egecagintepe/celery-reliability-engineer

Última actividad de origen registrada: 5 de noviembre de 2026 a las 09:47

Catálogo de SkillsMP actualizado: 12 de noviembre de 2026 a las 12:52

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<div align="center"> # Celery Reliability Engineer **Make background jobs resilient to crashes, retries, and duplicate delivery.** [![CI](https://github.com/egecagintepe/celery-reliability-engineer/actions/workflows/ci.yml/badge.svg)](https://github.com/egecagintepe/celery-reliability-engineer/actions) [![Release](https://img.shields.io/github/v/release/egecagintepe/celery-reliability-engineer?color=blue)](https://github.com/egecagintepe/celery-reliability-engineer/releases/latest) [![skills.sh](https://img.shields.io/badge/skills.sh-celery--reliability--engineer-blue)](https://skills.sh/egecagintepe/celery-reliability-engineer) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Python: 3.8+](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/downloads/) [![Agent Skills: v1](https://img.shields.io/badge/Agent%20Skills-v1.0-emerald.svg)](https://agentskills.io/specification) *Distributed task queue reliability toolkit, configuration linter, fail-closed locks, and full-jitter backoff for Celery and Redis.* <br /> ```bash npx skills add egecagintepe/celery-reliability-engineer ``` <br /> ``` Task Enqueued ──► [ AT-LEAST-ONCE BROKER ] ──► Worker Crash? │ │ ▼ ▼ [ REDIS DISTRIBUTED LOCK ] acks_late=True (Fail-Closed Coordination) (Re-queued to live worker) │ │ ▼ ▼ [ DB TRANSACTION COMMIT ] ──► Duplicate Prevented! ``` </div> --- ## Problem → Solution → Result - **The Problem**: Celery and Redis are the industry standard for Python asynchronous processing, but default setups fail catastrophically under load: 1. **At-Least-Once Duplication**: Brokers guarantee delivery, not single execution. A worker crash after a database commit causes the task to re-execute, double-billing customers or spamming emails. 2. **Silent Distributed Lock Degradation**: Unsafe lock libraries silently fall back to local in-memory dictionaries when Redis connections drop, letting workers in separate containers execute the same task concurrently. 3. **Thundering Herd Retry Storms**: When downstream APIs return HTTP 429 or 500, hundreds of workers retrying on fixed 10-second intervals strike the API at the exact same millisecond, turning temporary glitches into sustained outages. 4. **Prefetch Starvation**: Default `worker_prefetch_multiplier = 4` on mixed workloads (100ms jobs mixed with 15-minute reports) causes one worker to hoard long tasks while other workers sit starved and idle. - **The Solution**: `celery-reliability-engineer` provides fail-closed distributed locking (`RedisDistributedLock`), full-jitter exponential backoff schedules (`JitteredBackoff`), and an AST-based configuration linter (`celery_doctor.py`) that audits Celery settings against production failure modes. - **The Result**: Tasks fail closed instead of corrupting data during network blips, retries are decorrelated across random windows, workers share mixed queues evenly (`prefetch_multiplier = 1`), and concurrent tasks verify durable database state rather than dropping pending work. --- ## Quick Install (30 Seconds) ### Option 1: Using Agent Skills CLI (`skills.sh`) ```bash npx skills add egecagintepe/celery-reliability-engineer ``` ### Option 2: Direct Agent Integration (Claude Code, Cursor, Codex) Clone into your project's local agent skills directory: ```bash git clone https://github.com/egecagintepe/celery-reliability-engineer.git .agents/skills/celery-reliability-engineer ``` ### Option 3: Standalone Python Toolkit (Zero Dependencies) ```bash git clone https://github.com/egecagintepe/celery-reliability-engineer.git cd celery-reliability-engineer python scripts/celery_doctor.py --help ``` --- ## Before & After | Scenario | Naive Celery Setup | Celery Reliability Engineer | |---|---|---| | **Redis connection blips** | Silent memory fallback (separate workers double-process same job). | **Fail-Closed (`RedisDistributedLock`)**: Raises `RedisLockConnectionError`; never executes uncoordinated writes. | | **Worker dies during task execution (`SIGKILL`)** | Early ack: Task is marked done; work is lost forever. | **Late Ack (`acks_late=True`, `reject_on_worker_lost=True`)**: Unacknowledged message automatically requeued. | | **Downstream API returns HTTP 429** | Fixed 10s retry: All 50 workers strike API simultaneously (Thundering Herd). | **Full-Jitter Exponential Backoff**: Retries randomly decorrelated across time window. | | **Daily API Quota exhausted** | Worker retries every 10s all day (burns CPU and locks queue). | **Quota Reset Calculation**: Differentiates short rate-limits from daily quota resets; parks task until reset epoch. | | **Mixed fast & slow task queue** | Default prefetch=4: One worker pulls 8 long jobs; other workers sit idle. | **`prefetch_multiplier = 1`**: Ensures even task distribution across active worker pool. | --- ## Core Components ### 1. `RedisDistributedLock` (Fail-Closed Coordination) Zero-dependency, production-grade distributed lock with Lua atomic compare-and-delete: ```python from idempotency_helpers import RedisDistributedLock, TaskLockAcquisitionError, JitteredBackoff backoff = JitteredBackoff(base_delay=2.0, max_delay=120.0) @app.task(bind=True, acks_late=True, max_retries=5) def generate_monthly_invoice(self, customer_id): try: # Fails closed if Redis is down (never silently falls back to local memory) with RedisDistributedLock(key=f"invoice_{customer_id}", ttl_seconds=120, redis_client=redis_conn): # Safe execution boundary process_invoice(customer_id) except TaskLockAcquisitionError as exc: # Check durable database state: has the invoice already completed? if is_invoice_already_completed(customer_id): return {"status": "ALREADY_COMPLETED"} # Work is still pending: DO NOT silently acknowledge and drop! Re-queue with jitter delay = backoff.calculate_delay(self.request.retries + 1) logger.warning("Invoice in-flight by another worker. Re-queueing with jitter (delay: %.2fs).", delay) raise self.retry(exc=exc, countdown=delay) ``` ### 2. `JitteredBackoff` (Full-Jitter Exponential Retry) Eliminate thundering herds by decorrelating retry schedules: ```python from idempotency_helpers import JitteredBackoff backoff = JitteredBackoff(base_delay=2.0, max_delay=300.0) @app.task(bind=True, max_retries=5) def call_partner_webhook(self, url, payload): try: send_webhook(url, payload) except TransientNetworkError as exc: delay = backoff.calculate_delay(self.request.retries + 1) raise self.retry(exc=exc, countdown=delay) ``` See the full production task scenario in [examples/resilient_task_example.py](examples/resilient_task_example.py) and baseline settings in [examples/celery_conf_recommended.py](examples/celery_conf_recommended.py). --- ## CLI Diagnostics (`celery_doctor.py`) ### 1. Audit Celery Configuration Audits any settings file or Django settings module using AST analysis, distinguishing static values from dynamic runtime variables: ```bash # Heterogeneous mixed workload (default) python scripts/celery_doctor.py lint-config --config examples/celery_conf_recommended.py # Homogeneous uniform fast workload python scripts/celery_doctor.py lint-config --config examples/celery_conf_recommended.py --workload-profile homogeneous ``` Sample Output: ```text === Celery Reliability Audit: celery_conf.py (Profile: heterogeneous) === [EXPLICITLY UNSAFE]: ! worker_autoscale: Autoscale terminates busy workers mid-job during backlog recovery. Use fixed concurrency for non-trivial tasks. [DEFAULTS REQUIRING VERIFICATION]: ? worker_prefetch_multiplier: Value is 4. On heterogeneous workloads, prefetch > 1 causes idle workers while one worker hoards long tasks. [VERIFIED SAFE (5 settings)]: task_acks_late, task_reject_on_worker_lost, worker_max_tasks_per_child, task_time_limit, broker_connection_retry_on_startup Final Audit Verdict: PASS ``` ### 2. Calculate Jittered Backoff Schedules ```bash python scripts/celery_doctor.py calc-backoff --attempt 3 --base-delay 2.0 --max-delay 120.0 ``` ### 3. Simulate Thundering Herd Collision Elimination ```bash python scripts/celery_doctor.py simulate-herd --workers 50 --attempts 3 ``` Output: ```text Simulating 50 concurrent tasks over 3 retries: Fixed Interval Retry -> Peak Herd: 50 simultaneous requests at T=5s (THUNDERING HERD) Full Jitter Retry -> Peak Herd: 3 simultaneous requests at T=4s (SMOOTH DISTRIBUTION) ``` --- ## Frequently Asked Questions (FAQ) ### Does a distributed lock guarantee durable idempotency? **No.** A distributed lock provides *concurrency coordination* (preventing two workers from processing the same entity simultaneously). Durable idempotency requires transactional database status checkpoints (`UPDATE orders SET status = 'COMPLETED' WHERE id = 101 AND status = 'PENDING'`). Always combine locks with transactional state machine checks. ### What happens when a lock is contested by another worker? Our resilient pattern does not silently acknowledge and drop work. When `TaskLockAcquisitionError` occurs, the task inspects durable database state: - If the job is already `COMPLETED`, it acknowledges the duplicate safely (`ALREADY_COMPLETED`). - If the job is still `PENDING`, it raises `retry` with exponential jitter backoff so it can run if the competing worker crashes. ### Why is `worker_prefetch_multiplier = 1` critical for mixed workloads? Celery defaults to `prefetch_multiplier = 4`. If a worker with 4 concurrency starts, it reserves `4 * 4 = 16` tasks from the broker. If those tasks include 15-minute reports, that worker sits holding tasks for hours while other workers in the cluster sit idle with nothing to do. Setting prefetch to 1 guarantees fair work distribution. ### Does `celery_doctor.py` require a running Redis or Celery cluster? **No.** The linter performs static Abstract Syntax Tree (AST) analysis on your Python configuration files. It executes with zero external dependencies and zero live infrastructure requirements. --- ## Deep Documentation - [Idempotency Patterns & Distributed Locks](references/idempotency_patterns.md) — State machines vs locks - [Worker Concurrency & Prefetch Tuning](references/concurrency_and_prefetch.md) — Sizing worker pools safely - [Retry Storms & API Quota Handling](references/retry_storm_prevention.md) — Rate limits vs daily resets - [Broker Resilience & Connection Lifecycles](references/broker_resilience.md) — Visibility timeouts & keepalives - [Recommended Configuration Reference](examples/celery_conf_recommended.py) — Production baseline settings - [Resilient Task Scenario Example](examples/resilient_task_example.py) — End-to-end Python implementation --- ## Verified Compatibility | Environment | Supported | Notes | |---|---|---| | **Python** | 3.8, 3.9, 3.10, 3.11, 3.12 | Standard library only; optional `redis-py` integration. | | **Celery** | 4.x, 5.x | Config-agnostic AST analysis. | | **Brokers** | Redis, RabbitMQ | Locking primitives specialized for Redis. | | **Agent Skills** | Specification v1.0 | Validated with official `skills-ref validate`. | | **AI Agents** | Claude Code, Cursor, Codex, Antigravity | Indexed via `SKILL.md`. | --- ## Safety Boundaries & Limitations - **Locks Do Not Equal Durable Idempotency**: A distributed lock provides *concurrency control*. Durable idempotency requires transactional database checkpoints (`UPDATE ... WHERE status = 'PENDING'`). - **Lock TTL Expiration**: If a task takes longer than its TTL, the lock will expire in Redis. For long-running batch jobs, pair with soft time limits or heartbeat renewal. --- ## Testing Run the full automated test suite (22 tests): ```bash python -m unittest discover -s tests ``` --- ## License This project is licensed under the [MIT License](LICENSE).

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Distributed task queue reliability and architecture specialist for Celery and Redis. Prevents duplicate task execution, ghost worker loss, thundering herd retry storms (especially 429 quota exhaustion), prefetch starvation, autoscale traps, and uncheckpointed…

Idioma del texto original: inglés

2026-11-05

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