topic Re: Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema in Data Engineering

Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema

animeshjain — Mon, 06 Jul 2026 12:30:19 GMT

Environment:

Bundle engine: direct
Resource type: Volume (Unity Catalog)

Problem:

I'm trying to use the lifecycle field with prevent_destroy: true on a Unity Catalog volume resource in my Declarative Automation Bundle (DAB). the validation fails with:

Error: Error while loading volume
at resources.volumes.raw_zone

ValueError: Unexpected field 'lifecycle' for class Volume

```yaml

resources:
volumes:
raw_zone:
catalog_name: "datazone_dev"
schema_name: "source"
name: "raw_zone"
comment: "Volume created for raw zone data storage"
volume_type: "MANAGED"
lifecycle:
prevent_destroy: true
grants:
- principal: "user-name1"
privileges:
- READ_VOLUME
- WRITE_VOLUME

```

When I run databricks bundle schema, the JSON schema clearly shows that lifecycle is a valid field for Volume resources:

"resources.Volume": {
"type": "object",
"properties": {
"catalog_name": { ... },
"comment": { ... },
"grants": { ... },
"lifecycle": {
"description": "Settings that control the deployment lifecycle of the resource, such as preventing it from being destroyed.",
"$ref": "#/$defs/github.com/databricks/cli/bundle/config/resources.Lifecycle"
},
"name": { ... },
"schema_name": { ... },
"volume_type": { ... }
}
}

is there something where I am wrong to get this error

Re: Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema

balajij8 — Thu, 09 Jul 2026 05:38:53 GMT

What you are seeing is expected behavior when the local execution environment is out of sync with the latest target schema definitions. prevent_destroy: true is a fully supported property for Unity Catalog Volume resources within Databricks Asset Bundles (DABs). The ValueError: Unexpected field 'lifecycle' for class Volume error is a strict validation failure thrown by an older Databricks CLI bundle engine that does not yet recognize the lifecycle class for Volume resource types.

You can follow below

If you are running Databricks CLI version older than v1.6.0 - The local environment you use lacks the updated Python-based schema definitions that introduced volume lifecycle management. You must upgrade your Databricks CLI to v1.6.0 or higher. You can verify your current version by executing databricks --version in the terminal.
If your CLI is already up to date (v1.6.0+) but validation still fails - It can be due to corrupted local state cache or a YAML syntax misalignment. Delete the hidden .databricks caching directory in the project root to clear the state, ensure strict YAML indentation under the raw_zone block, and execute databricks bundle validate to force a clean parse.
If you are experiencing this error within an automated CI/CD pipeline - The pipeline runner (GitHub Actions, Azure DevOps, GitLab) is pulling a stale, cached version of the Databricks CLI. Ensure the pipeline configuration explicitly specifies the latest CLI version tag in its setup step.

Re: Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema

animeshjain — Fri, 10 Jul 2026 10:26:40 GMT

Hi thanks for reply
I am running cli version 1.6.0 and still getting this error ,i also removed the .databricks directory from the project root
attaching screenshot for the same

Re: Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema

balajij8 — Fri, 10 Jul 2026 13:23:35 GMT

Hi,

The configuration is valid and the CLI version supports the lifecycle field. However, the error is most likely coming from outdated Databricks Python packages in your virtual environment at /home/animesh/datazone/.venv/. CLI uses the packages from the venv to parse and validate the bundle. Those packages are from an older version that doesn't recognize lifecycle as a valid field for volumes and hence the Unexpected field 'lifecycle' for class Volume error.

You can activate your virtual environment and upgrade the databricks-sdk & all other packages in the environment. You can upgrade to python 3.13.2 if feasible for better management. lifecycle.prevent_destroy field will be recognized and your bundle will validate and deploy successfully after its upgraded.

Re: Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema

bala_sai — Fri, 10 Jul 2026 16:14:08 GMT

@animeshjain I think the error is coming from the local Python bundle parser, not from the schema itself, your CLI is showing a schema where lifecycle is valid for volume, but may be the installed databricks.bundles.volumes runtime that actually parses the YAML is older and still rejects that field.

You can try checking if multiple Databricks CLI installs are present by running below

which databricks databricks --version python -c "import databricks.bundles.volumes as v; print(v.__file__)"

and if that points to an old virtualenv/site-packages path, do a clean reinstall of the Databricks CLI and upgrade to the latest available version rather than staying on 1.6.0. Removing .databricks alone might not fix this because the failure is in the installed parser code, not bundle state.

Re: Bundle Validation Error: Volume lifecycle Field Rejected Despite Being in Schema

binlogreader — Sat, 18 Jul 2026 20:40:05 GMT

@animeshjain What's happening is that two different components are checking your config, and they are at different versions.

databricks bundle schema is generated from the CLI binary itself, and your CLI is latest enough enough to know about lifecycle. That's why the schema shows the field. But the error is not coming from the CLI.

Look at the traceback path: site-packages/databricks/bundles/build.py and core/_transform.py. That is the databricks-bundles Python package, which runs because your bundle has Python support enabled. With Python support on, every resource in the bundle, including ones defined in plain YAML like your volume, gets loaded into typed Python dataclasses so that Python code can see and mutate them. Those dataclasses are strict, and the transform step raises on any field the class does not declare. Your installed version of that package has a Volume class with no lifecycle field, so it rejects the config that your CLI already accepted.

Have you tried updating the Python package? You can check what you're on with

pip show databricks-bundles

If it reports anything below 0.268.0, that's your answer. Upgrade it:

pip install --upgrade databricks-bundles

or, more likely in your case, bump the pin in whatever requirements or pyproject file builds your bundle's Python environment. The
pythonEnv-.../site-packages path in your traceback shows the environment is built fresh from declared dependencies, so the old version is coming from a pin somewhere rather than from a stale machine. The package is on a 1.x version line now, so anything current is well past the fix. Nothing in your resource definition needs to change; after the upgrade, rerun validate and the volume should load cleanly, and prevent_destroy works as expected with the direct engine.