Skip to content

Latest commit

 

History

History
58 lines (45 loc) · 2.78 KB

README.md

File metadata and controls

58 lines (45 loc) · 2.78 KB
Raw

PyPI version pipeline coverage

gittrail - Linking data pipeline outputs to git history

Versioning of code with git is easy, versioning data pipeline inputs/outputs is hard.

GitTrail helps you to maintain a traceable data lineage by enforcing a link between data files and the commit history of your processing code.

Like blockchain, but easier.

How it works

GitTrail is used as a context manager around the code that executes your data processing:

with GitTrail(
    repo="/path/to/my_data_processing_code",
    data="/path/to/my_data_storage",
):
    # TODO: download the pipeline inputs to [data]

Inbetween GitTrail sessions you may edit your pipeline code, make commits etc.

When your next data processing stage is ready:

with GitTrail(
    repo="/path/to/my_data_processing_code",
    data="/path/to/my_data_storage",
):
    # TODO: run data analysis on inputs from [data]
    # TODO: save results to [data]

Upon entering the context GitTrail attaches a log handler to re-route all logging into a *.log file in a subdirectory of [data]. When the context exits, the logger is detached and session metadata is stored in a *.json file. The metadata includes the current git commit of your [repo], as well MD5 hashes of the files inside [data].

Within the context, the following two rules are enforced:

  1. The working tree of your code [repo] must be clean (no uncommitted changes).
  2. All files currently found in [data] must have been created/changed in a previous GitTrail context.

Taken together this means that:

  • You're not allowed to add/edit/anything in [data] by hand.
  • Your data processing code may continue to evolve as you're moving forward through your pipeline.
  • You can amend/rewind/rewrite git commits of your processing code, but the corresponding files in [data] and the audit trail session file must be deleted.
  • All files in the [data] are linked to the processing code that produced them.

Limitations

GitTrail can't police everything, so keep the following in mind:

  • Data outside of [data], for example a database, is not tracked. If you're reading/writing data outside of [data] think about how you can trace that in your git history and/or [data] audit trail.
  • Code outside of [repo] is not tracked. Unless your [repo] specifies exact dependency versions, your code may not be 100 % reproducible.
  • Audit trail files are not cryptographically signed, so if you mess with them that's not tracked.