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Accumulo >> mail # dev >> KeyRangePartitioner

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Is there any reason why changing the RangePartitioner so that it
understands Accumulo Key objects is bad? It seems like the only significant
change is passing key.getRow() into the binarySearch call in findPartition.
With this change it seems that the sort phase of map-reduce is sorting
accumulo keys properly.

Have i overlooked something?

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package org.apache.accumulo.core.client.mapreduce.lib.partition;

import java.io.BufferedReader;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.Arrays;
import java.util.Scanner;
import java.util.TreeSet;

import org.apache.commons.codec.binary.Base64;
import org.apache.hadoop.conf.Configurable;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.Partitioner;

import *org.apache.accumulo.core.data.Key;*

 * Hadoop partitioner that uses ranges, and optionally sub-bins based
on hashing.
public class KeyRangePartitioner extends Partitioner<Key,Writable>
implements Configurable {
  private static final String PREFIX = RangePartitioner.class.getName();
  private static final String CUTFILE_KEY = PREFIX + ".cutFile";
  private static final String NUM_SUBBINS = PREFIX + ".subBins";

  private Configuration conf;

  public int getPartition(Key key, Writable value, int numPartitions) {
    try {
      return findPartition(key, getCutPoints(), getNumSubBins());
    } catch (IOException e) {
      throw new RuntimeException(e);

  int findPartition(Key key, Text[] array, int numSubBins) {
    // find the bin for the range, and guarantee it is positive
    int index = Arrays.binarySearch(array, key.getRow());
    index = index < 0 ? (index + 1) * -1 : index;

    // both conditions work with numSubBins == 1, but this check is to avoid
    // hashing, when we don't need to, for speed
    if (numSubBins < 2)
      return index;
    return (key.toString().hashCode() & Integer.MAX_VALUE) %
numSubBins + index * numSubBins;

  private int _numSubBins = 0;

  private synchronized int getNumSubBins() {
    if (_numSubBins < 1) {
      // get number of sub-bins and guarantee it is positive
      _numSubBins = Math.max(1, getConf().getInt(NUM_SUBBINS, 1));
    return _numSubBins;

  private Text cutPointArray[] = null;

  private synchronized Text[] getCutPoints() throws IOException {
    if (cutPointArray == null) {
      String cutFileName = conf.get(CUTFILE_KEY);
      Path[] cf = DistributedCache.getLocalCacheFiles(conf);

      if (cf != null) {
        for (Path path : cf) {
          if (path.toUri().getPath().endsWith(cutFileName.substring(cutFileName.lastIndexOf('/'))))
            TreeSet<Text> cutPoints = new TreeSet<Text>();
            Scanner in = new Scanner(new BufferedReader(new
            try {
              while (in.hasNextLine())
            } finally {
            cutPointArray = cutPoints.toArray(new Text[cutPoints.size()]);
      if (cutPointArray == null)
        throw new FileNotFoundException(cutFileName + " not found in
distributed cache");
    return cutPointArray;

  public Configuration getConf() {
    return conf;

  public void setConf(Configuration conf) {
    this.conf = conf;

   * Sets the hdfs file name to use, containing a newline separated
list of Base64 encoded split points that represent ranges for
  public static void setSplitFile(JobContext job, String file) {
    URI uri = new Path(file).toUri();
    DistributedCache.addCacheFile(uri, job.getConfiguration());
    job.getConfiguration().set(CUTFILE_KEY, uri.getPath());

   * Sets the number of random sub-bins per range
  public static void setNumSubBins(JobContext job, int num) {
    job.getConfiguration().setInt(NUM_SUBBINS, num);