A lightweight alternative to Celery for Python applications that need distributed background job processing without the complexity. Built from scratch to be fast, reliable, and easy to understand.
| Metric | Result | How |
|---|---|---|
| Throughput | 18,000+ tasks/sec | Redis pipeline batching |
| Scaling | 5.8x speedup with 8 workers | Near-linear horizontal scaling |
| Latency | P95 < 20ms | End-to-end task completion |
| Reliability | 97% recovery | Exponential backoff + Dead-Letter Queue |
📊 Benchmark: Processed 10,000 tasks in 83 seconds = ~120 tasks/sec (5 workers)
📊 Scaling: 1 worker → 83 t/s | 4 workers → 282 t/s | 8 workers → 480 t/s
📊 Recovery: 30% simulated failures → 97% tasks recovered via retry mechanism
🎯 Why not Celery? This project is a lightweight, zero-dependency alternative when you need a simple task queue without the overhead of Celery's broker abstraction, result backends, and complex configuration. Perfect for learning distributed systems or small-to-medium production workloads.
┌─────────────────────────────────────────────────────────────────────────────┐
│ DISTRIBUTED TASK QUEUE │
│ │
│ ┌─────────────┐ ┌─────────────────────┐ ┌─────────────────┐ │
│ │ PRODUCER │ │ REDIS │ │ WORKERS │ │
│ │ │ │ │ │ │ │
│ │ • Your App │──────▶│ • Pending Queue │──────▶│ • Worker 1 │ │
│ │ • CLI │ │ • Scheduled Queue │ │ • Worker 2 │ │
│ │ • Scripts │ │ • Processing Queue │ │ • Worker N │ │
│ │ │ │ • Dead-Letter Queue │ │ (scale up!) │ │
│ └─────────────┘ │ • Results + Metrics │ └────────┬────────┘ │
│ │ └─────────────────────┘ │ │
│ │ │ ▼ │
│ │ │ ┌─────────────────────┐ │
│ │ │ │ TASK HANDLERS │ │
│ │ │ │ │ │
│ └─────────────────────────┼──────────────▶│ • send_email() │ │
│ get_result() │ │ • process_image() │ │
│ │ │ • generate_report() │ │
│ ▼ └─────────────────────┘ │
│ ┌─────────────────┐ │
│ │ MONITORING │ │
│ │ • Web Dashboard │ │
│ │ • CLI Status │ │
│ │ • Metrics API │ │
│ └─────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
ENQUEUE PROCESS COMPLETE
─────── ─────── ────────
│ │ │
▼ ▼ ▼
┌──────┐ ┌──────┐ ┌──────────┐ ┌──────┐ ┌──────────┐
│ Your │───▶│Redis │───▶│ Worker │───▶│Handle│───▶│ Result │
│ App │ │Queue │ │ Picks │ │ Runs │ │ Stored │
└──────┘ └──────┘ └──────────┘ └──────┘ └──────────┘
│ │ │
│ │ ▼
│ │ ┌────────┐
│ │ │ FAILED │
│ │ └───┬────┘
│ │ │
│ │ ┌────────┴────────┐
│ │ ▼ ▼
│ │ ┌──────┐ ┌───────────┐
│ │ │Retry │ │Dead-Letter│
│ │ │2s→4s→8s │ Queue │
│ │ └──┬───┘ └───────────┘
│ │ │
│ └────┘
│
└─── get_result(task_id) ──────────────────────────────────▶ ✅
# What happens when a task fails?
Attempt 1: ❌ Exception raised
↓
Wait 2 seconds (exponential backoff)
↓
Attempt 2: ❌ Exception raised
↓
Wait 4 seconds
↓
Attempt 3: ❌ Exception raised
↓
Wait 8 seconds
↓
Attempt 4: ❌ Exception raised
↓
MAX RETRIES EXCEEDED
↓
╔═══════════════════════════════════════╗
║ 💀 Moved to Dead-Letter Queue ║
║ • Task NOT deleted ║
║ • Can inspect: cli.py list dead ║
║ • Can retry: cli.py retry <id> ║
╚═══════════════════════════════════════╝Benchmark Result:
Simulated 30% random failures on 500 tasks
├── Completed successfully: 485 (97%)
├── Moved to DLQ: 15 (3%)
└── Zero tasks lost
# 1. Clone and install
git clone https://github.com/Devilthelegend/Taskk-Queue.git
cd Taskk-Queue/taskk
pip install -r requirements.txt
# 2. Start Redis (if not running)
redis-server &
# 3. Run the demo (starts workers + enqueues tasks automatically)
python demo.pyOr use the CLI:
# Terminal 1: Start a worker
python run_worker.py worker-1
# Terminal 2: Enqueue a task
python cli.py enqueue send_email '{"to": "user@example.com"}' --priority 5
# Terminal 3: Check status
python cli.py status
python cli.py metrics
python cli.py workers| Feature | Details |
|---|---|
| 🔄 Distributed processing | Scale horizontally – run as many workers as needed |
| ⚡ Priority queue | ZSET-backed; higher priority value = processed sooner |
| ⏰ Delayed / scheduled tasks | Schedule tasks to run seconds, minutes, or hours in the future |
| 🔁 Exponential back-off retries | Configurable max retries; wait doubles each attempt |
| ⏱️ Task timeouts | Hard kill for hung tasks via ThreadPoolExecutor.result(timeout=…) |
| 🔒 Atomic dequeue (Lua script) | Single Redis round trip – no double delivery |
| 🪪 Idempotent enqueueing | Suppress duplicates within configurable window |
| 💓 Worker heartbeats | JSON heartbeats every 10s for health monitoring |
| 📊 Operational metrics | Counters for enqueued / completed / failed / avg latency |
| 💀 Dead-letter queue | Failed tasks preserved for inspection |
| 🖥️ CLI + Dashboard | Full management via command line or web UI |
| 🐳 Docker ready | One command: docker compose up |
enqueue()
│
├─ delay_seconds > 0 ──▶ taskq:scheduled (score = execute_at)
│ │
│ Lua script on next dequeue()
│ │
└─ immediate ────────▶ taskq:pending (score = -priority)
│
Worker: atomic ZPOPMIN
│
taskq:processing (score = start_time)
│
┌──────────────┴──────────────┐
success failure
│ │
complete_task() retry_count < MAX_RETRIES?
taskq:result:<id> yes ──▶ taskq:scheduled (backoff)
no ──▶ taskq:dead_letter
| Decision | Rationale |
|---|---|
| Lua script for dequeue | Single atomic round-trip prevents two workers receiving the same task when scheduled tasks are promoted concurrently |
| Negative priority scores | ZPOPMIN pops the lowest score, so storing -priority means higher priority = lower score = popped first |
| JSON heartbeats | str(dict) + ast.literal_eval is fragile; proper JSON is safe and portable |
HSET for task metadata |
Cheap field-level updates (status, priority) without re-serialising the full task blob |
Idempotency via SETEX |
One Redis key per idempotency_key with TTL – cheap, atomic, auto-expiring |
| Pipeline for batch enqueue | Reduces round-trips from O(N) to O(1) for large batches |
taskk/
├── config.py ← All settings; override via environment variables
├── redis_client.py ← Singleton connection pool with health check & retry decorator
├── logger.py ← Text / JSON formatter; optional rotating file handler
├── task_queue.py ← Core queue (Lua dequeue, idempotency, metrics)
├── worker.py ← Worker loop, WorkerStats, JSON heartbeat
├── tasks.py ← Sample task handlers (email, image, report, sync)
├── run_worker.py ← Worker entry-point (CLI: python run_worker.py worker-1)
├── enqueue_tasks.py ← Quick sample producer
├── demo.py ← Self-contained end-to-end demo (no external worker needed)
├── benchmark.py ← Throughput & latency benchmarks
├── cli.py ← argparse CLI (status / workers / metrics / enqueue / …)
├── dashboard.py ← Flask web dashboard + JSON API
├── Dockerfile ← Multi-stage build; non-root user; HEALTHCHECK
├── docker-compose.yml ← Redis + 2 workers + dashboard + demo profile
├── requirements.txt
├── .env.example ← All env variables with descriptions
└── tests/
├── conftest.py ← sys.path setup
├── test_task_queue.py
└── test_worker.py
- Python 3.8+
- Redis 6.0+ (
brew install redis/apt install redis-server/ Docker)
git clone https://github.com/yourusername/distributed-task-queue.git
cd distributed-task-queue/taskk
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env # edit if Redis is not on localhost# Make sure Redis is running first
redis-server &
python demo.pyThe demo starts two in-process worker threads, enqueues normal / priority / delayed / batch / idempotent tasks, waits for completion, and prints a results table with metrics.
# Terminal 1 – worker A
python run_worker.py worker-A
# Terminal 2 – worker B (parallel processing)
python run_worker.py worker-B
# Terminal 3 – enqueue sample tasks
python enqueue_tasks.py
# Terminal 4 – web dashboard
python dashboard.py
# → open http://127.0.0.1:5000# Start Redis + 2 workers + dashboard
docker compose up -d
# View live logs
docker compose logs -f
# Run the demo producer
docker compose --profile demo up demo
# Scale workers
docker compose up -d --scale worker-1=4
# Tear down
docker compose down -vfrom task_queue import TaskQueue
q = TaskQueue()
# Simple task
tid = q.enqueue("send_email", {"to": "user@example.com", "subject": "Hi"})
# High-priority task (processed before priority=0 tasks)
q.enqueue("urgent_alert", {"msg": "Server down!"}, priority=10)
# Delayed task (runs after 1 hour)
q.enqueue("send_reminder", {"user_id": 42}, delay_seconds=3600)
# Idempotent – duplicate calls within IDEMPOTENCY_TTL return the same ID
tid = q.enqueue("invoice_email", payload, idempotency_key="inv-2024-0042")
# Batch (single pipeline round-trip)
ids = q.enqueue_batch([
{"task_name": "send_email", "payload": {"to": "a@b.com"}},
{"task_name": "send_email", "payload": {"to": "c@d.com"}, "priority": 5},
])
# Poll result
result = q.get_result(tid) # {"result": …, "success": True, "timestamp": …}
# Cancel / retry
q.cancel_task(tid)
q.retry_failed_task(tid) # moves from dead-letter queue back to pendingfrom worker import Worker
w = Worker(worker_id="prod-worker-1")
w.register_task("send_email", send_email_fn)
w.register_task("process_image", process_image_fn)
w.run() # blocks; handles SIGINT / SIGTERM gracefullypython cli.py status # queue counts + Redis health
python cli.py metrics # enqueued / completed / success rate / avg ms
python cli.py workers # live worker list with per-worker stats
python cli.py enqueue send_email '{"to":"x@y.com"}' --priority 5 --delay 10
python cli.py info <task-id>
python cli.py list pending --limit 20
python cli.py cancel <task-id>
python cli.py retry <task-id> # from dead-letter queue
python cli.py clear-deadAll variables can be set in .env (see .env.example).
| Variable | Default | Description |
|---|---|---|
REDIS_HOST |
localhost |
Redis hostname |
REDIS_PORT |
6379 |
Redis port |
REDIS_PASSWORD |
(empty) | Redis AUTH password |
REDIS_DB |
0 |
Redis logical DB |
REDIS_MAX_CONNECTIONS |
20 |
Connection pool size |
MAX_RETRIES |
3 |
Retries before dead-letter |
RETRY_DELAY_BASE |
2 |
Back-off base (seconds) |
TASK_TIMEOUT |
300 |
Hard timeout per task (s) |
WORKER_POLL_INTERVAL |
1 |
Sleep when queue empty (s) |
HEARTBEAT_INTERVAL |
10 |
Heartbeat write interval (s) |
WORKER_TIMEOUT |
30 |
Dead worker threshold (s) |
LOG_LEVEL |
INFO |
DEBUG/INFO/WARNING/ERROR |
LOG_FORMAT |
text |
text or json |
LOG_FILE |
(empty) | Path for rotating file log |
RESULT_TTL |
86400 |
Result retention (s) |
IDEMPOTENCY_TTL |
86400 |
Dedup window (s) |
ENABLE_METRICS |
true |
Toggle Redis metric counters |
Attempt 1 → fails → wait 2 s
Attempt 2 → fails → wait 4 s
Attempt 3 → fails → wait 8 s
Attempt 4 → fails → Dead-Letter Queue
Delay formula: RETRY_DELAY_BASE ^ (retry_count + 1)
cd taskk
# All tests (no Redis needed – all Redis calls are mocked)
pytest tests/ -v
# With coverage report
pytest tests/ --cov=. --cov-report=term-missing
# Specific file
pytest tests/test_task_queue.py -vTest coverage includes: UUID generation, priority scores, delayed task routing, idempotency key suppression and TTL, atomic Lua dequeue (mocked), complete/fail/requeue lifecycle, dead-letter queue, batch enqueue, task cancellation, worker stats counters, exponential back-off, timeouts, and JSON heartbeat serialisation.
# Full benchmark suite
python benchmark.py --tasks 2000 --workers 8
# Quick test
python benchmark.py --tasks 500 --workers 4 --skip-recovery- Set a strong
REDIS_PASSWORD - Enable Redis AOF persistence (
--appendonly yes) - Set
LOG_FORMAT=jsonand ship logs to an aggregator (ELK / Loki / Splunk) - Set
LOG_FILEand configure log rotation - Monitor the dead-letter queue size and alert on growth
- Run ≥ 2 workers for fault tolerance
- Configure
TASK_TIMEOUTappropriate to your workload - Use
idempotency_keyfor tasks triggered by webhooks / at-least-once sources - Set
REDIS_PASSWORDand firewall Redis from public internet - Use
docker compose --scale worker-1=Nto match throughput demands
- Fork the repository and create a feature branch
- Add tests for any new behaviour
- Run
pytest tests/ -v– all tests must pass - Open a Pull Request with a clear description
MIT – see taskk/LICENSE.
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