Alternatives to MassTransit logo

Alternatives to MassTransit

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

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

MassTransit alternatives & related posts

related RabbitMQ posts

James Cunningham
James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 112.5K views
atSentrySentry
RabbitMQ
RabbitMQ
Celery
Celery
#MessageQueue

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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Tim Abbott
Tim Abbott
Founder at Zulip · | 10 upvotes · 65.2K views
atZulipZulip
Redis
Redis
Python
Python
RabbitMQ
RabbitMQ

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).

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NServiceBus logo

NServiceBus

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Enterprise-grade scalability and reliability for your workflows and integrations
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    NServiceBus
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    MassTransit
    Azure Service Bus logo

    Azure Service Bus

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    Reliable cloud messaging as a service (MaaS)
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      MassTransit

      related Kafka posts

      Eric Colson
      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 270.3K views
      atStitch FixStitch Fix
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      Docker
      Docker
      PyTorch
      PyTorch
      R
      R
      Python
      Python
      Presto
      Presto
      Apache Spark
      Apache Spark
      Amazon S3
      Amazon S3
      PostgreSQL
      PostgreSQL
      Kafka
      Kafka
      #Data
      #DataStack
      #DataScience
      #ML
      #Etl
      #AWS

      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
      John Kodumal
      CTO at LaunchDarkly · | 15 upvotes · 138.9K views
      atLaunchDarklyLaunchDarkly
      Kafka
      Kafka
      Amazon Kinesis
      Amazon Kinesis
      Redis
      Redis
      Amazon EC2
      Amazon EC2
      Amazon ElastiCache
      Amazon ElastiCache
      Consul
      Consul
      Patroni
      Patroni
      TimescaleDB
      TimescaleDB
      PostgreSQL
      PostgreSQL
      Amazon RDS
      Amazon RDS

      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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      related Amazon SQS posts

      Tim Specht
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 63K views
      atDubsmashDubsmash
      Google BigQuery
      Google BigQuery
      Amazon SQS
      Amazon SQS
      AWS Lambda
      AWS Lambda
      Amazon Kinesis
      Amazon Kinesis
      Google Analytics
      Google Analytics
      #BigDataAsAService
      #RealTimeDataProcessing
      #GeneralAnalytics
      #ServerlessTaskProcessing

      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

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      Praveen Mooli
      Praveen Mooli
      Technical Leader at Taylor and Francis · | 11 upvotes · 161.7K views
      MongoDB Atlas
      MongoDB Atlas
      Amazon S3
      Amazon S3
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Serverless
      Serverless
      Docker
      Docker
      Terraform
      Terraform
      Travis CI
      Travis CI
      GitHub
      GitHub
      RxJS
      RxJS
      Angular 2
      Angular 2
      AWS Lambda
      AWS Lambda
      Amazon SQS
      Amazon SQS
      Amazon SNS
      Amazon SNS
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon Kinesis
      Amazon Kinesis
      Flask
      Flask
      Python
      Python
      ExpressJS
      ExpressJS
      Node.js
      Node.js
      Spring Boot
      Spring Boot
      Java
      Java
      #Data
      #Devops
      #Webapps
      #Eventsourcingframework
      #Microservices
      #Backend

      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

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      related Celery posts

      James Cunningham
      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 112.5K views
      atSentrySentry
      RabbitMQ
      RabbitMQ
      Celery
      Celery
      #MessageQueue

      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
      Michael Mota
      Michael Mota
      CEO & Founder at AlterEstate · | 4 upvotes · 11.9K views
      atAlterEstateAlterEstate
      Django
      Django
      RabbitMQ
      RabbitMQ
      Celery
      Celery

      Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

      We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

      See more
      ActiveMQ logo

      ActiveMQ

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      A message broker written in Java together with a full JMS client
      ActiveMQ logo
      ActiveMQ
      VS
      MassTransit logo
      MassTransit

      related ActiveMQ posts

      Naushad Warsi
      Naushad Warsi
      software developer at klingelnberg · | 1 upvotes · 48.9K views
      RabbitMQ
      RabbitMQ
      ActiveMQ
      ActiveMQ

      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
      ZeroMQ logo

      ZeroMQ

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      Fast, lightweight messaging library that allows you to design complex communication system without much effort
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      ZeroMQ
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      MassTransit
      MQTT logo

      MQTT

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      A machine-to-machine Internet of Things connectivity protocol
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        MQTT
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        MassTransit
        WCF logo

        WCF

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        A runtime and a set of APIs for building connected, service-oriented applications
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          WCF logo
          WCF
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          MassTransit
          Kafka Manager logo

          Kafka Manager

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          A tool for managing Apache Kafka, developed by Yahoo
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            Kafka Manager
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            MassTransit

            related Kafka Manager posts

            Conor Myhrvold
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 5 upvotes · 124.9K views
            atUber TechnologiesUber Technologies
            Kafka Manager
            Kafka Manager
            Kafka
            Kafka
            GitHub
            GitHub
            Apache Spark
            Apache Spark
            Hadoop
            Hadoop

            Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

            Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

            https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

            (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

            See more
            Mosquitto logo

            Mosquitto

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            An open source message broker that implements the MQTT protocol
            Mosquitto logo
            Mosquitto
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            MassTransit
            Confluent logo

            Confluent

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            A stream data platform to help companies harness their high volume real-time data streams
            Confluent logo
            Confluent
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            MassTransit
            XMPP logo

            XMPP

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            An open XML technology for real-time communication
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              XMPP
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              MassTransit
              IBM MQ logo

              IBM MQ

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              Enterprise-grade messaging middleware
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                IBM MQ logo
                IBM MQ
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                MassTransit