List of usage examples for org.apache.hadoop.mapred JobConf setNumMapTasks
public void setNumMapTasks(int n)
From source file:com.github.gaoyangthu.demo.mapred.PiEstimator.java
License:Apache License
/** * Run a map/reduce job for estimating Pi. * * @return the estimated value of Pi/*from w w w. j a v a 2 s. c om*/ */ public static BigDecimal estimate(int numMaps, long numPoints, JobConf jobConf) throws IOException { //setup job conf jobConf.setJobName(PiEstimator.class.getSimpleName()); jobConf.setInputFormat(SequenceFileInputFormat.class); jobConf.setOutputKeyClass(BooleanWritable.class); jobConf.setOutputValueClass(LongWritable.class); jobConf.setOutputFormat(SequenceFileOutputFormat.class); jobConf.setMapperClass(PiMapper.class); jobConf.setNumMapTasks(numMaps); jobConf.setReducerClass(PiReducer.class); jobConf.setNumReduceTasks(1); // turn off speculative execution, because DFS doesn't handle // multiple writers to the same file. jobConf.setSpeculativeExecution(false); //setup input/output directories final Path inDir = new Path(TMP_DIR, "in"); final Path outDir = new Path(TMP_DIR, "out"); FileInputFormat.setInputPaths(jobConf, inDir); FileOutputFormat.setOutputPath(jobConf, outDir); final FileSystem fs = FileSystem.get(jobConf); if (fs.exists(TMP_DIR)) { throw new IOException( "Tmp directory " + fs.makeQualified(TMP_DIR) + " already exists. Please remove it first."); } if (!fs.mkdirs(inDir)) { throw new IOException("Cannot create input directory " + inDir); } try { //generate an input file for each map task for (int i = 0; i < numMaps; ++i) { final Path file = new Path(inDir, "part" + i); final LongWritable offset = new LongWritable(i * numPoints); final LongWritable size = new LongWritable(numPoints); final SequenceFile.Writer writer = SequenceFile.createWriter(fs, jobConf, file, LongWritable.class, LongWritable.class, CompressionType.NONE); try { writer.append(offset, size); } finally { writer.close(); } System.out.println("Wrote input for Map #" + i); } //start a map/reduce job System.out.println("Starting Job"); final long startTime = System.currentTimeMillis(); JobClient.runJob(jobConf); final double duration = (System.currentTimeMillis() - startTime) / 1000.0; System.out.println("Job Finished in " + duration + " seconds"); //read outputs Path inFile = new Path(outDir, "reduce-out"); LongWritable numInside = new LongWritable(); LongWritable numOutside = new LongWritable(); SequenceFile.Reader reader = new SequenceFile.Reader(fs, inFile, jobConf); try { reader.next(numInside, numOutside); } finally { reader.close(); } //compute estimated value return BigDecimal.valueOf(4).setScale(20).multiply(BigDecimal.valueOf(numInside.get())) .divide(BigDecimal.valueOf(numMaps)).divide(BigDecimal.valueOf(numPoints)); } finally { fs.delete(TMP_DIR, true); } }
From source file:com.hadoopilluminated.examples.dancing.DistributedPentomino.java
License:Apache License
public int run(String[] args) throws Exception { JobConf conf; int depth = 5; int width = 9; int height = 10; Class<? extends Pentomino> pentClass; if (args.length == 0) { System.out.println("pentomino <output>"); ToolRunner.printGenericCommandUsage(System.out); return -1; }/*from ww w . j av a2 s . co m*/ conf = new JobConf(getConf()); width = conf.getInt("pent.width", width); height = conf.getInt("pent.height", height); depth = conf.getInt("pent.depth", depth); pentClass = conf.getClass("pent.class", OneSidedPentonimo.class, Pentomino.class); Path output = new Path(args[0]); Path input = new Path(output + "_input"); FileSystem fileSys = FileSystem.get(conf); try { FileInputFormat.setInputPaths(conf, input); FileOutputFormat.setOutputPath(conf, output); conf.setJarByClass(PentMap.class); conf.setJobName("dancingElephant"); Pentomino pent = ReflectionUtils.newInstance(pentClass, conf); pent.initialize(width, height); createInputDirectory(fileSys, input, pent, depth); // the keys are the prefix strings conf.setOutputKeyClass(Text.class); // the values are puzzle solutions conf.setOutputValueClass(Text.class); conf.setMapperClass(PentMap.class); conf.setReducerClass(IdentityReducer.class); conf.setNumMapTasks(2000); conf.setNumReduceTasks(1); JobClient.runJob(conf); } finally { fileSys.delete(input, true); } return 0; }
From source file:com.hadoopilluminated.examples.Join.java
License:Apache License
/** * The main driver for sort program. Invoke this method to submit the * map/reduce job.// ww w. j a v a 2 s. com * * @throws IOException When there is communication problems with the job * tracker. */ @Override public int run(String[] args) throws Exception { JobConf jobConf = new JobConf(getConf(), Sort.class); jobConf.setJobName("join"); jobConf.setMapperClass(IdentityMapper.class); jobConf.setReducerClass(IdentityReducer.class); JobClient client = new JobClient(jobConf); ClusterStatus cluster = client.getClusterStatus(); int num_maps = cluster.getTaskTrackers() * jobConf.getInt("test.sort.maps_per_host", 10); int num_reduces = (int) (cluster.getMaxReduceTasks() * 0.9); String sort_reduces = jobConf.get("test.sort.reduces_per_host"); if (sort_reduces != null) { num_reduces = cluster.getTaskTrackers() * Integer.parseInt(sort_reduces); } Class<? extends InputFormat> inputFormatClass = SequenceFileInputFormat.class; Class<? extends OutputFormat> outputFormatClass = SequenceFileOutputFormat.class; Class<? extends WritableComparable> outputKeyClass = BytesWritable.class; Class<? extends Writable> outputValueClass = TupleWritable.class; String op = "inner"; List<String> otherArgs = new ArrayList<String>(); for (int i = 0; i < args.length; ++i) { try { if ("-m".equals(args[i])) { num_maps = Integer.parseInt(args[++i]); } else if ("-r".equals(args[i])) { num_reduces = Integer.parseInt(args[++i]); } else if ("-inFormat".equals(args[i])) { inputFormatClass = Class.forName(args[++i]).asSubclass(InputFormat.class); } else if ("-outFormat".equals(args[i])) { outputFormatClass = Class.forName(args[++i]).asSubclass(OutputFormat.class); } else if ("-outKey".equals(args[i])) { outputKeyClass = Class.forName(args[++i]).asSubclass(WritableComparable.class); } else if ("-outValue".equals(args[i])) { outputValueClass = Class.forName(args[++i]).asSubclass(Writable.class); } else if ("-joinOp".equals(args[i])) { op = args[++i]; } else { otherArgs.add(args[i]); } } catch (NumberFormatException except) { System.out.println("ERROR: Integer expected instead of " + args[i]); return printUsage(); } catch (ArrayIndexOutOfBoundsException except) { System.out.println("ERROR: Required parameter missing from " + args[i - 1]); return printUsage(); // exits } } // Set user-supplied (possibly default) job configs jobConf.setNumMapTasks(num_maps); jobConf.setNumReduceTasks(num_reduces); if (otherArgs.size() < 2) { System.out.println("ERROR: Wrong number of parameters: "); return printUsage(); } FileOutputFormat.setOutputPath(jobConf, new Path(otherArgs.remove(otherArgs.size() - 1))); List<Path> plist = new ArrayList<Path>(otherArgs.size()); for (String s : otherArgs) { plist.add(new Path(s)); } jobConf.setInputFormat(CompositeInputFormat.class); jobConf.set("mapred.join.expr", CompositeInputFormat.compose(op, inputFormatClass, plist.toArray(new Path[0]))); jobConf.setOutputFormat(outputFormatClass); jobConf.setOutputKeyClass(outputKeyClass); jobConf.setOutputValueClass(outputValueClass); Date startTime = new Date(); System.out.println("Job started: " + startTime); JobClient.runJob(jobConf); Date end_time = new Date(); System.out.println("Job ended: " + end_time); System.out.println("The job took " + (end_time.getTime() - startTime.getTime()) / 1000 + " seconds."); return 0; }
From source file:com.hp.hplc.mr.driver.WordCount.java
License:Apache License
/** * The main driver for word count map/reduce program. * Invoke this method to submit the map/reduce job. * @throws IOException When there is communication problems with the * job tracker./* w w w . j a va 2 s . co m*/ */ public int run(String[] args) throws Exception { JobConf conf = new JobConf(getConf(), WordCount.class); conf.setJobName("wordcount"); // the keys are words (strings) conf.setOutputKeyClass(Text.class); // the values are counts (ints) conf.setOutputValueClass(IntWritable.class); conf.setMapperClass(MapClass.class); conf.setCombinerClass(Reduce.class); conf.setReducerClass(Reduce.class); List<String> other_args = new ArrayList<String>(); for (int i = 0; i < args.length; ++i) { try { if ("-m".equals(args[i])) { conf.setNumMapTasks(Integer.parseInt(args[++i])); } else if ("-r".equals(args[i])) { conf.setNumReduceTasks(Integer.parseInt(args[++i])); System.out.println("# of reduces: " + conf.getNumReduceTasks()); } else { other_args.add(args[i]); } } catch (NumberFormatException except) { System.out.println("ERROR: Integer expected instead of " + args[i]); return printUsage(); } catch (ArrayIndexOutOfBoundsException except) { System.out.println("ERROR: Required parameter missing from " + args[i - 1]); return printUsage(); } } // Make sure there are exactly 2 parameters left. if (other_args.size() != 2) { System.out.println("ERROR: Wrong number of parameters: " + other_args.size() + " instead of 2."); return printUsage(); } FileInputFormat.setInputPaths(conf, other_args.get(0)); FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1))); JobClient.runJob(conf); return 0; }
From source file:com.ibm.bi.dml.runtime.controlprogram.parfor.RemoteParForMR.java
License:Open Source License
/** * /*from ww w . ja v a 2s.c om*/ * @param pfid * @param program * @param taskFile * @param resultFile * @param _enableCPCaching * @param mode * @param numMappers * @param replication * @return * @throws DMLRuntimeException */ public static RemoteParForJobReturn runJob(long pfid, String program, String taskFile, String resultFile, MatrixObject colocatedDPMatrixObj, //inputs boolean enableCPCaching, int numMappers, int replication, int max_retry, long minMem, boolean jvmReuse) //opt params throws DMLRuntimeException { RemoteParForJobReturn ret = null; String jobname = "ParFor-EMR"; long t0 = DMLScript.STATISTICS ? System.nanoTime() : 0; JobConf job; job = new JobConf(RemoteParForMR.class); job.setJobName(jobname + pfid); //maintain dml script counters Statistics.incrementNoOfCompiledMRJobs(); try { ///// //configure the MR job //set arbitrary CP program blocks that will perform in the mapper MRJobConfiguration.setProgramBlocks(job, program); //enable/disable caching MRJobConfiguration.setParforCachingConfig(job, enableCPCaching); //set mappers, reducers, combiners job.setMapperClass(RemoteParWorkerMapper.class); //map-only //set input format (one split per row, NLineInputFormat default N=1) if (ParForProgramBlock.ALLOW_DATA_COLOCATION && colocatedDPMatrixObj != null) { job.setInputFormat(RemoteParForColocatedNLineInputFormat.class); MRJobConfiguration.setPartitioningFormat(job, colocatedDPMatrixObj.getPartitionFormat()); MatrixCharacteristics mc = colocatedDPMatrixObj.getMatrixCharacteristics(); MRJobConfiguration.setPartitioningBlockNumRows(job, mc.getRowsPerBlock()); MRJobConfiguration.setPartitioningBlockNumCols(job, mc.getColsPerBlock()); MRJobConfiguration.setPartitioningFilename(job, colocatedDPMatrixObj.getFileName()); } else //default case { job.setInputFormat(NLineInputFormat.class); } //set the input path and output path FileInputFormat.setInputPaths(job, new Path(taskFile)); //set output format job.setOutputFormat(SequenceFileOutputFormat.class); //set output path MapReduceTool.deleteFileIfExistOnHDFS(resultFile); FileOutputFormat.setOutputPath(job, new Path(resultFile)); //set the output key, value schema job.setMapOutputKeyClass(LongWritable.class); job.setMapOutputValueClass(Text.class); job.setOutputKeyClass(LongWritable.class); job.setOutputValueClass(Text.class); ////// //set optimization parameters //set the number of mappers and reducers job.setNumMapTasks(numMappers); //numMappers job.setNumReduceTasks(0); //job.setInt("mapred.map.tasks.maximum", 1); //system property //job.setInt("mapred.tasktracker.tasks.maximum",1); //system property //job.setInt("mapred.jobtracker.maxtasks.per.job",1); //system property //use FLEX scheduler configuration properties if (ParForProgramBlock.USE_FLEX_SCHEDULER_CONF) { job.setInt("flex.priority", 0); //highest job.setInt("flex.map.min", 0); job.setInt("flex.map.max", numMappers); job.setInt("flex.reduce.min", 0); job.setInt("flex.reduce.max", numMappers); } //set jvm memory size (if require) String memKey = "mapred.child.java.opts"; if (minMem > 0 && minMem > InfrastructureAnalyzer.extractMaxMemoryOpt(job.get(memKey))) { InfrastructureAnalyzer.setMaxMemoryOpt(job, memKey, minMem); LOG.warn("Forcing '" + memKey + "' to -Xmx" + minMem / (1024 * 1024) + "M."); } //disable automatic tasks timeouts and speculative task exec job.setInt("mapred.task.timeout", 0); job.setMapSpeculativeExecution(false); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //enables the reuse of JVMs (multiple tasks per MR task) if (jvmReuse) job.setNumTasksToExecutePerJvm(-1); //unlimited //set sort io buffer (reduce unnecessary large io buffer, guaranteed memory consumption) job.setInt("io.sort.mb", 8); //8MB //set the replication factor for the results job.setInt("dfs.replication", replication); //set the max number of retries per map task // disabled job-level configuration to respect cluster configuration // note: this refers to hadoop2, hence it never had effect on mr1 //job.setInt("mapreduce.map.maxattempts", max_retry); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); ///// // execute the MR job RunningJob runjob = JobClient.runJob(job); // Process different counters Statistics.incrementNoOfExecutedMRJobs(); Group pgroup = runjob.getCounters().getGroup(ParForProgramBlock.PARFOR_COUNTER_GROUP_NAME); int numTasks = (int) pgroup.getCounter(Stat.PARFOR_NUMTASKS.toString()); int numIters = (int) pgroup.getCounter(Stat.PARFOR_NUMITERS.toString()); if (DMLScript.STATISTICS && !InfrastructureAnalyzer.isLocalMode()) { Statistics.incrementJITCompileTime(pgroup.getCounter(Stat.PARFOR_JITCOMPILE.toString())); Statistics.incrementJVMgcCount(pgroup.getCounter(Stat.PARFOR_JVMGC_COUNT.toString())); Statistics.incrementJVMgcTime(pgroup.getCounter(Stat.PARFOR_JVMGC_TIME.toString())); Group cgroup = runjob.getCounters().getGroup(CacheableData.CACHING_COUNTER_GROUP_NAME.toString()); CacheStatistics .incrementMemHits((int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_MEM.toString())); CacheStatistics.incrementFSBuffHits( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_FSBUFF.toString())); CacheStatistics .incrementFSHits((int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_FS.toString())); CacheStatistics.incrementHDFSHits( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_HITS_HDFS.toString())); CacheStatistics.incrementFSBuffWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_FSBUFF.toString())); CacheStatistics.incrementFSWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_FS.toString())); CacheStatistics.incrementHDFSWrites( (int) cgroup.getCounter(CacheStatistics.Stat.CACHE_WRITES_HDFS.toString())); CacheStatistics .incrementAcquireRTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_ACQR.toString())); CacheStatistics .incrementAcquireMTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_ACQM.toString())); CacheStatistics .incrementReleaseTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_RLS.toString())); CacheStatistics .incrementExportTime(cgroup.getCounter(CacheStatistics.Stat.CACHE_TIME_EXP.toString())); } // read all files of result variables and prepare for return LocalVariableMap[] results = readResultFile(job, resultFile); ret = new RemoteParForJobReturn(runjob.isSuccessful(), numTasks, numIters, results); } catch (Exception ex) { throw new DMLRuntimeException(ex); } finally { // remove created files try { MapReduceTool.deleteFileIfExistOnHDFS(new Path(taskFile), job); MapReduceTool.deleteFileIfExistOnHDFS(new Path(resultFile), job); } catch (IOException ex) { throw new DMLRuntimeException(ex); } } if (DMLScript.STATISTICS) { long t1 = System.nanoTime(); Statistics.maintainCPHeavyHitters("MR-Job_" + jobname, t1 - t0); } return ret; }
From source file:com.ibm.bi.dml.runtime.matrix.CleanupMR.java
License:Open Source License
public static boolean runJob(DMLConfig conf) throws Exception { boolean ret = false; try {//from ww w . j ava 2 s. co m JobConf job; job = new JobConf(CleanupMR.class); job.setJobName("Cleanup-MR"); //set up SystemML local tmp dir String dir = conf.getTextValue(DMLConfig.LOCAL_TMP_DIR); MRJobConfiguration.setSystemMLLocalTmpDir(job, dir); //set mappers, reducers int numNodes = InfrastructureAnalyzer.getRemoteParallelNodes(); job.setMapperClass(CleanupMapper.class); //map-only job.setNumMapTasks(numNodes); //numMappers job.setNumReduceTasks(0); //set input/output format, input path String inFileName = conf.getTextValue(DMLConfig.SCRATCH_SPACE) + "/cleanup_tasks"; job.setInputFormat(NLineInputFormat.class); job.setOutputFormat(NullOutputFormat.class); Path path = new Path(inFileName); FileInputFormat.setInputPaths(job, path); writeCleanupTasksToFile(path, numNodes); //disable automatic tasks timeouts and speculative task exec job.setInt("mapred.task.timeout", 0); job.setMapSpeculativeExecution(false); ///// // execute the MR job RunningJob runjob = JobClient.runJob(job); ret = runjob.isSuccessful(); } catch (Exception ex) { //don't raise an exception, just gracefully an error message. LOG.error("Failed to run cleanup MR job. ", ex); } return ret; }
From source file: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 www . j a v a 2 s .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.jyz.study.hadoop.mapreduce.datajoin.DataJoinJob.java
License:Apache License
public static JobConf createDataJoinJob(String args[]) throws IOException { String inputDir = args[0];//w w w . j av a 2 s .co m String outputDir = args[1]; Class inputFormat = SequenceFileInputFormat.class; if (args[2].compareToIgnoreCase("text") != 0) { System.out.println("Using SequenceFileInputFormat: " + args[2]); } else { System.out.println("Using TextInputFormat: " + args[2]); inputFormat = TextInputFormat.class; } int numOfReducers = Integer.parseInt(args[3]); Class mapper = getClassByName(args[4]); Class reducer = getClassByName(args[5]); Class mapoutputValueClass = getClassByName(args[6]); Class outputFormat = TextOutputFormat.class; Class outputValueClass = Text.class; if (args[7].compareToIgnoreCase("text") != 0) { System.out.println("Using SequenceFileOutputFormat: " + args[7]); outputFormat = SequenceFileOutputFormat.class; outputValueClass = getClassByName(args[7]); } else { System.out.println("Using TextOutputFormat: " + args[7]); } long maxNumOfValuesPerGroup = 100; String jobName = ""; if (args.length > 8) { maxNumOfValuesPerGroup = Long.parseLong(args[8]); } if (args.length > 9) { jobName = args[9]; } Configuration defaults = new Configuration(); JobConf job = new JobConf(defaults, DataJoinJob.class); job.setJobName("DataJoinJob: " + jobName); FileSystem fs = FileSystem.get(defaults); fs.delete(new Path(outputDir), true); FileInputFormat.setInputPaths(job, inputDir); job.setInputFormat(inputFormat); job.setMapperClass(mapper); FileOutputFormat.setOutputPath(job, new Path(outputDir)); job.setOutputFormat(outputFormat); SequenceFileOutputFormat.setOutputCompressionType(job, SequenceFile.CompressionType.BLOCK); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(mapoutputValueClass); job.setOutputKeyClass(Text.class); job.setOutputValueClass(outputValueClass); job.setReducerClass(reducer); job.setNumMapTasks(1); job.setNumReduceTasks(numOfReducers); job.setLong("datajoin.maxNumOfValuesPerGroup", maxNumOfValuesPerGroup); return job; }
From source file:com.kadwa.hadoop.DistExec.java
License:Open Source License
/** * Calculate how many maps to run. Number of maps is equal to the number of files. * * @param fileCount Count of total files for job * @param job The job to configure * @return Count of maps to run./* www . j av a 2 s.c o m*/ */ private static void setMapCount(long fileCount, JobConf job) throws IOException { int numMaps = (int) fileCount; numMaps = Math.min(numMaps, job.getInt(MAX_MAPS_LABEL, MAX_MAPS_PER_NODE * new JobClient(job).getClusterStatus().getTaskTrackers())); job.setNumMapTasks(Math.max(numMaps, 1)); }
From source file:com.manning.hip.ch4.joins.improved.impl.OptimizedDataJoinJob.java
License:Apache License
public static JobConf createDataJoinJob(String args[]) throws IOException { String inputDir = args[0];//from w w w . j a v a 2 s.co m String outputDir = args[1]; Class inputFormat = SequenceFileInputFormat.class; if (args[2].compareToIgnoreCase("text") != 0) { System.out.println("Using SequenceFileInputFormat: " + args[2]); } else { System.out.println("Using TextInputFormat: " + args[2]); inputFormat = TextInputFormat.class; } int numOfReducers = Integer.parseInt(args[3]); Class mapper = getClassByName(args[4]); Class reducer = getClassByName(args[5]); Class mapoutputValueClass = getClassByName(args[6]); Class outputFormat = TextOutputFormat.class; Class outputValueClass = Text.class; if (args[7].compareToIgnoreCase("text") != 0) { System.out.println("Using SequenceFileOutputFormat: " + args[7]); outputFormat = SequenceFileOutputFormat.class; outputValueClass = getClassByName(args[7]); } else { System.out.println("Using TextOutputFormat: " + args[7]); } long maxNumOfValuesPerGroup = 100; String jobName = ""; if (args.length > 8) { maxNumOfValuesPerGroup = Long.parseLong(args[8]); } if (args.length > 9) { jobName = args[9]; } Configuration defaults = new Configuration(); JobConf job = new JobConf(defaults, OptimizedDataJoinJob.class); job.setJobName("DataJoinJob: " + jobName); FileSystem fs = FileSystem.get(defaults); fs.delete(new Path(outputDir)); FileInputFormat.setInputPaths(job, inputDir); job.setInputFormat(inputFormat); job.setMapperClass(mapper); FileOutputFormat.setOutputPath(job, new Path(outputDir)); job.setOutputFormat(outputFormat); SequenceFileOutputFormat.setOutputCompressionType(job, SequenceFile.CompressionType.BLOCK); job.setMapOutputKeyClass(CompositeKey.class); job.setMapOutputValueClass(mapoutputValueClass); job.setOutputKeyClass(Text.class); job.setOutputValueClass(outputValueClass); job.setReducerClass(reducer); job.setPartitionerClass(CompositeKeyPartitioner.class); job.setOutputKeyComparatorClass(CompositeKeyComparator.class); job.setOutputValueGroupingComparator(CompositeKeyOnlyComparator.class); job.setNumMapTasks(1); job.setNumReduceTasks(numOfReducers); job.setLong("datajoin.maxNumOfValuesPerGroup", maxNumOfValuesPerGroup); return job; }