Many articles discuss deploying AWS SQS, and this blog also provides an overview of CloudFormation-based deployments of AWS SQS in the AWS Cloud. However, this article focuses on the benefits and value that cloud-native services can provide, particularly how they can simplify operations and enhance operational stability, using AWS SQS as a prime example.
What is AWS SQS?
AWS SQS, which stands for Amazon Simple Queue Service, is a fully managed message queuing service provided by AWS. Like other queuing technologies, it facilitates the design and construction of decoupled, event-driven applications, where a queue or broker receives messages from producers and delivers them to consumers for further processing.
Costs
Like other cloud-native services, you only pay for the resources you consume. This enables highly cost-effective usage, as you don’t need to account for idle time. Ideally, services like AWS SQS should be deployed using CloudFormation templates. This allows for rapid and reusable setups that can be fully automated—for example, event-driven or on-demand setups. While configurations via the AWS CLI and AWS Console are possible, CloudFormation offers enhanced repeatability and consistency.
A significant cost-saving factor to consider is the deployment process itself. The time and effort required should be included in a cost model when comparing hosting services like RabbitMQ, AMQ, and similar solutions with native cloud services like AWS SQS. Once deployed, ongoing efforts such as OS patching, service updates/upgrades, and service health monitoring should also be factored into a full cost model.
Additionally, the frameworks used to install and maintain such services, like Chef or Puppet, require their own maintenance, uptime, and monitoring, which must be considered in a comprehensive 360° view. You can quickly end up with a complex web of dependencies requiring coordination among various teams to deploy and maintain a service effectively.
Conclusion: The chain of dependencies can significantly influence the complexity and time consumption of a self-managed deployment, impacting its overall cost. These factors must be carefully weighed when comparing cloud-native services with self-managed deployments.
Performance and Scale
AWS SQS can handle messages in a FIFO (First-In-First-Out) pattern when message order is critical, although FIFO queues have throughput limitations. The standard message handling is designed for maximum scale and throughput. While a SQS FIFO queue can process up to 3,000 messages per second, the standard SQS queue can handle nearly unlimited transactions per second (TPS).
This article uses AWS SQS as a specific example to compare cloud-native services with self-managed deployments; the performance and scalability characteristics described for AWS SQS are generally representative of cloud-native services. This allows for scaling up or down to meet current needs, effectively managing both performance and costs.
Deployment
Given that AWS SQS is relatively straightforward from a usage perspective, deployment is also generally easy. However, it’s important to carefully consider how producers and consumers handle messages, especially the frequency of read/pull operations. Each call to the SQS API to receive a message is charged, regardless of whether the response contains an empty body.
AWS SQS configuration parameters, such as the receive message wait time, help control costs by reducing empty responses. On the application side, incorporating logic to slow down message retrieval when an empty queue is detected can further reduce costs associated with empty calls. Since you only pay for what you use, minimizing empty calls is essential to keep costs as low as possible.
Operations, Maintenance
The alternative to a cloud-managed service is a self-deployed, hosted, and managed service. As previously discussed, the impact of a self-managed service on costs and effort can be substantial. While queuing services like RabbitMQ and AMQ are easy to deploy in principle, the actual efforts required for deployment, maintenance, and monitoring can become cumbersome.
Typically, a certain level of uptime and reliability is required, often necessitating failover mechanisms and clustered setups to support business needs. Simple tasks like OS patching can introduce a range of challenges:
- When to patch?
- What is the rollback plan?
- How long can the service be offline?
- What are the failover options?
- What if patching is ignored, and potential vulnerabilities remain unaddressed?
In highly regulated environments, specific procedures, such as OS patching, must be followed diligently.
The shared responsibility model in a cloud environment means that tasks like patching and ensuring environmental stability are handled by the cloud provider (e.g., Amazon AWS, Google GCP, Microsoft Azure).
What does AWS SQS provide from an Operations and Maintenance perspective?
AWS ensures high reliability. Although SQS is a regional service, AWS guarantees that compute node failures, network outages, or even Availability Zone (AZ) failures will not impact the SQS service itself.
This level of reliability is typically not achievable in a self-hosted environment without significant effort, such as containerization to abstract compute dependencies and clustering to ensure high availability and resilience.
If producers and consumers rely on a queuing service with ongoing, always-available, and consistent performance (true 24×7 availability), a self-hosted queue service will likely encounter limitations. Key questions to consider include:
- What if the underlying compute requires OS patching?
- Who is available 24×7 if the service needs maintenance?
- What are the impacts and follow-up costs if an internally hosted queue service becomes unresponsive?
- What efforts are required to ensure High Availability (HA)?
- Will the queue reliably store messages?
- What compute resources are needed to handle average and peak loads?
- What happens if producers send messages faster than consumers can process them?
- How large should the backlog be?
Use case experience
After several years of managing a self-managed queuing service (RabbitMQ) and addressing all the related implications as described in this article, a transition was made to integrate AWS SQS as a replacement.
The adaptation from a code and software perspective was straightforward, with no issues encountered thanks to careful planning and design.
Overall conclusion
While AWS SQS was used as a specific example in this article, the overall conclusion is quite clear: utilizing cloud-native services can outperform self-hosted, self-managed services on multiple levels. Specifically, when it comes to deployment, maintenance, and operations, cloud-native services offer significant advantages and should always be considered as viable alternatives.
With wise planning, costs can be controlled precisely and predictably. Overhead and dependencies across various layers can be eliminated entirely. Ad-hoc changes in requirements can be accommodated very easily, often in a fully automated manner.
Finally, the shared responsibility model allows public cloud providers like AWS to alleviate much of the burden of operating and maintaining a given service.
Cloud Architecture, Solutions and Designs 
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