9.9.25

[atambay37]

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Meeting notes:

• Design Document Planning and Execution Timeline: Anshul and Michael discussed breaking down tasks for the upcoming design document, aligning their efforts with Michael's travel schedule and aiming to begin execution in October, with Anshul planning to start documenting and Michael providing historical context from a previous collaboration.
  • Task Breakdown and Scheduling: Anshul proposed starting to break down the remaining work for the design document so that by October, the team can begin executing on the planned items, taking into account Michael's travel from September 20th to October 3rd and the expected quieter period for commits.
  • Historical Context and Approach: Michael shared that a similar design exercise was conducted with a collaborator in Denmark, which resulted in a comprehensive design document but stalled due to the collaborator's other commitments; he emphasized the importance of balancing planning with action to avoid delays.
  • Incremental Progress and Avoiding Over-Planning: Both agreed on the need to avoid excessive planning and to start development while refining the design document, ensuring that progress is made without getting stuck in prolonged conceptualization.
• Error Handling and API Design Considerations: Michael and Anshul reviewed current error handling in the data companion, discussed improving error messages, and considered how future API designs should support plug-and-play model integration, referencing specific tickets and user stories.
  • Improving Error Messages: Michael highlighted the need to enhance error handling in the data companion, specifically to provide more informative error messages to users instead of generic internal server errors, and suggested this as a manageable ticket for Anshul to address.
  • API Flexibility for Model Integration: Anshul raised the importance of designing the new API to allow easy integration of different models, ensuring that any new API design supports plug-and-play capabilities for future extensibility.
  • Using User Stories for Design: Michael suggested using the model integration scenario as a user story to guide the API design, ensuring practical requirements are met.
• Celery Task Processing and Database Write Strategies: Anshul and Michael analyzed the current Celery-based processing workflow, identifying concurrency issues with database writes, and discussed strategies to serialize write operations and reduce database load, including retry logic and task synchronization.
  • Current Workflow and Concurrency Issues: Anshul described the workflow where multiple save tasks are queued in Celery, potentially causing parallel writes to the database and leading to concurrency issues such as job log overwrites and out-of-order log entries.
  • Mitigation Strategies for Database Writes: Anshul proposed serializing write operations by ensuring only one write task executes at a time and adding retry logic with exponential backoff to prevent overloading the database, especially under high load.
  • Job Log Consistency: Michael noted that concurrent save or processing tasks can overwrite job logs, and Anshul suggested a pattern where asynchronous tasks are awaited in sequence to maintain log order and consistency.
• Celery Results Backend and Data Serialization Challenges: Michael and Anshul discussed the limitations of using Celery's results backend for large data payloads, identifying inefficiencies in serializing large JSON objects through the message queue and exploring alternatives for handling result data.
  • Results Backend Limitations: Michael explained that Celery's results backend, currently using RabbitMQ, is being used to store large JSON responses from processing tasks, which can lead to worker crashes and inefficiencies.
  • Serialization Overhead: Anshul pointed out that passing large JSON objects between tasks via the message queue results in unnecessary serialization and deserialization, increasing system load and latency.
  • Alternative Approaches: Both considered alternatives such as saving results directly to the database or object store, using thread pool executors for asynchronous saves within the same process, and minimizing the size of task inputs and outputs to only essential identifiers.
• Staging Environment Testing and Next Steps: Anshul and Michael agreed to use the staging environment for testing proposed fixes, with Anshul planning to implement and trial database write improvements and Michael providing access and support for code navigation and inspection tools.
  • Staging Environment Usage: Anshul suggested deploying the proposed database write fix to the staging environment, and Michael confirmed readiness to provide access and support for running trials to validate the solution.
  • Code Navigation and Tooling: Michael offered to share code pointers and recommended using Celery's inspect tools and the Flower graphical interface for monitoring and debugging task execution and results.
• Milestone Planning and Documentation: Anshul and Michael outlined two project milestones: stabilizing the current processing service (V1) and designing for scalability and extensibility (V2), with Anshul committing to documenting thoughts and drafting a pull request for review.
  • Milestone Definition: The team identified milestone one as stabilizing the current processing service and milestone two as enhancing scalability and extensibility, including improvements like the push API and better developer experience.
  • Documentation and Communication: Anshul committed to documenting their thoughts in the design document and drafting a pull request to communicate proposed fixes and gather feedback from Michael.

Follow-up tasks:

• Design Document Progress: Add thoughts to the design document and draft a PR outlining the proposed fix for review and feedback. (Anshul)
• Code Reference Sharing: Send pointers to the relevant code sections illustrating the ephemeral results back end and main Celery task for clarity. (Michael)
• Staging Environment Testing: Revive the staging environment to enable testing of the proposed write fix and run trials to assess if it resolves the issue with long jobs. (Michael)
• Diagram Sharing: Share the current architecture/process diagram with Anshul for reference and collaboration. (Michael)
• Ticket Creation: Create tickets related to the discussed milestones and tasks for tracking purposes. (Anshul)