Kafka vs Storm

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Apache Storm

Stacks207

Followers282

Votes25

GitHub Stars6.7K

Forks4.1K

Kafka vs Storm: What are the differences?

Kafka: Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design; Storm: Distributed and fault-tolerant realtime computation. Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Kafka belongs to "Message Queue" category of the tech stack, while Storm can be primarily classified under "Stream Processing".

Some of the features offered by Kafka are:

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)

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

Storm integrates with the queueing and database technologies you already use
Simple API
Scalable

"High-throughput" is the top reason why over 95 developers like Kafka, while over 7 developers mention "Flexible" as the leading cause for choosing Storm.

Kafka and Storm are both open source tools. It seems that Kafka with 12.7K GitHub stars and 6.81K forks on GitHub has more adoption than Storm with 5.75K GitHub stars and 3.91K GitHub forks.

According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Storm, which is listed in 37 company stacks and 8 developer stacks.

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Advice on Kafka, Apache Storm

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

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

Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k

Comments

Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

Reviewon

Kafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are:

you have to deal with Zookeeper
you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

10.8k views10.8k

Comments

Detailed Comparison

Kafka	Apache Storm
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
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	Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source
Statistics
GitHub Stars 31.2K	GitHub Stars 6.7K
GitHub Forks 14.8K	GitHub Forks 4.1K
Stacks 24.2K	Stacks 207
Followers 22.3K	Followers 282
Votes 607	Votes 25
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 10 Flexible 6 Easy setup 4 Event Processing 3 Clojure 2 Real Time

What are some alternatives to Kafka, Apache Storm?

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 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

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

Some of the features offered by Kafka are:

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)

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

Storm integrates with the queueing and database technologies you already use
Simple API
Scalable

"High-throughput" is the top reason why over 95 developers like Kafka, while over 7 developers mention "Flexible" as the leading cause for choosing Storm.

Kafka vs Storm