Partitioning, Shuffle and sort

Partitioning, Shuffle and sort  what happened?

- Partitioning

Partitioning is the process of determining which reducer instance will receive which intermediate keys and values. Each mapper must determine for all of its output (key, value) pairs which reducer will receive them. It is necessary that for any key, regardless of which mapper instance generated it, the destination partition is the same

Problem: how dose the hadoop make it? Use a hash function ? what is the function? 

here is code~

1 public class HashPartitioner<K, V> extends Partitioner<K, V> {
2     public int getPartition(K key, V value, int numReduceTasks) {
3         return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
4     }
5 }

解释：将key均匀分布在ReduceTasks上，举例若是Key为Text的话，Text的hashcode方法跟String的基本一致，都是采用的Horner公式计算，获得一个int，string太大的话这个int值可能会溢出变成负数，因此与上Integer.MAX_VALUE（即0111111111111111），而后再对reduce个数取余，这样就可让相同key分布在一个节点上，而且较为均匀的分布在reduce上

Horner规则:算法导论上有介绍这个，百度之

think about BloomFilter~ 保证这个任务任务分发的均匀是关键，因此要设计优秀的hash函数是关键

- Shuffle

After the first map tasks have completed, the nodes may still be performing several more map tasks each. But they also begin exchanging the intermediate outputs from the map tasks to where they are required by the reducers. This process of moving map outputs to the reducers is known as shuffling.

 

- Sort

Each reduce task is responsible for reducing the values associated with several intermediate keys. The set of intermediate keys on a single node is automatically sorted by  Hadoop before they are presented to the Reducer