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

Example 56 with RunningJob

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

the class MergeFileTask method execute.

/**
 * start a new map-reduce job to do the merge, almost the same as ExecDriver.
 */
@Override
public int execute(DriverContext driverContext) {
    Context ctx = driverContext.getCtx();
    boolean ctxCreated = false;
    RunningJob rj = null;
    int returnVal = 0;
    try {
        if (ctx == null) {
            ctx = new Context(job);
            ctxCreated = true;
        }
        HiveFileFormatUtils.prepareJobOutput(job);
        job.setInputFormat(work.getInputformatClass());
        job.setOutputFormat(HiveOutputFormatImpl.class);
        job.setMapperClass(MergeFileMapper.class);
        job.setMapOutputKeyClass(NullWritable.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(NullWritable.class);
        job.setNumReduceTasks(0);
        // create the temp directories
        Path outputPath = work.getOutputDir();
        Path tempOutPath = Utilities.toTempPath(outputPath);
        FileSystem fs = tempOutPath.getFileSystem(job);
        if (!fs.exists(tempOutPath)) {
            fs.mkdirs(tempOutPath);
        }
        ExecDriver.propagateSplitSettings(job, work);
        // set job name
        boolean noName = StringUtils.isEmpty(job.get(MRJobConfig.JOB_NAME));
        String jobName = null;
        if (noName && this.getQueryPlan() != null) {
            int maxlen = conf.getIntVar(HiveConf.ConfVars.HIVEJOBNAMELENGTH);
            jobName = Utilities.abbreviate(this.getQueryPlan().getQueryStr(), maxlen - 6);
        }
        if (noName) {
            // This is for a special case to ensure unit tests pass
            job.set(MRJobConfig.JOB_NAME, jobName != null ? jobName : "JOB" + Utilities.randGen.nextInt());
        }
        // add input path
        addInputPaths(job, work);
        // serialize work
        Utilities.setMapWork(job, work, ctx.getMRTmpPath(), true);
        // remove pwd from conf file so that job tracker doesn't show this logs
        String pwd = HiveConf.getVar(job, HiveConf.ConfVars.METASTOREPWD);
        if (pwd != null) {
            HiveConf.setVar(job, HiveConf.ConfVars.METASTOREPWD, "HIVE");
        }
        // submit the job
        JobClient jc = new JobClient(job);
        String addedJars = Utilities.getResourceFiles(job, SessionState.ResourceType.JAR);
        if (!addedJars.isEmpty()) {
            job.set("tmpjars", addedJars);
        }
        // make this client wait if job trcker is not behaving well.
        Throttle.checkJobTracker(job, LOG);
        // Finally SUBMIT the JOB!
        rj = jc.submitJob(job);
        this.jobID = rj.getJobID();
        returnVal = jobExecHelper.progress(rj, jc, ctx);
        success = (returnVal == 0);
    } catch (Exception e) {
        setException(e);
        String mesg = " with exception '" + Utilities.getNameMessage(e) + "'";
        if (rj != null) {
            mesg = "Ended Job = " + rj.getJobID() + mesg;
        } else {
            mesg = "Job Submission failed" + mesg;
        }
        // Has to use full name to make sure it does not conflict with
        // org.apache.commons.lang.StringUtils
        console.printError(mesg, "\n" + org.apache.hadoop.util.StringUtils.stringifyException(e));
        success = false;
        returnVal = 1;
    } finally {
        try {
            if (ctxCreated) {
                ctx.clear();
            }
            if (rj != null) {
                if (returnVal != 0) {
                    rj.killJob();
                }
            }
            // get the list of Dynamic partition paths
            if (rj != null) {
                if (work.getAliasToWork() != null) {
                    for (Operator<? extends OperatorDesc> op : work.getAliasToWork().values()) {
                        op.jobClose(job, success);
                    }
                }
            }
        } catch (Exception e) {
            // jobClose needs to execute successfully otherwise fail task
            LOG.warn("Job close failed ", e);
            if (success) {
                setException(e);
                success = false;
                returnVal = 3;
                String mesg = "Job Commit failed with exception '" + Utilities.getNameMessage(e) + "'";
                console.printError(mesg, "\n" + org.apache.hadoop.util.StringUtils.stringifyException(e));
            }
        } finally {
            HadoopJobExecHelper.runningJobs.remove(rj);
        }
    }
    return returnVal;
}

Example 57 with RunningJob

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

the class RemoteDPParForMR method runJob.

public static RemoteParForJobReturn runJob(long pfid, String itervar, String matrixvar, String program, // config params
String resultFile, // config params
MatrixObject input, // config params
PartitionFormat dpf, // config params
OutputInfo oi, // config params
boolean tSparseCol, // opt params
boolean enableCPCaching, // opt params
int numReducers, // opt params
int replication) {
    RemoteParForJobReturn ret = null;
    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
        // -1 for unlimited
        job.setNumTasksToExecutePerJvm(1);
        // 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;
}

Example 58 with RunningJob

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

the class CMCOVMR method runJob.

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;
    // 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
    // each output tag is a group
    MRJobConfiguration.setNumReducers(job, mapoutputIndexes.size(), 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, outputInfos, runjob.isSuccessful());
}

Example 59 with RunningJob

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

the class CSVReblockMR method runAssignRowIDMRJob.

public static AssignRowIDMRReturn runAssignRowIDMRJob(String[] inputs, InputInfo[] inputInfos, int[] brlens, int[] bclens, String reblockInstructions, int replication, String[] smallestFiles) 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;
    // 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(MRConfigurationNames.DFS_REPLICATION, replication);
    // set up custom map/reduce configurations
    DMLConfig config = ConfigurationManager.getDMLConfig();
    MRJobConfiguration.setupCustomMRConfigurations(job, config);
    // 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);
    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;
}

Example 60 with RunningJob

use of org.apache.hadoop.mapred.RunningJob in project incubator-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());
}