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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Kafka Manager vs Starling

Kafka Manager vs Starling

OverviewComparisonAlternatives

Overview

Starling
Starling
Stacks8
Followers11
Votes0
GitHub Stars463
Forks59
Kafka Manager
Kafka Manager
Stacks70
Followers173
Votes1

Kafka Manager vs Starling: What are the differences?

Developers describe Kafka Manager as "A tool for managing Apache Kafka, developed by Yahoo". 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. On the other hand, Starling is detailed as "A light weight server for reliable distributed message passing". Starling is a powerful but simple messaging server that enables reliable distributed queuing with an absolutely minimal overhead. It speaks the MemCache protocol for maximum cross-platform compatibility. Any language that speaks MemCache can take advantage of Starling's queue facilities.

Kafka Manager and Starling can be primarily classified as "Message Queue" tools.

Some of the features offered by Kafka Manager are:

  • Manage multiple clusters
  • Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution)
  • Run preferred replica election

On the other hand, Starling provides the following key features:

  • Written by Blaine Cook at Twitter
  • Starling is a Message Queue Server based on MemCached
  • Written in Ruby

Kafka Manager and Starling are both open source tools. It seems that Kafka Manager with 7.55K GitHub stars and 1.84K forks on GitHub has more adoption than Starling with 468 GitHub stars and 63 GitHub forks.

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Detailed Comparison

Starling
Starling
Kafka Manager
Kafka Manager

Starling is a powerful but simple messaging server that enables reliable distributed queuing with an absolutely minimal overhead. It speaks the MemCache protocol for maximum cross-platform compatibility. Any language that speaks MemCache can take advantage of Starling's queue facilities.

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 by Blaine Cook at Twitter;Starling is a Message Queue Server based on MemCached;Written in Ruby;Stores jobs in memory (message queue)
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
463
GitHub Stars
-
GitHub Forks
59
GitHub Forks
-
Stacks
8
Stacks
70
Followers
11
Followers
173
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    Better Insights for Kafka cluster
Integrations
No integrations available
Kafka
Kafka

What are some alternatives to Starling, Kafka Manager?

Kafka

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

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.

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