appdash
Application tracing system for Go, based on Google's Dapper.
Appdash traces how a single request travels through your application, showing a visual timeline to help you find slow steps and debug errors.
Appdash is a tool that helps you understand exactly what happens inside your application when it handles a request. If a user clicks a button and that action triggers a chain of events across different parts of your system, this tool traces the entire journey from start to finish. It shows you how long each step takes and displays the results as a visual timeline and tree, making it much easier to find performance bottlenecks or debug errors.
To use it, developers add small tracking notes to their code at key points, like when a web server receives a request or when a database query runs. These notes are organized into a hierarchy. For example, when a main web request triggers sub-tasks, the system groups them together into a "span." The tool collects all these notes, sends them to a central server, and pieces them together into a complete picture of how a single request traveled through your entire application.
This is designed for teams building applications in Go, though it also has client libraries for Python and Ruby. A typical user might be a developer trying to figure out why a web page takes too long to load. Instead of guessing which database call or external API is causing the delay, they can look at the generated timeline to pinpoint the exact slow step. It is especially useful for systems where a single user action touches multiple separate services before returning a result.
The project is based on a famous Google research paper about large-scale tracing, meaning its design follows proven architectural patterns. It also supports the OpenTracing API, an industry standard that makes it easier to swap out or integrate with other monitoring tools. One notable detail is that it bundles its visual interface templates directly into its binary, so the web viewer runs without needing extra setup.
Where it fits
- Trace why a web page is slow by viewing the request timeline
- Find which database call or external API causes a delay
- Track a request as it moves across multiple microservices