我們知道Mapreduce框架在feed數據給reducer之前會對map output key排序，這種排序機制保證了每一個reducer局部有序，Hadoop 默認的partitioner是HashPartitioner，它依賴於output key的hashcode，使得相同key會去相同reducer，但是不保證全局有序，如果想要獲得全局排序結果（比如獲取top N, bottom N），就需要用到TotalOrderPartitioner了，它保證了相同key去相同reducer的同時也保證了全局有序。
public class HashPartitioner<K, V> extends Partitioner<K, V> {
/** Use {@link Object#hashCode()} to partition. */
public int getPartition(K key, V value,
int numReduceTasks) {
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
/**
* Partitioner effecting a total order by reading split points from
* an externally generated source.
*/
@InterfaceAudience.Public
@InterfaceStability.Stable
public class TotalOrderPartitioner<K extends WritableComparable<?>,V>
extends Partitioner<K,V> implements Configurable {
// by construction, we know if our keytype
@SuppressWarnings("unchecked") // is memcmp-able and uses the trie
public int getPartition(K key, V value, int numPartitions) {
return partitions.findPartition(key);
}
}
TotalOrderPartitioner依賴於一個partition file來distribute keys，partition file是一個實現計算好的sequence file，如果我們設置的reducer number是N，那麼這個文件包含（N-1）個key分割點，並且是基於key comparator排好序的。TotalOrderPartitioner會檢查每一個key屬於哪一個reducer的范圍內，然後決定分發給哪一個reducer。
InputSampler類的writePartitionFile方法會對input files取樣並創建partition file。有三種取樣方法：
1. RandomSampler 隨機取樣
2. IntervalSampler 從s個split裡面按照一定間隔取樣，通常適用於有序數據
3. SplitSampler 從s個split中選取前n條記錄取樣
paritition file可以通過TotalOrderPartitioner.setPartitionFile(conf, partitionFile)來設置，在TotalOrderPartitioner instance創建的時候會調用setConf函數，這時會讀入partition file中key值，如果key是BinaryComparable(可以認為是字符串類型)的話會構建trie，時間復雜度是O(n), n是樹的深度。如果是非BinaryComparable類型就構建BinarySearchNode，用二分查找，時間復雜度O(log(n))，n是reduce數
boolean natOrder =
conf.getBoolean(NATURAL_ORDER, true);
if (natOrder && BinaryComparable.class.isAssignableFrom(keyClass)) {
partitions = buildTrie((BinaryComparable[])splitPoints, 0,
splitPoints.length, new byte[0],
// Now that blocks of identical splitless trie nodes are
// represented reentrantly, and we develop a leaf for any trie
// node with only one split point, the only reason for a depth
// limit is to refute stack overflow or bloat in the pathological
// case where the split points are long and mostly look like bytes
// iii...iixii...iii . Therefore, we make the default depth
// limit large but not huge.
conf.getInt(MAX_TRIE_DEPTH, 200));
} else {
partitions = new BinarySearchNode(splitPoints, comparator);
}
示例程序
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler.RandomSampler;
import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
public class TotalSortMR {
public static int runTotalSortJob(String[] args) throws Exception {
Path inputPath = new Path(args[0]);
Path outputPath = new Path(args[1]);
Path partitionFile = new Path(args[2]);
int reduceNumber = Integer.parseInt(args[3]);
// RandomSampler第一個參數表示key會被選中的概率，第二個參數是一個選取samples數，第三個參數是最大讀取input splits數
RandomSampler<Text, Text> sampler = new InputSampler.RandomSampler<Text, Text>(0.1, 10000, 10);
Configuration conf = new Configuration();
// 設置partition file全路徑到conf
TotalOrderPartitioner.setPartitionFile(conf, partitionFile);
Job job = new Job(conf);
job.setJobName("Total-Sort");
job.setJarByClass(TotalSortMR.class);
job.setInputFormatClass(KeyValueTextInputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setNumReduceTasks(reduceNumber);
// partitioner class設置成TotalOrderPartitioner
job.setPartitionerClass(TotalOrderPartitioner.class);
FileInputFormat.setInputPaths(job, inputPath);
FileOutputFormat.setOutputPath(job, outputPath);
outputPath.getFileSystem(conf).delete(outputPath, true);
// 寫partition file到mapreduce.totalorderpartitioner.path
InputSampler.writePartitionFile(job, sampler);
return job.waitForCompletion(true)? 0 : 1;
}
public static void main(String[] args) throws Exception{
System.exit(runTotalSortJob(args));
}
}
上面的例子是采用InputSampler來創建partition file，其實還可以使用mapreduce來創建，可以自定義一個inputformat來取樣，將output key輸出到一個reducer
ps:hive 0.12實現了parallel ORDER BY(https://issues.apache.org/jira/browse/HIVE-1402)，也是基於TotalOrderPartitioner，非常靠譜的new feature啊
更多Hadoop相關信息見Hadoop 專題頁面 http://www.linuxidc.com/topicnews.aspx?tid=13