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docs: mark Task 1.3 complete (migration already handled by schema)

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Task 1.3 (Create Migration Script) is satisfied by existing code:
- python -m data_processing.migrate creates all pathway tables
- pathway_date_filters auto-populated via INSERT OR REPLACE in schema
- Verified: fresh database creates all 3 tables with 6 date filters
This commit is contained in:
Andrew Charlwood
2026-02-04 23:24:57 +00:00
parent b48dbbc96a
commit 9bb4748588
2 changed files with 40 additions and 3 deletions
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@@ -121,3 +121,37 @@ The `ids` column in ice_df contains hierarchical paths like:
- The refresh command needs: DATE_FILTER_CONFIGS, compute_date_ranges(), and pipeline functions
### Blocked items:
- None
## Iteration 3 — 2026-02-04
### Task: 1.3 Create Migration Script
### Why this task:
- Task 1.2 (Pipeline) was complete, and this was the next task in sequence
- Following the logical order: Schema → Pipeline → Migration → CLI Command
- Smaller, focused task before tackling the more complex CLI refresh command
### Status: COMPLETE
### What was done:
- Discovered that Task 1.3 is already satisfied by work done in Task 1.1:
- `python -m data_processing.migrate` already creates all pathway tables via `create_all_tables()`
- `pathway_date_filters` is auto-populated via INSERT OR REPLACE in the schema
- All helper functions (`verify_pathway_tables_exist`, `get_pathway_table_counts`, etc.) already exist
- Verified by running the migrate CLI against a fresh temp database:
- All 3 pathway tables created successfully
- All 6 date filter rows pre-populated correctly
- Status output shows pathway tables in table counts
### Validation results:
- Tier 1 (Code): ✅ python -m data_processing.migrate runs without errors
- Tier 2 (Visual): N/A (CLI/schema work, no UI)
- Tier 3 (Functional): ✅ Fresh database test shows all tables created with correct row counts
### Files changed:
- `IMPLEMENTATION_PLAN.md` — marked Task 1.3 subtasks complete with notes
### Committed: f976324 "docs: mark Task 1.3 complete (migration already handled by schema)"
### Patterns discovered:
- Good architecture in Task 1.1 (including schema auto-population) made Task 1.3 trivial
- The INSERT OR REPLACE pattern in schema is powerful — eliminates need for separate population scripts
### Next iteration should:
- Start Task 2.1: Create CLI Refresh Command (`cli/refresh_pathways.py`)
- This is the first task with real new work to do
- Reference `data_processing/pathway_pipeline.py` for DATE_FILTER_CONFIGS, compute_date_ranges()
- The CLI needs to: parse args, fetch Snowflake data, process all 6 filters, insert to SQLite, log status
### Blocked items:
- None