Database Engines Ranking

db-engines-ranking-table provides some insight into some of the most popular database engines (312 of them to be precise).  Nothing too surprising there – Oracle and MySQL leading the charts, but it’s nice to have the numbers and trends.


There are, of course, many different ways how the popularity can be calculated.  Their method is based on the popularity of each engine in a variety of online outlets, from Google Search to social networks.

  • Number of mentions of the system on websites, measured as number of results in search engines queries. At the moment, we use Google, Bing and Yandex for this measurement. In order to count only relevant results, we are searching for <system name> together with the term database, e.g. “Oracle” and “database”.
  • General interest in the system. For this measurement, we use the frequency of searches in Google Trends.
  • Frequency of technical discussions about the system. We use the number of related questions and the number of interested users on the well-known IT-related Q&A sites Stack Overflow and DBA Stack Exchange.
  • Number of job offers, in which the system is mentioned. We use the number of offers on the leading job search engines Indeed and Simply Hired.
  • Number of profiles in professional networks, in which the system is mentioned. We use the internationally most popular professional networks LinkedIn and Upwork.
  • Relevance in social networks. We count the number of Twitter tweets, in which the system is mentioned.

It seems objective and representative enough to me.

WordPress now powers 27.1% of all websites on the Internet


WordPress Tavern states:

WordPress now powers 27.1% of all websites on the internet, up from 25% last year. While it may seem that WordPress is neatly adding 2% of the internet every year, its percentage increase fluctuates from year to year and the climb is getting more arduous with more weight to haul.

Linking to these statistics from W3Techs.  Impressive!

Those who think that WordPress is just a blogging system are far from the truth…

WordPress 4.7 Field Guide

WordPress 4.7 is just around the corner (this month).  Here is a field guide, detailing what are the changes (and there are plenty!) and what to pay attention to during and after upgrade of your site, as well as what plugin and theme developers should check for the maximum compatibility with the upcoming release.

WordPress 4.7 Field Guide

Holly Molly, that’s a lot of changes!

Over 447 bugs, 165 enhancements, 8 feature requests, and 15 blessed tasks have been marked as fixed in WordPress 4.7.


WordPress : Preferred Languages Research

Pascal Birchler of the WordPress blogs some interesting research he did in the area of handling preferred language and how different systems – ranging from browsers, wikis, and social networks to all kinds of content management systems – approach and solve the problem.



Drupal 8 has a rather powerful user interface text language detection mechanism. There is a per session, per user and per browser option in the detection settings. However, users can only choose one language, so they cannot say (in core at least) that they want German primarily and Spanish if German is not available. But the language selected by the user is part of the larger fallback system, so it may fall back further down to other options.

The Language fallback module allows defining one fallback for a language, while the Language Hierarchy module provides a GUI to change the language fallback system. It allows setting up language hierarchies where translations of a site’s content, settings and interface can fall back to parent language translations, without ever falling back to English. This module might be the most interesting one for our research.

Apart from the research itself, I think this is an interesting example of how complex some seemingly simple features are.

Drupal and Playboy

Slashdot has the details for the story, if you haven’t heard it yet.  Inappropriate? Maybe.  But then again, where do you draw the line of what’s inappropriate in the sponsor’s bag?  (Beer and other alcoholic beverages are very welcome, for example.)

I tend to take things on the lighter side, considering it to be somewhat entertaining and mildly funny.

Amazon Rekognition – Image Detection and Recognition Powered by Deep Learning

I know, I know, this blog is turning into an Amazon marketing blow-horn, but what can I do? Amazon re:Invent 2016 conference turned into an exciting stream of news for the regular Joe, like yours truly.

This time, Amazon Rekognition is announced, which is an image detection and recognition service, powered by deep learning.  This is yet another area traditionally difficult for the computers.

Like with the other Amazon AWS services, I was eager to try it out.  So I grabbed a few images from my Instagram stream, and uploaded them into the Rekognition Console.  I don’t think Rekognition actually uses Instagram to learn about the tags and such (but it is possible).  Just to make it a bit more difficult for them, I’ve used the generic image names like q1.jpg, q2.jpg, etc.

Here are the results.  Firstly, the burger.


This was spot on, with burger, food, and seasoning identified as labels.  The confidence for burger and food was almost 99%, which is correct.

Then, the beer can with a laptop in the background.


Can and tin labels are at 98% confidence. Beverage, drink, computer and electronics are at 69%, which is not bad at all.

Then I decided to try something with people.  Here goes my son Maxim, in a very grainy, low-light picture.


People, person, human at 99%, which is correct.  Portrait and selfie at 58%, which is accurate enough.  And then female at 53%, which is not exactly the case.  But with him being still a kid, that’s not too terrible.

Let’s see what it thinks of me then.


Human, people, person at 99% – yup. 98% for beard and hair is not bad.  But it completely missed out on the duck! :)  I guess it returns a limited number of labels, and while the duck is pretty obvious, the size of it, compared to how much of the picture is occupied by my ugly mug, is insignificant.

Overall, these are quite good results.  This blog post covers a few other cases, like figuring out the breed of a dog and emotional state of people in the picture, which is even cooler, than my tests.

Pricing-wise, I think the service is quite affordable as well:


$1 USD per 1,000 images is very reasonable.  The traditional Free Tier allows for 5,000 images per month.  And API calls that support more than 1 image per call, are still counted as a single image.

All I need now is a project where I can apply this awesomeness…

Amazon Polly – Text to Speech in 47 Voices and 24 Languages

Amazon announced a new service – Amazon Polly – text to speech in 47 voices and 24 languages.  This part got me intrigued:

Polly was designed to address many of the more challenging aspects of speech generation. For example, consider the difference in pronunciation of the word “live” in the phrases “I live in Seattle” and “Live from New York.” Polly knows that this pair of homographs are spelled the same but are pronounced quite differently. Or, what about the “St.” Depending on the language and the context, this could mean (and should be pronounced) as either “street” or “saint.” Again, Polly knows what to do here. Polly can also deal with units, fractions, abbreviations, currencies, dates, times, and other speech components in sophisticated, language-specific fashion.

I am not much involved with text to speech these days, but I did experiments in this area a few years ago.  Simple text to simple English has been around for a long time.  But support for other languages was always limited, and even with English, the voices always sounded very robotic, and often failed to understand the simplest of native language constructs.

I tried Amazon Polly and was blown away by the quality of the synthesis.  Here are the English samples of the text from this blog post:

US English, Kendra, female:

British English, Bryan, male:

Welsh English, Geraint, male:

With that, I wanted to see what happens with other languages.  The only other language I speak is Russian, so I pasted the Russian category description into the converter, selected the Russian language, and got this:

Russian, Maxim, male:

That is pretty good!  Going further, I pasted the content of this blog post, which is a quoted story that somebody else wrote.  It has a very informal flow to it and some weird punctuation.  Listen to what it turned into:

Russian, Maxim, male:

You can still make out that it’s a robot and not a human, but it’s way better than anything else I’ve heard so far.  By far!

So, how affordable is this technology now?  The pricing page answer is very simple:

Pay-as-you-go $4.00 per 1 million characters (when outside the free tier).

It also provides some examples of how this pricing converts to real-life scenarios:


I don’t know about you, but my mind is blown…

Things to learn about Linux

Julia Evans has this amazing list of things to learn about Linux.  I think, it doesn’t matter how new or experienced you are with the operating system, you’ll find a few points in this list that you either know nothing about or know very little.

Personally, I’ve been using and administrating Linux systems for almost two decades now, and my own knowledge of the things on that list is either very limited or not existing.  Sure, I know about pipes and signals, but even with basic things like permissions there are some tricky questions that I’m not sure I can get right on the first go.

Some of the topics mentioned are simple and straight-forward and will only need a few minutes or a couple of hours to get up to speed with.  Others – are huge areas which might take years, if not decades (like networking, for example).

I look forward to Julia’s drawings covering some of these.


PHP 7.1.0 Released!

PHP 7.1.0 release is out, bringing quite a bit of new features and improvements.  Here are some of the new things:

I guess I’ll wait for Fedora 26 or something to get a silent upgrade. :)