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Track Day, September 10th, Heartland Park

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Track day, June 3rd at Heartland Park

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Your idea sucks

I thought I had arrived. One of my open-source projects started gaining a small following on GitHub. No more nagging self-doubt, the thousand or so star-gazers of my project provided all the validation I needed. Here was something I could conjure up in moments of doubt, reminding myself that I truly was all those things I wanted to believe about myself. I never stopped to think that the stars might not be for me.

* * *

All this got turned on it's head, though, by one of those very people who I set so much store by. I'm thinking in particular of one person who was using my project to manage the backend data-storage for his company's platform. It was a critical function, and as the CTO of his organization, he was responsible for ensuring it was technically sound. He was very invested, professionally, in the direction of my project. This was a sharp contrast to most people I'd talked to, who were using my project for side-projects and hobbies of their own.

The combination of his expectations of me, as a maintainer, and my beliefs about my own motivations for sharing my code led to a pretty unbelievable series of events.

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Multi-threaded SQLite without the OperationalErrors

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SQLite's write lock and pysqlite's clunky transaction state-machine are a toxic combination for multi-threaded applications. Unless you are very diligent about keeping your write transactions as short as possible, you can easily wind up with one thread accidentally holding a write transaction open for an unnecessarily long time. Threads that are waiting to write will then have a much greater likelihood of timing out while waiting for the lock, giving the illusion of poor performance.

In this post I'd like to share a very effective technique for performing writes to a SQLite database from multiple threads.

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Monospace Font Favorites

For the past six months or so, I've been experimenting with a variety of monospace fonts in a quest to find the perfect coding font. While I haven't found a clear winner, I have found a dozen nice-looking fonts and learned a lot about typefaces in general. I've also learned quite a bit about font rendering on Linux, which I hope to summarize in a separate post soon.

In this post I'd like to share some screenshots (or "swatches") of my favorite fonts.

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Suffering for fashion: a glimpse into my Linux theming toolchain

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My desktop at the time of writing.

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Here it is a couple months later.

It's been over 2 years since I wrote about the tooling I use to theme my desktop, so I thought I'd post about my current scripts...

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"For Humans" makes me cringe

for chodes

When Kenneth Reitz created the requests library, the Python community rushed to embrace the project, as it provided (finally) a clean, sane API for making HTTP requests. He subtitled his project "Python HTTP Requests for Humans", referring, I suppose, to the fact that his API provided developer-friendly APIs. If naming things "for humans" had stopped there, that would have been fine with me, but instead there's been a steady stream of new projects describing themselves as being "For Humans" and I have issues with that.

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Measuring Nginx Cache Performance using Lua and Redis

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Shortly after launching my Nginx-based cache + thumbnailing web-service, I realized I had no visibility into the performance of the service. I was curious what my hit-ratios were like, how much time was spent during a cache-miss, basic stuff like that. Nginx has monitoring tools, but it looks like they're only available to people who pay for Nginx Plus, so I decided to see if I could roll my own. In this post, I'll describe how I used Lua, cosockets, and Redis to extract real-time metrics from my thumbnail service.

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Nginx: a caching, thumbnailing, reverse proxying image server?

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A month or two ago, I decided to remove Varnish from my site and replace it with Nginx's built-in caching system. I was already using Nginx to proxy to my Python sites, so getting rid of Varnish meant one less thing to fiddle with. I spent a few days reading up on how to configure Nginx's cache and overhauling the various config files for my Python sites (so much for saving time). In the course of my reading I bookmarked a number of interesting Nginx modules to return to, among them the Image Filter module.

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Five reasons you should use SQLite in 2016

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If you haven't heard, SQLite is an amazing database capable of doing real work in real production environments. In this post, I'll outline 5 reasons why I think you should use SQLite in 2016.

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Announcing sophy: fast Python bindings for Sophia Database

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Sophia is a powerful key/value database with loads of features packed into a simple C API. In order to use this database in some upcoming projects I've got planned, I decided to write some Python bindings and the result is sophy. In this post, I'll describe the features of Sophia database, and then show example code using sophy, the Python wrapper.

Here is an overview of the features of the Sophia database:

  • Append-only MVCC database
  • ACID transactions
  • Consistent cursors
  • Compression
  • Ordered key/value store
  • Range searches
  • Prefix searches

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Updated instructions for compiling BerkeleyDB with SQLite for use with Python

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About three years ago I posted some instructions for building the Python SQLite driver for use with BerkeleyDB. While those instructions still work, they have the unfortunate consequence of stomping on any other SQLite builds you've installed in /usr/local. I haven't been able to build pysqlite with BerkeleyDB compiled in, because the source amalgamation generated by BerkeleyDB is invalid. So that leaves us with dynamically linking, and that requires that we use the BerkeleyDB libsqlite, which is exactly what the previous post described.

In this post I'll describe a better approach. Instead of building a modified version of libsqlite3, we'll modify pysqlite to use the BerkeleyDB libdb_sql library.

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SQLite Table-Valued Functions with Python

One of the benefits of running an embedded database like SQLite is that you can configure SQLite to call into your application's code. SQLite provides APIs that allow you to create your own scalar functions, aggregate functions, collations, and even your own virtual tables. In this post I'll describe how I used the virtual table APIs to expose a nice API for creating table-valued (or, multi-value) functions in Python. The project is called sqlite-vtfunc and is hosted on GitHub.

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Using SQLite4's LSM Storage Engine as a Stand-alone NoSQL Database with Python

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SQLite and Key/Value databases are two of my favorite topics to blog about. Today I get to write about both, because in this post I will be demonstrating a Python wrapper for SQLite4's log-structured merge-tree (LSM) key/value store.

I don't actively follow SQLite's releases, but the recent release of SQLite 3.8.11 drew quite a bit of attention as the release notes described massive performance improvements over 3.8.0. While reading the release notes I happened to see a blurb about a new, experimental full-text search extension, and all this got me to wondering what was going on with SQLite4.

As I was reading about SQLite4, I saw that one of the design goals was to provide an interface for pluggable storage engines. At the time I'm writing this, SQLite4 has two built-in storage backends, one of which is an LSM key/value store. Over the past month or two I've been having fun with Cython, writing Python wrappers for the embedded key/value stores UnQLite and Vedis. I figured it would be cool to use Cython to write a Python interface for SQLite4's LSM storage engine.

After pulling down the SQLite4 source code and reading through the LSM header file (it's very small!), I started coding and the result is python-lsm-db (docs).

Read the rest of the post for examples of how to use the library.

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Connor Thomas Leifer

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Why I won't be switching to Disque

Disque's alpha release announcement generated some buzz on HackerNews. If you missed it, Disque is a distributed message broker from Salvatore Sanfilippo, the author of Redis.

In the Limitations section of the README, Salvatore has written:

Disque was designed a bit in astronaut mode, not triggered by an actual use case of mine, but more in response to what I was seeing people doing with Redis as a message queue and with other message queues.

This admission makes me wary of using Disque, even if it reaches a stable release, because of my own experience with similar projects I've created but never actually used. These projects are usually fun opportunities for learning, but when it comes to maintenance, my experience has shown me that they quickly become a burden. Usually the problem is masked by the fact that if I'm not using it usually nobody else is either, but in the rare case I do end up with users, then eventually those users are going to submit bug reports and feature requests.

For a problem as complex as a distribute message broker, I imagine that there are going to be a lot of bug reports, strange edge-cases, and feature requests to support exotic use-cases. I hope that, in addition to his work on Redis, Salvatore can find the time to support Disque!

The other reason I don't foresee using Disque is alluded to in the author's own comments. He observes that many people are using Redis as a message broker, and decides that maybe there is a need for a "Redis of messaging". I would say the opposite is true, and that instead of another message server, people want to use Redis!

Redis integrates very nicely into the stack for web-based projects. It can be used as a cache, for locking, as a primary data store, for write-heavy portions of the application, and yes, as a message broker.

Perhaps the reason people are using Redis as a message broker is because they don't want to use something else?

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A Tour of Tagging Schemas: Many-to-many, Bitmaps and More

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In this post I'll describe how to implement tagging with a relational database. What I mean by tagging are those little labels you see at the top of this blog post, which indicate how I've chosen to categorize the content. There are many ways to solve this problem, and I'll try to describe some of the more popular methods, as well as one unconventional approach using bitmaps. In each section I'll describe the database schema, try to list the benefits and drawbacks, and present example queries. I will use Peewee ORM for the example code, but hopefully these examples will easily translate to your tool-of-choice.

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Meet Scout, a Search Server Powered by SQLite

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In my continuing adventures with SQLite, I had the idea of writing a RESTful search server utilizing SQLite's full-text search extension. You might think of it as a poor man's ElasticSearch – a very, very poor man.

So what is this project? Well, the idea I had was that instead of building out separate search implementations for my various projects, I would build a single lightweight search service I could use everywhere. I really like SQLite (and have previously blogged about using SQLite's full-text search with Python), and the full-text search extension is quite good, so it didn't require much imagination to take the next leap and expose it as a web-service.

Read on for more details.

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How to make a Flask blog in one hour or less

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For fun, I thought I'd write a post describing how to build a blog using Flask, a Python web-framework. Building a blog seems like, along with writing a Twitter-clone, a quintessential experience when learning a new web framework. I remember when I was attending a five-day Django tutorial presented by Jacob Kaplan-Moss, one of my favorite projects we did was creating a blog. After setting up the core of the site, I spent a ton of time adding features and little tweaks here-and-there. My hope is that this post will give you the tools to build a blog, and that you have fun customizing the site and adding cool new features.

In this post we'll cover the basics to get a functional site, but leave lots of room for personalization and improvements so you can make it your own. The actual Python source code for the blog will be a very manageable 200 lines.

Who is this post for?

This post is intended for beginner to intermediate-level Python developers, or experienced developers looking to learn a bit more about Python and Flask. For the mother of all Flask tutorials, check out Miguel Grinberg's 18 part Flask mega-tutorial.

The spec

Here are the features:

  • Entries are formatted using markdown.
  • Entries support syntax highlighting, optionally using Github-style triple-backticks.
  • Automatic video / rich media embedding using OEmbed.
  • Very nice full-text search thanks to SQLite's FTS extension.
  • Pagination.
  • Draft posts.

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Querying the top N objects per group with Peewee ORM

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This post is a follow-up to my post about querying the top related item by group. In this post we'll go over ways to retrieve the top N related objects by group using the Peewee ORM. I've also presented the SQL and the underlying ideas behind the queries, so you can translate them to whatever ORM / query layer you are using.

Retrieving the top N per group is a pretty common task, for example:

  • Display my followers and their 10 most recent tweets.
  • In each of my inboxes, list the 5 most recent unread messages.
  • List the sections of the news site and the three latest stories in each.
  • List the five best sales in each department.

In this post we'll discuss the following types of solutions:

  • Solutions involving COUNT()
  • Solutions involving LIMIT
  • Window functions
  • Postgresql lateral joins

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Querying the top item by group with peewee ORM

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In this post I'd like to share some techniques for querying the top item by group using the Peewee ORM. For example,

  • List the most recent tweet by each of my followers.
  • List the highest severity open bug for each of my open source projects.
  • List the latest story in each section of a news site.

This is a common task, but one that can be a little tricky to implement in a single SQL query. To add a twist, we won't use window functions or other special SQL constructs, since they aren't supported by SQLite. If you're interested in finding the top N items per group, check out this follow-up post.

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Naive Bayes Classifier using Python and Kyoto Cabinet

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In this post I will describe how to build a simple naive bayes classifier with Python and the Kyoto Cabinet key/value database. I'll begin with a short description of how a probabilistic classifier works, then we will implement a simple classifier and put it to use by writing a spam detector. The training and test data will come from the Enron spam/ham corpora, which contains several thousand emails that have been pre-categorized as spam or ham.

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Walrus: Lightweight Python utilities for working with Redis

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A couple weekends ago I got it into my head that I would build a thin Python wrapper for working with Redis. Andy McCurdy's redis-py is a fantastic low-level client library with built-in support for connection-pooling and pipelining, but it does little more than provide an interface to Redis' built-in commands (and rightly so). I decided to build a project on top of redis-py that exposed pythonic containers for the Redis data-types. I went on to add a few extras, including a cache and a declarative model layer. The result is walrus.

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Extending SQLite with Python

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SQLite is an embedded database, which means that instead of running as a separate server process, the actual database engine resides within the application. This makes it possible for the database to call directly into the application when it would be beneficial to add some low-level, application-specific functionality. SQLite provides numerous hooks for inserting user code and callbacks, and, through virtual tables, it is even possible to construct a completely user-defined table. By extending the SQL language with Python, it is often possible to express things more elegantly than if we were to perform calculations after the fact.

In this post I'll describe how to extend SQLite with Python, adding functions and aggregates that will be callable directly from any SQL queries you execute. We'll wrap up by looking at SQLite's virtual table mechanism and seeing how to expose a SQL interface over external data sources.

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Querying Tree Structures in SQLite using Python and the Transitive Closure Extension

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I recently read a good write-up on tree structures in PostgreSQL. Hierarchical data is notoriously tricky to model in a relational database, and a variety of techniques have grown out of developers' attempts to optimize for certain types of queries.

In his post, Graeme describes several approaches to modeling trees, including:

  • Adjancency models, in which each node in the tree contains a foreign key to its parent row.
  • Materialized path model, in which each node stores its ancestral path in a denormalized column. Typically the path is stored as a string separated by a delimiter, e.g. "{root id}.{child id}.{grandchild id}".
  • Nested sets, in which each node defines an interval that encompasses a range of child nodes.
  • PostgreSQL arrays, in which the materialized path is stored in an array, and general inverted indexes are used to efficiently query the path.

In the comments, some users pointed out that the ltree extension could also be used to efficiently store and query materialized paths. LTrees support two powerful query languages (lquery and ltxtquery) for pattern-matching LTree labels and performing full-text searches on labels.

One technique that was not discussed in Graeme's post was the use of closure tables. A closure table is a many-to-many junction table storing all relationships between nodes in a tree. It is related to the adjacency model, in that each database row still stores a reference to its parent row. The closure table gets its name from the additional table, which stores each combination of ancestor/child nodes.

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Web-based SQLite Database Browser, powered by Flask and Peewee

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For the past week or two I've been spending some of my spare time working on a web-based SQLite database browser. I thought this would be a useful project, because I've switched all my personal projects over to SQLite and foresee using it for pretty much everything. It also dovetailed with some work I'd been doing lately on peewee regarding reflection and code generation. So it seemed like some pretty good bang/buck, especially given my perception that there weren't many SQLite browsers out there (it turns out there are quite a few, however). I'm sharing it in the hopes that other devs (and non-devs?) find it useful.

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Dear Diary, an Encrypted Command-Line Diary with Python

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In my last post, I wrote about how to work with encrypted SQLite databases with Python. As an example application of these libraries, I showed some code fragments for a fictional diary program. Because I was thinking the examples directory of the peewee repo was looking a little thin, I decided to flesh out the diary program and include it as an example.

In this post, I'll go over the diary code in the hopes that you may find it interesting or useful. The code shows how to use the peewee SQLCipher extension. I've also implemented a simple command-line menu loop. All told, the code is less than 100 lines!

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Saturday morning hacks: Building an Analytics App with Flask

Saturday morning hacks

A couple years back I wrote about building an Analytics service with Cassandra. As fun as that project was to build, the reality was that Cassandra was completely unsuitable for my actual needs, so I decided to switch to something simpler. I'm happy to say the replacement app has been running without a hitch for the past 5 months taking up only about 20 MB of RAM! In this post I'll show how to build a lightweight Analytics service using Flask.

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Encrypted SQLite Databases with Python and SQLCipher

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SQLCipher, created by Zetetic, is an open-source library that provides transparent 256-bit AES encryption for your SQLite databases. SQLCipher is used by a large number of organizations, including Nasa, SalesForce, Xerox and more. The project is open-source and BSD licensed. Best of all, there are open-source python bindings.

A GitHub user known as The Dod was kind enough to contribute a sqlcipher playhouse module, making it a snap to use Peewee with SQLCipher.

In this post, I'll show how to compile SQLCipher and the pysqlcipher bindings, then use peewee ORM to work with an encrypted SQLite database.

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Saturday morning hacks: Adding full-text search to the flask note-taking app

Saturday morning hacks

In preparation for the fourth and final installment in the "Flask Note-taking app" series, I found it necessary to improve the search feature of the note-taking app. In this post we will use SQLite's full-text search extension to improve the search feature.

To recap, the note-taking app provides a lightweight interface for storing markdown-formatted notes. Because I frequently find myself wanting to take notes on the spur of the moment, the note-taking app needed to be very mobile-friendly. By using twitter bootstrap and a hefty dose of JavaScript, we made an app that matches our spec and manages to look good doing it!

In part 2, we added email reminders and check-able task lists to the note-taking app. We also converted the backend to use flask-peewee's REST API extension, which made it easy to add pagination and search. And that is how I've left it for the last three months or so.

Below is a screenshot of the latest version of the notes app. The UI is much cleaner thanks to a stylesheet from bootswatch. The bootswatch stylesheet works as a drop-in replacement for the default bootstrap CSS file.

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All together, the note-taking app has the following features:

  • Flexible pinterest-style tiled layout that looks great on a variety of screen sizes.
  • Easy to create notes and reminders from the phone.
  • Notes support markdown and there is also a simple WYSIWYM markdown editing toolbar.
  • Links are converted to rich media objects where possible (e.g. a YouTube URL becomes an embedded player).
  • To-do lists (or task lists) can be embedded in notes.
  • Email reminders can be scheduled for a given note.
  • Simple full-text search.
  • Pagination.

You can browse or download the finished code from part 2 in this gist. If you're in a hurry, you can find all the code from this post in this gist.

In case you were curious, I've been using the notes app for things like:

  • Bookmarking interesting sites to read later.
  • Creating short to-do lists or writing down particular items to get from the store, etc.
  • Writing down interesting dreams or ideas I get in the middle of the night.
  • Appointment reminders, reminders to call people, etc.
  • Saving funny cat pics.
  • Writing down ideas for programming projects.
  • Saving code snippets or useful commands.

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SQLite: Small. Fast. Reliable. Choose any three.

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SQLite is a fantastic database and in this post I'd like to explain why I think that, for many scenarios, SQLite is actually a great choice. I hope to also clear up some common misconceptions about SQLite.

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JavaScript Canvas Fun: Pong

Earlier this week I rediscovered some old games I'd written, and I realized that I had not yet done a JavaScript version of Pong. I did versions of Tetris and Snake, perennial favorites of mine to implement, but somehow I'd forgotten about Pong. I think Pong was probably the first game I ever tried to copy, and it has a special place in my early-programmer's memory.

So I set out last night to put together a JavaScript canvas version of Pong. You can find a playable version in the post.

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Saturday morning hacks: Revisiting the notes app

Saturday morning hacks

My post from last month, Saturday Morning Hack, a Little Note-Taking App with Flask, was pretty well-received. Since I've made a number of improvements to the app, I thought I would write one more post to share some of the updates I've made to this project, in the hopes that they may be of interest to you.

A live demo is up and running on Python Anywhere, so feel free to check that out before continuing on with the post: http://beetlejuicer.pythonanywhere.com/

To briefly recap the previous post, I discussed how I built a lightweight note-taking app which I could use from my phone or desktop. It has a nice ajax-ey interface and some simple markdown helpers written with javascript. In addition to supporting markdown, it also supports oembed for automatically embedding YouTube videos and the like. Here is what it looked like when we left off a few weeks ago:

Notes on Desktop

And this is how it looks now!

New and improved notes app

So what's new? Well, I've made a couple changes under-the-hood, and added some entirely new features to the UI.

  • Allow creation of Task Lists with checkbox inputs.
  • Create reminders that will send me an email at the appointed time.
  • Built a RESTful API to interact with the Note model. Thanks to flask-peewee everything comes "for free".
  • Added search.
  • Added pagination (using Ajax).

This was super fun to hack on so I thought I'd share the new code and describe how I added these features. Honestly, I didn't really end up adding much in terms of implementation. Huey handles scheduling and sending the email reminders, even automatically retrying messages that fail to send. Similarly, Flask-Peewee's REST API provides search and pagination out-of-the-box, so all I had to do was write the JavaScript to communicate with it. Thanks to these libraries, I was able to focus on the things that made this project unique, and hopefully you enjoy reading about the code.

Read the rest of the post for the details.

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Using SQLite Full-Text Search with Python

Full-text search with SQLite

In this post I will show how to use SQLite full-text search with Python (and a lot of help from peewee ORM). We will see how to index content for searching, and how to order search results using two ranking algorithms.

Last week I migrated my site from Postgresql to SQLite. I had been using Redis to power my site's search, but since SQLite has an awesome full-text search extension, I decided to give it a try. I am really pleased with the results, and being able to specify boolean search queries is an added plus. Here is a brief overview of the types of search queries SQLite supports:

  • Simple phrase: peewee would return all docs containing the word peewee.
  • Prefix queries: py* would return docs containing Python, pypi, etc.
  • Quoted phrases: "sqlite extension"
  • NEAR: peewee NEAR sqlite would return docs containing the words peewee and sqlite with no more than 10 intervening words. You can also specify the max number of intervening words, e.g. peewee NEAR/3 sqlite.
  • AND, OR, NOT: sqlite OR postgresql AND NOT mysql would return docs about high-quality databases (just trollin).

Check out the full post for details on adding full-text search to your project.

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Saturday morning hack: personalized news digest with boolean query parser

Saturday morning hacks

Because I had so much fun writing my last Saturday morning hack, I thought I would share another little hack. I was thinking that I really enjoy my subscription to Python weekly and wouldn't it be great if I had a personal email digest containing just the types of things that interest me? I regularly cruise reddit and hacker hater news but in my opinion there's a pretty low signal-to-noise ratio. Occasionally I stumble on fascinating content and that's what keeps me coming back.

I wanted to write an app that would search the sites I read and automatically create an email digest based on search terms that I specified. I recently swapped my blog over to SQLite and I love that the SQLite full-text search extension lets you specify boolean queries. With that in mind, I decided that I would have a curated list of boolean search queries which would be used to filter content from the various sites I read. Any articles that match my search would then be emailed to me.

Here are some of my search terms, which I am viewing in the flask-peewee admin interface:

Search term admin

If you're interested in learning how to build your own version of this project, check out the rest of the post.

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Migrating to SQLite

Sqlite Logo

Small. Fast. Reliable. Choose any three.

I made the decision this week to migrate my personal sites and several other sites I host onto SQLite. Previously almost everything I hosted had been using Postgresql. The move was motivated by a couple factors:

  • SQLite is awesome!
  • Self-contained: does not require a separate server process
  • Data is stored in a single file, simplifying backups
  • Excellent Python (and peewee) support
  • Full-text search

At times it has seemed to me that there is a tacit agreement within the Flask / Django communities that if you're using SQL you should be using Postgresql. Postgresql is an amazing piece of engineering. I have spent the last five years of my career working exclusively with it, and I am continually impressed by its performance and the constant stream of great new features.

So why change things?

Well, as my list indicates, there are a handful of reasons. But the primary reason was that I wanted something lightweight. I'm running a fairly low-traffic, read-heavy site, so Postgresql was definitely overkill. My blog is deployed on a VPS with very limited resources, so every MB of RAM counts. Additionally, I wasn't using any special Postgresql features so there was nothing holding me back.

(continued...)

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Saturday morning hack: a little note-taking app with Flask

Saturday morning hacks

A couple Saturdays ago I spent the morning hacking together a note-taking app. I'm really pleased with the result, so I thought I'd share the code in case anyone else might find it useful.

The note-taking project idea came about out of necessity -- I wanted something that worked well from my phone. While I have a personal wiki site I've used for things like software installation notes or salsa recipes, I've also noticed that because it's so cumbersome to use from my phone, I often end up emailing things to myself. Plus a wiki implies a kind of permanence to the content, making it not a great fit for these impromptu notes. I also like to use markdown to format notes, but markdown isn't too easy on a phone because of the special characters or the need to indent blocks of text. With these considerations in mind, I set out to build a note-taking app that would be easy to use from my phone.

Here is how the app appears on a narrow screen like my phone:

Notes on Phone

And here it is on my laptop:

Notes on Desktop

Because markdown is a bit difficult to use when you're not in a nice text editor like vim, I've added some simple toolbar buttons to the editor:

Notes Toolbar

Read the full post for all the details!

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Lawrence, KS

I am proud to live in Lawrence, KS, a college town of about 100,000 which has been my home for the majority of my life. Perhaps the most striking feature about my home is the amazing sky here -- nowhere else I've lived comes close:

Cloudy winter day

Sunset over school

Being in the tech industry, I'm often asked if I have plans to move away to a place with more jobs. I always answer simply and somewhat apologetically that I intend to stay in Kansas. Answering that way is so much less embarassing than explaining why I love Kansas. My home is very much a part of me, though, and I'd like to write just once about why I am so happy to live here.

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The search for the missing link: what lies between SQL and Django's ORM?

I had the opportunity this week to write some fairly interesting SQL queries. I don't write "raw" SQL too often, so it was fun to use that part of my brain (by the way, does it bother anyone else when people call SQL "raw"?). At Counsyl we use Django for pretty much everything so naturally we also use the ORM. Every place I've worked there's a strong bias against using SQL when you've got an ORM on board, which makes sense -- if you choose a tool you should standardize on it if for no other reason than it makes maintenance easier.

So as I was saying, I had some pretty interesting queries to write and I struggled to think how to shoehorn them into Django's ORM. I've already written about some of the shortcomings of Django's ORM so I won't rehash those points. I'll just say that Django fell short and I found myself writing SQL. The queries I was working on joined models from very disparate parts of our codebase. The joins were on values that weren't necessarily foreign keys (think UUIDs) and this is something that Django just doesn't cope with. Additionally I was interested in aggregates on calculated values, and it seems like Django can only do aggregates on a single column.

As I was prototyping, I found several mistakes in my queries and decided to run them in the postgres shell before translating them into my code. I started to think that some of these errors could have been avoided if I could find an abstraction that sat between the ORM and a string of SQL. By leveraging the python interpreter, the obvious syntax errors could have been caught at module import time. By using composable data structures, methods I wrote that used similar table structures could have been more DRY. When I write less code, I think I generally write less bugs as well.

That got me started on my search for the "missing link" between SQL (represented as a string) and Django's ORM.

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Using python to generate awesome linux desktop themes

I remember spending hours when I was younger cycling through the various awesome color themes on my 386, in the glory days of windows 3.1. Remember hotdog stand?

Hotdog Stand

Well, I haven't changed much. I still enjoy making tweaks to the colors and appearance of my desktop. In this post I'll talk about a script I wrote that makes it easy for me to modify all the various colors and configuration files which control the appearance of my desktop.

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