Pipeline

The individual generators in compass (the arroyos and rivers, the caverns and meanders) are the fun part to show off, but they aren’t what makes the library usable. The part I’m actually proud of is the composition layer that sits on top, and it owes a lot to the runtime pipeline patterns I wrote about a while ago. Shapes times features Every room is described as a shape combined with a feature. There are 24 shapes: squares and circles, but also blobs, crystalline caverns, crescents, capsules, and a handful of hallway types. There are 18 features: pillars, islands, stepping stones, pools, moats, chasms, rubble, and four kinds of river. Any shape can pair with any feature, which is 432 distinct room configurations before you’ve placed a single one. A feature is just two functions: an initialize step that works out the geometry (the meandering river feature, for example, generates a sine-wave centerline and expands around it), and a content step that decides the terrain type for each tile inside the shape. Keeping those two responsibilities separate is what lets the same river feature drop into a square room or an L-shaped hallway without caring which it landed in. ...

Introduction Pipeline patterns are most commonly used for simple data workflows like general functional composition or user registration processes. After briefly describing the basics, this article focuses on a couple of behavioral software design patterns under the strategy pattern we’ll refer to as “runtime patterns” for use with pipelines. Their power in modern applications is allowing pipelines to expand or contract dynamically. Whether it be through behavior modeling with ai, blueprint fulfilling or more basic conditional pipeline adaptation, the runtime patterns are helpful and with forethought can be simply understood and implemented. ...