Multiple Flatten.pCollections() transforms generate an overwhelming number of tasks

The Flatten.pCollections() is translated into a Spark union operation. The resulting RDD will have the sum of the partitions in the originating RDDs.

If you flatten 2 PCollections with each 10 partitions, the result will have 20 partitions.

This is ok in small pipelins, but in our main pipeline, this means the number of tasks grows out of hand quite easily (over 500k tasks in one stage). This overloads the driver and crashes the process.

I have created a small repro case:

https://github.com/pbackx/beam-flatmap-test

A possible solution is to add a coalesce call after the union. We have been testing this and it seems to do exactly what we want, but I'm not sure if this fix is applicable for all cases. 

I will open a PR for this so that you can review my proposed change and discuss whether or not it's a good idea.