yieldgraph 0.1.0

A lightweight Python library for building interruptible, generator-driven ETL pipelines as directed graphs.

yieldgraph

Build interruptible, generator-driven ETL pipelines as directed graphs — in pure Python.

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What is yieldgraph?

yieldgraph lets you compose data-processing pipelines by connecting plain Python callables into a directed graph. Each callable becomes a Node. Nodes are linked by Edges — lightweight queues that carry data tuples downstream. Call graph.run() and the pipeline does the rest.

There is no framework to learn. Your business logic stays in ordinary Python functions. yieldgraph just wires them together and gets out of the way.

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Features

• Pure Python, zero runtime dependencies — stdlib only; loguru is optional.
• Generator-native — yield zero, one, or many results per input; everything stays lazy.
• Cooperative cancellation — set graph.cancelled = True or press Ctrl+C; the pipeline stops cleanly at the next yield.
• Fan-out branching — attach multiple downstream chains to any node with a single add_chain call.
• Threaded mode — flip YIELDGRAPH_THREADED=1 to run all nodes concurrently; edges become thread-safe blocking queues automatically.
• Built-in observability — every node exposes n_consumed, n_produced, errors, and a progress fraction you can poll at any time.

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Installation

pip install yieldgraph

From source:

git clone https://github.com/j4ggr/yieldgraph.git
cd yieldgraph
pip install -e .

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Quick start

from yieldgraph import Graph

def source(graph):
    """Emit raw records — receives the Graph instance as first argument."""
    for row in [
        {"name": "Alice", "score": 95},
        {"name": "Bob",   "score": 72},
        {"name": "Carol", "score": 88},
    ]:
        yield row

def grade(record):
    """Add a pass/fail label to each record."""
    record["grade"] = "pass" if record["score"] >= 85 else "fail"
    yield record

def format_output(record):
    """Format as a human-readable string."""
    yield f"{record['name']}: {record['grade']} ({record['score']})"

g = Graph()
g.add_chain(source, grade, format_output)
g.run()

for row in g.output:
    print(row[0])
# Alice: pass (95)
# Bob: fail (72)
# Carol: pass (88)

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Fan-out — multiple downstream chains

def source(graph):
    for x in range(1, 6):
        yield x

def store_db(x):
    yield f"db:{x}"

def send_queue(x):
    yield f"mq:{x}"

g = Graph()
g.add_chain(source, store_db)    # chain 1
g.add_chain(source, send_queue)  # chain 2 — same source, parallel branch
g.run()

print(g.output)
# [('db:1',), ('db:2',), ..., ('mq:1',), ('mq:2',), ...]

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Cooperative cancellation

def source(graph):
    for i in range(1_000_000):
        if i >= 5:
            graph.cancelled = True   # stop cleanly after this item
            return
        yield i

g = Graph()
g.add_chain(source, process)
g.run()

print(len(g.output))   # 5
print(g.cancelled)     # True

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Threaded execution

YIELDGRAPH_THREADED=1 python my_pipeline.py

Or in Python before the run:

import os
os.environ["YIELDGRAPH_THREADED"] = "1"

g = Graph()
g.add_chain(fetch_from_api, transform, write_to_db)
g.run()

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Error handling

Exceptions raised inside a node are caught per-item and stored — they never abort the pipeline. Inspect them after the run:

g.run()

for name, node in g.nodes.items():
    if node.n_errors:
        print(f"{name}: {node.n_errors} error(s)")
        for err in node.errors:
            print(f"  {type(err).__name__}: {err}")

if g.succeeded:
    print(f"Done — {len(g.output)} rows produced")

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Status & progress

Expression	Type	Description
g.output	list[tuple]	All tuples emitted by terminal nodes
g.succeeded	bool	Finished without errors
g.has_output	bool	Finished and produced at least one row
g.error	str	Graph-level error description (empty on success)
g.step	str	Human-readable label of the currently executing node
g.progress	int	0–100 % progress of the current node
g.cancelled	bool	Whether the run was cancelled
g.finished	bool	Whether run() has returned

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Documentation

Full guides and API reference at https://j4ggr.github.io/yieldgraph/

• Getting Started
• Patterns & Recipes
• Configuration
• API Reference

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Development

# Install dev dependencies
pdm install -G test -G doc

# Run tests
pdm run pytest

# Build docs locally
pdm run mkdocs serve

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License

MIT