celery-tasks

Orchestrating Celery tasks reliably requires structured queue architecture, idempotent task patterns, and robust retry strategies with backoff. This Skill provides templates and guidelines for coordinating tasks across default, etl, and beat workers to prevent fragility and duplication in Django projects.

Preventing duplicate Celery tasks with transaction.on_commit involves applying idempotent task patterns and explicit error handling. This Skill provides templates that enforce on_commit usage correctly, ensuring dependent tasks are only queued after database transactions complete successfully without creating duplicates.

Implementing distributed locking and retry strategies for Celery task queues requires enforcing time limits, backoff configurations, and recovery task documentation. This Skill offers production-grade patterns for distributed locking to prevent race conditions across concurrent workers.

Yes, this Celery task orchestration Skill is designed for Django projects and Python apps handling ETL workloads. It provides specific queue architecture guidance for fast, ETL, and periodic tasks across default, etl, and beat workers with practical examples for data synchronization.

Celery tasks fail and become hard to trace when coordinated across multiple queues without standard patterns, leading to fragility and duplication. This Skill addresses these issues by enforcing time limits, retry strategies, on_commit usage, and explicit error handling patterns for production-grade workflows.

Configuring time limits and recovery strategies for Celery periodic tasks involves setting explicit timeouts, backoff schedules, and documenting recovery procedures. This Skill provides templates and anti-pattern guidance to prevent runaway tasks and ensure reliable execution across beat workers.