The Relational Model & SQL
The relational model was proposed in a paper published in 1970 by Edgar Codd, a computer scientist working at IBM. In previous years, some storage systems had already emerged, but the relational model was first proposed with a strong theoretical basis.
As part of my exploration of a minimum set of devops tools, I’ve been learning how to stack containers full of Rails apps onto the Docker. There are plenty of examples of how to get started with Rails and Postgres on Docker, even one from the whale’s mouth, as it were. Working from this example, it was pretty clear to me that one of Docker’s major strengths is that it makes it really, really easy to get something running with a minimum of fuss; it took all of about a half day to learn enough Docker to hoist anchor, and even tweak a few things to my liking. One thing kept nagging me about the Docker example, though, and that was a warning from bundler when running docker-compose.
Software Engineering is about more than just writing code. It is a complex process that has a lot of moving parts. Requirements gathering, planning, testing, deployment and source control management are just a few of the pieces to the software engineering puzzle. So how do we manage all this complexity? Software methodologies come to the rescue.
In the past we would often treat a server as a machine which has a variety of roles. A single server may be responsible for serving web content, email, processing background jobs, and even hosting a database system. Your application is really only one of the many things that runs on that machine.
A common way to describe requirements on Agile projects is through the use of user story mapping, and, as a result, user stories. This post will not cover this process, but rather the process of taking an existing set of user stories and leveraging them within our development workflow to ensure that an application is built as accurately and efficiently as possible. To this effect, we will set up tools (Rails, RSpec, Capybara, FactoryGirl, and Guard, to be precise) for implementing our app using behavior-driven development. Structuring our app in this way gives us much better odds of producing robust, low-defect code that delivers on the requirements we set out to build.
I use Chrome extensions all the time and decided it was time to figure out how to make my own. I found it to be incredibly easy and I’d like to share with you some of the basics, as well as an example of an extension I made. Let’s get started!
I work from all over the place: Home, on public transit, the office, coffee shops, etc.
A big challenge to developing android apps in an environment where my laptop and phone are on different networks (wifi vs. LTE, or laptop tethered through phone) is the inability for my phone to see the API server that is often running locally on my laptop. Here is a simple tip to allow your phone to hit the backend over ADB and a usb cable.
To get a better handle on Erlang’s behavior, I decided to install a popular set of tools for debugging and performance profiling: EPER. I think it stands for “Erlang PERformance tools”, but it could also mean “Everything Proves Erlang Rules” or “Egrets Prefer to Eat Robots” or really anything for that matter. One thing is for certain, however: getting these tools built and running on Mac OS X was fraught with danger and build errors.
Graphic Processor Units are becoming more and more important in recent years and are spreading into many different fields, some of which include: computational finance, defense and intelligence, machine learning, fluid dynamics, structural mechanics, electronic biology, physics, chemistry, numerical analysis and security. There are many reasons why a person should learn how to write code for a GPU. For example, GPU’s have been used to successfully decrypt passwords (add reference) in record time, a 25-GPU cluster is able to crack any standard Windows password (95^8 combinations) in less than 6 hours. Other applications of the GPU include data mining for Bitcoin and also applying machine learning for sentimental analysis on tweets.