If you’re a developer that uses Sentry and you’re reading this in 2024, stop reading and head to rollback.sentry.io to get your very own Rollback! Just a few weeks ago, we released Sentry Rollback — our first ever year-in-review experience. Think Spotify Wrapped, but for recapping your year as a developer on Sentry.

Snuba, the primary storage and query service for event data that powers Sentry in production, has historically been doing rate limiting under the hood, making it hard to discover and increasing time to resolve customer support requests. This is not something you’d know the specifics of unless you were deep in the Snuba code. But as we triage support questions from customers, one issue tends to pop up: RateLimitExceeded. You got tired of not getting query results.

If you’ve ever been floored by a sub-100ms response time, you’ve likely got caching to thank. Caching is the unsung hero of performance, shaving precious milliseconds off your application’s response time by storing frequently accessed data, avoiding yet another round-trip request to the database or API. Let’s break down how caching works and explore a few common strategies.

Sentry is committed to helping developers fix broken code quickly and effectively. In this article, we’ll cover a range of intermediate to advanced techniques for debugging Python code using VS Code and the Sentry Python SDK.

Debugging. It’s a critical skill for all developers. And when you’re building a dynamic, high-performance application with Next.js, Chrome DevTools, and console.log() aren’t always enough. There are more effective and structured ways to debug Next.js apps as they scale. You will also find practical tips from our Next.js debugging workshop sprinkled throughout. Also, while this guide is focused primarily on Next.js, there is a similar guide for debugging React apps here.

Ensuring a seamless player experience is critical for game developers, and yet unanticipated crashes and performance issues continue to harm games’ reputations and disrupt player engagement. To address this developers need proactive error monitoring across multiple platforms. Luckily, Sentry offers a robust SDK designed specifically for Unreal Engine to help developers debug and maintain performance effectively.

Slow database queries drag down performance for both developers and users. They waste resources, slow down testing, and frustrate customers with laggy experiences. But often, there’s a surprisingly simple fix: indexing. Here’s how indexing works and when to use it, regardless of your schema.

This is a guest post from Artem Zakharchenko, creator of MSWJS, an API mocking library for Javascript. He also writes about testing for EpicWeb and on his personal blog. Test flakiness is a big issue. Not only can it be a colossal time investment to detect and fix, but it hurts perhaps the biggest value you get from your tests—their trustworthiness. A test you cannot trust is a useless test. Time spent maintaining a useless test is time wasted; time that could have been spent building.

As developers, we know that debugging can be a time-consuming process. Hunting down elusive bugs or trying to reproduce an issue based on vague user reports can turn a simple fix into an hours-long journey. While leveraging logs, metrics, and tracing to reproduce locally or try to understand what happened can help us identify a root case, we’re often missing a critical component to truly being able to understand the impact on our users.

Given that 100% of the databases I have interacted with in my professional career have been SQL databases, my data-based mental model (please enjoy my pun) has always defaulted to a relational one. However, when spinning up a tiny side project in 2020 (a bot to provide interactivity to my Twitch stream), my data-storing requirements didn’t call for a relational model at the time, so I chose a NoSQL solution: MongoDB.