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.
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.
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.
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...
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.
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.
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.
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.
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
- Ordered key/value store
- Range searches
- Prefix searches
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