The Problem: Python’s Task Queues Are Stuck in 2009

If you’re building async Python applications with FastAPI or aiohttp, you’ve hit this wall: every major task queue was designed before async/await even existed.

Celery? Built in 2009. RQ? 2011. Sure, they’ve bolted on async support, but that’s like putting a Tesla battery in a Model T—the foundation is still synchronous.

So you’re stuck choosing between:

• Celery → Fighting async/sync impedance mismatch
• ARQ → Locked into Redis forever
• Rolling your own → (Please. Don’t.)

After releasing AsyncTasQ, I’m here to show you a much better way.

What Makes AsyncTasQ Different

AsyncTasQ is a modern, async-first, type-safe task queue built from scratch for Python’s asyncio ecosystem. Think Laravel’s elegant queue API, rebuilt for async Python in 2025.

The differentiators:

🚀 True async-first architecture — Built with asyncio from day one, not retrofitted 🧠 Intelligent ORM serialization — Pass SQLAlchemy/Django/Tortoise models directly (90%+ smaller payloads) 🔄 Multi-backend flexibility — 5 production drivers (Redis, PostgreSQL, MySQL, RabbitMQ, AWS SQS), identical API ✨ Type-safe everything — Full Generic[T] support with IDE autocomplete ⚡ Performance that matters — 2-3x faster than Celery (benchmarks below)

The Numbers: AsyncTasQ vs Celery

Let’s cut to the chase. I ran comprehensive benchmarks comparing AsyncTasQ to Celery across three real-world scenarios.

Test Setup: Same hardware, same configuration

• NOOP: 20,000 tasks with minimal work (pure framework overhead)
• I/O: 10,000 I/O-bound tasks (API calls, database queries)
• CPU: 5,000 CPU-intensive tasks (data processing, ML inference)

Benchmark 1: Pure Framework Overhead (NOOP)

20,000 tasks are doing nothing essentially. This measures the amount of overhead the framework itself adds.

AsyncTasQ: 3,429 tasks/sec, completed in 0.10 seconds

Celery: 1,121 tasks/sec, completed in 9.33 seconds

📊 AsyncTasQ is 3.1x faster throughput, 93x faster completion

The difference is stark. While Celery takes over 9 seconds to process 20K minimal tasks, AsyncTasQ finishes in 0.1 seconds. The async-first architecture eliminates blocking operations in the critical path.

Benchmark 2: I/O-Bound Tasks (The Async Sweet Spot)

10,000 tasks making async I/O calls—simulating API requests, database queries, and file operations.

AsyncTasQ: 3,357 tasks/sec, completed in 0.10 seconds

Celery: 1,194 tasks/sec, completed in 4.30 seconds

📊 AsyncTasQ is 2.81x faster throughput, 43x faster completion

This is where native async/await architecture dominates. While Celery relies on threading or multiprocessing, AsyncTasQ uses the event loop for true asynchronous I/O.

Perfect for: Web scraping, API calls, database queries, webhooks, and email sending.

Benchmark 3: CPU-Intensive Tasks

5,000 CPU-bound tasks. Async typically doesn’t help here, but AsyncTasQ still wins.

AsyncTasQ: 1,473 tasks/sec, 279 MB memory, 2.02s completion

Celery: 972 tasks/sec, 335 MB memory, 2.87s completion

📊 AsyncTasQ is 1.51x faster + uses 16.7% less memory

Even with process pools (which both use for CPU work), AsyncTasQ’s architecture is more efficient with lower overhead.

The Verdict

Workload	AsyncTasQ	Celery	Speedup
Framework overhead	3,429 t/s	1,121 t/s	3.1x
I/O-bound	3,357 t/s	1,194 t/s	2.81x
CPU-bound	1,473 t/s	972 t/s	1.51x

Across every scenario, AsyncTasQ is 1.5-3.1x faster. The async architecture gives the biggest win on I/O workloads (where most real-world tasks live), but even CPU tasks benefit from reduced overhead.

Benchmark Setup: Tests conducted on dedicated hardware with identical configurations for fair comparison. Both frameworks configured with Redis backend, 10 concurrent workers, and default settings. Results represent average throughput across multiple runs. Your results may vary based on workload characteristics and infrastructure.

Game-Changer #1: ORM Auto-Serialization

This feature alone is worth switching for. It eliminates so much boilerplate.

The Old Way (Every Other Task Queue)

# The painful manual approach with Celery
@celery_task
def send_welcome_email(user_id: int):
# Manually re-fetch from database
user = User.query.get(user_id)
print(f"Sending email to {user.email}")

# Dispatch - manually extract ID first
send_welcome_email.delay(user.id)

Problems:

• ❌ Verbose: Extract ID → pass ID → re-fetch model
• ❌ Error-prone: Forget the re-fetch? Runtime error
• ❌ Large payloads: Full objects serialize everything

The AsyncTasQ Way

from asynctasq import task

@task
async def send_welcome_email(user: User):
# user is automatically re-fetched with fresh data
print(f"Sending email to {user.email}")

# Just pass the model directly
await send_welcome_email(user).dispatch()

That’s it. No ID extraction. No manual re-fetching. AsyncTasQ handles it.

How It Works

1. On dispatch: AsyncTasQ detects the ORM model and stores only the primary key
2. In queue: Lightweight reference goes into Redis/Postgres/SQS (4 bytes vs 400+)
3. On execution: Worker automatically re-fetches from database with fresh data
4. Parallel optimization: Multiple models? asyncio.gather() fetches them in parallel

The Impact

User model with 20 fields:

Without AsyncTasQ (standard serialization):

{
"id": 123,
"email": "user@example.com",
"name": "John Doe",
"created_at": "2025-01-01T00:00:00Z",
"address": "123 Main St",
"phone": "+1-555-0123",
# ... 14 more fields
}
# Total: ~450 bytes when serialized with msgpack

With AsyncTasQ (ORM reference):

{"__orm:sqlalchemy__": 123, "__orm_class__": "app.models.User"}
# Total: ~45 bytes (90% reduction!)

Real impact: 10,000 queued tasks = 4.5MB vs 450KB. That’s faster queue operations, lower memory usage, and cheaper infrastructure costs.

Supports: SQLAlchemy (async/sync), Django ORM, Tortoise ORM. Handles composite PKs, UUIDs, and foreign keys.

Game-Changer #2: Zero Vendor Lock-In

Most task queues chain you to one backend. ARQ? Redis only. RQ? Redis only. Celery? Three backends, but with different feature sets.

AsyncTasQ gives you 5 production drivers, one API:

1. Redis → Fast, simple, great default
2. PostgreSQL → ACID guarantees, dead-letter queues
3. MySQL → ACID with InnoDB row-level locking
4. RabbitMQ → AMQP protocol, advanced routing
5. AWS SQS → Fully managed, serverless-ready

Switch Backends in One Line

from asynctasq import init

# Dev: Redis
init({'driver': 'redis', 'redis': {'url': 'redis://localhost:6379'}})

# Prod: PostgreSQL (ACID guarantees)
init({'driver': 'postgres', 'postgres': {'dsn': 'postgresql://...'}})

# Serverless: AWS SQS
init({'driver': 'sqs', 'sqs': {'region': 'us-east-1'}})

# Your task code? Unchanged.

Why This Matters

• Start simple: Redis in dev, PostgreSQL in prod
• Use existing infrastructure: No need for new services
• ACID when needed: PostgreSQL/MySQL for critical workflows
• Go serverless: SQS for AWS Lambda
• Experiment freely: Try backends without code changes

Each Driver Has Superpowers

PostgreSQL/MySQL: ACID transactions, dead-letter queues, visibility timeouts Redis: Highest throughput, Pub/Sub events, sorted sets for delays AWS SQS: Fully managed, auto-scaling, IAM roles RabbitMQ: Advanced routing, exchange types, message acks

Game-Changer #3: Type Safety & Developer Experience

Modern Python is typed. AsyncTasQ was built for it.

Full Generic[T] Support

from asynctasq import AsyncTask

class ProcessPayment(AsyncTask[bool]):
async def execute(self) -> bool:
# IDE knows return type
# mypy validates at build time
return True

result: bool = await ProcessPayment(...).dispatch_sync()

Full IDE autocomplete. Full type checking. Zero runtime surprises.

Four Execution Modes for Every Workload

Mode	Concurrency	Best For
AsyncTask	1000s concurrent	API calls, async DB, webhooks
SyncTask	100s concurrent	requests, sync DB drivers
AsyncProcessTask	# CPU cores	Async + heavy compute
SyncProcessTask	# CPU cores	NumPy, Pandas, ML inference

from asynctasq import task

# Async I/O (handles 1000s concurrently)
@task
async def fetch_data(url: str):
async with httpx.AsyncClient() as client:
return await client.get(url)

# CPU-bound (bypasses GIL with process pool)
@task(process=True)
def crunch_numbers(matrix: list[list[float]]):
import numpy as np
return np.linalg.inv(np.array(matrix))

Laravel-Style Method Chaining

await send_email(to="user@example.com", subject="Welcome") \
.on_queue("high-priority") \
.delay(60) \
.max_attempts(5) \
.timeout(30) \
.dispatch()

Override any parameter at dispatch time. Zero need for separate task functions.

FastAPI: First-Class Integration

from fastapi import FastAPI
from asynctasq import AsyncTasQIntegration, task

asynctasq = AsyncTasQIntegration()
app = FastAPI(lifespan=asynctasq.lifespan)

@task
async def send_welcome_email(user_id: int):
print(f"Sending welcome email to user {user_id}")

@app.post("/users")
async def create_user(email: str):
user_id = 123  # Created user
task_id = await send_welcome_email(user_id).dispatch()
return {"user_id": user_id, "task_id": task_id}

Native lifespan integration = proper cleanup on shutdown.

Enterprise-Ready Out of the Box

ACID Guarantees (PostgreSQL/MySQL) → Transactional processing, exactly-once delivery, zero lost tasks

Dead-Letter Queues → Failed tasks auto-moved to DLQ for inspection and manual retry

Crash Recovery → Visibility timeouts ensure stuck tasks reappear if workers die

Graceful Shutdown → SIGTERM/SIGINT handlers let in-flight tasks complete

Real-time Monitoring (Redis Pub/Sub) → Stream events: task_started, task_completed, task_failed, worker_online

Built-in Metrics:

from asynctasq import MonitoringService

stats = await MonitoringService().get_queue_stats("emails")
# depth, processing, completed, failed

Beautiful CLI (powered by Rich):

$ asynctasq worker --queues default --concurrency 20

╭─────────────────────────────────────────╮
│  AsyncTasQ Worker                       │
│  Queues: default | Concurrency: 20      │
╰─────────────────────────────────────────╯
✓ Worker started • ⚡ Waiting for tasks...

When to Choose AsyncTasQ

Perfect for:

• ✅ Modern async apps (FastAPI, aiohttp)
• ✅ Type-safe teams (full IDE support)
• ✅ High-throughput systems (millions of tasks)
• ✅ ORM-heavy apps (SQLAlchemy, Django, Tortoise)
• ✅ Enterprise needs (ACID, DLQs, monitoring)
• ✅ Avoiding vendor lock-in (5 backends)

vs The Competition

Feature	AsyncTasQ	Celery	ARQ
Async-first	✅ Native	❌	✅
Type safety	✅ Generic[T]	⚠️ External	✅
Backends	5	3	1
ORM auto-serialization	✅	❌	❌
ACID guarantees	✅	❌	❌
Dead-letter queues	✅ Built-in	⚠️ Manual	❌
FastAPI integration	✅ Native	⚠️ Manual	⚠️ Manual
Performance vs Celery	1.5-3x faster	1x	—

Still choose Celery for: Mature plugin ecosystem, existing large codebases Still choose ARQ for: Simple Redis-only needs with cron

Getting Started in 30 Seconds

# Install
pip install asynctasq[redis]

# Generate .env template
asynctasq publish

# Edit .env with your settings
# ASYNCTASQ_DRIVER=redis
# ASYNCTASQ_REDIS_URL=redis://localhost:6379

from asynctasq import init, task, run

init()  # Load from .env

@task(queue='emails')
async def send_email(to: str, subject: str):
print(f"Sending to {to}: {subject}")
return f"Sent!"

async def main():
# Dispatch
task_id = await send_email(
to="user@example.com",
subject="Welcome!"
).dispatch()

# With delay
await send_email(to="...", subject="Reminder") \
.delay(60) \
.dispatch()

if __name__ == "__main__":
run(main())

# Run worker
asynctasq worker --queues emails --concurrency 20

Done. Tasks are processing.

Real-World Example: FastAPI + SQLAlchemy

from fastapi import FastAPI
from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
from asynctasq import AsyncTasQIntegration, task

# DB setup
engine = create_async_engine('postgresql+asyncpg://...')
async_session = async_sessionmaker(engine)
Base._asynctasq_session_factory = async_session

# FastAPI + AsyncTasQ
asynctasq = AsyncTasQIntegration()
app = FastAPI(lifespan=asynctasq.lifespan)

# Task - pass ORM models directly!
@task(queue='emails')
async def send_welcome_email(user: User):
print(f"Welcome {user.email}!")

# Endpoint
@app.post("/users")
async def create_user(email: str, name: str):
async with async_session() as session:
user = User(email=email, name=name)
session.add(user)
await session.commit()

# Pass the model directly
task_id = await send_welcome_email(user).dispatch()

return {"user_id": user.id, "task_id": task_id}

Magic happening here:

• Pass User model directly (not user.id)
• AsyncTasQ serializes only the PK (4 bytes vs 400+)
• Worker re-fetches with fresh data
• FastAPI lifespan ensures a clean shutdown

What’s Next

AsyncTasQ v1.6 is production-ready.

Coming soon:

• SQLite & Oracle drivers
• Task chaining & workflows (DAG-based)
• Rate limiting & priority queues
• Cron/scheduled tasks

The Bottom Line

After 8 weeks of building and testing, AsyncTasQ v1.6 is what modern Python task queues should be:

🚀 Fast → 1.5-3x faster than Celery 🧠 Smart → ORM auto-serialization 🔄 Flexible → 5 backends, one API ✨ Type-safe → Full Generic[T] support 🏢 Production-ready → ACID, DLQs, monitoring

The Python async ecosystem deserved a task queue built for async/await from day one. Not retrofitted. Not bolted on. Native async, all the way down.

Try It

• GitHub: github.com/adamrefaey/asynctasq (⭐ if you’re excited!)
• Docs: Full documentation
• PyPI: pypi.org/project/asynctasq

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Which feature excites you most? ORM auto-serialization? The 2-3x speedup? Multi-backend flexibility? Drop a comment!

If this was useful, star the repo—it helps other developers discover AsyncTasQ.

Built with ❤️ by Adam Refaey for the Python community.