How to Define Metrics with Aggregate Expressions in the Ossie Specification

In Apache Ossie, metrics are top-level objects within a semantic model that use SQL aggregate expressions—such as SUM, COUNT, or AVG—to define quantitative business calculations across one or more datasets.

The Apache Ossie specification provides a framework for building semantic models that standardize business metrics across data platforms. When you define metrics with aggregate expressions in the Ossie specification, you create portable, dialect-aware calculations that reference fields from any dataset in your model. These definitions reside in YAML configuration files governed by the Core Metadata Specification and the Expression Language Specification.

Metric Schema Structure

According to the core-spec/spec.md file, each metric follows the schema defined in the Metrics table. A metric requires a unique name and an expression object containing one or more SQL dialects, with ANSI_SQL serving as the default. You can optionally include a human-readable description, logical datatype, ai_context metadata for natural language interfaces, and vendor-specific custom_extensions.

Supported Aggregate Functions

The core-spec/expression_language.md file specifies the portable SQL subset that Ossie implementations must support. Required aggregation functions include SUM, COUNT, AVG, MEDIAN, and APPROX_COUNT_DISTINCT. Expressions can be single aggregates or compositions of aggregates, such as ratios that divide one aggregation by another (for example, SUM(sales) / COUNT(orders)).

Defining Aggregate Metrics in YAML

To create a metric with an aggregate expression, follow these steps:

1. Create a metric entry under the metrics array of the semantic model.
2. Assign a unique name to the metric.
3. Add an expression object containing a dialects list.
4. Write the aggregate expression in your chosen dialect (typically ANSI_SQL).
5. Optionally configure description, datatype, ai_context, and custom_extensions.

Simple Aggregation on a Single Dataset

This example defines a total revenue metric using the SUM aggregate on a single dataset:

metrics:
  - name: total_revenue
    description: Total revenue across all orders
    datatype: Decimal
    expression:
      dialects:
        - dialect: ANSI_SQL
          expression: SUM(orders.amount)
    ai_context:
      synonyms:
        - "total sales"
        - "revenue"

Cross-Dataset Calculations

Metrics can reference fields from multiple datasets to create composite calculations. This example calculates average order value per customer by aggregating across orders and customers datasets:

metrics:
  - name: avg_order_value
    description: Average revenue per distinct customer
    datatype: Decimal
    expression:
      dialects:
        - dialect: ANSI_SQL
          expression: SUM(orders.amount) / COUNT(DISTINCT customers.id)

Multi-Dialect Expressions

When working with platform-specific syntax, supply multiple dialects. Ossie implementations select dialects in deterministic order: ANSI SQL first, followed by Snowflake, Databricks, and others:

metrics:
  - name: monthly_sales
    description: Sales summed per calendar month
    datatype: Decimal
    expression:
      dialects:
        - dialect: ANSI_SQL
          expression: SUM(orders.amount) GROUP BY DATE_TRUNC('month', orders.order_date)
        - dialect: SNOWFLAKE
          expression: SUM(orders.amount) GROUP BY DATE_TRUNC('month', orders.order_date)

Approximate Aggregations

For large-scale datasets, use approximate aggregation functions to improve query performance:

metrics:
  - name: unique_customers_approx
    description: Approximate count of distinct customers
    datatype: Integer
    expression:
      dialects:
        - dialect: ANSI_SQL
          expression: APPROX_COUNT_DISTINCT(customers.id)

Expression Language and Dialect Handling

The Expression Language specification in core-spec/expression_language.md mandates support for standard SQL aggregates while allowing dialect-specific optimizations. When multiple dialects are provided, implementations process them in a deterministic order, defaulting to ANSI_SQL when no specific platform dialect matches. This ensures that metrics remain portable across different query engines while preserving calculation semantics.

Summary

• Metrics in Apache Ossie are defined in the core-spec/spec.md schema as top-level objects with unique names and SQL expressions.
• Aggregate expressions must use supported functions from the core-spec/expression_language.md specification, including SUM, COUNT, AVG, and APPROX_COUNT_DISTINCT.
• YAML configurations support multiple SQL dialects, with ANSI_SQL as the default and deterministic fallback ordering.
• Cross-dataset calculations allow metrics to reference fields from any dataset within the semantic model.

Frequently Asked Questions

What aggregate functions are supported in the Ossie Expression Language?

The Ossie Expression Language requires implementations to support standard SQL aggregates including SUM, COUNT, AVG, MEDIAN, and APPROX_COUNT_DISTINCT. You can also compose these into ratios or more complex expressions as defined in core-spec/expression_language.md.

Can a single metric reference fields from multiple datasets?

Yes. Metrics can reference fields from any dataset defined in the semantic model, enabling cross-dataset calculations such as dividing aggregated values from one dataset by distinct counts from another.

How does Ossie handle multiple SQL dialects in a single metric expression?

When you provide multiple dialects, Ossie implementations select them in a deterministic order: ANSI SQL first, followed by platform-specific dialects like Snowflake or Databricks. If no specific dialect matches, the system defaults to the ANSI_SQL expression.

What is the purpose of the ai_context field in metric definitions?

The ai_context field provides metadata such as synonyms and descriptions specifically designed for AI systems, helping natural language interfaces understand and correctly map user queries to the defined metric calculations.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}