コード例 #1
0
 @Override
 public void configure(JobConf conf) {
   this.threshold = conf.getFloat(PARAM_APS_THRESHOLD, DEFAULT_THRESHOLD);
   int reducerID = conf.getInt("mapred.task.partition", -1);
   int max = conf.getInt(PARAM_APS_MAXKEY, 0);
   int nstripes = conf.getInt(PARAM_APS_STRIPES, 1);
   int spread = conf.getInt(PARAM_APS_REDUCER_PER_STRIPE, 1);
   if (reducerID < 0 || max == 0) {
     LOG.error("Could not find stripe ID, reverting to whole rest file loading");
     LOG.debug("reducer = " + reducerID + "\t max = " + max + "\t nstripes = " + nstripes);
     // open the pruned part file in the DistrubutedCache
     haspruned = FileUtils.readRestFile(conf, pruned);
   } else {
     int stripe = GenericKey.StripePartitioner.findStripe(reducerID, spread);
     int from = GenericKey.StripePartitioner.minKeyInStripe(stripe, nstripes, max);
     int to = from + GenericKey.StripePartitioner.numKeysInStripe(stripe, nstripes, max);
     // read from 'from' included, to 'to' excluded
     LOG.info(
         "Reducer "
             + reducerID
             + " loading stripe "
             + stripe
             + " of "
             + nstripes
             + " ("
             + from
             + ","
             + (to - 1)
             + ")");
     haspruned = FileUtils.readRestFile(conf, pruned, from, to);
   }
   if (!haspruned) LOG.warn("No pruned file provided in DistributedCache");
   else LOG.info("Read " + pruned.size() + " entries from pruned file");
 }
コード例 #2
0
 @Override
 public void configure(JobConf conf) {
   threshold = conf.getFloat(PARAM_APS_THRESHOLD, DEFAULT_THRESHOLD);
   mos = new MultipleOutputs(conf);
   // open the maxWeight_i file in the DistributedCache
   boolean succeded = FileUtils.readMaxWiFile(conf, maxWi);
   if (!succeded) throw new AssertionError("Could not read maxWi file");
 }
コード例 #3
0
  @Override
  public int run(String[] args) throws IOException {
    OptionParser p = new OptionParser();
    OptionSpec<String> maxwiOpt =
        p.accepts(maxwiOptName, "location of maxWi map file (HDFS) REQUIRED")
            .withRequiredArg()
            .ofType(String.class);
    OptionSpec<Float> thresholdOpt =
        p.accepts(thresholdOptName, "similarity threshold")
            .withRequiredArg()
            .ofType(Float.class)
            .defaultsTo(DEFAULT_THRESHOLD);
    OptionSpec<Integer> stripesOpt =
        p.accepts(stripesOptName, "number of stripes to divide the similarity matrix")
            .withRequiredArg()
            .ofType(Integer.class)
            .defaultsTo(1);
    OptionSpec<Integer> spreadOpt =
        p.accepts(spreadOptName, "number of reducers per stripe")
            .withRequiredArg()
            .ofType(Integer.class)
            .defaultsTo(DEFAULT_SPREAD);
    OptionSpec<Integer> factorOpt =
        p.accepts(factorOptName, "number of mappers per reducer")
            .withRequiredArg()
            .ofType(Integer.class)
            .defaultsTo(DEFAULT_FACTOR);
    OptionSpec<Integer> maxVectorIDOpt =
        p.accepts(maxVectorIDOptName, "maximum vector ID").withRequiredArg().ofType(Integer.class);
    p.acceptsAll(Arrays.asList("h", "?"), "show help");

    OptionSet options = parseOptions(p, args);

    // to distinguish indexes built in successive runs
    DateFormat df = new SimpleDateFormat("yyyyMMdd-HHmmss");
    Date date = new Date();

    float threshold = options.valueOf(thresholdOpt); // threshold
    if (threshold < 0 || threshold >= 1) {
      System.err.println(thresholdOptName + " should be between 0 and 1");
      System.exit(1);
    }

    int numStripes = options.valueOf(stripesOpt); // number of stripes
    if (numStripes < 1) {
      System.err.println(stripesOptName + " should be > 0");
      System.exit(1);
    }

    // MapReduce parameters
    int spread = options.valueOf(spreadOpt); // how many reducers per stripe
    if (spread < 1) {
      System.err.println(spreadOptName + " should be > 0");
      System.exit(1);
    }

    int factor = options.valueOf(factorOpt); // how many mappers per reducer
    if (factor < 1) {
      System.err.println(factorOptName + " should be > 0");
      System.exit(1);
    }

    int maxKey = 0;
    if (options.has(maxVectorIDOpt)) {
      maxKey = options.valueOf(maxVectorIDOpt); // maximum value of the vector ID
      if (maxKey < 1) {
        System.err.println(maxVectorIDOptName + " should be > 0");
        System.exit(1);
      }
    }

    int numReducers = GenericKey.StripePartitioner.numReducers(numStripes, spread);
    int numMappers = numReducers * factor;
    int numBuckets = numMappers;

    // pick the file with max weights from command line
    String maxWiDir = options.valueOf(maxwiOpt);
    List<String> nonOptArgs = options.nonOptionArguments();

    LOG.info("Threshold set to " + threshold);
    LOG.info(
        String.format(
            "Buckets: %1$-10s Factor: %2$-10s Stripes: %3$-10s Spread: %4$-10s Reducers: %5$-10s",
            numBuckets, factor, numStripes, spread, numReducers));

    // start building the jobs
    JobConf conf1 = new JobConf(getConf(), Similarity.class);
    conf1.setFloat(PARAM_APS_THRESHOLD, threshold);
    conf1.setInt(PARAM_APS_STRIPES, numStripes);
    DistributedCache.addCacheFile(URI.create(maxWiDir), conf1);

    Path inputPath = new Path(nonOptArgs.get(0));
    Path indexPath =
        new Path(
            nonOptArgs.get(0) + "-index-" + threshold + "-s" + numStripes + "_" + df.format(date));
    // index filtering pruned nested directory
    Path indexOnlyPath = new Path(indexPath, "part*");
    Path outputPath = new Path(nonOptArgs.get(1) + "-" + threshold + "-s" + numStripes);
    FileInputFormat.setInputPaths(conf1, inputPath);
    FileOutputFormat.setOutputPath(conf1, indexPath);

    conf1.setInputFormat(SequenceFileInputFormat.class);
    conf1.setOutputFormat(SequenceFileOutputFormat.class);
    conf1.setMapOutputKeyClass(LongWritable.class);
    conf1.setMapOutputValueClass(IndexItem.class);
    conf1.setOutputKeyClass(LongWritable.class);
    conf1.setOutputValueClass(IndexItemArrayWritable.class);
    conf1.setMapperClass(IndexerMapper.class);
    conf1.setReducerClass(IndexerReducer.class);

    // assuming input is sorted according to the key (vectorID) so that the
    // part files are locally sorted
    MultipleOutputs.addNamedOutput(
        conf1,
        PRUNED,
        SequenceFileOutputFormat.class,
        IntWritable.class,
        VectorComponentArrayWritable.class);

    // remove the stuff we added from the job name
    conf1.set(
        "mapred.job.name",
        "APS-" + indexPath.getName().substring(0, indexPath.getName().length() - 16));
    conf1.setNumTasksToExecutePerJvm(-1); // JVM reuse
    conf1.setSpeculativeExecution(false);
    conf1.setCompressMapOutput(true);
    // hash the posting lists in different buckets to distribute the load
    conf1.setNumReduceTasks(numBuckets);

    RunningJob job1 = JobClient.runJob(conf1);

    // part 2
    JobConf conf2 = new JobConf(getConf(), Similarity.class);

    if (numStripes > 0) FileUtils.mergeRestFile(conf2, indexPath, PRUNED, INDEX_INTERVAL);

    MultipleInputs.addInputPath(
        conf2, indexOnlyPath, SequenceFileInputFormat.class, SimilarityMapperIndex.class);
    MultipleInputs.addInputPath(
        conf2, inputPath, SequenceFileInputFormat.class, SimilarityMapperInput.class);
    FileOutputFormat.setOutputPath(conf2, outputPath);
    conf2.setCombinerClass(SimilarityCombiner.class);
    conf2.setReducerClass(SimilarityReducer.class);
    conf2.setPartitionerClass(GenericKey.StripePartitioner.class);
    conf2.setOutputKeyComparatorClass(GenericKey.Comparator.class);
    conf2.setOutputValueGroupingComparator(GenericKey.PrimaryComparator.class);
    conf2.setMapOutputKeyClass(GenericKey.class);
    conf2.setMapOutputValueClass(GenericValue.class);
    conf2.setOutputKeyClass(VectorPair.class);
    conf2.setOutputValueClass(NullWritable.class);

    Counter numDocs =
        job1.getCounters()
            .findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS");
    maxKey = maxKey > 0 ? maxKey : (int) numDocs.getValue();
    LOG.info("Setting max key value in input to " + maxKey);
    conf2.setInt(PARAM_APS_MAXKEY, maxKey);

    conf2.setInt(PARAM_APS_STRIPES, numStripes);
    conf2.setFloat(PARAM_APS_THRESHOLD, threshold);
    conf2.setInt(PARAM_APS_REDUCER_PER_STRIPE, spread);
    conf2.set("mapred.job.name", "APS-" + outputPath.getName());

    conf2.setNumTasksToExecutePerJvm(-1); // JVM reuse
    conf2.setSpeculativeExecution(false);
    conf2.setCompressMapOutput(true);
    conf2.setNumReduceTasks(numReducers);

    JobClient.runJob(conf2);

    return 0;
  }