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ADR-010: Background Job Processing

Status: Planned
Date: 2025-11-10
Deciders: Development Team
Related: Tech Stack, ADR-004: Database Selection

Context

We needed a solution for handling background tasks and asynchronous processing in our system that would:

  • Support distributed task execution across multiple workers
  • Integrate well with our Python-based backend
  • Provide reliable task queuing and execution
  • Offer monitoring and management capabilities
  • Scale horizontally to handle varying workloads
  • Handle task retries and failure scenarios gracefully

Decision

We will use Celery with Redis as our distributed task queue system for background job processing (preferred approach, pending final evaluation).

Note: This decision is planned but not yet implemented. Alternatives like Dramatiq, Huey, and FastAPI BackgroundTasks will be evaluated during implementation.

Rationale

Celery provides a robust solution for our background processing needs:

  1. Python Native: First-class support for Python with excellent integration
  2. Multiple Broker Support: Works with Redis, RabbitMQ, and other message brokers
  3. Distributed Execution: Tasks can be executed across multiple worker nodes
  4. Flexible Routing: Advanced routing capabilities for different task types
  5. Monitoring Tools: Built-in monitoring and management tools (Flower)
  6. Retry Mechanisms: Configurable retry policies with exponential backoff
  7. Large Community: Extensive documentation and community support

Redis was chosen as the message broker because:

  • Simple setup and operation
  • Excellent performance for our expected workload
  • Persistence options for task durability
  • Pub/Sub capabilities for real-time features

Compared to alternatives:

  • RQ: Simpler but less feature-rich, sufficient for basic needs
  • Dramatiq: Modern alternative with better API, good performance
  • Huey: Lightweight Redis-based queue, simpler than Celery
  • FastAPI BackgroundTasks: Built-in but limited to single-process, no persistence
  • Custom solutions would require significant development effort
  • Other queue systems (like RabbitMQ) are more complex to operate

Consequences

Positive

  • Reliable distributed task processing
  • Horizontal scalability for background jobs
  • Built-in monitoring and management (Flower)
  • Flexible task scheduling options
  • Graceful handling of failures and retries
  • Large community and ecosystem

Negative

  • Additional infrastructure component (Redis) required
  • Learning curve for team members unfamiliar with Celery
  • Potential for increased complexity in debugging distributed tasks
  • Need for proper monitoring and alerting setup
  • Operational overhead compared to simpler solutions

Neutral

  • Decision deferred until implementation phase
  • Will evaluate simpler alternatives (Dramatiq, Huey) based on actual requirements
  • FastAPI BackgroundTasks may be sufficient for MVP phase

Implementation Details

Our Celery implementation will include:

  1. Task Organization:
  2. Separate modules for different task types
  3. Consistent naming conventions for tasks

  4. Clear separation between task definitions and business logic

  5. Worker Configuration:

  6. Dedicated worker processes for different task priorities
  7. Proper resource allocation and limits

  8. Supervision and auto-restart mechanisms

  9. Monitoring:

  10. Flower for real-time monitoring
  11. Custom metrics for task performance

  12. Alerting for failed tasks and queue backlogs

  13. Error Handling:

  14. Comprehensive logging for task execution
  15. Configurable retry policies
  16. Dead letter queue for repeatedly failing tasks

References