Alternatives to MQTT logo

Alternatives to MQTT

RabbitMQ, REST, XMPP, Google Cloud Messaging, and Kafka are the most popular alternatives and competitors to MQTT.
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What is MQTT and what are its top alternatives?

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.
MQTT is a tool in the Message Queue category of a tech stack.

Top Alternatives to MQTT

  • RabbitMQ
    RabbitMQ

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

  • REST
    REST

    An architectural style for developing web services. A distributed system framework that uses Web protocols and technologies. ...

  • XMPP
    XMPP

    It is a set of open technologies for instant messaging, presence, multi-party chat, voice and video calls, collaboration, lightweight middleware, content syndication, and generalized routing of XML data. ...

  • Google Cloud Messaging
    Google Cloud Messaging

    Google Cloud Messaging (GCM) is a free service that enables developers to send messages between servers and client apps. This includes downstream messages from servers to client apps, and upstream messages from client apps to servers. ...

  • Kafka
    Kafka

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

  • 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. ...

  • 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... ...

  • 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. ...

MQTT alternatives & related posts

RabbitMQ logo

RabbitMQ

17.9K
15.6K
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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
    Intuitive work through python
  • 18
    Standard protocols
  • 10
    Written primarily in Erlang
  • 8
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Scales to 1 million messages per second
  • 3
    Reliable
  • 2
    Distributed
  • 2
    Supports AMQP
  • 2
    Better than most traditional queue based message broker
  • 1
    Inubit Integration
  • 1
    Delayed messages
  • 1
    Supports MQTT
  • 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 · 1.4M 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 · 234K 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

See more
REST logo

REST

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A software architectural style
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PROS OF REST
  • 3
    Popularity
CONS OF REST
    Be the first to leave a con

    related REST posts

    XMPP logo

    XMPP

    53
    121
    0
    An open XML technology for real-time communication
    53
    121
    + 1
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    PROS OF XMPP
      Be the first to leave a pro
      CONS OF XMPP
        Be the first to leave a con

        related XMPP posts

        Google Cloud Messaging logo

        Google Cloud Messaging

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        230
        22
        Simple and reliable messaging to reach over a billion devices.
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        22
        PROS OF GOOGLE CLOUD MESSAGING
        • 9
          Free
        • 6
          Scalable
        • 4
          Easy setup
        • 2
          Easy iOS setup
        • 1
          IOS Support
        CONS OF GOOGLE CLOUD MESSAGING
        • 1
          Reliability

        related Google Cloud Messaging posts

        Kafka logo

        Kafka

        19.3K
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        Distributed, fault tolerant, high throughput pub-sub messaging system
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        596
        PROS OF KAFKA
        • 126
          High-throughput
        • 119
          Distributed
        • 90
          Scalable
        • 84
          High-Performance
        • 65
          Durable
        • 37
          Publish-Subscribe
        • 19
          Simple-to-use
        • 17
          Open source
        • 11
          Written in Scala and java. Runs on JVM
        • 8
          Message broker + Streaming system
        • 4
          Avro schema integration
        • 4
          Robust
        • 4
          KSQL
        • 2
          Suport Multiple clients
        • 2
          Partioned, replayable log
        • 1
          Flexible
        • 1
          Extremely good parallelism constructs
        • 1
          Simple publisher / multi-subscriber model
        • 1
          Fun
        CONS OF KAFKA
        • 30
          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 · 2.5M 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
        ZeroMQ logo

        ZeroMQ

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        72
        Fast, lightweight messaging library that allows you to design complex communication system without much effort
        226
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        PROS OF ZEROMQ
        • 24
          Fast
        • 20
          Lightweight
        • 11
          Transport agnostic
        • 7
          No broker required
        • 4
          Low level APIs are in C
        • 4
          Low latency
        • 1
          Open source
        • 1
          Publish-Subscribe
        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 · | 6 upvotes · 301.5K 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
        gRPC logo

        gRPC

        1.1K
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        A high performance, open-source universal RPC framework
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        PROS OF GRPC
        • 23
          Higth performance
        • 12
          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

          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
          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
          Amazon SQS logo

          Amazon SQS

          3.3K
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          Fully managed message queuing service
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          PROS OF AMAZON SQS
          • 60
            Easy to use, reliable
          • 39
            Low cost
          • 27
            Simple
          • 13
            Doesn't need to maintain it
          • 8
            It is Serverless
          • 4
            Has a max message size (currently 256K)
          • 3
            Easy to configure with Terraform
          • 3
            Triggers Lambda
          • 3
            Delayed delivery upto 15 mins only
          • 3
            Delayed delivery upto 12 hours
          • 1
            JMS compliant
          • 1
            Support for retry and dead letter queue
          • 1
            D
          CONS OF AMAZON SQS
          • 2
            Has a max message size (currently 256K)
          • 2
            Proprietary
          • 2
            Difficult to configure
          • 1
            Has a maximum 15 minutes of delayed messages only

          related Amazon SQS posts

          Praveen Mooli
          Engineering Manager at Taylor and Francis · | 17 upvotes · 2.5M views

          We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

          To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

          To build #Webapps we decided to use Angular 2 with RxJS

          #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

          See more
          Tim Specht
          ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 677.6K views

          In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

          While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

          In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

          #ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

          See more