AWS X-Ray Reviews

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.

My team has been working with AWS for about five years, and X-Ray is just a small portion of the observability part amongst CloudWatch insights, Lambda Insight, and so on. But in the last three years, X-Ray has been the most used observability tool. We recently observed if it should be or could be more useful and if we should spend more time diving into it or not.

The initial setup actually depends on how deep you want to go. You can segment it on Lambda levels with SDKs or dedicated libraries to get a more granular view. You can also sample it on different levels. But setting it up, I would rate it a seven out of ten for difficulty, where one is difficult, and ten is very easy.

We created a proof of concept to evaluate AWS X-Ray, specifically in exporting data sources or providing data sources to other tools like Rafael. It is to avoid the need for IM users to have permissions for certain environment types like production by outsourcing this responsibility to another tool.

Overall, we evaluated AWS X-Ray in terms of observed availability for our team, the insights it brings, and which surrounding tools we need to have an overall better experience of the system.

I wouldn't say Grafana is the most suitable solution, but some customer restrictions exist. The most suitable solution would be to use AWS X-Ray itself, but unfortunately, some customers are rendering this option unavailable.

If you have a small team, it's overkill. If you have a big team, it might only be sufficient if you also use other tools that provide insights or another perspective on your data.

Overall, I would rate AWS X-RAY an eight out of ten.

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.

The most promising feature of AWS X-Ray is that you can debug the issues through the proper logs. You can also get an analysis out of the logs for some use cases, though I haven't tried all the features of AWS X-Ray.

What needs to be better in AWS X-Ray is the log filtering. Predefined filters could be helpful because the power of analytics comes from how you can filter the data.

I also want to see more KPIs from AWS X-Ray.

I've been using AWS X-Ray for two years.

AWS X-Ray is a stable tool, so I'm giving it an eight in terms of stability.

AWS X-Ray has excellent scalability. It's a seven out of ten.

I've never tried contacting the AWS X-Ray technical support team.

My company used Logstash but switched to AWS X-Ray because, as a PaaS provider, the company uses AWS extensively, so it wants to use power of AWS. AWS is my company's primary cloud provider, so it prioritizes AWS products. The company found that AWS X-Ray meets all use cases, so it didn't need to go elsewhere or use another product.

Setting up AWS X-Ray is straightforward and takes one to two days maximum.

We saw a lot of ROI from AWS X-Ray.

The pricing for AWS X-Ray is a six out of ten.

I have experience with AWS X-Ray, though only with some of its functions. My experience with Amazon CloudWatch is more extensive than my AWS X-Ray experience.

As a solution architect, I want to know the use cases or what others want to implement AWS X-Ray for first. Still, I can say that for application usage monitoring and debugging, I'm recommending the tool to others.

AWS X-Ray is a good tool, so my rating is eight out of ten.

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.

The user interface is sometimes kind of confusing to understand. It's not very user-friendly. They should work to improve this aspect of the product. It should be easier to navigate.

While the configuration is not that difficult and you can follow the instructions in the AWS documentation, it's also not so easy to understand what exactly you have to do to configure everything. For example, in our applications, sometimes it's not clear the points I have to direct the interceptors to have the perfect trace of the endpoint. In fact, right now I'm working on a ticket here to solve a problem where the interceptor break is interrupted at a certain point due to a problem of configuration.

Between local services and AWS services, I'm working with tasks. These tasks use threads, and they are kind of getting lost due to the threads. In these cases, the configuration is a little bit difficult to understand in terms of what to do.

I've been dealing with the solution for under a year. It's been about six months or so.

The stability of the solution is good. It doesn't crash or freeze. There aren't bugs or glitches. It's mostly reliable.

We don't have problems with scalability. When we have to scale our applications, we can do so. Other companies would be able to do so as well.

The entire company uses the application. We did a job last year, at the beginning of 2020, to include X-Ray in all applications we have. Therefore, I can say that all the company uses the application. That's at least 600 people, if not more.

I've never specifically contacted technical support. I can't speak to their kowledgability or responsiveness.

AWS in general does have good support. I've used them for other issues and they were fine.

The initial is not that difficult. It's fairly straightforward for the most part. The problem is when the system starts to become more complex. It's a little bit hard to figure out where exactly to do to have the perfect, solid application.

Everybody working with back-end development, including DevOps, works on maintaining X-Ray.

I don't have any information in regards to licensing. I'm not on the DevOps team and typically they are the ones that handle it. It's not an aspect of the solution I'm familiar with.

We have or applications with Amazon. We have an enterprise relationship.

We use the up-to-date version of the solution.

I would recommend the solution. Despite all the difficulties we have faced in terms of configuration, it's very useful to trace everything and to have information about the application types we have in our endpoints. Overall it's a nice tool. Potentially, I just need more time with it to understand more of its functionalities and quirks.

I would rate the solution at a seven out of ten.