mrq vs Celery
Side-by-side — pricing, strengths, tradeoffs, and fit.
mrq
Distributed worker task queue in Python using Redis & gevent.
Celery
Asynchronous task queue/job queue based on distributed message passing.
At a glance
Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0
Overview
One-liner
One-liner
Distributed worker task queue in Python using Redis & gevent.
One-liner
Asynchronous task queue/job queue based on distributed message passing.
Maturity
Maturity
DecliningMaturity
Not enough dataOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Developers building Python-based systems that require efficient handling of asynchronous tasks.
Teams needing a scalable solution for distributed task processing.
✓ Best for
Developers building scalable Python applications that require asynchronous task processing
Teams needing to integrate distributed message passing into their application architecture
Projects requiring real-time and scheduled task execution capabilities
Not ideal for
✕ Not for
Projects requiring real-time data streaming and processing, as mrq is designed for batch jobs.
Applications where the overhead of Redis and gevent might be prohibitive.
✕ Not for
Applications where tasks need to be executed in a strict sequence without parallelism
Scenarios where the overhead of setting up a messaging system is not justified by the benefits
Strengths
↑ Strengths
Distributed task queue using Redis and gevent for concurrency.
Supports Python-based background tasks.
Flexible configuration options for scaling.
↑ Strengths
Distributed message passing for task execution
Supports real-time operation and scheduling
Flexible configuration options for scalability
Weaknesses
↓ Weaknesses
Performance bottlenecks when Redis becomes a bottleneck
↓ Weaknesses
Steep learning curve for non-Python developers
Frequent breaking changes between versions
Limited language support beyond Python
Complex setup and configuration requirements