Job Traceability, Management, and Audibility Overhaul#645
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Job Traceability, Management, and Audibility Overhaul#645bencap wants to merge 135 commits intorelease-2026.2.0from
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…cture Break down 1767-line jobs.py into domain-driven modules, improving maintainability and developer experience. - variant_processing/: Variant creation and VRS mapping - external_services/: ClinGen, UniProt, gnomAD integrations - data_management/: Database and view operations - utils/: Shared utilities (state, retry, constants) - registry.py: Centralized ARQ job configuration - constants.py: Environment configuration - redis.py: Redis connection settings - lifecycle.py: Worker lifecycle hooks - worker.py: Main ArqWorkerSettings class - All job functions maintain identical behavior - Registry provides BACKGROUND_FUNCTIONS/BACKGROUND_CRONJOBS lists for ARQ initialization - Test structure mirrors source organization This refactor ensures ARQ worker initialization is backwards compatible. The modular architecture establishes a more maintainable foundation for MaveDB's automated processing workflows while preserving all existing functionality.
Implement complete database foundation for pipeline-based job tracking and monitoring: Database Tables: • pipelines - High-level workflow grouping with correlation IDs for end-to-end tracing • job_runs - Individual job execution tracking with full lifecycle management • job_dependencies - Workflow orchestration with success/completion dependency types • job_metrics - Detailed performance metrics (CPU, memory, execution time, business metrics) • variant_annotation_status - Granular variant-level annotation tracking with success data Key Features: • Pipeline workflow management with dependency resolution • Comprehensive job lifecycle tracking (pending → running → completed/failed) • Retry logic with configurable limits and backoff strategies • Resource usage and performance metrics collection • Variant-level annotation status for debugging failures • Correlation ID support for request tracing across system • JSONB metadata fields for flexible job-specific data • Optimized indexes for common query patterns Schema Design: • Foreign key relationships maintain data integrity • Check constraints ensure valid enum values and positive numbers • Strategic indexes optimize dependency resolution and metrics queries • Cascade deletes prevent orphaned records • Version tracking for audit and debugging Models & Enums: • SQLAlchemy models with proper relationships and hybrid properties • Comprehensive enum definitions for job/pipeline status and failure categories
Add comprehensive job lifecycle management with status-based completion: * Implement convenience methods for common job outcomes: - succeed_job() for successful completion - fail_job() for error handling with exception details - cancel_job() for user/system cancellation - skip_job() for conditional job skipping * Enhance progress tracking with increment_progress() and set_progress_total() * Add comprehensive error handling with specific exception types * Improve job state validation and atomic transaction handling * Implement extensive test coverage for all job operations
- Created PipelineManager capable of coordinating jobs within a pipeline context - Introduced `construct_bulk_cancellation_result` to standardize cancellation result structures. - Added `job_dependency_is_met` to check job dependencies based on their types and statuses. - Created comprehensive tests for PipelineManager covering initialization, job coordination, status transitions, and error handling. - Implemented mocks for database and Redis dependencies to isolate tests. - Added tests for job enqueuing, cancellation, pausing, unpausing, and retrying functionalities.
Adds decorators for managed jobs and pipelines. These can be applied to async ARQ functions to automatically persist their state as they execute
…for score set mapping
- Updated `fetch_clinvar_variant_summary_tsv` to `fetch_clinvar_variant_data` for clarity. - Consolidated parsing logic into `fetch_clinvar_variant_data`, which now handles downloading, parsing, and caching. - Introduced a new caching mechanism that invalidates stale caches based on field changes. - Enhanced tests to cover new data fetching logic and ensure proper handling of cached data. - Removed unused constants and streamlined imports in related modules.
…ush for annotations to improve write performance
…' for better performance
…n history and current annotations
…te related models and logic
… with ARQ support Removes dedicated per-job scripts in favor of a generic handler for arbitrary named jobs/pipelines.
recovery, and concurrency limit - Wrap send_slack_error in try/except so Slack outages never break job lifecycle management or error recovery in decorators - Add failure_category field to JobExecutionOutcome with explicit categories on all job failure returns - Classify unhandled exceptions automatically via classify_exception() (ConnectionError → NETWORK_ERROR, TimeoutError → TIMEOUT, etc.) - Trim unused FailureCategory enum values (21 → 11) - Strip redundant Slack try/except wrappers from decorators - Implement UniProt polling retry via SERVICE_UNAVAILABLE category instead of silently skipping unfinished jobs - Add RUNNING to STARTABLE_JOB_STATUSES so start_job() handles crash recovery directly with a warning log - Set max_jobs=2 in ArqWorkerSettings to prevent event loop starvation from concurrent sync psycopg2 DB calls - Update worker instructions and README
… exception classification
- Removed assertions for progress updates in various test cases across multiple test files. - Simplified tests by eliminating unnecessary mocking of the JobManager's update_progress method. - Ensured that tests still validate the expected outcomes without relying on progress update calls.
- Removes keys from S3 if uploaded and raises to the user directly
…ent retry delay logic
- Introduced new FastAPI routers for job runs and pipelines, providing endpoints for listing and showing details with filtering options. - Implemented CLI scripts for job runs and pipelines, allowing operators to inspect state and progress via command line. - Enhanced view models for job runs and pipelines to include necessary fields and serialization. - Updated server main to include new routers and ensure proper routing. - Added tests for the new endpoints and CLI commands to ensure functionality and access control. - Updated versioning to indicate a development release.
… dependency states
…eueing
ARQ's enqueue_job silently returns None if arq:job:{id} or arq:result:{id}
already exists. This left the DB in QUEUED state while ARQ had no record
of the job — a worker would never pick it up. Two scenarios trigger it:
- Crashed RUNNING job: arq:job: survives because finish_job never ran
- Prior run within TTL: arq:result: lingers for up to 1 hour
Fix: make prepare_retry async and delete both keys before the caller
re-enqueues. All retry paths (cleanup, pipeline manager, job management
decorator) now benefit automatically.
- prepare_retry: made async, deletes arq:job: and arq:result: via
redis.delete before returning
- cleanup.py: await prepare_retry; treat enqueue_job returning None as
a hard error (RuntimeError) rather than silently succeeding
- job_management.py, pipeline_manager.py: await prepare_retry
- tests: convert prepare_retry tests to async; add unit tests for key
deletion and redis=None path; add regression integration tests that
seed stale keys and assert the job is present in ARQ's queue after
cleanup runs
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This PR introduces a robust, auditable, and maintainable background job system for MaveDB, supporting both standalone jobs and complex pipelines. It provides a strong foundation for future workflow automation, error recovery, and developer onboarding.
Features include:
1. Worker Job System Refactor & Enhancements
Refactored the monolithic worker job system into a modular architecture:
2. Job & Pipeline Management Infrastructure
Implemented JobManager and PipelineManager classes for robust job and pipeline lifecycle management: (05fc52b, ae18eeb, 3799d84, 1e447a7, 7b44346)
3. Decorator System for Jobs and Pipelines
Introduced decorators for job and pipeline management:
(c2100a2, 155e549, 4a4055d, 3c4e6b9, 010f15c)
Improved test mode support and simplified stacking/usage patterns.
4. Comprehensive Test Suite
Added and refactored unit and integration tests for all job modules, managers, and decorators.
(05fc52b, ae18eeb, a701d53, 806f8ed, 011522c, 010f15c, 8a22306, a716cc9, b0397b4, 8c5e225, 3c4e6b9, 4a4055d, 1fe076a, 1abe4c6)
Enhanced test coverage for error handling, state transitions, and job orchestration.
Introduced fixtures and utilities for easier test setup and mocking.
Categorized tests with markers for unit, integration, and network tests.
(16a5a50, f34939c)
5. Developer Documentation
Added detailed markdown documentation in the worker/jobs/] directory:
(1abe4c6)
6. Database & Model Changes
(1db6b68)
Alembic migration for pipeline and job tracking schema.
Updated models and enums to support new job/pipeline features.
7. Miscellaneous Improvements
Dependency updates (e.g., added asyncclick).
(a3f36d1)