/*
* Helper function to create Vertex for given ReduceWork.
*/
private Vertex createVertex(
JobConf conf,
ReduceWork reduceWork,
LocalResource appJarLr,
List<LocalResource> additionalLr,
FileSystem fs,
Path mrScratchDir,
Context ctx)
throws Exception {
// set up operator plan
Utilities.setReduceWork(conf, reduceWork, mrScratchDir, false);
// create the directories FileSinkOperators need
Utilities.createTmpDirs(conf, reduceWork);
// Call once here, will be updated when we find edges
MultiStageMRConfToTezTranslator.translateVertexConfToTez(conf, null);
// create the vertex
Vertex reducer =
new Vertex(
reduceWork.getName(),
new ProcessorDescriptor(ReduceTezProcessor.class.getName())
.setUserPayload(MRHelpers.createUserPayloadFromConf(conf)),
reduceWork.getNumReduceTasks(),
getContainerResource(conf));
Map<String, String> environment = new HashMap<String, String>();
MRHelpers.updateEnvironmentForMRTasks(conf, environment, false);
reducer.setTaskEnvironment(environment);
reducer.setJavaOpts(getContainerJavaOpts(conf));
Map<String, LocalResource> localResources = new HashMap<String, LocalResource>();
localResources.put(getBaseName(appJarLr), appJarLr);
for (LocalResource lr : additionalLr) {
localResources.put(getBaseName(lr), lr);
}
reducer.setTaskLocalResources(localResources);
return reducer;
}
/*
* Helper function to create Vertex from MapWork.
*/
private Vertex createVertex(
JobConf conf,
MapWork mapWork,
LocalResource appJarLr,
List<LocalResource> additionalLr,
FileSystem fs,
Path mrScratchDir,
Context ctx,
TezWork tezWork)
throws Exception {
Path tezDir = getTezDir(mrScratchDir);
// set up the operator plan
Utilities.setMapWork(conf, mapWork, mrScratchDir, false);
// create the directories FileSinkOperators need
Utilities.createTmpDirs(conf, mapWork);
// Tez ask us to call this even if there's no preceding vertex
MultiStageMRConfToTezTranslator.translateVertexConfToTez(conf, null);
// finally create the vertex
Vertex map = null;
// use tez to combine splits
boolean useTezGroupedSplits = false;
int numTasks = -1;
Class amSplitGeneratorClass = null;
InputSplitInfo inputSplitInfo = null;
Class inputFormatClass = conf.getClass("mapred.input.format.class", InputFormat.class);
boolean vertexHasCustomInput = false;
if (tezWork != null) {
for (BaseWork baseWork : tezWork.getParents(mapWork)) {
if (tezWork.getEdgeType(baseWork, mapWork) == EdgeType.CUSTOM_EDGE) {
vertexHasCustomInput = true;
}
}
}
if (vertexHasCustomInput) {
useTezGroupedSplits = false;
// grouping happens in execution phase. Setting the class to TezGroupedSplitsInputFormat
// here would cause pre-mature grouping which would be incorrect.
inputFormatClass = HiveInputFormat.class;
conf.setClass("mapred.input.format.class", HiveInputFormat.class, InputFormat.class);
// mapreduce.tez.input.initializer.serialize.event.payload should be set to false when using
// this plug-in to avoid getting a serialized event at run-time.
conf.setBoolean("mapreduce.tez.input.initializer.serialize.event.payload", false);
} else {
// we'll set up tez to combine spits for us iff the input format
// is HiveInputFormat
if (inputFormatClass == HiveInputFormat.class) {
useTezGroupedSplits = true;
conf.setClass(
"mapred.input.format.class", TezGroupedSplitsInputFormat.class, InputFormat.class);
}
}
if (HiveConf.getBoolVar(conf, ConfVars.HIVE_AM_SPLIT_GENERATION)) {
// if we're generating the splits in the AM, we just need to set
// the correct plugin.
amSplitGeneratorClass = MRInputAMSplitGenerator.class;
} else {
// client side split generation means we have to compute them now
inputSplitInfo =
MRHelpers.generateInputSplits(
conf, new Path(tezDir, "split_" + mapWork.getName().replaceAll(" ", "_")));
numTasks = inputSplitInfo.getNumTasks();
}
byte[] serializedConf = MRHelpers.createUserPayloadFromConf(conf);
map =
new Vertex(
mapWork.getName(),
new ProcessorDescriptor(MapTezProcessor.class.getName()).setUserPayload(serializedConf),
numTasks,
getContainerResource(conf));
Map<String, String> environment = new HashMap<String, String>();
MRHelpers.updateEnvironmentForMRTasks(conf, environment, true);
map.setTaskEnvironment(environment);
map.setJavaOpts(getContainerJavaOpts(conf));
assert mapWork.getAliasToWork().keySet().size() == 1;
String alias = mapWork.getAliasToWork().keySet().iterator().next();
byte[] mrInput = null;
if (useTezGroupedSplits) {
mrInput =
MRHelpers.createMRInputPayloadWithGrouping(
serializedConf, HiveInputFormat.class.getName());
} else {
mrInput = MRHelpers.createMRInputPayload(serializedConf, null);
}
map.addInput(
alias,
new InputDescriptor(MRInputLegacy.class.getName()).setUserPayload(mrInput),
amSplitGeneratorClass);
Map<String, LocalResource> localResources = new HashMap<String, LocalResource>();
localResources.put(getBaseName(appJarLr), appJarLr);
for (LocalResource lr : additionalLr) {
localResources.put(getBaseName(lr), lr);
}
if (inputSplitInfo != null) {
// only relevant for client-side split generation
map.setTaskLocationsHint(inputSplitInfo.getTaskLocationHints());
MRHelpers.updateLocalResourcesForInputSplits(
FileSystem.get(conf), inputSplitInfo, localResources);
}
map.setTaskLocalResources(localResources);
return map;
}
/** Execute a query plan using Hadoop. */
@SuppressWarnings({"deprecation", "unchecked"})
@Override
public int execute(DriverContext driverContext) {
IOPrepareCache ioPrepareCache = IOPrepareCache.get();
ioPrepareCache.clear();
boolean success = true;
Context ctx = driverContext.getCtx();
boolean ctxCreated = false;
Path emptyScratchDir;
MapWork mWork = work.getMapWork();
ReduceWork rWork = work.getReduceWork();
try {
if (ctx == null) {
ctx = new Context(job);
ctxCreated = true;
}
emptyScratchDir = ctx.getMRTmpPath();
FileSystem fs = emptyScratchDir.getFileSystem(job);
fs.mkdirs(emptyScratchDir);
} catch (IOException e) {
e.printStackTrace();
console.printError(
"Error launching map-reduce job",
"\n" + org.apache.hadoop.util.StringUtils.stringifyException(e));
return 5;
}
HiveFileFormatUtils.prepareJobOutput(job);
// See the javadoc on HiveOutputFormatImpl and HadoopShims.prepareJobOutput()
job.setOutputFormat(HiveOutputFormatImpl.class);
job.setMapperClass(ExecMapper.class);
job.setMapOutputKeyClass(HiveKey.class);
job.setMapOutputValueClass(BytesWritable.class);
try {
String partitioner = HiveConf.getVar(job, ConfVars.HIVEPARTITIONER);
job.setPartitionerClass(JavaUtils.loadClass(partitioner));
} catch (ClassNotFoundException e) {
throw new RuntimeException(e.getMessage(), e);
}
if (mWork.getNumMapTasks() != null) {
job.setNumMapTasks(mWork.getNumMapTasks().intValue());
}
if (mWork.getMaxSplitSize() != null) {
HiveConf.setLongVar(
job, HiveConf.ConfVars.MAPREDMAXSPLITSIZE, mWork.getMaxSplitSize().longValue());
}
if (mWork.getMinSplitSize() != null) {
HiveConf.setLongVar(
job, HiveConf.ConfVars.MAPREDMINSPLITSIZE, mWork.getMinSplitSize().longValue());
}
if (mWork.getMinSplitSizePerNode() != null) {
HiveConf.setLongVar(
job,
HiveConf.ConfVars.MAPREDMINSPLITSIZEPERNODE,
mWork.getMinSplitSizePerNode().longValue());
}
if (mWork.getMinSplitSizePerRack() != null) {
HiveConf.setLongVar(
job,
HiveConf.ConfVars.MAPREDMINSPLITSIZEPERRACK,
mWork.getMinSplitSizePerRack().longValue());
}
job.setNumReduceTasks(rWork != null ? rWork.getNumReduceTasks().intValue() : 0);
job.setReducerClass(ExecReducer.class);
// set input format information if necessary
setInputAttributes(job);
// Turn on speculative execution for reducers
boolean useSpeculativeExecReducers =
HiveConf.getBoolVar(job, HiveConf.ConfVars.HIVESPECULATIVEEXECREDUCERS);
HiveConf.setBoolVar(
job, HiveConf.ConfVars.HADOOPSPECULATIVEEXECREDUCERS, useSpeculativeExecReducers);
String inpFormat = HiveConf.getVar(job, HiveConf.ConfVars.HIVEINPUTFORMAT);
if (mWork.isUseBucketizedHiveInputFormat()) {
inpFormat = BucketizedHiveInputFormat.class.getName();
}
LOG.info("Using " + inpFormat);
try {
job.setInputFormat(JavaUtils.loadClass(inpFormat));
} catch (ClassNotFoundException e) {
throw new RuntimeException(e.getMessage(), e);
}
// No-Op - we don't really write anything here ..
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
// Transfer HIVEAUXJARS and HIVEADDEDJARS to "tmpjars" so hadoop understands
// it
String auxJars = HiveConf.getVar(job, HiveConf.ConfVars.HIVEAUXJARS);
String addedJars = HiveConf.getVar(job, HiveConf.ConfVars.HIVEADDEDJARS);
if (StringUtils.isNotBlank(auxJars) || StringUtils.isNotBlank(addedJars)) {
String allJars =
StringUtils.isNotBlank(auxJars)
? (StringUtils.isNotBlank(addedJars) ? addedJars + "," + auxJars : auxJars)
: addedJars;
LOG.info("adding libjars: " + allJars);
initializeFiles("tmpjars", allJars);
}
// Transfer HIVEADDEDFILES to "tmpfiles" so hadoop understands it
String addedFiles = HiveConf.getVar(job, HiveConf.ConfVars.HIVEADDEDFILES);
if (StringUtils.isNotBlank(addedFiles)) {
initializeFiles("tmpfiles", addedFiles);
}
int returnVal = 0;
boolean noName = StringUtils.isEmpty(HiveConf.getVar(job, HiveConf.ConfVars.HADOOPJOBNAME));
if (noName) {
// This is for a special case to ensure unit tests pass
HiveConf.setVar(job, HiveConf.ConfVars.HADOOPJOBNAME, "JOB" + Utilities.randGen.nextInt());
}
String addedArchives = HiveConf.getVar(job, HiveConf.ConfVars.HIVEADDEDARCHIVES);
// Transfer HIVEADDEDARCHIVES to "tmparchives" so hadoop understands it
if (StringUtils.isNotBlank(addedArchives)) {
initializeFiles("tmparchives", addedArchives);
}
try {
MapredLocalWork localwork = mWork.getMapRedLocalWork();
if (localwork != null && localwork.hasStagedAlias()) {
if (!ShimLoader.getHadoopShims().isLocalMode(job)) {
Path localPath = localwork.getTmpPath();
Path hdfsPath = mWork.getTmpHDFSPath();
FileSystem hdfs = hdfsPath.getFileSystem(job);
FileSystem localFS = localPath.getFileSystem(job);
FileStatus[] hashtableFiles = localFS.listStatus(localPath);
int fileNumber = hashtableFiles.length;
String[] fileNames = new String[fileNumber];
for (int i = 0; i < fileNumber; i++) {
fileNames[i] = hashtableFiles[i].getPath().getName();
}
// package and compress all the hashtable files to an archive file
String stageId = this.getId();
String archiveFileName = Utilities.generateTarFileName(stageId);
localwork.setStageID(stageId);
CompressionUtils.tar(localPath.toUri().getPath(), fileNames, archiveFileName);
Path archivePath = Utilities.generateTarPath(localPath, stageId);
LOG.info("Archive " + hashtableFiles.length + " hash table files to " + archivePath);
// upload archive file to hdfs
Path hdfsFilePath = Utilities.generateTarPath(hdfsPath, stageId);
short replication = (short) job.getInt("mapred.submit.replication", 10);
hdfs.copyFromLocalFile(archivePath, hdfsFilePath);
hdfs.setReplication(hdfsFilePath, replication);
LOG.info("Upload 1 archive file from" + archivePath + " to: " + hdfsFilePath);
// add the archive file to distributed cache
DistributedCache.createSymlink(job);
DistributedCache.addCacheArchive(hdfsFilePath.toUri(), job);
LOG.info(
"Add 1 archive file to distributed cache. Archive file: " + hdfsFilePath.toUri());
}
}
work.configureJobConf(job);
List<Path> inputPaths = Utilities.getInputPaths(job, mWork, emptyScratchDir, ctx, false);
Utilities.setInputPaths(job, inputPaths);
Utilities.setMapRedWork(job, work, ctx.getMRTmpPath());
if (mWork.getSamplingType() > 0 && rWork != null && job.getNumReduceTasks() > 1) {
try {
handleSampling(ctx, mWork, job);
job.setPartitionerClass(HiveTotalOrderPartitioner.class);
} catch (IllegalStateException e) {
console.printInfo("Not enough sampling data.. Rolling back to single reducer task");
rWork.setNumReduceTasks(1);
job.setNumReduceTasks(1);
} catch (Exception e) {
LOG.error("Sampling error", e);
console.printError(
e.toString(), "\n" + org.apache.hadoop.util.StringUtils.stringifyException(e));
rWork.setNumReduceTasks(1);
job.setNumReduceTasks(1);
}
}
// remove the pwd from conf file so that job tracker doesn't show this
// logs
String pwd = HiveConf.getVar(job, HiveConf.ConfVars.METASTOREPWD);
if (pwd != null) {
HiveConf.setVar(job, HiveConf.ConfVars.METASTOREPWD, "HIVE");
}
JobClient jc = new JobClient(job);
// make this client wait if job tracker is not behaving well.
Throttle.checkJobTracker(job, LOG);
if (mWork.isGatheringStats() || (rWork != null && rWork.isGatheringStats())) {
// initialize stats publishing table
StatsPublisher statsPublisher;
StatsFactory factory = StatsFactory.newFactory(job);
if (factory != null) {
statsPublisher = factory.getStatsPublisher();
List<String> statsTmpDir = Utilities.getStatsTmpDirs(mWork, job);
if (rWork != null) {
statsTmpDir.addAll(Utilities.getStatsTmpDirs(rWork, job));
}
StatsCollectionContext sc = new StatsCollectionContext(job);
sc.setStatsTmpDirs(statsTmpDir);
if (!statsPublisher.init(sc)) { // creating stats table if not exists
if (HiveConf.getBoolVar(job, HiveConf.ConfVars.HIVE_STATS_RELIABLE)) {
throw new HiveException(
ErrorMsg.STATSPUBLISHER_INITIALIZATION_ERROR.getErrorCodedMsg());
}
}
}
}
Utilities.createTmpDirs(job, mWork);
Utilities.createTmpDirs(job, rWork);
SessionState ss = SessionState.get();
if (HiveConf.getVar(job, HiveConf.ConfVars.HIVE_EXECUTION_ENGINE).equals("tez")
&& ss != null) {
TezSessionState session = ss.getTezSession();
TezSessionPoolManager.getInstance().close(session, true);
}
// Finally SUBMIT the JOB!
rj = jc.submitJob(job);
// replace it back
if (pwd != null) {
HiveConf.setVar(job, HiveConf.ConfVars.METASTOREPWD, pwd);
}
returnVal = jobExecHelper.progress(rj, jc, ctx.getHiveTxnManager());
success = (returnVal == 0);
} catch (Exception e) {
e.printStackTrace();
String mesg = " with exception '" + Utilities.getNameMessage(e) + "'";
if (rj != null) {
mesg = "Ended Job = " + rj.getJobID() + mesg;
} else {
mesg = "Job Submission failed" + mesg;
}
// Has to use full name to make sure it does not conflict with
// org.apache.commons.lang.StringUtils
console.printError(mesg, "\n" + org.apache.hadoop.util.StringUtils.stringifyException(e));
success = false;
returnVal = 1;
} finally {
Utilities.clearWork(job);
try {
if (ctxCreated) {
ctx.clear();
}
if (rj != null) {
if (returnVal != 0) {
rj.killJob();
}
jobID = rj.getID().toString();
}
} catch (Exception e) {
LOG.warn("Failed while cleaning up ", e);
} finally {
HadoopJobExecHelper.runningJobs.remove(rj);
}
}
// get the list of Dynamic partition paths
try {
if (rj != null) {
if (mWork.getAliasToWork() != null) {
for (Operator<? extends OperatorDesc> op : mWork.getAliasToWork().values()) {
op.jobClose(job, success);
}
}
if (rWork != null) {
rWork.getReducer().jobClose(job, success);
}
}
} catch (Exception e) {
// jobClose needs to execute successfully otherwise fail task
if (success) {
success = false;
returnVal = 3;
String mesg = "Job Commit failed with exception '" + Utilities.getNameMessage(e) + "'";
console.printError(mesg, "\n" + org.apache.hadoop.util.StringUtils.stringifyException(e));
}
}
return (returnVal);
}
