Pricing products using Python graphs and sets
summary
A model-driven pricing engine that represents items and rules as a product graph consumes XML/DB exports, matches a small set of supported patterns (includes, requires, max occurrences) via behavior tests written with freshen/BDD pattern tests, and returns charges as JSON over HTTP. It uses adjacency lookups and a Rete-like approach so only relevant rules run, handles odd cases (three-scoop tubs vs cones, a €7 oversized cone), and is production-ready with fast iteration and cached results for performance.
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chapters (14)
- 0:00Intro
- 6:50Cucumber
- 8:25Scenarios
- 11:48Introducing David
- 12:41Davids background
- 13:38Typical workflow
- 15:49How to express rules
- 17:58Examples of bad rules
- 20:07Why you should build a better Python
- 21:11The alternative
- 22:59The bad way
- 25:46Product lifecycle management
- 28:53How to interpret rules
- 35:39Summary
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