减少partition时，用coalesce效率更高

减少partition时，用coalesce效率更高

测试

repartition，shuffle 2.8G, 耗时10min39sec

    
    
    
    

     
     
     
     
df.rdd.repartition(1).saveAsTextFile("/gx/gziptest", classOf[org.apache.hadoop.io.compress.GzipCodec])
    
    
    
    

joe: start time: Tue Jul 07 12:43:06 CST 2015

joe: end time: Tue Jul 07 12:53:45 CST 2015

coalesce，没有shuffle， 耗时6min22sec

    
    
    
    

     
     
     
     
df.rdd.coalesce(1).saveAsTextFile("/gx/gziptest", classOf[org.apache.hadoop.io.compress.GzipCodec])
    
    
    
    

joe: start time: Tue Jul 07 13:39:16 CST 2015

joe: end time: Tue Jul 07 13:45:38 CST 2015

说明

repartition(numPartitions) 

Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.  等于 coalesce(numPartitions, shuffle = true)

coalesce(numPartitions)    

Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.

If you are decreasing the number of partitions in this RDD, consider using `coalesce`,  which can avoid performing a shuffle.

However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,  this may result in your computation taking place on fewer nodes than  you like (e.g. one node in the case of numPartitions = 1). To avoid this,  you can pass shuffle = true. This will add a shuffle step, but means the   current upstream partitions will be executed in parallel (per whatever  the current partitioning is).

来自为知笔记(Wiz)