List of usage examples for org.apache.hadoop.mapred JobConf setPartitionerClass
public void setPartitionerClass(Class<? extends Partitioner> theClass)
From source file:com.benchmark.mapred.terasort.TeraSort.java
License:Apache License
public int run(String[] args) throws Exception { LOG.info("starting"); JobConf job = (JobConf) getConf(); Path inputDir = new Path(args[0]); if (args.length != 3) { System.out.println("ERROR: Wrong number of parameters: " + args.length + " instead of 3."); }//from ww w. j a v a2 s. com inputDir = inputDir.makeQualified(inputDir.getFileSystem(job)); Path partitionFile = new Path(inputDir, TeraInputFormat.PARTITION_FILENAME); URI partitionUri = new URI(partitionFile.toString() + "#" + TeraInputFormat.PARTITION_FILENAME); TeraInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setJobName("TeraSort"); job.setJarByClass(TeraSort.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); job.setInputFormat(TeraInputFormat.class); job.setOutputFormat(TeraOutputFormat.class); job.setPartitionerClass(TotalOrderPartitioner.class); job.setNumReduceTasks(Integer.parseInt(args[2])); TeraInputFormat.writePartitionFile(job, partitionFile); DistributedCache.addCacheFile(partitionUri, job); DistributedCache.createSymlink(job); job.setInt("dfs.replication", 1); TeraOutputFormat.setFinalSync(job, true); Date startIteration = new Date(); JobClient.runJob(job); Date endIteration = new Date(); System.out.println( "The iteration took " + (endIteration.getTime() - startIteration.getTime()) / 1000 + " seconds."); LOG.info("done"); return 0; }
From source file:com.datasalt.pangool.benchmark.urlresolution.HadoopUrlResolution.java
License:Apache License
public final static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 3) { System.err.println("Usage: urlresolution <url-map> <url-register> <out>"); System.exit(2);/*from w w w .j a v a2s .c om*/ } JobConf job = new JobConf(conf); FileSystem fS = FileSystem.get(conf); fS.delete(new Path(otherArgs[2]), true); MultipleInputs.addInputPath(job, new Path(otherArgs[0]), TextInputFormat.class, UrlMapClass.class); MultipleInputs.addInputPath(job, new Path(otherArgs[1]), TextInputFormat.class, UrlRegisterMapClass.class); job.setJarByClass(HadoopUrlResolution.class); job.setPartitionerClass(KeyPartitioner.class); job.setOutputValueGroupingComparator(GroupingComparator.class); job.setMapOutputKeyClass(UrlRegJoinUrlMap.class); job.setMapOutputValueClass(NullWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(NullWritable.class); FileOutputFormat.setOutputPath(job, new Path(otherArgs[2])); Job j = new Job(job); j.setReducerClass(Reduce.class); j.waitForCompletion(true); }
From source file:com.ebay.erl.mobius.core.mapred.ConfigurableJob.java
License:Apache License
@Override protected synchronized void submit() { JobConf jobConf = this.getJobConf(); boolean isLocalHadoop = jobConf.get("mapred.job.tracker", "local").equals("local"); // the default partitioner is {@link com.ebay.erl.mobius.core.datajoin.DataJoinKeyPartitioner} // which is hash based. ////from ww w .j a v a2 s . c o m // If user choose to use even partitioner, Mobius will use // {@link com.ebay.erl.mobius.core.datajoin.EvenlyPartitioner} which // is sampling based partitioner of attempting to balance the load // for each reducer. String partitioner = jobConf.get("mobius.partitioner", "default"); if (!isLocalHadoop && jobConf.getNumReduceTasks() != 0 && partitioner.equals("even")) { // this job needs reducer, perform sampling on the keys to // make load on reducers are almost evenly distributed. double freq = jobConf.getFloat("mobius.sampler.freq", 0.1F); int numSamples = jobConf.getInt("mobius.sampler.num.samples", 50000); int maxSplits = jobConf.getInt("mobius.sampler.max.slipts.sampled", 5); // log sampling parameters so that user knows. LOGGER.info("Sampling parameters { " + "mobius.sampler.freq:" + format.format(freq) + ", " + "mobius.sampler.num.samples:" + numSamples + ", " + "mobius.sampler.max.slipts.sampled:" + maxSplits + "}"); InputSampler.Sampler<?, ?> sampler = new MobiusInputSampler(freq, numSamples, maxSplits); writePartitionFile(jobConf, sampler); // add to distributed cache try { URI partitionUri = new URI(TotalOrderPartitioner.getPartitionFile(jobConf) + "#_partitions"); LOGGER.info("Adding partition uri to distributed cache:" + partitionUri.toString()); DistributedCache.addCacheFile(partitionUri, jobConf); DistributedCache.createSymlink(jobConf); jobConf.setPartitionerClass(EvenlyPartitioner.class); LOGGER.info("Using " + EvenlyPartitioner.class.getCanonicalName() + " to partiton the keys evenly among reducers."); } catch (URISyntaxException e) { LOGGER.error(e.getMessage(), e); throw new RuntimeException(e); } // adding -XX:-UseParallelOldGC, this will automatically set -XX:-UseParallelGC // according to Oracle's specification String jvmOpts = jobConf.get("mapred.child.java.opts", ""); if (jvmOpts.isEmpty()) { jvmOpts = "-XX:-UseParallelOldGC"; } else { if (jvmOpts.indexOf("-XX:-UseParallelOldGC") < 0) { // remove " jvmOpts = jvmOpts.replaceAll("\"", ""); jvmOpts = jvmOpts.concat(" -XX:-UseParallelOldGC"); } } jobConf.set("mapred.child.java.opts", jvmOpts); this.setJobConf(jobConf); } LOGGER.info("Submiting job:" + jobConf.getJobName()); super.submit(); }
From source file:com.github.gaoyangthu.demo.mapred.terasort.TeraSort.java
License:Apache License
public int run(String[] args) throws Exception { LOG.info("starting"); JobConf job = (JobConf) getConf(); Path inputDir = new Path(args[0]); inputDir = inputDir.makeQualified(inputDir.getFileSystem(job)); Path partitionFile = new Path(inputDir, TeraInputFormat.PARTITION_FILENAME); URI partitionUri = new URI(partitionFile.toString() + "#" + TeraInputFormat.PARTITION_FILENAME); TeraInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setJobName("TeraSort"); job.setJarByClass(TeraSort.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); job.setInputFormat(TeraInputFormat.class); job.setOutputFormat(TeraOutputFormat.class); job.setPartitionerClass(TotalOrderPartitioner.class); TeraInputFormat.writePartitionFile(job, partitionFile); DistributedCache.addCacheFile(partitionUri, job); DistributedCache.createSymlink(job); job.setInt("dfs.replication", 1); TeraOutputFormat.setFinalSync(job, true); JobClient.runJob(job);//from w w w . jav a2 s . co m LOG.info("done"); return 0; }
From source file:com.hadoop.secondarysort.SecondarySort_MapRed.java
License:Apache License
public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: secondarysrot <in> <out>"); System.exit(2);// w w w. ja v a 2 s. c o m } JobConf jobConf = new JobConf(conf); jobConf.setMapperClass(MapClass.class); jobConf.setReducerClass(Reduce.class); jobConf.setPartitionerClass(FirstPartitioner.class); jobConf.setOutputValueGroupingComparator(FirstGroupingComparator.class); jobConf.setMapOutputKeyClass(IntPair.class); jobConf.setMapOutputValueClass(IntWritable.class); jobConf.setOutputKeyClass(Text.class); jobConf.setOutputValueClass(IntWritable.class); // // Job job = new Job(conf, "secondary sort"); // job.setJarByClass(SecondarySort_MapRed.class); // job.setMapperClass(MapClass.class); // job.setReducerClass(Reduce.class); // // // group and partition by the first int in the pair // job.setPartitionerClass(FirstPartitioner.class); // job.setGroupingComparatorClass(FirstGroupingComparator.class); // conf.setClass("mapred.output.key.comparator.class", // KeyComparator.class, RawComparator.class); // // job.setSortComparatorClass(SecondGroupingComparator.class); // // the map output is IntPair, IntWritable // job.setMapOutputKeyClass(IntPair.class); // job.setMapOutputValueClass(IntWritable.class); // // // the reduce output is Text, IntWritable // job.setOutputKeyClass(Text.class); // job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(jobConf, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(jobConf, new Path(otherArgs[1])); }
From source file:com.ibm.bi.dml.runtime.controlprogram.parfor.ResultMergeRemoteMR.java
License:Open Source License
/** * /*from w w w.ja v a 2s . c o m*/ * @param fname null if no comparison required * @param fnameNew * @param srcFnames * @param ii * @param oi * @param rlen * @param clen * @param brlen * @param bclen * @throws DMLRuntimeException */ @SuppressWarnings({ "unused", "deprecation" }) protected void executeMerge(String fname, String fnameNew, String[] srcFnames, InputInfo ii, OutputInfo oi, long rlen, long clen, int brlen, int bclen) throws DMLRuntimeException { String jobname = "ParFor-RMMR"; long t0 = DMLScript.STATISTICS ? System.nanoTime() : 0; JobConf job; job = new JobConf(ResultMergeRemoteMR.class); job.setJobName(jobname + _pfid); //maintain dml script counters Statistics.incrementNoOfCompiledMRJobs(); //warning for textcell/binarycell without compare boolean withCompare = (fname != null); if ((oi == OutputInfo.TextCellOutputInfo || oi == OutputInfo.BinaryCellOutputInfo) && !withCompare && ResultMergeLocalFile.ALLOW_COPY_CELLFILES) LOG.warn("Result merge for " + OutputInfo.outputInfoToString(oi) + " without compare can be realized more efficiently with LOCAL_FILE than REMOTE_MR."); try { Path pathCompare = null; Path pathNew = new Path(fnameNew); ///// //configure the MR job if (withCompare) { pathCompare = new Path(fname).makeQualified(FileSystem.get(job)); MRJobConfiguration.setResultMergeInfo(job, pathCompare.toString(), ii, LocalFileUtils.getWorkingDir(LocalFileUtils.CATEGORY_RESULTMERGE), rlen, clen, brlen, bclen); } else MRJobConfiguration.setResultMergeInfo(job, "null", ii, LocalFileUtils.getWorkingDir(LocalFileUtils.CATEGORY_RESULTMERGE), rlen, clen, bclen, bclen); //set mappers, reducers, combiners job.setMapperClass(ResultMergeRemoteMapper.class); job.setReducerClass(ResultMergeRemoteReducer.class); if (oi == OutputInfo.TextCellOutputInfo) { job.setMapOutputKeyClass(MatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixCell.class); job.setOutputKeyClass(NullWritable.class); job.setOutputValueClass(Text.class); } else if (oi == OutputInfo.BinaryCellOutputInfo) { job.setMapOutputKeyClass(MatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixCell.class); job.setOutputKeyClass(MatrixIndexes.class); job.setOutputValueClass(MatrixCell.class); } else if (oi == OutputInfo.BinaryBlockOutputInfo) { //setup partitioning, grouping, sorting for composite key (old API) job.setPartitionerClass(ResultMergeRemotePartitioning.class); //partitioning job.setOutputValueGroupingComparator(ResultMergeRemoteGrouping.class); //grouping job.setOutputKeyComparatorClass(ResultMergeRemoteSorting.class); //sorting job.setMapOutputKeyClass(ResultMergeTaggedMatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixBlock.class); job.setOutputKeyClass(MatrixIndexes.class); job.setOutputValueClass(MatrixBlock.class); } //set input format job.setInputFormat(ii.inputFormatClass); //set the input path Path[] paths = null; if (withCompare) { paths = new Path[srcFnames.length + 1]; paths[0] = pathCompare; for (int i = 1; i < paths.length; i++) paths[i] = new Path(srcFnames[i - 1]); } else { paths = new Path[srcFnames.length]; for (int i = 0; i < paths.length; i++) paths[i] = new Path(srcFnames[i]); } FileInputFormat.setInputPaths(job, paths); //set output format job.setOutputFormat(oi.outputFormatClass); //set output path MapReduceTool.deleteFileIfExistOnHDFS(fnameNew); FileOutputFormat.setOutputPath(job, pathNew); ////// //set optimization parameters //set the number of mappers and reducers //job.setNumMapTasks( _numMappers ); //use default num mappers long reducerGroups = _numReducers; if (oi == OutputInfo.BinaryBlockOutputInfo) reducerGroups = Math.max(rlen / brlen, 1) * Math.max(clen / bclen, 1); else //textcell/binarycell reducerGroups = Math.max((rlen * clen) / StagingFileUtils.CELL_BUFFER_SIZE, 1); job.setNumReduceTasks((int) Math.min(_numReducers, reducerGroups)); //use FLEX scheduler configuration properties if (ParForProgramBlock.USE_FLEX_SCHEDULER_CONF) { job.setInt("flex.map.min", 0); job.setInt("flex.map.max", _numMappers); job.setInt("flex.reduce.min", 0); job.setInt("flex.reduce.max", _numMappers); } //disable automatic tasks timeouts and speculative task exec job.setInt("mapred.task.timeout", 0); job.setMapSpeculativeExecution(false); //set up preferred custom serialization framework for binary block format if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) MRJobConfiguration.addBinaryBlockSerializationFramework(job); //enables the reuse of JVMs (multiple tasks per MR task) if (_jvmReuse) job.setNumTasksToExecutePerJvm(-1); //unlimited //enables compression - not conclusive for different codecs (empirically good compression ratio, but significantly slower) //job.set("mapred.compress.map.output", "true"); //job.set("mapred.map.output.compression.codec", "org.apache.hadoop.io.compress.GzipCodec"); //set the replication factor for the results job.setInt("dfs.replication", _replication); //set the max number of retries per map task // disabled job-level configuration to respect cluster configuration // note: this refers to hadoop2, hence it never had effect on mr1 //job.setInt("mapreduce.map.maxattempts", _max_retry); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); ///// // execute the MR job JobClient.runJob(job); //maintain dml script counters Statistics.incrementNoOfExecutedMRJobs(); } catch (Exception ex) { throw new DMLRuntimeException(ex); } if (DMLScript.STATISTICS) { long t1 = System.nanoTime(); Statistics.maintainCPHeavyHitters("MR-Job_" + jobname, t1 - t0); } }
From source file:com.ibm.bi.dml.runtime.matrix.CMCOVMR.java
License:Open Source License
public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String instructionsInMapper, String cmNcomInstructions, int numReducers, int replication, byte[] resultIndexes, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job = new JobConf(CMCOVMR.class); job.setJobName("CM-COV-MR"); //whether use block representation or cell representation MRJobConfiguration.setMatrixValueClassForCM_N_COM(job, true); //added for handling recordreader instruction String[] realinputs = inputs; InputInfo[] realinputInfos = inputInfos; long[] realrlens = rlens; long[] realclens = clens; int[] realbrlens = brlens; int[] realbclens = bclens; byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;/*from w ww. ja v a2 s.c om*/ //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, realIndexes, realinputs, realinputInfos, realbrlens, realbclens, true, ConvertTarget.WEIGHTEDCELL); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, realIndexes, realrlens, realclens); //set up the block size MRJobConfiguration.setBlocksSizes(job, realIndexes, realbrlens, realbclens); //set up unary instructions that will perform in the mapper MRJobConfiguration.setInstructionsInMapper(job, instructionsInMapper); //set up the aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setCM_N_COMInstructions(job, cmNcomInstructions); //set up the replication factor for the results job.setInt("dfs.replication", replication); //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, instructionsInMapper, null, cmNcomInstructions, resultIndexes); //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, new byte[resultIndexes.length], outputs, outputInfos, false); // configure mapper and the mapper output key value pairs job.setMapperClass(CMCOVMRMapper.class); job.setMapOutputKeyClass(TaggedFirstSecondIndexes.class); job.setMapOutputValueClass(CM_N_COVCell.class); job.setOutputKeyComparatorClass(TaggedFirstSecondIndexes.Comparator.class); job.setPartitionerClass(TaggedFirstSecondIndexes.TagPartitioner.class); //configure reducer job.setReducerClass(CMCOVMRReducer.class); //job.setReducerClass(PassThroughReducer.class); MatrixCharacteristics[] stats = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, instructionsInMapper, null, null, cmNcomInstructions, resultIndexes, mapoutputIndexes, false).stats; //set up the number of reducers MRJobConfiguration.setNumReducers(job, mapoutputIndexes.size(), numReducers);//each output tag is a group // Print the complete instruction if (LOG.isTraceEnabled()) inst.printCompleteMRJobInstruction(stats); // By default, the job executes in "cluster" mode. // Determine if we can optimize and run it in "local" mode. MatrixCharacteristics[] inputStats = new MatrixCharacteristics[inputs.length]; for (int i = 0; i < inputs.length; i++) { inputStats[i] = new MatrixCharacteristics(rlens[i], clens[i], brlens[i], bclens[i]); } //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); RunningJob runjob = JobClient.runJob(job); return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:com.ibm.bi.dml.runtime.matrix.MMCJMR.java
License:Open Source License
private static MatrixCharacteristics[] commonSetup(JobConf job, boolean inBlockRepresentation, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String instructionsInMapper, String aggInstructionsInReducer, String aggBinInstrction, int numReducers, int replication, byte resultDimsUnknown, String output, OutputInfo outputinfo) throws Exception { job.setJobName("MMCJ-MR"); if (numReducers <= 0) throw new Exception("MMCJ-MR has to have at least one reduce task!"); //whether use block representation or cell representation MRJobConfiguration.setMatrixValueClass(job, inBlockRepresentation); byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;// w w w .ja v a2 s. c o m //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, realIndexes, inputs, inputInfos, brlens, bclens, true, inBlockRepresentation ? ConvertTarget.BLOCK : ConvertTarget.CELL); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, realIndexes, rlens, clens); //set up the block size MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens); //set up unary instructions that will perform in the mapper MRJobConfiguration.setInstructionsInMapper(job, instructionsInMapper); //set up the aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setAggregateInstructions(job, aggInstructionsInReducer); //set up the aggregate binary operation for the mmcj job MRJobConfiguration.setAggregateBinaryInstructions(job, aggBinInstrction); //set up the replication factor for the results job.setInt("dfs.replication", replication); //set up preferred custom serialization framework for binary block format if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) MRJobConfiguration.addBinaryBlockSerializationFramework(job); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); byte[] resultIndexes = new byte[] { MRInstructionParser.parseSingleInstruction(aggBinInstrction).output }; byte[] resultDimsUnknown_Array = new byte[] { resultDimsUnknown }; // byte[] resultIndexes=new byte[]{AggregateBinaryInstruction.parseMRInstruction(aggBinInstrction).output}; //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, instructionsInMapper, aggInstructionsInReducer, aggBinInstrction, resultIndexes); //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, resultDimsUnknown_Array, new String[] { output }, new OutputInfo[] { outputinfo }, inBlockRepresentation); // configure mapper job.setMapperClass(MMCJMRMapper.class); job.setMapOutputKeyClass(TaggedFirstSecondIndexes.class); if (inBlockRepresentation) job.setMapOutputValueClass(MatrixBlock.class); else job.setMapOutputValueClass(MatrixCell.class); job.setOutputKeyComparatorClass(TaggedFirstSecondIndexes.Comparator.class); job.setPartitionerClass(TaggedFirstSecondIndexes.FirstIndexPartitioner.class); //configure combiner //TODO: cannot set up combiner, because it will destroy the stable numerical algorithms // for sum or for central moments //if(aggInstructionsInReducer!=null && !aggInstructionsInReducer.isEmpty()) // job.setCombinerClass(MMCJMRCombiner.class); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, instructionsInMapper, aggInstructionsInReducer, aggBinInstrction, null, resultIndexes, mapoutputIndexes, true); //set up the number of reducers if (AUTOMATIC_CONFIG_NUM_REDUCERS) { int numRed = determineNumReducers(rlens, clens, numReducers, ret.numReducerGroups); job.setNumReduceTasks(numRed); } else MRJobConfiguration.setNumReducers(job, ret.numReducerGroups, numReducers); //configure reducer // note: the alternative MMCJMRReducer is not maintained job.setReducerClass(MMCJMRReducerWithAggregator.class); return ret.stats; }
From source file:com.ibm.bi.dml.runtime.matrix.MMRJMR.java
License:Open Source License
public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String instructionsInMapper, String aggInstructionsInReducer, String aggBinInstrctions, String otherInstructionsInReducer, int numReducers, int replication, byte[] resultIndexes, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job = new JobConf(MMRJMR.class); job.setJobName("MMRJ-MR"); if (numReducers <= 0) throw new Exception("MMRJ-MR has to have at least one reduce task!"); // TODO: check w/ yuanyuan. This job always runs in blocked mode, and hence derivation is not necessary. boolean inBlockRepresentation = MRJobConfiguration.deriveRepresentation(inputInfos); //whether use block representation or cell representation MRJobConfiguration.setMatrixValueClass(job, inBlockRepresentation); byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;//ww w .j a v a2 s . c o m //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, realIndexes, inputs, inputInfos, brlens, bclens, true, inBlockRepresentation ? ConvertTarget.BLOCK : ConvertTarget.CELL); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, realIndexes, rlens, clens); //set up the block size MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens); //set up unary instructions that will perform in the mapper MRJobConfiguration.setInstructionsInMapper(job, instructionsInMapper); //set up the aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setAggregateInstructions(job, aggInstructionsInReducer); //set up the aggregate binary operation for the mmcj job MRJobConfiguration.setAggregateBinaryInstructions(job, aggBinInstrctions); //set up the instructions that will happen in the reducer, after the aggregation instrucions MRJobConfiguration.setInstructionsInReducer(job, otherInstructionsInReducer); //set up the replication factor for the results job.setInt("dfs.replication", replication); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); // byte[] resultIndexes=new byte[]{AggregateBinaryInstruction.parseMRInstruction(aggBinInstrction).output}; //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, instructionsInMapper, aggInstructionsInReducer, aggBinInstrctions, resultIndexes); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, instructionsInMapper, aggInstructionsInReducer, aggBinInstrctions, otherInstructionsInReducer, resultIndexes, mapoutputIndexes, false); MatrixCharacteristics[] stats = ret.stats; //set up the number of reducers MRJobConfiguration.setNumReducers(job, ret.numReducerGroups, numReducers); // Print the complete instruction if (LOG.isTraceEnabled()) inst.printCompleteMRJobInstruction(stats); byte[] dimsUnknown = new byte[resultIndexes.length]; for (int i = 0; i < resultIndexes.length; i++) { if (stats[i].getRows() == -1 || stats[i].getCols() == -1) { dimsUnknown[i] = (byte) 1; } else { dimsUnknown[i] = (byte) 0; } } //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, dimsUnknown, outputs, outputInfos, inBlockRepresentation); // configure mapper job.setMapperClass(MMRJMRMapper.class); job.setMapOutputKeyClass(TripleIndexes.class); if (inBlockRepresentation) job.setMapOutputValueClass(TaggedMatrixBlock.class); else job.setMapOutputValueClass(TaggedMatrixCell.class); job.setOutputKeyComparatorClass(TripleIndexes.Comparator.class); job.setPartitionerClass(TripleIndexes.FirstTwoIndexesPartitioner.class); //configure combiner //TODO: cannot set up combiner, because it will destroy the stable numerical algorithms // for sum or for central moments // if(aggInstructionsInReducer!=null && !aggInstructionsInReducer.isEmpty()) // job.setCombinerClass(MMCJMRCombiner.class); //configure reducer job.setReducerClass(MMRJMRReducer.class); // By default, the job executes in "cluster" mode. // Determine if we can optimize and run it in "local" mode. MatrixCharacteristics[] inputStats = new MatrixCharacteristics[inputs.length]; for (int i = 0; i < inputs.length; i++) { inputStats[i] = new MatrixCharacteristics(rlens[i], clens[i], brlens[i], bclens[i]); } //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); RunningJob runjob = JobClient.runJob(job); /* Process different counters */ Group group = runjob.getCounters().getGroup(MRJobConfiguration.NUM_NONZERO_CELLS); for (int i = 0; i < resultIndexes.length; i++) { // number of non-zeros stats[i].setNonZeros(group.getCounter(Integer.toString(i))); } return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:com.ibm.bi.dml.runtime.matrix.SortMR.java
License:Open Source License
@SuppressWarnings({ "unchecked", "rawtypes" })
public static JobReturn runJob(MRJobInstruction inst, String input, InputInfo inputInfo, long rlen, long clen,
int brlen, int bclen, String combineInst, String sortInst, int numReducers, int replication,
String output, OutputInfo outputInfo, boolean valueIsWeight) throws Exception {
boolean sortIndexes = getSortInstructionType(sortInst) == SortKeys.OperationTypes.Indexes;
String tmpOutput = sortIndexes ? MRJobConfiguration.constructTempOutputFilename() : output;
JobConf job = new JobConf(SortMR.class);
job.setJobName("SortMR");
//setup partition file
String pfname = MRJobConfiguration.setUpSortPartitionFilename(job);
Path partitionFile = new Path(pfname);
URI partitionUri = new URI(partitionFile.toString());
//setup input/output paths
Path inputDir = new Path(input);
inputDir = inputDir.makeQualified(inputDir.getFileSystem(job));
SamplingSortMRInputFormat.setInputPaths(job, inputDir);
Path outpath = new Path(tmpOutput);
FileOutputFormat.setOutputPath(job, outpath);
MapReduceTool.deleteFileIfExistOnHDFS(outpath, job);
//set number of reducers (1 if local mode)
if (InfrastructureAnalyzer.isLocalMode(job))
job.setNumReduceTasks(1);/* ww w. j av a 2 s.c o m*/
else
MRJobConfiguration.setNumReducers(job, numReducers, numReducers);
//setup input/output format
job.setInputFormat(SamplingSortMRInputFormat.class);
SamplingSortMRInputFormat.setTargetKeyValueClasses(job,
(Class<? extends WritableComparable>) outputInfo.outputKeyClass, outputInfo.outputValueClass);
//setup instructions and meta information
if (combineInst != null && !combineInst.trim().isEmpty())
job.set(COMBINE_INSTRUCTION, combineInst);
job.set(SORT_INSTRUCTION, sortInst);
job.setBoolean(VALUE_IS_WEIGHT, valueIsWeight);
boolean desc = getSortInstructionDescending(sortInst);
job.setBoolean(SORT_DECREASING, desc);
MRJobConfiguration.setBlockSize(job, (byte) 0, brlen, bclen);
MRJobConfiguration.setInputInfo(job, (byte) 0, inputInfo, brlen, bclen, ConvertTarget.CELL);
int partitionWith0 = SamplingSortMRInputFormat.writePartitionFile(job, partitionFile);
//setup mapper/reducer/partitioner/output classes
if (getSortInstructionType(sortInst) == SortKeys.OperationTypes.Indexes) {
MRJobConfiguration.setInputInfo(job, (byte) 0, inputInfo, brlen, bclen, ConvertTarget.CELL);
job.setOutputFormat(OutputInfo.BinaryBlockOutputInfo.outputFormatClass);
job.setMapperClass(IndexSortMapper.class);
job.setReducerClass(IndexSortReducer.class);
job.setMapOutputKeyClass(!desc ? IndexSortComparable.class : IndexSortComparableDesc.class);
job.setMapOutputValueClass(LongWritable.class);
job.setOutputKeyClass(MatrixIndexes.class);
job.setOutputValueClass(MatrixBlock.class);
} else { //default case: SORT w/wo weights
MRJobConfiguration.setInputInfo(job, (byte) 0, inputInfo, brlen, bclen, ConvertTarget.CELL);
job.setOutputFormat(CompactOutputFormat.class);
job.setMapperClass(ValueSortMapper.class);
job.setReducerClass(ValueSortReducer.class);
job.setOutputKeyClass(outputInfo.outputKeyClass); //double
job.setOutputValueClass(outputInfo.outputValueClass); //int
}
job.setPartitionerClass(TotalOrderPartitioner.class);
//setup distributed cache
DistributedCache.addCacheFile(partitionUri, job);
DistributedCache.createSymlink(job);
//setup replication factor
job.setInt("dfs.replication", replication);
MatrixCharacteristics[] s = new MatrixCharacteristics[1];
s[0] = new MatrixCharacteristics(rlen, clen, brlen, bclen);
// Print the complete instruction
if (LOG.isTraceEnabled())
inst.printCompleteMRJobInstruction(s);
//set unique working dir
MRJobConfiguration.setUniqueWorkingDir(job);
//run mr job
RunningJob runjob = JobClient.runJob(job);
Group group = runjob.getCounters().getGroup(NUM_VALUES_PREFIX);
numReducers = job.getNumReduceTasks();
//process final meta data
long[] counts = new long[numReducers];
long total = 0;
for (int i = 0; i < numReducers; i++) {
counts[i] = group.getCounter(Integer.toString(i));
total += counts[i];
}
//add missing 0s back to the results
long missing0s = 0;
if (total < rlen * clen) {
if (partitionWith0 < 0)
throw new RuntimeException("no partition contains 0, which is wrong!");
missing0s = rlen * clen - total;
counts[partitionWith0] += missing0s;
} else
partitionWith0 = -1;
if (sortIndexes) {
//run builtin job for shifting partially sorted blocks according to global offsets
//we do this in this custom form since it would not fit into the current structure
//of systemml to output two intermediates (partially sorted data, offsets) out of a
//single SortKeys lop
boolean success = runjob.isSuccessful();
if (success) {
success = runStitchupJob(tmpOutput, rlen, clen, brlen, bclen, counts, numReducers, replication,
output);
}
MapReduceTool.deleteFileIfExistOnHDFS(tmpOutput);
MapReduceTool.deleteFileIfExistOnHDFS(pfname);
return new JobReturn(s[0], OutputInfo.BinaryBlockOutputInfo, success);
} else {
MapReduceTool.deleteFileIfExistOnHDFS(pfname);
return new JobReturn(s[0], counts, partitionWith0, missing0s, runjob.isSuccessful());
}
}