When testing and monitoring websites end to end with Playwright, choosing the right locators is crucial. Proper locators help create tests that are less flaky and more reliable. Let's explore user-first locators and how to filter locators for more robust tests.

I've been in the observability market long before it even had that name. Over the years, observability has undergone a significant transformation. As someone who has witnessed these changes firsthand, I can attest to the dynamic nature of this field. In the early days, it was largely about basic monitoring: tracking system metrics, lots of logs, and simple alerts.

When I was first learning programming, I was implementing a markov chain and explained to my mentor that it was taking me more than a week to do. I was implementing input sanitization on my interface. “Don’t do that” said my mentor, “your tool has zero users, soon it will have one user, you.

What are the two things making your end-to-end test investment a failure? Firstly, it's test flakiness. If you've invested days (if not months) in creating your test suite and it didn't turn out to be like this one trustworthy friend you have in your life for decades, you failed. You failed because eventually, you'll discover that every moment waiting for retrying tests became a burden.

Our mission at Checkly is to enable engineers to detect and resolve issues 10x faster. We are very excited to announce $20 million in Series B funding led by Balderton, alongside the participation of our existing investors Accel and CRV.

If you bet on end-to-end testing or even synthetic monitoring, there’s a high chance that you use Microsoft's Playwright. And if you have Playwright in your toolchain, you probably adopted TypeScript, too. It's an easy choice because of its rock-solid auto-completion and type safety. With this setup, you can enjoy the beautiful DX (developer experience) and safely refactor your ever-growing code base without worrying about runtime exceptions because of TypeScript's type checking, right? Wrong!

Traditional monitoring has become insufficient for managing complex systems. Modern infrastructures consist of numerous interconnected services, and simply monitoring individual metrics and logs fails to provide a comprehensive view. This is where observability becomes crucial.

This guide goes into the fundamentals, practical applications and tips & tricks of using OpenTelemetry (OTel) to monitor your Next.js application. OpenTelemetry is gaining (a lot) of momentum outside of its historical niche of distributed, micro services based application stacks. But, as it turns out you can just as well use it for more traditional, three tiered, web applications and it comes with a host of benefits.

When you invest time and effort into creating a well-running end-to-end test suite or adopt Playwright synthetic monitoring with Checkly, you should focus on two things. Your tests must be stable because few things are worse than an unreliable test suite. But also, your tests must be fast because no one wants to wait hours to receive the green light when you're on the edge of your seat to deploy this critical production hotfix.

I'm excited to share that Gartner has included Monitoring as Code (MaC) as an emerging practice to their Hype Cycles for SREs again, the second year in a row. Since we founded Checkly, our vision has been that monitoring should sit in your repository, be codified, and scale with your software development. There is no alternative to MaC as it allows your engineering team(s) to work together, create and maintain checks, and ultimately own their monitoring.