[Three hundred and eighty-one]. The number of days it’s been since our Sentry offices shut down, in what we believed at that time to be a very short-term temporary measure while we took stock of the events unfolding around us.
Auto-instrumenting AWS Lambda Monitoring didn’t originate through a focus group or business plan. It started as a hackathon project that addressed the tedium of removing manual code instrumentation. Developer environments often include hundreds of AWS Lambda functions. And our existing instrumentation required initialization code to be manually placed on every single function.
The consequences of poor application performance are both real and terrifying: lost customers, lost trust from your team, and lost confidence in your abilities. It’s why every developer remembers those moments where their solution improves both process and performance. We’ve seen this happen firsthand with our customers, and want to share their Great Moments in Application Monitoring.
In the early days of web development, there was one way to measure code: WTFs per minute. It was a metric that could be applied across all languages, as every developer knew what WTF meant (Works That Frustrate, obviously). Today, however, code is too intricate — and important — for clever, opaque metrics. You need objective data that communicates the quality and stability of your code — KPIs such as events accepted, transaction outcomes, and crash-free sessions.
Like mechanics who restore their own cars or plastic surgeons who self-rhinoplasty, our developers put their skills to interesting uses during their free time. Here, Native Platform Engineer, Arpad Borsos breaks down how memory mappings and dynamic library loading works and how it relates to native Android libraries loaded from APKs. Libraries are key to modular programming, as they offer functionality in a single unit which can be shared with other developers.
Auto-instrumenting Lambda Monitoring didn’t originate through a focus group or business plan. It started as a hackathon project in which our growth team used Cloudwatch to build a prototype that could instrument Lambda functions with Sentry. We did this by using Cloudformation’s stack to automatically create resources in a customer environment while streaming CloudWatch Logs to Sentry through the Kinesis Firehose.
Top of search for you means top of mind for your customers. And with Google’s upcoming Page Experience update to Web Vitals taking place in a few weeks, now’s the right time to optimize your user experience. But before you can optimize your user’s experience, you need to be able to measure it. We’re kicking off Measurement May by breaking down breaking down how Google uses Web Vitals data — and how you can instrument that data with Sentry.
Responding to and ignoring notifications can be a full-contact sport. It makes sense, though, from GitHub, Slack, to Jira and Sentry; our world revolves around robots telling us everything is important, critical, and urgent. Just like that, it’s near impossible to see what actually matters so you can solve quicker and more comprehensively.
Recently, Sentry converted 100% of its frontend React codebase from JavaScript to TypeScript. This year-long effort spanned over a dozen members of the engineering team, 1,100 files, and 95,000 lines of code. In this blog post, we share our process, techniques, challenges, and ultimately, what we learned along this journey.
From testing in production to running A/B tests, feature flags have a range of uses. At Sentry, one way we use feature flags is to safely allow beta access to new features for some of our “Early Adopter” customers. Because you can set multiple combinations of feature flags, every user is likely to have a different experience.
