Solving and staying ahead of problems when scaling up a system of GitHub’s size is a delicate process. Here's a look at some of the tools in GitHub's toolbox, and how we’ve used them to solve problems.
The post How we improved availability through iterative simplification appeared first on The GitHub Blog.

How we improved availability through iterative simplification

GitHub switched to performing merges and rebases using merge-ort. Come behind the scenes to see why and how we made this change.

Scaling merge-ort across GitHub

Migrating Netflix to GraphQL Safely

Imagine a tool that can store and connect all the information you need to make decisions and solve problems. Most people would say it’s nice to think about, but not yet possible. The good news is this tool already exists - and it’s called a graph database. At LinkedIn, technologies like graph databases are essential to powering today's platform, while being flexible enough to scale for our future needs. In earlier blog posts, we defined what a graph database is and also shared how to index graph data for fast, constant-time access. One of the biggest (and most important) questions […]

How LIquid Connects Everything So Our Members Can Do Anything

Co-authors: Kenneth Tay and Xiaofeng Wang At Linkedin, we constantly evaluate the value our products and services deliver, so that we can provide the best possible experiences for our members and customers. This includes understanding how product changes impact key metrics related to those experiences. However, simply looking at connections between product changes and key metrics can be misleading. As we know, correlation does not always imply causation. When making decisions about the path forward for a product or feature, we need to know the causal impact of that change on our key […]

Ocelot: Scaling observational causal inference at LinkedIn

Through automated profiling and data collection of performance behavior, Microsoft’s M365 Core team can derive the context with which to inform the engineer about the impact of their code, as they write it. Randy Lehner likens it to the autonomic nervous system in this post on their Cloud Profiling and Reporting Pipeline.
The post The pursuit of an autonomic scale and efficiency system for Microsoft 365: Making it as easy as breathing appeared first on Engineering@Microsoft.

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