Distributed Pure Functions by Richard Feldman 

There's a lot of hand-waving when it comes to scaling purely functional code. It would be easy to get the impression that as long as all your functions have no side effects, and all your data is immutable - say, because the programming language guarantees it - then distributing the work across cores and machines becomes trivial. Theoretically, a purely functional language could even parallelize workloads automatically for you behind the scenes!
Naturally, things get trickier in practice. This talk dives into the practical considerations of distributing functions that are known to be pure. How much can really be done automatically? How do you avoid the accidentally slowing things down when the coordination costs get too high? What are the tradeoffs and edge cases involved?
Come find out what happens when pure functions get distributed!
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