You can set as an argument the number of the partitions you want. By default, Elixir will use the same number of processors on the machine and it’s not designed to work in a distributed fashion (Flow — Flow v1.2.4).

Flow.from_enumerable([1, 2, 3], stages: 3) # To use 3 processes

If I’m not mistaken, the reducer stage is where you join everything together. So, the computations are run in parallel on the mapping stage, not on the reducer stage. So, every time you call Flow.partition/2, new processes will be used by that computations. For example:

[1, 2, 3]
|> Flow.from_enumerable()
|> Flow.map(fn x -> x + 1 end) # [2, 3, 4]
|> Flow.partition() # A new group of processes will be spawned here.
|> Flow.map(fn x -> x * 2 end) # [4, 6, 8]
|> Flow.partition() # A new group of processes will be spawned here.
|> Flow.reduce(fn -> 0 end, fn x, acc -> x + acc end)

$> 18

Be in mind that Flow does not work in a distributed way - it only works on a single node.

I’m not sure if I understand your question. Can you elaborate more what you want to achieve?

Sorry, I have to revive this old topic because I have new… needs about Flow.
I know that at the level of 2017 Flow didn’t work in a distributed way.
Now is it possible to make it run on multiple nodes ? In such way to distribute partitions among nodes ?
Is there an alternative for distributed MapReduce in Elixir ?