dbx-tools-core

Dependency-free Python configuration, identity, and mise-backed executable helpers shared by dbx-tools packages.

Install from PyPI:

pip install dbx-tools-core

To install the current main branch directly from the repository instead:

pip install "dbx-tools-core @ git+https://github.com/reggie-db/dbx-tools.git@main#subdirectory=packages/py/core"

Key features

  • config.text() resolves scoped keys from constant data, the process environment, the nearest project .env file, the single App's config.env in validated Databricks bundle JSON, then app.yaml / app.yml env values. Root bundle variables are not a config source: they are authoring inputs interpolated into the bundle's own targets, resources, and paths, so reading one as a process setting resolves names the deployed App never sees. Reference a variable from config.env to make it one.
  • .env.<NODE_ENV> wins over .env, with production/prod and development/dev treated as aliases.
  • Bundle validation stays lazy: the Databricks CLI runs only after environment and dotenv lookup miss; app YAML runs only after bundle lookup misses. Parsed dotenv records are cached by file path, bundle output by bundle path plus Databricks profile, and app YAML by app path. Bundle value_from and App YAML valueFrom references resolve supported values from named resources. Config-file discovery and parsed results are single-attempt per source key: found paths, missing files, empty records, invalid records, and None results all cache.
  • Deployed Databricks Apps skip local files and bundle validation because the platform has already populated real environment variables.
  • DBX_TOOLS_DATABRICKS_APP_ENV=true or false forces Databricks App runtime detection; absent or unrecognized values retain automatic detection.
  • DBX_TOOLS_CONFIG_DOTENV, DBX_TOOLS_CONFIG_BUNDLE, and DBX_TOOLS_CONFIG_APP independently force each local source on or off, overriding the usual deployed-App skip.
  • Bundle reads default off when NODE_ENV=production unless DBX_TOOLS_CONFIG_BUNDLE=true explicitly enables them.
  • String, boolean, positive-number, positive-integer, and list helpers use the same loose configuration coercions as @dbx-tools/core.
  • Stable-key, FNV hash, and identifier functions preserve deterministic Node and Python identity contracts.
  • bin.resolve() reuses executables from PATH, otherwise performs a check-lock-check mise installation and returns the path reported by mise which.
  • bin.execute() has the asyncio.create_subprocess_exec calling convention, adds only mise_tool=, and returns the native asyncio.subprocess.Process. If mise itself is missing on macOS or Linux, the official checksum-verifying installer is run under the same cross-process lock.

Quick start

from dbx_tools.core import config

host = config.text("HOST", {"prefix": "SMTP"})
port = config.positive_int(None, "PORT", 587, {"prefix": "SMTP"})
endpoint = config.resolve_value(
    "lakebaseEndpoint",
    {
        "data": {"LAKEBASE_ENDPOINT": flags.endpoint},
        "sources": ["env", "dotenv", "bundle", "app"],
    },
)

Mise-backed async subprocesses retain the standard library process API:

import asyncio

from dbx_tools.core import bin

process = await bin.execute(
    "uv",
    "--version",
    mise_tool="uv@0.11",
    stdout=asyncio.subprocess.PIPE,
)
stdout, _ = await process.communicate()

Use bin.ensure_tool("neo4j@5.26.12").root when a caller needs an installed tool directory rather than one executable.

The default key order for HOST with prefix SMTP is DBX_TOOLS_SMTP_HOST, SMTP_HOST, then HOST. Pass config.ENV_ONLY when a caller must read the exact process environment without local file fallbacks. Constant data is first by default. If custom sources omit config, passed data is still read and appended last, as in the environment-first example. resolve_value() tries exact, uppercase, and tokenized-uppercase names through the same scope and prefix rules. config.is_databricks_app_env() validates the App name, HTTP(S) workspace host, and TCP port unless config.DATABRICKS_APP_ENV_KEY names a recognized boolean override in the environment. config.CONFIG_DOTENV_KEY, config.CONFIG_BUNDLE_KEY, and config.CONFIG_APP_KEY name the equivalent per-source overrides. Recognized booleans win; absent or unrecognized values read files outside a Databricks App and skip them inside one. Bundle validation also stays off by default in production.

Modules

  • bin — locked mise bootstrap, tool installation, executable resolution, and native asyncio subprocess creation;
  • config — layered environment, dotenv, Databricks bundle, and app YAML configuration;
  • hash.fnv_hash() — the single-string subset of TypeScript fnvHashWithOptions, including UTF-16 code-unit hashing and base-32 output;
  • object.to_stable_key() — strict structured identity canonicalization;
  • string.to_identifier() — readable identifier tokenization, with the same hyphen default as TypeScript and an explicit delimiter override for consumers such as the underscore-delimited Postgres bus channel.

The identity functions exist so Python packages do not copy the TypeScript algorithms locally and silently drift. Their shared behavior is exercised by colocated polygotTest callbacks in the owning TypeScript packages. Configuration precedence, dotenv discovery/parsing, bundle fallback, laziness, and parsed-record caching are covered by each runtime's native config tests.

Project links

Data verified by PyPI on Sep 2, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.6.201)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for reggie-db from gravatar.com reggie-db

GitHub Statistics

Data verified by PyPI on Sep 2, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python <3.14, >=3.10

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