Amazon SQS Reviews

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.

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.

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.

In our system, we have millions of users, and for certain actions, we need to send millions of messages, which Amazon SQS handles smoothly without any problems. It serves as a communication line between different applications or services. I use it to send messages between separate systems since we have multiple services built in a microservice architecture. These distributed services communicate with each other using SQS to send messages.

Amazon SQS handles a high volume of messages smoothly, without any problems, allowing efficient communication between services in our system.

The most valuable features include the ability to handle a huge number of messages and the presence of a dead letter queue. If any messages are missed from a service, they will go to the dead letter queue, allowing us to handle these cases. It's also a distributed queue, which is perfect for our system because we deal with large numbers of messages. Additionally, it provides data security, as failed messages go to the dead letter queue where they can be handled later.

I have a problem with Logstash when searching logs. The search should be more user-friendly, allowing me to search for a longer period of time and return results faster. This is my problem with AWS when searching the logs using Logstash.

I have been working with SQS for many years because it's a main component in our system. In one project, I worked with CloudFormation for around two months. My experience with CloudFormation was about a year and a half ago, as we built it one time and rarely updated the stack.

I haven't encountered any stability issues while using Amazon SQS.

The scalability is excellent. Amazon SQS can handle millions of messages smoothly and without issues.

I'm not directly involved in communication with AWS technical support; this is typically handled by the DevOps team.

Positive

Before Amazon SQS, the company used Kafka. SQS was implemented, possibly because it is scalable by itself and doesn't require extra effort from developers or cloud personnel to handle scalability and queue size.

The initial setup is straightforward. The documentation is clear, allowing anyone to read it in ten minutes and start using the solution. There are detailed developer documents available, which are useful for understanding how it works and its technical details.

The cost of AWS services, including SQS, can become high as the system scales. When handling a high volume or scalable system, the price increases and this might be a problem.

For me, no other message queue solutions have been used besides Amazon SQS.

For users considering Amazon SQS, they should consider their budget, whether it is low or high, as pricing can be a concern.

I'd 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.

The tool I use to transform and move data can read the entries from Amazon SQS. For example, to start some workflow orchestration, it checks Amazon SQS, reads new messages from it, and then runs some transformation. My responsibility was setting up the new SQS, setting up the right policies, adding some text, and allowing communication.

It's very quick and easy to build or set up Amazon SQS. It's a very stable solution, and we have never faced any downtime issues.

Sending or receiving messages takes some time, and it could be quicker.

I have been using Amazon SQS for one year.

I rate the solution ten out of ten for stability.

Around 500 users are using the solution in our organization.

I rate Amazon SQS ten out of ten for scalability.

The solution’s initial setup is straightforward.

Beginners can very easily set up Amazon SQS. It requires just a few clicks and then some permissions. The solution can be installed in 15 minutes.

Amazon SQS is moderately priced.

Users need to check the number of messages. Since the solution works on a pay-as-you-go model, it could be expensive if the number of messages is very large.

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

Amazon SQS is basically a queue service. Each message is treated as an event and added to a container. When a message needs to be processed, a trigger can be set.

One of the useful features is the ability to schedule a call after a certain number of messages accumulate in the container. For example, if there are ten messages in the container, you can perform a specific action.

Recently, I encountered an issue where I couldn't send a message to multiple recipients. If two subscribers are subscribed to the same channel, the message can only be sent to either one of them, not both. I believe this is an area that needs improvement.

So, I cannot send a message to multiple people simultaneously. It can only be sent to one recipient.

I've been using Amazon SQS for about six months.

Amazon SQS is not very stable. 

It does not that stable because you can't deliver the same message to two people; how can you add that feature on the AWS. So we have the option to add multiple subscribers to the same message, but it's not delivering this message to all the people at the same instant.

Amazon SQS is a scalable product. There are 30 people using Amazon SQS in our company.

Amazon SQS was easy to set up.

If you only have one job to run, I would recommend using it. However, if you need to handle multiple jobs, I would not recommend it.

Overall, I would rate the solution a five out of ten. Since it doesn't support sending the same message to multiple subscribers, it lacks usefulness in certain cases.