As part of our export functions for adjust.com, we generate CSV reports for our clients, which are typically used to create pivot tables in Office. Sometimes those reports can be 100’s of megabytes in size and Office will not be able to open them. And even those files that can be opened often take minutes to load.

Being asked one too many times to “quickly” check one of those reports I came across a very helpful little tool to transform CSVs into SQL without all the hustle of creating complex table structures.

“Unrolling” line charts are everywhere - where the lines gradually enter from origin, point by point. This is the world’s favourite way of animating a line chart, particularly as it makes a ton of sense when graphing a time series. d3 tends to transition line charts really weirdly, though. So what is d3 actually doing when creating transitions on line charts, and how can we make them prettier?

Here at adjust we use Redis as a fast in memory key-value data store. Today I want to show you how we are clustering Redis.

As adeven’s primary “tech support”, I’d like to discourage call-center connotations and phrases like “turn it off; turn it on again” from rolling off your tongue. We’re a lean, mean startup, and our internal tools for troubleshooting are called tail, head and grep.

Alright guys, as you all already know, Rails is dead or at least dying. It’s slow, it’s vulnerable and it does not scale, which is a huge disadvantage for your next gen social dating service with geolocation and badges. Today we will play rats abandoning a sinking ship of Ruby on Rails and learn how to handle a session created by a Rails application. I’m going to talk about web apps that use Rails 4.0.0 or greater, because even my grandmother can fake an authorization stored in a Base64 encoded cookie (and remember, if you don’t keep your app up-to-date, you will be eaten) very soon.

In my last article I talked about how redismq employs a simple Redis client to describe a message queue. The first iteration was aimed at proof of concept and to satisfy my own curiosity. Since then adeven decided to use it in production and so we developed the initial idea into a full featured queue.

On the way we picked up a lot of interesting stuff which I’d like to share with you.

Rport is an R package that greatly facilitates common tasks found in many R Business Intelligence apps. It bridges R and SQL analytics similarly to how Rails bridges Ruby and Web Development.

Understanding query locking is key to using postgres concurrently. So let’s look at an example to learn more about how locking works and how to see what’s going on within your database.

Golang is a young language that tries to make many things better than the “ancient” languages. It almost reaches the speed of C without the need of your own memory management. Also it respects modern patterns and makes writing concurrent programs easier than most other languages. But the strict model of Go has disadvantages when it comes to resolving third party dependencies and managing different branches of your Go project. Goem tries to fix this issues.

Redis is not just a very good in-memory database, it can also double as a message queue. I want to show you our first shot at implementing a queue in Redis that can fulfill all our requirements: reliable, persistent, fast, monitorable. While still being a Gedankenexperiment the basic idea should be a very good starting point.