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Querying the top N objects per group with Peewee ORM
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
COUNT() - Solutions involving
LIMIT - Window functions
- Postgresql lateral joins
Querying the top item by group with peewee ORM
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.
Naive Bayes Classifier using Python and Kyoto Cabinet
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.
Walrus: Lightweight Python utilities for working with Redis
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.