Architecting Weblate: Managing Asynchronous Translation and Data Consistency
Discover how Weblate separates UI and heavy file parsing with Celery, Redis, and PostgreSQL, and learn operational checks to keep the translation pipeline healthy.
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djangoSelecting the Right Apache Airflow Executor for Your Workload
A technical guide for choosing between Local, Celery, and Kubernetes executors in Apache Airflow based on scaling needs, infrastructure constraints, and fault tolerance.
kubernetesSynchronous TM suggestions vs deferred indexing for concurrent Weblate edits
Goal is to keep editor save latency predictable when multiple users edit the same project via Weblate UI or API. Weblate stores translation units with Django ORM transactions. Background work for commits, translation memory indexing and machine translation suggestions runs via Celery on the same database. With default queues, UI requests can contend for I/O
Weblate and Redis: Task Queue Consistency During Failed Upgrades
Weblate utilizes Celery and Redis to manage asynchronous translation tasks. During a version upgrade, the system executes Django migrations to update the PostgreSQL schema and updates the application logic to handle new task signatures. A critical boundary exists between the persistent database state and the volatile task queue in Redis. If an upgrade fails
redis