Databases & Programming

The world of UNIX appears to attract those interested in the more technical aspects of computing. Since Linux is cut from the same lineage, it is apt to include lists of computing languages. Both scripting and programming appear here despite the title, itself shortened for the sake of brevity. Since much code cutting involves working with databases, these appear here too.

In time, I plan to correct the imbalance between programming and scripting languages that currently exists. The original list was bare so descriptions have been added and will be more and more needed should there be any expansion of what you find here.

Programming and Scripting Languages

Apache Groovy

My first encounter with an implementation of this language was with that belonging to a statistical computing environment (SCE) and that remains an ongoing dalliance. It is easy to think of Groovy as a way of work with a Java-based API using a scripting language and it certainly feels like that. Saying that, it all works better if you know Java though you do have to watch for the development of domain-specific language capability. That last comment probably applies to the aforementioned SCE in that it has its own object and method hierarchy that means that not all standard Groovy functionality is available.


Computing languages often get strange names like single letters or small words like this one; that means that you need to look for “golang” in any online search. In any case, Go was originated at Google and numbered among its inventors was one of the creators of the C programming language. The intent here is massively multi-threaded system programming using stand-alone executable components while retaining or enhancing code readability. Another facet is the ability to function efficiently in distributed computing environments like those at Soundcloud or Uber. A variety of different tools have been written using the language and these include the ever pervasive Docker and Kubernetes.


It remains an odd decision to give a computing language a girl’s name but the purpose is a serious one. Often, there is a trade-off between speed of code writing and speed of execution with the result being that data programming involves prototyping in one language and porting to another for production usage. The first group includes R and Python while the second includes C, C++, FORTRAN and even Java so there is an element of translation involved that often means that different people are involved, which adds an element of error caused by misunderstandings. This gets characterised as the two language problem and Julia’s major raison d’être is the avoidance of that: its top line description is that it is as quick to program as Python but runs as fast as C because of its just-in-time compilation, multiple dispatch and in-built multithreading. This also allows for extensive capabilities for scientific computing that go beyond machine learning and an example comes in the number of differential equation solvers that are available. It also helps that meta-programming makes everything more generalisable.


It has been around since the 1980’s and still pervades though it is not as dominant as it once was for creating dynamic websites or system administration. PHP has taken on much of the former while Python is making inroads into the latter. Still, no list would be complete with complete without a mention of the once ubiquitous scripting language and it once powered my online photo gallery. It may be an easier language but there is plenty of documentation on the web with Perldoc, Perl Maven and Perlmeister being some good places to look and Dan Massey has some interesting articles on his site too. Not only that but it is extensible too with plenty of extra modules to be found on CPAN.


This usurper has taken the place of Perl for powering many of the world’s websites. That the language is less verbose probably helps its case and many if not most CMS packages make use of its versatility.


It may be Google’s preferred scripting language for system administration but it is its usefulness for Data Science where it really has shone in the eyes of many. There are numerous packages for data wrangling, data visualisation and machine learning that make the language ever present in any Data Scientist’s toolbox and looking in the PyPi archive will allow you to find what you need. It also has its place in web scripting too even it is not a pervasive as PHP though CMS’s like Plone run on Python and there is the Django framework together with the Gunicorn web server.


One of the acts of Jonathon Schwartz while he was head at Sun Microsystems was to make Java open source after more than a decade of its being largely proprietary and this is the website for the project. Of course, his more notable act at Sun was to sell the company to Oracle but that’s another story altogether…


This is an open source implementation of the S language that is much appreciated by statisticians and is much used in the teaching of the subject. The base language only has so much functionality but there are many packages available that do just that and there are many to find on repositories like the CRAN and others can be found on various GitHub repositories though these tend to be more experimental in nature. There are commonly used and well supported mainstays that everyone uses but there always is a need to verify that a particular package does what it claims to do. Given that, there are possibilities for data wrangling, data tabulation, data visualisation and data science. While quick to code, R is slow to execute compared with others and I have found that Python is faster but it still has a use for smaller data sets; both keep their temporary data sets in system memory so that will help.


It came as a surprise that this Mozilla-originated language is gaining traction in scientific data analysis, possibly because it is a fast multi-threaded counterpart to C and C++ with some added safety features (though these can be turned off if needed and extra care gets taken). The downsizing of Mozilla led to a sharp reduction in its team of Rust developers and the Rust Foundation has been set up to oversee the language instead. There are online books like The Rust Programming Language and the Rust Cookbook, with the first of these also having paper and eBook counterparts from No Starch Press. For those interested in a more interactive introduction, there also is the Tour of Rust.



This essentially is a fork of MySQL (see below) now that Oracle owns it. The originators of MySQL are the creators of MariaDB so their claims of it being a drop-in replacement for it may be have some traction. So far, I have seen no exodus from MySQL though.


After being in the hands of a number of owners until it incongruously came into the custodianship of Oracle (who of course already had and still have one of their own), the database system that powers so many dynamic websites almost is a de facto standard and looks set to remain thus for now.


This may a document-based and not a relationship database like many of us understand them but it still is being touted as an alternative to the more mainstream competition. Database technology isn’t just about SQL and MongoDB champions a NoSQL approach; it sounds as if the emergence of XML might be what’s facilitating the NoSQL database technologies.


This project may have more open source credibility than MySQL but it seems to remain in its shadow. It so happens that this is what Debian installs if you specify the web server option at operating system installation time.