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Amazon RDS for PostgreSQL vs Amazon SQS: What are the differences?
Amazon RDS for PostgreSQL and Amazon SQS are two different AWS services with distinct functionalities. In this comparison, we will focus on highlighting the key differences between these two services.
Data Storage vs Message Queueing: The primary difference between Amazon RDS for PostgreSQL and Amazon SQS lies in their core functionalities. RDS for PostgreSQL is a managed relational database service that provides storage and retrieval of structured data, while SQS is a fully managed message queuing service that enables decoupling of components in distributed systems.
Data Persistence vs Data Transmission: Another major difference is in how these services handle data. RDS for PostgreSQL provides durable storage for data and allows users to write, read, and modify the data directly. On the other hand, SQS enables message transmission between different components, allowing for asynchronous and reliable communication.
Structured Data vs Unstructured Messages: RDS for PostgreSQL focuses on storing and retrieving structured data in a tabular format, making it ideal for transactional and analytical workloads. In contrast, SQS handles unstructured messages, allowing developers to pass messages between systems without the need for tight coupling.
Database Management vs Event-driven Communication: RDS for PostgreSQL provides a managed database environment, taking care of backups, patching, and scalability. It is suitable for scenarios where data consistency and database management are critical. SQS, on the other hand, facilitates event-driven communication between distributed systems, making it ideal for asynchronous and loosely coupled architectures.
Real-time Data Access vs Delayed Processing: With RDS for PostgreSQL, data can be accessed and modified in real-time, ensuring immediate consistency for applications relying on up-to-date data. In contrast, SQS operates asynchronously, allowing for delayed message processing and reducing dependencies between different components.
Scaling Constraints: When it comes to scaling, RDS for PostgreSQL provides vertical scaling where users can increase the compute and storage capacity of a database instance. In contrast, SQS offers horizontal scalability, allowing users to handle increased message traffic by adding more message queues or leveraging features like message sharding.
In summary, Amazon RDS for PostgreSQL focuses on data storage and retrieval, providing a managed relational database service. On the other hand, Amazon SQS enables asynchronous and reliable message transmission, facilitating decoupling and event-driven communication in distributed systems.
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.
Hello, i highly recommend Apache Kafka, to me it's the best. You can deploy it in cluster mode inside K8S, thus you can have a Highly available system (also auto scalable).
Good luck
Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to: * Not loose messages in services outages * Safely restart service without losing messages (ZeroMQ seems to need to close the socket in the receiver before restart manually)
Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?
Thank you for your time
ZeroMQ is fast but you need to build build reliability yourself. There are a number of patterns described in the zeromq guide. I have used RabbitMQ before which gives lot of functionality out of the box, you can probably use the worker queues
example from the tutorial, it can also persists messages in the queue.
I haven't used Amazon SQS before. Another tool you could use is Kafka.
Both would do the trick, but there are some nuances. We work with both.
From the sound of it, your main focus is "not losing messages". In that case, I would go with RabbitMQ with a high availability policy (ha-mode=all) and a main/retry/error queue pattern.
Push messages to an exchange, which sends them to the main queue. If an error occurs, push the errored out message to the retry exchange, which forwards it to the retry queue. Give the retry queue a x-message-ttl and set the main exchange as a dead-letter-exchange. If your message has been retried several times, push it to the error exchange, where the message can remain until someone has time to look at it.
This is a very useful and resilient pattern that allows you to never lose messages. With the high availability policy, you make sure that if one of your rabbitmq nodes dies, another can take over and messages are already mirrored to it.
This is not really possible with SQS, because SQS is a lot more focused on throughput and scaling. Combined with SNS it can do interesting things like deduplication of messages and such. That said, one thing core to its design is that messages have a maximum retention time. The idea is that a message that has stayed in an SQS queue for a while serves no more purpose after a while, so it gets removed - so as to not block up any listener resources for a long time. You can also set up a DLQ here, but these similarly do not hold onto messages forever. Since you seem to depend on messages surviving at all cost, I would suggest that the scaling/throughput benefit of SQS does not outweigh the difference in approach to messages there.
I want to schedule a message. Amazon SQS provides a delay of 15 minutes, but I want it in some hours.
Example: Let's say a Message1 is consumed by a consumer A but somehow it failed inside the consumer. I would want to put it in a queue and retry after 4hrs. Can I do this in Amazon MQ? I have seen in some Amazon MQ videos saying scheduling messages can be done. But, I'm not sure how.
Mithiridi, I believe you are talking about two different things. 1. If you need to process messages with delays of more 15m or at specific times, it's not a good idea to use queues, independently of tool SQM, Rabbit or Amazon MQ. you should considerer another approach using a scheduled job. 2. For dead queues and policy retries RabbitMQ, for example, doesn't support your use case. https://medium.com/@kiennguyen88/rabbitmq-delay-retry-schedule-with-dead-letter-exchange-31fb25a440fc I'm not sure if that is possible SNS/SQS support, they have a maximum delay for delivery (maxDelayTarget) in seconds but it's not clear the number. You can check this out: https://docs.aws.amazon.com/sns/latest/dg/sns-message-delivery-retries.html
Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.
Good balance between easy to manage, pricing, docs and features.
DigitalOcean's offering is pretty solid. Easy to scale, great UI, automatic daily backups, decent pricing.
Pros of Amazon RDS for PostgreSQL
- Easy setup, backup, monitoring25
- Geospatial support13
- Master-master replication using Multi-AZ instance2
Pros of Amazon SQS
- Easy to use, reliable62
- Low cost40
- Simple28
- Doesn't need to maintain it14
- It is Serverless8
- Has a max message size (currently 256K)4
- Triggers Lambda3
- Easy to configure with Terraform3
- Delayed delivery upto 15 mins only3
- Delayed delivery upto 12 hours3
- JMS compliant1
- Support for retry and dead letter queue1
- D1
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Cons of Amazon RDS for PostgreSQL
Cons of Amazon SQS
- Has a max message size (currently 256K)2
- Proprietary2
- Difficult to configure2
- Has a maximum 15 minutes of delayed messages only1