Alternatives to RabbitMQ logo

Alternatives to RabbitMQ

Kafka, ActiveMQ, ZeroMQ, Amazon SNS, and Redis are the most popular alternatives and competitors to RabbitMQ.
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What is RabbitMQ and what are its top alternatives?

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
RabbitMQ is a tool in the Message Queue category of a tech stack.
RabbitMQ is an open source tool with 9K GitHub stars and 3.4K GitHub forks. Here’s a link to RabbitMQ's open source repository on GitHub

Top Alternatives to RabbitMQ

  • Kafka

    Kafka

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

  • 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. ...

  • Amazon SNS

    Amazon SNS

    Amazon Simple Notification Service makes it simple and cost-effective to push to mobile devices such as iPhone, iPad, Android, Kindle Fire, and internet connected smart devices, as well as pushing to other distributed services. Besides pushing cloud notifications directly to mobile devices, SNS can also deliver notifications by SMS text message or email, to Simple Queue Service (SQS) queues, or to any HTTP endpoint. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • 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. ...

  • Beanstalkd

    Beanstalkd

    Beanstalks's interface is generic, but was originally designed for reducing the latency of page views in high-volume web applications by running time-consuming tasks asynchronously. ...

  • gRPC

    gRPC

    gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking... ...

RabbitMQ alternatives & related posts

Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 122
    High-throughput
  • 116
    Distributed
  • 87
    Scalable
  • 81
    High-Performance
  • 65
    Durable
  • 36
    Publish-Subscribe
  • 19
    Simple-to-use
  • 15
    Open source
  • 10
    Written in Scala and java. Runs on JVM
  • 6
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 2
    Suport Multiple clients
  • 2
    Robust
  • 2
    KSQL
  • 2
    Partioned, replayable log
  • 1
    Fun
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Flexible
CONS OF KAFKA
  • 27
    Non-Java clients are second-class citizens
  • 26
    Needs Zookeeper
  • 7
    Operational difficulties
  • 2
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

See more
ActiveMQ logo

ActiveMQ

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A message broker written in Java together with a full JMS client
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PROS OF ACTIVEMQ
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
  • 5
    Scalable
  • 3
    Support XA (distributed transactions)
  • 3
    Persistence
  • 2
    Distributed Network of brokers
  • 1
    Highly configurable
  • 1
    Docker delievery
  • 0
    RabbitMQ
CONS OF ACTIVEMQ
  • 1
    Support
  • 1
    Low resilience to exceptions and interruptions
  • 1
    Difficult to scale

related ActiveMQ posts

I want to choose Message Queue with the following features - Highly Available, Distributed, Scalable, Monitoring. I have RabbitMQ, ActiveMQ, Kafka and Apache RocketMQ in mind. But I am confused which one to choose.

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Naushad Warsi
software developer at klingelnberg · | 1 upvote · 609.6K views
Shared insights
on
ActiveMQActiveMQRabbitMQRabbitMQ

I use ActiveMQ because RabbitMQ have stopped giving the support for AMQP 1.0 or above version and the earlier version of AMQP doesn't give the functionality to support OAuth.

If OAuth is not required and we can go with AMQP 0.9 then i still recommend rabbitMq.

See more
ZeroMQ logo

ZeroMQ

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Fast, lightweight messaging library that allows you to design complex communication system without much effort
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PROS OF ZEROMQ
  • 24
    Fast
  • 20
    Lightweight
  • 11
    Transport agnostic
  • 7
    No broker required
  • 4
    Low latency
  • 4
    Low level APIs are in C
  • 1
    Open source
CONS OF ZEROMQ
  • 5
    No message durability
  • 3
    Not a very reliable system - message delivery wise
  • 1
    M x N problem with M producers and N consumers

related ZeroMQ posts

Meili Triantafyllidi
Software engineer at Digital Science · | 5 upvotes · 165.8K views
Shared insights
on
Amazon SQSAmazon SQSRabbitMQRabbitMQZeroMQZeroMQ

Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to: * Not loose messages in services outages * Safely restart service without losing messages (ZeroMQ seems to need to close the socket in the receiver before restart manually)

Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?

Thank you for your time

See more
Amazon SNS logo

Amazon SNS

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Fully managed push messaging service
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PROS OF AMAZON SNS
  • 10
    Low cost
  • 5
    Supports multi subscribers
CONS OF AMAZON SNS
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    related Amazon SNS posts

    Cyril Duchon-Doris

    We decided to use AWS Lambda for several serverless tasks such as

    • Managing AWS backups
    • Processing emails received on Amazon SES and stored to Amazon S3 and notified via Amazon SNS, so as to push a message on our Redis so our Sidekiq Rails workers can process inbound emails
    • Pushing some relevant Amazon CloudWatch metrics and alarms to Slack
    See more
    Manish Mishra
    Lead Consultant at Knoldus Software LLp · | 6 upvotes · 55.7K views
    Shared insights
    on
    Amazon PinpointAmazon PinpointAmazon SNSAmazon SNS

    Instead of Amazon SNS, which is currently being used to send outbound push notification and including SMS, we want to build the 2 Way SMS using Amazon Pinpoint. Just want to know about Pinpoint and any outstanding issues if we drop SNS since it does not support 2 Way and use Pinpoint for both incoming and outgoing flow.

    See more
    Redis logo

    Redis

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    An in-memory database that persists on disk
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    PROS OF REDIS
    • 875
      Performance
    • 535
      Super fast
    • 511
      Ease of use
    • 441
      In-memory cache
    • 321
      Advanced key-value cache
    • 190
      Open source
    • 179
      Easy to deploy
    • 163
      Stable
    • 153
      Free
    • 120
      Fast
    • 40
      High-Performance
    • 39
      High Availability
    • 34
      Data Structures
    • 32
      Very Scalable
    • 23
      Replication
    • 20
      Great community
    • 19
      Pub/Sub
    • 17
      "NoSQL" key-value data store
    • 14
      Hashes
    • 12
      Sets
    • 10
      Sorted Sets
    • 9
      Lists
    • 8
      BSD licensed
    • 8
      NoSQL
    • 7
      Async replication
    • 7
      Integrates super easy with Sidekiq for Rails background
    • 7
      Bitmaps
    • 6
      Open Source
    • 6
      Keys with a limited time-to-live
    • 5
      Strings
    • 5
      Lua scripting
    • 4
      Awesomeness for Free!
    • 4
      Hyperloglogs
    • 3
      outstanding performance
    • 3
      Runs server side LUA
    • 3
      Networked
    • 3
      LRU eviction of keys
    • 3
      Written in ANSI C
    • 3
      Feature Rich
    • 3
      Transactions
    • 2
      Data structure server
    • 2
      Performance & ease of use
    • 1
      Existing Laravel Integration
    • 1
      Automatic failover
    • 1
      Easy to use
    • 1
      Object [key/value] size each 500 MB
    • 1
      Simple
    • 1
      Channels concept
    • 1
      Scalable
    • 1
      Temporarily kept on disk
    • 1
      Dont save data if no subscribers are found
    • 0
      Jk
    CONS OF REDIS
    • 14
      Cannot query objects directly
    • 2
      No secondary indexes for non-numeric data types
    • 1
      No WAL

    related Redis posts

    Robert Zuber

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

    See more

    I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

    We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

    Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

    We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

    Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

    See more
    Gearman logo

    Gearman

    69
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    A generic application framework to farm out work to other machines or processes
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    + 1
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    PROS OF GEARMAN
    • 11
      Ease of use and very simple APIs
    • 11
      Free
    • 6
      Polyglot
    • 5
      No single point of failure
    • 3
      Scalable
    • 3
      High-throughput
    • 2
      Foreground & background processing
    • 2
      Very fast
    • 1
      Different Programming Languages Channel
    • 1
      Many supported programming languages
    CONS OF GEARMAN
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      related Gearman posts

      Beanstalkd logo

      Beanstalkd

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      A simple, fast work queue
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      PROS OF BEANSTALKD
      • 23
        Fast
      • 12
        Free
      • 12
        Does one thing well
      • 9
        Scalability
      • 8
        Simplicity
      • 3
        External admin UI developer friendly
      • 3
        Job delay
      • 2
        Job prioritization
      • 2
        External admin UI
      CONS OF BEANSTALKD
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        related Beanstalkd posts

        Frédéric MARAND
        Core Developer at OSInet · | 2 upvotes · 186.3K views

        I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.

        So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.

        I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.

        See more
        gRPC logo

        gRPC

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        A high performance, open-source universal RPC framework
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        PROS OF GRPC
        • 19
          Higth performance
        • 10
          The future of API
        • 10
          Easy setup
        • 4
          Contract-based
        • 3
          Polyglot
        CONS OF GRPC
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          related gRPC posts

          Shared insights
          on
          gRPCgRPCSignalRSignalR.NET.NET

          We need to interact from several different Web applications (remote) to a client-side application (.exe in .NET Framework, Windows.Console under our controlled environment). From the web applications, we need to send and receive data and invoke methods to client-side .exe on javascript events like users onclick. SignalR is one of the .Net alternatives to do that, but it adds overhead for what we need. Is it better to add SignalR at both client-side application and remote web application, or use gRPC as it sounds lightest and is multilingual?

          SignalR or gRPC are always sending and receiving data on the client-side (from browser to .exe and back to browser). And web application is used for graphical visualization of data to the user. There is no need for local .exe to send or interact with remote web API. Which architecture or framework do you suggest to use in this case?

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          Shared insights
          on
          KafkaKafkagRPCgRPC
          at

          By mid-2015, Uber’s rider growth coupled with its cadence of releasing new services, like Eats and Freight, was pressuring the infrastructure. To allow the decoupling of consumption from production, and to add an abstraction layer between users, developers, and infrastructure, Uber built Catalyst, a serverless internal service mesh.

          Uber decided to build their own severless solution, rather that using something like AWS Lambda, speed for its global production environments as well as introspectability.

          See more