All Performance Case Studies

We’ve written extensively about the lengths CouchDB goes to ensure your data is kept safe. CouchDB is incredibly reliable, but there are still a host of reasons to monitor your CouchDB metrics and statistics, from ensuring you’re ready to scale to learning where you can optimise for your use case and hardware right now.

We’ve offered a CouchDB diagnostics tool called “Opservatory” for quite a few years now to help make sense of your CouchDB monitoring and metrics data. Based on user feedback, plus the longstanding urge to update a beloved creation that will be familiar to many readers, we’re happy to announce a major overhaul in the form of Opservatory 2.0!

While other databases out there might shard, CouchDB is one of the few that does it automatically and saves you the annoying — read error-prone — work of setting it up yourself. Being unique in this way, it’s a topic you may not know too well. The arcane-sounding term (especially if it reminds you of the prismatic variety) doesn’t need to conjure confusion or intimidation. In this post, we’re going to take a deeper look at scaling CouchDB with shards: what sharding is, plus why and how to do it.

All software benchmarks and claims of performance are carefully crafted lies and this write-up is no different. Instead of giving you a quick “do steps one, two, three for a magic speedup”, we aim to explain how we arrived at the changes we made and how we rigorously tested those changes to make sure we understand their impact.

All this to give you the tools to follow the process yourself, at the end of which, you might come to very different conclusions that are valid for your particular situation which is very likely different from what our customer was dealing with.

If you need help with your CouchDB, Erlang, or just regular Unix networking performance, book a call today.

Hold on to your hats and get ready for a journey of performance discovery! We will learn about CouchDB and Erlang internals, performance tooling and how to find the next bottleneck in your CouchDB cluster setup.