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  1. Stackups
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  4. Message Queue
  5. Kafka vs MSMQ

Kafka vs MSMQ

OverviewDecisionsComparisonAlternatives

Overview

Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K
MSMQ
MSMQ
Stacks33
Followers118
Votes3

Kafka vs MSMQ: What are the differences?

Introduction

In this article, we will explore the key differences between Kafka and MSMQ. Kafka and MSMQ are both messaging systems used for asynchronous communication between applications. However, they differ in several aspects, which are described below.

  1. Message Persistence: Kafka is designed to handle high-volume data streams and provides durable message storage by persisting messages to disk. This ensures that messages are not lost even if the system crashes. On the other hand, MSMQ provides reliable message storage, but it does not persist messages to disk by default. It relies on the underlying operating system for message persistence.

  2. Message Delivery Guarantees: Kafka provides at-least-once message delivery semantics, which ensures that messages are delivered to consumers at least once. It guarantees message order within a partition but does not provide global ordering across partitions. MSMQ provides exactly-once message delivery semantics, which ensures that messages are delivered exactly once to consumers. It supports global ordering across all queues.

  3. Scalability: Kafka is designed for high throughput and can handle a large number of messages per second. It achieves scalability by distributing the message processing load across multiple partitions and consumers. MSMQ, on the other hand, is limited in scalability and is more suitable for low to medium message volumes.

  4. Message Retention: Kafka retains messages for a configurable period of time, allowing consumers to process historical data. It supports long-term data storage and replayability of messages. On the other hand, MSMQ only retains messages as long as they are present in the queues. Once consumed, the messages are removed from the queues.

  5. Data Replication: Kafka replicates messages across multiple brokers to provide fault tolerance and high availability. It uses a distributed architecture and ensures that messages are available even if some brokers fail. MSMQ does not have built-in data replication mechanisms and relies on backup and recovery strategies for fault tolerance.

  6. Supported Programming Languages: Kafka provides client libraries and drivers for a wide range of programming languages, making it easier to integrate with applications written in different languages. MSMQ, on the other hand, is primarily designed for Windows environments and has better integration with .NET applications.

In summary, Kafka and MSMQ differ in terms of message persistence, delivery guarantees, scalability, message retention, data replication, and supported programming languages. Kafka is more suitable for high-volume data streams, provides durable message storage, and offers at-least-once delivery semantics. MSMQ is better suited for low to medium message volumes, provides reliable message storage, and offers exactly-once delivery semantics.

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Advice on Kafka, MSMQ

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
Ishfaq
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
Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

ReviewonKafkaKafka

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)
10.9k views10.9k
Comments

Detailed Comparison

Kafka
Kafka
MSMQ
MSMQ

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

This technology enables applications running at different times to communicate across heterogeneous networks and systems that may be temporarily offline. Applications send messages to queues and read messages from queues.

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
-
Statistics
GitHub Stars
31.2K
GitHub Stars
-
GitHub Forks
14.8K
GitHub Forks
-
Stacks
24.2K
Stacks
33
Followers
22.3K
Followers
118
Votes
607
Votes
3
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
  • 2
    Easy to learn
  • 1
    Cloud not needed
Cons
  • 1
    Windows dependency

What are some alternatives to Kafka, MSMQ?

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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