At a glance, the hosting industry may not appear exciting, but it’s grunts in data centres the world over that keep our industry going. They are, quite literally, the backbone of the Internet, and as such they make everything possible: from e-commerce sites, to smart mobile apps for our latest toys. The heavy lifting is done in boring data centres, not on our flashy smartphones and wafer thin notebooks.
During the last few years, the hottest word on everyone’s lip has been “productivity.” In the rapidly evolving Internet world, getting something done fast always gets an upvote. Despite needing to implement real business logic quickly and accurately, as an experienced PHP developer I still spent hundreds of hours on other tasks, such as setting up database or caches, deploying projects, monitoring online statistics, and so on. Many developers have struggled with these so called miscellaneous tasks for years, wasting time instead concentrating on the project logic.
One of the most exciting events in 2015 in the PHP world was the release of PHP 7, 10 years on from the release of the last major version, PHP 5. With a major step forward, PHP 7 introduces plenty of new features and performance upgrades.
However, it also removes old, deprecated functionality, which introduces some compatibility breaks, making it harder for older applications to migrate to the new version. This guide should serve as a quick tour on what to expect if you plan on moving your existing applications, or building new ones, on top of PHP 7.
I first heard of Spark in late 2013 when I became interested in Scala, the language in which Spark is written. Some time later, I did a fun data science project trying to predict survival on the Titanic. This turned out to be a great way to get further introduced to Spark concepts and programming. I highly recommend it for any aspiring Spark developers looking for a place to get started.
Today, Spark is being adopted by major players like Amazon, eBay, and Yahoo! Many organizations run Spark on clusters with thousands of nodes. According to the Spark FAQ, the largest known cluster has over 8000 nodes. Indeed, Spark is a technology well worth taking note of and learning about.
This article provides an introduction to Spark including use cases and examples. It contains information from the Apache Spark website as well as the book Learning Spark – Lightning-Fast Big Data Analysis.
As someone who’s been writing high performance networking code for a number of years now (my doctoral dissertation was on the topic of a Cache Server for Distributed Applications Adapted to Multicore Systems), I see many tutorials on the subject that completely miss or omit any discussion of the fundamentals of network server models. This article is therefore intended as a hopefully useful overview and comparison of network server models, with the goal being to take some of the mystery out of writing high performance networking code.
This article is intended for “system programmers”, i.e., back-end developers who will work with the low-level details of their applications, implementing network server code. This will usually be done in C++ or C, though nowadays most modern languages and frameworks offer decent low-level functionality, with various levels of efficiency.
I’ll take as common knowledge that since it’s easier to scale CPUs by adding cores, it’s only natural to adapt the software to use these cores as best it can. Thus, the question becomes how to partition software among threads (or processes) which can be executed in parallel on multiple CPUs.