[SOLVED] What are the Spark transformations that causes a Shuffle?

What are the Spark transformations that causes a Shuffle?

I have trouble to find in the Spark documentation operations that causes a shuffle and operation that does not. In this list, which ones does cause a shuffle and which ones does not?

Map and filter does not. However, I am not sure with the others.

map(func)
filter(func)
flatMap(func)
mapPartitions(func)
mapPartitionsWithIndex(func)
sample(withReplacement, fraction, seed)
union(otherDataset)
intersection(otherDataset)
distinct([numTasks]))
groupByKey([numTasks])
reduceByKey(func, [numTasks])
aggregateByKey(zeroValue)(seqOp, combOp, [numTasks])
sortByKey([ascending], [numTasks])
join(otherDataset, [numTasks])
cogroup(otherDataset, [numTasks])
cartesian(otherDataset)
pipe(command, [envVars])
coalesce(numPartitions)

Solution

It is actually extremely easy to find this out, without the documentation. For any of these functions just create an RDD and call to debug string, here is one example you can do the rest on ur own.

scala> val a  = sc.parallelize(Array(1,2,3)).distinct
scala> a.toDebugString
MappedRDD[5] at distinct at <console>:12 (1 partitions)
  MapPartitionsRDD[4] at distinct at <console>:12 (1 partitions)
    **ShuffledRDD[3] at distinct at <console>:12 (1 partitions)**
      MapPartitionsRDD[2] at distinct at <console>:12 (1 partitions)
        MappedRDD[1] at distinct at <console>:12 (1 partitions)
          ParallelCollectionRDD[0] at parallelize at <console>:12 (1 partitions)

So as you can see distinct creates a shuffle. It is also particularly important to find out this way rather than docs because there are situations where a shuffle will be required or not required for a certain function. For example join usually requires a shuffle but if you join two RDD's that branch from the same RDD spark can sometimes elide the shuffle.