Celery remains the default distributed task queue for Python in 2026. According to Celery’s official documentation, Celery is a distributed task queue system for Python that processes vast amounts of messages while supporting real-time processing and task scheduling. Celery 5.6 (Recovery) is the current stable release, with Python 3.9 through Python 3.13 support (and PyPy3.11+), critical memory-leak fixes (especially on Python 3.11+), security fixes for broker credentials in logs, and stability improvements that make 5.6 the recommended upgrade. Backends and brokers document Redis and RabbitMQ as stable, feature-complete brokers—Redis for fast transport of small messages and dual broker/backend use, RabbitMQ for larger messages and production scale. For Python developers, first steps with Celery and the @app.task decorator with delay() and apply_async() are the standard way to offload background jobs without blocking the web process. This article examines where Celery stands in 2026, why Python and Celery matter for async workloads, and how Redis and RabbitMQ power Google Discover–worthy infrastructure coverage.
Why Celery Matters in 2026
Distributed task queues offload long-running or heavy work from web requests so that APIs stay responsive and scalable. Celery introduction explains that Celery requires a message broker to send and receive messages between clients and workers; tasks are defined in Python, sent to the broker, and executed by worker processes. Python is the only language for defining Celery tasks—@app.task, delay(), apply_async(), retries, and routing are all Python APIs. In 2026, Celery is the default choice for Python teams building email sending, report generation, image processing, data pipelines, and scheduled jobs. For Google News and Google Discover, the story is Python at the center of async task processing and distributed workloads.
Celery 5.6 (Recovery): Stability, Memory, and Security
Celery 5.6 (Recovery) and the Celery changelog document critical fixes: memory leaks (especially on Python 3.11+ due to traceback reference cycles), security (broker credentials no longer logged in plaintext during delayed delivery), and stability (many long-standing bugs resolved). The project encourages upgrading to 5.6 as soon as possible. Python 3.9 through 3.13 are supported; Python 3.8 is dropped, and Python 3.14 has initial support. For Python developers, Celery 5.6 means reliable workers and safer credentials—so that production task queues stay stable and auditable for Google Discover–relevant infrastructure stories.
Brokers: Redis and RabbitMQ
Celery backends and brokers describe Redis and RabbitMQ as stable brokers. Redis works well for rapid transport of small messages and can serve as both broker and result backend; large messages can congest the system, and Redis is more susceptible to data loss on abrupt termination. RabbitMQ is feature-complete, handles larger messages better, and is recommended for production; at very high volume, scaling can be a concern unless run at scale. A common configuration is RabbitMQ as broker and Redis as backend; for long-term result persistence, PostgreSQL, MySQL, or Cassandra are recommended as backends. Python teams choose Redis for simplicity and speed or RabbitMQ for durability and scale—so that in 2026, Celery and Python deliver async task processing with flexible broker choice.
First Steps with Celery: Python Tasks and delay()
Celery first steps walk through installing Celery (pip install celery), choosing a broker (Redis or RabbitMQ), and creating a Celery app and tasks. Tasks are defined with the @app.task decorator and invoked with delay() (a shortcut for apply_async()) or apply_async() for countdown, eta, and expires. A minimal Python example defines a task and sends it to a worker:
from celery import Celery
app = Celery("myapp", broker="redis://localhost:6379/0")
@app.task
def add(x, y):
return x + y
# Send task to worker
add.delay(4, 6)
That pattern—Python for app logic, @app.task for task definition, delay() or apply_async() for invocation—is the norm in 2026 for background jobs in Python. Calling tasks documents countdown, eta, expires, retry, and routing so that Python developers can schedule and retry tasks without blocking the main process. In 2026, Python and Celery together form the default for distributed task queues in the Python ecosystem.
Result Backends, Scheduling, and Next Steps
Celery supports result backends (Redis, PostgreSQL, etc.) so that task results can be retrieved by ID. Celery Beat provides periodic task scheduling (cron-like). Celery next steps and the Celery getting started index describe routing, queues, retries, monitoring, and production deployment. Python developers use result.get() (blocking) or AsyncResult for async result retrieval, and Celery Beat for scheduled tasks—so that Python and Celery deliver full task-queue semantics for Google News and Google Discover audiences.
Conclusion: Celery as the Task Queue Default in 2026
In 2026, Celery is the default distributed task queue for Python. Celery 5.6 (Recovery) brings stability, memory-leak fixes, and security improvements; Redis and RabbitMQ are stable brokers; Python 3.9–3.13 are supported. @app.task, delay(), and apply_async() power background jobs from Python without blocking the web process. For Google News and Google Discover, the story in 2026 is clear: Celery is where Python async task processing runs, and Python is how distributed workloads are defined and invoked.
