> ## Documentation Index
> Fetch the complete documentation index at: https://elementary-devin-1782754750-bigquery-permissions-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Capture compiled code for microbatch models

Elementary can capture and store the compiled SQL of [dbt microbatch incremental models](https://docs.getdbt.com/docs/build/incremental-microbatch) in `dbt_run_results.compiled_code`. By default dbt does not surface compiled code for the microbatch strategy, so this column is empty for microbatch models until you enable the setup below.

## How it works

Elementary provides the `capture_and_execute_microbatch_compiled_code_sql` macro that captures the compiled SQL of each batch as it runs. The captured code is cached during the invocation and later written to `dbt_run_results.compiled_code`, so microbatch models populate this column the same way other incremental strategies do.

## Enabling microbatch compiled code capture

<Steps>
  <Step title="Override the microbatch strategy macro in your project">
    Add a macro that delegates to Elementary's implementation. Place it under your project's `macros/` directory:

    ```sql filename="macros/get_incremental_microbatch_sql.sql" theme={null}
    {% macro get_incremental_microbatch_sql(arg_dict) %}
      {{ return(elementary.capture_and_execute_microbatch_compiled_code_sql(arg_dict)) }}
    {% endmacro %}
    ```
  </Step>

  <Step title="Enable the dbt behavior flag">
    Add the `require_batched_execution_for_custom_microbatch_strategy` flag to your `dbt_project.yml`:

    ```yaml filename="dbt_project.yml" theme={null}
    flags:
      require_batched_execution_for_custom_microbatch_strategy: True
    ```

    This flag tells dbt to use your project-level override of the microbatch strategy with batched execution.
  </Step>

  <Step title="Run your microbatch models">
    On the next `dbt run` or `dbt build`, Elementary captures the compiled SQL of each microbatch model and writes it to `dbt_run_results.compiled_code`.
  </Step>
</Steps>

## Unsupported configurations

<Warning>
  The override flow is currently not supported on the following adapters:

  * Spark
  * BigQuery
  * Athena
  * ClickHouse
  * Dremio
  * Vertica

  It is also not supported on dbt Fusion.

  On unsupported adapters and on Fusion, microbatch models continue to run normally but `dbt_run_results.compiled_code` remains empty for them.
</Warning>