Every developer has likely at least considered writing their own framework or CMS. Until you start to realize just how much work it is and how much of your problems have actually been solved by someone else already. Then you throw in the towel and start using (and hopefully, contributing) to existing open source projects that suit your needs. Writing a minifier is very much alike.
A 2-dimensional location on our earth can be represented via a coordinate system similar to an X & Y-axis. These axes are called latitude (lat) & longitude (lng).
Latitude is the north-south axis with a minimum of -90 (south pole) and maximum of 90 degrees (north pole). The equator is zero degrees latitude.
Longitude is the X-axis equivalent, running around the globe from east to west: from -180 to +180 degrees. The Greenwich meridian is 0 degrees longitude. Everything west and east from it is respectively negative and positive on the longitude scale, up until the middle of the Pacific Ocean, near the International Date Line, where -180° longitude crosses over to 180°.
A myriad of features may prompt the need to aggregate your data, like showing an average score based on multiple values, or even simply showing the amount of entries that abide to a certain condition. Usually this is a trivial query, but this is often untrue when dealing with a huge dataset.
In content-heavy websites, it becomes increasingly important to provide capable search possibilities to help your users find exactly what they’re looking for. The most obvious solution is searching your MySQL database directly, but implementing a generic MySQL search is not at all trivial. Here’s how to avoid those pitfalls and build a robust MySQL-powered search engine for you website.
This article will solely focus on the most common text-based search (as opposed to e.g. geography- or time-based)
In PHP, there are multiple ways to process data asynchronously, although not one will work in every single environment. There is no one true solution, and whichever suits you best will mostly come down to your specific task.
Although both multithreading and multiprocessing can be used to process code in parallel, it probably makes sense to first distinguish between the two.