Lessons learned scaling PostgreSQL database to 1.2bn records/month

“This isn’t my first rodeo with large datasets. The authentication and product management database that I have designed for the largest UK public Wi-Fi provider had impressive volumes too. We were tracking authentication for millions of devices daily. However, that project had a funding that allowed us to pick any hardware, any supporting services and hire any DBAs to assist with replication/data warehousing/troubleshooting. Furthermore, all analytics queries/reporting were done off logical replicas and there were multiple sysadmins that looked after the supporting infrastructure. Whereas this was a venture of my own, with limited funding and 20x the volume.

Others’ mistakes

This is not to say that if we did have loadsamoney we would have spent it on purchasing top-of-the-line hardware, flashy monitoring systems or DBAs (Okay, maybe having a dedicated DBA would have been nice). Over many years of consulting I have developed a view that the root of all evil lies in the unnecessarily complex data processing pipeline. You don’t need a message queue for ETL and you don’t need an application-layer cache for database queries. More often than not, these are workarounds for the underlying database issues (e.g. latency, poor indexing strategy) that create more issues down the line. In ideal scenario, you want to have all data contained within a single database and all data loading operations abstracted into atomic transactions. My goal was not to repeat these mistakes.

Our goals

As you have already guessed, our PostgreSQL database became the central piece of the business (aptly called ‘mother’, although my co-founder insists that me calling various infrastructure components ‘mother’, ‘mothership’, ‘motherland’, etc is worrying). We don’t have a standalone message queue service, cache service or replicas for data warehousing. Instead of maintaining the supporting infrastructure, I have dedicated my efforts to eliminating any bottlenecks by minimizing latency, provisioning the most suitable hardware, and carefully planning the database schema. What we have is an easy to scale infrastructure with a single database and many data processing agents. I love the simplicity of it — if something breaks, we can pin point and fix the issue within minutes. However, a lot of mistakes were done along the way — this articles summarizes some of them…”


Containers the hard way: Gocker: A mini Docker written in Go

They are popular and they are misunderstood. Containers have become the default way applications are packaged and run on servers, initially popularized by Docker. Now, Docker itself is misunderstood. It is the name of a company and a command (a suite of commands, rather) that allow you to manage containers (create, run, delete, network) easily. Containers themselves however, are created from a set of operating system primitives. In this article, we shall concern ourselves with containers on the Linux operating system and simply act as though containers on Windows do not exist at all.

There is no single system call under Linux that creates containers. They are a loose construct made by utilizing Linux namespaces and control groups or cgroups…


Riding the Tiger: Lessons Learned Implementing Istio

Recently I (along with a few others much smarter than me) had occasion to implement a ‘real’ production system with Istio, running on a managed cloud-provided Kubernetes service.

“My next ulcer will be called ‘istio'” by Ian Miell

Istio has a reputation for being difficult to build with and administer, but I haven’t read many war stories about trying to make it work, so I thought it might be useful to actually write about what it’s like in the trenches for a ‘typical’ team trying to implement this stuff. The intention is very much not to bury Istio, but to praise it (it does so much that is useful/needed for ‘real’ Kubernetes clusters – skip to the end if impatient) while warning those about to step into the breach what comes if you’re not prepared…

A Mathematical Explanation of Naive Bayes in 5 Minutes

Photo by Courtney Cook on Unsplash

Naive Bayes. What may seem like a very confusing algorithm is actually one of the simplest algorithms once understood. Part of why it’s so simple to understand and implement is because of the assumptions that it inherently makes. However, that’s not to say that it’s a poor algorithm despite the strong assumptions that it holds — in fact, Naive Bayes is widely used in the data science world and has a lot of real-life applications.

In this article, we’ll look at what Naive Bayes is, how it works with an example to make it easy to understand, the different types of Naive Bayes, the pros and cons, and some real-life applications of it…