mlflow/mlflow · error · ExecutionException

Got unexpected error response when checking whether file {db

Error message

Got unexpected error response when checking whether file {dbfs_path} exists in DBFS: {json_response_obj}

What it means

After successfully parsing the DBFS status response as JSON, `_dbfs_path_exists` expects an `error_code` field only to carry RESOURCE_DOES_NOT_EXIST. Any other Databricks error code in the JSON means the existence check failed unexpectedly, so it raises ExecutionException embedding the full response object.

Source

Thrown at mlflow/projects/databricks.py:152

        response = rest_utils.http_request(
            host_creds=host_creds,
            endpoint="/api/2.0/dbfs/get-status",
            method="GET",
            json={"path": f"/{dbfs_path}"},
        )
        try:
            json_response_obj = json.loads(response.text)
        except Exception:
            raise MlflowException(
                f"API request to check existence of file at DBFS path {dbfs_path} failed with "
                f"status code {response.status_code}. Response body: {response.text}"
            )
        # If request fails with a RESOURCE_DOES_NOT_EXIST error, the file does not exist on DBFS
        error_code_field = "error_code"
        if error_code_field in json_response_obj:
            if json_response_obj[error_code_field] == "RESOURCE_DOES_NOT_EXIST":
                return False
            raise ExecutionException(
                f"Got unexpected error response when checking whether file {dbfs_path} "
                f"exists in DBFS: {json_response_obj}"
            )
        return True

    def _upload_project_to_dbfs(self, project_dir, experiment_id):
        """
        Tars a project directory into an archive in a temp dir and uploads it to DBFS, returning
        the HDFS-style URI of the tarball in DBFS (e.g. dbfs:/path/to/tar).

        Args:
            project_dir: Path to a directory containing an MLflow project to upload to DBFS (e.g.
                a directory containing an MLproject file).
        """
        with tempfile.TemporaryDirectory() as temp_tarfile_dir:
            temp_tar_filename = os.path.join(temp_tarfile_dir, "project.tar.gz")

            def custom_filter(x):

View on GitHub (pinned to 6a27f2decc)

Solutions

Read the error_code in the message (e.g. PERMISSION_DENIED) and fix the underlying Databricks-side issue
Grant the token/service principal DBFS read/write permissions
Validate the DBFS path format passed to run / storage paths
Handle throttling by retrying later if the code indicates rate limiting

Example fix

null

Defensive patterns

Strategy: try-catch

Validate before calling

null

Try / catch

from mlflow.exceptions import ExecutionException
try:
    run_databricks_spark_job(...)
except ExecutionException as e:
    if 'exists in DBFS' in str(e):
        log.error('DBFS check failed: inspect error_code in message, fix permissions/path')

Prevention

Grant the service principal DBFS read/write access
Validate dbfs:// paths before launching
Check for permission/rate-limit error codes in the message

When it happens

Trigger: DBFS API returns a structured error other than RESOURCE_DOES_NOT_EXIST, e.g. PERMISSION_DENIED for a token lacking DBFS access, INVALID_PARAMETER_VALUE for a malformed path, or rate limiting, while checking project upload paths.

Common situations: Service principal lacking DBFS permissions; malformed dbfs:// path; workspace restrictions; quota/throttling errors on busy workspaces.

Related errors

AI-assisted analysis of mlflow/mlflow@6a27f2decc (2026-08-29). Data as JSON: /api/errors/d0856b5dce41aa99. Report an issue: GitHub.