async-jobs（异步作业）

Async Jobs

Patterns for background task processing with Celery, ARQ, and Redis. Covers task queues, canvas workflows, scheduling, retry strategies, rate limiting, and production monitoring. Each category has individual rule files in references/ loaded on-demand.

Quick Reference

Total: 10 rules across 9 categories

Quick Start

@app.task(bind=True, max_retries=3, default_retry_delay=60)
def process_payment(self, order_id: str):
    try:
        return gateway.charge(order_id)
    except TransientError as exc:
        raise self.retry(exc=exc, countdown=2 ** self.request.retries * 60)

Load more examples: Read("${CLAUDE_SKILL_DIR}/references/quick-start-examples.md") for Celery retry task and ARQ/FastAPI integration patterns.

Configuration

Production Celery app configuration with secure defaults and worker tuning.

Key Patterns

• JSON serialization with task_serializer="json" for safety
• Late acknowledgment with task_acks_late=True to prevent task loss on crash
• Time limits with both task_time_limit (hard) and task_soft_time_limit (soft)
• Fair distribution with worker_prefetch_multiplier=1
• Reject on lost with task_reject_on_worker_lost=True

Key Decisions

Task Routing

Priority queue configuration with multi-queue workers and dynamic routing.

Key Patterns

• Named queues for critical/high/default/low/bulk separation
• Redis priority with queue_order_strategy: "priority" and 0-9 levels
• Task router classes for dynamic routing based on task attributes
• Per-queue workers with tuned concurrency and prefetch settings
• Content-based routing for dynamic workflow dispatch

Key Decisions

Canvas Workflows

Celery canvas primitives for sequential, parallel, and fan-in/fan-out workflows.

Key Patterns

• Chain for sequential ETL pipelines with result passing
• Group for parallel execution of independent tasks
• Chord for fan-out/fan-in with aggregation callback
• Immutable signatures (si()) for steps that ignore input
• Nested workflows combining groups inside chains
• Link error callbacks for workflow-level error handling

Key Decisions

Retry Strategies

Retry patterns with exponential backoff, idempotency, and dead letter queues.

Key Patterns

• Exponential backoff with retry_backoff=True and retry_backoff_max
• Jitter with retry_jitter=True to prevent thundering herd
• Idempotency keys in Redis to prevent duplicate processing
• Dead letter queues for failed tasks requiring manual review
• Task locking to prevent concurrent execution of singleton tasks
• Base task classes with shared retry configuration

Key Decisions

Scheduling

Celery Beat periodic task configuration with crontab, database-backed schedules, and overlap prevention.

Key Patterns

• Crontab for time-based schedules (daily, weekly, monthly)
• Interval for fixed-frequency tasks (every N seconds)
• Database scheduler with django-celery-beat for dynamic schedules
• Schedule locks to prevent overlapping long-running scheduled tasks
• Adaptive polling with self-rescheduling tasks

Key Decisions

Monitoring

Production monitoring with Flower, custom signals, health checks, and Prometheus metrics.

Key Patterns

• Flower dashboard for real-time task monitoring
• Celery signals (task_prerun, task_postrun, task_failure) for metrics
• Health check endpoint verifying broker connection and active workers
• Queue depth monitoring for autoscaling decisions
• Beat monitoring for scheduled task dispatch tracking

Key Decisions

Result Backends

Task result storage, custom states, and progress tracking patterns.

Key Patterns

• Redis backend for task status and small results
• Custom task states (VALIDATING, PROCESSING, UPLOADING) for progress
• update_state() for real-time progress reporting
• S3/database for large result storage (never Redis)
• AsyncResult for querying task state and progress

Key Decisions

ARQ Patterns

Lightweight async Redis Queue for FastAPI and simple background tasks.

Key Patterns

• Native async/await with arq for FastAPI integration
• Worker lifecycle with startup/shutdown hooks for resource management
• Job enqueue from FastAPI routes with enqueue_job()
• Job status tracking with Job.status() and Job.result()
• Delayed tasks with _delay=timedelta() for deferred execution

Key Decisions

Tool Selection

Load: Read("${CLAUDE_SKILL_DIR}/references/quick-start-examples.md") for the full tool comparison table (ARQ, Celery, RQ, Dramatiq, FastAPI BackgroundTasks).

Anti-Patterns (FORBIDDEN)

Load details: Read("${CLAUDE_SKILL_DIR}/references/anti-patterns.md") for full list.

Key rules: never run long tasks in request handlers, never block on results inside tasks, never store large results in Redis, always use idempotency for retried tasks.

Temporal Workflows

Durable execution engine for reliable distributed applications with Temporal.io.

Key Patterns

• Workflow definitions with @workflow.defn and deterministic code
• Saga pattern with compensation for multi-step transactions
• Signals and queries for external interaction with running workflows
• Timers with workflow.wait_condition() for human-in-the-loop
• Parallel activities via asyncio.gather inside workflows

Key Decisions

Temporal Activities

Activity and worker patterns for Temporal.io I/O operations.

Key Patterns

• Activity definitions with @activity.defn for all I/O
• Heartbeating for long-running activities (> 60s)
• Error classification with ApplicationError(non_retryable=True) for business errors
• Worker configuration with dedicated task queues
• Testing with WorkflowEnvironment.start_local()

Key Decisions

Related Skills

• ork:python-backend - FastAPI, asyncio, SQLAlchemy patterns
• ork:langgraph - LangGraph workflow patterns (use for LLM workflows, not Celery)
• ork:distributed-systems - Resilience patterns, circuit breakers
• ork:monitoring-observability - Metrics and alerting

Capability Details

Load details: Read("${CLAUDE_SKILL_DIR}/references/capability-details.md") for full keyword index and problem-solution mapping across all 8 capabilities.