Amazon SQS Reviews

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 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.

We use Amazon SQS for an asynchronous solution. We receive data through API calls, which we process and log to our database. To avoid doing this during the API call, we offload it to Amazon SQS. We have a service that monitors Amazon SQS to process the data in the background.

It helps with high volumes of data on the API, preventing us from doing too much processing for every API call. By using Amazon SQS as a holding area, we process data in the background, which does not affect the user experience on the front end.

Amazon SQS is reliable, with no issues to date. It handles its load well, and the graphs for monitoring are good. It offers durable storage, reducing data loss. The messages remain until processed and deleted, with a retention period of a maximum of 14 days.

It would be beneficial to have the ability to peek at messages currently in Amazon SQS without needing to monitor incoming messages. Additionally, when using Azure, I could look at messages while they were there. Such a feature would be useful in Amazon SQS as well.

I have used Amazon SQS for about two years now.

Amazon SQS is stable and handles its load effectively.

While Amazon SQS itself is not scalable, it supports scalability. It acts as a holding area between high volumes of messages, allowing us to scale and receive more messages without worrying about whether Amazon SQS can handle it.

I have not needed to escalate any issues to Amazon SQS customer support. It has been reliable without any need for assistance.

Positive

I have used Azure queues and Azure services. Azure allowed me to see the messages in the queue, which I found beneficial. However, Amazon SQS can store more messages at a time than Azure.

The initial setup was fairly straightforward.

From a programming and reliability standpoint, Amazon SQS is a good part of the infrastructure. It saves a lot of headaches and helps maintain system integrity.

The pricing and licensing details are abstracted from me, so I have not looked into them.

I have considered Azure queues and Azure services.

Using queues in an infrastructure is a good idea for certain scenarios. If you're going to use AWS and need queues, then Amazon SQS is the solution. I would recommend its use if you have simple enough needs.

I'd rate the solution eight out of ten.

I mainly use SQS to buffer tasks or jobs, so that our system can handle spikes in traffic without losing messages or slowing down processing.

Amazon SQS (Simple Queue Service) has significantly improved the way our organization manages asynchronous communication between different components of our system. Before adopting SQS, our services were tightly coupled, meaning that if one service experienced a delay or failure, it would directly impact other parts of the system. This led to bottlenecks, failed processes, and a lot of manual intervention to recover lost or delayed tasks. Introducing SQS allowed us to decouple these services, enabling each component to operate independently while still reliably communicating with others.

One of the most noticeable improvements has been in system reliability. By placing messages in SQS queues, we can ensure that tasks are not lost even if a downstream service is temporarily unavailable. The visibility timeout and retry mechanisms built into SQS have been particularly valuable. They have reduced the risk of message duplication or loss and have allowed us to handle failures gracefully without human intervention. Additionally, dead-letter queues have been instrumental in tracking and troubleshooting messages that repeatedly fail, improving our operational efficiency and reducing the time spent debugging errors.

SQS has also had a major impact on scalability. Our system now handles varying workloads more efficiently because messages can be queued and processed at a pace that matches downstream processing capabilities. During peak traffic, requests no longer overwhelm our services, as SQS buffers them effectively. This has not only improved performance but has also made our infrastructure more resilient to spikes in demand.

The features of Amazon SQS that I have found most valuable are message durability, decoupling, dead-letter queues, and visibility timeouts.

1. Message durability ensures that messages are never lost, even if downstream services fail temporarily. This reliability is critical for maintaining business processes without manual intervention.

2. Decoupling of services has been a game-changer. By placing a queue between components, we can scale, update, or maintain individual services independently without affecting the overall system. This reduces bottlenecks and simplifies architecture.

3. Dead letter Queues are incredibly useful for error handling. They allow us to capture and analyze messages that fail repeatedly, helping us troubleshoot issues efficiently without impacting other processes.

A few points are there which can be improve the product a lot.

1. Message Ordering: Standard queues don’t guarantee order, FIFO queues have throughput limits. Better handling of high-volume ordered messages would help.

2. Real-Time Streaming: SQS is not designed for instant analytics or event streaming, faster propagation would be valuable.

3. Monitoring & Debugging: More built-in tools for tracing message flow and identifying bottlenecks could simplify operations.

I have been using Amazon SQS for 4 years.

The solution is working fine. Whenever there is some issue around it, we look into it.  Sometimes, there are issues with its important sensors. Also, there are issues with the data center.

I rate the solution’s stability an eight or nine out of ten.

There are three or four users, but multiple instances are connected this way between different applications.

I rate the solution’s scalability a ten out of ten.

Customer support is very bad. There's no tech support from the AWS side. If you want it, you must pay a lot of money, which is 2,000 dollars. They have a lot of customers. They have an open application and use a support team. If you're willing to pay that much, something significant is happening with your application. There's no direct connection if a small customer wants to get some information from AWS.

Negative

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

The initial setup is easy. It depends on the time limit for setting up the queues, but whatever standard time is defined should be sufficient.

The solution costs 200 dollars. We don't manage it if we don't use any queues.

Initially, we didn't have any code. Our lead engineers had the most knowledge about our system. We decided to pursue a customized design due to cost concerns. We soon realized that we could not focus on our product because we were constantly distracted by tasks such as deployments and managing scalability. That's why we decided to transition to SQS fully. SQS provides scalability and fixes related issues. Whenever we require a set of messages, we need to set them up in our system, and SQS takes care of the rest. The keys are functioning perfectly fine for now.

Overall, I rate the solution a ten out of ten.