async-jobs

What this skill does

# 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

| Category | Rules | Impact | When to Use |
|----------|-------|--------|-------------|
| [Configuration](#configuration) | celery-config | HIGH | Celery app setup, broker, serialization, worker tuning |
| [Task Routing](#task-routing) | task-routing | HIGH | Priority queues, multi-queue workers, dynamic routing |
| [Canvas Workflows](#canvas-workflows) | canvas-workflows | HIGH | Chain, group, chord, nested workflows |
| [Retry Strategies](#retry-strategies) | retry-strategies | HIGH | Exponential backoff, idempotency, dead letter queues |
| [Scheduling](#scheduling) | scheduled-tasks | MEDIUM | Celery Beat, crontab, database-backed schedules |
| [Monitoring](#monitoring) | monitoring-health | MEDIUM | Flower, custom events, health checks, metrics |
| [Result Backends](#result-backends) | result-backends | MEDIUM | Redis results, custom states, progress tracking |
| [ARQ Patterns](#arq-patterns) | arq-patterns | MEDIUM | Async Redis Queue for FastAPI, lightweight jobs |
| [Temporal Workflows](#temporal-workflows) | temporal-workflows | HIGH | Durable workflow definitions, sagas, signals, queries |
| [Temporal Activities](#temporal-activities) | temporal-activities | HIGH | Activity patterns, workers, heartbeats, testing |

**Total: 10 rules across 9 categories**

## Quick Start

```python
@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

| Decision | Recommendation |
|----------|----------------|
| Serializer | JSON (never pickle) |
| Ack mode | Late ack (`task_acks_late=True`) |
| Prefetch | 1 for fair, 4-8 for throughput |
| Time limit | soft < hard (e.g., 540/600) |
| Timezone | UTC always |

## 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

| Decision | Recommendation |
|----------|----------------|
| Queue count | 3-5 (critical/high/default/low/bulk) |
| Priority levels | 0-9 with Redis `x-max-priority` |
| Worker assignment | Dedicated workers per queue |
| Prefetch | 1 for critical, 4-8 for bulk |
| Routing | Router class for 5+ routing rules |

## 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

| Decision | Recommendation |
|----------|----------------|
| Sequential | Chain with `s()` |
| Parallel | Group for independent tasks |
| Fan-in | Chord (all must succeed for callback) |
| Ignore input | Use `si()` immutable signature |
| Error in chain | Reject stops chain, retry continues |
| Partial failures | Return error dict in chord tasks |

## 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

| Decision | Recommendation |
|----------|----------------|
| Retry delay | Exponential backoff with jitter |
| Max retries | 3-5 for transient, 0 for permanent |
| Idempotency | Redis key with TTL |
| Failed tasks | DLQ for manual review |
| Singleton | Redis lock with TTL |

## 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

| Decision | Recommendation |
|----------|----------------|
| Schedule type | Crontab for time-based, interval for frequency |
| Dynamic | Database scheduler (`django-celery-beat`) |
| Overlap | Redis lock with timeout |
| Beat process | Separate process (not embedded) |
| Timezone | UTC always |

## 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

| Decision | Recommendation |
|----------|----------------|
| Dashboard | Flower with persistent storage |
| Metrics | Prometheus via celery signals |
| Health | Broker + worker + queue depth |
| Alerting | Signal on task_failure |
| Autoscale | Queue depth > threshold |

## 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

| Decision | Recommendation |
|----------|----------------|
| Status storage | Redis result backend |
| Large results | S3 or database (never Redis) |
| Progress | Custom states with `update_state()` |
| Result query | AsyncResult with state checks |

## 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

| Decision | Recommendation |
|----------|----------------|
| Simple async | ARQ (native async) |
| Complex workflows | Celery (chains, cho