List of usage examples for org.apache.hadoop.mapred JobConf setOutputKeyClass
public void setOutputKeyClass(Class<?> theClass)
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.//www . j a v a 2s. 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.RemoteDPParForMR.java
License:Open Source License
/** * // w ww . j av a2s . 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 itervar, String matrixvar, String program, String resultFile, MatrixObject input, PDataPartitionFormat dpf, OutputInfo oi, boolean tSparseCol, //config params boolean enableCPCaching, int numReducers, int replication, int max_retry) //opt params throws DMLRuntimeException { 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, 1, 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("mapred.task.timeout", 0); job.setMapSpeculativeExecution(false); //set up preferred custom serialization framework for binary block format if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) MRJobConfiguration.addBinaryBlockSerializationFramework(job); //set up map/reduce memory configurations (if in AM context) DMLConfig config = ConfigurationManager.getConfig(); DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config); //disable JVM reuse job.setNumTasksToExecutePerJvm(1); //-1 for unlimited //set the replication factor for the results job.setInt("dfs.replication", replication); //set the max number of retries per map task //note: currently disabled to use cluster config //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(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.controlprogram.parfor.RemoteParForMR.java
License:Open Source License
/** * /*from w w w . java 2s . c o m*/ * @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.controlprogram.parfor.ResultMergeRemoteMR.java
License:Open Source License
/** * /*from w ww. ja va 2 s.com*/ * @param fname null if no comparison required * @param fnameNew * @param srcFnames * @param ii * @param oi * @param rlen * @param clen * @param brlen * @param bclen * @throws DMLRuntimeException */ @SuppressWarnings({ "unused", "deprecation" }) protected void executeMerge(String fname, String fnameNew, String[] srcFnames, InputInfo ii, OutputInfo oi, long rlen, long clen, int brlen, int bclen) throws DMLRuntimeException { String jobname = "ParFor-RMMR"; long t0 = DMLScript.STATISTICS ? System.nanoTime() : 0; JobConf job; job = new JobConf(ResultMergeRemoteMR.class); job.setJobName(jobname + _pfid); //maintain dml script counters Statistics.incrementNoOfCompiledMRJobs(); //warning for textcell/binarycell without compare boolean withCompare = (fname != null); if ((oi == OutputInfo.TextCellOutputInfo || oi == OutputInfo.BinaryCellOutputInfo) && !withCompare && ResultMergeLocalFile.ALLOW_COPY_CELLFILES) LOG.warn("Result merge for " + OutputInfo.outputInfoToString(oi) + " without compare can be realized more efficiently with LOCAL_FILE than REMOTE_MR."); try { Path pathCompare = null; Path pathNew = new Path(fnameNew); ///// //configure the MR job if (withCompare) { pathCompare = new Path(fname).makeQualified(FileSystem.get(job)); MRJobConfiguration.setResultMergeInfo(job, pathCompare.toString(), ii, LocalFileUtils.getWorkingDir(LocalFileUtils.CATEGORY_RESULTMERGE), rlen, clen, brlen, bclen); } else MRJobConfiguration.setResultMergeInfo(job, "null", ii, LocalFileUtils.getWorkingDir(LocalFileUtils.CATEGORY_RESULTMERGE), rlen, clen, bclen, bclen); //set mappers, reducers, combiners job.setMapperClass(ResultMergeRemoteMapper.class); job.setReducerClass(ResultMergeRemoteReducer.class); if (oi == OutputInfo.TextCellOutputInfo) { job.setMapOutputKeyClass(MatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixCell.class); job.setOutputKeyClass(NullWritable.class); job.setOutputValueClass(Text.class); } else if (oi == OutputInfo.BinaryCellOutputInfo) { job.setMapOutputKeyClass(MatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixCell.class); job.setOutputKeyClass(MatrixIndexes.class); job.setOutputValueClass(MatrixCell.class); } else if (oi == OutputInfo.BinaryBlockOutputInfo) { //setup partitioning, grouping, sorting for composite key (old API) job.setPartitionerClass(ResultMergeRemotePartitioning.class); //partitioning job.setOutputValueGroupingComparator(ResultMergeRemoteGrouping.class); //grouping job.setOutputKeyComparatorClass(ResultMergeRemoteSorting.class); //sorting job.setMapOutputKeyClass(ResultMergeTaggedMatrixIndexes.class); job.setMapOutputValueClass(TaggedMatrixBlock.class); job.setOutputKeyClass(MatrixIndexes.class); job.setOutputValueClass(MatrixBlock.class); } //set input format job.setInputFormat(ii.inputFormatClass); //set the input path Path[] paths = null; if (withCompare) { paths = new Path[srcFnames.length + 1]; paths[0] = pathCompare; for (int i = 1; i < paths.length; i++) paths[i] = new Path(srcFnames[i - 1]); } else { paths = new Path[srcFnames.length]; for (int i = 0; i < paths.length; i++) paths[i] = new Path(srcFnames[i]); } FileInputFormat.setInputPaths(job, paths); //set output format job.setOutputFormat(oi.outputFormatClass); //set output path MapReduceTool.deleteFileIfExistOnHDFS(fnameNew); FileOutputFormat.setOutputPath(job, pathNew); ////// //set optimization parameters //set the number of mappers and reducers //job.setNumMapTasks( _numMappers ); //use default num mappers long reducerGroups = _numReducers; if (oi == OutputInfo.BinaryBlockOutputInfo) reducerGroups = Math.max(rlen / brlen, 1) * Math.max(clen / bclen, 1); else //textcell/binarycell reducerGroups = Math.max((rlen * clen) / StagingFileUtils.CELL_BUFFER_SIZE, 1); job.setNumReduceTasks((int) Math.min(_numReducers, reducerGroups)); //use FLEX scheduler configuration properties if (ParForProgramBlock.USE_FLEX_SCHEDULER_CONF) { job.setInt("flex.map.min", 0); job.setInt("flex.map.max", _numMappers); job.setInt("flex.reduce.min", 0); job.setInt("flex.reduce.max", _numMappers); } //disable automatic tasks timeouts and speculative task exec job.setInt("mapred.task.timeout", 0); job.setMapSpeculativeExecution(false); //set up preferred custom serialization framework for binary block format if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) MRJobConfiguration.addBinaryBlockSerializationFramework(job); //enables the reuse of JVMs (multiple tasks per MR task) if (_jvmReuse) job.setNumTasksToExecutePerJvm(-1); //unlimited //enables compression - not conclusive for different codecs (empirically good compression ratio, but significantly slower) //job.set("mapred.compress.map.output", "true"); //job.set("mapred.map.output.compression.codec", "org.apache.hadoop.io.compress.GzipCodec"); //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 JobClient.runJob(job); //maintain dml script counters Statistics.incrementNoOfExecutedMRJobs(); } catch (Exception ex) { throw new DMLRuntimeException(ex); } if (DMLScript.STATISTICS) { long t1 = System.nanoTime(); Statistics.maintainCPHeavyHitters("MR-Job_" + jobname, t1 - t0); } }
From source file:com.ibm.bi.dml.runtime.matrix.CSVReblockMR.java
License:Open Source License
public static AssignRowIDMRReturn runAssignRowIDMRJob(String[] inputs, InputInfo[] inputInfos, int[] brlens, int[] bclens, String reblockInstructions, int replication, String[] smallestFiles, boolean transform, String naStrings, String specFile) throws Exception { AssignRowIDMRReturn ret = new AssignRowIDMRReturn(); JobConf job; job = new JobConf(CSVReblockMR.class); job.setJobName("Assign-RowID-MR"); byte[] realIndexes = new byte[inputs.length]; for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;//from www . ja va 2 s.c om //set up the input files and their format information MRJobConfiguration.setUpMultipleInputs(job, realIndexes, inputs, inputInfos, brlens, bclens, false, ConvertTarget.CELL); job.setStrings(SMALLEST_FILE_NAME_PER_INPUT, smallestFiles); //set up the aggregate instructions that will happen in the combiner and reducer MRJobConfiguration.setCSVReblockInstructions(job, reblockInstructions); //set up the replication factor for the results job.setInt("dfs.replication", replication); //set up the number of reducers job.setNumReduceTasks(1); // Print the complete instruction //if (LOG.isTraceEnabled()) //inst.printCompelteMRJobInstruction(); // configure mapper and the mapper output key value pairs job.setMapperClass(CSVAssignRowIDMapper.class); job.setMapOutputKeyClass(ByteWritable.class); job.setMapOutputValueClass(OffsetCount.class); //configure reducer job.setReducerClass(CSVAssignRowIDReducer.class); //turn off adaptivemr job.setBoolean("adaptivemr.map.enable", false); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); //set up the output file ret.counterFile = new Path(MRJobConfiguration.constructTempOutputFilename()); job.setOutputFormat(SequenceFileOutputFormat.class); FileOutputFormat.setOutputPath(job, ret.counterFile); job.setOutputKeyClass(ByteWritable.class); job.setOutputValueClass(OffsetCount.class); // setup properties relevant to transform job.setBoolean(MRJobConfiguration.TF_TRANSFORM, transform); if (transform) { if (naStrings != null) // Adding "dummy" string to handle the case of na_strings = "" job.set(MRJobConfiguration.TF_NA_STRINGS, TfUtils.prepNAStrings(naStrings)); job.set(MRJobConfiguration.TF_SPEC_FILE, specFile); } RunningJob runjob = JobClient.runJob(job); /* Process different counters */ Group rgroup = runjob.getCounters().getGroup(NUM_ROWS_IN_MATRIX); Group cgroup = runjob.getCounters().getGroup(NUM_COLS_IN_MATRIX); ret.rlens = new long[inputs.length]; ret.clens = new long[inputs.length]; for (int i = 0; i < inputs.length; i++) { // number of non-zeros ret.rlens[i] = rgroup.getCounter(Integer.toString(i)); ret.clens[i] = cgroup.getCounter(Integer.toString(i)); } return ret; }
From source file:com.ibm.bi.dml.runtime.matrix.SortMR.java
License:Open Source License
@SuppressWarnings({ "unchecked", "rawtypes" })
public static JobReturn runJob(MRJobInstruction inst, String input, InputInfo inputInfo, long rlen, long clen,
int brlen, int bclen, String combineInst, String sortInst, int numReducers, int replication,
String output, OutputInfo outputInfo, boolean valueIsWeight) throws Exception {
boolean sortIndexes = getSortInstructionType(sortInst) == SortKeys.OperationTypes.Indexes;
String tmpOutput = sortIndexes ? MRJobConfiguration.constructTempOutputFilename() : output;
JobConf job = new JobConf(SortMR.class);
job.setJobName("SortMR");
//setup partition file
String pfname = MRJobConfiguration.setUpSortPartitionFilename(job);
Path partitionFile = new Path(pfname);
URI partitionUri = new URI(partitionFile.toString());
//setup input/output paths
Path inputDir = new Path(input);
inputDir = inputDir.makeQualified(inputDir.getFileSystem(job));
SamplingSortMRInputFormat.setInputPaths(job, inputDir);
Path outpath = new Path(tmpOutput);
FileOutputFormat.setOutputPath(job, outpath);
MapReduceTool.deleteFileIfExistOnHDFS(outpath, job);
//set number of reducers (1 if local mode)
if (InfrastructureAnalyzer.isLocalMode(job))
job.setNumReduceTasks(1);/*w ww. ja v a 2s. c o m*/
else
MRJobConfiguration.setNumReducers(job, numReducers, numReducers);
//setup input/output format
job.setInputFormat(SamplingSortMRInputFormat.class);
SamplingSortMRInputFormat.setTargetKeyValueClasses(job,
(Class<? extends WritableComparable>) outputInfo.outputKeyClass, outputInfo.outputValueClass);
//setup instructions and meta information
if (combineInst != null && !combineInst.trim().isEmpty())
job.set(COMBINE_INSTRUCTION, combineInst);
job.set(SORT_INSTRUCTION, sortInst);
job.setBoolean(VALUE_IS_WEIGHT, valueIsWeight);
boolean desc = getSortInstructionDescending(sortInst);
job.setBoolean(SORT_DECREASING, desc);
MRJobConfiguration.setBlockSize(job, (byte) 0, brlen, bclen);
MRJobConfiguration.setInputInfo(job, (byte) 0, inputInfo, brlen, bclen, ConvertTarget.CELL);
int partitionWith0 = SamplingSortMRInputFormat.writePartitionFile(job, partitionFile);
//setup mapper/reducer/partitioner/output classes
if (getSortInstructionType(sortInst) == SortKeys.OperationTypes.Indexes) {
MRJobConfiguration.setInputInfo(job, (byte) 0, inputInfo, brlen, bclen, ConvertTarget.CELL);
job.setOutputFormat(OutputInfo.BinaryBlockOutputInfo.outputFormatClass);
job.setMapperClass(IndexSortMapper.class);
job.setReducerClass(IndexSortReducer.class);
job.setMapOutputKeyClass(!desc ? IndexSortComparable.class : IndexSortComparableDesc.class);
job.setMapOutputValueClass(LongWritable.class);
job.setOutputKeyClass(MatrixIndexes.class);
job.setOutputValueClass(MatrixBlock.class);
} else { //default case: SORT w/wo weights
MRJobConfiguration.setInputInfo(job, (byte) 0, inputInfo, brlen, bclen, ConvertTarget.CELL);
job.setOutputFormat(CompactOutputFormat.class);
job.setMapperClass(ValueSortMapper.class);
job.setReducerClass(ValueSortReducer.class);
job.setOutputKeyClass(outputInfo.outputKeyClass); //double
job.setOutputValueClass(outputInfo.outputValueClass); //int
}
job.setPartitionerClass(TotalOrderPartitioner.class);
//setup distributed cache
DistributedCache.addCacheFile(partitionUri, job);
DistributedCache.createSymlink(job);
//setup replication factor
job.setInt("dfs.replication", replication);
MatrixCharacteristics[] s = new MatrixCharacteristics[1];
s[0] = new MatrixCharacteristics(rlen, clen, brlen, bclen);
// Print the complete instruction
if (LOG.isTraceEnabled())
inst.printCompleteMRJobInstruction(s);
//set unique working dir
MRJobConfiguration.setUniqueWorkingDir(job);
//run mr job
RunningJob runjob = JobClient.runJob(job);
Group group = runjob.getCounters().getGroup(NUM_VALUES_PREFIX);
numReducers = job.getNumReduceTasks();
//process final meta data
long[] counts = new long[numReducers];
long total = 0;
for (int i = 0; i < numReducers; i++) {
counts[i] = group.getCounter(Integer.toString(i));
total += counts[i];
}
//add missing 0s back to the results
long missing0s = 0;
if (total < rlen * clen) {
if (partitionWith0 < 0)
throw new RuntimeException("no partition contains 0, which is wrong!");
missing0s = rlen * clen - total;
counts[partitionWith0] += missing0s;
} else
partitionWith0 = -1;
if (sortIndexes) {
//run builtin job for shifting partially sorted blocks according to global offsets
//we do this in this custom form since it would not fit into the current structure
//of systemml to output two intermediates (partially sorted data, offsets) out of a
//single SortKeys lop
boolean success = runjob.isSuccessful();
if (success) {
success = runStitchupJob(tmpOutput, rlen, clen, brlen, bclen, counts, numReducers, replication,
output);
}
MapReduceTool.deleteFileIfExistOnHDFS(tmpOutput);
MapReduceTool.deleteFileIfExistOnHDFS(pfname);
return new JobReturn(s[0], OutputInfo.BinaryBlockOutputInfo, success);
} else {
MapReduceTool.deleteFileIfExistOnHDFS(pfname);
return new JobReturn(s[0], counts, partitionWith0, missing0s, runjob.isSuccessful());
}
}
From source file:com.ibm.bi.dml.runtime.matrix.SortMR.java
License:Open Source License
/** * /*from ww w. ja v a 2 s. co m*/ * @param input * @param rlen * @param clen * @param brlen * @param bclen * @param counts * @param numReducers * @param replication * @param output * @throws Exception */ private static boolean runStitchupJob(String input, long rlen, long clen, int brlen, int bclen, long[] counts, int numReducers, int replication, String output) throws Exception { JobConf job = new JobConf(SortMR.class); job.setJobName("SortIndexesMR"); //setup input/output paths Path inpath = new Path(input); Path outpath = new Path(output); FileInputFormat.setInputPaths(job, inpath); FileOutputFormat.setOutputPath(job, outpath); MapReduceTool.deleteFileIfExistOnHDFS(outpath, job); //set number of reducers (1 if local mode) if (InfrastructureAnalyzer.isLocalMode(job)) job.setNumReduceTasks(1); else MRJobConfiguration.setNumReducers(job, numReducers, numReducers); //setup input/output format InputInfo iinfo = InputInfo.BinaryBlockInputInfo; OutputInfo oinfo = OutputInfo.BinaryBlockOutputInfo; job.setInputFormat(iinfo.inputFormatClass); job.setOutputFormat(oinfo.outputFormatClass); CompactInputFormat.setKeyValueClasses(job, MatrixIndexes.class, MatrixBlock.class); //setup mapper/reducer/output classes MRJobConfiguration.setInputInfo(job, (byte) 0, InputInfo.BinaryBlockInputInfo, brlen, bclen, ConvertTarget.BLOCK); job.setMapperClass(IndexSortStitchupMapper.class); job.setReducerClass(IndexSortStitchupReducer.class); job.setOutputKeyClass(oinfo.outputKeyClass); job.setOutputValueClass(oinfo.outputValueClass); MRJobConfiguration.setBlockSize(job, (byte) 0, brlen, bclen); MRJobConfiguration.setMatricesDimensions(job, new byte[] { 0 }, new long[] { rlen }, new long[] { clen }); //compute shifted prefix sum of offsets and put into configuration long[] cumsumCounts = new long[counts.length]; long sum = 0; for (int i = 0; i < counts.length; i++) { cumsumCounts[i] = sum; sum += counts[i]; } job.set(SORT_INDEXES_OFFSETS, Arrays.toString(cumsumCounts)); //setup replication factor job.setInt("dfs.replication", replication); //set unique working dir MRJobConfiguration.setUniqueWorkingDir(job); //run mr job RunningJob runJob = JobClient.runJob(job); return runJob.isSuccessful(); }
From source file:com.ibm.bi.dml.runtime.transform.ApplyTfCSVMR.java
License:Open Source License
public static JobReturn runJob(String inputPath, String specPath, String mapsPath, String tmpPath, String outputPath, String partOffsetsFile, CSVFileFormatProperties inputDataProperties, long numCols, int replication, String headerLine) throws IOException, ClassNotFoundException, InterruptedException { JobConf job = new JobConf(ApplyTfCSVMR.class); job.setJobName("ApplyTfCSV"); /* Setup MapReduce Job */ job.setJarByClass(ApplyTfCSVMR.class); // set relevant classes job.setMapperClass(ApplyTfCSVMapper.class); job.setNumReduceTasks(0);//www. j a v a2s . c o m // Add transformation metadata file as well as partOffsetsFile to Distributed cache DistributedCache.addCacheFile((new Path(mapsPath)).toUri(), job); DistributedCache.createSymlink(job); Path cachefile = new Path(partOffsetsFile); DistributedCache.addCacheFile(cachefile.toUri(), job); DistributedCache.createSymlink(job); // set input and output properties job.setInputFormat(TextInputFormat.class); job.setOutputFormat(TextOutputFormat.class); job.setMapOutputKeyClass(NullWritable.class); job.setMapOutputValueClass(Text.class); job.setOutputKeyClass(NullWritable.class); job.setOutputValueClass(Text.class); job.setInt("dfs.replication", replication); FileInputFormat.addInputPath(job, new Path(inputPath)); // delete outputPath, if exists already. Path outPath = new Path(outputPath); FileSystem fs = FileSystem.get(job); fs.delete(outPath, true); FileOutputFormat.setOutputPath(job, outPath); job.set(MRJobConfiguration.TF_HAS_HEADER, Boolean.toString(inputDataProperties.hasHeader())); job.set(MRJobConfiguration.TF_DELIM, inputDataProperties.getDelim()); if (inputDataProperties.getNAStrings() != null) // Adding "dummy" string to handle the case of na_strings = "" job.set(MRJobConfiguration.TF_NA_STRINGS, TfUtils.prepNAStrings(inputDataProperties.getNAStrings())); job.set(MRJobConfiguration.TF_SPEC_FILE, specPath); job.set(MRJobConfiguration.TF_SMALLEST_FILE, CSVReblockMR.findSmallestFile(job, inputPath)); job.set(MRJobConfiguration.OUTPUT_MATRICES_DIRS_CONFIG, outputPath); job.setLong(MRJobConfiguration.TF_NUM_COLS, numCols); job.set(MRJobConfiguration.TF_TXMTD_PATH, mapsPath); job.set(MRJobConfiguration.TF_HEADER, headerLine); job.set(CSVReblockMR.ROWID_FILE_NAME, cachefile.toString()); job.set(MRJobConfiguration.TF_TMP_LOC, tmpPath); //turn off adaptivemr job.setBoolean("adaptivemr.map.enable", false); // Run the job RunningJob runjob = JobClient.runJob(job); // Since transform CSV produces part files w/ prefix transform-part-*, // delete all the "default" part-..... files deletePartFiles(fs, outPath); MatrixCharacteristics mc = new MatrixCharacteristics(); return new JobReturn(new MatrixCharacteristics[] { mc }, runjob.isSuccessful()); }
From source file:com.ibm.bi.dml.runtime.transform.GenTfMtdMR.java
License:Open Source License
public static long runJob(String inputPath, String txMtdPath, String specFileWithIDs, String smallestFile, String partOffsetsFile, CSVFileFormatProperties inputDataProperties, long numCols, int replication, String headerLine) throws IOException, ClassNotFoundException, InterruptedException { JobConf job = new JobConf(GenTfMtdMR.class); job.setJobName("GenTfMTD"); /* Setup MapReduce Job */ job.setJarByClass(GenTfMtdMR.class); // set relevant classes job.setMapperClass(GTFMTDMapper.class); job.setReducerClass(GTFMTDReducer.class); // set input and output properties job.setInputFormat(TextInputFormat.class); job.setOutputFormat(NullOutputFormat.class); job.setMapOutputKeyClass(IntWritable.class); job.setMapOutputValueClass(DistinctValue.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); job.setInt("dfs.replication", replication); FileInputFormat.addInputPath(job, new Path(inputPath)); // delete outputPath, if exists already. Path outPath = new Path(txMtdPath); FileSystem fs = FileSystem.get(job); fs.delete(outPath, true);/*from w w w . ja va 2 s . c o m*/ FileOutputFormat.setOutputPath(job, outPath); job.set(MRJobConfiguration.TF_HAS_HEADER, Boolean.toString(inputDataProperties.hasHeader())); job.set(MRJobConfiguration.TF_DELIM, inputDataProperties.getDelim()); if (inputDataProperties.getNAStrings() != null) // Adding "dummy" string to handle the case of na_strings = "" job.set(MRJobConfiguration.TF_NA_STRINGS, TfUtils.prepNAStrings(inputDataProperties.getNAStrings())); job.set(MRJobConfiguration.TF_SPEC_FILE, specFileWithIDs); job.set(MRJobConfiguration.TF_SMALLEST_FILE, smallestFile); job.setLong(MRJobConfiguration.TF_NUM_COLS, numCols); job.set(MRJobConfiguration.TF_HEADER, headerLine); job.set(MRJobConfiguration.OUTPUT_MATRICES_DIRS_CONFIG, txMtdPath); // offsets file to store part-file names and offsets for each input split job.set(MRJobConfiguration.TF_OFFSETS_FILE, partOffsetsFile); //turn off adaptivemr job.setBoolean("adaptivemr.map.enable", false); // Run the job RunningJob runjob = JobClient.runJob(job); Counters c = runjob.getCounters(); long tx_numRows = c.findCounter(MRJobConfiguration.DataTransformCounters.TRANSFORMED_NUM_ROWS).getCounter(); return tx_numRows; }
From source file:com.ibm.jaql.fail.io.ErrorOutputConfigurator.java
License:Apache License
@Override protected void registerSerializers(JobConf conf) { conf.setMapOutputKeyClass(JsonHolderDefault.class); conf.setMapOutputValueClass(JsonHolderDefault.class); conf.setOutputKeyClass(LongWritable.class); conf.setOutputValueClass(ErrorWritable.class); HadoopSerializationDefault.register(conf); }