mirror of https://github.com/buster-so/buster.git
82 lines
4.5 KiB
Markdown
82 lines
4.5 KiB
Markdown
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# Model Schema Documentation
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This document describes a YAML-based schema for defining data models, designed for an AI data analyst (LLM) to interpret and generate SQL queries. The schema supports complex multi-table relationships, filters, and metrics, aiming for simplicity, clarity, and LLM usability while mirroring business entities (e.g., Palantir ontology inspiration).
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## Overview
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- **Purpose**: Digitally clone a business by modeling entities (tables/models), their attributes, relationships, and analytical logic.
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- **Key Features**:
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- Modular models with dimensions, measures, metrics, filters, and entities.
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- Multi-table `filters` and `metrics` using columns from related models via `entities`.
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- Parameterized `filters` and `metrics` for dynamic queries.
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- Structured `entities` for reliable join parsing.
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## Top-Level Structure
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- **`models`**: Array of model objects, each representing a table or view.
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### Model Fields
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- **`name`** (required, string): Unique model identifier (e.g., `culture`).
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- **`description`** (optional, string): Human-readable description.
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### Dimensions
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Filterable fields or identifiers (e.g., categorical attributes).
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- **`name`** (required, string): Column name in the data source.
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- **`description`** (optional, string): Field explanation.
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- **`type`** (optional, string): Data type (e.g., `character`); inferred if omitted.
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- **`searchable`** (optional, boolean, default: `false`): Index for search.
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- **`options`** (optional, array of strings, default: `null`): Valid values (e.g., `["active", "inactive"]`).
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### Measures
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Raw quantitative fields for analysis.
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- **`name`** (required, string): Column name in the data source.
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- **`description`** (optional, string): Field explanation.
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- **`type`** (optional, string): Data type (e.g., `integer`); inferred if omitted.
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### Metrics
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Aggregated or derived values, optionally parameterized.
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- **`name`** (required, string): Metric name (e.g., `total_revenue`).
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- **`expr`** (required, string): Expression (e.g., `SUM(revenue)`). Can use `model.column` syntax (e.g., `logins.login_count`) for entity columns.
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- **`description`** (optional, string): Metric explanation.
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- **`args`** (optional, array of objects, default: `null`):
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- **`name`** (required, string): Argument name (e.g., `days`).
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- **`type`** (required, string): Data type (e.g., `integer`).
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- **`description`** (optional, string): Argument purpose.
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- **Notes**: For `many-to-many` relationships, pre-aggregate entity data to avoid duplication (e.g., use a subquery).
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### Filters
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Reusable boolean conditions, optionally parameterized.
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- **`name`** (required, string): Filter name (e.g., `active_customer`).
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- **`expr`** (required, string): Boolean expression (e.g., `login_count > 1`). Can use `model.column` for entity columns.
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- **`description`** (optional, string): Filter explanation.
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- **`args`** (optional, array of objects, default: `null`):
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- **`name`** (required, string): Argument name.
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- **`type`** (required, string): Data type.
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- **`description`** (optional, string): Argument purpose.
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- **Notes**: Use `EXISTS` or subqueries for `many-to-many` to preserve intent without duplication.
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### Entities
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Relationships to other models, enabling multi-table joins.
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- **`name`** (required, string): Related model name (e.g., `logins`).
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- **`primary_key`** (required, string): Current model’s join column (e.g., `cultureid`).
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- **`foreign_key`** (required, string): Entity model’s join column (e.g., `cultureid`).
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- **`type`** (optional, string): Join type (`LEFT`, `INNER`, `RIGHT`, `FULL`); LLM decides if omitted based on `expr` context.
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- **`cardinality`** (optional, string, default: `null`): Relationship type (e.g., `one-to-many`, `many-to-many`).
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- **`description`** (optional, string): Relationship explanation.
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- **Notes**:
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- Join is `<current>.<primary_key> = <entity>.<foreign_key>` with LLM-chosen `type`.
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- `cardinality` hints at duplication risk (e.g., `many-to-many` may need subqueries).
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## SQL Compilation
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- **Joins**: Use `entities` to join models, with LLM selecting `type` if unspecified.
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- **Many-to-Many**:
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- Pre-aggregate entity data (e.g., `GROUP BY cultureid`) or use `EXISTS` to avoid duplicating base rows.
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- Example: `SELECT SUM(revenue) FROM culture WHERE EXISTS (SELECT 1 FROM culture_products WHERE ...)`.
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## Design Choices
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- **Option 3**: `filters` and `metrics` can reference entity columns, reducing model sprawl.
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- **Key Pairs**: `primary_key`/`foreign_key` over `join_on` for structured parsing and LLM ease.
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- **Dynamic Joins**: Optional `type` lets the LLM adapt to query context, balancing flexibility and simplicity.
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