Helper code to mimic Clojure fns in Scala

I’ve finished my 3.5 year stint writing Scala, and I haven’t stopped missing writing Clojure. The knowledge of Clojure continues to heighten and inform my programmer sensibilities. One thing that I appreciated about Scala is that it was as good of a medium as you might practically find to allow writing Clojure without writing Clojure. I liked to think of Scala as the canvas on which I painted my Clojure ideas. Because Scala makes itself amenable to many styles of programming at once (at least, FP and OOP), it was possible to write code by imagining what the Clojure code would look like, and then writing that in Scala syntax. Interestingly, the more I did this, and the more faithfully I did so, the more people implicitly (no pun intended!) acknowledged the code as “good Scala code”. Because, you know, most Scala programmers agree that good Scala code puts “val”s at the top of a function body, uses immutable collections exclusively, prefers functions over (object) methods, and makes functions small, stateless, and composable. More on that later. Here, I want to simply release some of the code that I wrote in Scala to fill in a few perceived gaps in Scala’s Seq abstraction, where the perception is based on what I was accustomed to using in Clojure.
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Tips for Writing SQL Queries

In my previous job, I had a basic grasp of writing a SQL query, but I was never quite comfortable with “advanced” queries. (By “advanced”, it’s more like intermediate at best — it’s the nuances of joins, group-bys, having vs. where.) I was told that whatever SQL I didn’t know would be “easy” to pick up and would happen naturally, although in practice that never quite happened. It wasn’t until I started to come up with a system for solving interview-style programming problems that I started to similarly come up with a system for writing any SQL query. The following is the result, which is less of a “tutorial” for “beginner SQL” and more of a systematic process for constructing a SQL query:
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Updates from the last 20 months

The last 18 months have been eventful even if my updates have been sparse. Here’s a quick rundown of some of the things that I’ve been up to:

Happy New Year, and hoping that 2018 is a good year!

Tamil Internet Conference 2017 – Prefix Trees for Language Processing – slides and paper

The Tamil Internet Conference for 2017 in Toronto, Ontario, Canada just concluded. I presented a more in-depth explanation of my previous post on prefix trees along with specific examples of how I have used them.

Here is the full paper that I submitted for the conference proceedings, entitled “Prefix Trees (Tries) for Tamil Language Processing”. Here is the slide deck for the presentation I gave in the conference.

The following is the full text of the paper from the link above:
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Using Clojure to Create Multi-Threaded Spark Jobs

I recently was tasked with performing an ETL task that should be done as efficiently and quickly as possible. The work led me to learn more about parallel and distributed processing in Clojure. In addition to having a greater appreciation for what Clojure enables (once again), I also pushed the boundaries of what I thought is possible using the available tools. I ultimately ended up writing a Spark job whose executors are each running N threads (currently, N=3). But the path to that solution taught as much by what didn’t work as much as what did work.
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Compiling a Leiningen Project from Maven

For those of you with experience with Maven, you might be wondering why anyone who is using Leiningen to build a project would then want to run that build tool from Maven, which is itself another build tool. There is a reason why I even ventured down this path. I would like to share what I have found so far, in case it benefits anyone else, but I would also like to get feedback from people who know of a better way of accomplishing the same goals.
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Using Prefix Trees for Thamil Language Processing

Thamil computing has made a lot of progress in the past 10-20 years. Much of the work that has reached the public has been in the areas of fonts/rendering and input methods. Thanks to the continuing efforts in these areas, most of those issues have been solved, Thamil text has standardized on a single character set (Unicode), and we have nice fonts and input methods for major operating systems and mobile devices. The new environment has enabled the widespread creation and consumption of digital content in Thamil.

Now, the next set of problems to solve are handling Thamil text that is written using the Unicode character set. Unicode is designed for all languages’ fonts to standardize, but the slight cost to Thamil language processing has been its complexity. But the challenges can be handled easily by representing the data in a suitable data structure, which in this case is a prefix tree (or “trie”).

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