scala - Using Futures within Spark

Question

A Spark job makes a remote web service for every element in an RDD. A simple implementation might look something like this:

def webServiceCall(url: String) = scala.io.Source.fromURL(url).mkString
rdd2 = rdd1.map(x => webServiceCall(x.field1))

(The above example has been kept simple and does not handle timeouts).

There is no interdependency between any of the results for different elements of the RDD.

Would the above be improved by using Futures to optimise performance by making parallel calls to the web service for each element of the RDD? Or does Spark itself have that level of optimization built in, so that it will run the operations on each element in the RDD in parallel?

If the above can be optimized by using Futures, does anyone have some code examples showing the correct way to use Futures within a function passed to a Spark RDD.

Thanks

Answer 1

Or does Spark itself have that level of optimization built in, so that it will run the operations on each element in the RDD in parallel?

It doesn't. Spark parallelizes tasks at the partition level but by default every partition is processed sequentially in a single thread.

Would the above be improved by using Futures

It could be an improvement but is quite hard to do it right. In particular:

• every Future has to be completed in the same stage before any reshuffle takes place.
• given lazy nature of the Iterators used to expose partition data you cannot do it high level primitives like map (see for example Spark job with Async HTTP call).
• you can build your custom logic using mapPartitions but then you have to deal with all the consequences of non-lazy partition evaluation.