Alternatives to Azure Service Bus logo

Alternatives to Azure Service Bus

NServiceBus, RabbitMQ, Kafka, MSMQ, and IBM MQ are the most popular alternatives and competitors to Azure Service Bus.
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What is Azure Service Bus and what are its top alternatives?

It is a cloud messaging system for connecting apps and devices across public and private clouds. You can depend on it when you need highly-reliable cloud messaging service between applications and services, even when one or more is offline.
Azure Service Bus is a tool in the Message Queue category of a tech stack.

Top Alternatives to Azure Service Bus

  • NServiceBus

    NServiceBus

    Performance, scalability, pub/sub, reliable integration, workflow orchestration, and everything else you could possibly want in a service bus. ...

  • 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. ...

  • MSMQ

    MSMQ

    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. ...

  • IBM MQ

    IBM MQ

    It is a messaging middleware that simplifies and accelerates the integration of diverse applications and business data across multiple platforms. It offers proven, enterprise-grade messaging capabilities that skillfully and safely move information. ...

  • 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. ...

  • 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. ...

  • 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. ...

Azure Service Bus alternatives & related posts

NServiceBus logo

NServiceBus

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93
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Enterprise-grade scalability and reliability for your workflows and integrations
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93
+ 1
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PROS OF NSERVICEBUS
    Be the first to leave a pro
    CONS OF NSERVICEBUS
      Be the first to leave a con

      related NServiceBus posts

      RabbitMQ logo

      RabbitMQ

      13.6K
      11.7K
      511
      Open source multiprotocol messaging broker
      13.6K
      11.7K
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      511
      PROS OF RABBITMQ
      • 226
        It's fast and it works with good metrics/monitoring
      • 79
        Ease of configuration
      • 57
        I like the admin interface
      • 49
        Easy to set-up and start with
      • 20
        Durable
      • 18
        Intuitive work through python
      • 18
        Standard protocols
      • 10
        Written primarily in Erlang
      • 7
        Simply superb
      • 6
        Completeness of messaging patterns
      • 3
        Reliable
      • 3
        Scales to 1 million messages per second
      • 2
        Distributed
      • 2
        Supports AMQP
      • 2
        Better than most traditional queue based message broker
      • 1
        High performance
      • 1
        Reliability
      • 1
        Clusterable
      • 1
        Inubit Integration
      • 1
        Clear documentation with different scripting language
      • 1
        Great ui
      • 1
        Runs on Open Telecom Platform
      • 1
        Better routing system
      • 1
        Supports MQTT
      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 · 1.2M views
      Shared insights
      on
      Celery
      RabbitMQ
      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
      Tim Abbott
      Shared insights
      on
      RabbitMQ
      Python
      Redis
      at

      We've been using RabbitMQ as Zulip's queuing system since we needed a queuing system. What I like about it is that it scales really well and has good libraries for a wide range of platforms, including our own Python. So aside from getting it running, we've had to put basically 0 effort into making it scale for our needs.

      However, there's several things that could be better about it: * It's error messages are absolutely terrible; if ever one of our users ends up getting an error with RabbitMQ (even for simple things like a misconfigured hostname), they always end up needing to get help from the Zulip team, because the errors logs are just inscrutable. As an open source project, we've handled this issue by really carefully scripting the installation to be a failure-proof configuration (in this case, setting the RabbitMQ hostname to 127.0.0.1, so that no user-controlled configuration can break it). But it was a real pain to get there and the process of determining we needed to do that caused a significant amount of pain to folks installing Zulip. * The pika library for Python takes a lot of time to startup a RabbitMQ connection; this means that Zulip server restarts are more disruptive than would be ideal. * It's annoying that you need to run the rabbitmqctl management commands as root.

      But overall, I like that it has clean, clear semanstics and high scalability, and haven't been tempted to do the work to migrate to something like Redis (which has its own downsides).

      See more
      Kafka logo

      Kafka

      14.3K
      13.4K
      562
      Distributed, fault tolerant, high throughput pub-sub messaging system
      14.3K
      13.4K
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      562
      PROS OF KAFKA
      • 120
        High-throughput
      • 114
        Distributed
      • 86
        Scalable
      • 79
        High-Performance
      • 64
        Durable
      • 35
        Publish-Subscribe
      • 18
        Simple-to-use
      • 14
        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 · 1.9M 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
      MSMQ logo

      MSMQ

      24
      66
      1
      A technology for asynchronous messaging
      24
      66
      + 1
      1
      PROS OF MSMQ
      • 1
        Easy to learn
      CONS OF MSMQ
      • 1
        Windows dependency

      related MSMQ posts

      IBM MQ logo

      IBM MQ

      81
      120
      9
      Enterprise-grade messaging middleware
      81
      120
      + 1
      9
      PROS OF IBM MQ
      • 3
        Reliable for banking transactions
      • 2
        Useful for big enteprises
      • 2
        Secure
      • 1
        Many deployment options (containers, cloud, VM etc)
      • 1
        High Availability
      CONS OF IBM MQ
      • 2
        Cost

      related IBM MQ posts

      Amazon SQS logo

      Amazon SQS

      1.8K
      1.6K
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      Fully managed message queuing service
      1.8K
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      163
      PROS OF AMAZON SQS
      • 58
        Easy to use, reliable
      • 39
        Low cost
      • 26
        Simple
      • 13
        Doesn't need to maintain it
      • 8
        It is Serverless
      • 4
        Has a max message size (currently 256K)
      • 3
        Delayed delivery upto 15 mins only
      • 3
        Triggers Lambda
      • 3
        Easy to configure with Terraform
      • 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 · | 14 upvotes · 1.8M 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 · 602.8K 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
      Celery logo

      Celery

      1.3K
      1.2K
      260
      Distributed task queue
      1.3K
      1.2K
      + 1
      260
      PROS OF CELERY
      • 93
        Task queue
      • 59
        Python integration
      • 36
        Django integration
      • 28
        Scheduled Task
      • 18
        Publish/subsribe
      • 6
        Easy to use
      • 6
        Various backend broker
      • 5
        Great community
      • 4
        Free
      • 4
        Workflow
      • 1
        Dynamic
      CONS OF CELERY
      • 4
        Sometimes loses tasks
      • 1
        Depends on broker

      related Celery posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 1.2M views
      Shared insights
      on
      Celery
      RabbitMQ
      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
      Pulkit Sapra

      Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

      See more
      ActiveMQ logo

      ActiveMQ

      423
      1K
      72
      A message broker written in Java together with a full JMS client
      423
      1K
      + 1
      72
      PROS OF ACTIVEMQ
      • 16
        Easy to use
      • 13
        Open source
      • 12
        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
        Low resilience to exceptions and interruptions
      • 1
        Difficult to scale
      • 1
        Support

      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.

      See more
      Naushad Warsi
      software developer at klingelnberg · | 1 upvote · 583K views
      Shared insights
      on
      ActiveMQ
      RabbitMQ

      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