List of usage examples for org.apache.hadoop.fs FileStatus getBlockSize
public long getBlockSize()
From source file:PageInputFormat.java
License:Apache License
public InputSplit[] getSplits(JobConf job, int num) throws IOException { long minSize = 1; long maxSize = getMaxSplitSize(job); // generate splits List<InputSplit> splits = new ArrayList<InputSplit>(); FileStatus[] files = listStatus(job); for (FileStatus file : files) { Path path = file.getPath(); long length = file.getLen(); if (length != 0) { BlockLocation[] blkLocations; FileSystem fs = path.getFileSystem(job); blkLocations = fs.getFileBlockLocations(file, 0, length); if (isSplitable(path.getFileSystem(job), path)) { long blockSize = file.getBlockSize(); long splitSize = computeSplitSize(blockSize, minSize, maxSize); long bytesRemaining = length; while (((double) bytesRemaining) / splitSize > SPLIT_SLOP) { int blkIndex = getBlockIndex(blkLocations, length - bytesRemaining); splits.add(makeSplit(path, length - bytesRemaining, splitSize, blkLocations[blkIndex].getHosts())); bytesRemaining -= splitSize; }/*from w w w . j a v a 2 s .c om*/ if (bytesRemaining != 0) { int blkIndex = getBlockIndex(blkLocations, length - bytesRemaining); splits.add(makeSplit(path, length - bytesRemaining, bytesRemaining, blkLocations[blkIndex].getHosts())); } } else splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts())); } else splits.add(makeSplit(path, 0, length, new String[0])); } // Save the number of input files for metrics/loadgen job.setLong(NUM_INPUT_FILES, files.length); return splits.toArray(new InputSplit[0]); }
From source file:TestRawParascaleFileSystemBase.java
License:Apache License
public void testGetFileStatus() throws IOException { final FileStatus fileStatus = fs.getFileStatus(fs.getHomeDirectory()); assertEquals(64 * 1024 * 1024, fileStatus.getBlockSize()); fs.getConf().setLong(String.format(RawParascaleFileSystem.PS_VFS_BLOCKSIZE_FORMAT, "myvirtualFs"), 64); try {// ww w . j a v a 2s . c o m fs.getFileStatus(new Path("psdfs://myvirtualFs@10.200.2.10")); fail("wrong fs"); } catch (final IllegalArgumentException e) { // expected } }
From source file:StreamWikiDumpInputFormat.java
License:Apache License
/** * Generate the list of files and make them into FileSplits. * //from ww w . j a v a2 s . c om * @param job * the job context * @throws IOException */ @Override public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException { LOG.info("StreamWikiDumpInputFormat.getSplits job=" + job + " n=" + numSplits); // InputSplit[] oldSplits = super.getSplits(job, numSplits); List<InputSplit> splits = new ArrayList<InputSplit>(); FileStatus[] files = listStatus(job); // Save the number of input files for metrics/loadgen job.setLong("mapreduce.input.num.files", files.length); long totalSize = 0; // compute total size for (FileStatus file : files) { // check we have valid files if (file.isDir()) { throw new IOException("Not a file: " + file.getPath()); } totalSize += file.getLen(); } long minSize = 1; long goalSize = totalSize / (numSplits == 0 ? 1 : numSplits); for (FileStatus file : files) { if (file.isDir()) { throw new IOException("Not a file: " + file.getPath()); } long blockSize = file.getBlockSize(); long splitSize = computeSplitSize(goalSize, minSize, blockSize); LOG.info(String.format("goalsize=%d splitsize=%d blocksize=%d", goalSize, splitSize, blockSize)); // System.err.println(String.format("goalsize=%d splitsize=%d blocksize=%d", // goalSize, splitSize, blockSize)); for (InputSplit x : getSplits(job, file, pageBeginPattern, splitSize)) splits.add(x); } System.err.println("splits=" + splits); return splits.toArray(new InputSplit[splits.size()]); }
From source file:MRDriver.java
License:Apache License
public int run(String args[]) throws Exception { FileSystem fs = null;// ww w . j av a 2 s .c om 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:alluxio.hadoop.HadoopUtils.java
License:Apache License
/** * Returns a string representation of a Hadoop {@link FileStatus}. * * @param fs Hadoop {@link FileStatus}/* w w w . jav a 2 s . co m*/ * @return its string representation */ public static String toStringHadoopFileStatus(FileStatus fs) { StringBuilder sb = new StringBuilder(); sb.append("HadoopFileStatus: Path: ").append(fs.getPath()); sb.append(" , Length: ").append(fs.getLen()); // Use isDir instead of isDirectory for compatibility with hadoop 1. sb.append(" , IsDir: ").append(fs.isDir()); sb.append(" , BlockReplication: ").append(fs.getReplication()); sb.append(" , BlockSize: ").append(fs.getBlockSize()); sb.append(" , ModificationTime: ").append(fs.getModificationTime()); sb.append(" , AccessTime: ").append(fs.getAccessTime()); sb.append(" , Permission: ").append(fs.getPermission()); sb.append(" , Owner: ").append(fs.getOwner()); sb.append(" , Group: ").append(fs.getGroup()); return sb.toString(); }
From source file:alluxio.underfs.hdfs.HdfsUnderFileSystem.java
License:Apache License
@Override public long getBlockSizeByte(String path) throws IOException { Path tPath = new Path(path); if (!mFileSystem.exists(tPath)) { throw new FileNotFoundException(path); }//from w w w . j av a 2 s . co m FileStatus fs = mFileSystem.getFileStatus(tPath); return fs.getBlockSize(); }
From source file:ca.sparkera.adapters.mapred.MainframeVBInputFormat.java
License:Apache License
/** * Splits files returned by {@link #listStatus(JobConf)} when they're too * big./*from w w w. ja va 2 s . c om*/ */ @Override @SuppressWarnings("deprecation") public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException { FileStatus[] files = listStatus(job); for (FileStatus file : files) { // check we have valid files if (file.isDir()) { throw new IOException("Not a file: " + file.getPath()); } totalSize += file.getLen(); } long goalSize = totalSize / (numSplits == 0 ? 1 : numSplits); long minSize = Math.max(job.getLong("mapred.min.split.size", 1), minSplitSize); // generate splits ArrayList<FileSplit> splits = new ArrayList<FileSplit>(numSplits); for (FileStatus file : files) { Path path = file.getPath(); FileSystem fs = path.getFileSystem(job); FSDataInputStream fileIn; InputStream inputStream; fileIn = fs.open(path); inputStream = fileIn; filePosition = fileIn; long offset = 0; long length = file.getLen(); BlockLocation[] blkLocations = fs.getFileBlockLocations(file, 0, length); if ((length != 0) && isSplitable(fs, path)) { long blockSize = file.getBlockSize(); long bytesRemaining = length; long splitSize = 0; while (offset < length) { splitSize = computeSplitSize(goalSize, minSize, blockSize, inputStream); int blkIndex = getBlockIndex(blkLocations, length - bytesRemaining); splits.add(new FileSplit(path, length - bytesRemaining, splitSize, blkLocations[blkIndex].getHosts())); bytesRemaining -= splitSize; offset = length - bytesRemaining; } if (bytesRemaining != 0) { throw new IOException( "Partial record(length = " + bytesRemaining + ") found at the end of file " + path); } } else if (length != 0) { splits.add(new FileSplit(path, 0, length, blkLocations[0].getHosts())); } else { // Create empty hosts array for zero length files splits.add(new FileSplit(path, 0, length, new String[0])); } if (inputStream != null) { inputStream.close(); inputStream = null; } } java.util.Date date = new java.util.Date(); System.out.println((new Timestamp(date.getTime())) + ",\t Split = 100% Total Splits - " + (++splitCount) + "\t Total Records in VB file - " + totalRecords); LOG.debug("Total # of splits: " + splits.size()); return splits.toArray(new FileSplit[splits.size()]); }
From source file:cascading.tap.hadoop.Hfs.java
License:Open Source License
/** * Method getBlockSize returns the {@code blocksize} specified by the underlying file system for this resource. * * @param conf of JobConf/*from w ww . j a va 2 s . c o m*/ * @return long * @throws IOException when */ public long getBlockSize(Configuration conf) throws IOException { if (!resourceExists(conf)) return 0; FileStatus fileStatus = getFileSystem(conf).getFileStatus(getPath()); if (fileStatus.isDir()) return 0; return fileStatus.getBlockSize(); }
From source file:ch.cern.db.hdfs.Main.java
License:GNU General Public License
private void printFileStatus(FileStatus status) { System.out.println();//from www . jav a 2 s .c om System.out.println("Showing metadata for: " + status.getPath()); System.out.println(" isDirectory: " + status.isDirectory()); System.out.println(" isFile: " + status.isFile()); System.out.println(" isSymlink: " + status.isSymlink()); System.out.println(" encrypted: " + status.isEncrypted()); System.out.println(" length: " + status.getLen()); System.out.println(" replication: " + status.getReplication()); System.out.println(" blocksize: " + status.getBlockSize()); System.out.println(" modification_time: " + new Date(status.getModificationTime())); System.out.println(" access_time: " + new Date(status.getAccessTime())); System.out.println(" owner: " + status.getOwner()); System.out.println(" group: " + status.getGroup()); System.out.println(" permission: " + status.getPermission()); System.out.println(); }
From source file:cn.lhfei.hadoop.ch03.ShowFileStatusTest.java
License:Apache License
@Test public void fileStatusForFile() throws IOException { Path path = new Path("/dir/file"); FileStatus stat = fs.getFileStatus(path); Assert.assertEquals(stat.getPath().toUri().getPath(), "/dir/file"); Assert.assertTrue(stat.isFile());//w ww . j a v a 2 s . c o m stat.getBlockSize(); }