Celery vs IBM MQ

Overview

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

IBM MQ

Stacks118

Followers187

Votes11

Celery vs IBM MQ: What are the differences?

Data Processing Model: Celery is a distributed task queue that follows the worker-based model where tasks are sent to worker nodes for processing, whereas IBM MQ is a message-oriented middleware that uses a message queue system for communication between applications.
Language Support: Celery is written in Python and is commonly used with Python applications, while IBM MQ supports multiple programming languages such as Java, C, and Python, making it versatile for a wide range of applications.
Message Durability: Celery does not inherently guarantee message durability, as it relies on the underlying message broker for persistence, whereas IBM MQ ensures message durability by storing messages persistently until they are consumed or expire.
Scalability: Celery provides horizontal scalability by adding more worker nodes to distribute the workload, whereas IBM MQ offers vertical scalability by increasing the resources of a single message queue manager to handle more messages.
Fault Tolerance: Celery lacks built-in fault tolerance mechanisms, requiring external configurations for fault tolerance, while IBM MQ features built-in high availability and failover configurations for reliable message delivery even in case of failures.
Integration Capabilities: Celery integrates well with various message brokers like Redis, RabbitMQ, and Amazon SQS, providing flexibility in choosing a backend, whereas IBM MQ is a standalone messaging platform with its own messaging system, offering robust integration within the IBM ecosystem.

In Summary, Celery and IBM MQ differ in their data processing model, language support, message durability, scalability options, fault tolerance mechanisms, and integration capabilities.

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Detailed Comparison

Celery	IBM MQ
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.	It is a messaging middleware that simplifies and accelerates the integration of diverse applications and business data across multiple platforms. It offers proven, enterprise-grade messaging capabilities that skillfully and safely move information.
-	Once-and-once-only delivery; Asynchronous messaging; Powerful protection; Simplified, smart management; Augmented security; Expanded client application options
Statistics
GitHub Stars 27.5K	GitHub Stars -
GitHub Forks 4.9K	GitHub Forks -
Stacks 1.7K	Stacks 118
Followers 1.6K	Followers 187
Votes 280	Votes 11
Pros & Cons
Pros 99 Task queue 63 Python integration 40 Django integration 30 Scheduled Task 19 Publish/subsribe Cons 4 Sometimes loses tasks 1 Depends on broker	Pros 3 Useful for big enteprises 3 Reliable for banking transactions 2 Secure 1 High Availability 1 Many deployment options (containers, cloud, VM etc) Cons 2 Cost

What are some alternatives to Celery, IBM MQ?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

Related Comparisons

Celery vs IBM MQ: What are the differences?

Data Processing Model: Celery is a distributed task queue that follows the worker-based model where tasks are sent to worker nodes for processing, whereas IBM MQ is a message-oriented middleware that uses a message queue system for communication between applications.
Language Support: Celery is written in Python and is commonly used with Python applications, while IBM MQ supports multiple programming languages such as Java, C, and Python, making it versatile for a wide range of applications.
Message Durability: Celery does not inherently guarantee message durability, as it relies on the underlying message broker for persistence, whereas IBM MQ ensures message durability by storing messages persistently until they are consumed or expire.
Scalability: Celery provides horizontal scalability by adding more worker nodes to distribute the workload, whereas IBM MQ offers vertical scalability by increasing the resources of a single message queue manager to handle more messages.
Fault Tolerance: Celery lacks built-in fault tolerance mechanisms, requiring external configurations for fault tolerance, while IBM MQ features built-in high availability and failover configurations for reliable message delivery even in case of failures.
Integration Capabilities: Celery integrates well with various message brokers like Redis, RabbitMQ, and Amazon SQS, providing flexibility in choosing a backend, whereas IBM MQ is a standalone messaging platform with its own messaging system, offering robust integration within the IBM ecosystem.

In Summary, Celery and IBM MQ differ in their data processing model, language support, message durability, scalability options, fault tolerance mechanisms, and integration capabilities.

Celery vs IBM MQ

Overview