dbt-labs/dbt-core · error · minijinja::Error::InvalidOperation

parse_columns_and_constraints is only available for Databric

Error message

parse_columns_and_constraints is only available for Databricks/Spark adapter

What it means

parse_columns_and_constraints parses existing columns plus model constraints into Databricks/Spark constraint syntax, and is explicitly restricted to the Databricks and Spark adapters. Any other adapter type immediately raises an InvalidOperation error before any parsing happens.

Source

Thrown at crates/dbt-adapter/src/adapter/adapter_impl.rs:4643

    ///
    /// Returns [enriched_columns, typed_constraints] for use with get_column_and_constraints_sql
    /// and relation.enrich().
    ///
    /// DatabricksAdapter https://github.com/databricks/dbt-databricks/blob/45351e11517d3f37c5ac7a736b5fcba453d3f368/dbt/adapters/databricks/impl.py#L1038
    pub fn parse_columns_and_constraints(
        &self,
        _state: &State,
        existing_columns: &Value,
        model_columns: &Value,
        model_constraints: &Value,
        contract_enforced: bool,
        model_name: &str,
    ) -> Result<Value, minijinja::Error> {
        use crate::relation::databricks::typed_constraint;
        use std::collections::{BTreeMap, BTreeSet};

        if self.adapter_type() != Databricks && self.adapter_type() != Spark {
            return Err(minijinja::Error::new(
                minijinja::ErrorKind::InvalidOperation,
                "parse_columns_and_constraints is only available for Databricks/Spark adapter",
            ));
        }

        let columns: Vec<Column> = existing_columns
            .try_iter()
            .map_err(|e| {
                minijinja::Error::new(
                    minijinja::ErrorKind::InvalidOperation,
                    format!("existing_columns must be iterable: {e}"),
                )
            })?
            .map(|v| {
                v.downcast_object_ref::<Column>().cloned().ok_or_else(|| {
                    minijinja::Error::new(
                        minijinja::ErrorKind::InvalidOperation,
                        "existing_columns must contain Column objects",

View on GitHub (pinned to 0267ce9170)

Solutions

1. Only call parse_columns_and_constraints from Databricks or Spark models/macros.
2. Add an adapter-type guard in your macro with a graceful fallback for other adapters.
3. Move constraint parsing into adapter-conditional logic if the materialization is shared.
4. Check the adapter configured in profiles.yml matches what the model's materialization expects.

Example fix

// before
parsed = adapter.parse_columns_and_constraints(existing_columns, model_columns, model_name)

// after
if adapter.type() in ('databricks', 'spark'):
    parsed = adapter.parse_columns_and_constraints(existing_columns, model_columns, model_name)
else:
    parsed = model_columns

def supports_columns_and_constraints(adapter_type):
    return adapter_type in ('databricks', 'spark')

def is_databricks_or_spark(adapter_type):
    return adapter_type in ('databricks', 'spark')

try:
    parsed = adapter.parse_columns_and_constraints(existing, model_columns, name)
except Exception as e:
    if 'Databricks/Spark adapter' in str(e):
        parsed = model_columns  # fallback for other adapters
    else:
        raise

When it happens

Trigger: Calling parse_columns_and_constraints while self.adapter_type() is neither Databricks nor Spark — e.g. under Postgres, Snowflake, or BigQuery.

Common situations: Reusing a Databricks custom materialization macro on another warehouse; running a project with mixed adapters where the macro doesn't dispatch on adapter type.

Understand the failure class

Background: UnsupportedOperationException and "is not supported" errors: when a library deliberately refuses a call — this error's family across 30 libraries.

Related errors

• render_constraints_for_create is only available for Databric
• {type(df)} is not a supported type for dbt Python materializ
• only available with Databricksadapter
• only available with Databricks adapter
• resolve_file_format is only supported in Databricks

AI-assisted analysis of dbt-labs/dbt-core@0267ce9170 (2026-09-07). Data as JSON: /api/errors/4c9f54f278b11aa2. Report an issue: GitHub.