Author: jeff

On the way to conciliator 3.0

When I started writing conciliator, I focused solely on implementing the main URL endpoint for reconciliation, since that’s what I needed at the time. There are actually several APIs that work together in OpenRefine: the Preview, Suggest, and recently added Data Extension APIs provide functionality that complements Reconciliation. And there may be more in the future as OpenRefine continues to evolve.

My code didn’t extend very easily, so I’ve rewritten a ton of stuff. This is currently in the master branch and it’s running on http://refine.codefork.com. I will release 3.0 probably sometime in January.

Most users don’t need to do anything. Out of the box, it should work as the previous version did. If you modified the conciliator.properties file, you should look at the changes in that file.

Here are the changes under the hood so far:

– Spring controllers and components are now used more effectively for better separation of concerns, less manual plumbing, better extensibility and maintainability.
– The classes representing data in/out for the various APIs are more fully fleshed out.
– The conciliator.properties file allows for less configurability than before, but I don’t know how useful that ever was, really.
– Tests have been rewritten for more “real world” coverage.
– Custom cache code has been replaced with Ehcache.
– Requires Java 8.

With this scaffolding in place, I can start actually implementing Data Extension for specific data sources. I plan to start with OpenLibrary as it provides the richest data.

Stay tuned.

Thanks to everyone who contributed bug reports and suggestions! I’m amazed and gratified that this software gets used as much as it does.

What’s Worse?

What’s worse: a website that is intermittently down or completely down?

The latter is worse, right? Isn’t it better that a site serve, say, 80% of requests, than 0%? This is the cloud-think we’ve all become accustomed to.

Here’s the thing: when a web application or service is intermittently down, it can hide the fact that there are any problems at all. It’s easy to dismiss problems as due to factors beyond your control, or momentary blips that will clear up on their own. In the meantime, what happens is that a user going through a sequence of, say, 6 requests to complete a workflow, will experience failure on that 6th request serviced by the one bad host or container in the cloud. And they will get frustrated and give up. And they’ll start to associate your application with being flakey and unreliable.

And you won’t notice, because it’s not happening to everyone, and the problem persists for a long while before it’s detected and fixed.

This is how the “high availability” mentality of the cloud lures you into a false sense of security.

I’ve been seeing this happen with Docker Swarm, where, under certain conditions, some newly started containers will have intermittent connectivity problems with other containers. Unless you’re paying close attention to error logs, you may not notice any problems, even though some users are definitely experiencing them.

But when a site is completely down, everyone knows, and you can’t help but address the problem.

Okay, sure, the answer of which is worse depends a lot on the type of website or web application. My point is simply that there’s often the presumption that putting things in the cloud alleviates the pressure upon individual instances of an application or service to be up and functioning correctly. This just isn’t true. And at the point where you need to care about and closely monitor individual containers because you take availability seriously, well, at that point, the cloud maybe hasn’t bought you as much as you thought it would.

True Empowerment

I fixed a bug in the blacklight-marc gem recently. It involved this line of Ruby code:

vals << (v == 'AD') ? 'Atlas' : 'Map'

Contrary to what it looks like, this line adds a boolean value to the vals array. The << operation returns true, so the entire line of code always evaluates to ‘Atlas’. Then nothing happens with that string.

Obviously, this isn’t what was intended. The problem is that << has higher precedence than the if-else operators. So here’s the fix:

vals << (v == 'AD' ? 'Atlas' : 'Map')

This code path wasn’t being taken all the time, and it also didn’t raise any exceptions: the calling code uses the result as an array of strings, so the booleans get automatically converted to “true” and “false” strings. I just happened to notice those weird values where they didn’t make sense, and thought to dig into it.

Let’s be honest: this is the kind of mistake anyone could easily make. I’m 100% certain I’ve done something similar. In fact, I innocently asked some co-workers what the original line of code did, and of course, they interpreted it incorrectly. It’s a tricky little bug.

I thought to post about this because it’s a perfect example of how, in a loosely typed, dynamic language like Ruby, you’re really on your own.

Dynamic languages can often feel “empowering” because they place trust in the programmer. It’s your responsibility not to write code that does anything really crazy or stupid. But there are a lot of these “gotcha” cases, where you’re writing code that’s quite reasonable, and you simply made a mistake that the language lets you get away with, because it’s interpeted differently from what you intended. It’s valid code. And you won’t figure it out until much later, when it shows up as a symptom elsewhere.

By contrast, with Java or Scala, you wouldn’t be able to do this. The compiler would check the types, and meaningfully, say, “Sorry buddy, it doesn’t make sense to me to add a boolean to a List of Strings,” and you’d immediately notice the problem with operator precedence. And you’d fix it.

Your program would never even be able to run with that error in it. Which is some awfully nice work that the language is doing for you there. That feels like true empowerment to me.

Final note: you could argue that good test coverage would catch this. That’s true, but we all know the difficulties of achieving thorough test coverage under deadlines. And this example is particularly annoying to get thorough coverage for, because the line of code is one case of many different cases of values for the variable ‘v’.

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.