openedx / openedx/openedx-platform
Merge several outline processors in into UserPartitionOutlineProcessor
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- Python
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Description
From this list of processors, the following processors wrap concepts which, under the hood, are implemented on the user_partitions framework:
- ContentGatingOutlineProcessor
- EnrollmentTrackPartitionGroupsOutlineProcessor
- CohortPartitionGroupsOutlineProcessor
- TeamPartitionGroupsOutlineProcessor (new as of this PR)
Theoretically, they could all be merged into a single UserPartitionOutlineProcessor, which would dedupe some business logic and ensure that any new user_partition schemes automatically work with learning_sequences.
Open questions:
- is this as straightforward as I think it is, or are there leaky abstractions / edge cases we're going to hit?
- performance regressions?
- could the same refactoring be applied to block transformers?
CC @ormsbee
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start with the processor list in openedx/core/djangoapps/content/learning_sequences/api/outlines.py around line 324, then inspect the four named outline processors and the user_partitions framework they wrap. Investigate whether their logic can be unified without edge cases or performance regressions, and whether block transformers are affected. The issue does not define concrete acceptance criteria, so confirm the intended scope before starting.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100