List of usage examples for org.apache.hadoop.mapred JobConf setJobName
public void setJobName(String name)
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.j a v 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); //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.CombineMR.java
License:Open Source 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; 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;//from w w w . j a v a2 s .c o m //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("dfs.replication", replication); //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:com.ibm.bi.dml.runtime.matrix.CSVReblockMR.java
License:Open Source License
public static AssignRowIDMRReturn runAssignRowIDMRJob(String[] inputs, InputInfo[] inputInfos, int[] brlens, int[] bclens, String reblockInstructions, int replication, String[] smallestFiles, boolean transform, String naStrings, String specFile) throws Exception { AssignRowIDMRReturn ret = new AssignRowIDMRReturn(); JobConf job; job = new JobConf(CSVReblockMR.class); job.setJobName("Assign-RowID-MR"); byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;/* w ww . ja v a 2 s . c o m*/ //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 aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setCSVReblockInstructions(job, reblockInstructions); //set up the replication factor for the results job.setInt("dfs.replication", replication); //set up the number of reducers job.setNumReduceTasks(1); // Print the complete instruction //if (LOG.isTraceEnabled()) //inst.printCompelteMRJobInstruction(); // configure mapper and the mapper output key value pairs job.setMapperClass(CSVAssignRowIDMapper.class); job.setMapOutputKeyClass(ByteWritable.class); job.setMapOutputValueClass(OffsetCount.class); //configure reducer job.setReducerClass(CSVAssignRowIDReducer.class); //turn off adaptivemr job.setBoolean("adaptivemr.map.enable", false); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); //set up the output file ret.counterFile = new Path(MRJobConfiguration.constructTempOutputFilename()); job.setOutputFormat(SequenceFileOutputFormat.class); FileOutputFormat.setOutputPath(job, ret.counterFile); job.setOutputKeyClass(ByteWritable.class); job.setOutputValueClass(OffsetCount.class); // setup properties relevant to transform job.setBoolean(MRJobConfiguration.TF_TRANSFORM, transform); if (transform) { if (naStrings != null) // Adding "dummy" string to handle the case of na_strings = "" job.set(MRJobConfiguration.TF_NA_STRINGS, TfUtils.prepNAStrings(naStrings)); job.set(MRJobConfiguration.TF_SPEC_FILE, specFile); } RunningJob runjob = JobClient.runJob(job); /* Process different counters */ Group rgroup = runjob.getCounters().getGroup(NUM_ROWS_IN_MATRIX); Group cgroup = runjob.getCounters().getGroup(NUM_COLS_IN_MATRIX); ret.rlens = new long[inputs.length]; ret.clens = new long[inputs.length]; for (int i = 0; i < inputs.length; i++) { // number of non-zeros ret.rlens[i] = rgroup.getCounter(Integer.toString(i)); ret.clens[i] = cgroup.getCounter(Integer.toString(i)); } return ret; }
From source file:com.ibm.bi.dml.runtime.matrix.CSVReblockMR.java
License:Open Source 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; 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;/*from ww w . j a v a 2s. c o m*/ //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("dfs.replication", replication); //set up preferred custom serialization framework for binary block format if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) MRJobConfiguration.addBinaryBlockSerializationFramework(job); //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, ConfigurationManager.getConfig().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:com.ibm.bi.dml.runtime.matrix.DataGenMR.java
License:Open Source License
/** * <p>Starts a Rand MapReduce job which will produce one or more random objects.</p> * //from w w w. j a va 2s . co m * @param numRows number of rows for each random object * @param numCols number of columns for each random object * @param blockRowSize number of rows in a block for each random object * @param blockColSize number of columns in a block for each random object * @param minValue minimum of the random values for each random object * @param maxValue maximum of the random values for each random object * @param sparsity sparsity for each random object * @param pdf probability density function for each random object * @param replication file replication * @param inputs input file for each random object * @param outputs output file for each random object * @param outputInfos output information for each random object * @param instructionsInMapper instruction for each random object * @param resultIndexes result indexes for each random object * @return matrix characteristics for each random object * @throws Exception if an error occurred in the MapReduce phase */ 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] = genInst.getBaseDir() + "tmp" + _seqRandInput.getNextID() + ".randinput"; maxsparsity = Math.max(maxsparsity, randInst.getSparsity()); FSDataOutputStream fsOut = fs.create(new Path(inputs[i])); PrintWriter pw = new PrintWriter(fsOut); //for obj reuse and preventing repeated buffer re-allocations StringBuilder sb = new StringBuilder(); //seed generation Well1024a bigrand = LibMatrixDatagen.setupSeedsForRand(randInst.getSeed()); long[] nnz = LibMatrixDatagen.computeNNZperBlock(rlens[i], clens[i], brlens[i], bclens[i], randInst.getSparsity()); int nnzIx = 0; 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(nnz[nnzIx++]); sb.append(','); sb.append(bigrand.nextLong()); pw.println(sb.toString()); sb.setLength(0); numblocks++; } } pw.close(); fsOut.close(); 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; // 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 = 1 + (long) Math.floor((to - from) / 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; FSDataOutputStream fsOut = fs.create(new Path(inputs[i])); PrintWriter pw = new PrintWriter(fsOut); 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(','); /* // Need not include block size while generating seq() sb.append(curBlockRowSize); sb.append(','); sb.append(1); sb.append(',');*/ sb.append(block_from); sb.append(','); sb.append(block_to); sb.append(','); sb.append(incr); pw.println(sb.toString()); //System.out.println("MapTask " + r + ": " + sb.toString()); sb.setLength(0); numblocks++; } pw.close(); fsOut.close(); 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("dfs.replication", replication); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(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:com.ibm.bi.dml.runtime.matrix.GMR.java
License:Open Source License
/** * inBlockRepresentation: indicate whether to use block representation or cell representation * inputs: input matrices, the inputs are indexed by 0, 1, 2, .. based on the position in this string * inputInfos: the input format information for the input matrices * rlen: the number of rows for each matrix * clen: the number of columns for each matrix * brlen: the number of rows per block/*from w w w. ja v a 2s . c om*/ * bclen: the number of columns per block * instructionsInMapper: in Mapper, the set of unary operations that need to be performed on each input matrix * aggInstructionsInReducer: in Reducer, right after sorting, the set of aggreagte operations that need * to be performed on each input matrix, * otherInstructionsInReducer: the mixed operations that need to be performed on matrices after the aggregate operations * numReducers: the number of reducers * replication: the replication factor for the output * resulltIndexes: the indexes of the result matrices that needs to be outputted. * outputs: the names for the output directories, one for each result index * outputInfos: output format information for the output matrices */ @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, (NumItemsByEachReducerMetaData) 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, (NumItemsByEachReducerMetaData) 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, (NumItemsByEachReducerMetaData) inputInfos[ins.input1].metadata, ins.cst, 1 - ins.cst); realrlens[ins.input1] = UtilFunctions.getLengthForInterQuantile( (NumItemsByEachReducerMetaData) 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; } } 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("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); //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); //MatrixCharacteristics[] stats=new MatrixCharacteristics[resultIndexes.length]; 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); return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }
From source file:com.ibm.bi.dml.runtime.matrix.GroupedAggMR.java
License:Open Source 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;// w ww.ja v a 2s . 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); 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("dfs.replication", replication); //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(TaggedInt.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: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;// ww w . ja 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, 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;// w w w .j a va 2s.co 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.ReblockMR.java
License:Open Source License
public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, long[] nnz, String instructionsInMapper, String reblockInstructions, String otherInstructionsInReducer, int numReducers, int replication, boolean jvmReuse, byte[] resultIndexes, String[] outputs, OutputInfo[] outputInfos) throws Exception { JobConf job = new JobConf(ReblockMR.class); job.setJobName("Reblock-MR"); byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;// ww w. j a v a 2 s.c o m //set up the input files and their format information //(internally used input converters: text2bc for text, identity for binary inputs) MRJobConfiguration.setUpMultipleInputsReblock(job, realIndexes, inputs, inputInfos, brlens, bclens); //set up the dimensions of input matrices MRJobConfiguration.setMatricesDimensions(job, realIndexes, rlens, clens, nnz); //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.setReblockInstructions(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("dfs.replication", replication); //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); //enable jvm reuse (based on SystemML configuration) 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, reblockInstructions, null, otherInstructionsInReducer, resultIndexes); MatrixChar_N_ReducerGroups ret = MRJobConfiguration.computeMatrixCharacteristics(job, realIndexes, instructionsInMapper, reblockInstructions, null, null, otherInstructionsInReducer, resultIndexes, mapoutputIndexes, false); MatrixCharacteristics[] stats = ret.stats; //set up the number of reducers (according to output size) int numRed = determineNumReducers(rlens, clens, nnz, ConfigurationManager.getConfig().getIntValue(DMLConfig.NUM_REDUCERS), ret.numReducerGroups); job.setNumReduceTasks(numRed); //setup in-memory reduce buffers budget (reblock reducer dont need much memory) //job.set("mapred.job.reduce.input.buffer.percent", "0.70"); // Print the complete 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; } } //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(ReblockMapper.class); job.setMapOutputKeyClass(MatrixIndexes.class); //represent key offsets for block job.setMapOutputValueClass(TaggedAdaptivePartialBlock.class); //binary cell/block //configure reducer job.setReducerClass(ReblockReducer.class); // By default, the job executes in "cluster" mode. // Determine if we can optimize and run it in "local" mode. // at this point, both reblock_binary and reblock_text are similar 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))); // System.out.println("result #"+resultIndexes[i]+" ===>\n"+stats[i]); } return new JobReturn(stats, outputInfos, runjob.isSuccessful()); }