Amazon SQS Reviews

I primarily use SQL Server for messaging services, and I need to offer loose couplings. SQS is handy for offloading non-urgent tasks that can be reverted later. I use it as a queue management service for deferring processing or ensuring important messages are preserved in the queue before being processed by Lambda integrators. Lambda can pull messages from the queue, process them to completion, and then erase them, providing reliable communication.

The most valuable feature for me is the variety of queues offered, such as the standard and FIFO queues, providing reliable communication. FIFO queues ensure a message is processed once, preventing duplicate processing. This product is serverless, managed, and scalable, with benefits similar to Lambda Compute. It offers high scalability, availability, and protection against failures, eliminating my need for EC2 infrastructure for messaging processing. My campus has become cashless, with all transactions digitized and utilizing AWS for scalability. The migration to the cloud has satisfied our students, staff, and finance department, with a reduced responsibility. There are no concerns regarding the solution's cost.

AWS provides another messaging service, which is fine for certain purposes. SQS meets the cloud messaging workload requirements. However, combining the features of both products could be an easier option for me. Currently, it mainly supports HTTP, HTTPS communication protocols and lacks support for others. In hybrid workloads where existing apps use different messaging protocols, AWS directs me to another service. Combining features into a single product would ease my adaptation.

I started using Lambda and SQS in 2018.

It is very stable for me because it's a regional service, not dependent on a single point of failure. I would rate its availability as nine out of ten.

Peak workloads show me that SQS doesn't have scalability limits, similar to Lambda. The product scales automatically, handling peak and off-peak hours. I've never lost messages within SQS, and all message infrastructure is managed by AWS.

Positive

Proprietary protocols like GMS, AMQP, etc., were in place previously. With workloads migrated to the cloud, SQS now meets all my messaging workloads requirements.

The setup is straightforward for me, as it's HTTPS based. Communication and setup are easy, and target SDKs are available for different client frameworks. Due to its simplified deployment and SDK availability, I would rate it nine out of ten.

I am not the project owner for SQS, but I'm aware of its usage. I have not heard complaints about it from my team.

It's quite economical for me. I would say a rating of four to five out of five is appropriate, as charges are based on usage.

Overall, I'd rate it nine out of ten. The serverless stack offered by AWS provides me with good guidance. Serverless patterns from AWS need better reference. Cloud adoption will have general discussions, but more proprietary cloud frameworks should learn from serverless tech, facilitating infrastructure as code. Open-source frameworks for building serverless applications are beneficial for setting up full stack apps promptly. 

Our primary use case was to connect SQS with S3 so that we could count the number of downloads of our objects within S3. However, we had to switch to using the CloudFront URL instead. We initially used SQS to help in decoupling and to find the number of messages coming in.

The most valuable feature is the ability to decouple components. It is beneficial because our architecture usually has different components and the communication aspect of components is crucial. SQS is effective in decoupling or buffering to prevent overwhelming components in case there is increased traffic, aiding in the management of communication loads. By design, it is scalable, and its SAGRAM feature helps to lower loads during surges.

The primary issue was the increase in costs due to frequent polling for messages. The cost became an issue, leading us to consider other solutions.

We used SQS for a project, however, due to cost concerns, we had to switch to a different solution.

We did not encounter any challenges with the stability of SQS. The only challenge we faced was with the cost.

SQS is scalable. It is designed to handle varying loads well, with the SAGRAM feature assisting in managing and lowering loads during increased traffic.

Understanding the service requires prior knowledge, and we could not just use it directly. We needed to first understand the service itself for deployment.

The main challenge we encountered was related to the cost increase due to frequent polling for messages.

We switched from using SQS to CloudFront, as CloudFront was a better fit for our needs and was more cost-effective.

I would recommend SQS to others depending on their use case. 

Overall, I would rate SQS a nine out of ten.

I have been heavily using Amazon SQS for the last more than four years in serverless and decoupled solutions. 

We use it in workflows like order creation, where the order creation task is queued, allowing consumers to pull and process the information in batches. 

SQS helps in loose coupling between producers and consumers and is valuable for storing failed record processing through DLQs.

One of the most valuable features of Amazon SQS is its ability to handle large-scale data with millions of records per queue. The scale it manages is quite impressive. 

Additionally, features such as message retention up to 14 days are impactful, as they allow for the processing of messages if consumer systems are slow or unavailable. Integration capabilities with services like EC2, Lambda, EventBridge, and SNS for a fan-out pattern further enhance its functionality.

A primary area of improvement for Amazon SQS is the message size limitation, which is currently restricted to 256 kilobytes per message. If this could be increased, it would benefit many use cases. Additionally, SQS uses a pull-based mechanism rather than a push-based one, which requires consumers to poll for new messages.

I have been using Amazon SQS for more than four years.

Amazon SQS is very stable. It was one of the first services launched by AWS, indicating its mature and reliable nature.

Amazon SQS can handle an increased load and scale vertically without any issues. However, one should be aware of possible message duplicates and sequence changes when handling multiple producers and consumers.

No questions have been escalated to technical support.

The initial setup of Amazon SQS is straightforward, requiring a few minutes if the policies are clearly understood. Proper permissions need to be set in both the SQS policy and resource-based policy to ensure successful message delivery and consumption.

SQS has proven its value in handling sudden spikes of active users and maintaining message integrity. By using DLQs and SNS fan-out methods, workflows are not disrupted by failures. These methods also allow for processing messages later if they're initially unsuccessful.

Amazon SQS offers a generous free tier, beyond which it remains very cost-effective. The cost per million messages is less than a dollar, making it an economical choice.

I would recommend Amazon SQS, especially for designing decoupled systems where one component doesn't depend on another. It's an optimal choice for high availability and reliably handling queuing mechanisms. In terms of size and scalability, SQS excels.

I'd rate the solution nine out of ten.

We use Amazon SQS for notifying, queuing servers, queuing messages, and notifying the people for alerting systems. We have been using Amazon SQS in one of the projects for queuing messages. For alerting, we have production support where SQS is mostly used.

I have been using Amazon SQS for four to five years.

We had no issues with the solution’s stability.

Amazon SQS is highly scalable, both horizontally and vertically. We don't see any scalability issues with the solution. More than 500 users are using the solution in our organization.

I created a support ticket one or two years ago. The technical support team responded promptly and helped us with the issues. I am very much happy with the solution’s technical support. They are knowledgeable, understand our problem easily, and then figure out the issues.

On a scale from one to ten, where one is bad and ten is excellent, I rate the solution’s technical support nine and a half out of ten.

Positive

I've previously used Kafka, which is similar to Amazon SQS but not exactly the same feature-wise. Kafka has different storage functionality, which we don't have in Amazon SQS. I'd say Amazon SQS is the best solution for messaging service.

The solution's initial setup is easy. Compared to other complex services in AWS, Amazon SQS is simple to configure. It is also simple to provision through the console and through Terraform.

Compared to EC2 and other services, Amazon SQS' pricing is cheaper.

Anyone with a basic cloud experience can use the solution. I would recommend Amazon SQS for any support project that needs message queuing and faster and more reliable processing.

Overall, I rate the solution ten out of ten.

The tool helps to process events in a microservices cluster. We use it in the financial industry. 

I like how we can subscribe to multiple topics in Amazon SQS. It's also much simpler and quicker to set up than other solutions. It also supports patterns like Kafka and RapidMQ's fan-out pattern but with easier implementation.

Amazon SQS is costly. I think there could be improvements in how it facilitates comparisons between different AWS products. A calculator would be helpful. The calculator for Kafka is based on factors like throughput or storage used in the last month. In contrast, the calculator for Amazon SQS is based on the number of transactions processed. These different approaches make it challenging to compare them directly. I suggest AWS provide a straightforward calculator where I can input one aspect, and it calculates costs for multiple solutions. 

I have been working with the product for two years. 

I rate the product's stability a ten out of ten. 

I rate the solution's scalability a ten out of ten. My company has 100 users. 

I rate the solution's deployment ease an eight out of ten. Its deployment is generally quick, but it involves considerations around security, which are essential for DevOps teams. Typically, it takes about one week for deployment. However, if I handled it for my project personally, the deployment time would likely be shorter.

I rate the tool's pricing a nine out of ten. 

We manage and monitor our Amazon SQS performance and costs using DataOps. It helps us with intuitive data. Transitioning from our legacy tools to Amazon SQS would be beneficial because it's simple to set up and can serve as a pilot for our approach.

I rate the overall product an eight out of ten. 

We use it for event-driven messaging and workflows.

We use SNS as the publisher, and our procurement service subscribes to those events using SQS.

In the past, we relied on time-based or batch-based processes to send data between services on-premises. With SQS, we can trigger actions based on real-time changes in business processes, improving reliability.

Moreover, SQS can grow with our needs.

SQS message delay feature and redundant retention policies helped us to avoid replaying events due to errors and ensure our messages are processed reliably.

We use CloudWatch for monitoring.

It's easy to implement and cost-effective.

The visibility timeout feature is very nice. We use the visibility timeout in our internal processes to ensure that if a message fails to process, it becomes available for other consumers after a set period.

The current visibility timeout of five minutes is okay. However, I'd like to explore the possibility of extending it for specific use cases.

I have been using it for five to six years now. We (my company) use SQS quite extensively, and it has been quite a good service till now.

It is a stable product.

It is a scalable product. We can handle 10,000 events easily. 

We have a lot of end users using it in my company. We have around 2,000 end users using it. We have multiple locations. 

I have used RabbitMQ. 

The initial setup is very straightforward. It's a simple checkbox-kinda process. 

It is not difficult to maintain it. It is very easy. Overall, it is a very straightforward solution. 

It does a very good job. The cost was the main issue for us.

It's quite expensive. 

It's a great solution. I would recommend using it. 

Overall, I would rate the solution a nine out of ten. I've used it, and it seems to be a solid solution.

We use Amazon SQS for triggering other resources, such as Lambda, API Gateway, EC2 instances, and various tasks running inside different compute services. 

It's primarily used for queuing different tasks, alerting, and monitoring pipelines. Additionally, it is used for message distribution.

All Amazon Web Services resources are easy to configure. The configuration process is straightforward, making it easy to use. It's easy to get started with it.

There is room for improvement in all the resources. That said, it's currently pretty good. For example, improvement is needed in terms of troubleshooting and logs, as there is not much logging in SNS and SQS services. More specific logs would be helpful, as it's hard to troubleshoot without them.

I've been using it for quite a while, about four years.

Stability-wise, it's pretty good. It doesn't really break down, but it does freeze sometimes. However, the freezing is not that significant.

It is very scalable. Scalability-wise, I would give it a ten out of ten.

Personally, it's pretty good because, from my workplace, we receive special support. However, for the general public, it's not as good. The more you pay, the more support you get.

Neutral

I previously used both cloud and on-prem solutions. Of course, cloud is better than on-prem since there's no maintenance, but it comes at a cost.

It is expensive. The cloud is expensive.

I would recommend this product to bigger businesses.

I'd rate the solution nine out of ten.

Amazon SQS is used when the need arises to publish a message. This could be a natural message or a message to a service to execute a process. The consumer, which is usually a service, will catch it, consume it, and execute the process based on the message that is passed to the queue. This describes the general concept of our use case.

Amazon SQS helps process all the requests that come from the clients, and it enhances the client-side performance, making it faster. It ensures data is processed correctly with no data loss, especially with the Dead Letter Queue (DLQ) feature. This feature ensures that even if a message is not processed properly, it can be reprocessed later on.

The dead-letter queue is very helpful in maintaining the messages that come into the queue. It allows the consumer to process it again later when it is available, preventing the queue from being overlooked when the same message has been retried more than the specified attempts.

There could be improvements in the UI for security and scalability. Initially, I struggled to understand the scalability and get the general gist of how it works, but over time, it became clearer.

I have been working with Amazon Suite for a couple of months now.

The stability of Amazon SQS is really high, and I would rate it a nine out of ten. The stability ensures the data is processed correctly without any loss.

The scalability is pretty good, and I rate it around nine out of ten. It is possible to configure Amazon SQS to have several queues that may serve the same line but are divided into several consumers.

Currently, I haven't communicated with technical support for Amazon SQS since I haven't faced any specific problems requiring their support.

Positive

There was no previous solution before Amazon SQS.

The initial setup was mostly straightforward. I would rate my experience with it a nine. The problem was not with the configuration but with understanding the AWS implementation.

Only I was needed for the deployment since it was straightforward.

Amazon SQS contributes greatly to processing client requests, improves client-side performance, and maintains a high level of satisfaction for the consumers.

The pricing is rather affordable, and I would rate it at two to three out of ten, with ten being the most expensive.

I haven't evaluated alternate solutions for the use case of SQS.

I'd advise new users to look at the documentation and try to understand the basic queue and the implementation in Amazon SQS.

I'd rate the solution nine out of ten.