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* Copyright 2011 Red Hat Inc. and/or its affiliates and other
* contributors as indicated by the @author tags. All rights reserved.
* See the copyright.txt in the distribution for a full listing of
* individual contributors.
*
* This is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of
* the License, or (at your option) any later version.
*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this software; if not, write to the Free
* Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
* 02110-1301 USA, or see the FSF site: http://www.fsf.org.
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package org.infinispan.distexec.mapreduce;
import org.infinispan.AdvancedCache;
import org.infinispan.Cache;
import org.infinispan.CacheException;
import org.infinispan.commands.CommandsFactory;
import org.infinispan.commands.read.MapReduceCommand;
import org.infinispan.context.InvocationContextContainer;
import org.infinispan.distribution.DistributionManager;
import org.infinispan.factories.ComponentRegistry;
import org.infinispan.interceptors.InterceptorChain;
import org.infinispan.lifecycle.ComponentStatus;
import org.infinispan.marshall.Marshaller;
import org.infinispan.marshall.StreamingMarshaller;
import org.infinispan.remoting.responses.ExceptionResponse;
import org.infinispan.remoting.responses.Response;
import org.infinispan.remoting.responses.SuccessfulResponse;
import org.infinispan.remoting.rpc.RpcManager;
import org.infinispan.remoting.transport.Address;
import org.infinispan.util.Util;
import org.infinispan.util.concurrent.AbstractInProcessFuture;
import org.infinispan.util.concurrent.FutureListener;
import org.infinispan.util.concurrent.NotifyingFuture;
import org.infinispan.util.concurrent.NotifyingNotifiableFuture;
import org.infinispan.util.logging.Log;
import org.infinispan.util.logging.LogFactory;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Future;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import static org.infinispan.factories.KnownComponentNames.CACHE_MARSHALLER;
/**
* MapReduceTask is a distributed task allowing a large scale computation to be transparently
* parallelized across Infinispan cluster nodes.
* <p>
*
* 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 {@link Mapper}
* and {@link Reducer} seamlessly across Infinispan nodes.
* <p>
*
* Unless otherwise specified using {@link MapReduceTask#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:
*
* <pre>
* 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();
* </pre>
*
* The final result is a map where key is a word and value is a word count for that particular word.
* <p>
*
* Accompanying {@link Mapper} and {@link Reducer} are defined as follows:
*
* <pre>
* 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;
* }
* }
* </pre>
*
* Note that {@link Mapper} and {@link 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.
*
* <p>
*
* If you are not familiar with concept of map reduce distributed execution model
* start with Google's MapReduce research <a href="http://labs.google.com/papers/mapreduce.html">paper</a>.
*
*
* @author Manik Surtani
* @author Vladimir Blagojevic
* @author Sanne Grinovero
*
* @since 5.0
*/
public class MapReduceTask<KIn, VIn, KOut, VOut> {
private static final Log log = LogFactory.getLog(MapReduceTask.class);
private Mapper<KIn, VIn, KOut, VOut> mapper;
private Reducer<KOut, VOut> reducer;
private final Collection<KIn> keys;
private final AdvancedCache<KIn, VIn> cache;
protected final Marshaller marshaller;
/**
* Create a new MapReduceTask given a master cache node. All distributed task executions will be
* initiated from this cache node.
*
* @param masterCacheNode
* cache node initiating map reduce task
*/
public MapReduceTask(Cache<KIn, VIn> masterCacheNode) {
if (masterCacheNode == null)
throw new IllegalArgumentException("Can not use null cache for MapReduceTask");
ensureProperCacheState(masterCacheNode.getAdvancedCache());
this.cache = masterCacheNode.getAdvancedCache();
this.keys = new LinkedList<KIn>();
this.marshaller = cache.getComponentRegistry().getComponent(StreamingMarshaller.class, CACHE_MARSHALLER);
}
/**
* Rather than use all available keys as input <code>onKeys</code> allows users to specify a
* subset of keys as input to this task
*
* @param input
* input keys for this task
* @return this task
*/
public MapReduceTask<KIn, VIn, KOut, VOut> onKeys(KIn... input) {
Collections.addAll(keys, input);
return this;
}
/**
* Specifies Mapper to use for this MapReduceTask
* <p>
* Note that {@link 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.
*
* @param mapper
* @return
*/
public MapReduceTask<KIn, VIn, KOut, VOut> mappedWith(Mapper<KIn, VIn, KOut, VOut> mapper) {
if (mapper == null)
throw new IllegalArgumentException("A valid reference of Mapper is needed");
this.mapper = mapper;
return this;
}
/**
* Specifies Reducer to use for this MapReduceTask
*
* <p>
* Note that {@link 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.
*
* @param reducer
* @return
*/
public MapReduceTask<KIn, VIn, KOut, VOut> reducedWith(Reducer<KOut, VOut> reducer) {
if (reducer == null)
throw new IllegalArgumentException("A valid reference of Mapper is needed");
this.reducer = reducer;
return this;
}
/**
* Executes this task across Infinispan cluster nodes.
*
* @return a Map where each key is an output key and value is reduced value for that output key
*/
@SuppressWarnings("unchecked")
public Map<KOut, VOut> execute() throws CacheException {
if (mapper == null)
throw new NullPointerException("A valid reference of Mapper is not set " + mapper);
if (reducer == null)
throw new NullPointerException("A valid reference of Reducer is not set " + reducer);
ComponentRegistry registry = cache.getComponentRegistry();
RpcManager rpc = cache.getRpcManager();
InvocationContextContainer icc = cache.getInvocationContextContainer();
DistributionManager dm = cache.getDistributionManager();
InterceptorChain invoker = registry.getComponent(InterceptorChain.class);
CommandsFactory factory = registry.getComponent(CommandsFactory.class);
MapReduceCommand cmd = null;
MapReduceCommand selfCmd = null;
Map<Address, Response> results = new HashMap<Address, Response>();
if (inputTaskKeysEmpty()) {
cmd = factory.buildMapReduceCommand(mapper, reducer, rpc.getAddress(), keys);
selfCmd = cmd;
try {
log.debugf("Invoking %s across entire cluster ", cmd);
Map<Address, Response> map = rpc.invokeRemotely(null, cmd, true, false, false);
log.debugf("Invoked %s across entire cluster, results are %s", cmd, map);
results.putAll(map);
} catch (Throwable e) {
throw new CacheException("Could not invoke MapReduce task on remote nodes ", e);
}
} else {
Map<Address, List<KIn>> keysToNodes = mapKeysToNodes();
log.debugf("Keys to nodes mapping is " + keysToNodes);
List<MapReduceFuture> futures = new ArrayList<MapReduceFuture>();
for (Entry<Address, List<KIn>> e : keysToNodes.entrySet()) {
Address address = e.getKey();
List<KIn> keys = e.getValue();
if (address.equals(rpc.getAddress())) {
selfCmd = factory.buildMapReduceCommand(clone(mapper), clone(reducer), rpc.getAddress(), keys);
} else {
cmd = factory.buildMapReduceCommand(mapper, reducer, rpc.getAddress(), keys);
try {
log.debugf("Invoking %s on %s", cmd, address);
MapReduceFuture future = new MapReduceFuture();
futures.add(future);
rpc.invokeRemotelyInFuture(Collections.singleton(address), cmd, future);
log.debugf("Invoked %s on %s ", cmd, address);
} catch (Exception ex) {
throw new CacheException("Could not invoke MapReduceTask on remote node " + address, ex);
}
}
}
for (MapReduceFuture future : futures) {
Map<Address, Response> result;
try {
result = (Map<Address, Response>) future.get();
results.putAll(result);
log.debugf("Received result from future %s", result);
} catch (Exception e1) {
throw new CacheException("Could not retrieve MapReduceTask result from remote node", e1);
}
}
}
boolean selfInvoke = selfCmd != null;
Object localCommandResult = null;
if (selfInvoke) {
log.debugf("Invoking %s locally", cmd);
selfCmd.init(factory, invoker, icc, dm, rpc.getAddress());
try {
localCommandResult = selfCmd.perform(null);
log.debugf("Invoked %s locally", cmd);
} catch (Throwable e1) {
throw new CacheException("Could not invoke MapReduce task locally ", e1);
}
}
// we have results from all nodes now, group intermediate keys for final reduction
Map<KOut, List<VOut>> reduceMap = new HashMap<KOut, List<VOut>>();
for (Entry<Address, Response> e : results.entrySet()) {
Response rsp = e.getValue();
if (rsp.isSuccessful() && rsp.isValid()) {
Map<KOut, VOut> reducedResponse = (Map<KOut, VOut>) ((SuccessfulResponse) rsp).getResponseValue();
groupKeys(reduceMap, reducedResponse);
} else if (rsp instanceof ExceptionResponse) {
throw new CacheException("MapReduce task on remote node " + e.getKey()
+ " threw Exception", ((ExceptionResponse) rsp).getException());
} else {
throw new CacheException("MapReduce task on remote node " + e.getKey() + " failed ");
}
}
if (selfInvoke) {
groupKeys(reduceMap, (Map<KOut, VOut>) localCommandResult);
}
// final reduce
//TODO parallelize into Executor
Map<KOut, VOut> result = new HashMap<KOut, VOut>();
for (Entry<KOut, List<VOut>> entry : reduceMap.entrySet()) {
VOut reduced = reducer.reduce(entry.getKey(), (entry.getValue()).iterator());
result.put(entry.getKey(), reduced);
}
return result;
}
/**
* Executes this task across Infinispan cluster nodes asynchronously.
*
* @return a Future wrapping a Map where each key is an output key and value is reduced value for
* that output key
*/
public Future<Map<KOut, VOut>> executeAsynchronously() {
final Callable<Map<KOut, VOut>> call = new Callable<Map<KOut, VOut>>() {
@Override
public Map<KOut, VOut> call() throws Exception {
return execute();
}
};
return new AbstractInProcessFuture<Map<KOut, VOut>>() {
@Override
public Map<KOut, VOut> get() throws InterruptedException, ExecutionException {
try {
return call.call();
} catch (Exception e) {
throw new ExecutionException(e);
}
}
};
}
/**
* Executes this task across Infinispan cluster but the final result is collated using specified
* {@link Collator}
*
* @param collator
* a Collator to use
*
* @return collated result
*/
public <R> R execute(Collator<KOut, VOut, R> collator) {
Map<KOut, VOut> execute = execute();
return collator.collate(execute);
}
/**
* Executes this task asynchronously across Infinispan cluster; final result is collated using
* specified {@link Collator} and wrapped by Future
*
* @param collator
* a Collator to use
*
* @return collated result
*/
public <R> Future<R> executeAsynchronously(final Collator<KOut, VOut, R> collator) {
final Callable<R> call = new Callable<R>() {
@Override
public R call() throws Exception {
return execute(collator);
}
};
return new AbstractInProcessFuture<R>() {
@Override
public R get() throws InterruptedException, ExecutionException {
try {
return call.call();
} catch (Exception e) {
throw new ExecutionException(e);
}
}
};
}
protected void groupKeys(Map<KOut, List<VOut>> finalReduced, Map<KOut, VOut> mapReceived) {
for (Entry<KOut, VOut> entry : mapReceived.entrySet()) {
List<VOut> l;
if (!finalReduced.containsKey(entry.getKey())) {
l = new LinkedList<VOut>();
finalReduced.put(entry.getKey(), l);
} else {
l = finalReduced.get(entry.getKey());
}
l.add(entry.getValue());
}
}
protected Map<Address, List<KIn>> mapKeysToNodes() {
DistributionManager dm = cache.getDistributionManager();
Map<Address, List<KIn>> addressToKey = new HashMap<Address, List<KIn>>();
for (KIn key : keys) {
Address ownerOfKey = dm.getPrimaryLocation(key);
List<KIn> keysAtNode = addressToKey.get(ownerOfKey);
if (keysAtNode == null) {
keysAtNode = new ArrayList<KIn>();
addressToKey.put(ownerOfKey, keysAtNode);
}
keysAtNode.add(key);
}
return addressToKey;
}
protected Mapper<KIn, VIn, KOut, VOut> clone(Mapper<KIn, VIn, KOut, VOut> mapper){
return Util.cloneWithMarshaller(marshaller, mapper);
}
protected Reducer<KOut, VOut> clone(Reducer<KOut, VOut> reducer){
return Util.cloneWithMarshaller(marshaller, reducer);
}
private void ensureProperCacheState(AdvancedCache<KIn, VIn> cache) throws NullPointerException,
IllegalStateException {
if (cache.getRpcManager() == null)
throw new IllegalStateException("Can not use non-clustered cache for MapReduceTask");
if (cache.getStatus() != ComponentStatus.RUNNING)
throw new IllegalStateException("Invalid cache state " + cache.getStatus());
if (cache.getDistributionManager() == null) {
throw new IllegalStateException("Cache mode should be DIST, rather than "
+ cache.getConfiguration().getCacheModeString());
}
}
private boolean inputTaskKeysEmpty() {
return keys == null || keys.isEmpty();
}
private static class MapReduceFuture implements NotifyingNotifiableFuture<Object>{
private Future<Object> futureResult;
@Override
public NotifyingFuture<Object> attachListener(
FutureListener<Object> listener) {
return this;
}
@Override
public boolean cancel(boolean mayInterruptIfRunning) {
return false;
}
@Override
public boolean isCancelled() {
return false;
}
@Override
public boolean isDone() {
return false;
}
@Override
public Object get() throws InterruptedException,
ExecutionException {
return futureResult.get();
}
@Override
public Object get(long timeout, TimeUnit unit)
throws InterruptedException, ExecutionException, TimeoutException {
return futureResult.get(timeout, unit);
}
@Override
public void notifyDone() {
}
@Override
public void setNetworkFuture(Future<Object> future) {
this.futureResult = future;
}
}
}