Pipeline Toolkit

A small functional pipeline toolkit for Python.

pipeline-toolkit provides a simple way to build sequential pipelines from ordinary Python callables.

It also provides small utilities for composing functions, configuring callable steps, applying side effects, and managing pipeline state.

Features

• Sequential functional pipeline execution.
• Asynchronous execution using a worker thread.
• Positional and keyword arguments for pipeline steps.
• Configurable pipeline defaults.
• Stop, skip, wait, and rerun execution.
• Manual synchronous step execution.
• Optional execution delays.
• Configurable daemon worker threads.
• Optional stop-on-error behavior.
• Result and error history using a stack.
• Pipeline modification with add(), insert(), pop(), and clear().
• Sequential callable composition with compose().
• Configurable callable steps with pipe and step.
• Side-effect operations with tap().

Installation

Install from PyPI:

pip install pipeline-toolkit

Or install directly from GitHub:

pip install git+https://github.com/Hoang-Long2012/pipeline-toolkit.git

Quick Start

A pipeline is created from an iterable of steps.

Each step is a tuple whose first item is a callable.

from pipeline import Pipeline

def add(value, amount):
	return value + amount

def multiply(value, factor):
	return value * factor

pipeline = Pipeline([
	(add, (5,)),
	(multiply, (2,)),
])

pipeline.run(10).wait()

print(pipeline.results.get())

The execution flow is:

10
 ↓
add(10, 5)
 ↓
15
 ↓
multiply(15, 2)
 ↓
30

The final result is 30.

Pipeline Steps

Each step can use one of four supported forms.

Callable only

(function,)

The callable receives the previous result:

pipeline = Pipeline([
	(str.upper,),
])

pipeline.run("hello").wait()

The step is executed as:

str.upper(previous_result)

Positional arguments

(function, args)

where args is a tuple:

pipeline = Pipeline([
	(add, (5,)),
	(multiply, (2,)),
])

A step such as:

(add, (5,))

is executed as:

add(previous_result, 5)

Keyword arguments

(function, kwargs)

where kwargs is a mapping:

pipeline = Pipeline([
	(pow, {"exp": 2}),
])

The step is executed as:

pow(previous_result, exp=2)

Positional and keyword arguments

(function, args, kwargs)

For example:

pipeline = Pipeline([
	(my_function, (1, 2), {"option": True}),
])

The callable receives the previous result followed by the supplied positional and keyword arguments.

Execution

run()

Start the pipeline asynchronously.

pipeline.run(default=None, delay=0, daemon=False, stop_on_error=True)

The default value becomes the initial result.

If it is None, the default value configured when creating the pipeline is used.

pipeline = Pipeline([
	(add, (5,)),
], default=10)

pipeline.run().wait()

delay specifies the delay in seconds between steps.

daemon controls whether the worker thread is a daemon thread.

stop_on_error controls whether execution stops after the first exception.

run() returns the pipeline instance, allowing calls such as:

pipeline.run(10).wait()

The pipeline is snapshotted when execution starts. Changes made to pipeline.pipeline after run() begins do not affect the current execution.

wait()

Wait for the current execution to finish.

pipeline.wait()

It returns the pipeline instance.

stop()

Request the running pipeline to stop and wait for its worker thread to terminate.

step = pipeline.stop()

The return value is the current one-based step index when execution is stopped, or 0 if the pipeline was not running.

skip()

Request the worker to skip the next step that reaches its skip check.

pipeline.skip()

The method returns the pipeline instance.

rerun()

Stop the current execution and start the pipeline again.

pipeline.rerun(10)

Arguments are passed directly to run().

Manual Step Execution

run_step() executes one configured step synchronously.

result = pipeline.run_step(2, 10)

Unlike run(), this method:

• does not create a worker thread
• does not modify pipeline execution state
• does not store the result in results
• does not store exceptions in errors
• allows exceptions to propagate to the caller

This makes it useful when a single pipeline step needs to be executed manually.

Results and Errors

The pipeline provides two Stack instances:

pipeline.results
pipeline.errors

results contains the initial value and the results produced by executed steps.

For example:

pipeline.run(10).wait()

print(pipeline.results.get())

errors contains exceptions raised by pipeline steps.

When stop_on_error=True, execution stops after the first exception.

When stop_on_error=False, the exception is stored in errors and execution continues with the previous result.

Managing Pipeline Steps

Pipeline steps can be modified before or between executions.

add()

Append a step:

pipeline.add((str.upper,))

insert()

Insert a step at a one-based position:

pipeline.insert(2, (str.strip,))

Positions range from 1 to len(pipeline) + 1.

pop()

Remove and return a step:

step = pipeline.pop(1)

Pipeline indexes are one-based.

clear()

Remove all configured steps:

pipeline.clear()

Pipeline State

The running property indicates whether the worker thread is currently running:

if pipeline.running:
	print("Pipeline is running")

The step attribute contains the one-based index of the currently executing step. It is 0 when the pipeline is not running.

A Pipeline instance can also be used as a boolean:

if pipeline:
	print("Pipeline is running")

Calling a pipeline instance is equivalent to calling run():

pipeline(10)

is equivalent to:

pipeline.run(10)

The length of a pipeline is the number of configured steps:

len(pipeline)

A callable can be checked with the in operator:

if add in pipeline:
	print("add is part of the pipeline")

Callable membership uses identity comparison.

Functional Utilities

compose()

compose() applies callables sequentially to a value.

The result of each callable is passed as the first argument to the next callable.

from pipeline import compose

def add(value, amount):
	return value + amount

def multiply(value, factor):
	return value * factor

result = compose(
	lambda value: add(value, 5),
	lambda value: multiply(value, 2),
	default=10,
)

print(result)

The execution flow is:

10
 ↓
add(10, 5)
 ↓
15
 ↓
multiply(15, 2)
 ↓
30

compose() executes the callables immediately and returns the final result.

An empty composition returns the supplied default value.

pipe

pipe wraps a callable as a step factory.

It is useful when the same callable needs to be configured with different arguments.

from pipeline import pipe

@pipe
def add(value, amount):
	return value + amount

step = add(5)
print(step(10))

The pipe object itself can be called to create a step:

add(5)

add(5) returns a step containing add and the argument 5.

It can then be used directly in a pipeline:

from pipeline import Pipeline, pipe

@pipe
def add(value, amount):
	return value + amount

pipeline = Pipeline([
	(add(5),),
])

step

step represents a callable with preconfigured arguments.

from pipeline import step

add_five = step(add, 5)

print(add_five(10))

A step passes its supplied value as the first argument to the wrapped callable, followed by its configured positional and keyword arguments.

It can also be used with the pipe operator:

result = 10 | add_five

This is equivalent to:

result = add_five(10)

A step can be repeated with the multiplication operator:

def add(value, amount):
	return (value or 0) + amount

result = step(add, 5) * 3

print(result)  # 15

The wrapped callable is executed once for each repetition, with each result passed to the next execution.

Repeated execution starts with None; each result is passed to the next execution.

step also provides convenience support for file-like objects through < and >:

step = step(process)

result = step < file
step > file

These operations use the file-like object's read() and write() methods respectively.

tap

tap() applies a side effect to a deep copy of a value and returns the original value unchanged.

This makes it useful for logging, inspection, debugging, or other side effects that should not interrupt a functional chain.

from pipeline.tap import tap

value = {"count": 10}

def show(data):
	print(data)

result = tap(value, show)

print(result is value)

The function receives a deep copy, so mutations made by the side-effect function do not modify the original value.

tap() can also be used as a pipeline step:

from pipeline import Pipeline
from pipeline.tap import tap

def add(value, amount):
	return value + amount

pipeline = Pipeline([
	(add, (5,)),
	(tap, (print,)),
	(add, (10,)),
])

pipeline.run(10).wait()

The value printed by tap() is still passed unchanged to the next step.

Stack

Stack is a simple LIFO stack container with optional capacity limits.

It supports common stack operations such as pushing, retrieving and removing items, with dedicated exceptions for overflow and underflow conditions.

Import it directly from its submodule:

from pipeline.stack import Stack

stack = Stack()

stack.push("first")
stack.push("second")

print(stack.get())

Stack is also used internally by Pipeline for storing results and errors.

For detailed stack operations and behavior, see the pipeline.stack module.

API Overview

Pipeline

Functional Utilities

Requirements

• Python 3.8 or newer

Changelog

See CHANGELOG.md.

License

This project is licensed under the MIT License. See LICENSE for details.

Contribution

If you'd like to contribute, feel free to submit a pull request.

If you'd like to report a bug or request a feature, please open an issue.

Copyright (C) 2026 Hoàng Long