AWS X-Ray Reviews

I used X-Ray for the performance of my application. I have used X-Ray to check the performance of my applications to identify bottlenecks or lagging issues. I just use the tracing marks in X-Ray to address any latency.

It benefits my application by helping me check any performance bottlenecks.

I have two use cases with my customers. One involves using AWS Fargate, and the other involves an application running on WordPress. The WordPress application was very slow, and we used AWS X-Ray to understand the main problem. X-Ray helped us identify issues with our EFS, allowing us to adjust throughput and solve the problem.

AWS X-Ray shows us exactly when there are delays, helping us understand the depth of issues and what is happening point-to-point. The service map is incredibly helpful for troubleshooting, allowing us to view all information in the same place. It combines different logs and metrics efficiently.

It's basically like a cloud stack. 

You can trace your entire HTTP request. Once you have submitted any request, it will be addressed as a 500 Error or 401 Error, or if there is any exception happening when this request, and how much time will take. X-Ray can trace all the information about the request and response.

Wherever we require any API for any logging purpose, we use AWS X-Ray in our code.

By using AWS X-Ray, we know which risk takes how many milliseconds. For example, if there is a request during user onboarding, such as uploading documentation that takes three seconds to pass verification, we can do the analytics and divide the action into the sub milliseconds, for example, document verification takes this long, submit form takes that long, et cetera.  

We integrate solutions based on customer requirements. Therefore, the primary use case is to understand the system's behavior after several rounds of developers going into the team and building the system. Also, the big picture is sometimes missing when the system goes up. So, the ServiceNet and the segmentation, ordering, and timeline views on the segments, especially on the link traces, help understand how the system and microservices are interconnected.

The second use case is to identify bottlenecks in terms of stability and performance and how long certain data lives in terms of response time and duration. These are important aspects on our side.

Interpreting data is an important aspect to consider when discussing improvement, along with identification. It involves not only generating data and KPIs using a tool but also understanding and evaluating those KPIs from different perspectives. Based on this evaluation, solutions can be suggested to either improve or confirm that everything is working well.

First, you have to detect and name the problems, and then the second step is finding ways to mitigate those issues. It is not about needing extra tools but more about the decision if we want to spend more manpower or not because currently, there wouldn't be any tools unless it's some kind of AI, automated solution.

It provides a telemetry solution, so we track activity within our applications in our implementation at Amazon.

We use it for the work we're doing for the federal government where they need to know a lot of things. We monitor serverless applications, data usage, and things like that. We provide a global solution, and if there are any bottlenecks or latency, especially in specific regions, we can address those regions better. Those problems that we can't address automatically through the scalability of our system go back to the support center for addressing.

The solution is used to trace dashboard databases and also as a new layer for Codesphere.

AWS X-Ray is a strong solution and has a smooth integration process.

We use AWS X-Ray daily. We use it much for monitoring, but we use Amazon CloudWatch more for cloud groups.

Both AWS X-Ray and Amazon CloudWatch have helped our organization, especially when we have applications where the use cases are failing. For example, we've dockerized our microservices into AWS CCS, and all of the logs are being watched through AWS CloudWatch, so whenever some use cases fail, our first point of contact would be AWS CloudWatch, and then we'll use AWS X-Ray for monitoring the logs. Based on the logs, we're alerted on which problems to look into through AWS X-Ray.

In our company, we work with microservices. We use X-Ray to trace the flow of our endpoints and our requests. That's basically the only use case for us.

The solution has made it easier for us to trace the problems that we have with our requests and to monitor the timing of each step in each request we do in our endpoints. 

The solution does allow for various configurations.

The initials setup is pretty straightforward.

We've found the stability t be quite good.