Starting today, Checkly users can send their traces from synthetics checks to Coralogix to view in-depth synthetic user data along with back-end APM based tracing. This gives SRE’s and Operations engineers a new insight into how the system is responding to automated synthetic tests of your service. For Checkly users, integrating with Coralogix data means it’s easy to correlate end-to-end user experience with backend performance, and track poor performance to its root cause.

It’s 1am, and something has gone very wrong. The head of sales is in the incident response channel because our top customer is reporting a system-wide outage. Everyone’s running around trying to figure it out. As you look at service maps and traces, you get a sinking feeling. Earlier the previous evening, you got an alert that user-access-service was running out of memory.

Today, I’m thrilled to announce two changes to our leadership team. We at Checkly aim to deliver the best synthetic monitoring platform that allows you to identify and resolve issues 10x faster. I’m proud to have crossed that 1,000-customer mark and aim to enhance your experience even further as we are just getting started and are excited about what technologies like Open Telemetry, Clickhouse and others will enable us to do in the future.

With an ever-growing market of digital products, it is becoming increasingly important for every business to ensure a high level of customer satisfaction. In the past, companies might have been able to get away with slow or messy websites. Today, if a customer gets frustrated even once, they will likely abandon your product in search of a better replacement.

We are very excited to announce that we redesigned our monitoring results chart to make it easier for you to understand check performance over time and easily investigate any past anomaly. The redesign is a result of our UX research that showed that the old check overview chart made it challenging for users to find check results from the past. While we were redesigning our monitoring results charts, we wanted to achieve two things: And, we achieved this in three attempts. Let’s dive in.

I wish someone had told me that I shouldn’t hop between frameworks. Just like learning four programming languages in your first year, in my experience spending time content switching as a beginner is wasted effort. If I’d spent a solid year learning how to deploy services on AWS, then when it was time to learn Azure, I’d see more similarities than differences and find it a lot easier to pick up a second public cloud.

Playwright offers robust capabilities for automating browser tests. A common question among developers, however, revolves around the best practices for structuring Playwright projects, especially when tests involve significant environment changes, resource creation, or database updates. This blog post describes strategies for running Playwright tests either in parallel or in sequence, optimizing your testing workflow for efficiency and reliability.

Cloud-native monitoring is all about keeping an eye on how well your cloud-based apps and services are doing. It's like having a health check-up for your digital operations, making sure everything runs smoothly and efficiently across distributed cloud systems.

With end-to-end testing, efficiency is key. Every millisecond counts, especially when you're running a large suite of tests. Today, we'll dive into how you can use request interception in Playwright to significantly reduce the runtime of your scripts.

When you’re implementing automated testing, clarity and maintainability of test scripts are as crucial as the tests themselves. Playwright offers a feature that enhances the readability and ease of debugging of your tests: test steps. This article explores how to use test steps in Playwright to document your test cases effectively.