |
execute(Collator<KOut,VOut,R> collator)
Executes this task across Infinispan cluster but the final result is collated using specified
Collator |
Future<Map<KOut,VOut>> |
executeAsynchronously()
Executes this task across Infinispan cluster nodes asynchronously.
|
<R> Future<R> |
executeAsynchronously(Collator<KOut,VOut,R> collator)
Executes this task asynchronously across Infinispan cluster; final result is collated using
specified
Collator and wrapped by Future |
protected Set<KOut> |
executeMapPhase(boolean useCompositeKeys) |
protected Map<KOut,VOut> |
executeMapPhaseWithLocalReduction() |
protected Map<KOut,VOut> |
executeReducePhase(Set<KOut> allMapPhasesResponses,
boolean useCompositeKeys) |
protected void |
executeTaskInit(String tmpCacheName) |
int |
hashCode() |
protected boolean |
inputTaskKeysEmpty() |
protected <T> Map<Address,List<T>> |
mapKeysToNodes(Collection<T> keysToMap) |
protected <T> Map<Address,List<T>> |
mapKeysToNodes(Collection<T> keysToMap,
boolean useIntermediateCompositeKey) |
protected <T> Map<Address,List<T>> |
mapKeysToNodes(DistributionManager dm,
Collection<T> keysToMap,
boolean useIntermediateCompositeKey) |
MapReduceTask<KIn,VIn,KOut,VOut> |
mappedWith(Mapper<KIn,VIn,KOut,VOut> mapper)
Specifies Mapper to use for this MapReduceTask
|
MapReduceTask<KIn,VIn,KOut,VOut> |
onKeys(KIn... input)
Rather than use all available keys as input
onKeys allows users to specify a
subset of keys as input to this task |
MapReduceTask<KIn,VIn,KOut,VOut> |
reducedWith(Reducer<KOut,VOut> reducer)
Specifies Reducer to use for this MapReduceTask
|
String |
toString() |
protected boolean |
useIntermediatePerTaskCache() |
protected boolean |
useIntermediateSharedCache() |
public static final String DEFAULT_TMP_CACHE_CONFIGURATION_NAME
protected final boolean distributeReducePhase
protected final boolean useIntermediateSharedCache
protected final Collection<KIn> keys
protected final AdvancedCache<KIn,VIn> cache
protected final Marshaller marshaller
protected final MapReduceManager mapReduceManager
protected final CancellationService cancellationService
protected final List<org.infinispan.distexec.mapreduce.MapReduceTask.CancellableTaskPart> cancellableTasks
protected final UUID taskId
public MapReduceTask(Cache<KIn,VIn> masterCacheNode)
Large and data intensive tasks whose reduction phase would exceed working memory of one Infinispan node should use distributed reduce phase
masterCacheNode
- cache node initiating map reduce taskpublic MapReduceTask(Cache<KIn,VIn> masterCacheNode, boolean distributeReducePhase)
masterCacheNode
- cache node initiating map reduce taskdistributeReducePhase
- if true this task will use distributed reduce phase executionpublic MapReduceTask(Cache<KIn,VIn> masterCacheNode, boolean distributeReducePhase, boolean useIntermediateSharedCache)
masterCacheNode
- cache node initiating map reduce taskdistributeReducePhase
- if true this task will use distributed reduce phase executionuseIntermediateSharedCache
- if true this tasks will share intermediate value cache with other executing
MapReduceTasks on the grid. Otherwise, if false, this task will use its own
dedicated cache for intermediate valuespublic MapReduceTask<KIn,VIn,KOut,VOut> onKeys(KIn... input)
onKeys
allows users to specify a
subset of keys as input to this taskinput
- input keys for this taskpublic MapReduceTask<KIn,VIn,KOut,VOut> mappedWith(Mapper<KIn,VIn,KOut,VOut> mapper)
Note that Mapper
should not be specified as inner class. Inner classes declared in
non-static contexts contain implicit non-transient references to enclosing class instances,
serializing such an inner class instance will result in serialization of its associated outer
class instance as well.
mapper
- used to execute map phase of MapReduceTaskpublic MapReduceTask<KIn,VIn,KOut,VOut> reducedWith(Reducer<KOut,VOut> reducer)
Note that Reducer
should not be specified as inner class. Inner classes declared in
non-static contexts contain implicit non-transient references to enclosing class instances,
serializing such an inner class instance will result in serialization of its associated outer
class instance as well.
reducer
- used to reduce results of map phasepublic MapReduceTask<KIn,VIn,KOut,VOut> combinedWith(Reducer<KOut,VOut> combiner)
Note that Reducer
should not be specified as inner class. Inner classes declared in
non-static contexts contain implicit non-transient references to enclosing class instances,
serializing such an inner class instance will result in serialization of its associated outer
class instance as well.
combiner
- used to immediately combine results of map phase before reduce phase is invokedpublic Map<KOut,VOut> execute() throws CacheException
CacheException
protected boolean distributeReducePhase()
protected boolean useIntermediateSharedCache()
protected boolean useIntermediatePerTaskCache()
protected void executeTaskInit(String tmpCacheName)
protected Set<KOut> executeMapPhase(boolean useCompositeKeys) throws InterruptedException, ExecutionException
protected Map<KOut,VOut> executeMapPhaseWithLocalReduction() throws InterruptedException, ExecutionException
protected <V> org.infinispan.distexec.mapreduce.MapReduceTask.MapTaskPart<V> createTaskMapPart(MapCombineCommand<KIn,VIn,KOut,VOut> cmd, Address target, boolean distributedReduce)
protected Map<KOut,VOut> executeReducePhase(Set<KOut> allMapPhasesResponses, boolean useCompositeKeys) throws InterruptedException, ExecutionException
protected <V> org.infinispan.distexec.mapreduce.MapReduceTask.ReduceTaskPart<V> createReducePart(ReduceCommand<KOut,VOut> cmd, Address target, String destCacheName)
public Future<Map<KOut,VOut>> executeAsynchronously()
public <R> R execute(Collator<KOut,VOut,R> collator)
Collator
collator
- a Collator to usepublic <R> Future<R> executeAsynchronously(Collator<KOut,VOut,R> collator)
Collator
and wrapped by Futurecollator
- a Collator to useprotected void aggregateReducedResult(Map<KOut,List<VOut>> finalReduced, Map<KOut,VOut> mapReceived)
protected <T> Map<Address,List<T>> mapKeysToNodes(DistributionManager dm, Collection<T> keysToMap, boolean useIntermediateCompositeKey)
protected <T> Map<Address,List<T>> mapKeysToNodes(Collection<T> keysToMap, boolean useIntermediateCompositeKey)
protected <T> Map<Address,List<T>> mapKeysToNodes(Collection<T> keysToMap)
protected boolean inputTaskKeysEmpty()
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