New post on How my Data Toolset evolved in 2012: scale, speed, unlocking new data types in 2013, & props to @ben_fry http://t.co/vkpWEdul

— Ben Lorica 罗瑞卡 (@bigdata) January 1, 2013

I’m slowly following in Ben Lorica’s big data footsteps. On his recommendation I’ve decided to jump into Scala, starting with Odersky et. al.’s Programming in Scala. Like what I’m seeing so far. Functional programming, with a somewhat Pythonic sensibility, and performant exploitation of the JVM.

Before I generate a set of resolutions for 2103, I thought it would be worthwhile to check in on the results of last year’s goals:

• Improve Physical Health. Wash. Lost some weight, pretty close to target, but didn’t really incorporate exercise. Improved the diet, but failed to play any ultimate.
• Maintain Financial Health. Achieved. Hit all targets.
• Shine The Skillz. Wash. Did not get into any real programmer events, but work offered a lot more hacking opportunity than I anticipated.
• Expand The Network. Fail. I joined the MIT DC Alumni Club, but haven’t had a chance to attend any events. Missed on everything else.
• Get Rid of Stuff. Complete Fail.
• Build A Tribe. I’ll call it a fail, although the jury’s still out, pending my performance reviews at work. Even without that feedback, I’m still disappointed I haven’t been able to energize a really high performing group of people, with a good sense of camaraderie.
• Less Watch, More Do. Achieved. Took a sabbatical from college football to good effect. Also severely cut down on the channel surfing.

A couple of wins, a couple of washes, and three fails, so an overall “enh” from me. These were intended to be stretch goals so I can’t complain too much.

Given a few other things that happened in my life in 2012, I’ll happily bid goodbye to the annum, but hold back on the good riddance.

Interesting. Despite being a pretty consistent poster over the last 15 months or so, I’ve never had a 40 post month. New goal for 2013.

Here’s an interesting hack I don’t really don’t have time to execute on. Take a BoingBoing data dump and dissect either references to Boing Boing pages or outlinks from the archive using the Common Crawl dataset. Might be some interesting intersections, or you could trace out the patterns of BoingBoing influence.

Tangential side project, build up a set of mrjob modules to work with either dataset. The BoingBoing stuff comes in one big file so it might be useful for someone else to bust it up into some smaller units and stuff onto Amazon S3, or otherwise make publicly available.

DEFEAT!! After a great run at the end of the season, I got hit with one of those vexing fantasy defeats to flush me out of title contention. Appropriate on this final weekend of the NFL regular season that I document my collapse.

On the one hand, I got beat by a sizeable margin, 122.7 to 85.30. So it would appear my roster choices really wouldn’t have made a difference. On the other hand, consider this:

• I listened to the fantasy “experts” and at the last minute substituted the red hot Danario Alexander instead of my usual Eric Decker. Alexander goose egg, Decker 23 fantasy points, me as GM -23.
• On the same expert advice I played the St. Louis Rams DEF for a grand total of 1 point. I could have easily played one or two other units for 6-9 more points
• I stuck with Lawrence Tynes as my kicker, admittedly with no encouragement, for the only game where the Giants got shut out. That means Tynes got 0 points too.
• Arian Foster came up short of his projection by 8 points.
• I pooh poohed a roster move by my opponent benching Torrey Smith and playing Brandon Lloyd. +23 for him.
• The opposition survived a 0.1 performance from his QB position.

So as I float into the fantasy off-season, I’ll console myself with the thought that my optimal lineup wouldn’t have won. But three self-inflicted zeros leaves a quite bitter taste. If I had added 30 points who knows what could have happened.

After months of sometimes painful experimentation here’s a technique others may find useful. This is for those who need to ingest millions of JSON objects, collected from the wild, stored as line ordered text files, into a PostgreSQL table.

1. Transform your JSON lines into base64 hex encoded lines
2. Start a transaction
3. Create a temp table that deletes on commit
4. COPY from your hex encoded file into the temp table
5. Run an INSERT into your destination table that draws from a SELECT that base64 decodes the JSON column
6. COMMIT, dropping the the temp table
7. Profit!!
8. For bonus points, use the temp table to delete duplicate records or other cleanup

The benefit of this approach is that it saves you from dealing with escaping and encoding horrors. Basically, JSON can contain any character so it’s almost impossible to safely outsmart stupid users and PgSQL strictness. Once you’re over a million records, you’ll get bit by one or the other.

Reliably applied to over 300 million Tweets from the Twitter streaming API.

Update. If you’re doing this with Python make sure to use psycopg2 since you can control the transaction isolation and it supports COPY.

Hilary Mason has some advice:

I get quite a few e-mail messages from very smart people who are looking to get started in data science. Here’s what I usually tell them:

The best way to get started in data science is to DO data science!

First, data scientists do three fundamentally different things: math, code (and engineer systems), and communicate. Figure out which one of these you’re weakest at, and do a project that enhances your capabilities. Then figure out which one of these you’re best at, and pick a project which shows off your abilities.

Good input, although I’m still somewhat partial to Sean Taylor’s “Ask, Answer, Tell” for what data scientists do. I think coding is important, but while engineering systems can be scientific, the practice is much more pragmatic and provides much different challenges than scientific exploration. Probably a minor quibble but engineering is quite different from science.

Still, getting your hands dirty is better than noodling in the abstract on the sidelines.

If I’m going to get serious about learning Scala, I’m going to need a serious development environment. My default would be to figure something out with Emacs, but it’s time to check out the modern kit.

Jeethu Rao documented getting IntelliJ IDEA set up for Scala:

The class used the Typesafe Scala IDE, which is built on Eclipse. I’ve been using JetBrains PyCharm for most of my python work and when they had IntelliJ 12 Ultimate Edition on sale at a 75% discount last week, I bought it. Last night, I spent a bit of time setting up IntelliJ to work with Scala on my Retina MBP. I’m posting this as a future reference.

Hope IDEA is a bit lighter weight than Eclipse, whose enormity has always confounded me.

DBpedia has always felt like a great resource to me, but I’ve never taken the time to play with the data, being somewhat daunted by RDF and triplestores.

Jean-Louis Fuchs has done yeoman’s work in creating a repository of DBpedia infoboxes in SQLite and releasing his import script. May have to grab and experiment with.

When I learned about DBpedia, I wanted to have it installed locally, I read the tutorials on sparql and how to install DBpedia: that has to be simpler. I kind of worship Simple and I didn’t want to learn yet another query language. So I hacked an SQLite import script for DBpedia types and infobox-properties in one evening. The script takes about 50 hours to read instance_types_en.nt and infobox_properties_en.nt. It creates a table per type and a table per property, after tables are created everything gets an index, the whole database is analyzed and finally vacuumed.

An aside thought, wonder if there’s a straightforward way to take DBpedia data and stuff it into a full text indexing system like ElasticSearch? You’d give up the reasoning properties of a SPARQL based triplestore, but would have quick and dirty access to a significant semi-structured knowledge repository.

I’ve been putting mrjob to the task quite a bit recently. Two quick thoughts on a highly useful package.

First, Map/Reduce is a fairly handy data processing framework even if you don’t need a ton of scalability. Any complex UNIX text file processing involving transforms, filters, and aggregations might be more easily expressed as a Map/Reduce computation. mrjob makes that quite simple to do on a single machine at a high level.

Second, mrjob supports the bundling and uploading of custom code and data to support the job. I’ve mainly been using it to add Python extension modules, but there’s no reason one couldn’t include supplemental data in a binary form, like a Python pickle, or an SQLite DB. Very handy for map side augmentation or filtering presuming the data set isn’t too large.

Yeah, I knew he was a top-N villain from It’s A Wonderful Life but jeez, Potter knew Uncle Billy lost that money, knew George Bailey was covering for him, and still had to kick poor George while he was down. Jerk!

Greatest Christmas movie ever. If you haven’t had a shitty day like George Bailey, you’re not human. And hopefully you made it over the hump.

Cheers!!

Be it resolved for 2013, learn and apply D3.js, the recent 3.0 release taking the JavaScript visualization library to 11:

In addition to all of the above, D3 includes a handful of bug fixes, performance improvements, and minor new features. A new d3.shuffle method provides the Fisher–Yates shuffle. The d3.format class now supports align and fill, and both d3.format and d3.time.format support POSIX localization at build time. The d3.layout.treemap now supports the “slice-and-dice” algorithm. Lastly, all of the D3 examples have been moved to bl.ocks.org and GitHub Gist for easier browsing & forking.

As the year winds down I’ve been looking at some of my older MPR content. One noticeable difference is the amount of original content. Definitely has decreased over time. There’s been a discernible increase in embeds and pull quotes, probably correlating to increased posting from iOS devices.

Something to think about going into next year. On the one hand quoting and linking makes it easier to post consistently. On the other, I’d really rather not publish a link blog although I could be accused of doing that already.

Looking for a good time series data storage and analysis service. Maybe it’s bamboo: OEmbed Link rot on URL: https://twitter.com/pypi/status/282975117741527040

bamboo is an application that systematizes realtime data analysis. bamboo provides an interface for merging, aggregating and adding algebraic calculations to dynamic datasets. Clients can interact with bamboo through a a REST web interface and through Python.

bamboo supports a simple querying language to build calculations (e.g. student teacher ratio) and aggregations (e.g. average number of students per district) from datasets. These are updated as new data is received.

Need to check out UltraJSON at work:

UltraJSON is an ultra fast JSON encoder and decoder written in pure C with bindings for Python 2.5+ and 3.

Mark Farina: Urban Coyotes Tour

PRE-ORDER Your copy of Mark Farina's latest CD featuring 15 tracks all from the… http://t.co/qBgZdI0M

— Mark Farina (@djmarkfarina) December 20, 2012

URBAN COYOTES - Mixed By Mark Farina - Digi-Pak CD

PRE-ORDER Your copy of Mark Farina’s latest CD featuring 15 tracks all from the Coyote Cuts imprint mixed by the man himself with all the fixins. Released as a 4 panel digi-pak with art by Aaron Miller and Josh Adams. CD’s will begin to ship around the last week of December.

Done!

Wow! I didn’t realize how easy it was to run a Spark/Mesos cluster on Amazon EC2:

The spark-ec2 script, located in Spark’s ec2 directory, allows you to launch, manage and shut down Spark clusters on Amazon EC2. It automatically sets up Mesos, Spark and HDFS on the cluster for you. This guide describes how to use spark-ec2 to launch clusters, how to run jobs on them, and how to shut them down. It assumes you’ve already signed up for an EC2 account on the Amazon Web Services site.

Might be fun to just spin one up and run against some Common Crawl data.

Jean-Louis Fuchs has collected some essential IPython videos:

These are videos you have to see, if you don’t know ipython yet! I’ve worked as a programmer for more than 15 years and I thought I’ve seen it by now, but ipython got me as excited as I was when I dialed into a BBS first time… it bursts your horizon.

Parkin’ this in case it helps someone else, including me in the future. Oddly difficult to find documentation on the feature, the closest being an aside on distutils config files.

When building a Python package that has a C extension you can build the extension separately, using the build_ext command. This allows you to control the include directories, link directories, and link libraries. Handy for those ornery libraries that you just can’t install in /usr/lib or thereabouts. I recently needed it for geos and Shapely.

I don’t know if the games are any good, but I’ll give Michael Cook a +1 for a moby hack. Growing games is sort of cool.

But +11 for a tail-recursive acronym:

On Friday, however, Cook released a game called A Puzzling Present which shows off the much of the hard work Cook’s put into Angelina (a tail-recursive acronym for “A Novel Game-Evolving Labrat I’ve Named ANGELINA”). Instead of a simple platformer with maps and obstacles, the latest games by Angelina now include new mechanics for the player (in this particular seasonal game, the character is Santa Claus) at each level.

The call for papers for ICWSM, the International Conference on Weblogs and Social Media, 2013 is out. I’ve been a reviewer and attendee in the past and may actually be able to submit something this year. Yeah!!

But, according to Danah Boyd there might have been a bit of a botch:

In the end, I found that there was such extensive miscommunication that the gulf between what I had stated upfront as being key to me running the conference was miles away from what AAAI would find acceptable. Both of us felt as though we were contorting ourselves to make this work and it left both of us very bitter and unable to work with one another. I decided that I could not run a conference that wasn’t accessible to many of the core parts of my research community just to please other parts of the research community. I felt trapped and realized that it would be better to walk away than to let go of my principles.

Boooo!!

Victory!! Your’s truly ends the season on a five game winning streak and takes the regular season title on point differential. Onwards to the Fantasy Playoffs!!

The matchup was unnecessarily tense in that I started RGIII (17.47 points) over Cam Newton (39.17 points). Given the weather, rainy, and opponent, Ravens, I was seriously leaning towards benching Griffin. Instead I went with the “don’t get cute” mantra. Similarly, I went with an oversold Greg Jennings instead of reliable Randall Cobb.

Thankfully, I made a great waiver wire pickup in Knowshon Moreno for 24 points. And no one else underperformed. Thankfully Arian Foster collected just enough garbage time points to get me over the hump. A five point squeaker that had me sweating into the second half of Monday Night Football.

Feeling good about my playoff chances although my opponent has some scary explosive players in Josh Freeman (hot, good matchup), Torrey Smith (overdue), Calvin Johnson (he’s Megatron), and Adrian Peterson (’nuff said). I’m guessing I’ll need someone to go way over projection to pull this one out.

Criminy, I know it’s the Verizon Center, but there’s no need to jam the AT&T cells!!

Or at least that’s what it feels like whenever I come to a Wizards game. All I want is to check some stats, lookup random trivia, and pen the occasional blog post. Is that too much to ask? #firstworldproblems

IPython dev is funded, starting in January. We won’t have to relegate contributions to nights and weekends. 2013 will be an exciting year.

— IPython Developers (@IPythonDev) December 14, 2012

Cool! Hoping the IPython notebook get’s even more useful and interesting.

I like Python. I like PostgreSQL. I like PyCon. I should like PyPgDay.

In coordination with the Postgres community and PyCon US 2013, PyPgDay is occurring in Santa Clara on March 13 from 9am-6pm.

PyPgDay will be a full-day event with seven talks about PostgreSQL and Python, including talks by contributors to PostgreSQL, Django, PostGIS, and Python. Half the talks will help PostgreSQL DBAs, and the other half will focus on developing Python applications using Postgres features.

Link parkin’: Crawley Project

Crawley is Pythonic Crawling / Scraping framework intented to change the way you think about extracting data from the internet

It’s never actually materialized, but I’m still hoping that user grade, commodity cost, focused crawlers become a reality. Seems like the tech has caught up with the concept. Maybe something like Crawley plus Python’s copious machine learning and data mining toolkits could provide the foundation.

I believe not actually telling folks you’re exploiting focused crawling is one of the key tricks.

I’m not in the “everybody should code” cargo cult, but a bit of knowledgeable advocacy for one’s discipline is always welcome. Looks like Douglas Rushkoff is getting a chance to do some stumping for Computer Science education:

This week is Computer Science Education Week, which is being observed around the United States with events aimed at highlighting the promise — and paucity — of digital education. The climax of the festivities, for me anyway, will be the opportunity to address members of Congress and their staffers on Wednesday in Washington about the value of digital literacy. I’ve been an advocate of digital culture for the past 20 years, and this feels like the culmination of a lifetime of arguing.

Knock ’em dead!

Memo to self, read Program or Be Programmed over the holidays.

Link parkin’: graph-tool

graph-tool is an efficient python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers a level of performance which is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

The Boost Graph Library gives me the willies though. I’ve never had much luck building or using it embedded within Python. Weird linking or dynamic library loading issues got me. At a 2.x release though, this module might be worth a test drive.

Via @hmason

“graph-tool is an efficient python module for manipulation and statistical analysis of graphs” http://t.co/0UFbVjCY

— Hilary Mason (@hmason) December 8, 2012

This nugget from Jacob Kaplan-Moss is a bit old, and from a Django-centric perspective, but captures what’s been tripping me up with Python web app frameworks and building RESTful APIs:

Conflating models and resources

In the REST world, the resource is key, and it’s really tempting to simply look at a Django model and make a direct link between resources and models — one model, one resource. This fails, though, as soon as you need to provide any sort of aggregated resource, and it really fails with highly denormalized models. Think about a Superhero model: a single GET /heros/superman/ought to return all his vital stats along with a list of related Power objects, a list of his related Friend objects, etc. So the data associated with a resource might actually come out of a bunch of models. Think select_related(), except arbitrary.

Victory! A satisfying win over the league leader avenging an earlier loss this fantasy season. That makes for four wins in a row. RG III closed the door on Monday Night Football, but if I’d have started Cam Newton, the deal would have been done Sunday night.

On my side, it was mostly pedestrian performances with RG III scoring 18, Arian Foster and Stevan Ridley for 13 apiece, and Tony Gonzalez (Go Bears!!) for 17. I will pat myself on the back though for grabbing Cecil Shorts off of the waiver wire and getting 17 points out of him.

I also lucked into my opponent’s QB, Drew Brees, having an uncharacteristically bad game, delivering 1.37 points. Brees is usually good for 20+. Hey, whatever it takes.

Now I’m locked into the first or second seed for our fantasy playoffs with one regular season game to go. Need a little help, but I already like how week 14 is working out.

Jan YESely.

— Wiz Win Last Night? (@DidTheWizWin) December 5, 2012

Somehow I lucked out and had a ticket for the second Washington Wizards victory of the season. Against reigning NBA champs, the Miami Heat, no less.

Have to say I was fairly subdued until the final minute, when I realized the Wiz could actually win. Otherwise, I was anticipating collapse, but the Heat never really turned on the defense.

I’m not rooting for Windows 8, but I hope it spurs Apple to do something interesting with the well nigh useless (for me) iPhone lockscreen. Brent Caswell takes a crack at a redesign:

What isn’t very straightforward is the lockscreen. I set out to make the lockscreen flexible and open to the apps on your device, without throwing everything that works really well out the window. But before I get to my ideas, how does the lockscreen work now?

Seems like a missed opportunity for both high utility and magnificent delight.

Via Daring Fireball

P.S. Without having to jailbreak the device.

Link parkin’: OEmbed Link rot on URL: https://twitter.com/pypi/status/275454924970680320

simplekml is a python package which enables you to generate KML with as little effort as possible.

Took a quick look at the docs and seems quite elegant, thorough, and useful.

On the drive in to work today I was listening to the local guy talk, faux sports talk, AM show. They opened a segment with a freestyle rap which I could immediately ID as Q-Tip but couldn’t place the track name.

DJ finally announced it as Excursions from The Low End Theory. Hit the parking garage, got outside of the building, and had it downloaded in 5 minutes through the modern miracle of LTE.

Life’s good, but why the hell have I been sleepin’ on The Low End Theory? Gotta’ get that on my Jesusphone 5 post haste.

TIL that Jacque Vaughn is the head coach of the Orlando Magic. I remember how I used to love college basketball and Vaughn’s Kansas Jayhawks team was one to watch every game.

Man I’m old.

The list of PyCon 2013 talks was announced:

As you may already know, this was an incredibly hard decision for the Program Commitee: we had over 450 submissions for only 114 slots on the program. Further, the quality of submissions was very high; the committee debated each and every talk very closely. I want to sincerely thank everyone who submitted a talk: the quality of PyCon comes from our speakers, and this year you all blew it out of the water.

Sounds like the potential for a great program. Unfortunately, I can’t be as excited as I was last year since I’m not as confident that I can get away. Still, I’m quite interested in the more hardcore groupings like “Big Data” (yeah I know buzzword compliant) and even more so in the tutorials lineup.

Following the Python PyPi Twitter stream reveals a number of toolkits that help you build RESTful APIs. These modules usually get you most of the way mapping models from an ORM into an API. However, where they’ve always fallen short for me is in easing the generation of search end points. Maybe I’m doing something wrong, but I need more help in 1) handling incoming query args, 2) turning them into searches against the model(s), and 3) generating a RESTful response, especially the resource URLs.

LazyWeb, make it so!

Torque is a (relatively) recently announced toolkit built on top of CartoDB 2.0 for performant browser rendering of large geospatially oriented time series data:

Torque allows you to create beautiful visualizations with big temporal datasets by bundling HTML5 browser rendering technologies with a generic and efficient temporal data transfer format created using the CartoDB SQL API. Torque visualisations work on desktop and ipads, and work well on temporal datasets with hundreds of thousands or even millions of datapoints. In anticipation of the Strata Conference starting this week in London, we have prepared some examples to share. Simon Tokumine will be there, so ping us for a private demo there.

Previously, I had found CartoDB a bit daunting for a hosted self-installation, but looking at the requirements now it doesn’t seem that bad. Feels like one of those tools where if you absorb the pain and get in on the ground floor, one can build a distinct competitive advantage. Working on tablets and desktops is a nice tease.

Finally got a chance to put supervisord into action at work. It’s very handy for organizing, launching, and managing a bunch of server processes. I’m especially liking the interactive command shell provided by supervisorctl. Plus I think it’s easily launchable from a cron @reboot action which is great for this non-root user.

How have I not heard of Pydoop until now?! OEmbed Link rot on URL: https://twitter.com/pypi/status/274179982736121856

Welcome to Pydoop. Pydoop is a package that provides a Python API for Hadoop MapReduce and HDFS. Pydoop has several advantages 1 over Hadoop’s built-in solutions for Python programming, i.e., Hadoop Streaming and Jython: being a CPython package, it allows you to access all standard library and third party modules, some of which may not be available for other Python implementations – e.g., NumPy; in addition, Pydoop provides a Python HDFS API which, to the best of our knowledge, is not available in other solutions.