org.apache.crunch.impl.spark.SparkRuntime Maven / Gradle / Ivy
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* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
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package org.apache.crunch.impl.spark;
import com.google.common.collect.ImmutableList;
import com.google.common.collect.ImmutableMap;
import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import com.google.common.collect.Sets;
import com.google.common.util.concurrent.AbstractFuture;
import com.google.common.util.concurrent.ListeningExecutorService;
import com.google.common.util.concurrent.MoreExecutors;
import org.apache.crunch.CombineFn;
import org.apache.crunch.PCollection;
import org.apache.crunch.PipelineCallable;
import org.apache.crunch.PipelineExecution;
import org.apache.crunch.PipelineResult;
import org.apache.crunch.SourceTarget;
import org.apache.crunch.Target;
import org.apache.crunch.fn.IdentityFn;
import org.apache.crunch.impl.dist.collect.PCollectionImpl;
import org.apache.crunch.impl.spark.fn.MapFunction;
import org.apache.crunch.impl.spark.fn.OutputConverterFunction;
import org.apache.crunch.impl.spark.fn.PairMapFunction;
import org.apache.crunch.io.CrunchOutputs;
import org.apache.crunch.io.MapReduceTarget;
import org.apache.crunch.io.PathTarget;
import org.apache.crunch.materialize.MaterializableIterable;
import org.apache.crunch.types.Converter;
import org.apache.crunch.types.PType;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.FileUtil;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.WritableUtils;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.CounterGroup;
import org.apache.hadoop.mapreduce.Counters;
import org.apache.hadoop.mapreduce.Job;
import org.apache.spark.Accumulator;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaRDDLike;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.storage.StorageLevel;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.net.URI;
import java.util.Comparator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.concurrent.atomic.AtomicReference;
public class SparkRuntime extends AbstractFuture implements PipelineExecution {
private static final Logger LOG = LoggerFactory.getLogger(SparkRuntime.class);
private SparkPipeline pipeline;
private JavaSparkContext sparkContext;
private Configuration conf;
private CombineFn combineFn;
private SparkRuntimeContext ctxt;
private Accumulator>> counters;
private Map, Set> outputTargets;
private Map, MaterializableIterable> toMaterialize;
private Map, StorageLevel> toCache;
private Map, Set> allPipelineCallables;
private Set> activePipelineCallables;
private final CountDownLatch doneSignal = new CountDownLatch(1);
private AtomicReference status = new AtomicReference(Status.READY);
private boolean started;
private Thread monitorThread;
// Note that this is the oppposite of the MR sort
static final Comparator> DEPTH_COMPARATOR = new Comparator>() {
@Override
public int compare(PCollectionImpl left, PCollectionImpl right) {
int cmp = left.getDepth() - right.getDepth();
if (cmp == 0) {
// Ensure we don't throw away two output collections at the same depth.
// Using the collection name would be nicer here, but names aren't
// necessarily unique.
cmp = new Integer(left.hashCode()).compareTo(right.hashCode());
}
return cmp;
}
};
public SparkRuntime(SparkPipeline pipeline,
JavaSparkContext sparkContext,
Configuration conf,
Map, Set> outputTargets,
Map, MaterializableIterable> toMaterialize,
Map, StorageLevel> toCache,
Map, Set> allPipelineCallables) {
this.pipeline = pipeline;
this.sparkContext = sparkContext;
this.conf = conf;
this.counters = sparkContext.accumulator(Maps.>newHashMap(), pipeline.getName(),
new CounterAccumulatorParam());
this.ctxt = new SparkRuntimeContext(sparkContext.appName(), counters,
sparkContext.broadcast(WritableUtils.toByteArray(conf)));
this.outputTargets = Maps.newTreeMap(DEPTH_COMPARATOR);
this.outputTargets.putAll(outputTargets);
this.toMaterialize = toMaterialize;
this.toCache = toCache;
this.allPipelineCallables = allPipelineCallables;
this.activePipelineCallables = allPipelineCallables.keySet();
this.status.set(Status.READY);
this.monitorThread = new Thread(new Runnable() {
@Override
public void run() {
monitorLoop();
}
});
}
public void setCombineFn(CombineFn combineFn) {
this.combineFn = combineFn;
}
public CombineFn getCombineFn() {
CombineFn ret = combineFn;
this.combineFn = null;
return ret;
}
private void distributeFiles() {
try {
URI[] uris = DistributedCache.getCacheFiles(conf);
if (uris != null) {
URI[] outURIs = new URI[uris.length];
for (int i = 0; i < uris.length; i++) {
Path path = new Path(uris[i]);
FileSystem fs = path.getFileSystem(conf);
if (fs.isFile(path)) {
outURIs[i] = uris[i];
} else {
Path mergePath = new Path(path.getParent(), "sparkreadable-" + path.getName());
FileUtil.copyMerge(fs, path, fs, mergePath, false, conf, "");
outURIs[i] = mergePath.toUri();
}
sparkContext.addFile(outURIs[i].toString());
}
DistributedCache.setCacheFiles(outURIs, conf);
}
} catch (IOException e) {
throw new RuntimeException("Error retrieving cache files", e);
}
}
public synchronized SparkRuntime execute() {
if (!started) {
monitorThread.start();
started = true;
}
return this;
}
public JavaSparkContext getSparkContext() {
return sparkContext;
}
public SparkRuntimeContext getRuntimeContext() {
return ctxt;
}
public Configuration getConfiguration() {
return conf;
}
public boolean isValid(JavaRDDLike rdd) {
return (rdd != null); //TODO: support multi-contexts
}
public StorageLevel getStorageLevel(PCollection pcollection) {
return toCache.get(pcollection);
}
@Override
public String getPlanDotFile() {
return "";
}
@Override
public Map getNamedDotFiles() {
return ImmutableMap.of("", "");
}
@Override
public void waitFor(long timeout, TimeUnit timeUnit) throws InterruptedException {
doneSignal.await(timeout, timeUnit);
}
@Override
public void waitUntilDone() throws InterruptedException {
doneSignal.await();
}
private void runCallables(Set unfinished) {
Set> oldCallables = activePipelineCallables;
activePipelineCallables = Sets.newHashSet();
List> callablesToRun = Lists.newArrayList();
List> failedCallables = Lists.newArrayList();
for (PipelineCallable pipelineCallable : oldCallables) {
if (Sets.intersection(allPipelineCallables.get(pipelineCallable), unfinished).isEmpty()) {
if (pipelineCallable.runSingleThreaded()) {
try {
if (pipelineCallable.call() != PipelineCallable.Status.SUCCESS) {
failedCallables.add(pipelineCallable);
}
} catch (Throwable t) {
pipelineCallable.setMessage(t.getLocalizedMessage());
failedCallables.add(pipelineCallable);
}
} else {
callablesToRun.add(pipelineCallable);
}
} else {
// Still need to run this one
activePipelineCallables.add(pipelineCallable);
}
}
ListeningExecutorService es = MoreExecutors.listeningDecorator(Executors.newCachedThreadPool());
try {
List> res = es.invokeAll(callablesToRun);
for (int i = 0; i < res.size(); i++) {
if (res.get(i).get() != PipelineCallable.Status.SUCCESS) {
failedCallables.add((PipelineCallable) callablesToRun.get(i));
}
}
} catch (Throwable t) {
t.printStackTrace();
failedCallables.addAll((List) callablesToRun);
} finally {
es.shutdownNow();
}
if (!failedCallables.isEmpty()) {
LOG.error("{} callable failure(s) occurred:", failedCallables.size());
for (PipelineCallable c : failedCallables) {
LOG.error("{} : {}", c.getName(), c.getMessage());
}
status.set(Status.FAILED);
set(PipelineResult.EMPTY);
doneSignal.countDown();
}
}
private void monitorLoop() {
status.set(Status.RUNNING);
long start = System.currentTimeMillis();
Map, Set> targetDeps = Maps.newTreeMap(DEPTH_COMPARATOR);
Set unfinished = Sets.newHashSet();
for (PCollectionImpl pcollect : outputTargets.keySet()) {
targetDeps.put(pcollect, pcollect.getTargetDependencies());
unfinished.addAll(outputTargets.get(pcollect));
}
runCallables(unfinished);
while (!targetDeps.isEmpty() && doneSignal.getCount() > 0) {
Set allTargets = Sets.newHashSet();
for (PCollectionImpl pcollect : targetDeps.keySet()) {
allTargets.addAll(outputTargets.get(pcollect));
}
Map, JavaRDDLike> pcolToRdd = Maps.newTreeMap(DEPTH_COMPARATOR);
for (PCollectionImpl pcollect : targetDeps.keySet()) {
if (Sets.intersection(allTargets, targetDeps.get(pcollect)).isEmpty()) {
JavaRDDLike rdd = ((SparkCollection) pcollect).getJavaRDDLike(this);
pcolToRdd.put(pcollect, rdd);
}
}
distributeFiles();
for (Map.Entry, JavaRDDLike> e : pcolToRdd.entrySet()) {
JavaRDDLike rdd = e.getValue();
PType ptype = e.getKey().getPType();
Set targets = outputTargets.get(e.getKey());
if (targets.size() > 1) {
rdd.rdd().cache();
}
for (Target t : targets) {
Configuration conf = new Configuration(getConfiguration());
getRuntimeContext().setConf(sparkContext.broadcast(WritableUtils.toByteArray(conf)));
if (t instanceof MapReduceTarget) { //TODO: check this earlier
Converter c = t.getConverter(ptype);
IdentityFn ident = IdentityFn.getInstance();
JavaPairRDD outRDD;
if (rdd instanceof JavaRDD) {
outRDD = ((JavaRDD) rdd)
.map(new MapFunction(c.applyPTypeTransforms() ? ptype.getOutputMapFn() : ident, ctxt))
.mapToPair(new OutputConverterFunction(c));
} else {
outRDD = ((JavaPairRDD) rdd)
.map(new PairMapFunction(c.applyPTypeTransforms() ? ptype.getOutputMapFn() : ident, ctxt))
.mapToPair(new OutputConverterFunction(c));
}
try {
Job job = new Job(conf);
if (t instanceof PathTarget) {
PathTarget pt = (PathTarget) t;
pt.configureForMapReduce(job, ptype, pt.getPath(), "out0");
CrunchOutputs.OutputConfig outConfig =
CrunchOutputs.getNamedOutputs(job.getConfiguration()).get("out0");
job.setOutputFormatClass(outConfig.bundle.getFormatClass());
job.setOutputKeyClass(outConfig.keyClass);
job.setOutputValueClass(outConfig.valueClass);
outConfig.bundle.configure(job.getConfiguration());
job.getConfiguration().set("crunch.namedoutput", "out0");
Path tmpPath = pipeline.createTempPath();
outRDD.saveAsNewAPIHadoopFile(
tmpPath.toString(),
c.getKeyClass(),
c.getValueClass(),
job.getOutputFormatClass(),
job.getConfiguration());
pt.handleOutputs(job.getConfiguration(), tmpPath, -1);
} else { //if (t instanceof MapReduceTarget) {
MapReduceTarget mrt = (MapReduceTarget) t;
mrt.configureForMapReduce(job, ptype, pipeline.createTempPath(), "out0");
CrunchOutputs.OutputConfig outConfig =
CrunchOutputs.getNamedOutputs(job.getConfiguration()).get("out0");
job.setOutputFormatClass(outConfig.bundle.getFormatClass());
job.setOutputKeyClass(outConfig.keyClass);
job.setOutputValueClass(outConfig.valueClass);
outRDD.saveAsHadoopDataset(new JobConf(job.getConfiguration()));
}
} catch (Exception et) {
LOG.error("Spark Exception", et);
status.set(Status.FAILED);
set(PipelineResult.EMPTY);
doneSignal.countDown();
}
}
}
unfinished.removeAll(targets);
}
if (status.get() == Status.RUNNING) {
for (PCollectionImpl output : pcolToRdd.keySet()) {
if (toMaterialize.containsKey(output)) {
MaterializableIterable mi = toMaterialize.get(output);
if (mi.isSourceTarget()) {
output.materializeAt((SourceTarget) mi.getSource());
}
}
targetDeps.remove(output);
}
}
runCallables(unfinished);
}
if (status.get() != Status.FAILED || status.get() != Status.KILLED) {
status.set(Status.SUCCEEDED);
set(new PipelineResult(
ImmutableList.of(new PipelineResult.StageResult("Spark", getCounters(),
start, System.currentTimeMillis())),
Status.SUCCEEDED));
} else {
set(PipelineResult.EMPTY);
}
doneSignal.countDown();
}
private Counters getCounters() {
Counters c = new Counters();
Map> values = counters.value();
for (Map.Entry> e : values.entrySet()) {
CounterGroup cg = c.getGroup(e.getKey());
for (Map.Entry f : e.getValue().entrySet()) {
cg.findCounter(f.getKey()).setValue(f.getValue());
}
}
return c;
}
@Override
public PipelineResult get() throws InterruptedException, ExecutionException {
if (getStatus() == Status.READY) {
execute();
}
return super.get();
}
@Override
public PipelineResult get(long timeout, TimeUnit unit) throws InterruptedException, TimeoutException,
ExecutionException {
if (getStatus() == Status.READY) {
execute();
}
return super.get(timeout, unit);
}
@Override
public Status getStatus() {
return status.get();
}
@Override
public PipelineResult getResult() {
try {
return get();
} catch (Exception e) {
LOG.error("Exception retrieving PipelineResult, returning EMPTY", e);
return PipelineResult.EMPTY;
}
}
@Override
public void kill() throws InterruptedException {
if (started) {
sparkContext.stop();
set(PipelineResult.EMPTY);
}
}
}
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