Alternatives to ZeroMQ logo

Alternatives to ZeroMQ

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

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.
ZeroMQ is a tool in the Message Queue category of a tech stack.
ZeroMQ is an open source tool with 8.4K GitHub stars and 2.2K GitHub forks. Here’s a link to ZeroMQ's open source repository on GitHub

Top Alternatives to ZeroMQ

  • RabbitMQ
    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Kafka
    Kafka

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

  • MQTT
    MQTT

    It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

  • 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. ...

  • nanomsg
    nanomsg

    It is a socket library that provides several common communication patterns. It aims to make the networking layer fast, scalable, and easy to use. Implemented in C, it works on a wide range of operating systems with no further dependencies. ...

  • 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... ...

  • NATS
    NATS

    Unlike traditional enterprise messaging systems, NATS has an always-on dial tone that does whatever it takes to remain available. This forms a great base for building modern, reliable, and scalable cloud and distributed systems. ...

ZeroMQ alternatives & related posts

RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 232
    It's fast and it works with good metrics/monitoring
  • 79
    Ease of configuration
  • 58
    I like the admin interface
  • 50
    Easy to set-up and start with
  • 20
    Durable
  • 18
    Standard protocols
  • 18
    Intuitive work through python
  • 10
    Written primarily in Erlang
  • 8
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Scales to 1 million messages per second
  • 3
    Reliable
  • 2
    Better than most traditional queue based message broker
  • 2
    Distributed
  • 2
    Supports MQTT
  • 2
    Supports AMQP
  • 1
    Inubit Integration
  • 1
    Open-source
  • 1
    Delayed messages
  • 1
    Runs on Open Telecom Platform
  • 1
    High performance
  • 1
    Reliability
  • 1
    Clusterable
  • 1
    Clear documentation with different scripting language
  • 1
    Great ui
  • 1
    Better routing system
CONS OF RABBITMQ
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.5M views
Shared insights
on
CeleryCeleryRabbitMQRabbitMQ
at

As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

#MessageQueue

See more
Yogesh Bhondekar
Product Manager | SaaS | Traveller · | 15 upvotes · 310.9K views

Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.

I need advise finalizing the tech stack for this app. I am considering below tech stack:

  • Frontend: React
  • Backend: Node.js
  • Database: MongoDB
  • IAAS: #AWS
  • Containers & Orchestration: Docker / Kubernetes
  • DevOps: GitLab, Terraform
  • Brokers: Redis / RabbitMQ

I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.

Also, please suggest what could be a better tech stack for my app?

#SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack

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Kafka logo

Kafka

20.9K
19.7K
600
Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 126
    High-throughput
  • 119
    Distributed
  • 91
    Scalable
  • 85
    High-Performance
  • 65
    Durable
  • 37
    Publish-Subscribe
  • 19
    Simple-to-use
  • 18
    Open source
  • 11
    Written in Scala and java. Runs on JVM
  • 8
    Message broker + Streaming system
  • 4
    KSQL
  • 4
    Robust
  • 4
    Avro schema integration
  • 3
    Suport Multiple clients
  • 2
    Partioned, replayable log
  • 1
    Flexible
  • 1
    Extremely good parallelism constructs
  • 1
    Fun
  • 1
    Simple publisher / multi-subscriber model
CONS OF KAFKA
  • 31
    Non-Java clients are second-class citizens
  • 28
    Needs Zookeeper
  • 8
    Operational difficulties
  • 3
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.7M 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

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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.

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MQTT logo

MQTT

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A machine-to-machine Internet of Things connectivity protocol
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PROS OF MQTT
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Lightweight with a relatively small data footprint
  • 2
    Very easy to configure and use with open source tools
CONS OF MQTT
  • 1
    Easy to configure in an unsecure manner

related MQTT posts

Kindly suggest the best tool for generating 10Mn+ concurrent user load. The tool must support MQTT traffic, REST API, support to interfaces such as Kafka, websockets, persistence HTTP connection, auth type support to assess the support /coverage.

The tool can be integrated into CI pipelines like Azure Pipelines, GitHub, and Jenkins.

See more
A Nielsen
Fullstack Dev at ADTELA · | 2 upvotes · 47.2K views

Hi Marc,

For the com part, depending of more details not provided, i'd use SSE, OR i'd run either Mosquitto or RabbitMQ running on Amazon EC2 instances and leverage MQTT or amqp 's subscribe/publish features with my users running mqtt or amqp clients (tcp or websockets) somehow. (publisher too.. you don't say how and who gets to update the document(s).

I find "a ton of end users", depending on how you define a ton (1k users ;) ?) and how frequent document updates are, that can mean a ton of ressources, can't cut it at some point, even using SSE

how many, how big, how persistant do the document(s) have to be ? Db-wise,can't say for lack of details and context, yeah could also be Redis, any RDBMS or nosql or even static json files stored on an Amazon S3 bucket .. anything really

Good luck!

See more
Redis logo

Redis

54.3K
41.3K
3.9K
Open source (BSD licensed), in-memory data structure store
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PROS OF REDIS
  • 882
    Performance
  • 540
    Super fast
  • 510
    Ease of use
  • 441
    In-memory cache
  • 321
    Advanced key-value cache
  • 191
    Open source
  • 180
    Easy to deploy
  • 163
    Stable
  • 153
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 31
    Very Scalable
  • 23
    Replication
  • 21
    Great community
  • 21
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Async replication
  • 7
    Bitmaps
  • 6
    Keys with a limited time-to-live
  • 6
    Open Source
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Hyperloglogs
  • 4
    Awesomeness for Free!
  • 3
    Transactions
  • 3
    Runs server side LUA
  • 3
    outstanding performance
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 2
    Performance & ease of use
  • 2
    Data structure server
  • 1
    Simple
  • 1
    Channels concept
  • 1
    Scalable
  • 1
    Temporarily kept on disk
  • 1
    Dont save data if no subscribers are found
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Existing Laravel Integration
  • 1
    Object [key/value] size each 500 MB
CONS OF REDIS
  • 15
    Cannot query objects directly
  • 3
    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.

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ActiveMQ logo

ActiveMQ

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1.2K
77
A message broker written in Java together with a full JMS client
764
1.2K
+ 1
77
PROS OF ACTIVEMQ
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
  • 5
    Scalable
  • 3
    Distributed Network of brokers
  • 3
    Persistence
  • 3
    Support XA (distributed transactions)
  • 1
    Docker delievery
  • 1
    Highly configurable
  • 0
    RabbitMQ
CONS OF ACTIVEMQ
  • 1
    ONLY Vertically Scalable
  • 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 · 719.7K 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
nanomsg logo

nanomsg

10
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0
A socket library
10
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+ 1
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PROS OF NANOMSG
    Be the first to leave a pro
    CONS OF NANOMSG
      Be the first to leave a con

      related nanomsg posts

      gRPC logo

      gRPC

      1.3K
      1.2K
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      A high performance, open-source universal RPC framework
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      PROS OF GRPC
      • 25
        Higth performance
      • 14
        The future of API
      • 12
        Easy setup
      • 4
        Polyglot
      • 4
        Contract-based
      CONS OF GRPC
        Be the first to leave a con

        related gRPC posts

        Dylan Krupp
        Shared insights
        on
        gRPCgRPCGraphQLGraphQL

        I used GraphQL extensively at a previous employer a few years ago and really appreciated the data-driven schema etc alongside the many other benefits it provided. At that time, it seemed like it was set to replace RESTful APIs and many companies were adopting it.

        However, as of late, it seems like interest has been waning for GraphQL as opposed to increasing as I had assumed it would. Am I missing something here? What is the current perspective regarding this technology?

        Currently, I'm working with gRPC and was curious as to the state of everything now.

        See more
        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?

        See more
        NATS logo

        NATS

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        450
        59
        Lightweight publish-subscribe & distributed queueing messaging system
        323
        450
        + 1
        59
        PROS OF NATS
        • 22
          Fastest pub-sub system out there
        • 16
          Rock solid
        • 11
          Easy to grasp
        • 4
          Light-weight
        • 4
          Easy, Fast, Secure
        • 2
          Robust Security Model
        CONS OF NATS
        • 2
          Persistence with Jetstream supported
        • 1
          No Order
        • 1
          No Persistence

        related NATS posts

        Reza Saadat
        IoT Solutions Architect at GreenEdge · | 5 upvotes · 55.4K views
        Shared insights
        on
        MQTTMQTTNATSNATS

        I want to use NATS for my IoT Platform and replace it instead of the MQTT broker. is there any preferred added value to do that?

        See more