One-liners

I stupidly created some directories with a colon in the filename, which confuses some programs. I wanted to change these colons to underscores. Shell scripting makes my head hurt, so I turned to do it in Ruby instead…


Not a bad one-liner. For all I complain about dynamic languages, they can sure be handy.

What would this look like in Scala (which also has a handy REPL)? Almost a one-liner, if you don’t count the import:

Scala will never be a popular scripting tool, obviously, but it’s cool that you can achieve a Ruby-like level of compactness with it.

Lumen: A port of Blacklight to Scala and the Play framework

I’ve done some work the past two years using Blacklight, a great discovery interface for Solr with a lot of library catalog features. It’s quality software with years of work invested in it by some very smart people.

Over time, “the Ruby way” of doing things, as well as “the Rails way,” has bugged me more and more. Things like the use of naming conventions for hooks, passing arrays and hashes around in lieu of actual data structures, the varying use of hashes and OpenStructs, the ability to monkey patch, the difficulty of looking at a method and not being able to tell what its arguments are or can be, the need to go digging around in the source code of a gem to figure out how certain APIs are dynamically created because those methods can’t get automatically documented by tools like rubydoc or yard. These things often make life easier when you’re writing new code and trying to do it quickly, but they create nightmares when you try to refactor stuff or upgrade gem dependencies.

The last few months, I’ve been slowly porting Blacklight to Scala and the Play framework. I’m calling this new project Lumen.

Scala is a powerful statically typed, compiled language that permits you to mix object-oriented and functional paradigms, and it allows you to take advantage of the enormous ecosystem of existing Java libraries. It also incorporates some of the innovations of the last two decades of dynamic languages that make programmers happy. I think it’s a language that privileges software quality over rapid development.

So far, Lumen has been a hobby project to learn Scala, so I’ve approached it in a less disciplined fashion than I otherwise might. This means there are lots of TODOs scattered throughout, and style/design inconsistencies as I’ve learned better ways to do things but haven’t always gone back to change things everywhere. That’s life when you’re noodling around in your spare time. Lumen is largely an experiment right now, but I hope it will eventually grow into a full-featured, production-quality piece of software. We’ll see.

You can check out a demo here: http://lumen-demo.codefork.com.

Announcing conciliator

I’ve just created a github repository for conciliator, a growing collection of OpenRefine reconciliation services, as well as a Java framework for creating them.

conciliator is a major refactoring of my refine_viaf project and supercedes it. This new project cleanly separates the VIAF-specific parts and the more “boilerplate” pieces needed for any OpenRefine reconciliation service. The result is a framework that allows you to easily write new reconciliation services. My intent here is to make some existing code way more flexible, so that it might be useful to more users and have a longer lifespan.

http://refine.codefork.com has already been running conciliator for a week now; if you’ve been using it, you don’t need to make any changes in OpenRefine.

Currently, conciliator out-of-the-box can query VIAF exactly like refine_viaf does, down to the same URLs. Additionally, conciliator can now query ORCID names. This was a somewhat arbitrary choice; I’ve been doing some ORCID integration at work so it was convenient for me to implement a data source for it as a proof of concept.

With VIAF and ORCID, conciliator acts as an intermediate or “bridge” service, but it would be possible to use conciliator to query other types of data sources as well: files, SQL databases, etc. Right now, you’d have to write your own code to read and parse files, open database connections, etc. But in the future, I hope to add support for these options to make them easier to implement.

For details on how to write your own service in Java using conciliator, see the README.

Are there data sources you’d like to see available as a reconciliation service? Leave a comment to this post. No promises, but I’ll at least consider all requests. And if you write your own service for a data source, please consider submitting your code as a pull request so that others can use it too!

The Myth of Artisanal Programming

Paul Chiusano, the author of the excellent Functional Programming in Scala from Manning (one of the few tech publishers I buy from; worth every penny), recently wrote a blog post titled, “The advantages of static typing, simply stated”.

Lately all I seem to do is rant to people about this exact topic. Paul’s post is way more succinct than anything I can write, so go over there and read it.

While he takes pains to give a balanced treatment of static vs dynamic type systems, it seems much more cut and dry to me. Dynamic languages are easier and faster for development when you’re getting started on a project, and it’s great if that project never gets very big. But they scale very poorly, for all the reasons he describes. Recently, I had the daunting task of reading almost ~10k lines of Perl code (pretty good Perl, in my opinion). It was hard to make sense of and figure out how to modify and extend, whereas the MUCH larger Java codebase (over 100k lines, if I recall) that I worked with years ago felt very manageable.

My own history as a programmer matches Paul’s very closely. I started with Java, which was annoying but not a bad language by any means. Then Python came along and seemed like a liberation from Java’s rigidity and verbosity. But Python, Ruby and others are showing their weaknesses, and it’s no mystery why people are turning to the newer generation of statically typed languages like Scala, Haskell, Go, etc.

People who haven’t been around as long don’t necessarily have this perspective.

In retrospect, it’s interesting to me how we programmers “got sold” on dynamic languages, from a cultural perspective. You might recall that a big selling point was using simple text editors rather than IDEs, and there was this sense that writing code this way made you closer to the software somehow. Java was corporate, while Python was hand-crafted. There was a vague implicit notion of “artisanal” programming in these circles.

The upshot, of course, is that every time you read a chunk of code or call a function or method, your brain has to do a lot of the work that a statically typed language would be able to enforce and verify for you. But in a dynamic language, you won’t know what happens until the code runs. In large measure, the quality of software hinges on how much you can tell, a priori, about code before it runs at all. In a dynamic world, anything can happen, and often does.

This is a nightmare, pure and simple. Much of the strong focus on writing automated tests is to basically make up for the lack of static typing.

True artisanship lies in design: namely, thinking hard about the data structures and code organization you’re committing to. It’s not about being able to take liberties that can result in things that make no sense to the machine and that can cause errors at runtime that could have been caught beforehand.

Data Streams in Ruby

Recently I wrote up some notes on how to do data processing using streams (lazy enumerators) in Ruby. Doing so served two purposes: 1) to help clarify my own thinking about better ways to write code for common data-munging tasks, 2) to pass along to co-workers in the hopes of establishing some informal best practices and initiating some conversations.

I decided to post my notes on github. Take a peek if this sort of thing interests you.