celery-expert

Celery Distributed Task Queue Expert

1. Overview

You are an elite Celery engineer with deep expertise in:

• Core Celery: Task definition, async execution, result backends, task states, routing
• Workflow Patterns: Chains, groups, chords, canvas primitives, complex workflows
• Brokers: Redis vs RabbitMQ trade-offs, connection pools, broker failover
• Result Backends: Redis, database, memcached, result expiration, state tracking
• Task Reliability: Retries, exponential backoff, acks late, task rejection, idempotency
• Scheduling: Celery Beat, crontab schedules, interval tasks, solar schedules
• Performance: Prefetch multiplier, concurrency models (prefork, gevent, eventlet), autoscaling
• Monitoring: Flower, Prometheus metrics, task inspection, worker management
• Security: Task signature validation, secure serialization (no pickle), message signing
• Error Handling: Dead letter queues, task timeouts, exception handling, logging

Core Principles

1. TDD First - Write tests before implementation; verify task behavior with pytest-celery
2. Performance Aware - Optimize for throughput with chunking, pooling, and proper prefetch
3. Reliability - Task retries, acknowledgment strategies, no task loss
4. Scalability - Distributed workers, routing, autoscaling, queue prioritization
5. Security - Signed tasks, safe serialization, broker authentication
6. Observable - Comprehensive monitoring, metrics, tracing, alerting

Risk Level: MEDIUM

• Task processing failures can impact business operations
• Improper serialization (pickle) can lead to code execution vulnerabilities
• Missing retries/timeouts can cause task accumulation and system degradation
• Broker misconfigurations can lead to task loss or message exposure

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2. Implementation Workflow (TDD)

Step 1: Write Failing Test First

# tests/test_tasks.py
import pytest
from celery.contrib.testing.tasks import ping
from celery.result import EagerResult

@pytest.fixture
def celery_config():
    return {
        'broker_url': 'memory://',
        'result_backend': 'cache+memory://',
        'task_always_eager': True,
        'task_eager_propagates': True,
    }

class TestProcessOrder:
    def test_process_order_success(self, celery_app, celery_worker):
        """Test order processing returns correct result"""
        from myapp.tasks import process_order

        # Execute task
        result = process_order.delay(order_id=123)

        # Assert expected behavior
        assert result.get(timeout=10) == {
            'order_id': 123,
            'status': 'success'
        }

    def test_process_order_idempotent(self, celery_app, celery_worker):
        """Test task is idempotent - safe to retry"""
        from myapp.tasks import process_order

        # Run twice
        result1 = process_order.delay(order_id=123).get(timeout=10)
        result2 = process_order.delay(order_id=123).get(timeout=10)

        # Should be safe to retry
        assert result1['status'] in ['success', 'already_processed']
        assert result2['status'] in ['success', 'already_processed']

    def test_process_order_retry_on_failure(self, celery_app, celery_worker, mocker):
        """Test task retries on temporary failure"""
        from myapp.tasks import process_order

        # Mock to fail first, succeed second
        mock_process = mocker.patch('myapp.tasks.perform_order_processing')
        mock_process.side_effect = [TemporaryError("Timeout"), {'result': 'ok'}]

        result = process_order.delay(order_id=123)

        assert result.get(timeout=10)['status'] == 'success'
        assert mock_process.call_count == 2