How Ossie Handles Multiple SQL Dialects: Native Multi-Database Expression Support

Apache Ossie stores every logical expression as a set of dialect-specific SQL renderings, allowing converters to automatically select the appropriate variant for each target platform while falling back to ANSI SQL when needed.

Ossie is an open-source semantic modeling framework designed to eliminate vendor lock-in by letting analytics teams define metrics and fields once and deploy them across heterogeneous data platforms. Instead of relying on runtime SQL translation, Ossie treats each expression as a collection of dialect-specific variants, ensuring consistent semantics across multiple SQL dialects including Snowflake, Databricks, and BigQuery.

The Core Expression Object Model

Dialect Enumeration in the Specification

The Ossie core specification defines a comprehensive enumeration of supported dialect identifiers in the Dialects table of core-spec/spec.md. Valid values include ANSI_SQL, SNOWFLAKE, DATABRICKS, BIGQUERY, MDX, TABLEAU, and MAQL, among others. The core-spec/expression_language.md file further describes the portable subset of SQL that all implementations must support, ensuring that ANSI_SQL expressions behave consistently across converters.

The Dialects Array Structure

Every field or metric in Ossie contains an expression object whose dialects array holds one or more entries. Each entry specifies a dialect (matching the enumeration) and the concrete SQL text for that dialect. According to core-spec/spec.md#Expression-Object, this structure allows a single logical concept to carry multiple physical implementations optimized for different engines.

# A field with three dialect versions

- name: email_normalized
  expression:
    dialects:
      - dialect: ANSI_SQL
        expression: LOWER(email)
      - dialect: SNOWFLAKE
        expression: LOWER(email)::VARCHAR
      - dialect: BIGQUERY
        expression: SAFE_CAST(LOWER(email) AS STRING)
  description: Normalized email address

Converter Dialect Selection Logic

When a converter exports the model to a specific platform, it implements a deterministic selection algorithm documented in converters/README.md#Expression-dialect-selection. The converter scans the dialects list and selects the entry whose dialect value matches the target platform identifier.

If the platform-specific dialect is absent, the converter automatically falls back to the generic ANSI_SQL expression. This guarantees that models remain functional even when optimized variants for every dialect are not provided. Concrete command-line usage and dialect handling examples are provided in the Omni converter documentation at converters/omni/README.md.

# A metric that prefers Snowflake but falls back to ANSI_SQL

- name: total_revenue
  expression:
    dialects:
      - dialect: SNOWFLAKE
        expression: SUM(ORDER_AMT)
      - dialect: ANSI_SQL
        expression: SUM(orders.amount)
  description: Total revenue across all orders

Fallback Behavior and Error Handling

When neither a platform-specific dialect nor ANSI_SQL is available, converter behavior depends on the target tool's capabilities. Converters may emit a warning such as "UNSUPPORTED_DIALECT" or drop the metric entirely to prevent runtime errors. The Wisdom and Orionbelt converter implementations document these specific error-handling strategies.

Extensibility and Future-Proofing

The Ossie specification allows new dialect identifiers to be added to the enumeration as the analytics ecosystem evolves. Converters can be extended to recognize these new identifiers while maintaining the deterministic fallback path to ANSI_SQL. This architecture ensures that semantic models remain portable and future-proof without requiring rewrites when new databases emerge.

Summary

• Ossie stores expressions as arrays of dialect-specific SQL variants rather than single strings.
• The core-spec/spec.md defines the Dialects enumeration and Expression Object structure.
• Converters automatically select the best matching dialect and fall back to ANSI_SQL when needed.
• Unsupported dialects trigger warnings or metric suppression based on converter configuration.
• The model is extensible, allowing new dialects to be added without breaking existing implementations.

Frequently Asked Questions

What SQL dialects does Ossie support out of the box?

The Ossie core specification defines dialect identifiers including ANSI_SQL, SNOWFLAKE, DATABRICKS, BIGQUERY, MDX, TABLEAU, and MAQL. This list is maintained in the Dialects table of core-spec/spec.md.

How does Ossie handle unsupported SQL dialects?

When a converter encounters an expression without a matching dialect or valid ANSI_SQL fallback, it either emits a warning (such as "UNSUPPORTED_DIALECT") or drops the metric entirely, depending on the specific converter implementation and target tool limitations.

Can I add custom dialects to the Ossie specification?

Yes, the specification is designed for extensibility. New dialect identifiers can be added to the enumeration, and converters can be extended to recognize them while maintaining backward compatibility through the ANSI_SQL fallback mechanism.

Does Ossie automatically translate SQL between dialects?

No, Ossie does not perform automatic SQL translation. Instead, it relies on model authors to provide explicit dialect variants for each expression. Converters then select the appropriate pre-written variant, ensuring predictable behavior and optimal performance on each platform.

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    "instagit": {
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}