Entries tagged 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.
Back in September, word started getting around trendy programming circles about a new file that had appeared in the SQLite fossil repo named json1.c. I originally wrote up a post that contained some gross hacks in order to get pysqlite to compile and work with the new
json1 extension. With the release of SQLite 3.9.0, those hacks are no longer necessary.
SQLite 3.9.0 is a fantastic release. In addition to the much anticipated
json1 extension, there is a new version of the full-text search extension called
fts5 improves performance of complex search queries and provides an out-of-the-box BM25 ranking implementation. You can also boost the significance of particular fields in the ranking. I suggest you check out the release notes for the full list of enhancements
This post will describe how to compile SQLite with support for
fts5. We'll use the new SQLite library to compile a python driver so we can use the new features from python. Because I really like
apsw, I've included instructions for building both of them. Finally, we'll use peewee ORM to run queries using the
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.
Read the rest of the post for examples of how to use the library.
About a year ago, I blogged about some Python bindings I wrote for the embedded NoSQL document store UnQLite. One year later I'm happy to announce that I've rewritten the library using Cython and operations are, in most cases, an order of magnitude faster.
This was my first real attempt at using Cython and the experience was just the right mix of challenging and rewarding. I bought the O'Reilly Cython Book which came in super handy, so if you're interested in getting started with Cython I recommend picking up a copy.
In this post I'll quickly touch on the features of UnQLite, then show you how to use the Python bindings. When you're done reading you should hopefully be ready to use UnQLite in your next Python project.
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.
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.
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.
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.
- Draft posts.
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
- Solutions involving
- Window functions
- Postgresql lateral joins
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.
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.