Documentation Index
Fetch the complete documentation index at: https://honeydew.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.
Recipes
Each recipe pairs a modeling pattern with a step-by-step implementation - showing how to combine entities, attributes, metrics, and parameters to solve real business questions in Honeydew.What a recipe contains
Most recipes include some or all of these sections:- When would you use this? - the business question the recipe answers
- Prerequisites - setup needed before you start
- Step-by-step implementation - the objects to create, with full YAML
- Sample output - results you can verify against
- Key design notes - design decisions and trade-offs
Dataset: tasty_bytes
All recipes use thetasty_bytes workspace - a food-truck order dataset. The
key entities are:
| Entity | Type | Description |
|---|---|---|
order_detail | Fact | Line-item orders - revenue per item sold |
order_header | Fact | Order-level - customer, date, city, truck |
menu | Dimension | Menu items - category and subcategory |
date | Time Spine | Calendar spine used by time-based metrics |
Available recipes
Filtered metrics
Metric for a specific category
Isolate revenue for a specific product category using
a filtered metric.
Category share of total
Compute a specific category’s share of total revenue
using ratio metrics.
Time-based metrics
Active customers & stickiness
Measure customer engagement with daily, weekly, and
monthly active user counts.
Period-over-period growth
Compare revenue to the same period last year and
compute the growth rate.
Fixed and dynamic grouping
Average order value
Average spend per order, computed correctly regardless
of how the data is sliced.
Share above a threshold
Share of orders above a threshold, using either a fixed
amount or a data-driven percentile.
Average daily revenue
Average revenue per day using dynamic grouping that
adapts to the user’s slicing context.
BI parameters
BI parameters
Route a single metric to different calculations
based on a BI user’s filter selection.
Related reading
- Entities, Attributes, and Metrics - the primitives used across recipes
- Advanced Modeling
- patterns like time metrics, shared dimensions, and slowly changing dimensions
- Conditional filtering
GET_FIELD_SELECTIONreference for the BI parameters recipe