Amazon RDS for PostgreSQL vs Amazon SQS: What are the differences?
Developers describe Amazon RDS for PostgreSQL as "* 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. On the other hand, *Amazon SQS** is detailed as "Fully managed message queuing service". 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.
Amazon RDS for PostgreSQL belongs to "PostgreSQL as a Service" category of the tech stack, while Amazon SQS can be primarily classified 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, Amazon SQS provides the following key features:
- A queue can be created in any region.
- The message payload can contain up to 256KB of text in any format. Each 64KB ‘chunk’ of payload is billed as 1 request. For example, a single API call with a 256KB payload will be billed as four requests.
- Messages can be sent, received or deleted in batches of up to 10 messages or 256KB. Batches cost the same amount as single messages, meaning SQS can be even more cost effective for customers that use batching.
"Easy setup, backup, monitoring" is the top reason why over 22 developers like Amazon RDS for PostgreSQL, while over 45 developers mention "Easy to use, reliable" as the leading cause for choosing Amazon SQS.
According to the StackShare community, Amazon SQS has a broader approval, being mentioned in 381 company stacks & 101 developers stacks; compared to Amazon RDS for PostgreSQL, which is listed in 164 company stacks and 27 developer stacks.
What is Amazon RDS for PostgreSQL?
What is Amazon SQS?
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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...
In the beginning we thought we wanted to start using something like RabbitMQ or maybe Kafka or maybe ActiveMQ. Back then we only had a few developers and no ops people. That has changed now, but we didn't really look forward to setting up a queuing cluster and making sure that all works.
What we did instead was we looked at what services Amazon offers to see if we can use those to build our own messaging system within those services. That's basically what we did. We wrote some clients in Ruby that can basically do the entire orchestration for us, and we run all our messaging on both SNS and SQS. Basically what you can do in Amazon services is you can use Amazon Simple Notification Service, so SNS, for creating topics and you can use queues to subscribe to these topics. That's basically all you need for a messaging system. You don't have to worry about scalability at all. That's what really appealed to us.
This isn't exactly low-latency (10s to 100s of milliseconds), but it has good throughput and a simple API. There is good reliability, and there is no configuration necessary to get up and running. A hosted queue is important when trying to move fast.
SQS is the bridge between our new Lambda services and our incumbent Rails applications. Extremely easy to use when you're already using other AWS infrastructure.
Primary message queue. Enqueueing operations revert to a local file-system-based queue when SQS is unavailable.
I can't afford to lose data if Dynamo throttles my writes, so everything goes into a message queue first.
Using PostGIS to serve GeoJSON data for the Leaflet front-end.