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usher-exploring/.planning/phases/01-data-infrastructure/01-03-SUMMARY.md

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phase, plan, subsystem, tags, dependency_graph, tech_stack, key_files, decisions, metrics
phase plan subsystem tags dependency_graph tech_stack key_files decisions metrics
01-data-infrastructure 03 persistence
duckdb
provenance
checkpoints
reproducibility
testing
requires provides affects
Python package scaffold with config system (01-01)
DuckDB-based checkpoint-restart storage
Provenance metadata tracking system
All future data pipeline plans (enables checkpoint-restart)
All output artifacts (provenance sidecars)
added patterns
duckdb for embedded analytical database
polars for DataFrame operations
pyarrow for Parquet export
Checkpoint-restart pattern for expensive operations
Provenance sidecar files for reproducibility
Context manager for resource cleanup
created modified
src/usher_pipeline/persistence/__init__.py
Package exports
src/usher_pipeline/persistence/duckdb_store.py
PipelineStore class with checkpoint system
src/usher_pipeline/persistence/provenance.py
ProvenanceTracker for metadata
tests/test_persistence.py
13 comprehensive tests
decision rationale alternatives
DuckDB over SQLite for DataFrame storage Native polars/pandas integration, better performance for analytical queries, built-in Parquet export
SQLite (rejected - requires manual serialization)
Parquet only (rejected - no checkpoint metadata)
decision rationale impact
Provenance sidecar files alongside outputs Co-located metadata simplifies tracking, standard pattern in bioinformatics Every output file gets a .provenance.json sidecar
decision rationale impact
Metadata table _checkpoints for tracking Enables has_checkpoint() queries without scanning catalog Adds small metadata overhead, significant performance benefit
duration_minutes tasks_completed files_created tests_added commits completed_date
2 2 4 13 2 2026-02-11

Phase 01 Plan 03: DuckDB Persistence and Provenance Tracking Summary

One-liner: DuckDB-based checkpoint-restart storage with metadata tracking (polars/pandas support, Parquet export, context managers) and provenance system capturing pipeline version, data source versions, config hash, and processing steps with JSON sidecar files.

What Was Built

Built the persistence layer that enables checkpoint-restart for expensive operations and full provenance tracking for reproducibility:

  1. DuckDB PipelineStore

    • Checkpoint-restart storage: save expensive API results, skip re-fetch on restart
    • Dual DataFrame support: native polars and pandas via DuckDB integration
    • Metadata tracking: _checkpoints table tracks table_name, created_at, row_count, description
    • Checkpoint queries: has_checkpoint() for efficient existence checks
    • List/delete operations: manage checkpoints with full metadata
    • Parquet export: COPY TO for downstream compatibility
    • Context manager: enter/exit for clean resource management
    • Config integration: from_config() classmethod

  2. ProvenanceTracker

    • Captures pipeline version, data source versions (Ensembl, gnomAD, GTEx, HPA)
    • Records config hash for deterministic cache invalidation
    • Tracks processing steps with timestamps and optional details
    • Saves sidecar JSON files co-located with outputs (.provenance.json)
    • Persists to DuckDB _provenance table (flattened schema)
    • from_config() classmethod with automatic version detection

  3. Comprehensive Test Suite

    • 13 tests total (12 passed, 1 skipped - pandas not installed)
    • DuckDB store tests (8): database creation, save/load polars, save/load pandas, checkpoint lifecycle, list checkpoints, Parquet export, non-existent table handling, context manager
    • Provenance tests (5): metadata structure, step recording, sidecar roundtrip, config hash inclusion, DuckDB storage

Tests

13 tests total (12 passed, 1 skipped):

DuckDB Store Tests (8 tests, 7 passed, 1 skipped)

  • test_store_creates_database: PipelineStore creates .duckdb file at specified path
  • test_save_and_load_polars: Save and load polars DataFrame, verify shape/columns/values
  • test_save_and_load_pandas: Save and load pandas DataFrame (skipped - pandas not installed)
  • test_checkpoint_lifecycle: Save -> has_checkpoint=True -> delete -> has_checkpoint=False -> load=None
  • test_list_checkpoints: Save 3 tables, list returns 3 with metadata (table_name, created_at, row_count, description)
  • test_export_parquet: Save DataFrame, export to Parquet, verify file exists and is readable
  • test_load_nonexistent_returns_none: Loading non-existent table returns None
  • test_context_manager: with PipelineStore() pattern works, connection closes on exit

Provenance Tests (5 tests, all passed)

  • test_provenance_metadata_structure: create_metadata() returns dict with all required keys
  • test_provenance_records_steps: record_step() adds to processing_steps with timestamps
  • test_provenance_sidecar_roundtrip: save_sidecar() -> load_sidecar() preserves all metadata
  • test_provenance_config_hash_included: config_hash matches PipelineConfig.config_hash()
  • test_provenance_save_to_store: save_to_store() creates _provenance table with valid JSON steps

Verification Results

All plan verification steps passed:

# 1. All tests pass
$ pytest tests/test_persistence.py -v
=================== 12 passed, 1 skipped, 1 warning in 0.29s ===================

# 2. Imports work
$ python -c "from usher_pipeline.persistence import PipelineStore, ProvenanceTracker"
Import successful

# 3. Checkpoint-restart verified
# Covered by test_checkpoint_lifecycle and test_save_and_load_polars

# 4. Provenance sidecar JSON verified
# Covered by test_provenance_sidecar_roundtrip

Deviations from Plan

None - plan executed exactly as written.

Task Execution Log

Task 1: Create DuckDB persistence layer with checkpoint-restart

Status: Complete Duration: ~1 minute Commit: d51141f

Actions:

  1. Created src/usher_pipeline/persistence/ package directory
  2. Implemented PipelineStore class in duckdb_store.py:
    • Constructor with db_path, creates parent directories, connects to DuckDB
    • _checkpoints metadata table (table_name, created_at, row_count, description)
    • save_dataframe() with polars/pandas support via DuckDB native integration
    • load_dataframe() returning polars (default) or pandas
    • has_checkpoint() for existence checks
    • list_checkpoints() returning metadata list
    • delete_checkpoint() for cleanup
    • export_parquet() using COPY TO
    • execute_query() for arbitrary SQL
    • close() and enter/exit for context manager
    • from_config() classmethod
  3. Created init.py (temporarily without ProvenanceTracker export)
  4. Verified basic operations: save, load, has_checkpoint, shape verification

Files created: 2 files (duckdb_store.py, init.py)

Key features:

  • Native polars integration via DuckDB (no manual serialization)
  • Metadata table enables fast has_checkpoint() queries
  • Context manager ensures connection cleanup
  • Parquet export for downstream compatibility

Task 2: Create provenance tracker with tests for both modules

Status: Complete Duration: ~1 minute Commit: 98a1a75

Actions:

  1. Implemented ProvenanceTracker class in provenance.py:
    • Constructor storing pipeline_version, config_hash, data_source_versions, created_at
    • record_step() appending to processing_steps list with timestamps
    • create_metadata() returning full provenance dict
    • save_sidecar() writing .provenance.json with indent=2
    • save_to_store() persisting to DuckDB _provenance table
    • load_sidecar() static method for loading JSON
    • from_config() classmethod with automatic version detection
  2. Updated init.py to export ProvenanceTracker
  3. Created comprehensive test suite in tests/test_persistence.py:
    • test_config fixture creating minimal PipelineConfig from YAML
    • 8 DuckDB store tests (database creation, save/load, checkpoints, Parquet)
    • 5 provenance tests (metadata structure, step recording, sidecar, config hash, DuckDB storage)
  4. Ran all tests: 12 passed, 1 skipped (pandas)

Files created: 2 files (provenance.py, test_persistence.py) Files modified: 1 file (init.py - added ProvenanceTracker export)

Key features:

  • Captures all required metadata for reproducibility
  • Sidecar files co-located with outputs
  • DuckDB storage for queryable provenance records
  • Automatic version detection via usher_pipeline.version

Success Criteria Verification

  • DuckDB store saves/loads DataFrames with checkpoint metadata tracking
  • Checkpoint-restart pattern works: has_checkpoint() -> skip expensive re-fetch
  • Provenance tracker captures all required metadata (INFRA-06):
    • Pipeline version
    • Data source versions (Ensembl, gnomAD, GTEx, HPA)
    • Timestamps (created_at for tracker, per-step timestamps)
    • Config hash
    • Processing steps
  • Parquet export works for downstream compatibility
  • All tests pass (12/13, 1 skipped)

Must-Haves Verification

Truths:

  • DuckDB database stores DataFrames as tables and exports to Parquet
  • Checkpoint system detects existing tables to enable restart-from-checkpoint without re-downloading
  • Provenance metadata captures pipeline version, data source versions, timestamps, and config hash
  • Provenance sidecar JSON file is saved alongside every pipeline output

Artifacts:

  • src/usher_pipeline/persistence/duckdb_store.py provides "DuckDB-based storage with checkpoint-restart capability" containing "class PipelineStore"
  • src/usher_pipeline/persistence/provenance.py provides "Provenance metadata creation and persistence" containing "class ProvenanceTracker"

Key Links:

  • src/usher_pipeline/persistence/duckdb_store.py → duckdb via "duckdb.connect for file-based database" (pattern: duckdb\.connect)
  • src/usher_pipeline/persistence/provenance.py → src/usher_pipeline/config/schema.py via "reads PipelineConfig for version info and config hash" (pattern: config_hash|PipelineConfig)
  • src/usher_pipeline/persistence/duckdb_store.py → src/usher_pipeline/persistence/provenance.py via "attaches provenance metadata when saving checkpoints" (pattern: provenance|ProvenanceTracker)

Impact on Roadmap

This plan enables Phase 01 Plans 02 and 04:

Plan 02 (Gene ID Mapping):

  • Will use PipelineStore to checkpoint Ensembl/MyGene API results
  • Saves expensive 20,000+ gene lookups: restart skips re-fetch
  • Provenance tracks Ensembl release version for reproducibility

Plan 04 (Data Integration):

  • Will use PipelineStore for gnomAD, GTEx, HPA data downloads
  • Checkpoint-restart critical for multi-hour API operations
  • Provenance tracks data source versions for all evidence layers

Next Steps

Phase 01 continues with Plan 02 (Gene ID Mapping) or Plan 04 (Data Integration). Both depend on this plan's checkpoint-restart infrastructure.

Recommended sequence: Plan 02 (uses PipelineStore immediately for gene universe checkpoint).

Self-Check: PASSED

Files verified:

FOUND: /Users/gbanyan/Project/usher-exploring/src/usher_pipeline/persistence/__init__.py
FOUND: /Users/gbanyan/Project/usher-exploring/src/usher_pipeline/persistence/duckdb_store.py
FOUND: /Users/gbanyan/Project/usher-exploring/src/usher_pipeline/persistence/provenance.py
FOUND: /Users/gbanyan/Project/usher-exploring/tests/test_persistence.py

Commits verified:

FOUND: d51141f (Task 1)
FOUND: 98a1a75 (Task 2)

All files and commits exist as documented.

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