While building Sentry, we also use Sentry to identify bugs, performance slowdowns, and issues that worsen our users’ experience. With our focus on keeping developers in their flow as much as possible, that often means identifying, fixing, and improving our integrations with other critical developer tools. Recently, one of our customers reported an issue with our Slack integration that I was able to debug and resolve with the help of our Trace View.

Having the right tools to support debugging is crucial for improving application performance and delivering an enhanced user experience. Traditional observability tools provide insights into application health and with a shift towards actionability, they also directly aid in the debuggability of your system, helping you pinpoint the root cause of issues in real-time.

Interaction to Next Paint is the newest of Google’s Core Web Vitals. The three metrics that make up the CWVs are Google’s attempt at defining proxy metrics for measuring things they believe are critical to a good user experience on the web. The three metrics are: Debugging and fixing these metrics can be quite complicated. In this post, I’m going to walk through how you can use Honeycomb for Frontend Observability to debug INP, which was just promoted to a stable Core Web Vital in March.

Let’s start with things we already know - complex projects are inherently hard to debug. The more complicated they are, the harder it is to debug them. The size of the project naturally defines complexity’s lower bounds, but even the smallest projects can become unnecessarily complex and messy if you don’t pay attention to how you structure them. Though we can’t eliminate complexity, we can manage it effectively with the right approach.

In this guide, you’ll gather how to identify and solve the most common bugs and performance issues. We’ll cover debugging client-side React, if you have a React app that uses server-side rendering, you can also look at our Node.js debugging guide or on-demand workshop. In the below sections you’ll learn.

In this article, we will learn how memory usage patterns can affect the real-time performance of an embedded application, drawing from a recent experience tracing an audio DSP application running on an embedded Linux platform. First, I will introduce the product in question and the real-time audio software I developed for it. Then, I’ll describe the issues I encountered with audio callbacks and the strategy I followed to determine the cause of the issues, ending with my solution and lessons learned.