List of usage examples for org.apache.hadoop.mapred JobConf setPartitionerClass
public void setPartitionerClass(Class<? extends Partitioner> theClass)
From source file:MRDriver.java
License:Apache License
public int run(String args[]) throws Exception { FileSystem fs = null;/*from w ww.ja va 2 s . c o m*/ Path samplesMapPath = null; float epsilon = Float.parseFloat(args[0]); double delta = Double.parseDouble(args[1]); int minFreqPercent = Integer.parseInt(args[2]); int d = Integer.parseInt(args[3]); int datasetSize = Integer.parseInt(args[4]); int numSamples = Integer.parseInt(args[5]); double phi = Double.parseDouble(args[6]); Random rand; /************************ Job 1 (local FIM) Configuration ************************/ JobConf conf = new JobConf(getConf()); /* * Compute the number of required "votes" for an itemsets to be * declared frequent */ // The +1 at the end is needed to ensure reqApproxNum > numsamples / 2. int reqApproxNum = (int) Math .floor((numSamples * (1 - phi)) - Math.sqrt(numSamples * (1 - phi) * 2 * Math.log(1 / delta))) + 1; int sampleSize = (int) Math.ceil((2 / Math.pow(epsilon, 2)) * (d + Math.log(1 / phi))); //System.out.println("reducersNum: " + numSamples + " reqApproxNum: " + reqApproxNum); conf.setInt("PARMM.reducersNum", numSamples); conf.setInt("PARMM.datasetSize", datasetSize); conf.setInt("PARMM.minFreqPercent", minFreqPercent); conf.setInt("PARMM.sampleSize", sampleSize); conf.setFloat("PARMM.epsilon", epsilon); // Set the number of reducers equal to the number of samples, to // maximize parallelism. Required by our Partitioner. conf.setNumReduceTasks(numSamples); // XXX: why do we disable the speculative execution? MR conf.setBoolean("mapred.reduce.tasks.speculative.execution", false); conf.setInt("mapred.task.timeout", MR_TIMEOUT_MILLI); /* * Enable compression of map output. * * We do it for this job and not for the aggregation one because * each mapper there only print out one record for each itemset, * so there isn't much to compress, I'd say. MR * * In Amazon MapReduce compression of the map output seems to be * happen by default and the Snappy codec is used, which is * extremely fast. */ conf.setBoolean("mapred.compress.map.output", true); //conf.setMapOutputCompressorClass(com.hadoop.compression.lzo.LzoCodec.class); conf.setJarByClass(MRDriver.class); conf.setMapOutputKeyClass(IntWritable.class); conf.setMapOutputValueClass(Text.class); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(DoubleWritable.class); conf.setInputFormat(SequenceFileInputFormat.class); // We write the collections found in a reducers as a SequenceFile conf.setOutputFormat(SequenceFileOutputFormat.class); SequenceFileOutputFormat.setOutputPath(conf, new Path(args[9])); // set the mapper class based on command line option switch (Integer.parseInt(args[7])) { case 1: System.out.println("running partition mapper..."); SequenceFileInputFormat.addInputPath(conf, new Path(args[8])); conf.setMapperClass(PartitionMapper.class); break; case 2: System.out.println("running binomial mapper..."); SequenceFileInputFormat.addInputPath(conf, new Path(args[8])); conf.setMapperClass(BinomialSamplerMapper.class); break; case 3: System.out.println("running coin mapper..."); SequenceFileInputFormat.addInputPath(conf, new Path(args[8])); conf.setMapperClass(CoinFlipSamplerMapper.class); case 4: System.out.println("running sampler mapper..."); SequenceFileInputFormat.addInputPath(conf, new Path(args[8])); conf.setMapperClass(InputSamplerMapper.class); // create a random sample of size T*m rand = new Random(); long sampling_start_time = System.nanoTime(); int[] samples = new int[numSamples * sampleSize]; for (int i = 0; i < numSamples * sampleSize; i++) { samples[i] = rand.nextInt(datasetSize); } // for each key in the sample, create a list of all T samples to which this key belongs Hashtable<LongWritable, ArrayList<IntWritable>> hashTable = new Hashtable<LongWritable, ArrayList<IntWritable>>(); for (int i = 0; i < numSamples * sampleSize; i++) { ArrayList<IntWritable> sampleIDs = null; LongWritable key = new LongWritable(samples[i]); if (hashTable.containsKey(key)) sampleIDs = hashTable.get(key); else sampleIDs = new ArrayList<IntWritable>(); sampleIDs.add(new IntWritable(i % numSamples)); hashTable.put(key, sampleIDs); } /* * Convert the Hastable to a MapWritable which we will * write to HDFS and distribute to all Mappers using * DistributedCache */ MapWritable map = new MapWritable(); for (LongWritable key : hashTable.keySet()) { ArrayList<IntWritable> sampleIDs = hashTable.get(key); IntArrayWritable sampleIDsIAW = new IntArrayWritable(); sampleIDsIAW.set(sampleIDs.toArray(new IntWritable[sampleIDs.size()])); map.put(key, sampleIDsIAW); } fs = FileSystem.get(URI.create("samplesMap.ser"), conf); samplesMapPath = new Path("samplesMap.ser"); FSDataOutputStream out = fs.create(samplesMapPath, true); map.write(out); out.sync(); out.close(); DistributedCache.addCacheFile(new URI(fs.getWorkingDirectory() + "/samplesMap.ser#samplesMap.ser"), conf); // stop the sampling timer long sampling_end_time = System.nanoTime(); long sampling_runtime = (sampling_end_time - sampling_start_time) / 1000000; System.out.println("sampling runtime (milliseconds): " + sampling_runtime); break; // end switch case case 5: System.out.println("running random integer partition mapper..."); conf.setInputFormat(WholeSplitInputFormat.class); Path inputFilePath = new Path(args[8]); WholeSplitInputFormat.addInputPath(conf, inputFilePath); conf.setMapperClass(RandIntPartSamplerMapper.class); // Compute number of map tasks. fs = inputFilePath.getFileSystem(conf); FileStatus inputFileStatus = fs.getFileStatus(inputFilePath); long len = inputFileStatus.getLen(); long blockSize = inputFileStatus.getBlockSize(); conf.setLong("mapred.min.split.size", blockSize); conf.setLong("mapred.max.split.size", blockSize); int mapTasksNum = ((int) (len / blockSize)) + 1; conf.setNumMapTasks(mapTasksNum); //System.out.println("len: " + len + " blockSize: " // + blockSize + " mapTasksNum: " + mapTasksNum); // Extract random integer partition of total sample // size into up to mapTasksNum partitions. // XXX I'm not sure this is a correct way to do // it. rand = new Random(); IntWritable[][] toSampleArr = new IntWritable[mapTasksNum][numSamples]; for (int j = 0; j < numSamples; j++) { IntWritable[] tempToSampleArr = new IntWritable[mapTasksNum]; int sum = 0; int i; for (i = 0; i < mapTasksNum - 1; i++) { int size = rand.nextInt(sampleSize - sum); tempToSampleArr[i] = new IntWritable(size); sum += size; if (sum > numSamples * sampleSize) { System.out.println("Something went wrong generating the sample Sizes"); System.exit(1); } if (sum == sampleSize) { break; } } if (i == mapTasksNum - 1) { tempToSampleArr[i] = new IntWritable(sampleSize - sum); } else { for (; i < mapTasksNum; i++) { tempToSampleArr[i] = new IntWritable(0); } } Collections.shuffle(Arrays.asList(tempToSampleArr)); for (i = 0; i < mapTasksNum; i++) { toSampleArr[i][j] = tempToSampleArr[i]; } } for (int i = 0; i < mapTasksNum; i++) { DefaultStringifier.storeArray(conf, toSampleArr[i], "PARMM.toSampleArr_" + i); } break; default: System.err.println("Wrong Mapper ID. Can only be in [1,5]"); System.exit(1); break; } /* * We don't use the default hash partitioner because we want to * maximize the parallelism. That's why we also fix the number * of reducers. */ conf.setPartitionerClass(FIMPartitioner.class); conf.setReducerClass(FIMReducer.class); /************************ Job 2 (aggregation) Configuration ************************/ JobConf confAggr = new JobConf(getConf()); confAggr.setInt("PARMM.reducersNum", numSamples); confAggr.setInt("PARMM.reqApproxNum", reqApproxNum); confAggr.setInt("PARMM.sampleSize", sampleSize); confAggr.setFloat("PARMM.epsilon", epsilon); // XXX: Why do we disable speculative execution? MR confAggr.setBoolean("mapred.reduce.tasks.speculative.execution", false); confAggr.setInt("mapred.task.timeout", MR_TIMEOUT_MILLI); confAggr.setJarByClass(MRDriver.class); confAggr.setMapOutputKeyClass(Text.class); confAggr.setMapOutputValueClass(DoubleWritable.class); confAggr.setOutputKeyClass(Text.class); confAggr.setOutputValueClass(Text.class); confAggr.setMapperClass(AggregateMapper.class); confAggr.setReducerClass(AggregateReducer.class); confAggr.setInputFormat(CombineSequenceFileInputFormat.class); SequenceFileInputFormat.addInputPath(confAggr, new Path(args[9])); FileOutputFormat.setOutputPath(confAggr, new Path(args[10])); long FIMjob_start_time = System.currentTimeMillis(); RunningJob FIMjob = JobClient.runJob(conf); long FIMjob_end_time = System.currentTimeMillis(); RunningJob aggregateJob = JobClient.runJob(confAggr); long aggrJob_end_time = System.currentTimeMillis(); long FIMjob_runtime = FIMjob_end_time - FIMjob_start_time; long aggrJob_runtime = aggrJob_end_time - FIMjob_end_time; if (args[7].equals("4")) { // Remove samplesMap file fs.delete(samplesMapPath, false); } Counters counters = FIMjob.getCounters(); Counters.Group FIMMapperStartTimesCounters = counters.getGroup("FIMMapperStart"); long[] FIMMapperStartTimes = new long[FIMMapperStartTimesCounters.size()]; int i = 0; for (Counters.Counter counter : FIMMapperStartTimesCounters) { FIMMapperStartTimes[i++] = counter.getCounter(); } Counters.Group FIMMapperEndTimesCounters = counters.getGroup("FIMMapperEnd"); long[] FIMMapperEndTimes = new long[FIMMapperEndTimesCounters.size()]; i = 0; for (Counters.Counter counter : FIMMapperEndTimesCounters) { FIMMapperEndTimes[i++] = counter.getCounter(); } Counters.Group FIMReducerStartTimesCounters = counters.getGroup("FIMReducerStart"); long[] FIMReducerStartTimes = new long[FIMReducerStartTimesCounters.size()]; i = 0; for (Counters.Counter counter : FIMReducerStartTimesCounters) { FIMReducerStartTimes[i++] = counter.getCounter(); } Counters.Group FIMReducerEndTimesCounters = counters.getGroup("FIMReducerEnd"); long[] FIMReducerEndTimes = new long[FIMReducerEndTimesCounters.size()]; i = 0; for (Counters.Counter counter : FIMReducerEndTimesCounters) { FIMReducerEndTimes[i++] = counter.getCounter(); } Counters countersAggr = aggregateJob.getCounters(); Counters.Group AggregateMapperStartTimesCounters = countersAggr.getGroup("AggregateMapperStart"); long[] AggregateMapperStartTimes = new long[AggregateMapperStartTimesCounters.size()]; i = 0; for (Counters.Counter counter : AggregateMapperStartTimesCounters) { AggregateMapperStartTimes[i++] = counter.getCounter(); } Counters.Group AggregateMapperEndTimesCounters = countersAggr.getGroup("AggregateMapperEnd"); long[] AggregateMapperEndTimes = new long[AggregateMapperEndTimesCounters.size()]; i = 0; for (Counters.Counter counter : AggregateMapperEndTimesCounters) { AggregateMapperEndTimes[i++] = counter.getCounter(); } Counters.Group AggregateReducerStartTimesCounters = countersAggr.getGroup("AggregateReducerStart"); long[] AggregateReducerStartTimes = new long[AggregateReducerStartTimesCounters.size()]; i = 0; for (Counters.Counter counter : AggregateReducerStartTimesCounters) { AggregateReducerStartTimes[i++] = counter.getCounter(); } Counters.Group AggregateReducerEndTimesCounters = countersAggr.getGroup("AggregateReducerEnd"); long[] AggregateReducerEndTimes = new long[AggregateReducerEndTimesCounters.size()]; i = 0; for (Counters.Counter counter : AggregateReducerEndTimesCounters) { AggregateReducerEndTimes[i++] = counter.getCounter(); } long FIMMapperStartMin = FIMMapperStartTimes[0]; for (long l : FIMMapperStartTimes) { if (l < FIMMapperStartMin) { FIMMapperStartMin = l; } } long FIMMapperEndMax = FIMMapperEndTimes[0]; for (long l : FIMMapperEndTimes) { if (l > FIMMapperEndMax) { FIMMapperEndMax = l; } } System.out.println("FIM job setup time (milliseconds): " + (FIMMapperStartMin - FIMjob_start_time)); System.out.println("FIMMapper total runtime (milliseconds): " + (FIMMapperEndMax - FIMMapperStartMin)); long[] FIMMapperRunTimes = new long[FIMMapperStartTimes.length]; long FIMMapperRunTimesSum = 0; for (int l = 0; l < FIMMapperStartTimes.length; l++) { FIMMapperRunTimes[l] = FIMMapperEndTimes[l] - FIMMapperStartTimes[l]; FIMMapperRunTimesSum += FIMMapperRunTimes[l]; } System.out.println("FIMMapper average task runtime (milliseconds): " + FIMMapperRunTimesSum / FIMMapperStartTimes.length); long FIMMapperRunTimesMin = FIMMapperRunTimes[0]; long FIMMapperRunTimesMax = FIMMapperRunTimes[0]; for (long l : FIMMapperRunTimes) { if (l < FIMMapperRunTimesMin) { FIMMapperRunTimesMin = l; } if (l > FIMMapperRunTimesMax) { FIMMapperRunTimesMax = l; } } System.out.println("FIMMapper minimum task runtime (milliseconds): " + FIMMapperRunTimesMin); System.out.println("FIMMapper maximum task runtime (milliseconds): " + FIMMapperRunTimesMax); long FIMReducerStartMin = FIMReducerStartTimes[0]; for (long l : FIMReducerStartTimes) { if (l < FIMReducerStartMin) { FIMReducerStartMin = l; } } long FIMReducerEndMax = FIMReducerEndTimes[0]; for (long l : FIMReducerEndTimes) { if (l > FIMReducerEndMax) { FIMReducerEndMax = l; } } System.out .println("FIM job shuffle phase runtime (milliseconds): " + (FIMReducerStartMin - FIMMapperEndMax)); System.out.println("FIMReducer total runtime (milliseconds): " + (FIMReducerEndMax - FIMReducerStartMin)); long[] FIMReducerRunTimes = new long[FIMReducerStartTimes.length]; long FIMReducerRunTimesSum = 0; for (int l = 0; l < FIMReducerStartTimes.length; l++) { FIMReducerRunTimes[l] = FIMReducerEndTimes[l] - FIMReducerStartTimes[l]; FIMReducerRunTimesSum += FIMReducerRunTimes[l]; } System.out.println("FIMReducer average task runtime (milliseconds): " + FIMReducerRunTimesSum / FIMReducerStartTimes.length); long FIMReducerRunTimesMin = FIMReducerRunTimes[0]; long FIMReducerRunTimesMax = FIMReducerRunTimes[0]; for (long l : FIMReducerRunTimes) { if (l < FIMReducerRunTimesMin) { FIMReducerRunTimesMin = l; } if (l > FIMReducerRunTimesMax) { FIMReducerRunTimesMax = l; } } System.out.println("FIMReducer minimum task runtime (milliseconds): " + FIMReducerRunTimesMin); System.out.println("FIMReducer maximum task runtime (milliseconds): " + FIMReducerRunTimesMax); System.out.println("FIM job cooldown time (milliseconds): " + (FIMjob_end_time - FIMReducerEndMax)); long AggregateMapperStartMin = AggregateMapperStartTimes[0]; for (long l : AggregateMapperStartTimes) { if (l < AggregateMapperStartMin) { AggregateMapperStartMin = l; } } long AggregateMapperEndMax = AggregateMapperEndTimes[0]; for (long l : AggregateMapperEndTimes) { if (l > AggregateMapperEndMax) { AggregateMapperEndMax = l; } } System.out.println( "Aggregation job setup time (milliseconds): " + (AggregateMapperStartMin - FIMjob_end_time)); System.out.println("AggregateMapper total runtime (milliseconds): " + (AggregateMapperEndMax - AggregateMapperStartMin)); long[] AggregateMapperRunTimes = new long[AggregateMapperStartTimes.length]; long AggregateMapperRunTimesSum = 0; for (int l = 0; l < AggregateMapperStartTimes.length; l++) { AggregateMapperRunTimes[l] = AggregateMapperEndTimes[l] - AggregateMapperStartTimes[l]; AggregateMapperRunTimesSum += AggregateMapperRunTimes[l]; } System.out.println("AggregateMapper average task runtime (milliseconds): " + AggregateMapperRunTimesSum / AggregateMapperStartTimes.length); long AggregateMapperRunTimesMin = AggregateMapperRunTimes[0]; long AggregateMapperRunTimesMax = AggregateMapperRunTimes[0]; for (long l : AggregateMapperRunTimes) { if (l < AggregateMapperRunTimesMin) { AggregateMapperRunTimesMin = l; } if (l > AggregateMapperRunTimesMax) { AggregateMapperRunTimesMax = l; } } System.out.println("AggregateMapper minimum task runtime (milliseconds): " + AggregateMapperRunTimesMin); System.out.println("AggregateMapper maximum task runtime (milliseconds): " + AggregateMapperRunTimesMax); long AggregateReducerStartMin = AggregateReducerStartTimes[0]; for (long l : AggregateReducerStartTimes) { if (l < AggregateReducerStartMin) { AggregateReducerStartMin = l; } } long AggregateReducerEndMax = AggregateReducerEndTimes[0]; for (long l : AggregateReducerEndTimes) { if (l > AggregateReducerEndMax) { AggregateReducerEndMax = l; } } System.out.println("Aggregate job round shuffle phase runtime (milliseconds): " + (AggregateReducerStartMin - AggregateMapperEndMax)); System.out.println("AggregateReducer total runtime (milliseconds): " + (AggregateReducerEndMax - AggregateReducerStartMin)); long[] AggregateReducerRunTimes = new long[AggregateReducerStartTimes.length]; long AggregateReducerRunTimesSum = 0; for (int l = 0; l < AggregateReducerStartTimes.length; l++) { AggregateReducerRunTimes[l] = AggregateReducerEndTimes[l] - AggregateReducerStartTimes[l]; AggregateReducerRunTimesSum += AggregateReducerRunTimes[l]; } System.out.println("AggregateReducer average task runtime (milliseconds): " + AggregateReducerRunTimesSum / AggregateReducerStartTimes.length); long AggregateReducerRunTimesMin = AggregateReducerRunTimes[0]; long AggregateReducerRunTimesMax = AggregateReducerRunTimes[0]; for (long l : AggregateReducerRunTimes) { if (l < AggregateReducerRunTimesMin) { AggregateReducerRunTimesMin = l; } if (l > AggregateReducerRunTimesMax) { AggregateReducerRunTimesMax = l; } } System.out.println("AggregateReducer minimum task runtime (milliseconds): " + AggregateReducerRunTimesMin); System.out.println("AggregateReducer maximum task runtime (milliseconds): " + AggregateReducerRunTimesMax); System.out.println( "Aggregation job cooldown time (milliseconds): " + (aggrJob_end_time - AggregateReducerEndMax)); System.out .println("total runtime (all inclusive) (milliseconds): " + (aggrJob_end_time - FIMjob_start_time)); System.out.println("total runtime (no FIM job setup, no aggregation job cooldown) (milliseconds): " + (AggregateReducerEndMax - FIMMapperStartMin)); System.out.println("total runtime (no setups, no cooldowns) (milliseconds): " + (FIMReducerEndMax - FIMMapperStartMin + AggregateReducerEndMax - AggregateMapperStartMin)); System.out.println("FIM job runtime (including setup and cooldown) (milliseconds): " + FIMjob_runtime); System.out.println("FIM job runtime (no setup, no cooldown) (milliseconds): " + (FIMReducerEndMax - FIMMapperStartMin)); System.out.println( "Aggregation job runtime (including setup and cooldown) (milliseconds): " + aggrJob_runtime); System.out.println("Aggregation job runtime (no setup, no cooldown) (milliseconds): " + (AggregateReducerEndMax - AggregateMapperStartMin)); return 0; }
From source file:SleepJob.java
License:Apache License
public JobConf setupJobConf(int numMapper, int numReducer, long mapSleepTime, int mapSleepCount, long reduceSleepTime, int reduceSleepCount) { JobConf job = new JobConf(getConf(), SleepJob.class); job.setNumMapTasks(numMapper);/* w w w. ja v a2 s . c o m*/ job.setNumReduceTasks(numReducer); job.setMapperClass(SleepJob.class); job.setMapOutputKeyClass(IntWritable.class); job.setMapOutputValueClass(NullWritable.class); job.setReducerClass(SleepJob.class); job.setOutputFormat(NullOutputFormat.class); job.setInputFormat(SleepInputFormat.class); job.setPartitionerClass(SleepJob.class); job.setSpeculativeExecution(false); FileInputFormat.addInputPath(job, new Path("ignored")); job.setLong("sleep.job.map.sleep.time", mapSleepTime); job.setLong("sleep.job.reduce.sleep.time", reduceSleepTime); job.setInt("sleep.job.map.sleep.count", mapSleepCount); job.setInt("sleep.job.reduce.sleep.count", reduceSleepCount); return job; }
From source file:NaivePageRank.java
License:Apache License
public static void main(String[] args) throws Exception { int iteration = -1; String inputPath = args[0];/* www .ja v a 2 s .c o m*/ String outputPath = args[1]; int specIteration = 0; if (args.length > 2) { specIteration = Integer.parseInt(args[2]); } int numNodes = 100000; if (args.length > 3) { numNodes = Integer.parseInt(args[3]); } int numReducers = 32; if (args.length > 4) { numReducers = Integer.parseInt(args[4]); } System.out.println("specified iteration: " + specIteration); long start = System.currentTimeMillis(); /** * job to count out-going links for each url */ JobConf conf = new JobConf(NaivePageRank.class); conf.setJobName("PageRank-Count"); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(Text.class); conf.setMapperClass(CountMapper.class); conf.setReducerClass(CountReducer.class); conf.setInputFormat(TextInputFormat.class); conf.setOutputFormat(TextOutputFormat.class); FileInputFormat.setInputPaths(conf, new Path(inputPath)); FileOutputFormat.setOutputPath(conf, new Path(outputPath + "/count")); conf.setNumReduceTasks(numReducers); JobClient.runJob(conf); /******************** Initial Rank Assignment Job ***********************/ conf = new JobConf(NaivePageRank.class); conf.setJobName("PageRank-Initialize"); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(Text.class); conf.setMapperClass(InitialRankAssignmentMapper.class); conf.setReducerClass(InitialRankAssignmentReducer.class); conf.setInputFormat(TextInputFormat.class); conf.setOutputFormat(TextOutputFormat.class); FileInputFormat.setInputPaths(conf, new Path(inputPath)); FileOutputFormat.setOutputPath(conf, new Path(outputPath + "/i" + iteration)); conf.setNumReduceTasks(numReducers); // conf.setIterative(false); JobClient.runJob(conf); iteration++; do { /****************** Join Job ********************************/ conf = new JobConf(NaivePageRank.class); conf.setJobName("PageRank-Join"); conf.setOutputKeyClass(Text.class); // conf.setOutputValueClass(Text.class); conf.setMapperClass(ComputeRankMap.class); conf.setReducerClass(ComputeRankReduce.class); conf.setMapOutputKeyClass(TextPair.class); conf.setInputFormat(TextInputFormat.class); conf.setOutputFormat(TextOutputFormat.class); conf.setPartitionerClass(FirstPartitioner.class); conf.setOutputKeyComparatorClass(KeyComparator.class); conf.setOutputValueGroupingComparator(GroupComparator.class); // relation table FileInputFormat.setInputPaths(conf, new Path(inputPath)); // rank table FileInputFormat.addInputPath(conf, new Path(outputPath + "/i" + (iteration - 1))); // count table FileInputFormat.addInputPath(conf, new Path(outputPath + "/count")); FileOutputFormat.setOutputPath(conf, new Path(outputPath + "/i" + iteration)); conf.setNumReduceTasks(numReducers); JobClient.runJob(conf); iteration++; /******************** Rank Aggregate Job ***********************/ conf = new JobConf(NaivePageRank.class); conf.setJobName("PageRank-Aggregate"); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(Text.class); conf.setMapOutputKeyClass(Text.class); conf.setMapperClass(RankAggregateMapper.class); conf.setReducerClass(RankAggregateReducer.class); conf.setInputFormat(TextInputFormat.class); conf.setOutputFormat(TextOutputFormat.class); FileInputFormat.setInputPaths(conf, new Path(outputPath + "/i" + (iteration - 1))); FileOutputFormat.setOutputPath(conf, new Path(outputPath + "/i" + iteration)); conf.setNumReduceTasks(numReducers); conf.setInt("haloop.num.nodes", numNodes); JobClient.runJob(conf); iteration++; } while (iteration < 2 * specIteration); long end = System.currentTimeMillis(); System.out.println("running time " + (end - start) / 1000 + "s"); }
From source file:SleepJobWithArray.java
License:Apache License
public JobConf setupJobConf(int numMapper, int numReducer, long mapSleepTime, int mapSleepCount, long reduceSleepTime, int reduceSleepCount) { JobConf job = new JobConf(getConf(), SleepJobWithArray.class); job.setNumMapTasks(numMapper);/*from www . j av a 2 s . c o m*/ job.setNumReduceTasks(numReducer); job.setMapperClass(SleepJobWithArray.class); job.setMapOutputKeyClass(IntWritable.class); job.setMapOutputValueClass(NullWritable.class); job.setReducerClass(SleepJobWithArray.class); job.setOutputFormat(NullOutputFormat.class); job.setInputFormat(SleepInputFormat.class); job.setPartitionerClass(SleepJobWithArray.class); job.setSpeculativeExecution(false); FileInputFormat.addInputPath(job, new Path("ignored")); job.setLong("sleep.job.map.sleep.time", mapSleepTime); job.setLong("sleep.job.reduce.sleep.time", reduceSleepTime); job.setInt("sleep.job.map.sleep.count", mapSleepCount); job.setInt("sleep.job.reduce.sleep.count", reduceSleepCount); return job; }
From source file:cascading.flow.FlowStep.java
License:Open Source License
protected JobConf getJobConf(JobConf parentConf) throws IOException { JobConf conf = parentConf == null ? new JobConf() : new JobConf(parentConf); // set values first so they can't break things downstream if (hasProperties()) { for (Map.Entry entry : getProperties().entrySet()) conf.set(entry.getKey().toString(), entry.getValue().toString()); }/*from w w w. java2 s. c om*/ // disable warning conf.setBoolean("mapred.used.genericoptionsparser", true); conf.setJobName(getStepName()); conf.setOutputKeyClass(Tuple.class); conf.setOutputValueClass(Tuple.class); conf.setMapperClass(FlowMapper.class); conf.setReducerClass(FlowReducer.class); // set for use by the shuffling phase TupleSerialization.setSerializations(conf); initFromSources(conf); initFromSink(conf); initFromTraps(conf); if (sink.getScheme().getNumSinkParts() != 0) { // if no reducer, set num map tasks to control parts if (getGroup() != null) conf.setNumReduceTasks(sink.getScheme().getNumSinkParts()); else conf.setNumMapTasks(sink.getScheme().getNumSinkParts()); } conf.setOutputKeyComparatorClass(TupleComparator.class); if (getGroup() == null) { conf.setNumReduceTasks(0); // disable reducers } else { // must set map output defaults when performing a reduce conf.setMapOutputKeyClass(Tuple.class); conf.setMapOutputValueClass(Tuple.class); // handles the case the groupby sort should be reversed if (getGroup().isSortReversed()) conf.setOutputKeyComparatorClass(ReverseTupleComparator.class); addComparators(conf, "cascading.group.comparator", getGroup().getGroupingSelectors()); if (getGroup().isGroupBy()) addComparators(conf, "cascading.sort.comparator", getGroup().getSortingSelectors()); if (!getGroup().isGroupBy()) { conf.setPartitionerClass(CoGroupingPartitioner.class); conf.setMapOutputKeyClass(IndexTuple.class); // allows groups to be sorted by index conf.setMapOutputValueClass(IndexTuple.class); conf.setOutputKeyComparatorClass(IndexTupleCoGroupingComparator.class); // sorts by group, then by index conf.setOutputValueGroupingComparator(CoGroupingComparator.class); } if (getGroup().isSorted()) { conf.setPartitionerClass(GroupingPartitioner.class); conf.setMapOutputKeyClass(TuplePair.class); if (getGroup().isSortReversed()) conf.setOutputKeyComparatorClass(ReverseGroupingSortingComparator.class); else conf.setOutputKeyComparatorClass(GroupingSortingComparator.class); // no need to supply a reverse comparator, only equality is checked conf.setOutputValueGroupingComparator(GroupingComparator.class); } } // perform last so init above will pass to tasks conf.setInt("cascading.flow.step.id", id); conf.set("cascading.flow.step", Util.serializeBase64(this)); return conf; }
From source file:cascading.flow.hadoop.HadoopFlowStep.java
License:Open Source License
public JobConf createInitializedConfig(FlowProcess<JobConf> flowProcess, JobConf parentConfig) { JobConf conf = parentConfig == null ? new JobConf() : HadoopUtil.copyJobConf(parentConfig); // disable warning conf.setBoolean("mapred.used.genericoptionsparser", true); conf.setJobName(getStepDisplayName(conf.getInt("cascading.display.id.truncate", Util.ID_LENGTH))); conf.setOutputKeyClass(Tuple.class); conf.setOutputValueClass(Tuple.class); conf.setMapRunnerClass(FlowMapper.class); conf.setReducerClass(FlowReducer.class); // set for use by the shuffling phase TupleSerialization.setSerializations(conf); initFromSources(flowProcess, conf);/* w ww.j a v a2 s.c o m*/ initFromSink(flowProcess, conf); initFromTraps(flowProcess, conf); initFromStepConfigDef(conf); int numSinkParts = getSink().getScheme().getNumSinkParts(); if (numSinkParts != 0) { // if no reducer, set num map tasks to control parts if (getGroup() != null) conf.setNumReduceTasks(numSinkParts); else conf.setNumMapTasks(numSinkParts); } else if (getGroup() != null) { int gatherPartitions = conf.getNumReduceTasks(); if (gatherPartitions == 0) gatherPartitions = conf.getInt(FlowRuntimeProps.GATHER_PARTITIONS, 0); if (gatherPartitions == 0) throw new FlowException(getName(), "a default number of gather partitions must be set, see FlowRuntimeProps"); conf.setNumReduceTasks(gatherPartitions); } conf.setOutputKeyComparatorClass(TupleComparator.class); if (getGroup() == null) { conf.setNumReduceTasks(0); // disable reducers } else { // must set map output defaults when performing a reduce conf.setMapOutputKeyClass(Tuple.class); conf.setMapOutputValueClass(Tuple.class); conf.setPartitionerClass(GroupingPartitioner.class); // handles the case the groupby sort should be reversed if (getGroup().isSortReversed()) conf.setOutputKeyComparatorClass(ReverseTupleComparator.class); addComparators(conf, "cascading.group.comparator", getGroup().getKeySelectors(), this, getGroup()); if (getGroup().isGroupBy()) addComparators(conf, "cascading.sort.comparator", getGroup().getSortingSelectors(), this, getGroup()); if (!getGroup().isGroupBy()) { conf.setPartitionerClass(CoGroupingPartitioner.class); conf.setMapOutputKeyClass(IndexTuple.class); // allows groups to be sorted by index conf.setMapOutputValueClass(IndexTuple.class); conf.setOutputKeyComparatorClass(IndexTupleCoGroupingComparator.class); // sorts by group, then by index conf.setOutputValueGroupingComparator(CoGroupingComparator.class); } if (getGroup().isSorted()) { conf.setPartitionerClass(GroupingSortingPartitioner.class); conf.setMapOutputKeyClass(TuplePair.class); if (getGroup().isSortReversed()) conf.setOutputKeyComparatorClass(ReverseGroupingSortingComparator.class); else conf.setOutputKeyComparatorClass(GroupingSortingComparator.class); // no need to supply a reverse comparator, only equality is checked conf.setOutputValueGroupingComparator(GroupingComparator.class); } } // perform last so init above will pass to tasks String versionString = Version.getRelease(); if (versionString != null) conf.set("cascading.version", versionString); conf.set(CASCADING_FLOW_STEP_ID, getID()); conf.set("cascading.flow.step.num", Integer.toString(getOrdinal())); HadoopUtil.setIsInflow(conf); Iterator<FlowNode> iterator = getFlowNodeGraph().getTopologicalIterator(); String mapState = pack(iterator.next(), conf); String reduceState = pack(iterator.hasNext() ? iterator.next() : null, conf); // hadoop 20.2 doesn't like dist cache when using local mode int maxSize = Short.MAX_VALUE; int length = mapState.length() + reduceState.length(); if (isHadoopLocalMode(conf) || length < maxSize) // seems safe { conf.set("cascading.flow.step.node.map", mapState); if (!Util.isEmpty(reduceState)) conf.set("cascading.flow.step.node.reduce", reduceState); } else { conf.set("cascading.flow.step.node.map.path", HadoopMRUtil.writeStateToDistCache(conf, getID(), "map", mapState)); if (!Util.isEmpty(reduceState)) conf.set("cascading.flow.step.node.reduce.path", HadoopMRUtil.writeStateToDistCache(conf, getID(), "reduce", reduceState)); } return conf; }
From source file:cn.edu.xmu.dm.mapreduce.Sort.java
License:Apache License
/** * The main driver for sort program. Invoke this method to submit the * map/reduce job./*w w w . j av a2 s . co m*/ * * @throws IOException * When there is communication problems with the job tracker. */ public int run(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "Sorter"); job.setJarByClass(Sort.class); JobConf jobConf = new JobConf(getConf(), Sort.class); jobConf.setJobName("sorter"); jobConf.setMapperClass(IdentityMapper.class); jobConf.setReducerClass(IdentityReducer.class); JobClient client = new JobClient(jobConf); ClusterStatus cluster = client.getClusterStatus(); 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 = BytesWritable.class; List<String> otherArgs = new ArrayList<String>(); InputSampler.Sampler<K, V> sampler = null; for (int i = 0; i < args.length; ++i) { try { if ("-m".equals(args[i])) { jobConf.setNumMapTasks(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 ("-totalOrder".equals(args[i])) { double pcnt = Double.parseDouble(args[++i]); int numSamples = Integer.parseInt(args[++i]); int maxSplits = Integer.parseInt(args[++i]); if (0 >= maxSplits) maxSplits = Integer.MAX_VALUE; sampler = new InputSampler.RandomSampler<K, V>(pcnt, numSamples, maxSplits); } 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.setNumReduceTasks(num_reduces); jobConf.setInputFormat(inputFormatClass); jobConf.setOutputFormat(outputFormatClass); jobConf.setOutputKeyClass(outputKeyClass); jobConf.setOutputValueClass(outputValueClass); // Make sure there are exactly 2 parameters left. if (otherArgs.size() != 2) { System.out.println("ERROR: Wrong number of parameters: " + otherArgs.size() + " instead of 2."); return printUsage(); } FileInputFormat.setInputPaths(jobConf, otherArgs.get(0)); FileOutputFormat.setOutputPath(jobConf, new Path(otherArgs.get(1))); if (sampler != null) { System.out.println("Sampling input to effect total-order sort..."); jobConf.setPartitionerClass(TotalOrderPartitioner.class); Path inputDir = FileInputFormat.getInputPaths(jobConf)[0]; inputDir = inputDir.makeQualified(inputDir.getFileSystem(jobConf)); Path partitionFile = new Path(inputDir, "_sortPartitioning"); TotalOrderPartitioner.setPartitionFile(jobConf, partitionFile); InputSampler.<K, V>writePartitionFile(jobConf, sampler); URI partitionUri = new URI(partitionFile.toString() + "#" + "_sortPartitioning"); DistributedCache.addCacheFile(partitionUri, jobConf); DistributedCache.createSymlink(jobConf); } System.out.println("Running on " + cluster.getTaskTrackers() + " nodes to sort from " + FileInputFormat.getInputPaths(jobConf)[0] + " into " + FileOutputFormat.getOutputPath(jobConf) + " with " + num_reduces + " reduces."); Date startTime = new Date(); System.out.println("Job started: " + startTime); jobResult = 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.alexholmes.hadooputils.sort.Sort.java
License:Apache License
/** * The driver for the sort MapReduce job. * * @param jobConf sort configuration * @param numMapTasks number of map tasks * @param numReduceTasks number of reduce tasks * @param sampler sampler, if required * @param codecClass the compression codec for compressing final outputs * @param mapCodecClass the compression codec for compressing intermediary map outputs * @param createLzopIndexes whether or not a MR job should be launched to create LZOP indexes * for the job output files * @param inputDirAsString input directory in CSV-form * @param outputDirAsString output directory * @return true if the job completed successfully * @throws IOException if something went wrong * @throws URISyntaxException if a URI wasn't correctly formed */// www . j a v a 2s . co m public boolean runJob(final JobConf jobConf, final Integer numMapTasks, final Integer numReduceTasks, final InputSampler.Sampler<K, V> sampler, final Class<? extends CompressionCodec> codecClass, final Class<? extends CompressionCodec> mapCodecClass, final boolean createLzopIndexes, final String inputDirAsString, final String outputDirAsString) throws IOException, URISyntaxException { jobConf.setJarByClass(Sort.class); jobConf.setJobName("sorter"); JobClient client = new JobClient(jobConf); ClusterStatus cluster = client.getClusterStatus(); if (numMapTasks != null) { jobConf.setNumMapTasks(numMapTasks); } if (numReduceTasks != null) { jobConf.setNumReduceTasks(numReduceTasks); } else { int numReduces = (int) (cluster.getMaxReduceTasks() * 0.9); String sortReduces = jobConf.get("test.sort.reduces_per_host"); if (sortReduces != null) { numReduces = cluster.getTaskTrackers() * Integer.parseInt(sortReduces); } // Set user-supplied (possibly default) job configs jobConf.setNumReduceTasks(numReduces); } jobConf.setMapperClass(IdentityMapper.class); jobConf.setReducerClass(SortReduce.class); jobConf.setInputFormat(SortInputFormat.class); jobConf.setMapOutputKeyClass(Text.class); jobConf.setMapOutputValueClass(Text.class); jobConf.setOutputKeyClass(Text.class); jobConf.setOutputValueClass(Text.class); if (mapCodecClass != null) { jobConf.setMapOutputCompressorClass(mapCodecClass); } if (codecClass != null) { jobConf.setBoolean("mapred.output.compress", true); jobConf.setClass("mapred.output.compression.codec", codecClass, CompressionCodec.class); } FileInputFormat.setInputPaths(jobConf, inputDirAsString); FileOutputFormat.setOutputPath(jobConf, new Path(outputDirAsString)); if (sampler != null) { System.out.println("Sampling input to effect total-order sort..."); jobConf.setPartitionerClass(TotalOrderPartitioner.class); Path inputDir = FileInputFormat.getInputPaths(jobConf)[0]; FileSystem fileSystem = FileSystem.get(jobConf); if (fileSystem.exists(inputDir) && fileSystem.isFile(inputDir)) { inputDir = inputDir.getParent(); } inputDir = inputDir.makeQualified(inputDir.getFileSystem(jobConf)); Path partitionFile = new Path(inputDir, "_sortPartitioning"); TotalOrderPartitioner.setPartitionFile(jobConf, partitionFile); InputSampler.writePartitionFile(jobConf, sampler); URI partitionUri = new URI(partitionFile.toString() + "#" + "_sortPartitioning"); DistributedCache.addCacheFile(partitionUri, jobConf); DistributedCache.createSymlink(jobConf); } System.out.println("Running on " + cluster.getTaskTrackers() + " nodes to sort from " + FileInputFormat.getInputPaths(jobConf)[0] + " into " + FileOutputFormat.getOutputPath(jobConf) + " with " + jobConf.getNumReduceTasks() + " reduces."); Date startTime = new Date(); System.out.println("Job started: " + startTime); jobResult = JobClient.runJob(jobConf); Date endTime = new Date(); System.out.println("Job ended: " + endTime); System.out.println("The job took " + TimeUnit.MILLISECONDS.toSeconds(endTime.getTime() - startTime.getTime()) + " seconds."); if (jobResult.isSuccessful()) { if (createLzopIndexes && codecClass != null && LzopCodec.class.equals(codecClass)) { new LzoIndexer(jobConf).index(new Path(outputDirAsString)); } return true; } return false; }
From source file:com.benchmark.mapred.SleepJob.java
License:Apache License
public JobConf setupJobConf(int numMapper, int numReducer, long mapSleepTime, int mapSleepCount, long reduceSleepTime, int reduceSleepCount) { JobConf job = new JobConf(getConf(), SleepJob.class); job.setNumMapTasks(numMapper);//from www . j av a 2 s .c om job.setNumReduceTasks(numReducer); job.setMapperClass(SleepJob.class); job.setMapOutputKeyClass(IntWritable.class); job.setMapOutputValueClass(NullWritable.class); job.setReducerClass(SleepJob.class); job.setOutputFormat(NullOutputFormat.class); job.setInputFormat(SleepInputFormat.class); job.setPartitionerClass(SleepJob.class); job.setSpeculativeExecution(false); job.setJobName("Sleep job"); FileInputFormat.addInputPath(job, new Path("ignored")); job.setLong("sleep.job.map.sleep.time", mapSleepTime); job.setLong("sleep.job.reduce.sleep.time", reduceSleepTime); job.setInt("sleep.job.map.sleep.count", mapSleepCount); job.setInt("sleep.job.reduce.sleep.count", reduceSleepCount); return job; }
From source file:com.benchmark.mapred.Sort.java
License:Apache License
/** * The main driver for sort program./* w w w . jav a 2 s . co m*/ * Invoke this method to submit the map/reduce job. * @throws IOException When there is communication problems with the * job tracker. */ public int run(String[] args) throws Exception { JobConf jobConf = new JobConf(getConf(), Sort.class); jobConf.setJobName("sorter"); jobConf.setMapperClass(IdentityMapper.class); jobConf.setReducerClass(IdentityReducer.class); JobClient client = new JobClient(jobConf); ClusterStatus cluster = client.getClusterStatus(); 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 = BytesWritable.class; List<String> otherArgs = new ArrayList<String>(); InputSampler.Sampler<K, V> sampler = null; for (int i = 0; i < args.length; ++i) { try { if ("-m".equals(args[i])) { jobConf.setNumMapTasks(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 ("-totalOrder".equals(args[i])) { double pcnt = Double.parseDouble(args[++i]); int numSamples = Integer.parseInt(args[++i]); int maxSplits = Integer.parseInt(args[++i]); if (0 >= maxSplits) maxSplits = Integer.MAX_VALUE; sampler = new InputSampler.RandomSampler<K, V>(pcnt, numSamples, maxSplits); } 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.setNumReduceTasks(num_reduces); jobConf.setInputFormat(inputFormatClass); jobConf.setOutputFormat(outputFormatClass); jobConf.setOutputKeyClass(outputKeyClass); jobConf.setOutputValueClass(outputValueClass); // Make sure there are exactly 2 parameters left. if (otherArgs.size() != 2) { System.out.println("ERROR: Wrong number of parameters: " + otherArgs.size() + " instead of 2."); return printUsage(); } FileInputFormat.setInputPaths(jobConf, otherArgs.get(0)); FileOutputFormat.setOutputPath(jobConf, new Path(otherArgs.get(1))); if (sampler != null) { System.out.println("Sampling input to effect total-order sort..."); jobConf.setPartitionerClass(TotalOrderPartitioner.class); Path inputDir = FileInputFormat.getInputPaths(jobConf)[0]; inputDir = inputDir.makeQualified(inputDir.getFileSystem(jobConf)); Path partitionFile = new Path(inputDir, "_sortPartitioning"); TotalOrderPartitioner.setPartitionFile(jobConf, partitionFile); InputSampler.<K, V>writePartitionFile(jobConf, sampler); URI partitionUri = new URI(partitionFile.toString() + "#" + "_sortPartitioning"); DistributedCache.addCacheFile(partitionUri, jobConf); DistributedCache.createSymlink(jobConf); } System.out.println("Running on " + cluster.getTaskTrackers() + " nodes to sort from " + FileInputFormat.getInputPaths(jobConf)[0] + " into " + FileOutputFormat.getOutputPath(jobConf) + " with " + num_reduces + " reduces."); Date startTime = new Date(); System.out.println("Job started: " + startTime); jobResult = 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; }