Examples with RunningJob org.apache.hadoop.mapred.RunningJob used on opensource projects

Example 66 with RunningJob

use of org.apache.hadoop.mapred.RunningJob in project systemml by apache.

the class CombineMR method runJob.

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;
    // 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());
}

Example 67 with RunningJob

use of org.apache.hadoop.mapred.RunningJob in project systemml by apache.

the class DataGenMR method runJob.

/**
 * <p>Starts a Rand MapReduce job which will produce one or more random objects.</p>
 *
 * @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]);
        MRType 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 == MRType.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());
                for (long r = 0; r < Math.max(rlens[i], 1); r += brlens[i]) {
                    long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r));
                    for (long c = 0; c < Math.max(clens[i], 1); 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(bigrand.nextLong());
                        pw.println(sb.toString());
                        sb.setLength(0);
                        numblocks++;
                    }
                }
            } finally {
                IOUtilFunctions.closeSilently(pw);
            }
            inputInfos[i] = InputInfo.TextCellInputInfo;
        } else if (mrtype == MRType.Seq) {
            SeqInstruction seqInst = (SeqInstruction) mrins;
            inputs[i] = genInst.getBaseDir() + System.currentTimeMillis() + ".seqinput";
            // always dense
            maxsparsity = 1.0;
            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;
                    // next block starts from here
                    temp = block_to + incr;
                    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);
        }
    }
    // remove the first ","
    dataGenInsStr = dataGenInsStr.substring(1);
    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());
}

Example 68 with RunningJob

use of org.apache.hadoop.mapred.RunningJob in project systemml by apache.

the class GMR method runJob.

/**
 * Execute job.
 *
 * @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());
}

Example 69 with RunningJob

use of org.apache.hadoop.mapred.RunningJob in project systemml by apache.

the class GroupedAggMR method runJob.

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;
    // 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());
}

Example 70 with RunningJob

use of org.apache.hadoop.mapred.RunningJob in project systemml by apache.

the class MMCJMR method runJob.

public static JobReturn runJob(MRJobInstruction inst, String[] inputs, InputInfo[] inputInfos, long[] rlens, long[] clens, int[] brlens, int[] bclens, String instructionsInMapper, String aggInstructionsInReducer, String aggBinInstrction, int numReducers, int replication, String output, OutputInfo outputinfo) throws Exception {
    JobConf job = new JobConf(MMCJMR.class);
    // TODO: check w/ yuanyuan. This job always runs in blocked mode, and hence derivation is not necessary.
    boolean inBlockRepresentation = MRJobConfiguration.deriveRepresentation(inputInfos);
    // by default, assume that dimensions of MMCJ's output are known at compile time
    byte resultDimsUnknown = (byte) 0;
    MatrixCharacteristics[] stats = commonSetup(job, inBlockRepresentation, inputs, inputInfos, rlens, clens, brlens, bclens, instructionsInMapper, aggInstructionsInReducer, aggBinInstrction, numReducers, replication, resultDimsUnknown, output, outputinfo);
    // Print the complete instruction
    if (LOG.isTraceEnabled())
        inst.printCompleteMRJobInstruction(stats);
    // There is always a single output
    if (stats[0].getRows() == -1 || stats[0].getCols() == -1) {
        resultDimsUnknown = (byte) 1;
        // if the dimensions are unknown, then setup done in commonSetup() must be updated
        byte[] resultIndexes = new byte[] { MRInstructionParser.parseSingleInstruction(aggBinInstrction).output };
        byte[] resultDimsUnknown_Array = new byte[] { resultDimsUnknown };
        // set up the multiple output files, and their format information
        MRJobConfiguration.setUpMultipleOutputs(job, resultIndexes, resultDimsUnknown_Array, new String[] { output }, new OutputInfo[] { outputinfo }, inBlockRepresentation);
    }
    AggregateBinaryInstruction ins = (AggregateBinaryInstruction) MRInstructionParser.parseSingleInstruction(aggBinInstrction);
    MatrixCharacteristics dim1 = MRJobConfiguration.getMatrixCharactristicsForBinAgg(job, ins.input1);
    MatrixCharacteristics dim2 = MRJobConfiguration.getMatrixCharactristicsForBinAgg(job, ins.input2);
    if (dim1.getRowsPerBlock() > dim1.getRows())
        dim1.setRowsPerBlock((int) dim1.getRows());
    if (dim1.getColsPerBlock() > dim1.getCols())
        dim1.setColsPerBlock((int) dim1.getCols());
    if (dim2.getRowsPerBlock() > dim2.getRows())
        dim2.setRowsPerBlock((int) dim2.getRows());
    if (dim2.getColsPerBlock() > dim2.getCols())
        dim2.setColsPerBlock((int) dim2.getCols());
    long blockSize1 = 77 + 8 * dim1.getRowsPerBlock() * dim1.getColsPerBlock();
    long blockSize2 = 77 + 8 * dim2.getRowsPerBlock() * dim2.getColsPerBlock();
    long blockSizeResult = 77 + 8 * dim1.getRowsPerBlock() * dim2.getColsPerBlock();
    long cacheSize = -1;
    // cache the first result
    if (dim1.getRows() < dim2.getCols()) {
        long numBlocks = (long) Math.ceil((double) dim1.getRows() / (double) dim1.getRowsPerBlock());
        cacheSize = numBlocks * (20 + blockSize1) + 32;
    } else // cache the second result
    {
        long numBlocks = (long) Math.ceil((double) dim2.getCols() / (double) dim2.getColsPerBlock());
        cacheSize = numBlocks * (20 + blockSize2) + 32;
    }
    // add known memory consumption (will be substracted from output buffer)
    cacheSize += // the cached key-value pair  (plus input instance)
    2 * Math.max(blockSize1, blockSize2) + // the cached single result
    blockSizeResult + // misc memory requirement by hadoop
    MRJobConfiguration.getMiscMemRequired(job);
    MRJobConfiguration.setMMCJCacheSize(job, (int) cacheSize);
    // set unique working dir
    MRJobConfiguration.setUniqueWorkingDir(job);
    // run mmcj job
    RunningJob runjob = JobClient.runJob(job);
    /* Process different counters */
    // NOTE: MMCJ job always has only a single output.
    // Hence, no need to scan resultIndexes[] like other jobs
    int outputIndex = 0;
    Byte outputMatrixID = MRInstructionParser.parseSingleInstruction(aggBinInstrction).output;
    Group group = runjob.getCounters().getGroup(MRJobConfiguration.NUM_NONZERO_CELLS);
    // number of non-zeros
    stats[outputIndex].setNonZeros(group.getCounter(Byte.toString(outputMatrixID)));
    return new JobReturn(stats[outputIndex], outputinfo, runjob.isSuccessful());
}