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Using Go for Anomaly Detection

As engineers, we need to know how our applications and services are performing in realtime and isolate any problems as quickly as possible. While there is a plethora of tools to collect, store and graph metrics from infrastructure, there are fewer tools for performing analysis on these metrics.

Go at Sourcegraph - Serving Terabytes of Git Data, Tracing App Performance, and Caching HTTP Resources

Sourcegraph is a code search and review application that supports and analyzes code in multiple languages: Go, Python, Java, Ruby, JavaScript, and soon more. Even though we have experts in each language on our team, Sourcegraph’s core has been written in Go since day one, and we’ve chosen Go for each new project and system we’ve built.

Go at Datadog

Go at Datadog In the last year, Go has started to supplant parts of our intake pipeline at Datadog that were previously written in Python.

Advanced Reflection with Go at HashiCorp

Advanced Reflection with Go at HashiCorp HashiCorp builds a diverse set of popular DevOps tools written in Go: Packer, Serf, Consul, and Terraform. While these tools range from desktop software to highly scalable distributed systems, their internals all have one thing in common: they use reflection, and a lot of it.

Go at CoreOS

Go at CoreOS When we launched the CoreOS project we knew from the very beginning that everything we built would be written in Go. This was not to make a fashion statement, but rather Go happened to be the perfect platform for reaching our goals – to build products that make distributed computing as easy as installing a Linux distro.

How Go Helped Shape Splice's engineering culture

Go is a trendy programming language, but let’s be honest: the Go language doesn’t have anything new that wasn’t first implemented somewhere else. As a matter of fact, Go is a pretty boring programming language.