run_config_smoke_tests

Public callable

Run 00_env_config readiness smoke checks for configuration bootstrap.

Use this during environment bootstrap to verify Spark availability, Fabric runtime context access, required path mappings, notebook naming policy, and optional AI/IO import readiness before executing downstream notebook steps.

Parameters:

Name	Type	Description	Default
config	FrameworkConfig	Validated framework configuration to evaluate.	required
env	str	Environment key used when resolving required target paths.	"Sandbox"
required_targets	list[str] | None	Required targets expected in config.path_config. Defaults to ["Source", "Unified"] when not provided.	None
check_ai	bool	Whether to run the Fabric AI availability check.	True
check_io_import	bool	Whether to test importability of fabric_io helpers.	False
notebook_name	str | None	Notebook name to validate against configured naming prefixes.	None
ai_result	dict[str, Any] | None	Optional precomputed AI availability payload to reuse instead of re-running the runtime import check.	None

Returns:

Type	Description
list[ConfigSmokeCheckResult]	Ordered check results with pass, warn, fail, or skipped statuses for each readiness dimension.

Raises:

Type	Description
ValueError	Propagated from config/path validation helpers when required targets or configured environments are invalid.

Notes

This helper performs validation and lightweight import/runtime checks only. It does not create or mutate Fabric resources.

Examples:

>>> checks = run_config_smoke_tests(config=my_config, env="Sandbox", notebook_name="00_env_config")
>>> any(c.status == "fail" for c in checks)
False

Source code in src/fabricops_kit/config.py

def run_config_smoke_tests(
    config: FrameworkConfig,
    env: str = "Sandbox",
    required_targets: list[str] | None = None,
    check_ai: bool = True,
    check_io_import: bool = False,
    notebook_name: str | None = None,
    ai_result: dict[str, Any] | None = None,
) -> list[ConfigSmokeCheckResult]:
    """Run 00_env_config readiness smoke checks for configuration bootstrap.

    Use this during environment bootstrap to verify Spark availability, Fabric
    runtime context access, required path mappings, notebook naming policy, and
    optional AI/IO import readiness before executing downstream notebook steps.

    Parameters
    ----------
    config : FrameworkConfig
        Validated framework configuration to evaluate.
    env : str, default="Sandbox"
        Environment key used when resolving required target paths.
    required_targets : list[str] | None, optional
        Required targets expected in ``config.path_config``. Defaults to
        ``["Source", "Unified"]`` when not provided.
    check_ai : bool, default=True
        Whether to run the Fabric AI availability check.
    check_io_import : bool, default=False
        Whether to test importability of ``fabric_io`` helpers.
    notebook_name : str | None, optional
        Notebook name to validate against configured naming prefixes.
    ai_result : dict[str, Any] | None, optional
        Optional precomputed AI availability payload to reuse instead of
        re-running the runtime import check.

    Returns
    -------
    list[ConfigSmokeCheckResult]
        Ordered check results with ``pass``, ``warn``, ``fail``, or ``skipped``
        statuses for each readiness dimension.

    Raises
    ------
    ValueError
        Propagated from config/path validation helpers when required targets or
        configured environments are invalid.

    Notes
    -----
    This helper performs validation and lightweight import/runtime checks only.
    It does not create or mutate Fabric resources.

    Examples
    --------
    >>> checks = run_config_smoke_tests(config=my_config, env="Sandbox", notebook_name="00_env_config")
    >>> any(c.status == "fail" for c in checks)
    False
    """
    from .runtime import validate_notebook_name

    results: list[ConfigSmokeCheckResult] = []
    required_targets = required_targets or ["Source", "Unified"]
    spark_ready, spark_message = _check_spark_session()
    results.append(ConfigSmokeCheckResult("spark_session", "pass" if spark_ready else "warn", spark_message))

    runtime_meta = _get_fabric_runtime_metadata(notebook_name=notebook_name)
    runtime_status = "pass" if runtime_meta.get("runtime_available") else "skipped"
    runtime_message = "Fabric runtime context is readable." if runtime_meta.get("runtime_available") else "notebookutils.runtime unavailable outside Fabric runtime."
    results.append(ConfigSmokeCheckResult("fabric_runtime_context", runtime_status, runtime_message))
    try:
        for target in required_targets:
            p = get_path(env=env, target=target, config=config)
            missing = [attr for attr in ("workspace_id", "house_id", "house_name", "root") if not getattr(p, attr, None)]
            if missing:
                results.append(ConfigSmokeCheckResult(f"path:{target}", "fail", f"Missing required fields: {missing}"))
            elif str(p.root).startswith("abfss://"):
                results.append(ConfigSmokeCheckResult(f"path:{target}", "pass", "Path is populated and ABFSS formatted."))
            else:
                results.append(ConfigSmokeCheckResult(f"path:{target}", "warn", "Path root is populated but not ABFSS-formatted."))
    except Exception as exc:
        results.append(ConfigSmokeCheckResult("path_resolution", "fail", str(exc)))

    if notebook_name:
        errors = validate_notebook_name(notebook_name, config=config)
        results.append(ConfigSmokeCheckResult("notebook_naming", "pass" if not errors else "fail", "; ".join(errors) or "Notebook name is valid."))
    else:
        results.append(ConfigSmokeCheckResult("notebook_naming", "skipped", "Notebook name check skipped."))

    if check_ai:
        ai_status = ai_result or check_fabric_ai_functions_available()
        results.append(ConfigSmokeCheckResult("fabric_ai", "pass" if ai_status.get("available") else "warn", ai_status.get("message", "")))
    else:
        results.append(ConfigSmokeCheckResult("fabric_ai", "skipped", "AI check disabled."))

    if check_io_import:
        try:
            from .fabric_io import lakehouse_table_read  # noqa: F401
            results.append(ConfigSmokeCheckResult("fabric_io_import", "pass", "fabric_io helpers are importable."))
        except Exception as exc:
            results.append(ConfigSmokeCheckResult("fabric_io_import", "fail", str(exc)))
    else:
        results.append(ConfigSmokeCheckResult("fabric_io_import", "skipped", "IO import check disabled."))
    return results