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  5. Kafka Manager vs Zookeeper

Kafka Manager vs Zookeeper

OverviewComparisonAlternatives

Overview

Zookeeper
Zookeeper
Stacks889
Followers1.0K
Votes43
Kafka Manager
Kafka Manager
Stacks70
Followers173
Votes1

Kafka Manager vs Zookeeper: What are the differences?

Introduction

Kafka Manager and Zookeeper are two important components in a Kafka ecosystem. While both play a crucial role, there are some key differences between them.

  1. Kafka Manager: Kafka Manager is a web-based tool used for managing and monitoring Apache Kafka clusters. It provides a user-friendly interface to perform various administrative tasks such as creating, modifying, and monitoring topics, as well as managing Kafka consumers and producers. Kafka Manager allows for easy visualization of cluster metadata and provides features like partition reassignment, leader election, and consumer lag monitoring. It simplifies Kafka cluster management by providing a centralized platform for administrators to manage multiple clusters efficiently.

  2. Zookeeper: Zookeeper is a distributed coordination service at the core of Kafka's architecture. It is responsible for maintaining and managing the overall state of the Kafka cluster. Zookeeper provides features like distributed synchronization, configuration management, and leader election. It acts as a centralized repository for storing and managing metadata of Kafka brokers, consumers, and topics. Zookeeper ensures fault tolerance by replicating data across multiple nodes. It enables Kafka to function in a distributed manner by providing a reliable and consistent view of the cluster.

  3. Ownership of Responsibilities: The key difference between Kafka Manager and Zookeeper lies in their ownership of responsibilities. Kafka Manager primarily focuses on providing a user-friendly interface for managing Kafka clusters and simplifying administrative tasks. It is a tool designed specifically for ease of use by administrators. On the other hand, Zookeeper is responsible for handling critical coordination and management tasks within the Kafka ecosystem. It ensures the consistency and reliability of the cluster and acts as a centralized coordination service.

  4. Granularity of Functionality: Kafka Manager offers a higher level of granularity in terms of functionality compared to Zookeeper. It provides features like graphical visualization of topics, consumer lag monitoring, and partition reassignment. Kafka Manager simplifies cluster management by offering an intuitive user interface with features catering to the specific needs of administrators. In contrast, Zookeeper focuses on low-level coordination and provides the foundation for Kafka's distributed architecture.

  5. Ease of Use: Kafka Manager is designed to be user-friendly, offering a simple and intuitive web-based interface. It provides easy access to essential Kafka management tasks and features, making it easier for administrators to navigate and perform their duties. Zookeeper, while powerful, requires more technical expertise to configure and manage. Its command-line interface and configuration files might be more complex and less intuitive to those unfamiliar with its intricacies.

  6. Scalability and Availability: Zookeeper has built-in support for high availability and scalability. It uses a replicated architecture to maintain data consistency and fault tolerance. It can handle a large number of nodes and provides automatic failover in case of node failures. Kafka Manager, being a tool built on top of Zookeeper, inherits the scalability and availability features of Zookeeper. However, Kafka Manager itself is not intrinsic to the core Kafka infrastructure and can be scaled and deployed separately.

In summary, Kafka Manager is a user-friendly web-based tool for managing Kafka clusters, providing a simplified interface and specific administrative features. Zookeeper, on the other hand, is a distributed coordination service responsible for critical coordination and management tasks within the Kafka ecosystem, ensuring consistency and fault tolerance.

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Detailed Comparison

Zookeeper
Zookeeper
Kafka Manager
Kafka Manager

A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications.

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
Stacks
889
Stacks
70
Followers
1.0K
Followers
173
Votes
43
Votes
1
Pros & Cons
Pros
  • 11
    High performance ,easy to generate node specific config
  • 8
    Kafka support
  • 8
    Java
  • 5
    Spring Boot Support
  • 3
    Supports extensive distributed IPC
Pros
  • 1
    Better Insights for Kafka cluster
Integrations
No integrations available
Kafka
Kafka

What are some alternatives to Zookeeper, Kafka Manager?

Consul

Consul

Consul is a tool for service discovery and configuration. Consul is distributed, highly available, and extremely scalable.

Eureka

Eureka

Eureka is a REST (Representational State Transfer) based service that is primarily used in the AWS cloud for locating services for the purpose of load balancing and failover of middle-tier servers.

etcd

etcd

etcd is a distributed key value store that provides a reliable way to store data across a cluster of machines. It’s open-source and available on GitHub. etcd gracefully handles master elections during network partitions and will tolerate machine failure, including the master.

Keepalived

Keepalived

The main goal of this project is to provide simple and robust facilities for loadbalancing and high-availability to Linux system and Linux based infrastructures.

SkyDNS

SkyDNS

SkyDNS is a distributed service for announcement and discovery of services. It leverages Raft for high-availability and consensus, and utilizes DNS queries to discover available services. This is done by leveraging SRV records in DNS, with special meaning given to subdomains, priorities and weights (more info here: http://blog.gopheracademy.com/skydns).

SmartStack

SmartStack

Scaling a web infrastructure requires services, and building a service-oriented infrastructure is hard. Make it EASY, with SmartStack’s automated, transparent service discovery and registration: cruise control for your distributed infrastructure.

Kafka REST

Kafka REST

It provides a RESTful interface to a Kafka cluster. It makes it easy to produce and consume messages, view the state of the cluster, and perform administrative actions without using the native Kafka protocol or clients. Examples of use cases include reporting data to Kafka from any frontend app built in any language, ingesting messages into a stream processing framework that doesn't yet support Kafka, and scripting administrative actions.

rdkafka

rdkafka

This gem is a modern Kafka client library for Ruby based on librdkafka. It wraps the production-ready C client using the ffi gem and targets Kafka 1.0+ and Ruby 2.3+.

Kafka UI

Kafka UI

It is a simple tool that makes your data flows observable, helps find and troubleshoot issues faster and deliver optimal performance. Its lightweight dashboard makes it easy to track key metrics of your Kafka clusters - Brokers, Topics, Partitions, Production, and Consumption.

Serf

Serf

Serf is a service discovery and orchestration tool that is decentralized, highly available, and fault tolerant. Serf runs on every major platform: Linux, Mac OS X, and Windows. It is extremely lightweight: it uses 5 to 10 MB of resident memory and primarily communicates using infrequent UDP messages.

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