Entries tagged with python

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

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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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Examples of using Walrus, a lightweight Redis Toolkit

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walrus is my go-to toolkit for working with Redis in Python, and hopefully this post will convince you that it can be your go-to as well. I've tried to include lots of high-level Python APIs built on Redis primitives and the result is quite a lot of functionality. In this post I'll take you on a tour of the library and show examples of how it might be useful in your next project.

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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 Spellfix Extension and Python

The SQLite source tree is full of wonders. There is the the lemon parser generator, a btree implementation (well, kind of not surprising), multiple search engines, a json library, and more. It looks like Dr. Hipp is also experimenting with integrating the LSM key/value store from SQLite4 as a standalone virtual table.

I've written about the json and full-text search extensions, the lsm key/value store, and the transitive closure extension (useful when querying hierarchical data). In this post I'll be covering another interesting extension, the spellfix1 extension (documentation).

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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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Advanced filtering and SQLite FTS5 with Scout search engine

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I've been working on some new features for Scout and thought they might be worth a short blog post. The super-short version is that Scout now supports complex filtering on metadata, adding another layer of filtering besides the full-text search. Additionally, I've added support for SQLite FTS5, using it by default if it's available otherwise falling back to FTS4.

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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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