This library makes it easy to run a linear DAG while extracting magical parallelism if some steps don't make changes to some values.

Sure, this is a dataflow-esque library that allows you to do transforms on items in one direction towards a goal. Where it differs from other dataflow models is that there is only map and the items can never never change type (for a given key).

Additionally all steps and inputs need to be known up front, but that isn't a restriction of the core algorithm, just in the name of readability.

In exchange for those restrictions, you get a lot of API simplicity, as well as the ability to run future steps eagerly given sufficient slots and automatic bundling into "batches" of items like xargs does to amortize child set-up times.

• The steps need to be decided up front (although it's cheap to have steps that maybe don't do anything). This includes the order that they will apply in.
• Steps ought to be deterministic and idempotent within a run (if they aren't, you should mark individual steps eager=False or enable deliberate=True on the Run, which only uses intra-step parallelism).
• Steps ought to have static relationships between the inputs and output keys such as %.py input changes potentially affecting %.java outputs, using the wildcard % you might know from Make. If you don't (say, files can include other arbitrary files), then you might need to model this as any input change invalidating all output keys which will tend to be inefficient.
• Steps ought to not change the type of a key's value (although they can create new keys, or delete existing keys, so you can work around this by including the type in the key and still get correctness). If you wanted to support str <=> int transformations on the same key, this will only work if all subsequent steps work with either.
• Your input values, as well as all intermediate output values, need to fit in memory. Nothing keeps you from using filenames, urls, or CAS keys as the value though.

Usage of this library should work back to 3.8, but development (and mypy compatibility) only on 3.10-3.12. Linting requires 3.12 for full fidelity.

This library follows meanver which basically means semver along with a promise to rename when the major version changes.

feedforward is copyright Tim Hatch, and licensed under the MIT license. See the LICENSE file for details.