Dbt Fusion & Elementary Package: Compatibility Issues

Oct 21, 2025 by SLV Team 54 views

Hey guys! Today, we're diving into a tricky issue that some of you might encounter when using the Elementary dbt package with dbt Fusion. Specifically, we'll be dissecting a compatibility problem that arises during the execution of the dbt_snowflake.materialization_incremental_snowflake macro. If you've been scratching your head over this, you're in the right place! Let's break it down and see what's going on.

The Bug: A Deep Dive

So, what's the actual problem? The error manifests itself during the materialization process, particularly when running Elementary with dbt Fusion. It's like trying to fit a square peg in a round hole – things just don't quite line up. The error message you might see looks something like this:

Failed [ 7.12s] model dbt_yragheb_DBT_METADATA.dbt_models (incremental)
error: dbt1501: Error executing materialization macro 'dbt_snowflake.materialization_incremental_snowflake' for model model.elementary.dbt_models: Failed to eval the compiled Jinja expression undefined value
(in compiled/models/edr/dbt_artifacts/dbt_models.sql:1:14)
(in dbt_internal_packages/dbt-snowflake/macros/materializations/incremental.sql:172:3)
...
--> compiled/models/edr/dbt_artifacts/dbt_models.sql:1:14

This error essentially means that the macro dbt_snowflake.materialization_incremental_snowflake is failing because it can't find a value it expects. It's like asking for an ingredient that isn't in the recipe. Specifically, the issue revolves around an undefined value within the Jinja expression, which is used to dynamically generate SQL code.

Steps to Reproduce

If you're the hands-on type and want to see this in action, here’s how you can reproduce the bug:

Upgrade your Elementary package: Make sure you're using a version within the [>=0.20.0, <0.21.0] range in your packages.yml file. This is crucial because the issue is specific to these versions.
```
packages:
  - package: elementary-data/elementary
    version: [">=0.20.0", "<0.21.0"]
```
Ensure dbt Fusion Compatibility: Adapt your codebase to be compatible with dbt Fusion. This might involve some changes in how you structure your dbt project or how you call certain functions.
Run Elementary models with dbt Fusion: Use the command dbtf run --select elementary to execute the Elementary package models. This is where the magic (or rather, the error) happens.
Observe the Error: You should now see the error message we discussed earlier, indicating the failure of the materialization macro.

Expected Behavior

Ideally, all models should run smoothly without any hiccups. You'd expect dbt Fusion and the Elementary package to play nicely together, but in this case, there's a bit of friction.

Environment Details

To give you a complete picture, here’s the environment where this issue was observed:

Elementary dbt package version: 0.20.1
dbt version: dbt-fusion 2.0.0-preview.45
Data warehouse: Snowflake
Infrastructure: MacOS, dev environment

Knowing these details can help narrow down the problem and find a solution that works for your setup.

The Root Cause: `thread_id` and dbt Fusion

So, what's the real reason behind this error? After some digging, it turns out the culprit is the thread_id variable. In older versions of dbt, thread_id was a global variable that could be accessed from anywhere in your dbt project. However, dbt Fusion changed things up, and this global thread_id is no longer available.

The Macro in Question

The specific area where this issue pops up is within the get_duration_context_stack() macro. This macro, typically found around lines 99-103 of the relevant Elementary package files, attempts to use thread_id. Because thread_id isn't defined in dbt Fusion's environment, the macro throws an error.

Why This Matters

This is a classic case of a breaking change in a software update. dbt Fusion's architectural changes inadvertently affected the Elementary package, which relied on the global thread_id. It highlights the importance of understanding how different parts of your data stack interact and the potential for unexpected issues when upgrading components.

The Suggested Solution: Macro Overriding

Now that we know the problem, what's the fix? One suggested solution, and a pretty effective one at that, is to override the problematic macro. This involves creating your own version of the get_duration_context_stack() macro within your dbt project and telling dbt to use your version instead of the one in the Elementary package.

How to Override the Macro

Here’s a step-by-step guide to overriding the macro:

Create a new macro file: In your dbt project, create a new file in your macros directory. You can name it something descriptive, like override_get_duration_context_stack.sql.
Define the overriding macro: In this file, define a macro with the same name as the one you want to override (get_duration_context_stack).
```
{% macro get_duration_context_stack() %}
  {# Override the macro and set the default thread to main #}
  {% set thread_id = 'main' %}
  {{ return('') }}
{% endmacro %}
```
Key Insight: Here, we are manually setting the thread_id to 'main'. This sidesteps the issue of the undefined thread_id in dbt Fusion. It's a pragmatic solution that allows the macro to function without relying on the missing global variable.
Test your changes: Run your dbt project, including the Elementary models, to ensure the override works as expected. You should no longer see the undefined value error.

Why This Works

Macro overriding is a powerful feature in dbt. It allows you to customize and extend the behavior of packages without directly modifying their code. This is particularly useful when dealing with compatibility issues or when you need to tweak functionality to fit your specific needs.

By overriding the get_duration_context_stack() macro and providing a default value for thread_id, we're essentially providing a fallback mechanism that dbt Fusion can use. This ensures that the macro can execute successfully, even though the global thread_id is no longer available.

Additional Considerations and Future Steps

While overriding the macro is a solid workaround, it's essential to consider the bigger picture. Here are a few things to keep in mind:

Package Updates

The Elementary package maintainers are likely aware of this issue and may release an updated version that includes a proper fix. Keep an eye on the package's release notes and consider upgrading when a new version becomes available. This is often the best long-term solution, as it ensures you're using the most up-to-date and compatible code.

dbt Fusion Updates

Similarly, dbt Fusion itself may undergo changes that address this compatibility issue. Stay informed about dbt Fusion's updates and consider how they might impact your project.

Contributing to the Community

The individual who initially reported this issue indicated they weren't able to contribute a fix directly. However, if you're comfortable with dbt and Python, consider contributing to the Elementary package or dbt Fusion. Your contributions can help improve the experience for everyone in the community.

Wrapping Up

So, there you have it – a deep dive into the compatibility issue between the Elementary dbt package and dbt Fusion. We've covered the bug, how to reproduce it, the root cause, a practical solution, and some additional considerations. This issue highlights the complexities of working with evolving data tools and the importance of understanding how different components interact.

By staying informed, leveraging techniques like macro overriding, and participating in the community, you can navigate these challenges and build robust and reliable data pipelines. Happy dbt-ing, folks!