reduceByKey和groupByKey的区别:

打开源码查看英文解释:

/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
* parallelism level.
*/
def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
reduceByKey(defaultPartitioner(self), func)
}

也就是说,reduceBykey在Mapper端对每个分区的key预先进行一次合并,类似于mapreduce当中的combiner归约,之后reducer端再把合并后的数据拉取过来,这样做的好处就是减少了mapper端到reducer端的数据量传输,提高了IO性能,也就提高了效率

/**
* Group the values for each key in the RDD into a single sequence. Allows controlling the
* partitioning of the resulting key-value pair RDD by passing a Partitioner.
* The ordering of elements within each group is not guaranteed, and may even differ
* each time the resulting RDD is evaluated.
*
* Note: This operation may be very expensive. If you are grouping in order to perform an
* aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
* or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
*
* Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any
* key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
*/
def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = {
// groupByKey shouldn't use map side combine because map side combine does not
// reduce the amount of data shuffled and requires all map side data be inserted
// into a hash table, leading to more objects in the old gen.
val createCombiner = (v: V) => CompactBuffer(v)
val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
val bufs = combineByKey[CompactBuffer[V]](
createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine=false)
bufs.asInstanceOf[RDD[(K, Iterable[V])]]
}

groupByKey：groupByKey会对每一个RDD中的value值进行聚合形成一个序列(Iterator)，此操作发生在reduce端，所以势必会将所有的数据通过网络进行传输，造成不必要的浪费。同时如果数据量十分大，可能还会造成OutOfMemoryError。

举例说明:

reduceByKey和groupByKey的区别:

总结:

通过以上对比可以发现在进行大量数据的reduce操作时候建议使用reduceByKey。不仅可以提高速度，还是可以防止使用groupByKey造成的内存溢出问题。

reduceByKey和groupByKey的区别:

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