Alternatives to NServiceBus logo

Alternatives to NServiceBus

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

Performance, scalability, pub/sub, reliable integration, workflow orchestration, and everything else you could possibly want in a service bus.
NServiceBus is a tool in the Message Queue category of a tech stack.

Top Alternatives to NServiceBus

  • Azure Service Bus

    Azure Service Bus

    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. ...

  • 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. ...

  • Akka

    Akka

    Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM. ...

  • MassTransit

    MassTransit

    It is free software/open-source .NET-based Enterprise Service Bus software that helps Microsoft developers route messages over MSMQ, RabbitMQ, TIBCO and ActiveMQ service busses, with native support for MSMQ and RabbitMQ. ...

  • 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. ...

  • Hangfire

    Hangfire

    It is an open-source framework that helps you to create, process and manage your background jobs, i.e. operations you don't want to put in your request processing pipeline. It supports all kind of background tasks – short-running and long-running, CPU intensive and I/O intensive, one shot and recurrent. ...

  • Apache Camel

    Apache Camel

    An open source Java framework that focuses on making integration easier and more accessible to developers. ...

NServiceBus alternatives & related posts

Azure Service Bus logo

Azure Service Bus

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Reliable cloud messaging as a service (MaaS)
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PROS OF AZURE SERVICE BUS
  • 2
    Cloud Native
  • 1
    Easy Integration with .Net
CONS OF AZURE SERVICE BUS
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    related Azure Service Bus posts

    RabbitMQ logo

    RabbitMQ

    13.6K
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    Open source multiprotocol messaging broker
    13.6K
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    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
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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
    Akka logo

    Akka

    750
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    79
    Build powerful concurrent & distributed applications more easily
    750
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    PROS OF AKKA
    • 29
      Great concurrency model
    • 15
      Fast
    • 10
      Actor Library
    • 9
      Open source
    • 6
      Resilient
    • 5
      Message driven
    • 5
      Scalable
    CONS OF AKKA
    • 3
      Mixing futures with Akka tell is difficult
    • 2
      Closing of futures
    • 2
      No type safety
    • 1
      Typed actors still not stable
    • 0
      Very difficult to refactor

    related Akka posts

    To solve the problem of scheduling and executing arbitrary tasks in its distributed infrastructure, PagerDuty created an open-source tool called Scheduler. Scheduler is written in Scala and uses Cassandra for task persistence. It also adds Apache Kafka to handle task queuing and partitioning, with Akka to structure the library’s concurrency.

    The service’s logic schedules a task by passing it to the Scheduler’s Scala API, which serializes the task metadata and enqueues it into Kafka. Scheduler then consumes the tasks, and posts them to Cassandra to prevent data loss.

    See more
    Shared insights
    on
    Akka
    Kafka

    I decided to use Akka instead of Kafka streams because I have personal relationships at @Lightbend.

    See more
    MassTransit logo

    MassTransit

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    Lightweight message bus for creating distributed applications
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    + 1
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    PROS OF MASSTRANSIT
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      CONS OF MASSTRANSIT
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        related MassTransit posts

        MSMQ logo

        MSMQ

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

        related MSMQ posts

        Hangfire logo

        Hangfire

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        Perform background processing in .NET and .NET Core applications
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        + 1
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        PROS OF HANGFIRE
        • 6
          Integrated UI dashboard
        • 5
          Simple
        • 2
          In Memory
        • 2
          Robust
        • 0
          Cons
        • 0
          Simole
        CONS OF HANGFIRE
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          related Hangfire posts

          Apache Camel logo

          Apache Camel

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          A versatile open source integration framework
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          PROS OF APACHE CAMEL
          • 5
            Based on Enterprise Integration Patterns
          • 4
            Free (open source)
          • 4
            Highly configurable
          • 3
            Open Source
          • 3
            Has over 250 components
          • 2
            Has great community
          CONS OF APACHE CAMEL
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            related Apache Camel posts