Metrics Generation Guide¶

Overview¶

The metrics generation feature helps you convert SQL queries into reusable MetricFlow metric definitions. Using an AI assistant, you can analyze SQL business logic and automatically generate standardized YAML metric configurations that can be queried consistently across your organization.

What is a Metric?¶

A metric is a reusable business calculation built on top of semantic models. Metrics provide: - Consistent Business Logic: One definition, used everywhere - Type Safety: Validated structure and measure references - Metadata: Display names, formats, business context - Composability: Build complex metrics from simpler ones

Example: Instead of writing SELECT SUM(revenue) / COUNT(DISTINCT customer_id) repeatedly, define an avg_customer_revenue metric once.

Important Limitations¶

⚠️ Single Table Queries Only

The current version only supports generating metrics from single-table SQL queries. Multi-table JOINs are not supported.

Supported:

SELECT SUM(revenue) FROM transactions WHERE status = 'completed'
SELECT COUNT(DISTINCT customer_id) / COUNT(*) FROM orders

Not Supported:

SELECT SUM(o.amount)
FROM orders o
JOIN customers c ON o.customer_id = c.id  -- ❌ JOIN not supported

How It Works¶

Start Datus CLI with datus --namespace <namespace>, and use the metrics generation subagent:

/gen_metrics Generate a metric from this SQL: SELECT SUM(amount) FROM transactions, the coresponding question is total amount of all transactions

Generation Workflow¶

User provides SQL and question → Agent analyzes logic → Finds semantic model → Reads measures →
Checks for duplicates → Generates metric YAML → Appends to file → Validates →
User confirms → Syncs to Knowledge Base

Interactive Confirmation¶

After generation, you'll see:

==========================================================
Generated YAML: transactions.yml
Path: /Users/you/.datus/data/semantic_models/transactions.yml
==========================================================
[YAML content with syntax highlighting showing the new metric]

  SYNC TO KNOWLEDGE BASE?

  1. Yes - Save to Knowledge Base
  2. No - Keep file only

Please enter your choice: [1/2]

Options: - Option 1: Syncs the metric to your Knowledge Base for AI-powered semantic search - Option 2: Keeps the YAML file only without syncing to the Knowledge Base

Configuration¶

Agent Configuration¶

Most configurations are built-in. In agent.yml, minimal setup is needed:

agentic_nodes:
  gen_metrics:
    model: claude        # Optional: defaults to configured model
    max_turns: 40        # Optional: defaults to 30

Built-in configurations (automatically enabled): - Tools: Generation tools and filesystem tools - Hooks: User confirmation workflow in interactive mode - MCP Server: MetricFlow validation server - System Prompt: Built-in template version 1.0 - Workspace: ~/.datus/data/{namespace}/semantic_models

Configuration Options¶

Parameter	Required	Description	Default
`model`	No	LLM model to use	Uses default configured model
`max_turns`	No	Maximum conversation turns	30

Subject Tree Categorization¶

Subject tree allows organizing metrics by domain and layers for better management. In CLI mode, include it in your question:

Example with subject_tree:

/gen_metrics Generate a metric from this SQL: SELECT SUM(amount) FROM transactions, subject_tree: finance/revenue/transactions

Example without subject_tree:

/gen_metrics Generate a metric from this SQL: SELECT SUM(amount) FROM transactions

When subject_tree is provided, the metric will be categorized accordingly (e.g., domain: finance, layer1: revenue, layer2: transactions). If not provided, the agent operates in learning mode and may suggest categories based on existing metrics in the Knowledge Base.

Usage Examples¶

Example 1: Simple Aggregation¶

User Input:

/gen_metrics Generate a metric for total order count

Agent Actions: 1. Finds orders.yml semantic model 2. Reads file to discover order_count measure 3. Generates MEASURE_PROXY metric:

---
metric:
  name: total_orders
  description: Total number of orders
  type: measure_proxy
  type_params:
    measure: order_count
  locked_metadata:
    display_name: "Total Orders"
    increase_is_good: true

Example 2: Conversion Rate¶

User Input:

/gen_metrics Create a metric from this SQL:
SELECT
  COUNT(DISTINCT CASE WHEN status = 'completed' THEN order_id END) /
  COUNT(DISTINCT order_id) AS completion_rate
FROM orders

Agent Actions: 1. Analyzes SQL logic (ratio with conditional counting) 2. Finds orders.yml and reads measures 3. Generates RATIO metric with constraint:

---
metric:
  name: order_completion_rate
  description: Percentage of orders that reached completed status
  type: ratio
  type_params:
    numerator:
      name: order_count
      constraint: status = 'completed'
    denominator: order_count
  locked_metadata:
    display_name: "Order Completion Rate"
    value_format: ".2%"
    increase_is_good: true

Example 3: Complex Calculation¶

User Input:

/gen_metrics Generate average basket size metric:
SELECT SUM(total_amount) / COUNT(DISTINCT order_id)
FROM order_items

Agent Actions: 1. Locates order_items.yml 2. Identifies this as a RATIO (average) 3. Generates metric:

---
metric:
  name: avg_basket_size
  description: Average order value (basket size)
  type: ratio
  type_params:
    numerator: total_amount
    denominator: order_count
  locked_metadata:
    display_name: "Average Basket Size"
    value_format: "$$,.2f"
    unit: "dollars"
    increase_is_good: true

How Metrics Are Stored¶

File Organization¶

Metrics are appended to existing semantic model files using the YAML document separator ---:

# Existing semantic model
data_source:
  name: transactions
  sql_table: transactions
  measures:
    - name: revenue
      agg: SUM
      expr: amount
  dimensions:
    - name: transaction_date
      type: TIME

---
# First metric (appended)
metric:
  name: total_revenue
  type: measure_proxy
  type_params:
    measure: revenue

---
# Second metric (appended)
metric:
  name: avg_transaction_value
  type: ratio
  type_params:
    numerator: revenue
    denominator: transaction_count

Why append instead of separate files? - Keeps related metrics close to their semantic model - Easier maintenance and validation - MetricFlow can validate all definitions together

Knowledge Base Storage¶

When you choose "1. Yes - Save to Knowledge Base", the metric is stored in a Vector Database with:

Metadata: Name, description, type, domain/layer classification
LLM Text: Natural language representation for semantic search
References: Associated semantic model name
Timestamp: Creation date

Summary¶

The metrics generation feature provides:

✅ SQL-to-Metric Conversion: Analyze SQL queries and generate MetricFlow metrics ✅ Intelligent Type Detection: Automatically selects the right metric type ✅ Duplicate Prevention: Checks for existing metrics before generation ✅ Validation: MetricFlow validation ensures correctness ✅ Interactive Workflow: Review and approve before syncing ✅ Knowledge Base Integration: Semantic search for metric discovery ✅ File Management: Appends to existing semantic model files safely