AWS X-Ray Reviews

I have been using AWS X-Ray for creating insights to our applications we have developed. It is used to correlate different services in AWS when a transaction happens, allowing us to see the flow of them.

We use AWS X-Ray to figure out where we have bottlenecks. My team looks through the full chain to see where we have code that is not acting as fast as possible, and then we either address it or determine if the performance is acceptable. That is the insight AWS X-Ray gives us.

AWS X-Ray helps us in error tracing by tracing processes through the system, enabling us to identify vulnerabilities and similar issues.

The best feature of AWS X-Ray is the ability to correlate data out of the box. When enabled, AWS will ensure that if one service calls another service, it will provide a full flow of what has happened. There is no need to build or develop anything around it as it handles everything automatically.

AWS X-Ray is a very powerful tool for developers and managed services. When issues occur, we can easily identify their location without having to search through numerous logs across VMs to determine the exact problem. It quickly provides us with an understanding of the situation.

The challenges we faced with AWS X-Ray were that some of the AWS services we were using did not support it, which we discovered at a later stage. This was potentially a design consideration we should have known about, requiring us to redeploy some services in another SKU version to get it functioning. This is the only challenge, as AWS X-Ray is relatively new. It is important to consider that not all AWS services support it out of the box.

My recommendation for AWS X-Ray would be broader support. We have been working in a relatively small area of AWS, using approximately six or seven services, so I have not explored all other available services. In general, the more services it supports, the better it would be.

AWS X-Ray should be able to support more services for improved functionality.

I have one year of experience working with AWS X-Ray.

We did not need to contact technical support for any issues with AWS X-Ray.

Neutral

We used CloudWatch, which is a basic classic logging system for storing logs before implementing AWS X-Ray.

AWS X-Ray is easy to set up if you understand the system, though some knowledge about it is necessary. It becomes relatively straightforward once you understand the process.

AWS X-Ray is native for AWS, so it is available through Marketplace, though we simply enabled it by logging in.

Dynamic sampling rules in AWS X-Ray will help us in adapting to application changes, though we have not implemented these changes yet.

For those planning to use AWS X-Ray, I recommend trying it out. It is relatively easy to get started, and you can always disable it if needed. The solution is most beneficial in environments where multiple services communicate together. When enabled, AWS X-Ray provides quick insights into system operations. It is similar to App Insights in Azure, offering visibility into application performance and AWS resources while identifying bottlenecks. The system provides good, reliable data that is simple to obtain.

On a scale of 1-10, I rate AWS X-Ray an 8 out of 10.

Positive

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.

The most beneficial feature is that it shows a dashboard for performance intervals, which reveals latencies. From the dashboard, we can see if there are performance issues and look into them. The error analysis capabilities of X-Ray are really good.

It should have X-Ray SDKs for different languages like Node.js, Python, or Java. It should also have custom annotations and better instruments for external API services. 

In addition to these, X-Ray should integrate with CloudPort for more insights. If there are performance issues, an alarm should be added to the application. 

It should also include anomaly detection capabilities, similar to machine learning, and have custom metadata to trace vulnerabilities. A significant downside is that it is very expensive.

I have been using X-Ray for about six months.

The product is quite stable. I didn't face any issues with AWS or anything else.

The solution is quite scalable.

I did not escalate any questions about X-Ray to technical support.

Neutral

Before AWS X-Ray, we were using OpenTelemetry for performance bottlenecks, Dataflow, and FDM. Since AWS X-Ray is an internal service of AWS, that's why I moved to it.

The initial setup took around fifteen to twenty days. There are different challenges that I faced, such as inserting code for AWS X-Ray into every call, configuring sampling rules, managing permissions, and integrating with third-party APIs.

I had help from my senior to set this up.

It really benefits me for my application to check any performance bottlenecks, but the cost is high.

AWS X-Ray is very beneficial, however, it is expensive. It should be more cost-effective.

Before AWS X-Ray, we were using OpenTelemetry.

The advice is to manage sampling and annotations according to the application. Use AWS insights or CloudWatch metrics for any performance issues.

I'd rate the solution seven out of ten.

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.

Sometimes, the collector agents are confusing to configure initially. It requires research to understand the correct configurations, especially when deploying on-premises or on an EC2 instance. Using AWS services like Fargate or Lambda is straightforward, but configuring collector agents can be challenging.

I have used AWS X-Ray since 2021, so for about four years.

I do not have support enabled for AWS X-Ray because we are an AWS partner. However, whenever I have used AWS support for other services, it has been good for understanding and resolving issues.

Positive

istio

Configuring the collector agents can be confusing initially. Once configured, the service is easy to use for AWS Fargate or Lambda, but the initial setup requires research.

Compared to other open-source tools, AWS X-Ray needs improvement in providing discounts. Customers often seek cheaper solutions. When using many AWS resources, pricing can be a mid-range cost, given the high dollar rate in Brazil.

Overall, I would recommend AWS X-Ray and rate it a six or seven out of ten. 

I also advise not to use the company name due to contractual NDAs limiting exposure of customer information.

Our customers used AWS X-Ray mainly because they were already utilizing other AWS products. The primary purpose of using AWS X-Ray was to observe code, product, and system performance. However, if we were seeking a top-tier solution, we would often opt for third-party products like AppDynamics, Instana, or Datadog.

AWS X-Ray's endpoints and user monitoring are considered the most valuable features. The end-user monitoring interface is particularly understandable and easy to implement. While it is beneficial for those aspects, there is room for improvement in making the development side more user-friendly.

AWS X-Ray should improve its implementation process to make it easier for developers. The current manual instrumentation is challenging compared to the almost automatic instrumentation of other products like Instana or AppDynamics. There is a need for an interface overhaul to enhance user-friendliness, particularly from a developer's perspective.

I worked with AWS X-Ray for about one year. It was employed at my previous company six months ago before I changed jobs.

Datadog, Instana, and AppDynamics are easier to use for customers compared to AWS X-Ray.

I would rate AWS X-Ray six out of ten. The product needs significant enhancements in terms of user-friendliness, especially when compared to third-party products like Instana and AppDynamics. It might benefit from adopting features from these third-party tools.

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 most important one is compliance. We're able to achieve our regulatory levels. We're able to achieve the security level that we need for the federal government. 

Performance is another one. It helps in making certain that we're providing excellent performance to those we serve. Having an understanding of how people are using the system and how events and other things are flowing through the system is super important to us because of the security level we need to maintain.

Like most Amazon products, the user interface, configuration, and tuning aren't the easiest. That's the biggest reason why people tend to go to products like TerraForm and Terragrunt. We use TerraForm and Terragrunt. So, for setting things up and interacting with X-Ray, it's definitely the user interface that can be better.

It has been about eight years. 

We have an open issue. It doesn't seem to be an issue in X-Ray. It seems to be an issue in a POP that's offshore. There seems to be data loss where we are having issues, but I'm not going to set a bug at this point in time because we haven't yet been able to pin it down. That's the only known open topic that we have with Amazon where X-Ray is involved.

We've not run into any problems. We have only three people on the X-Ray team. Our user base is about a hundred and twelve million. This instance is for the federal government. I also have commercial ones, but for the federal government, it's around a hundred million. There are a lot more users than that, but those are the users that are being monitored at this point in time.

I'd rate their support an eight out of ten.

Positive

It was easy for us.

I'm not involved with government pricing. If I switch to the commercial side, the pricing gets better once you get into the billable mode. The pricing gets better as you scale up. As you develop a relationship with Amazon, your pricing gets lower. You get credits for the amount of the system you use, and then if you're the government, you can get government pricing. For commercial users, there's a hump when you go from small to medium to big enterprise. Small businesses can live pretty easily off the free tier in a lot of cases, but when you go from a medium to a big enterprise, it becomes more expensive on a per-user basis. I'd like to see that curve going in a different way where pricing can be driven down while people are trying to adopt the technology.

Get somebody who knows what they're doing to install Amazon products. Don't just get a cloud architect. Make sure that they have experience and make sure you follow all the different things that Amazon has to offer in terms of architecture and things like that.

I'd rate AWS X-Ray an eight out of ten.

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.  

If we get any error, we can go to X-Ray and get all the requests for a certain error in one place. It makes it easy to find the issue in your own product. You can look at logs and error logs and find loopholes. You can do direct queries to get specific requests over the course of a day. It's very good at error reporting and quite fast.  You also get a lot of information, including IP addresses, user locations, et cetera. 

Right now, the solution works quite well. I do not have any notes in terms of improvements. 

I'm working with AWS X-Ray in my current project. I've used it for the last seven or eight months. 

I haven't found any stability issues. There are no bugs or glitches, and it doesn't crash or freeze. I'd rate it ten out of ten. It is reliable. 

It is a very scalable solution. I'd rate it ten out of ten in terms of ease of scaling. It also has a lot of other features as well that make it even more expansive in terms of use cases. 

I've never used technical support. I've never needed their help.

Previously, we added all the logs manually in AWS CloudWatch. Doing it that way, it's very hard to find any errors and to report. When we did find errors, we would send emails. Sometimes, they would not be received. We weren't getting to the actual issue efficiently.

Now, with AWS X-Ray, we can directly go to the AWS X-Ray dashboard, and we can file a query and get all error requests. It's much faster and more efficient.

I would rate the initial setup seven out of ten. With a bit of study and with the help of documentation, it's doable. 

It's very fast to deploy. You can handle the process within a matter of minutes. 

Usually, you just need to do some configuration in AWS CloudFormation, and then there is some line of code you need to add to your project.

You only need one person for the deployment and maintenance of the solution. 

We didn't need to use any third parties when setting up the solution. We did it ourselves. It is purely connected with AWS services. 

I've never investigated ROI for the solution.

Companies are charged based on use. 

The pricing is okay. I'd rate the affordability seven or eight out of ten.

It's relatively cheap. 

We did not consider other options previously. 

We are customers. 

You can always get the latest version from AWS. 

This solution was only recently launched by AWS and is already a very good technology. If we implement it nicely in our product, then we can use a lot of features. I'd advise others to try it. It will be really helpful to developers that need to reduce the time and effort in terms of finding issues.

I'd rate the solution ten out of ten.

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.

They can improve how traces are sent to other providers.

The support is good. 

Positive

The solution is a bit expensive. 

We need it to make a transformation based on the external products. I rate the solution a seven out of ten. 

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.