Generator Expressions

I discovered Python generator expressions this week. They’ve been around since v2.4, way back in 2004, and yet, somehow, they managed to escape my notice until now. I don’t think I’ve ever seen them used in any codebase I’ve worked on.

Generator expressions are almost the same as list comprehension syntax, except instead of square brackets, you use parentheses. And instead of returning a list, the comprehension returns a generator object.

This is quite a nice bit of syntactic sugar that helps to keep things more succinct than writing iterators and generator functions. But it’s not as succinct as, say, the chained method syntax you find in Scala or Ruby, which I think is vastly superior in terms of clarity. Several years ago, I wrote up some notes about Ruby streams in a github repo for my co-workers because it was a valuable technique in some data processing scripts we were working on. (Since then, I haven’t done much Ruby.)

Here’s a bit of Python code that duplicates the simple Ruby example in the linked repo above, with the identical output. Again, it’s not nearly as nice to look at as chained method calls, but still better than having to write separate generator functions or classes.

nums = list(range(1,6))

add10 = lambda i: print(f"adding 10 to {i}") or i + 10
filter_even = lambda i: print(f"filtering {i} on evenness") or i % 2 == 0
output = lambda i: print(f"in each: {i}")

# list comprehensions

r1 = [ add10(x) for x in nums ]
r2 = [ x for x in r1 if filter_even(x) ]
r3 = [ output(x) for x in r2 ]

# generator expressions

r1 = ( add10(x) for x in nums )
r2 = ( x for x in r1 if filter_even(x) )
r3 = ( output(x) for x in r2 )

Data as Coping Mechanism

Life under this pandemic has been hard. Oddly, one of the things that’s helped me deal is to play around with the coronavirus data. The numbers in the U.S. are horrifying, of course, but they’ve also been soothing at a technical level, maybe because working with the data is somewhat different from the work I do for my job. It’s also been neat to do hands-on validation of reporting in the media and various claims made about trends. I’ve been showing my results to friends who seem to find them insightful.

The repository is here. There are links in the README to the charts and visualizations.

Some technical reflections on this little hobby project:

I used Python and SQLite. Apache Spark seemed like overkill and I haven’t spent enough time with Spark to be able to troubleshoot intermediate pipeline steps as easily as I can in a plain SQL database. SQLite is fantastic for doing ETL or ELT. I can’t recommend it enough for non-“big data” scenarios. It’s fast (if you use local disk access), has enough SQL features for most ELT/ETL work, and is well-suited for use by a single user. It’s also good if the end goal is to produce data that will ultimately get imported into another system, say, a full-fledged RDBMS data warehouse that serves multiple users.

Currently, with just over 5 months of county- and state-level data, it takes ~2 minutes to to load all the raw data, transform it into dimensional tables that calculate various measures, and create data files used by the web pages that display tables and charts. The SQLite file is 850 MB which includes a lot of stage tables. This is on my laptop with a i5-7300U processor. Not too bad.

I created a Makefile to handle dependencies in the data pipeline, so that it only re-runs parts as needed. It’s currently not as fine-grained as it could be. For example, any change in the JHU CSSE data files will reload ALL the files into the database, but that portion of the code takes only maybe 10s total anyway. Similarly, all the dimensional models are created in a single process and could be split out. I’m happy with how it turned out overall with the qualification that writing and maintaining the Makefile is a bit of a pain. I might try using Apache Airflow instead at some point.

Storing data files in a git repo feels gross. But I did this so the chart and map web pages served through GitHub Pages could load the static data files. It’s a simple and free hosting solution.

In general, I like how simple this setup turned out to be and how easy it’s been to add new measures or tweak existing ones.

EDIT: In November 2020, I switched to using BigQuery.

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.

A Major Update to refine_viaf

I’ve rewritten my refine_viaf project in Java. It’s what is now running. The old python code is considered deprecated and will no longer be maintained, but will remain available in the python-deprecated branch on github.

The only thing most users need to know is that refine_viaf should return better results now. For the curious, this post explains the subtle but important differences in the new version and some reasons for the rewrite.


In a nutshell, the main difference/improvement is that searches now behave more like the VIAF website.

This is due mainly to how sources (i.e. “LC” for Library of Congress) are handled. Previously, either the source specified on the URL or the “preferred source” from the config file was used to filter out search results, but it did NOT get passed into the actual VIAF search query. This could give you some weird results. The new version works like VIAF’s website: if you don’t specify a source, everything gets searched; if you do specify one, it DOES get passed to the VIAF search query. Simple.

The old version had weird rules for which name in each VIAF “cluster” result it actually displayed. In the new version, if you don’t specify a source, the most popular name (ie. the name used by the most sources) for a search result is used for display. If you specify a source, then its name is always used.

The old version supported a comma-separated list of sources at the end of the URL path. In the new version, only a single source is supported, since that’s what VIAF’s API accepts.

Lastly, the licenses are different: the python version was distributed under a BSD license. The new version is GNU GPL.

Other reasons for the rewrite

The changes above could have been implemented in python. I decided to rewrite it in Java for a few reasons:

– Overall performance is better in Java. The Django app used BeautifulSoup because VIAF’s XML used to be janky, but it appears this is no longer the case; Java’s SAX parser works great with their XML these days and is very fast. BeautifulSoup would leak memory and consume a lot of CPU, to the point where it would trigger automated warnings from my VPS provider. My server is very modest and needs to run other things, so these were real problems. Running the service as a single multi-threaded Java process keeps memory usage low and predictable, and it never spikes the CPU.

– Running a Java jar file is MUCH easier for people who want to run their own service, especially on Windows. With the python version, you had to install pip, install a bunch of packages, and create and configure a Django app, all of which put the software out of reach of many users who might want to run it.

– I don’t care what other people think: I like Java. Plus I wanted to experiment with Spring Boot. There are much leaner web frameworks I could have used to save some memory, but it was interesting to play with Spring.

Leave a comment!

If you use this thing, please take a second and leave a comment on this post. I’m interested to know how many people really run this on their own computers.


Managing Dependencies in Python vs Ruby

Ruby's Bundler tool is amazing.
Ruby’s Bundler tool is amazing.

With Python projects, the standard way of doing things is to set up a virtualenv and use pip to install packages from PyPI specified in a requirements.txt file. This way, each of your project’s dependencies are kept separate, installed in their own directories in isolated sandboxed environments.

This works pretty well. But sometimes, when I am debugging a third party package, I want to be able to get the source code from git and use it instead of the package from PyPI, so I can make changes, troubleshoot, experiment, etc. This is a pain in the butt. You have to remove the installed package and either 1) install manually (and repeatedly, as you work) from your cloned repository, or 2) add the repository directory to your Python library path somehow. Then you have to undo these changes to go back to using the PyPI package. Either way, it’s clunky and annoying.

Ruby’s bundler tool has a very different approach to dependencies. It, too, downloads appropriate versions of gems (which is what packages are called), which are listed in a Gemfile. But unlike pip, it can store multiple versions of a gem, and even let you specify that a gem lives in a github or local repository; moreover, it makes the right packages available each time you run your program! That is, each time you run the “bundle exec” wrapper to run Rails or anything else, it sets up a custom set of directories for Ruby’s library path that point ONLY to the versions you want, ignoring the others.

I did this today when trying to pin down the source of some deprecation warnings I was seeing after some gem upgrades. My Gemfile had these lines in it:

gem 'sunspot_rails', '~> 2.1.0'
gem 'sunspot_solr', '~> 2.1.0'

I cloned the sunspot repository containing those gems. Then I ran:

# bundle config local.sunspot_rails ~/sunspot
# bundle config local.sunspot_solr ~/sunspot

And changed the Gemfile lines:

gem 'sunspot_rails', :github => 'sunspot/sunspot_rails', :branch => 'master'
gem 'sunspot_solr', :github => 'sunspot/sunspot_solr', :branch => 'master'

Finally, I ran “bundler update”. That’s it! I could make changes to my cloned repository, restart Rails, and see the changes immediately.

When I was done messing around, I changed my Gemfile back, ran “bundler update” again, and I was back to using my original gems.

Being able to work so easily with third party code allowed me to quickly figure out where the deprecated calls were being made and file an issue with the sunspot project.