Amazon Simple Queue Service (SQS) is a fully managed messaging service from Amazon Web Services (AWS). It enables developers to create highly scalable, reliable and resilient applications using a microservices architecture.
Before delving deeper into the topic, it’s important to understand what a messaging service is in the context of cloud computing: in this context, such a service allows different components of a distributed application to communicate with each other by transmitting messages. These messages can be requests for processing, information, notifications, and more.
What are the features of SQS AWS?
Amazon Simple Queue Service is rich in features. The table below will tell you more about the most common ones:
|This is a key feature of SQS. There are two types of queues:
|With asynchronous messaging capability, SQS allows offloading applications from constant monitoring of new message arrivals.
|Message delivery is guaranteed at least once, and SQS also offers replication and redundancy mechanisms to prevent losses.
|SQS can handle very large message volumes and workloads.
|Delays can be imposed to prevent messages from being processed too quickly or too late.
|Through REST APIs or Software Deployment Kits (SDKs) for various programming languages, developers can easily integrate SQS into their applications.
|SQS seamlessly integrates with other AWS services such as S3, EC2, Lambda, or SNS to create distributed architectures.
|Authentication and access management via IAM make SQ S highly secure, and it also offers data encryption features.
Understanding how Amazon SQS works
A distributed messaging system consists of three main components: the components of the distributed system, the queue (managed on Amazon SQS servers), and the pending messages.
In the following example, our system has multiple producers (message producers, components responsible for sending messages to the queue) and consumers (message consumers, components responsible for receiving pending messages). The queue stores messages on the SQS servers.
Let’s take a look at the following figure:
➡️ The producer (component 1) sends message A to the queue, and this message is distributed redundantly across Amazon SQS servers.
➡️ When a consumer (component 2) is ready to handle the message, it is said to “consume” it from the queue, and message A is returned, starting a visibility timer (configurable) in the process.
➡️ During its processing, it remains in the queue and is not delivered to subsequent receive requests for the duration of the timer. The consumer deletes message A from the queue to prevent it from being received and processed again once the visibility timeout has expired.
When can SQS AWS be used?
Amazon SQS includes the following use cases:
1. Decoupling of Applications: This allows for better scalability, greater resilience, and improved isolation between components.
2. Background Processing: SQS can handle background tasks to free up server resources, thereby enhancing overall performance.
3. Queue Management: Applications can process messages at their own pace without the need for real-time processing.
4. Microservices Architecture: SQS improves communication between different microservices.
5. Distributed Task Processing: The ability to process large volumes of data enhances overall application performance and reduces processing times.
Amazon SQS versus Apache Kafka
These two real-time messaging solutions are used in microservices and distributed application architectures. Often compared, they have some key differences:
1. Architecture: SQS is entirely managed by Amazon, allowing users to avoid dealing with infrastructure, availability, and security aspects. Kafka, on the other hand, is open-source and requires more advanced configuration and management.
2. Throughput and Latency: Kafka is often preferred when high throughput and minimal latency are required. While SQS provides latency in the range of milliseconds, Kafka is generally faster.
3. Scalability and Flexibility: SQS is designed to be highly scalable but has fewer features compared to Kafka, which is highly flexible and can be used in a wider range of use cases.
4. Security: SQS, built on Amazon’s infrastructure, offers very high security. Kafka can also be secured but requires more investment and advanced configurations.
In summary, SQS is well-suited for AWS-based applications, while Kafka, being open-source, is more flexible and feature-rich but demands more investment in configuration and management.
Case study: NASA
The National Aeronautics and Space Administration (NASA) is a well-known agency dedicated to space and aeronautics. In 2017, NASA launched a content library called the “NASA Image and Video Library,” which allows users to search, view, and download various multimedia content from NASA. However, creating such a platform posed several technical challenges for the development teams, one of which was managing the queue of media processing tasks.
To address this challenge, NASA turned to the Amazon SQS service. This allowed them to decouple media processing tasks from the main queue, reducing the load on their servers and improving system performance.
In addition to Amazon SQS, NASA also utilized other Amazon services such as Amazon S3 for file storage, Amazon EC2, RDS, and DynamoDB. The integration between these Amazon services helped NASA overcome the challenges of their content platform by providing a reliable and scalable messaging solution for managing media processing tasks.
Amazon SQS is a reliable and scalable asynchronous messaging service that enables distributed applications and microservices to communicate with each other.
It’s the ideal choice if you want to simplify communication between the various components of your application.
If you’d like to find out more about Amazon SQS, take a look at our Amazon Web Services training course.