Celery vs Kafka Manager

Overview

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Kafka Manager

Stacks70

Followers173

Votes1

Celery vs Kafka Manager: What are the differences?

Key Differences between Celery and Kafka Manager:

Message Brokers vs. Management Tool: Celery is a distributed task queue system that acts as a message broker, allowing task scheduling and message passing between applications. On the other hand, Kafka Manager is a tool designed specifically for managing and monitoring Apache Kafka clusters, providing a web-based interface to perform administrative tasks such as creating topics, managing partitions, and monitoring consumer groups.
Focus on Asynchronous Processing vs. Cluster Management: Celery is primarily focused on enabling asynchronous processing and distributed task execution, allowing applications to offload time-consuming tasks to background workers. Kafka Manager, on the other hand, is focused on simplifying the management and administration of Kafka clusters, providing essential features and functionalities to monitor and control Kafka infrastructure.
Task Queue vs. Log Streaming: Celery organizes tasks into queues, allowing applications to prioritize and distribute tasks among workers efficiently. It provides features like task result storage, retries, and priority handling. In contrast, Kafka Manager deals with log streaming and distributed event messaging. Kafka's message log conceptually replaces traditional task queues, making it suitable for real-time data streaming and event-driven architectures.
Language Agnosticism vs. Java-Centric: Celery is a multi-language library that supports various programming languages such as Python, Ruby, Java, and .NET. This versatility allows developers to integrate Celery into their applications regardless of the programming language used. Kafka Manager, on the other hand, primarily targets Java-based applications that leverage Apache Kafka as their messaging backbone.
Dynamic Scaling vs. Cluster Management: Celery supports dynamic scaling by allowing the addition or removal of worker nodes to handle varying workloads. It provides automatic load balancing and scalability features to effectively utilize the available resources. Kafka Manager, on the other hand, focuses on managing and monitoring the Kafka cluster by providing an intuitive web-based interface. It does not directly handle dynamic scaling of Kafka brokers.
Advanced Stream Processing vs. Administrative Features: Celery integrates with various stream processing frameworks like Apache Spark, Apache Storm, and Apache Flink to enable advanced data processing capabilities. It provides a way to process data streams in parallel, perform transformations, aggregations, and machine learning tasks. In contrast, Kafka Manager does not provide native stream processing capabilities and primarily focuses on administrative features and cluster management.

In Summary, Celery and Kafka Manager differ in their primary purpose – Celery serves as a distributed task queue system for asynchronous processing, while Kafka Manager is a management tool specifically designed for Apache Kafka clusters. Celery emphasizes message passing and task distribution, while Kafka Manager focuses on cluster management and administrative tasks related to Kafka infrastructure.

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Advice on Celery, Kafka Manager

Shantha

Sep 30, 2020

Needs adviceon

RabbitMQ

Celery

MongoDB

I am just a beginner at these two technologies.

Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.

Here these users can be treated as messages. Each REST API request is a task.

I am confused about whether I should go for RabbitMQ alone or Celery.

If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.

If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.

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Comments

Detailed Comparison

Celery	Kafka Manager
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.	This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.
-	Manage multiple clusters;Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution);Run preferred replica election;Generate partition assignments (based on current state of cluster);Run reassignment of partition (based on generated assignments)
Statistics
GitHub Stars 27.5K	GitHub Stars -
GitHub Forks 4.9K	GitHub Forks -
Stacks 1.7K	Stacks 70
Followers 1.6K	Followers 173
Votes 280	Votes 1
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 1 Better Insights for Kafka cluster
Integrations
No integrations available	Kafka

What are some alternatives to Celery, Kafka Manager?

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 Kafka Manager: What are the differences?

Key Differences between Celery and Kafka Manager:

Message Brokers vs. Management Tool: Celery is a distributed task queue system that acts as a message broker, allowing task scheduling and message passing between applications. On the other hand, Kafka Manager is a tool designed specifically for managing and monitoring Apache Kafka clusters, providing a web-based interface to perform administrative tasks such as creating topics, managing partitions, and monitoring consumer groups.
Focus on Asynchronous Processing vs. Cluster Management: Celery is primarily focused on enabling asynchronous processing and distributed task execution, allowing applications to offload time-consuming tasks to background workers. Kafka Manager, on the other hand, is focused on simplifying the management and administration of Kafka clusters, providing essential features and functionalities to monitor and control Kafka infrastructure.
Task Queue vs. Log Streaming: Celery organizes tasks into queues, allowing applications to prioritize and distribute tasks among workers efficiently. It provides features like task result storage, retries, and priority handling. In contrast, Kafka Manager deals with log streaming and distributed event messaging. Kafka's message log conceptually replaces traditional task queues, making it suitable for real-time data streaming and event-driven architectures.
Language Agnosticism vs. Java-Centric: Celery is a multi-language library that supports various programming languages such as Python, Ruby, Java, and .NET. This versatility allows developers to integrate Celery into their applications regardless of the programming language used. Kafka Manager, on the other hand, primarily targets Java-based applications that leverage Apache Kafka as their messaging backbone.
Dynamic Scaling vs. Cluster Management: Celery supports dynamic scaling by allowing the addition or removal of worker nodes to handle varying workloads. It provides automatic load balancing and scalability features to effectively utilize the available resources. Kafka Manager, on the other hand, focuses on managing and monitoring the Kafka cluster by providing an intuitive web-based interface. It does not directly handle dynamic scaling of Kafka brokers.
Advanced Stream Processing vs. Administrative Features: Celery integrates with various stream processing frameworks like Apache Spark, Apache Storm, and Apache Flink to enable advanced data processing capabilities. It provides a way to process data streams in parallel, perform transformations, aggregations, and machine learning tasks. In contrast, Kafka Manager does not provide native stream processing capabilities and primarily focuses on administrative features and cluster management.

Celery vs Kafka Manager

Overview

Celery vs Kafka Manager: What are the differences?