dbx-tools-config 0.1.8

Build and fingerprint databricks.sdk Config instances from server-supplied env or kwargs.

dbx-tools-config

Tiny wrapper around databricks.sdk.config.Config for services that build a Config per request from caller-supplied inputs - MCP servers, brokers, multi-tenant backends, sidecars, agent frameworks, etc.

The Databricks SDK auto-discovers config from os.environ of the host process, which is the wrong scope for a service serving many callers. This module lets each request bring its own config-shaped inputs and materialise a Config (or just a fingerprint) from them. A typical mapping for an HTTP/MCP-style request:

Source	dbx-tools-config layer
Request headers (env-shaped)	env=
POST body / RPC payload Config fields	**kwargs
Pre-resolved Config baseline	config=

Precedence is kwargs > env > config (last write wins). Every layer is optional.

API

Three public helpers, all with the same signature:

def config_params(
    config: Config | None = None,
    env: Mapping[str, Iterable[str] | None] | None = None,
    **kwargs,
) -> dict[str, Any]: ...

def config_params_hash(
    config: Config | None = None,
    env: Mapping[str, Iterable[str] | None] | None = None,
    **kwargs,
) -> str: ...

def create_config(
    config: Config | None = None,
    env: Mapping[str, Iterable[str] | None] | None = None,
    **kwargs,
) -> Config: ...

• config_params(...) merges config.as_dict() + recognised env keys
  • kwargs into a single dict suitable for Config(**...).
• config_params_hash(...) returns a SHA-256 hex digest of the merged kwargs. Cheap: pure in-memory compute, no Config constructed. See Hashing.
• create_config(...) is a one-liner for Config(**config_params(...)). Expensive: triggers Config.__init__'s host-metadata HTTP probe, ~/.databrickscfg read and credential strategy bootstrap.

Install

Published on PyPI:

pip install dbx-tools-config

In pyproject.toml:

[project]
dependencies = [
    "dbx-tools-config",
]

Alternative: install directly from GitHub

Useful for pinning to an unreleased commit or pulling from a fork. Works with pip, uv, poetry, etc. via the PEP 508 direct URL form.

pyproject.toml:

[project]
dependencies = [
    "dbx-tools-config @ git+https://github.com/reggie-db/dbx-tools-config",
]

Pin to a tag, branch or commit with the standard @<ref> suffix:

[project]
dependencies = [
    # tag
    "dbx-tools-config @ git+https://github.com/reggie-db/dbx-tools-config@v0.1.4",
    # branch
    "dbx-tools-config @ git+https://github.com/reggie-db/dbx-tools-config@main",
    # commit SHA
    "dbx-tools-config @ git+https://github.com/reggie-db/dbx-tools-config@<sha>",
]

Or install ad-hoc without editing pyproject.toml:

pip install 'git+https://github.com/reggie-db/dbx-tools-config'
uv add 'dbx-tools-config @ git+https://github.com/reggie-db/dbx-tools-config'

Usage

Server-style: per-request Config from headers + body

import dbx_tools_config
from databricks.sdk import WorkspaceClient

# An MCP-style handler. Headers carry env-shaped names, the body
# carries Config field overrides.
def handle_request(request):
    config = dbx_tools_config.create_config(
        env=request.headers,         # e.g. {"DATABRICKS_HOST": "...",
                                     #       "DATABRICKS_TOKEN": "..."}
        **request.json(),            # e.g. {"warehouse_id": "abc",
                                     #       "cluster_id": "xyz"}
    )
    return WorkspaceClient(config=config).do_work(...)

Other shapes

# From an arbitrary env-shaped mapping
config = dbx_tools_config.create_config(env={
    "DATABRICKS_HOST": "https://myworkspace.cloud.databricks.com",
    "DATABRICKS_TOKEN": "dapi...",
})

# From the process environment (single-tenant CLIs, scripts, tests)
import os
config = dbx_tools_config.create_config(env=os.environ)

# Kwargs always win over env
config = dbx_tools_config.create_config(
    host="https://override.cloud.databricks.com",
    env=client_env,
)

# Round-trip an existing Config (e.g. as a baseline)
config = dbx_tools_config.create_config(config=other_config, host="https://override...")

# Just the merged kwargs, without constructing a Config
kwargs = dbx_tools_config.config_params(config=other_config, env=client_env)

Env value semantics

Each value in the env mapping may be:

Value	Behavior
str	Used directly.
None	Sets the field to None (clears any baseline from config=).
Iterable[str]	First element wins; matches multi-value HTTP / multidict frames.
empty iterable	Field is left untouched.

Env key resolution

Each key in the env mapping is matched against the SDK's declared ConfigAttribute.env (and any env_aliases) on Config. Examples that the SDK declares today:

• DATABRICKS_HOST -> host
• DATABRICKS_TOKEN -> token
• DATABRICKS_CLUSTER_ID -> cluster_id
• DATABRICKS_OIDC_TOKEN_FILE -> oidc_token_filepath (alias)
• DATABRICKS_AZURE_RESOURCE_ID -> azure_workspace_resource_id
• ARM_TENANT_ID -> azure_tenant_id
• GOOGLE_CREDENTIALS -> google_credentials

Keys that don't match a declared env name (or alias) are silently ignored.

Note: this module does not perform string-to-bool/int/float coercion. Values are forwarded to Config(**kwargs) as-is and the SDK's descriptor transform (typically just the annotated type) does any conversion. Be aware that the SDK uses bool(value) for boolean fields, so the string "false" will resolve to True. Pass real Python booleans via kwargs if you care.

Out of scope: ambient env vars

A handful of databricks-sdk features read env vars directly from os.environ instead of going through Config:

• DATABRICKS_RUNTIME_VERSION (DBR detection / user-agent)
• IS_IN_DB_MODEL_SERVING_ENV, IS_IN_DATABRICKS_MODEL_SERVING_ENV, DATABRICKS_MODEL_SERVING_HOST_URL, DB_MODEL_SERVING_HOST_URL (model serving auto-auth)
• ACTIONS_ID_TOKEN_REQUEST_TOKEN, ACTIONS_ID_TOKEN_REQUEST_URL (GitHub Actions OIDC)
• SYSTEM_ACCESSTOKEN, SYSTEM_* (Azure DevOps OIDC)
• AGENT (user-agent)

Forwarding these through dbx_tools_config.create_config(env=...) has no effect because they bypass Config entirely. If you need them in a service context, set them on os.environ of the worker process before constructing the SDK client.

Hashing

dbx_tools_config.config_params_hash(...) returns a stable SHA-256 hex digest of the resolved kwargs without constructing a Config. This matters because Config.__init__ is not free - it does (in order):

1. _resolve_host_metadata - HTTP GET host/.well-known/databricks-config to discover account_id, workspace_id, cloud, discovery_url.
2. _known_file_config_loader - reads ~/.databrickscfg from disk if no auth is configured directly.
3. _validate - checks for conflicting auth methods.
4. init_auth - bootstraps the credential strategy (which itself may shell out to the Databricks CLI, fetch a token from disk, etc).

For a service that fans many requests over a small set of logical identities, hashing first lets you cache (or rate-limit) clients without paying any of the above per request:

import dbx_tools_config
from databricks.sdk import WorkspaceClient

_clients: dict[str, WorkspaceClient] = {}

def client_for(request):
    key = dbx_tools_config.config_params_hash(env=request.headers, **request.json())
    client = _clients.get(key)
    if client is None:
        config = dbx_tools_config.create_config(env=request.headers, **request.json())
        client = _clients[key] = WorkspaceClient(config=config)
    return client

The digest deliberately ignores fields that don't change which workspace / account is being addressed or how it's being authenticated:

Group	Fields ignored
Source / lookup	profile, config_file, databricks_cli_path
Derived during init	auth_type, databricks_environment

So two configs that resolve to the same identity but were loaded from different DATABRICKS_CONFIG_PROFILE / DATABRICKS_CONFIG_FILE paths, via a different CLI binary, or that happened to be tagged with a different derived auth_type, fingerprint the same way.

Normalisation:

• Mapping keys are sorted (after JSON-encoding) so dict ordering does not affect the digest.
• None collapses with the empty string so an explicit None value hashes the same as an explicit "".
• Iterables (other than strings) preserve their order.
• Scalar values are stringified via str() and JSON-quoted before being streamed into the digest.

Development

uv sync
uv build
uv run pytest