distributed computing - Spark - repartition() vs coalesce()

Question

According to Learning Spark

Keep in mind that repartitioning your data is a fairly expensive operation. Spark also has an optimized version of repartition() called coalesce() that allows avoiding data movement, but only if you are decreasing the number of RDD partitions.

One difference I get is that with repartition() the number of partitions can be increased/decreased, but with coalesce() the number of partitions can only be decreased.

If the partitions are spread across multiple machines and coalesce() is run, how can it avoid data movement?

Answer 1

It avoids a full shuffle. If it's known that the number is decreasing then the executor can safely keep data on the minimum number of partitions, only moving the data off the extra nodes, onto the nodes that we kept.

So, it would go something like this:

Node 1 = 1,2,3
Node 2 = 4,5,6
Node 3 = 7,8,9
Node 4 = 10,11,12

Then coalesce down to 2 partitions:

Node 1 = 1,2,3 + (10,11,12)
Node 3 = 7,8,9 + (4,5,6)

Notice that Node 1 and Node 3 did not require its original data to move.