Alternatives to CloudAMQP logo

Alternatives to CloudAMQP

RabbitMQ, IronMQ, Kafka, Amazon SQS, and Celery are the most popular alternatives and competitors to CloudAMQP.
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What is CloudAMQP and what are its top alternatives?

Fully managed, highly available RabbitMQ servers and clusters, on all major compute platforms.
CloudAMQP is a tool in the Message Queue category of a tech stack.

Top Alternatives to CloudAMQP

  • RabbitMQ

    RabbitMQ

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

  • IronMQ

    IronMQ

    An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance. ...

  • Kafka

    Kafka

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

  • 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. ...

  • 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. ...

  • Apache NiFi

    Apache NiFi

    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. ...

CloudAMQP alternatives & related posts

RabbitMQ logo

RabbitMQ

13.2K
11.4K
510
Open source multiprotocol messaging broker
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510
PROS OF RABBITMQ
  • 225
    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).

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IronMQ logo

IronMQ

35
46
36
Message Queue for any deployment
35
46
+ 1
36
PROS OF IRONMQ
  • 12
    Great Support
  • 8
    Heroku Add-on
  • 3
    Push support
  • 3
    Delayed delivery upto 7 days
  • 2
    Super fast
  • 2
    Language agnostic
  • 2
    Good analytics/monitoring
  • 2
    Ease of configuration
  • 2
    GDPR Compliant
CONS OF IRONMQ
  • 1
    Can't use rabbitmqadmin

related IronMQ posts

Kafka logo

Kafka

13.8K
13K
557
Distributed, fault tolerant, high throughput pub-sub messaging system
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13K
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557
PROS OF KAFKA
  • 119
    High-throughput
  • 113
    Distributed
  • 85
    Scalable
  • 78
    High-Performance
  • 64
    Durable
  • 35
    Publish-Subscribe
  • 17
    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 · 1.8M 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
Amazon SQS logo

Amazon SQS

1.8K
1.6K
163
Fully managed message queuing service
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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
    Proprietary
  • 2
    Difficult to configure
  • 2
    Has a max message size (currently 256K)
  • 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

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Tim Specht
‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 595.2K 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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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

409
1K
72
A message broker written in Java together with a full JMS client
409
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.

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Naushad Warsi
software developer at klingelnberg · | 1 upvote · 571.2K 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.

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MQTT logo

MQTT

298
326
4
A machine-to-machine Internet of Things connectivity protocol
298
326
+ 1
4
PROS OF MQTT
  • 2
    Varying levels of Quality of Service to fit a range of
  • 1
    Very easy to configure and use with open source tools
  • 1
    Lightweight with a relatively small data footprint
CONS OF MQTT
  • 1
    Easy to configure in an unsecure manner

related MQTT posts

Apache NiFi logo

Apache NiFi

234
479
55
A reliable system to process and distribute data
234
479
+ 1
55
PROS OF APACHE NIFI
  • 14
    Visual Data Flows using Directed Acyclic Graphs (DAGs)
  • 7
    Free (Open Source)
  • 5
    Simple-to-use
  • 4
    Reactive with back-pressure
  • 4
    Scalable horizontally as well as vertically
  • 3
    Bi-directional channels
  • 3
    Fast prototyping
  • 2
    Data provenance
  • 2
    Built-in graphical user interface
  • 2
    End-to-end security between all nodes
  • 2
    Can handle messages up to gigabytes in size
  • 1
    Hbase support
  • 1
    Kudu support
  • 1
    Hive support
  • 1
    Slack integration
  • 1
    Support for custom Processor in Java
  • 1
    Lot of articles
  • 1
    Lots of documentation
CONS OF APACHE NIFI
  • 2
    Memory-intensive
  • 1
    HA support is not full fledge

related Apache NiFi posts

I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

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