databricks-metric-views（databricks 指标视图）

Unity Catalog Metric Views

Define reusable, governed business metrics in YAML that separate measure definitions from dimension groupings for flexible querying.

When to Use

Use this skill when:

• Defining standardized business metrics (revenue, order counts, conversion rates)
• Building KPI layers shared across dashboards, Genie, and SQL queries
• Creating metrics with complex aggregations (ratios, distinct counts, filtered measures)
• Defining window measures (moving averages, running totals, period-over-period, YTD)
• Modeling star or snowflake schemas with joins in metric definitions
• Enabling materialization for pre-computed metric aggregations

Prerequisites

• Databricks Runtime 17.2+ (for YAML version 1.1)
• SQL warehouse with CAN USE permissions
• SELECT on source tables, CREATE TABLE + USE SCHEMA in the target schema

Quick Start

Inspect Source Table Schema

Before creating a metric view, call get_table_stats_and_schema to understand available columns for dimensions and measures:

get_table_stats_and_schema(
    catalog="catalog",
    schema="schema",
    table_names=["orders"],
    table_stat_level="SIMPLE"  # Use "DETAILED" for cardinality, min/max, histograms
)

Create a Metric View

CREATE OR REPLACE VIEW catalog.schema.orders_metrics
WITH METRICS
LANGUAGE YAML
AS $$
  version: 1.1
  source: catalog.schema.orders
  comment: "Orders KPIs for sales analysis"
  filter: order_date > '2020-01-01'
  dimensions:
    - name: Order Month
      expr: DATE_TRUNC('MONTH', order_date)
      comment: "Month of order"
    - name: Order Status
      expr: CASE
        WHEN status = 'O' THEN 'Open'
        WHEN status = 'P' THEN 'Processing'
        WHEN status = 'F' THEN 'Fulfilled'
        END
      comment: "Human-readable order status"
  measures:
    - name: Order Count
      expr: COUNT(1)
    - name: Total Revenue
      expr: SUM(total_price)
      comment: "Sum of total price"
    - name: Revenue per Customer
      expr: SUM(total_price) / COUNT(DISTINCT customer_id)
      comment: "Average revenue per unique customer"
$$

Query a Metric View

All measures must use the MEASURE() function. SELECT * is NOT supported.

SELECT
  `Order Month`,
  `Order Status`,
  MEASURE(`Total Revenue`) AS total_revenue,
  MEASURE(`Order Count`) AS order_count
FROM catalog.schema.orders_metrics
WHERE extract(year FROM `Order Month`) = 2024
GROUP BY ALL
ORDER BY ALL

Reference Files

MCP Tools

Use the manage_metric_views tool for all metric view operations:

Create via MCP

manage_metric_views(
    action="create",
    full_name="catalog.schema.orders_metrics",
    source="catalog.schema.orders",
    or_replace=True,
    comment="Orders KPIs for sales analysis",
    filter_expr="order_date > '2020-01-01'",
    dimensions=[
        {"name": "Order Month", "expr": "DATE_TRUNC('MONTH', order_date)", "comment": "Month of order"},
        {"name": "Order Status", "expr": "status"},
    ],
    measures=[
        {"name": "Order Count", "expr": "COUNT(1)"},
        {"name": "Total Revenue", "expr": "SUM(total_price)", "comment": "Sum of total price"},
    ],
)

Query via MCP

manage_metric_views(
    action="query",
    full_name="catalog.schema.orders_metrics",
    query_measures=["Total Revenue", "Order Count"],
    query_dimensions=["Order Month"],
    where="extract(year FROM `Order Month`) = 2024",
    order_by="ALL",
    limit=100,
)

Describe via MCP

manage_metric_views(
    action="describe",
    full_name="catalog.schema.orders_metrics",
)

Grant Access

manage_metric_views(
    action="grant",
    full_name="catalog.schema.orders_metrics",
    principal="data-consumers",
    privileges=["SELECT"],
)

YAML Spec Quick Reference

version: 1.1                    # Required: "1.1" for DBR 17.2+
source: catalog.schema.table    # Required: source table/view
comment: "Description"          # Optional: metric view description
filter: column > value          # Optional: global WHERE filter

dimensions:                     # Required: at least one
  - name: Display Name          # Backtick-quoted in queries
    expr: sql_expression        # Column ref or SQL transformation
    comment: "Description"      # Optional (v1.1+)

measures:                       # Required: at least one
  - name: Display Name          # Queried via MEASURE(`name`)
    expr: AGG_FUNC(column)      # Must be an aggregate expression
    comment: "Description"      # Optional (v1.1+)

joins:                          # Optional: star/snowflake schema
  - name: dim_table
    source: catalog.schema.dim_table
    on: source.fk = dim_table.pk

materialization:                # Optional (experimental)
  schedule: every 6 hours
  mode: relaxed

Key Concepts

Dimensions vs Measures

Why Metric Views vs Standard Views?

Common Issues

Integrations

Metric views work natively with:

• AI/BI Dashboards - Use as datasets for visualizations
• AI/BI Genie - Natural language querying of metrics
• Alerts - Set threshold-based alerts on measures
• SQL Editor - Direct SQL querying with MEASURE()
• Catalog Explorer UI - Visual creation and browsing

Resources

• Metric Views Documentation
• YAML Syntax Reference
• Joins
• Window Measures (Experimental)
• Materialization
• MEASURE() Function