How to Define a Metric with Multi-Dialect SQL Expressions in Apache Ossie

Apache Ossie models metrics as first-class semantic objects that support multiple SQL dialects within a single expression field, enabling cross-platform portability and graceful fallback for BI tools and AI engines.

Apache Ossie treats metrics as core components of its semantic modeling layer, allowing data teams to define calculations once and execute them across different warehouses. The repository's specification enables multi-dialect SQL expressions within individual metric definitions, ensuring that the same logical business measure can adapt to ANSI SQL, BigQuery, Snowflake, or other engines without rewriting the underlying model.

Understanding the Multi-Dialect Expression Structure

In core-spec/spec.md, the metric schema defines an expression field that contains a dialects list rather than a single SQL string【/cache/repos/github.com/apache/ossie/main/core-spec/spec.md#L30-L33】. Each entry in this array must specify:

• dialect – The identifier for the SQL variant (e.g., ANSI_SQL, BIGQUERY, SNOWFLAKE)
• expression – The SQL fragment implementing the metric logic for that specific engine

When a consumer such as a BI tool, AI engine, or conversion utility reads the model, it iterates through the dialects list and selects the first entry it understands. If no matching dialect is found, the metric remains defined but may be treated as vendor-specific or ignored, preserving the definition for future processing.

Defining the Metric Schema

The specification mandates that every metric containing multi-dialect expressions follows a strict schema. According to the core specification, the expression object structure ensures forward compatibility while supporting vendor extensions【/cache/repos/github.com/apache/ossie/main/core-spec/spec.md#L30-L33】.

Key requirements include:

1. Dialect specificity – Each SQL variant must be explicitly named using standardized identifiers
2. Expression validity – The SQL fragment must conform to the subset defined in core-spec/expression_language.md
3. Ordering priority – List dialects from most specific to most general (e.g., SNOWFLAKE before ANSI_SQL) to ensure optimal execution path selection

Practical Implementation Examples

Basic Metric with ANSI SQL and BigQuery

Define a revenue metric that works across standard SQL warehouses and Google BigQuery:

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

Cross-Dataset Calculations

Create metrics that reference multiple tables using standard ANSI SQL syntax for broad compatibility:

- name: avg_orders_per_customer
  expression:
    - dialect: ANSI_SQL
      expression: SUM(orders.amount) / COUNT(DISTINCT customers.id)
  description: Average order value per distinct customer

Vendor-Specific Extensions

Leverage warehouse-specific functions while maintaining fallback options. This example uses Snowflake's COUNT_DISTINCT window function with an ANSI SQL fallback:

- name: monthly_active_users
  expression:
    - dialect: SNOWFLAKE
      expression: COUNT_DISTINCT(user_id) OVER (PARTITION BY month)
    - dialect: ANSI_SQL
      expression: COUNT(DISTINCT user_id)
  description: Number of unique users active each month

Cross-Platform Portability and Fallback Behavior

The multi-dialect design enables three critical capabilities for enterprise data architectures:

• Cross-platform portability – The same logical metric executes on different warehouses without model rewriting
• Graceful fallback – Tools default to ANSI_SQL when native dialects are unavailable, ensuring broad compatibility
• Vendor extensions – Additional dialects can be added without schema changes, maintaining forward compatibility

As documented in docs/index.md, this architectural pattern ensures that semantic models remain portable across the modern data stack while accommodating vendor-specific optimizations【/cache/repos/github.com/apache/ossie/main/docs/index.md#L198-L200】.

Expression Language Compliance

All dialect implementations must support the SQL subset defined in core-spec/expression_language.md【/cache/repos/github.com/apache/ossie/main/core-spec/expression_language.md】. This specification establishes the baseline operations that every expression must support, ensuring that fallback dialects produce semantically equivalent results to their optimized counterparts.

When authoring multi-dialect expressions, verify that:

• Aggregate functions follow the standardized syntax
• Window functions include explicit framing clauses where required
• Cross-table references use unambiguous table aliases

Summary

• Apache Ossie metrics support multiple SQL dialects through a structured dialects list within the expression field
• Each dialect entry requires a dialect identifier and corresponding expression string
• Consumers select the first compatible dialect, falling back to ANSI_SQL when specific variants are unavailable
• The core specification (core-spec/spec.md) and expression language definition (core-spec/expression_language.md) govern valid syntax and structure
• This architecture enables cross-platform portability while allowing vendor-specific optimizations without breaking model compatibility

Frequently Asked Questions

How does Apache Ossie handle unsupported SQL dialects?

When a consumer encounters a metric containing only unrecognized dialects, the metric remains present in the semantic model but may be ignored or treated as vendor-specific. The definition is preserved for future processing, allowing models to maintain forward compatibility as tools add support for additional dialects.

Can I mix ANSI SQL with multiple vendor-specific dialects in one metric?

Yes. The dialects array supports unlimited entries, allowing you to define SNOWFLAKE, BIGQUERY, and ANSI_SQL variants within the same metric. List them in order of preference, with specialized dialects first and ANSI_SQL last as the universal fallback.

Where is the expression language subset defined in the repository?

The SQL subset that all implementations must support is defined in core-spec/expression_language.md【/cache/repos/github.com/apache/ossie/main/core-spec/expression_language.md】. This document ensures that expressions written for ANSI_SQL will execute consistently across any compliant engine, while vendor dialects may extend beyond this baseline for optimization.

What is the recommended order for listing dialects in a metric?

List dialects from most specific to most general. Place vendor-optimized expressions (e.g., SNOWFLAKE, BIGQUERY) at the beginning of the array, followed by ANSI_SQL at the end. This ensures engines select their native implementation when available while maintaining broad compatibility through the standard SQL fallback.

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