Alternatives to Amazon MQ logo

Alternatives to Amazon MQ

Amazon SQS, RabbitMQ, IBM MQ, ActiveMQ, and Kafka are the most popular alternatives and competitors to Amazon MQ.
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What is Amazon MQ and what are its top alternatives?

Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud.
Amazon MQ is a tool in the Message Queue category of a tech stack.

Top Alternatives to Amazon MQ

  • 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. ...

  • RabbitMQ
    RabbitMQ

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

  • 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. ...

  • 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. ...

  • Kafka
    Kafka

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

  • 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. ...

  • 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. ...

  • MQTT
    MQTT

    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. ...

Amazon MQ alternatives & related posts

Amazon SQS logo

Amazon SQS

2.8K
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Fully managed message queuing service
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PROS OF AMAZON SQS
  • 61
    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 · | 18 upvotes · 2.7M 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 · 698.9K 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

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RabbitMQ logo

RabbitMQ

18.9K
16.5K
523
Open source multiprotocol messaging broker
18.9K
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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
    Standard protocols
  • 18
    Intuitive work through python
  • 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 MQTT
  • 2
    Supports AMQP
  • 1
    Inubit Integration
  • 1
    Open-source
  • 1
    Delayed messages
  • 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 · 1.5M 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
Product Manager | SaaS | Traveller · | 15 upvotes · 287.7K 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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IBM MQ logo

IBM MQ

108
169
10
Enterprise-grade messaging middleware
108
169
+ 1
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PROS OF IBM MQ
  • 3
    Useful for big enteprises
  • 3
    Reliable for banking transactions
  • 2
    Secure
  • 1
    Many deployment options (containers, cloud, VM etc)
  • 1
    High Availability
CONS OF IBM MQ
  • 2
    Cost

related IBM MQ 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
ActiveMQ logo

ActiveMQ

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A message broker written in Java together with a full JMS client
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PROS OF ACTIVEMQ
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
  • 5
    Scalable
  • 3
    Distributed Network of brokers
  • 3
    Persistence
  • 3
    Support XA (distributed transactions)
  • 1
    Docker delievery
  • 1
    Highly configurable
  • 0
    RabbitMQ
CONS OF ACTIVEMQ
  • 1
    ONLY Vertically Scalable
  • 1
    Support
  • 1
    Low resilience to exceptions and interruptions
  • 1
    Difficult to scale

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 · 707.5K views
Shared insights
on
ActiveMQActiveMQRabbitMQRabbitMQ

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
Kafka logo

Kafka

20.4K
19.3K
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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 126
    High-throughput
  • 119
    Distributed
  • 90
    Scalable
  • 85
    High-Performance
  • 65
    Durable
  • 37
    Publish-Subscribe
  • 19
    Simple-to-use
  • 18
    Open source
  • 11
    Written in Scala and java. Runs on JVM
  • 8
    Message broker + Streaming system
  • 4
    Robust
  • 4
    Avro schema integration
  • 4
    KSQL
  • 3
    Suport Multiple clients
  • 2
    Partioned, replayable log
  • 1
    Extremely good parallelism constructs
  • 1
    Fun
  • 1
    Flexible
  • 1
    Simple publisher / multi-subscriber model
CONS OF KAFKA
  • 31
    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 · 2.6M 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.

See more
Azure Service Bus logo

Azure Service Bus

514
469
7
Reliable cloud messaging as a service (MaaS)
514
469
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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
Celery logo

Celery

1.5K
1.5K
279
Distributed task queue
1.5K
1.5K
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279
PROS OF CELERY
  • 98
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
  • 8
    Various backend broker
  • 6
    Easy to use
  • 5
    Great community
  • 5
    Workflow
  • 4
    Free
  • 1
    Dynamic
CONS OF CELERY
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker

related Celery posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.5M 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
Michael Mota

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
MQTT logo

MQTT

445
515
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A machine-to-machine Internet of Things connectivity protocol
445
515
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PROS OF MQTT
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Lightweight with a relatively small data footprint
  • 2
    Very easy to configure and use with open source tools
CONS OF MQTT
  • 1
    Easy to configure in an unsecure manner

related MQTT posts

Kindly suggest the best tool for generating 10Mn+ concurrent user load. The tool must support MQTT traffic, REST API, support to interfaces such as Kafka, websockets, persistence HTTP connection, auth type support to assess the support /coverage.

The tool can be integrated into CI pipelines like Azure Pipelines, GitHub, and Jenkins.

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A Nielsen
Fullstack Dev at ADTELA · | 2 upvotes · 35.8K views

Hi Marc,

For the com part, depending of more details not provided, i'd use SSE, OR i'd run either Mosquitto or RabbitMQ running on Amazon EC2 instances and leverage MQTT or amqp 's subscribe/publish features with my users running mqtt or amqp clients (tcp or websockets) somehow. (publisher too.. you don't say how and who gets to update the document(s).

I find "a ton of end users", depending on how you define a ton (1k users ;) ?) and how frequent document updates are, that can mean a ton of ressources, can't cut it at some point, even using SSE

how many, how big, how persistant do the document(s) have to be ? Db-wise,can't say for lack of details and context, yeah could also be Redis, any RDBMS or nosql or even static json files stored on an Amazon S3 bucket .. anything really

Good luck!

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