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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 (yes, irony). 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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Using the SQLite JSON1 and FTS5 Extensions with Python

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. 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 json1 and 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 pysqlite and apsw, I've included instructions for building both of them. Finally, we'll use peewee ORM to run queries using the json1 and fts5 extensions.

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My List of Python and SQLite Resources

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This post is going to be a greatest hits of my open-source libraries and blog posts concerning the use of SQLite with Python. I'll also share a list of some other neat SQLite projects that you may not have heard of before.

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Introduction to the fast new UnQLite Python Bindings

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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.

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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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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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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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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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Postgresql HStore, JSON data-type and Arrays with Peewee ORM

I've developed an interest in some of the more advanced features of SQLite after reading the O'Reilly title Using SQLite (Small. Fast. Reliable. Choose Any Three). For personal projects I like using SQLite, but when I need something more powerful I turn to Postgresql. Because peewee supports both of these databases (as well as MySQL), it is limited to a lowest-common-denominator feature set. While this encompasses a broad range of features, each database engine has its own extensions and I've been interested in adding some pythonic support for the cooler extensions.

Here are some of the fun things you can find in peewee's playhouse (collection of extensions):

This post will showcase the peewee support for HStore, JSON document store, and arrays. I've written elsewhere about SQLite's full-text search, so if you're a SQLite user you may want to check out that post.

To follow along at home, feel free to install peewee:

pip install peewee

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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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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: 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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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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Structuring flask apps, a how-to for those coming from Django

The other day a friend of mine was trying out flask-peewee and he had some questions about the best way to structure his app to avoid triggering circular imports. For someone new to flask, this can be a bit of a puzzler, especially if you're coming from django which automatically imports your modules. In this post I'll walk through how I like to structure my flask apps to avoid circular imports. In my examples I'll be showing how to use "flask-peewee", but the same technique should be applicable for other flask plugins.

I'll walk through the modules I commonly use in my apps, then show how to tie them all together and provide a single entrypoint into your app.

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Using python and k-means to find the dominant colors in images

I'm working on a little photography website for my Dad and thought it would be neat to extract color information from photographs. I tried a couple of different approaches before finding one that works pretty well. This approach uses k-means clustering to cluster the pixels in groups based on their color. The center of those resulting clusters are then the "dominant" colors. k-means is a great fit for this problem because it is (usually) fast.

Here's an example

Akira motorcycles

The results:

                                

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