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
  • 4
    Easy Integration with .Net
  • 2
    Cloud Native
  • 1
    Use while high messaging need
CONS OF AZURE SERVICE BUS
  • 1
    Observability of messages in the queue is lacking

related Azure Service Bus posts

Shared insights
on
Azure Service BusAzure Service BusIBM MQIBM MQ

Want to get the differences in features and enhancement, pros and cons, and also how to Migrate from IBM MQ to Azure Service Bus.

See more
RabbitMQ logo

RabbitMQ

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14.9K
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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
    Better than most traditional queue based message broker
  • 2
    Distributed
  • 2
    Supports AMQP
  • 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

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Yogesh Bhondekar
Co-Founder at weconnect.chat · | 15 upvotes · 193.6K 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

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 125
    High-throughput
  • 119
    Distributed
  • 89
    Scalable
  • 83
    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
  • 29
    Non-Java clients are second-class citizens
  • 27
    Needs Zookeeper
  • 7
    Operational difficulties
  • 2
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M 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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Akka logo

Akka

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Build powerful concurrent & distributed applications more easily
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PROS OF AKKA
  • 32
    Great concurrency model
  • 16
    Fast
  • 11
    Actor Library
  • 10
    Open source
  • 7
    Resilient
  • 5
    Scalable
  • 5
    Message driven
CONS OF AKKA
  • 3
    Mixing futures with Akka tell is difficult
  • 2
    Closing of futures
  • 2
    No type safety
  • 1
    Very difficult to refactor
  • 1
    Typed actors still not stable

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.

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Shared insights
on
AkkaAkkaKafkaKafka

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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PROS OF MASSTRANSIT
    Be the first to leave a pro
    CONS OF MASSTRANSIT
      Be the first to leave a con

      related MassTransit posts

      MSMQ logo

      MSMQ

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      A technology for asynchronous messaging
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      PROS OF MSMQ
      • 1
        Cloud not needed
      • 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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      PROS OF HANGFIRE
      • 7
        Integrated UI dashboard
      • 5
        Simple
      • 3
        Robust
      • 2
        In Memory
      • 0
        Simole
      CONS OF HANGFIRE
        Be the first to leave a con

        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
          Be the first to leave a con

          related Apache Camel posts