org.apache.hadoop.util.bloom.BloomFilter Java Examples

protected void buildAllPhrases(ArrayList<Collection<String>> terms, String zone, RawRecordContainer event, int position, BloomFilter alreadyIndexedTerms,
                TaskInputOutputContext<KEYIN,? extends RawRecordContainer,KEYOUT,VALUEOUT> context, ContextWriter<KEYOUT,VALUEOUT> contextWriter,
                StatusReporter reporter) throws IOException, InterruptedException {
    if (terms.size() < 2) {
        // An empty list has no tokens/phrases to emit and phrases of length one
        // were already handled
        return;
    }
    StringBuilder sb = new StringBuilder();
    for (String term : terms.get(0)) {
        if (term.length() <= tokenHelper.getTermLengthMinimum()) {
            continue;
        }
        sb.append(term);
        // Need to move the position pointer back by the amount of the phrase lengths
        // accounting for zero-indexing
        completePhrase(sb, terms.subList(1, terms.size()), zone, event, position - (terms.size() - 1), alreadyIndexedTerms, context, contextWriter,
                        reporter);
        
        sb.setLength(0);
    }
}
 

@Override
public Tuple exec(Tuple input) throws IOException {
    if (input == null || input.size() == 0) return null;

    // Strip off the initial level of bag
    DataBag values = (DataBag)input.get(0);
    Iterator<Tuple> it = values.iterator();
    Tuple t = it.next();

    // If the input tuple has only one field, then we'll extract
    // that field and serialize it into a key.  If it has multiple
    // fields, we'll serialize the whole tuple.
    byte[] b;
    if (t.size() == 1) b = DataType.toBytes(t.get(0));
    else b = DataType.toBytes(t, DataType.TUPLE);

    Key k = new Key(b);
    filter = new BloomFilter(vSize, numHash, hType);
    filter.add(k);

    return TupleFactory.getInstance().newTuple(bloomOut());
}
 

private void init() throws IOException {
    filter = new BloomFilter();
    String dir = "./" + getFilenameFromPath(bloomFile);
    String[] partFiles = new File(dir)
            .list(new FilenameFilter() {
                @Override
                public boolean accept(File current, String name) {
                    return name.startsWith("part");
                }
            });

    String dcFile = dir + "/" + partFiles[0];
    DataInputStream dis = new DataInputStream(new FileInputStream(dcFile));
    try {
        filter.readFields(dis);
    } finally {
        dis.close();
    }
}
 

@Override
public void add(Key key) {
  if (key == null) {
    throw new NullPointerException("Key can not be null");
  }

  org.apache.hadoop.util.bloom.BloomFilter bf = getActiveStandardBF();

  if (bf == null) {
    addRow();
    bf = matrix[matrix.length - 1];
    currentNbRecord = 0;
  }

  bf.add(key);

  currentNbRecord++;
}
 

protected BloomFilter bloomIn(DataByteArray b) throws IOException {
    DataInputStream dis = new DataInputStream(new
        ByteArrayInputStream(b.get()));
    BloomFilter f = new BloomFilter();
    f.readFields(dis);
    return f;
}
 

protected DataByteArray bloomOr(Tuple input) throws IOException {
    filter = new BloomFilter(vSize, numHash, hType);

    try {
        DataBag values = (DataBag)input.get(0);
        for (Iterator<Tuple> it = values.iterator(); it.hasNext();) {
            Tuple t = it.next();
            filter.or(bloomIn((DataByteArray)t.get(0)));
        }
    } catch (ExecException ee) {
        throw new IOException(ee);
    }

    return bloomOut();
}
 

/**
 * For testing only, do not use directly.
 */
public void setFilter(DataByteArray dba) throws IOException {
    DataInputStream dis = new DataInputStream(new
        ByteArrayInputStream(dba.get()));
    filter = new BloomFilter();
    filter.readFields(dis);
}
 

public static BloomFilter readFromAvro(InputStream is) throws IOException {
  DataFileStream<Object> reader =
      new DataFileStream<Object>(
          is, new GenericDatumReader<Object>());

  reader.hasNext();
  BloomFilter filter = new BloomFilter();
  AvroBytesRecord
      .fromGenericRecord((GenericRecord) reader.next(), filter);
  IOUtils.closeQuietly(is);
  IOUtils.closeQuietly(reader);

  return filter;
}
 

@Override
public void reduce(NullWritable key, Iterator<BloomFilter> values,
                   OutputCollector<AvroWrapper<GenericRecord>,
                       NullWritable> output,
                   Reporter reporter) throws IOException {
  while (values.hasNext()) {
    BloomFilter bf = values.next();
    filter.or(bf);
    System.out.println(filter);
  }
  collector = output;
}
 

@Override
public void map(Text key, Text value,
                OutputCollector<NullWritable, BloomFilter> output,
                Reporter reporter) throws IOException {

  System.out.println("K[" + key + "]");

  int age = Integer.valueOf(value.toString());
  if (age > 30) {
    filter.add(new Key(key.toString().getBytes()));
  }
  collector = output;
}
 

/**
 * The MapReduce driver - setup and launch the job.
 *
 * @param args the command-line arguments
 * @return the process exit code
 * @throws Exception if something goes wrong
 */
public int run(final String[] args) throws Exception {

  Cli cli = Cli.builder().setArgs(args).addOptions(CliCommonOpts.MrIoOpts.values()).build();
  int result = cli.runCmd();

  if (result != 0) {
    return result;
  }

  Path inputPath = new Path(cli.getArgValueAsString(CliCommonOpts.MrIoOpts.INPUT));
  Path outputPath = new Path(cli.getArgValueAsString(CliCommonOpts.MrIoOpts.OUTPUT));

  Configuration conf = super.getConf();

  JobConf job = new JobConf(conf);
  job.setJarByClass(BloomFilterCreator.class);

  job.set(AvroJob.OUTPUT_SCHEMA, AvroBytesRecord.SCHEMA.toString());
  job.set(AvroJob.OUTPUT_CODEC, SnappyCodec.class.getName());

  job.setInputFormat(KeyValueTextInputFormat.class);
  job.setOutputFormat(AvroOutputFormat.class);

  job.setMapperClass(Map.class);
  job.setReducerClass(Reduce.class);

  job.setMapOutputKeyClass(NullWritable.class);
  job.setMapOutputValueClass(BloomFilter.class);

  job.setOutputKeyClass(NullWritable.class);
  job.setOutputValueClass(BloomFilter.class);

  FileInputFormat.setInputPaths(job, inputPath);
  FileOutputFormat.setOutputPath(job, outputPath);

  return JobClient.runJob(job).isSuccessful() ? 0 : 1;
}
 

public static BloomFilter readFromAvro(InputStream is) throws IOException {
  DataFileStream<Object> reader =
      new DataFileStream<Object>(
          is, new GenericDatumReader<Object>());

  reader.hasNext();
  BloomFilter filter = new BloomFilter();
  AvroBytesRecord
      .fromGenericRecord((GenericRecord) reader.next(), filter);
  IOUtils.closeQuietly(is);
  IOUtils.closeQuietly(reader);

  return filter;
}
 

/**
 * Returns the active standard Bloom filter in <i>this</i> dynamic Bloom filter.
 *
 * @return BloomFilter The active standard Bloom filter.
 * <code>Null</code> otherwise.
 */
private BloomFilter getActiveStandardBF() {
  if (reachedMax) {
    return matrix[curMatrixIndex++ % matrix.length];
  }

  if (currentNbRecord >= nr && (matrix.length * nr) < maxNr) {
    return null;
  } else if (currentNbRecord >= nr && (matrix.length * nr) >= maxNr) {
    reachedMax = true;
    return matrix[0];
  }
  return matrix[matrix.length - 1];
}
 

/**
 * Adds a new row to <i>this</i> dynamic Bloom filter.
 */
private void addRow() {
  BloomFilter[] tmp = new BloomFilter[matrix.length + 1];
  System.arraycopy(matrix, 0, tmp, 0, matrix.length);
  tmp[tmp.length - 1] = new BloomFilter(vectorSize, nbHash, hashType);
  matrix = tmp;
}
 

@Override
public void write(DataOutput out) throws IOException {
  super.write(out);
  out.writeInt(nr);
  out.writeInt(currentNbRecord);
  out.writeInt(matrix.length);
  for (BloomFilter bloomFilter : matrix) {
    bloomFilter.write(out);
  }
}
 

@Override
public String toString() {
  StringBuilder res = new StringBuilder();

  for (BloomFilter bloomFilter : matrix) {
    res.append(bloomFilter);
    res.append(Character.LINE_SEPARATOR);
  }
  return res.toString();
}
 

@Override
public void readFields(DataInput in) throws IOException {
  super.readFields(in);
  nr = in.readInt();
  currentNbRecord = in.readInt();
  int len = in.readInt();
  matrix = new org.apache.hadoop.util.bloom.BloomFilter[len];
  for (int i = 0; i < matrix.length; i++) {
    matrix[i] = new org.apache.hadoop.util.bloom.BloomFilter();
    matrix[i].readFields(in);
  }
}
 

private void completePhrase(StringBuilder baseTerm, List<Collection<String>> terms, String zone, RawRecordContainer event, int position,
                BloomFilter alreadyIndexedTerms, TaskInputOutputContext<KEYIN,? extends RawRecordContainer,KEYOUT,VALUEOUT> context,
                ContextWriter<KEYOUT,VALUEOUT> contextWriter, StatusReporter reporter) throws IOException, InterruptedException {
    if (terms.isEmpty()) {
        return;
    }
    for (String term : terms.get(0)) {
        if (term == null) {
            continue;
        }
        boolean properLen = term.length() >= tokenHelper.getTermLengthMinimum();
        // Add the current term and emit the phrase if the current term isn't empty
        if (properLen) {
            baseTerm.append(SPACE).append(term);
            
            counters.increment(ContentIndexCounters.PHRASES_PROCESSED_COUNTER, reporter);
            
            processTermAndZone(event, position, new TermAndZone(baseTerm.toString(), zone), alreadyIndexedTerms, context, contextWriter, reporter);
        }
        
        // If we have more terms to add to this phrase, recurse
        if (terms.size() > 1) {
            completePhrase(baseTerm, terms.subList(1, terms.size()), zone, event, position, alreadyIndexedTerms, context, contextWriter, reporter);
        }
        
        // Only remove the space and term if we actually added one
        if (properLen) {
            // Remove the space and the token we appended last
            baseTerm.setLength(baseTerm.length() - 1 - term.length());
        }
    }
}
 

@Override
public boolean membershipTest(Key key) {
  if (key == null) {
    return true;
  }

  for (BloomFilter bloomFilter : matrix) {
    if (bloomFilter.membershipTest(key)) {
      return true;
    }
  }

  return false;
}
 

/**
 * Constructor.
 * <p>
 * Builds an empty Dynamic Bloom filter.
 *
 * @param vectorSize The number of bits in the vector.
 * @param nbHash     The number of hash function to consider.
 * @param hashType   type of the hashing function (see {@link org.apache.hadoop.util.hash.Hash}).
 * @param nr         The threshold for the maximum number of keys to record in a dynamic Bloom filter row.
 */
public InternalDynamicBloomFilter(int vectorSize, int nbHash, int hashType, int nr, int maxNr) {
  super(vectorSize, nbHash, hashType);

  this.nr = nr;
  this.currentNbRecord = 0;
  this.maxNr = maxNr;

  matrix = new org.apache.hadoop.util.bloom.BloomFilter[1];
  matrix[0] = new org.apache.hadoop.util.bloom.BloomFilter(this.vectorSize, this.nbHash, this.hashType);
}
 

@Nonnull
public static BloomFilter newBloomFilter(@Nonnegative final int expectedNumberOfElements,
        @Nonnegative final float errorRate) {
    // k = ceil(-log_2(false prob.))
    int nbHash = Math.max(2, (int) Math.ceil(-(Math.log(errorRate) / LOG2)));
    return newBloomFilter(expectedNumberOfElements, errorRate, nbHash);
}
 

@Nonnull
public static BloomFilter newBloomFilter(@Nonnegative final int expectedNumberOfElements,
        @Nonnegative final float errorRate, @Nonnegative final int nbHash) {
    // vector size should be `-kn / (ln(1 - c^(1/k)))` bits for
    // single key, where `k` is the number of hash functions,
    // `n` is the number of keys and `c` is the desired max error rate.
    int vectorSize = (int) Math.ceil((-nbHash * expectedNumberOfElements)
            / Math.log(1.d - Math.pow(errorRate, 1.d / nbHash)));
    return new BloomFilter(vectorSize, nbHash, Hash.MURMUR_HASH);
}
 

/**
 * Process a term and zone by writting all applicable keys to the context.
 * 
 * @param event
 * @param position
 * @param termAndZone
 * @param alreadyIndexedTerms
 * @param context
 * @param contextWriter
 * @param reporter
 * @throws IOException
 * @throws InterruptedException
 */
private void processTermAndZone(RawRecordContainer event, int position, TermAndZone termAndZone, BloomFilter alreadyIndexedTerms,
                TaskInputOutputContext<KEYIN,? extends RawRecordContainer,KEYOUT,VALUEOUT> context, ContextWriter<KEYOUT,VALUEOUT> contextWriter,
                StatusReporter reporter) throws IOException, InterruptedException {
    
    // Make sure the term length is greater than the minimum allowed length
    if (termAndZone.term.length() < tokenHelper.getTermLengthMinimum()) {
        log.debug("Ignoring token of length " + termAndZone.term.length() + " because it is too short");
        counters.increment(ContentIndexCounters.TOO_SHORT_COUNTER, reporter);
        return;
    }
    
    // Track all tokens (including synonyms) processed
    counters.increment(ContentIndexCounters.ALL_PROCESSED_COUNTER, reporter);
    
    // Normalize the term since it won't be auto-normalized through the eventFields map
    NormalizedFieldAndValue normFnV = new NormalizedFieldAndValue(termAndZone.zone, termAndZone.term);
    Set<NormalizedContentInterface> ncis = this.ingestHelper.normalize(normFnV);
    // nfv = (NormalizedFieldAndValue) this.ingestHelper.normalize(nfv);
    
    for (NormalizedContentInterface nci : ncis) {
        if (!(nci instanceof NormalizedFieldAndValue)) {
            log.warn("Can't handle a " + nci.getClass() + "; must be a NormalizedFieldAndValue.");
        }
        NormalizedFieldAndValue nfv = (NormalizedFieldAndValue) nci;
        byte[] fieldVisibility = getVisibility(event, nfv);
        
        // Build the event column key/value
        createShardEventColumn(event, contextWriter, context, nfv, this.shardId, fieldVisibility);
        
        // Create a index normalized variant of the term and zone for indexing purposes
        TermAndZone indexedTermAndZone = new TermAndZone(nfv.getIndexedFieldValue(), nfv.getIndexedFieldName());
        
        org.apache.hadoop.util.bloom.Key alreadySeen = null;
        if ((alreadyIndexedTerms != null)
                        && alreadyIndexedTerms.membershipTest(alreadySeen = new org.apache.hadoop.util.bloom.Key(indexedTermAndZone.getToken().getBytes()))) {
            if (log.isDebugEnabled()) {
                log.debug("Not creating index mutations for " + termAndZone + " as we've already created mutations for it.");
            }
            counters.increment(ContentIndexCounters.BLOOM_FILTER_EXISTS, reporter);
        } else if ((tokenOffsetCache != null) && tokenOffsetCache.containsKey(indexedTermAndZone)) {
            if (log.isDebugEnabled()) {
                log.debug("Not creating index mutations for " + termAndZone + " as we've already created mutations for it.");
            }
            counters.increment(ContentIndexCounters.TOKEN_OFFSET_CACHE_EXISTS, reporter);
        } else {
            // create the index
            createShardIndexColumns(event, contextWriter, context, nfv, this.shardId, fieldVisibility);
            
            if (alreadyIndexedTerms != null) {
                alreadyIndexedTerms.add(alreadySeen);
                counters.increment(ContentIndexCounters.BLOOM_FILTER_ADDED, reporter);
            }
        }
        
        // Now add the offset to the token offset queue, and if we overflow then output the overflow
        if (tokenOffsetCache != null) {
            OffsetList<Integer> overflow = tokenOffsetCache.addOffset(indexedTermAndZone, position);
            if (overflow != null) {
                // no need to normalize as that was already done upon insertion into the token offset cache
                NormalizedFieldAndValue overflowNfv = new NormalizedFieldAndValue(overflow.termAndZone.zone, overflow.termAndZone.term);
                byte[] overflowFieldVisibility = getVisibility(event, overflowNfv);
                
                // Build the field index key/value
                createTermFrequencyIndex(event, contextWriter, context, this.shardId, overflowNfv, overflow.offsets, overflowFieldVisibility,
                                this.ingestHelper.getDeleteMode());
                counters.increment(ContentIndexCounters.TOKENIZER_OFFSET_CACHE_OVERFLOWS, reporter);
                counters.incrementValue(ContentIndexCounters.TOKENIZER_OFFSET_CACHE_POSITIONS_OVERFLOWED, overflow.offsets.size(), reporter);
            }
        } else {
            createTermFrequencyIndex(event, contextWriter, context, this.shardId, nfv, Arrays.asList(position), fieldVisibility,
                            this.ingestHelper.getDeleteMode());
        }
    }
}
 

/**
 * Process a term and zone by writting all applicable keys to the context.
 * 
 * @param event
 * @param position
 * @param term
 * @param alreadyIndexedTerms
 * @param context
 * @param contextWriter
 * @param fieldName
 * @param fieldNameToken
 * @param reporter
 * @throws IOException
 * @throws InterruptedException
 */
protected void processTerm(RawRecordContainer event, int position, String term, BloomFilter alreadyIndexedTerms,
                TaskInputOutputContext<KEYIN,? extends RawRecordContainer,KEYOUT,VALUEOUT> context, ContextWriter<KEYOUT,VALUEOUT> contextWriter,
                String fieldName, String fieldNameToken, StatusReporter reporter) throws IOException, InterruptedException {
    
    // Track all tokens (including synonyms) processed
    if (context != null) {
        counters.increment(ContentIndexCounters.ALL_PROCESSED_COUNTER, reporter);
    }
    
    // Normalize the term since it won't be auto-normalized through the eventFields map
    NormalizedFieldAndValue normFnV = new NormalizedFieldAndValue(fieldNameToken, term);
    Set<NormalizedContentInterface> ncis = this.ingestHelper.normalize(normFnV);
    
    for (NormalizedContentInterface nci : ncis) {
        if (!(nci instanceof NormalizedFieldAndValue)) {
            log.warn("Can't handle a " + nci.getClass() + "; must be a NormalizedFieldAndValue.");
        }
        NormalizedFieldAndValue nfv = (NormalizedFieldAndValue) nci;
        byte[] fieldVisibility = getVisibility(event, nfv);
        
        // Build the event column key/value
        createShardEventColumn(event, contextWriter, context, nfv, this.shardId, fieldVisibility);
        
        // Create a index normalized variant of the term and zone for indexing purposes
        TermAndZone indexedTermAndZone = new TermAndZone(nfv.getIndexedFieldValue(), nfv.getIndexedFieldName());
        
        if ((tokenOffsetCache != null) && tokenOffsetCache.containsKey(indexedTermAndZone)) {
            if (log.isDebugEnabled()) {
                log.debug("Not creating index mutations for " + term + " as we've already created mutations for it.");
            }
            counters.increment(ContentIndexCounters.TOKEN_OFFSET_CACHE_EXISTS, reporter);
        } else {
            // create the index
            createShardIndexColumns(event, contextWriter, context, nfv, this.shardId, fieldVisibility);
        }
        
        // Now add the offset to the token offset queue, and if we overflow then output the overflow
        if (tokenOffsetCache != null) {
            OffsetList overflow = tokenOffsetCache.addOffset(indexedTermAndZone, position);
            if (overflow != null) {
                // no need to normalize as that was already done upon insertion into the token offset cache
                NormalizedFieldAndValue overflowNfv = new NormalizedFieldAndValue(overflow.termAndZone.zone, overflow.termAndZone.term);
                byte[] overflowFieldVisibility = getVisibility(event, overflowNfv);
                
                // Build the field index key/value
                createTermFrequencyIndex(event, contextWriter, context, this.shardId, overflowNfv, overflow.offsets, overflowFieldVisibility, false);
                counters.increment(ContentIndexCounters.TOKENIZER_OFFSET_CACHE_OVERFLOWS, reporter);
                counters.incrementValue(ContentIndexCounters.TOKENIZER_OFFSET_CACHE_POSITIONS_OVERFLOWED, overflow.offsets.size(), reporter);
            }
        } else {
            createTermFrequencyIndex(event, contextWriter, context, this.shardId, nfv, Arrays.asList(position), fieldVisibility, false);
        }
    }
}
 

@Nonnull
public static BloomFilter newBloomFilter(@Nonnegative final int expectedNumberOfElements) {
    return newBloomFilter(expectedNumberOfElements, DEFAULT_ERROR_RATE);
}
 

public static BloomFilter fromFile(File f) throws IOException {
  return readFromAvro(FileUtils.openInputStream(f));
}
 

@Override
protected void reduce(NullWritable key, Iterable<BloomFilter> values, Context context) throws IOException, InterruptedException {
  for (BloomFilter bf : values) {
    filter.or(bf);
  }
}
 

@Override
public void not() {
  for (BloomFilter bloomFilter : matrix) {
    bloomFilter.not();
  }
}
 

public static BloomFilter fromPath(Configuration config, Path path) throws IOException {
  FileSystem hdfs = path.getFileSystem(config);

  return readFromAvro(hdfs.open(path));
}
 

public static BloomFilter fromFile(File f) throws IOException {
  return readFromAvro(FileUtils.openInputStream(f));
}