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Elementary can capture and store the compiled SQL of dbt microbatch incremental models 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.

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How it works

Elementary provides an override macro for dbt’s get_incremental_microbatch_sql 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.

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Enabling microbatch compiled code capture

1

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:
{% macro get_incremental_microbatch_sql(arg_dict) %}
  {{ return(elementary.get_incremental_microbatch_sql(arg_dict)) }}
{% endmacro %}
2

Enable the dbt behavior flag

Add the require_batched_execution_for_custom_microbatch_strategy flag to your dbt_project.yml:
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.
3

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.

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Unsupported configurations

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.