List of usage examples for org.apache.hadoop.mapred RunningJob isSuccessful
public boolean isSuccessful() throws IOException;
From source file:org.apache.sysml.runtime.controlprogram.parfor.RemoteDPParForMR.java
License:Apache License
public static RemoteParForJobReturn runJob(long pfid, String itervar, String matrixvar, String program, String resultFile, MatrixObject input, PartitionFormat dpf, OutputInfo oi, boolean tSparseCol, //config params boolean enableCPCaching, int numReducers, int replication) //opt params throws DMLRuntimeException { RemoteParForJobReturn ret = null;/* ww w . j a v a2 s .c o m*/ String jobname = "ParFor-DPEMR"; long t0 = DMLScript.STATISTICS ? System.nanoTime() : 0; JobConf job; job = new JobConf(RemoteDPParForMR.class); job.setJobName(jobname + pfid); //maintain dml script counters Statistics.incrementNoOfCompiledMRJobs(); try { ///// //configure the MR job //set arbitrary CP program blocks that will perform in the reducers MRJobConfiguration.setProgramBlocks(job, program); //enable/disable caching MRJobConfiguration.setParforCachingConfig(job, enableCPCaching); //setup input matrix Path path = new Path(input.getFileName()); long rlen = input.getNumRows(); long clen = input.getNumColumns(); int brlen = (int) input.getNumRowsPerBlock(); int bclen = (int) input.getNumColumnsPerBlock(); MRJobConfiguration.setPartitioningInfo(job, rlen, clen, brlen, bclen, InputInfo.BinaryBlockInputInfo, oi, dpf._dpf, dpf._N, input.getFileName(), itervar, matrixvar, tSparseCol); job.setInputFormat(InputInfo.BinaryBlockInputInfo.inputFormatClass); FileInputFormat.setInputPaths(job, path); //set mapper and reducers classes job.setMapperClass(DataPartitionerRemoteMapper.class); job.setReducerClass(RemoteDPParWorkerReducer.class); //set output format job.setOutputFormat(SequenceFileOutputFormat.class); //set output path MapReduceTool.deleteFileIfExistOnHDFS(resultFile); FileOutputFormat.setOutputPath(job, new Path(resultFile)); //set the output key, value schema //parfor partitioning outputs (intermediates) job.setMapOutputKeyClass(LongWritable.class); if (oi == OutputInfo.BinaryBlockOutputInfo) job.setMapOutputValueClass(PairWritableBlock.class); else if (oi == OutputInfo.BinaryCellOutputInfo) job.setMapOutputValueClass(PairWritableCell.class); else throw new DMLRuntimeException("Unsupported intermrediate output info: " + oi); //parfor exec output job.setOutputKeyClass(LongWritable.class); job.setOutputValueClass(Text.class); ////// //set optimization parameters //set the number of mappers and reducers job.setNumReduceTasks(numReducers); //disable automatic tasks timeouts and speculative task exec job.setInt(MRConfigurationNames.MR_TASK_TIMEOUT, 0); job.setMapSpeculativeExecution(false); //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.getDMLConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //set up custom map/reduce configurations MRJobConfiguration.setupCustomMRConfigurations(job, config); //disable JVM reuse job.setNumTasksToExecutePerJvm(1); //-1 for unlimited //set the replication factor for the results job.setInt(MRConfigurationNames.DFS_REPLICATION, replication); //set the max number of retries per map task //note: currently disabled to use cluster config //job.setInt(MRConfigurationNames.MR_MAP_MAXATTEMPTS, max_retry); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); ///// // execute the MR job RunningJob runjob = JobClient.runJob(job); // Process different counters Statistics.incrementNoOfExecutedMRJobs(); Group pgroup = runjob.getCounters().getGroup(ParForProgramBlock.PARFOR_COUNTER_GROUP_NAME); int numTasks = (int) pgroup.getCounter(Stat.PARFOR_NUMTASKS.toString()); int numIters = (int) pgroup.getCounter(Stat.PARFOR_NUMITERS.toString()); if (DMLScript.STATISTICS && !InfrastructureAnalyzer.isLocalMode()) { Statistics.incrementJITCompileTime(pgroup.getCounter(Stat.PARFOR_JITCOMPILE.toString())); Statistics.incrementJVMgcCount(pgroup.getCounter(Stat.PARFOR_JVMGC_COUNT.toString())); Statistics.incrementJVMgcTime(pgroup.getCounter(Stat.PARFOR_JVMGC_TIME.toString())); Group cgroup = runjob.getCounters().getGroup(CacheableData.CACHING_COUNTER_GROUP_NAME.toString()); CacheStatistics .incrementMemHits((int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_MEM.toString())); CacheStatistics.incrementFSBuffHits( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_FSBUFF.toString())); CacheStatistics .incrementFSHits((int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_FS.toString())); CacheStatistics.incrementHDFSHits( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_HDFS.toString())); CacheStatistics.incrementFSBuffWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_FSBUFF.toString())); CacheStatistics.incrementFSWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_FS.toString())); CacheStatistics.incrementHDFSWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_HDFS.toString())); CacheStatistics .incrementAcquireRTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_ACQR.toString())); CacheStatistics .incrementAcquireMTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_ACQM.toString())); CacheStatistics .incrementReleaseTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_RLS.toString())); CacheStatistics .incrementExportTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_EXP.toString())); } // read all files of result variables and prepare for return LocalVariableMap[] results = readResultFile(job, resultFile); ret = new RemoteParForJobReturn(runjob.isSuccessful(), numTasks, numIters, results); } catch (Exception ex) { throw new DMLRuntimeException(ex); } finally { // remove created files try { MapReduceTool.deleteFileIfExistOnHDFS(new Path(resultFile), job); } catch (IOException ex) { throw new DMLRuntimeException(ex); } } if (DMLScript.STATISTICS) { long t1 = System.nanoTime(); Statistics.maintainCPHeavyHitters("MR-Job_" + jobname, t1 - t0); } return ret; }
From source file:org.apache.sysml.runtime.controlprogram.parfor.RemoteParForMR.java
License:Apache License
public static RemoteParForJobReturn runJob(long pfid, String program, String taskFile, String resultFile, MatrixObject colocatedDPMatrixObj, //inputs boolean enableCPCaching, int numMappers, int replication, int max_retry, long minMem, boolean jvmReuse) //opt params throws DMLRuntimeException { RemoteParForJobReturn ret = null;/* ww w .j av a2 s .c om*/ String jobname = "ParFor-EMR"; long t0 = DMLScript.STATISTICS ? System.nanoTime() : 0; JobConf job; job = new JobConf(RemoteParForMR.class); job.setJobName(jobname + pfid); //maintain dml script counters Statistics.incrementNoOfCompiledMRJobs(); try { ///// //configure the MR job //set arbitrary CP program blocks that will perform in the mapper MRJobConfiguration.setProgramBlocks(job, program); //enable/disable caching MRJobConfiguration.setParforCachingConfig(job, enableCPCaching); //set mappers, reducers, combiners job.setMapperClass(RemoteParWorkerMapper.class); //map-only //set input format (one split per row, NLineInputFormat default N=1) if (ParForProgramBlock.ALLOW_DATA_COLOCATION && colocatedDPMatrixObj != null) { job.setInputFormat(RemoteParForColocatedNLineInputFormat.class); MRJobConfiguration.setPartitioningFormat(job, colocatedDPMatrixObj.getPartitionFormat()); MatrixCharacteristics mc = colocatedDPMatrixObj.getMatrixCharacteristics(); MRJobConfiguration.setPartitioningBlockNumRows(job, mc.getRowsPerBlock()); MRJobConfiguration.setPartitioningBlockNumCols(job, mc.getColsPerBlock()); MRJobConfiguration.setPartitioningFilename(job, colocatedDPMatrixObj.getFileName()); } else //default case { job.setInputFormat(NLineInputFormat.class); } //set the input path and output path FileInputFormat.setInputPaths(job, new Path(taskFile)); //set output format job.setOutputFormat(SequenceFileOutputFormat.class); //set output path MapReduceTool.deleteFileIfExistOnHDFS(resultFile); FileOutputFormat.setOutputPath(job, new Path(resultFile)); //set the output key, value schema job.setMapOutputKeyClass(LongWritable.class); job.setMapOutputValueClass(Text.class); job.setOutputKeyClass(LongWritable.class); job.setOutputValueClass(Text.class); ////// //set optimization parameters //set the number of mappers and reducers job.setNumMapTasks(numMappers); //numMappers job.setNumReduceTasks(0); //job.setInt("mapred.map.tasks.maximum", 1); //system property //job.setInt("mapred.tasktracker.tasks.maximum",1); //system property //job.setInt("mapred.jobtracker.maxtasks.per.job",1); //system property //set jvm memory size (if require) String memKey = MRConfigurationNames.MR_CHILD_JAVA_OPTS; if (minMem > 0 && minMem > InfrastructureAnalyzer.extractMaxMemoryOpt(job.get(memKey))) { InfrastructureAnalyzer.setMaxMemoryOpt(job, memKey, minMem); LOG.warn("Forcing '" + memKey + "' to -Xmx" + minMem / (1024 * 1024) + "M."); } //disable automatic tasks timeouts and speculative task exec job.setInt(MRConfigurationNames.MR_TASK_TIMEOUT, 0); job.setMapSpeculativeExecution(false); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getDMLConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //set up custom map/reduce configurations MRJobConfiguration.setupCustomMRConfigurations(job, config); //enables the reuse of JVMs (multiple tasks per MR task) if (jvmReuse) job.setNumTasksToExecutePerJvm(-1); //unlimited //set sort io buffer (reduce unnecessary large io buffer, guaranteed memory consumption) job.setInt(MRConfigurationNames.MR_TASK_IO_SORT_MB, 8); //8MB //set the replication factor for the results job.setInt(MRConfigurationNames.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(MRConfigurationNames.MR_MAP_MAXATTEMPTS, max_retry); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); ///// // execute the MR job RunningJob runjob = JobClient.runJob(job); // Process different counters Statistics.incrementNoOfExecutedMRJobs(); Group pgroup = runjob.getCounters().getGroup(ParForProgramBlock.PARFOR_COUNTER_GROUP_NAME); int numTasks = (int) pgroup.getCounter(Stat.PARFOR_NUMTASKS.toString()); int numIters = (int) pgroup.getCounter(Stat.PARFOR_NUMITERS.toString()); if (DMLScript.STATISTICS && !InfrastructureAnalyzer.isLocalMode()) { Statistics.incrementJITCompileTime(pgroup.getCounter(Stat.PARFOR_JITCOMPILE.toString())); Statistics.incrementJVMgcCount(pgroup.getCounter(Stat.PARFOR_JVMGC_COUNT.toString())); Statistics.incrementJVMgcTime(pgroup.getCounter(Stat.PARFOR_JVMGC_TIME.toString())); Group cgroup = runjob.getCounters().getGroup(CacheableData.CACHING_COUNTER_GROUP_NAME.toString()); CacheStatistics .incrementMemHits((int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_MEM.toString())); CacheStatistics.incrementFSBuffHits( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_FSBUFF.toString())); CacheStatistics .incrementFSHits((int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_FS.toString())); CacheStatistics.incrementHDFSHits( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_HDFS.toString())); CacheStatistics.incrementFSBuffWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_FSBUFF.toString())); CacheStatistics.incrementFSWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_FS.toString())); CacheStatistics.incrementHDFSWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_HDFS.toString())); CacheStatistics .incrementAcquireRTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_ACQR.toString())); CacheStatistics .incrementAcquireMTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_ACQM.toString())); CacheStatistics .incrementReleaseTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_RLS.toString())); CacheStatistics .incrementExportTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_EXP.toString())); } // read all files of result variables and prepare for return LocalVariableMap[] results = readResultFile(job, resultFile); ret = new RemoteParForJobReturn(runjob.isSuccessful(), numTasks, numIters, results); } catch (Exception ex) { throw new DMLRuntimeException(ex); } finally { // remove created files try { MapReduceTool.deleteFileIfExistOnHDFS(new Path(taskFile), job); MapReduceTool.deleteFileIfExistOnHDFS(new Path(resultFile), job); } catch (IOException ex) { throw new DMLRuntimeException(ex); } } if (DMLScript.STATISTICS) { long t1 = System.nanoTime(); Statistics.maintainCPHeavyHitters("MR-Job_" + jobname, t1 - t0); } return ret; }
From source file:org.apache.sysml.runtime.matrix.CleanupMR.java
License:Apache License
public static boolean runJob(DMLConfig conf) throws Exception { boolean ret = false; try {//from ww w.jav a2 s . c om JobConf job; job = new JobConf(CleanupMR.class); job.setJobName("Cleanup-MR"); //set up SystemML local tmp dir String dir = conf.getTextValue(DMLConfig.LOCAL_TMP_DIR); MRJobConfiguration.setSystemMLLocalTmpDir(job, dir); //set mappers, reducers int numNodes = InfrastructureAnalyzer.getRemoteParallelNodes(); job.setMapperClass(CleanupMapper.class); //map-only job.setNumMapTasks(numNodes); //numMappers job.setNumReduceTasks(0); //set input/output format, input path String inFileName = conf.getTextValue(DMLConfig.SCRATCH_SPACE) + "/cleanup_tasks"; job.setInputFormat(NLineInputFormat.class); job.setOutputFormat(NullOutputFormat.class); Path path = new Path(inFileName); FileInputFormat.setInputPaths(job, path); writeCleanupTasksToFile(path, numNodes); //disable automatic tasks timeouts and speculative task exec job.setInt(MRConfigurationNames.MR_TASK_TIMEOUT, 0); job.setMapSpeculativeExecution(false); ///// // execute the MR job RunningJob runjob = JobClient.runJob(job); ret = runjob.isSuccessful(); } catch (Exception ex) { //don't raise an exception, just gracefully an error message. LOG.error("Failed to run cleanup MR job. ", ex); } return ret; }
From source file:org.apache.sysml.runtime.matrix.CMCOVMR.java
License:Apache 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 ww w . j a 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(MRConfigurationNames.DFS_REPLICATION, replication); //set up custom map/reduce configurations DMLConfig config = ConfigurationManager.getDMLConfig(); MRJobConfiguration.setupCustomMRConfigurations(job, config); //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:org.apache.sysml.runtime.matrix.CombineMR.java
License:Apache License
public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String combineInstructions, int numReducers, int replication, byte[] resultIndexes, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job;/* w ww . j a va 2 s .com*/ job = new JobConf(CombineMR.class); job.setJobName("Standalone-MR"); boolean inBlockRepresentation = MRJobConfiguration.deriveRepresentation(inputInfos); //whether use block representation or cell representation MRJobConfiguration.setMatrixValueClass(job, inBlockRepresentation); byte[] inputIndexes = new byte[inputs.length]; for (byte b = 0; b < inputs.length; b++) inputIndexes[b] = b; //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, inputIndexes, inputs, inputInfos, brlens, bclens, true, inBlockRepresentation ? ConvertTarget.BLOCK : ConvertTarget.CELL); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, inputIndexes, rlens, clens); //set up the block size MRJobConfiguration.setBlocksSizes(job, inputIndexes, brlens, bclens); //set up unary instructions that will perform in the mapper MRJobConfiguration.setInstructionsInMapper(job, ""); //set up the aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setAggregateInstructions(job, ""); //set up the instructions that will happen in the reducer, after the aggregation instrucions MRJobConfiguration.setInstructionsInReducer(job, ""); MRJobConfiguration.setCombineInstructions(job, combineInstructions); //set up the replication factor for the results job.setInt(MRConfigurationNames.DFS_REPLICATION, replication); //set up custom map/reduce configurations DMLConfig config = ConfigurationManager.getDMLConfig(); MRJobConfiguration.setupCustomMRConfigurations(job, config); //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, inputIndexes, null, null, combineInstructions, resultIndexes); //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, null, outputs, outputInfos, inBlockRepresentation); // configure mapper and the mapper output key value pairs job.setMapperClass(GMRMapper.class); job.setMapOutputKeyClass(MatrixIndexes.class); if (inBlockRepresentation) job.setMapOutputValueClass(TaggedMatrixBlock.class); else job.setMapOutputValueClass(TaggedMatrixCell.class); //configure reducer job.setReducerClass(InnerReducer.class); //job.setReducerClass(PassThroughReducer.class); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, inputIndexes, null, null, null, combineInstructions, 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); // 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, runjob.isSuccessful()); }
From source file:org.apache.sysml.runtime.matrix.CSVReblockMR.java
License:Apache License
private static JobReturn runCSVReblockJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String reblockInstructions, String otherInstructionsInReducer, int numReducers, int replication, byte[] resultIndexes, String[] outputs, OutputInfo[] outputInfos, Path counterFile, String[] smallestFiles) throws Exception { JobConf job;//from w ww . java 2s .c o m job = new JobConf(ReblockMR.class); job.setJobName("CSV-Reblock-MR"); byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b; //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, realIndexes, inputs, inputInfos, brlens, bclens, false, ConvertTarget.CELL); job.setStrings(SMALLEST_FILE_NAME_PER_INPUT, smallestFiles); //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 the aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setCSVReblockInstructions(job, reblockInstructions); //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(MRConfigurationNames.DFS_REPLICATION, replication); //set up preferred custom serialization framework for binary block format if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) MRJobConfiguration.addBinaryBlockSerializationFramework(job); //set up custom map/reduce configurations DMLConfig config = ConfigurationManager.getDMLConfig(); MRJobConfiguration.setupCustomMRConfigurations(job, config); //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, null, reblockInstructions, null, otherInstructionsInReducer, resultIndexes); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, null, reblockInstructions, null, null, otherInstructionsInReducer, resultIndexes, mapoutputIndexes, false); MatrixCharacteristics[] stats = ret.stats; //set up the number of reducers int numRed = WriteCSVMR.determineNumReducers(rlens, clens, config.getIntValue(DMLConfig.NUM_REDUCERS), ret.numReducerGroups); job.setNumReduceTasks(numRed); // Print the complete instruction //if (LOG.isTraceEnabled()) // inst.printCompelteMRJobInstruction(stats); // Update resultDimsUnknown based on computed "stats" byte[] resultDimsUnknown = new byte[resultIndexes.length]; for (int i = 0; i < resultIndexes.length; i++) { if (stats[i].getRows() == -1 || stats[i].getCols() == -1) { resultDimsUnknown[i] = (byte) 1; } else { resultDimsUnknown[i] = (byte) 0; } } //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, resultDimsUnknown, outputs, outputInfos, true, true); // configure mapper and the mapper output key value pairs job.setMapperClass(CSVReblockMapper.class); job.setMapOutputKeyClass(TaggedFirstSecondIndexes.class); job.setMapOutputValueClass(BlockRow.class); //configure reducer job.setReducerClass(CSVReblockReducer.class); //turn off adaptivemr job.setBoolean("adaptivemr.map.enable", false); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); Path cachefile = new Path(counterFile, "part-00000"); DistributedCache.addCacheFile(cachefile.toUri(), job); DistributedCache.createSymlink(job); job.set(ROWID_FILE_NAME, cachefile.toString()); RunningJob runjob = JobClient.runJob(job); MapReduceTool.deleteFileIfExistOnHDFS(counterFile, 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))); // System.out.println("result #"+resultIndexes[i]+" ===>\n"+stats[i]); } return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:org.apache.sysml.runtime.matrix.DataGenMR.java
License:Apache License
/** * <p>Starts a Rand MapReduce job which will produce one or more random objects.</p> * //from ww w. j av a 2 s . c o m * @param inst MR job instruction * @param dataGenInstructions array of data gen instructions * @param instructionsInMapper instructions in mapper * @param aggInstructionsInReducer aggregate instructions in reducer * @param otherInstructionsInReducer other instructions in reducer * @param numReducers number of reducers * @param replication file replication * @param resultIndexes result indexes for each random object * @param dimsUnknownFilePrefix file path prefix when dimensions unknown * @param outputs output file for each random object * @param outputInfos output information for each random object * @return matrix characteristics for each random object * @throws Exception if Exception occurs */ public static JobReturn runJob(MRJobInstruction inst, String[] dataGenInstructions, String instructionsInMapper, String aggInstructionsInReducer, String otherInstructionsInReducer, int numReducers, int replication, byte[] resultIndexes, String dimsUnknownFilePrefix, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job = new JobConf(DataGenMR.class); job.setJobName("DataGen-MR"); //whether use block representation or cell representation MRJobConfiguration.setMatrixValueClass(job, true); byte[] realIndexes = new byte[dataGenInstructions.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b; String[] inputs = new String[dataGenInstructions.length]; InputInfo[] inputInfos = new InputInfo[dataGenInstructions.length]; long[] rlens = new long[dataGenInstructions.length]; long[] clens = new long[dataGenInstructions.length]; int[] brlens = new int[dataGenInstructions.length]; int[] bclens = new int[dataGenInstructions.length]; FileSystem fs = FileSystem.get(job); String dataGenInsStr = ""; int numblocks = 0; int maxbrlen = -1, maxbclen = -1; double maxsparsity = -1; for (int i = 0; i < dataGenInstructions.length; i++) { dataGenInsStr = dataGenInsStr + Lop.INSTRUCTION_DELIMITOR + dataGenInstructions[i]; MRInstruction mrins = MRInstructionParser.parseSingleInstruction(dataGenInstructions[i]); MRINSTRUCTION_TYPE mrtype = mrins.getMRInstructionType(); DataGenMRInstruction genInst = (DataGenMRInstruction) mrins; rlens[i] = genInst.getRows(); clens[i] = genInst.getCols(); brlens[i] = genInst.getRowsInBlock(); bclens[i] = genInst.getColsInBlock(); maxbrlen = Math.max(maxbrlen, brlens[i]); maxbclen = Math.max(maxbclen, bclens[i]); if (mrtype == MRINSTRUCTION_TYPE.Rand) { RandInstruction randInst = (RandInstruction) mrins; inputs[i] = LibMatrixDatagen.generateUniqueSeedPath(genInst.getBaseDir()); maxsparsity = Math.max(maxsparsity, randInst.getSparsity()); PrintWriter pw = null; try { pw = new PrintWriter(fs.create(new Path(inputs[i]))); //for obj reuse and preventing repeated buffer re-allocations StringBuilder sb = new StringBuilder(); //seed generation Well1024a bigrand = LibMatrixDatagen.setupSeedsForRand(randInst.getSeed()); LongStream nnz = LibMatrixDatagen.computeNNZperBlock(rlens[i], clens[i], brlens[i], bclens[i], randInst.getSparsity()); PrimitiveIterator.OfLong nnzIter = nnz.iterator(); for (long r = 0; r < rlens[i]; r += brlens[i]) { long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r)); for (long c = 0; c < clens[i]; c += bclens[i]) { long curBlockColSize = Math.min(bclens[i], (clens[i] - c)); sb.append((r / brlens[i]) + 1); sb.append(','); sb.append((c / bclens[i]) + 1); sb.append(','); sb.append(curBlockRowSize); sb.append(','); sb.append(curBlockColSize); sb.append(','); sb.append(nnzIter.nextLong()); sb.append(','); sb.append(bigrand.nextLong()); pw.println(sb.toString()); sb.setLength(0); numblocks++; } } } finally { IOUtilFunctions.closeSilently(pw); } inputInfos[i] = InputInfo.TextCellInputInfo; } else if (mrtype == MRINSTRUCTION_TYPE.Seq) { SeqInstruction seqInst = (SeqInstruction) mrins; inputs[i] = genInst.getBaseDir() + System.currentTimeMillis() + ".seqinput"; maxsparsity = 1.0; //always dense double from = seqInst.fromValue; double to = seqInst.toValue; double incr = seqInst.incrValue; //handle default 1 to -1 for special case of from>to incr = LibMatrixDatagen.updateSeqIncr(from, to, incr); // Correctness checks on (from, to, incr) boolean neg = (from > to); if (incr == 0) throw new DMLRuntimeException("Invalid value for \"increment\" in seq()."); if (neg != (incr < 0)) throw new DMLRuntimeException("Wrong sign for the increment in a call to seq()"); // Compute the number of rows in the sequence long numrows = UtilFunctions.getSeqLength(from, to, incr); if (rlens[i] > 0) { if (numrows != rlens[i]) throw new DMLRuntimeException( "Unexpected error while processing sequence instruction. Expected number of rows does not match given number: " + rlens[i] + " != " + numrows); } else { rlens[i] = numrows; } if (clens[i] > 0 && clens[i] != 1) throw new DMLRuntimeException( "Unexpected error while processing sequence instruction. Number of columns (" + clens[i] + ") must be equal to 1."); else clens[i] = 1; PrintWriter pw = null; try { pw = new PrintWriter(fs.create(new Path(inputs[i]))); StringBuilder sb = new StringBuilder(); double temp = from; double block_from, block_to; for (long r = 0; r < rlens[i]; r += brlens[i]) { long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r)); // block (bid_i,bid_j) generates a sequence from the interval [block_from, block_to] (inclusive of both end points of the interval) long bid_i = ((r / brlens[i]) + 1); long bid_j = 1; block_from = temp; block_to = temp + (curBlockRowSize - 1) * incr; temp = block_to + incr; // next block starts from here sb.append(bid_i); sb.append(','); sb.append(bid_j); sb.append(','); sb.append(block_from); sb.append(','); sb.append(block_to); sb.append(','); sb.append(incr); pw.println(sb.toString()); sb.setLength(0); numblocks++; } } finally { IOUtilFunctions.closeSilently(pw); } inputInfos[i] = InputInfo.TextCellInputInfo; } else { throw new DMLRuntimeException("Unexpected Data Generation Instruction Type: " + mrtype); } } dataGenInsStr = dataGenInsStr.substring(1);//remove the first "," RunningJob runjob; MatrixCharacteristics[] stats; try { //set up the block size MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens); //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, realIndexes, inputs, inputInfos, brlens, bclens, false, ConvertTarget.BLOCK); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, realIndexes, rlens, clens); MRJobConfiguration.setDimsUnknownFilePrefix(job, dimsUnknownFilePrefix); //set up the block size MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens); //set up the rand Instructions MRJobConfiguration.setRandInstructions(job, dataGenInsStr); //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 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(MRConfigurationNames.DFS_REPLICATION, replication); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getDMLConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //set up custom map/reduce configurations MRJobConfiguration.setupCustomMRConfigurations(job, config); //determine degree of parallelism (nmappers: 1<=n<=capacity) //TODO use maxsparsity whenever we have a way of generating sparse rand data int capacity = InfrastructureAnalyzer.getRemoteParallelMapTasks(); long dfsblocksize = InfrastructureAnalyzer.getHDFSBlockSize(); //correction max number of mappers on yarn clusters if (InfrastructureAnalyzer.isYarnEnabled()) capacity = (int) Math.max(capacity, YarnClusterAnalyzer.getNumCores()); int nmapers = Math .max(Math.min((int) (8 * maxbrlen * maxbclen * (long) numblocks / dfsblocksize), capacity), 1); job.setNumMapTasks(nmapers); //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, dataGenInsStr, instructionsInMapper, null, aggInstructionsInReducer, otherInstructionsInReducer, resultIndexes); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, dataGenInsStr, instructionsInMapper, null, aggInstructionsInReducer, null, otherInstructionsInReducer, resultIndexes, mapoutputIndexes, false); stats = ret.stats; //set up the number of reducers MRJobConfiguration.setNumReducers(job, ret.numReducerGroups, numReducers); // print the complete MRJob instruction if (LOG.isTraceEnabled()) inst.printCompleteMRJobInstruction(stats); // Update resultDimsUnknown based on computed "stats" byte[] resultDimsUnknown = new byte[resultIndexes.length]; for (int i = 0; i < resultIndexes.length; i++) { if (stats[i].getRows() == -1 || stats[i].getCols() == -1) { resultDimsUnknown[i] = (byte) 1; } else { resultDimsUnknown[i] = (byte) 0; } } boolean mayContainCtable = instructionsInMapper.contains("ctabletransform") || instructionsInMapper.contains("groupedagg"); //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, resultDimsUnknown, outputs, outputInfos, true, mayContainCtable); // configure mapper and the mapper output key value pairs job.setMapperClass(DataGenMapper.class); if (numReducers == 0) { job.setMapOutputKeyClass(Writable.class); job.setMapOutputValueClass(Writable.class); } else { job.setMapOutputKeyClass(MatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixBlock.class); } //set up combiner if (numReducers != 0 && aggInstructionsInReducer != null && !aggInstructionsInReducer.isEmpty()) job.setCombinerClass(GMRCombiner.class); //configure reducer job.setReducerClass(GMRReducer.class); //job.setReducerClass(PassThroughReducer.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); 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))); } String dir = dimsUnknownFilePrefix + "/" + runjob.getID().toString() + "_dimsFile"; stats = MapReduceTool.processDimsFiles(dir, stats); MapReduceTool.deleteFileIfExistOnHDFS(dir); } finally { for (String input : inputs) MapReduceTool.deleteFileIfExistOnHDFS(new Path(input), job); } return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:org.apache.sysml.runtime.matrix.GMR.java
License:Apache License
/** * Execute job./*from w w w . jav a 2 s . co m*/ * * @param inst MR job instruction * @param inputs input matrices, the inputs are indexed by 0, 1, 2, .. based on the position in this string * @param inputInfos the input format information for the input matrices * @param rlens array of number of rows * @param clens array of number of columns * @param brlens array of number of rows in block * @param bclens array of number of columns in block * @param partitioned boolean array of partitioned status * @param pformats array of data partition formats * @param psizes does nothing * @param recordReaderInstruction record reader instruction * @param instructionsInMapper in Mapper, the set of unary operations that need to be performed on each input matrix * @param aggInstructionsInReducer in Reducer, right after sorting, the set of aggreagte operations * that need to be performed on each input matrix * @param otherInstructionsInReducer the mixed operations that need to be performed on matrices after the aggregate operations * @param numReducers the number of reducers * @param replication the replication factor for the output * @param jvmReuse if true, reuse JVM * @param resultIndexes the indexes of the result matrices that needs to be outputted * @param dimsUnknownFilePrefix file path prefix when dimensions unknown * @param outputs the names for the output directories, one for each result index * @param outputInfos output format information for the output matrices * @return job return object * @throws Exception if Exception occurs */ @SuppressWarnings({ "unchecked", "rawtypes" }) public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, boolean[] partitioned, PDataPartitionFormat[] pformats, int[] psizes, String recordReaderInstruction, String instructionsInMapper, String aggInstructionsInReducer, String otherInstructionsInReducer, int numReducers, int replication, boolean jvmReuse, byte[] resultIndexes, String dimsUnknownFilePrefix, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job = new JobConf(GMR.class); job.setJobName("G-MR"); boolean inBlockRepresentation = MRJobConfiguration.deriveRepresentation(inputInfos); //whether use block representation or cell representation MRJobConfiguration.setMatrixValueClass(job, inBlockRepresentation); //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; if (recordReaderInstruction != null && !recordReaderInstruction.isEmpty()) { assert (inputs.length <= 2); PickByCountInstruction ins = (PickByCountInstruction) PickByCountInstruction .parseInstruction(recordReaderInstruction); PickFromCompactInputFormat.setKeyValueClasses(job, (Class<? extends WritableComparable>) inputInfos[ins.input1].inputKeyClass, inputInfos[ins.input1].inputValueClass); job.setInputFormat(PickFromCompactInputFormat.class); PickFromCompactInputFormat.setZeroValues(job, (MetaDataNumItemsByEachReducer) inputInfos[ins.input1].metadata); if (ins.isValuePick) { double[] probs = MapReduceTool.readColumnVectorFromHDFS(inputs[ins.input2], inputInfos[ins.input2], rlens[ins.input2], clens[ins.input2], brlens[ins.input2], bclens[ins.input2]); PickFromCompactInputFormat.setPickRecordsInEachPartFile(job, (MetaDataNumItemsByEachReducer) inputInfos[ins.input1].metadata, probs); realinputs = new String[inputs.length - 1]; realinputInfos = new InputInfo[inputs.length - 1]; realrlens = new long[inputs.length - 1]; realclens = new long[inputs.length - 1]; realbrlens = new int[inputs.length - 1]; realbclens = new int[inputs.length - 1]; realIndexes = new byte[inputs.length - 1]; byte realIndex = 0; for (byte i = 0; i < inputs.length; i++) { if (i == ins.input2) continue; realinputs[realIndex] = inputs[i]; realinputInfos[realIndex] = inputInfos[i]; if (i == ins.input1) { realrlens[realIndex] = rlens[ins.input2]; realclens[realIndex] = clens[ins.input2]; realbrlens[realIndex] = 1; realbclens[realIndex] = 1; realIndexes[realIndex] = ins.output; } else { realrlens[realIndex] = rlens[i]; realclens[realIndex] = clens[i]; realbrlens[realIndex] = brlens[i]; realbclens[realIndex] = bclens[i]; realIndexes[realIndex] = i; } realIndex++; } } else { //PickFromCompactInputFormat.setPickRecordsInEachPartFile(job, (NumItemsByEachReducerMetaData) inputInfos[ins.input1].metadata, ins.cst, 1-ins.cst); PickFromCompactInputFormat.setRangePickPartFiles(job, (MetaDataNumItemsByEachReducer) inputInfos[ins.input1].metadata, ins.cst, 1 - ins.cst); realrlens[ins.input1] = UtilFunctions.getLengthForInterQuantile( (MetaDataNumItemsByEachReducer) inputInfos[ins.input1].metadata, ins.cst); realclens[ins.input1] = clens[ins.input1]; realbrlens[ins.input1] = 1; realbclens[ins.input1] = 1; realIndexes[ins.input1] = ins.output; } } boolean resetDistCache = setupDistributedCache(job, instructionsInMapper, otherInstructionsInReducer, realinputs, realrlens, realclens); //set up the input files and their format information boolean[] distCacheOnly = getDistCacheOnlyInputs(realIndexes, recordReaderInstruction, instructionsInMapper, aggInstructionsInReducer, otherInstructionsInReducer); MRJobConfiguration.setUpMultipleInputs(job, realIndexes, realinputs, realinputInfos, realbrlens, realbclens, distCacheOnly, true, inBlockRepresentation ? ConvertTarget.BLOCK : ConvertTarget.CELL); MRJobConfiguration.setInputPartitioningInfo(job, pformats); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, realIndexes, realrlens, realclens); MRJobConfiguration.setDimsUnknownFilePrefix(job, dimsUnknownFilePrefix); //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.setAggregateInstructions(job, aggInstructionsInReducer); //set up the instructions that will happen in the reducer, after the aggregation instructions MRJobConfiguration.setInstructionsInReducer(job, otherInstructionsInReducer); //set up the replication factor for the results job.setInt(MRConfigurationNames.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.getDMLConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //set up custom map/reduce configurations MRJobConfiguration.setupCustomMRConfigurations(job, config); //set up jvm reuse (incl. reuse of loaded dist cache matrices) if (jvmReuse) job.setNumTasksToExecutePerJvm(-1); //set up what matrices are needed to pass from the mapper to reducer HashSet<Byte> mapoutputIndexes = MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, instructionsInMapper, aggInstructionsInReducer, otherInstructionsInReducer, resultIndexes); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, instructionsInMapper, aggInstructionsInReducer, null, 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); // Update resultDimsUnknown based on computed "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; } } //MRJobConfiguration.updateResultDimsUnknown(job,resultDimsUnknown); //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, dimsUnknown, outputs, outputInfos, inBlockRepresentation, true); // configure mapper and the mapper output key value pairs job.setMapperClass(GMRMapper.class); if (numReducers == 0) { job.setMapOutputKeyClass(Writable.class); job.setMapOutputValueClass(Writable.class); } else { job.setMapOutputKeyClass(MatrixIndexes.class); if (inBlockRepresentation) job.setMapOutputValueClass(TaggedMatrixBlock.class); else job.setMapOutputValueClass(TaggedMatrixPackedCell.class); } //set up combiner if (numReducers != 0 && aggInstructionsInReducer != null && !aggInstructionsInReducer.isEmpty()) { job.setCombinerClass(GMRCombiner.class); } //configure reducer job.setReducerClass(GMRReducer.class); //job.setReducerClass(PassThroughReducer.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); Group group = runjob.getCounters().getGroup(MRJobConfiguration.NUM_NONZERO_CELLS); for (int i = 0; i < resultIndexes.length; i++) stats[i].setNonZeros(group.getCounter(Integer.toString(i))); //cleanups String dir = dimsUnknownFilePrefix + "/" + runjob.getID().toString() + "_dimsFile"; stats = MapReduceTool.processDimsFiles(dir, stats); MapReduceTool.deleteFileIfExistOnHDFS(dir); if (resetDistCache) MRBaseForCommonInstructions.resetDistCache(); return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:org.apache.sysml.runtime.matrix.GroupedAggMR.java
License:Apache License
public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String grpAggInstructions, String simpleReduceInstructions/*only scalar or reorg instructions allowed*/, int numReducers, int replication, byte[] resultIndexes, String dimsUnknownFilePrefix, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job = new JobConf(GroupedAggMR.class); job.setJobName("GroupedAgg-MR"); //whether use block representation or cell representation //MRJobConfiguration.setMatrixValueClassForCM_N_COM(job, true); MRJobConfiguration.setMatrixValueClass(job, false); //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 ww w.j av a 2 s .c o m*/ //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); MRJobConfiguration.setDimsUnknownFilePrefix(job, dimsUnknownFilePrefix); //set up the block size MRJobConfiguration.setBlocksSizes(job, realIndexes, realbrlens, realbclens); //set up the grouped aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setGroupedAggInstructions(job, grpAggInstructions); //set up the instructions that will happen in the reducer, after the aggregation instrucions MRJobConfiguration.setInstructionsInReducer(job, simpleReduceInstructions); //set up the number of reducers MRJobConfiguration.setNumReducers(job, numReducers, numReducers); //set up the replication factor for the results job.setInt(MRConfigurationNames.DFS_REPLICATION, replication); //set up custom map/reduce configurations DMLConfig config = ConfigurationManager.getDMLConfig(); MRJobConfiguration.setupCustomMRConfigurations(job, config); //set up what matrices are needed to pass from the mapper to reducer MRJobConfiguration.setUpOutputIndexesForMapper(job, realIndexes, null, null, grpAggInstructions, resultIndexes); MatrixCharacteristics[] stats = new MatrixCharacteristics[resultIndexes.length]; for (int i = 0; i < resultIndexes.length; i++) stats[i] = new MatrixCharacteristics(); // Print the complete instruction if (LOG.isTraceEnabled()) inst.printCompleteMRJobInstruction(stats); byte[] resultDimsUnknown = new byte[resultIndexes.length]; // Update resultDimsUnknown based on computed "stats" for (int i = 0; i < resultIndexes.length; i++) resultDimsUnknown[i] = (byte) 2; //set up the multiple output files, and their format information MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, resultDimsUnknown, outputs, outputInfos, false); // configure mapper and the mapper output key value pairs job.setMapperClass(GroupedAggMRMapper.class); job.setCombinerClass(GroupedAggMRCombiner.class); job.setMapOutputKeyClass(TaggedMatrixIndexes.class); job.setMapOutputValueClass(WeightedCell.class); //configure reducer job.setReducerClass(GroupedAggMRReducer.class); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); //execute job RunningJob runjob = JobClient.runJob(job); //get important output statistics Group group = runjob.getCounters().getGroup(MRJobConfiguration.NUM_NONZERO_CELLS); for (int i = 0; i < resultIndexes.length; i++) { // number of non-zeros stats[i] = new MatrixCharacteristics(); stats[i].setNonZeros(group.getCounter(Integer.toString(i))); } String dir = dimsUnknownFilePrefix + "/" + runjob.getID().toString() + "_dimsFile"; stats = MapReduceTool.processDimsFiles(dir, stats); MapReduceTool.deleteFileIfExistOnHDFS(dir); return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:org.apache.sysml.runtime.matrix.MMRJMR.java
License:Apache 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;/*from w w w.j a va2 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(MRConfigurationNames.DFS_REPLICATION, replication); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getDMLConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //set up custom map/reduce configurations MRJobConfiguration.setupCustomMRConfigurations(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()); }