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  5. Kafka vs Kafka Manager

Kafka vs Kafka Manager

OverviewDecisionsComparisonAlternatives

Overview

Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K
Kafka Manager
Kafka Manager
Stacks70
Followers173
Votes1

Kafka vs Kafka Manager: What are the differences?

Introduction

Kafka and Kafka Manager are two related tools that are commonly used in the field of distributed systems and stream processing. Kafka is a distributed event streaming platform that is designed to handle massive amounts of data in a fault-tolerant and scalable manner. On the other hand, Kafka Manager is a web-based tool that provides a user-friendly interface to manage and monitor Apache Kafka clusters.

  1. Data processing: Kafka is primarily focused on storing and streaming data in a distributed manner, whereas Kafka Manager is focused on providing a user-friendly interface to manage and monitor Kafka clusters. While Kafka allows users to ingest, process, and store large volumes of data in real-time, Kafka Manager enhances this functionality by providing an easy-to-use interface for managing Kafka clusters and monitoring their health and performance.

  2. Cluster management: Kafka provides built-in features for cluster management, such as replication, partitioning, and fault tolerance. On the other hand, Kafka Manager is specifically designed to work alongside Kafka and provides additional features for managing and monitoring Kafka clusters. These features include managing topics, brokers, partitions, and consumer groups, as well as monitoring the overall health and performance of the cluster.

  3. User interface: Kafka provides a command-line interface and APIs for interacting with the cluster, whereas Kafka Manager provides a web-based user interface that makes it easier for users to manage and monitor Kafka clusters. The user interface of Kafka Manager allows users to perform various operations, such as creating and deleting topics, adding and removing brokers, and monitoring the health and performance of the cluster.

  4. Security features: Kafka provides various security features, such as authentication, authorization, and encryption, that help in securing the data and the cluster. While Kafka Manager does not provide any security features directly, it can be integrated with other security tools to enhance the security of the Kafka cluster. This integration allows users to configure and manage the security features of the Kafka cluster using the Kafka Manager interface.

  5. Ease of use: Kafka Manager is designed to be user-friendly and provides a simplified interface for managing and monitoring Kafka clusters. It allows users to perform various tasks, such as creating and deleting topics, monitoring the health of the cluster, and managing consumer groups, in a simple and intuitive manner. Kafka, on the other hand, provides a more flexible and powerful set of tools and APIs that require a deeper understanding of the system.

  6. Scalability: Kafka is designed to be highly scalable and can handle large volumes of data and high-throughput workloads. It provides features for parallel processing, load balancing, and fault tolerance that allow it to scale horizontally as the data and workload increase. Kafka Manager, on the other hand, does not directly add any scalability features but can help in managing and monitoring the scalability of the Kafka cluster.

In summary, Kafka is a distributed event streaming platform focused on storing and streaming data, while Kafka Manager is a web-based tool designed to facilitate the management and monitoring of Kafka clusters. Kafka Manager provides a user-friendly interface for managing topics, brokers, and consumer groups, as well as monitoring the health and performance of the cluster.

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Advice on Kafka, Kafka Manager

Tarun
Tarun

Senior Software Developer at Okta

Dec 4, 2021

Review

We have faced the same question some time ago. Before I begin, DO NOT use Redis as a message broker. It is fast and easy to set up in the beginning but it does not scale. It is not made to be reliable in scale and that is mentioned in the official docs. This analysis of our problems with Redis may help you.

We have used Kafka and RabbitMQ both in scale. We concluded that RabbitMQ is a really good general purpose message broker (for our case) and Kafka is really fast but limited in features. That’s the trade off that we understood from using it. In-fact I blogged about the trade offs between Kafka and RabbitMQ to document it. I hope it helps you in choosing the best pub-sub layer for your use case.

153k views153k
Comments
viradiya
viradiya

Apr 12, 2020

Needs adviceonAngularJSAngularJSASP.NET CoreASP.NET CoreMSSQLMSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k
Comments
Kirill
Kirill

GO/C developer at Duckling Sales

Feb 16, 2021

Decided

Maybe not an obvious comparison with Kafka, since Kafka is pretty different from rabbitmq. But for small service, Rabbit as a pubsub platform is super easy to use and pretty powerful. Kafka as an alternative was the original choice, but its really a kind of overkill for a small-medium service. Especially if you are not planning to use k8s, since pure docker deployment can be a pain because of networking setup. Google PubSub was another alternative, its actually pretty cheap, but I never tested it since Rabbit was matching really good for mailing/notification services.

266k views266k
Comments

Detailed Comparison

Kafka
Kafka
Kafka Manager
Kafka Manager

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

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.

Written at LinkedIn in Scala;Used by LinkedIn to offload processing of all page and other views;Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled);Supports both on-line as off-line processing
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
31.2K
GitHub Stars
-
GitHub Forks
14.8K
GitHub Forks
-
Stacks
24.2K
Stacks
70
Followers
22.3K
Followers
173
Votes
607
Votes
1
Pros & Cons
Pros
  • 126
    High-throughput
  • 119
    Distributed
  • 92
    Scalable
  • 86
    High-Performance
  • 66
    Durable
Cons
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging
Pros
  • 1
    Better Insights for Kafka cluster

What are some alternatives to Kafka, Kafka Manager?

RabbitMQ

RabbitMQ

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

Celery

Celery

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.

Amazon SQS

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

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

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

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

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

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

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

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.

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