Elevating Your Monitoring with Application Tracing Tools
• Understand what Application Tracing is
• Learn about three modern-day Application Tracing tools
Application tracing is a technique for troubleshooting cross-application traces in more depth than a regular log. It also provides runtime logging information, which helps with diagnostics and debugging features. All of this is possible because application tracing tools typically utilize distributed context propagation to navigate from the beginning to the end of the request flow, passing through databases, event streamings, queues, and many other services used by the application. Let’s explore the concept of application tracing and look at the features and benefits of three popular application tracing tools.
What Is Application Tracing?
Application tracing can be seen as part of the evolution of logging. With the adoption of microservices architectures, multi-tiered applications have become part of many developers’ routines. These applications have many different microservices, each responsible for a single system piece. For example, you might have a single microservice that handles authentication or logging. In this model, an application’s components are more distributed (as opposed to being located in a single location), making monitoring much more difficult.
Just as the logs of monolithic systems require monitoring for vital information, organizations needed a way to follow tracing across microservices throughout the entire process. Application tracing aims to do just that. For example, with application tracing, you can follow a trace to determine the amount of time that each part of a process takes, which helps you find bottlenecks. In addition, application tracing solves some of the challenges introduced by containerization, such as the need to monitor information related to containers and pods, then correlate it with the data gleaned from monitoring other services.
Three Application Tracing Tools to Elevate Your Monitoring
Now, let’s explore three application tracing tools to elevate your monitoring and application performance management.
New Relic is one of the first monitoring tools to implement features aimed primarily at developers rather than network and system administrators. Therefore, it supports advanced cross-application tracing, including distributed tracing. It also supports the W3C Trace Context standard, which communicates and traces context data across services and networks. New Relic already has agents that can collect telemetry data from the services, but you can configure it to work with OpenTelemetry.
New Relic supports three approaches to sampling data:
- Head-based sampling: New Relic selects a simple sample representing the captured activities and avoids storing large amounts of data.
- Tail-based sampling: New Relic uses infinite tracing, meaning that tracing decisions happen after receiving all of the data.
- No Sampling: The user must define which data is essential to trace by configuring the Trace API accordingly.
Datadog is a customizable interface monitoring tool with distributed tracing capability that provides visibility into your application’s performance. The traces within Service Maps can produce an interactive map that allows you to navigate inside each component used by the process. Each click takes you deeper into the trace until you reach the log information, and you can access the entire stack this way.
In addition, it is essential to mention that Datadog can integrate with other cloud services (like Lambda) to provide accurate end-to-end monitoring. Like the other application tracing tools discussed in this article, Datadog has an optimized retention policy that uses tags to define retention filters. Datadog also supports both OpenTracing and OpenTelemetry, enabling users to get started or migrate from another application tracing tool.
Jaeger, one of the newest application tracing tools, solves the problem of monitoring distributed transactions, and it has focused on monitoring microservices since its inception. It was also built with high scalability since it aims to support Uber’s tracking data. When it comes to sampling data, its default strategy is to use a probabilistic algorithm to select 1 out of 1,000 traces (to keep it commensurate with performance concerns). Still, it can be customized to suit users’ needs.
One of Jaeger’s most significant benefits is its trace comparison feature. When in visual mode, it will display a tree with the paths to each trace. You will also be able to see the difference between successful and failed traces, which will help you identify the points where errors usually occur.
Jaeger has its way of handling connections and obtaining data, but developers should use OpenTelemetry APIs and SDKs with newer versions. It’s also worth noting that Jaeger will require more time to set up because you will have to install and configure it yourself.
There are currently many monitoring tools on the market that can perform application tracing for distributed systems. The best way to find the right one for your organization is to evaluate your system’s particular needs and the costs, then experiment with the options.
Regardless of which application tracing tool you choose, you should take the time to properly configure it and create a dashboard and link data that will give you better visibility into your application’s performance. And don’t forget to create alerts that notify you about actionable events without making extra monitoring noise.
Finally, you’ll probably want to choose an application tracing tool that already uses OpenTelemetry to create and collect telemetry data. Using this standard will allow you to integrate with most frameworks and libraries. Still, it will also make it much easier to switch to another application tracing tool if the need arises.