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· 6 min read


In this article, I will introduce you to flamegraphs, how to use them, how to read them, what makes them unique, and their use cases.

What is a flamegraph#

A flamegraph is a complete visualization of hierarchical data (e.g stack traces, file system contents, etc) with a metric, typically resource usage, attached to the data. Flamegraphs were originally invented by Brendan Gregg. He was inspired by the inability to view, read, and understand stack traces using the regular profilers to debug performance issues. The flamegraph was created to fix this exact problem.

Flamegraphs allow you to view a call stack in a much more visual way. They give you insight into your code performance and allow you to debug efficiently by drilling down to the origin of a bug, thereby increasing the performance of your application.

· 5 min read

Coming from a background working as a frontend developer at Grafana I'm no stranger to open source performance monitoring. I was part of a team that was responsible for the overall user experience of Grafana and performance was one of the key considerations. Along the line, I learned about a debugging technique known as profiling for monitoring application performance and fell in love ever since.

chrome browser profiler

What is continuous profiling#

“Profiling” is a dynamic method of analyzing the complexity of a program, such as CPU utilization or the frequency and duration of function calls. With profiling, you can locate exactly which parts of your application are consuming the most resources. “Continuous profiling” is a more powerful version of profiling that adds the dimension of time. By understanding your system's resources over time, you can then locate, debug, and fix issues related to performance.

As a frontend developer, my experience with profiling was limited to the browser. However, in the course of my study, I discovered a new pattern of profiling that seems exciting– continuous profiling. Similar to how you use the profiler in the dev console to understand frontend performance issues, continuous profiling allows you to profile servers from various languages 24/7 and be able to understand resource usage at any particular time.

While continuous profiling is new to many, the concept is actually relatively old. In 2010, Google released a paper titled “Google Wide profiling: A continuous profiling infrastructure for data centers” where they make the case for the value of adding continuous profiling to your applications.

Industry traction for continuous profiling#

Since then, many major performance monitoring solutions have joined them in releasing continuous profiling products. As time has gone on, the continuous profiling space has been getting increasingly popular as various companies/VCs are more frequently making major investments in Continuous Profiling to keep up with demand for this type of monitoring.

continuous profiling trends

· 4 min read


Why we added adhoc profiling#

While most profilers are built for more static or adhoc analysis (ie.profiling a script), Pyroscope's continuous profiling gives you the opportunity to jump around to any point in time. This fluid profiling is beneficial for understanding performance issues in your application. However, as we continued to improve on Pyroscope UI/UX, we identified ideal situations to use static profiling instead of running a profiler continuously, including profiling scripts and attaching a running process.

Our goal is to make Pyroscope a one-stop-shop for all profiling needs. That means supporting all languages, all flamegraph formats, continuously profiling servers, and, of course, quickly profiling an adhoc script.

Introducing Adhoc profiling#

That being said, we are excited to officially release Adhoc profiling mode for Pyroscope! With adhoc mode, you get all the convenience and simplicity of profiling a script, as well as Pyroscope's stellar visualization and UI functionality.

· 6 min read

How we improved performance of our Go application#

Recently we released a new feature where users can run Pyroscope in pull mode. It allows you to pull profiling data from applications and it has various discovery mechanisms so that you can easily integrate with things like kubernetes and start profiling all of your pods with minimum setup.

For Pyroscope, the difference between push and pull mode is that:

  • Push mode: Sends a POST request with profiling data from the application to the Pyroscope server and return a simple response
  • Pull mode: Pyroscope sends a GET request to targets (identified in config file) and the targets return profiling data in the response.