As of October 15, 2020, Mattermost Extended Support Release (ESR) version 5.19 will no longer be supported. If any of your servers are not on ESR 5.25 or later, we highly recommend that you upgrade immediately. With our simple upgrade steps, it takes only a few minutes. Mattermost adopts a monthly tick-tock release cycle, with a new version shipping on the 16th of each month.

A lot will happen over the next 1,000 years, and the codebase for the Mattermost open source project will be along for the entire ride. On July 8, GitHub successfully deposited 21 terabytes of open source repository data in the Arctic World Archive, a (very) long-term storage facility located on the Svalbard archipelago in Norway near the North Pole.

It’s been more than a year-and-a-half since we started using Cypress for our automated functional testing, and it has been well worth the investment. Cypress has now become an essential part of our process to automate regression testing, which helps us ship new releases faster, with increased quality.

One of our favorite things about Mattermost is that it makes it easy for teams to collaborate on projects and topics in specific channels. Today, we’re excited to announce that Mattermost users can now easily carry conversations over to a video conference and screen sharing with Jitsi, a leading open-source video conferencing solution.

Here at Mattermost, our team of developers and quality assurance analysts are proud of what we build and work hard to ship a quality product on the 16th of each month. However, maintaining our high bar for quality month over month isn’t without its challenges!

Mattermost Release v5.25 is generally available today. The new Extended Support Release offers many bug fixes for increased stability (see changelog for details) as well as the following new features.

One of the most important parts of the Mattermost source code is the one responsible for accessing the Mattermost database: the store. Every single database access is handled by the store, so we needed to find a way to extend its functionality while introducing as little complexity as possible. This is the reason behind the current layered approach using struct embedding.

Lately we’ve been working on improving different parts of the Mattermost server, including our monitoring and observability capabilities. We’ve been using Prometheus and Grafana to monitor our cluster for a while now, and you can read this great post where my colleague Stylianos explains how we have them working for our multi-cluster environment.

Large-scale cloud applications are usually built using interconnected services that can be rather hard to troubleshoot. When a service is scaled, simple logging doesn’t cut it anymore and a more in-depth view into system’s flow is required. That’s where distributed tracing comes into play; it allows developers and SREs to get a detailed view of a request as it travels through the system of services.