Amazon RDS for PostgreSQL vs RabbitMQ: What are the differences?
Amazon RDS for PostgreSQL: * Set up, operate, and scale PostgreSQL deployments in the cloud. Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS; *RabbitMQ:** A messaging broker - an intermediary for messaging. RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
Amazon RDS for PostgreSQL can be classified as a tool in the "PostgreSQL as a Service" category, while RabbitMQ is grouped under "Message Queue".
Some of the features offered by Amazon RDS for PostgreSQL are:
- Monitoring and Metrics –Amazon RDS provides Amazon CloudWatch metrics for you DB Instance deployments at no additional charge.
- DB Event Notifications –Amazon RDS provides Amazon SNS notifications via email or SMS for your DB Instance deployments.
- Automatic Software Patching – Amazon RDS will make sure that the PostgreSQL software powering your deployment stays up-to-date with the latest patches.
On the other hand, RabbitMQ provides the following key features:
- Robust messaging for applications
- Easy to use
- Runs on all major operating systems
"Easy setup, backup, monitoring" is the primary reason why developers consider Amazon RDS for PostgreSQL over the competitors, whereas "It's fast and it works with good metrics/monitoring" was stated as the key factor in picking RabbitMQ.
RabbitMQ is an open source tool with 5.88K GitHub stars and 1.73K GitHub forks. Here's a link to RabbitMQ's open source repository on GitHub.
9GAG, CircleCI, and OpenTable are some of the popular companies that use RabbitMQ, whereas Amazon RDS for PostgreSQL is used by Netflix, Product Hunt, and Instacart. RabbitMQ has a broader approval, being mentioned in 921 company stacks & 532 developers stacks; compared to Amazon RDS for PostgreSQL, which is listed in 164 company stacks and 27 developer stacks.
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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.
The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.
I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.
For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).
I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.
So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.
I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.
We use Amazon RDS for PostgreSQL because RDS and Amazon DynamoDB are two distinct database systems. DynamoDB is NoSQL DB whereas RDS is a relational database on the cloud. The pricing will mainly differ in the type of application you are using and your requirements. For some applications, both DynamoDB and RDS, can serve well, for some it might not. I do not think DynamoDB is cheaper. Right now we are helping Companies in Silicon Valley and in Southern California go SERVERLESS - drastically lowering costs if you are interested in hearing how we go about it.
I could spin up an Amazon EC2 instance and install PostgreSQL myself, review latest configuration best practices, sort Amazon EBS storage for data, set up a snapshot process etc.
Alternatively I could use Amazon RDS, Amazon RDS for PostgreSQL or Heroku Postgres and have most of that work handled for me, by a team of world experts...
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.
I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.
If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.
The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.
Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.
Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.
RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.
RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.
Using PostGIS to serve GeoJSON data for the Leaflet front-end.