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

september 12, 2014 11:13pm / peewee postgresql python / 2 comments

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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The Octopus Attack and the Sick Cat

july 30, 2014 12:04pm / kansas thoughts / 0 comments

It's been a great summer so far! Here are some of the things I've been up to.

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I was trying to get some work done the other day and there was an octopus attack. I survived by eating them.

Read on for more!

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Playing with Python Magic Methods to make a nicer Regex API

july 19, 2014 11:24am / python regex / 5 comments

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A co-worker of mine mentioned that he missed Ruby's syntactic sugar for regular expressions. I haven't used Ruby's regular expressions, but I'm familiar enough with Python's to know that the API is a bit wanting in syntactic sweetness.

In this post I'll show how you might use python's magic methods to make a nicer API for working with regular expressions.

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

july 14, 2014 12:37pm / berkeleydb peewee python sqlite / 10 comments

Sqlite Logo

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

july 09, 2014 08:54am / canvas javascript pong / 0 comments

Earlier this week I posted about 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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Mutant killer, Mario and more: rediscovering old projects

july 06, 2014 10:44pm / programming / 0 comments

I was reorganizing some folders on my laptop and ran across some really old code I'd written. I knew the code was there, but I hadn't looked at it in years and thought it would be fun to take a peek, so I created a WindowsXP virtual machine and fiddled around trying to get the various programs to run.

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Completely un-scientific benchmarks of some embedded databases with Python

june 29, 2014 01:31am / nosql python / 5 comments

I've spent some time over the past couple weeks playing with the embedded NoSQL databases Vedis and UnQLite. Vedis, as its name might indicate, is an embedded data-structure database modeled after Redis. UnQLite is a JSON document store (like MongoDB, I guess??). Beneath the higher-level APIs, both Vedis and UnQLite are key/value stores, which puts them in the same category as BerkeleyDB, KyotoCabinet and LevelDB. The Python standard library also includes some dbm-style databases, including gdbm.

For fun, I thought I would put together a completely un-scientific benchmark showing the relative speeds of these various databases for storing and retrieving simple keys and values.

Here are the databases and drivers that I used for the test:

I'm running these tests with:

For the test, I simply recorded the time it took to store 100K simple key/value pairs (no collisions). Then I recorded the time it took to read back all these values. The results are in seconds elapsed:

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Python bindings for UnQLite, an embedded NoSQL database/JSON document store

june 26, 2014 11:46am / nosql python unqlite / 1 comments

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I'm happy to write that I've just released some python bindings for UnQLite, an embedded NoSQL database and JSON document store. UnQLite might be characterized as the SQLite of NoSQL databases, though it's JSON document-store and Jx9 scripting language make it a pretty unique offering. UnQLite is created by Symisc Systems, who are also responsible for Vedis, an embedded Redis-like database (I also wrote some python bindings for vedis). Here is a quick overview of some of UnQLite's features, as described on the project homepage:

In the rest of this post I will show some basic usage of the unqlite-python library. If you'd like to follow along, you can use pip to install unqlite:

pip install unqlite

You can find the project source code hosted on GitHub and the documentation is available on readthedocs.

Read on for the details!

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Python bindings for Vedis, the Embedded NoSQL Database

june 19, 2014 10:22am / nosql python vedis / 0 comments

vedis-python logo

Over the past week I've been writing some python bindings to the embedded NoSQL database Vedis, a transactional data-store modeled after Redis. Like Redis, Vedis could be characterized as an advanced key-value store that supports hash, set and list data-structures. Vedis has over 70 available commands for working with the various data types. Unlike Redis, which is run as a separate server process, Vedis is embedded in the host process like SQLite. Vedis works with either in-memory databases or on-disk databases. Vedis is transactional (ACID) and also thread-safe. If you'd like more information, check out the Vedis FAQ.

Vedis-python

Vedis-python allows you to use Vedis in your Python apps. Vedis-python supports all the Vedis data-types, and also allows you extend Vedis by writing your own commands in Python. As I mentioned, this project is very new so while I have written pretty extensive unit tests, the library has certainly not been battle-tested yet.

If you'd like to give it a try, you can use pip to install vedis-python. At the time of writing the current version is 0.1.5.

$ pip install vedis-python

Just a word of caution, I've tested the installation on various flavors of Linux (including on my raspberry pi), and Mac OSX, but have not tested on Windows.

Read the rest of the post for the details.

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Gangster as hell

june 12, 2014 06:48pm / huey mickey / 0 comments

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