Transducers.jl: Efficient transducers for Julia

Transducers.jl provides composable algorithms on "sequence" of inputs.
They are called transducers, first introduced in Clojure language
by Rich Hickey.

Using transducers is quite straightforward, especially if you already
know similar concepts in iterator libraries:

juliaCopy
using Transducers
1:40 |> Partition(7) |> Filter(x -> prod(x) % 11 == 0) |> Cat() |> Scan(+) |> sum

However, the protocol used for the transducers is quite different from
iterators and results in a better performance for complex
compositions. Furthermore, some transducers support parallel
execution. If a transducer is composed of such transducers, it can be
automatically re-used both in sequential (foldl etc.) and parallel
(reduce etc.) contexts.

See more in the documentation.

If you are interested in parallel programming in general, see also:
A quick introduction to data parallelism in Julia

Installation

juliaCopy
using Pkg
Pkg.add("Transducers")

Related packages

Following packages are supported by Transducers.jl. In particular, they rely on
the Transducers.jl protocol to support multi-threading, multi-processing, and
GPU-based parallelism.

• Folds.jl implements parallelized
  Base-like API based on Transducers.jl. This package can be used without
  knowing anything about transducers.
• FLoops.jl provides for-loop
  syntax for using the loop executed by the Transducers.jl protocol.
• BangBang.jl implements
  mutate-or-widen API. This is the foundation of
  typocalypse-free
  map/collect-like functions. Functions such as append!!,
  merge!!, mergewith!!, union!!, etc. are useful as a reducing
  function.
• InitialValues.jl
  provides a framework for initial/identity element of folds.
• MicroCollections.jl
  provides empty and singleton collections (arrays, dicts and sets).
  They are useful when writing transducers and reducing functions that
  construct a data collection.