goeieDAG

/ɣu.jə.ˈdɑx/: hello, good day (Dutch greeting used during daytime)

goeieDAG provides a unified Python API to Ninja and Make (TODO) build systems, aiming to make it extremely easy to benefit from parallel processing in any graph-like workflow.

Usage

from pathlib import Path

import goeiedag
from goeiedag import ALL_INPUTS, INPUT, OUTPUT

workdir = Path("output")

graph = goeiedag.CommandGraph()

# Extract OS name from /etc/os-release
graph.add(["grep", "^NAME=", INPUT, ">", OUTPUT],
          inputs=["/etc/os-release"],
          outputs=["os-name.txt"])
# Get username
graph.add(["whoami", ">", OUTPUT],
          inputs=[],
          outputs=["username.txt"])
# Glue together to produce output
graph.add(["cat", ALL_INPUTS, ">", OUTPUT.result],
          inputs=["os-name.txt", "username.txt"],
          outputs=dict(result="result.txt"))  # can also use a dictionary and refer to inputs/outputs by name

goeiedag.build_all(graph, workdir)

# Print output
print((workdir / "result.txt").read_text())

Q&A

Why use the files and commands model rather than Python objects and functions?

• It is a tested and proven paradigm (make traces back to 1976!)
• It provides an obvious way of evaluating which products need rebuilding (subject to an accurate dependency graph)
• It naturally isolates and parallelizes individual build tasks
• It is agnostic as to how data objects are serialized (convenient for the library author...)
• Graph edges are implicitly defined by input/output file names
• A high-quality executor (Ninja) is available and installable via a Python package

How is this different from using the Ninja package directly?

• Simpler mental model & usage: no need to separately define build rules or think about implicit/explicit inputs and outputs
• API accepts Paths; no need to cast everything to str!
• Higher-level API in general (for example, the output directory is created automatically)

Similar projects

• Ninja (Python package) -- provides a lower-level API, used by goeieDAG as back-end
• TaskGraph -- similar project, but centered around Python functions and in-process parallelism
• Snakemake -- similar goals, but a stand-alone tool rather than a library
• Dask -- different execution model; caching of intermediate results is left up to the user
• doit