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.
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:
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!
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:
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.
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.
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”).
I’m excited to be selected as a speaker at the upcoming Clojure/West 2015 conference next month in Portland! I’ll be talking on how Clojure can be used to program in other human languages (other than English). There are interesting opportunities related to diversity and access. I will be drawing on my experiences with programming in/for Thamil in the clj-thamil library. And I’ll see what other interesting, related ideas I can slip in (turtles that draw?)… and put a bird on them.
Or: A clear example of what macros can do
I started working on a library called clj-thamil that I envision as a general-purpose library for Thamil language computing (ex: mobile & web input method), but a slight excursion in that work has led me to some very deep, intriguing ideas — some of which are technical, and some of which are socio-cultural. But they all fit together in my mind — Clojure, macros, opportunity and diversity (in computing), and the non English-speaking world.
I think that the implications are things that we should all think about. But if nothing else, hopefully you can read this account and understand something about macros — the kind of power they uniquely provide and at least good one use case where they are necessary.
Back around the December – January time frame, I was trying to implement the Lambda Architecture as described by Nathan Marz. At that time, the early-release version of his upcoming Big Data book was just at chapter 5 or 6, but my goal was to tackle what seemed liked the harder part — real-time (Storm). The book chapters hadn’t yet caught up to it. A few slide decks mentioned their current implementations of a fully thought-out, end-to-end Lambda Architecture implementation that included Storm, but no reliable, easy-to-deploy code was readily available from the interwebs.
In installing Storm, it quickly seemed apparent that having Kafka running upstream of it was one way to support both real-time and batch processing of incoming data, and probably the one of least resistance. So I added installing Kafka to my to-do list.
Cutting edge technology means dealing with rough edges. I downloaded the latest versions of the relevant software components, but the integration of all of them didn’t work. As I found out, the reason was that versions of components that finally worked together with each other for me were not the most recent, but instead maybe a version or two behind.
The code that I ended up with to get Kafka and Storm working together on a toy example using the Twitter Dev Stream is on github here: