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Getting started🔗

Install the library🔗

Install the latest version from PyPI using pip:

pip install bandsaw

Define the individual tasks of your workflow🔗

Import the @task decorator from the bandsaw package and decorate a function with it:

import bandsaw


def my_function(x):
    return x

Configure bandsaw🔗

Create a new python module bandsaw_config and add first advice that just adds some additional logging when a task is executed:

import bandsaw

configuration = bandsaw.Configuration().add_advice_chain(

Run your workflow🔗

When you now run your workflow, bandsaw intercepts the execution of my_function and its LoggingAdvice prints out additional log messages before() and after() it, e.g.:

$ python
2021-10-27 13:13:51,940  2290 bandsaw.advices.logging INFO: BEFORE 0d268ac0..4213:76560cb4..a37d with context {}
2021-10-27 13:13:52,127  2290 bandsaw.advices.logging INFO: AFTER 0d268ac0..4213:76560cb4..a37d with context {}

The log messages contain the task_id (0d268ac0..4213), which is derived from the code that is decorated, and the run_id (76560cb4..a37d), derived from the arguments that my_function was called with.

Where to go from here?🔗

Read the user guide for some more in-depth explanation about bandsaw and its concepts.

Alternatively, bandsaw brings with it a couple of useful advice classes, that can be used just by adding them to its configuration:

Last update: 2021-10-28