Spark

create_workspace_custom_pool(workspace, display_name=None, *, auto_scale_enabled=None, min_node_count=None, max_node_count=None, dynamic_executor_allocation_enabled=None, min_executors=None, max_executors=None, node_family='MemoryOptimized', node_size='Small', df=True)

Creates a new workspace custom pool in the specified workspace.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
display_name	str	The display name for the new workspace custom pool.	None
auto_scale_enabled	bool	Whether auto-scaling is enabled.	None
min_node_count	int	The minimum number of nodes.	None
max_node_count	int	The maximum number of nodes.	None
dynamic_executor_allocation_enabled	bool	Whether dynamic executor allocation is enabled.	None
min_executors	int	The minimum number of executors.	None
max_executors	int	The maximum number of executors. Always less than max_node_count.	None
node_family	str	The node family. Default is 'MemoryOptimized'.	'MemoryOptimized'
node_size	Literal['Small', 'Medium', 'Large']	The node size. Default is 'Small'.	'Small'
df	Optional[bool]	If True or not provided, returns a DataFrame with flattened keys. If False, returns a list of dictionaries.	True

Returns:

Type	Description
Union[DataFrame, Dict[str, Any], None]	The created workspace custom pool details if successful, otherwise None.

Examples:

create_workspace_custom_pool(
    'MyProjectWorkspace',
    'MyNewCustomPool',
    auto_scale_enabled=True,
    min_node_count=1,
    max_node_count=10,
    dynamic_executor_allocation_enabled=True,
    min_executors=1,
    max_executors=9,
    node_family='MemoryOptimized',
    node_size='Small'
)

Source code in src/pyfabricops/items/spark.py

@df
def create_workspace_custom_pool(
    workspace: str,
    display_name: str | None = None,
    *,
    auto_scale_enabled: bool | None = None,
    min_node_count: int | None = None,
    max_node_count: int | None = None,
    dynamic_executor_allocation_enabled: bool | None = None,
    min_executors: int | None = None,
    max_executors: int | None = None,
    node_family: str | None = "MemoryOptimized",
    node_size: Literal["Small", "Medium", "Large"] = "Small",
    df: bool | None = True,
) -> DataFrame | dict[str, Any] | None:
    """
    Creates a new workspace custom pool in the specified workspace.

    Args:
        workspace (str): The workspace name or ID.
        display_name (str, optional): The display name for the new workspace custom pool.
        auto_scale_enabled (bool, optional): Whether auto-scaling is enabled.
        min_node_count (int, optional): The minimum number of nodes.
        max_node_count (int, optional): The maximum number of nodes.
        dynamic_executor_allocation_enabled (bool, optional): Whether dynamic executor allocation is enabled.
        min_executors (int, optional): The minimum number of executors.
        max_executors (int, optional): The maximum number of executors. Always less than max_node_count.
        node_family (str, optional): The node family. Default is 'MemoryOptimized'.
        node_size (Literal['Small', 'Medium', 'Large'], optional): The node size. Default is 'Small'.
        df (Optional[bool]): If True or not provided, returns a DataFrame with flattened keys.
            If False, returns a list of dictionaries.

    Returns:
        (Union[DataFrame, Dict[str, Any], None]): The created workspace custom pool details if successful, otherwise None.

    Examples:
        ```python
        create_workspace_custom_pool(
            'MyProjectWorkspace',
            'MyNewCustomPool',
            auto_scale_enabled=True,
            min_node_count=1,
            max_node_count=10,
            dynamic_executor_allocation_enabled=True,
            min_executors=1,
            max_executors=9,
            node_family='MemoryOptimized',
            node_size='Small'
        )
        ```
    """
    workspace_id = resolve_workspace(workspace)

    payload = {}

    payload["name"] = display_name

    if auto_scale_enabled is not None:
        payload["autoScale"] = {}
        payload["autoScale"]["enabled"] = auto_scale_enabled
    if min_node_count is not None:
        payload["autoScale"]["minNodeCount"] = min_node_count
    if max_node_count is not None:
        payload["autoScale"]["maxNodeCount"] = max_node_count
    if dynamic_executor_allocation_enabled is not None:
        payload["dynamicExecutorAllocation"] = {}
        payload["dynamicExecutorAllocation"]["enabled"] = (
            dynamic_executor_allocation_enabled
        )
    if min_executors is not None:
        payload["dynamicExecutorAllocation"]["minExecutors"] = min_executors
    if max_executors is not None:
        payload["dynamicExecutorAllocation"]["maxExecutors"] = max_executors
    if node_family:
        payload["nodeFamily"] = node_family
    if node_size:
        payload["nodeSize"] = node_size

    return api_request(
        endpoint="/workspaces/" + workspace_id + "/spark/pools",
        method="post",
        payload=payload,
    )

delete_workspace_custom_pool(workspace, workspace_custom_pool)

Delete a custom pool from the specified workspace.

Parameters:

Name	Type	Description	Default
workspace	str	The name or ID of the workspace to delete.	required
workspace_custom_pool	str	The name or ID of the workspace custom pool to delete.	required

Returns:

Name	Type	Description
None	None	If the workspace custom pool is successfully deleted.

Raises:

Type	Description
ResourceNotFoundError	If the specified workspace is not found.

Examples:

delete_workspace_custom_pool('MyProjectWorkspace', 'MyCustomPool')
delete_workspace_custom_pool('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000')

Source code in src/pyfabricops/items/spark.py

def delete_workspace_custom_pool(
    workspace: str, workspace_custom_pool: str
) -> None:
    """
    Delete a custom pool from the specified workspace.

    Args:
        workspace (str): The name or ID of the workspace to delete.
        workspace_custom_pool (str): The name or ID of the workspace custom pool to delete.

    Returns:
        None: If the workspace custom pool is successfully deleted.

    Raises:
        ResourceNotFoundError: If the specified workspace is not found.

    Examples:
        ```python
        delete_workspace_custom_pool('MyProjectWorkspace', 'MyCustomPool')
        delete_workspace_custom_pool('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000')
        ```
    """
    workspace_id = resolve_workspace(workspace)

    workspace_custom_pool_id = resolve_workspace_custom_pool(
        workspace_id, workspace_custom_pool
    )

    return api_request(
        endpoint="/workspaces/"
        + workspace_id
        + "/spark/pools/"
        + workspace_custom_pool_id,
        method="delete",
    )

get_workspace_custom_pool(workspace, workspace_custom_pool, *, df=True)

Retrieves a specific custom pool from the workspace.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
workspace_custom_pool	str	The name or ID of the workspace custom pool to retrieve.	required
df	Optional[bool]	If True or not provided, returns a DataFrame with flattened keys. If False, returns a list of dictionaries.	True

Returns:

Type	Description
Union[DataFrame, Dict[str, Any], None]	The workspace custom pool details as a dictionary or DataFrame, or None if not found.

Examples:

get_workspace_custom_pool('MyProjectWorkspace', 'MyCustomPool')
get_workspace_custom_pool('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000', df=True)

Source code in src/pyfabricops/items/spark.py

@df
def get_workspace_custom_pool(
    workspace: str,
    workspace_custom_pool: str,
    *,
    df: bool | None = True,
) -> DataFrame | dict[str, Any] | None:
    """
    Retrieves a specific custom pool from the workspace.

    Args:
        workspace (str): The workspace name or ID.
        workspace_custom_pool (str): The name or ID of the workspace custom pool to retrieve.
        df (Optional[bool]): If True or not provided, returns a DataFrame with flattened keys.
            If False, returns a list of dictionaries.

    Returns:
        (Union[DataFrame, Dict[str, Any], None]): The workspace custom pool details as a dictionary or DataFrame, or None if not found.

    Examples:
        ```python
        get_workspace_custom_pool('MyProjectWorkspace', 'MyCustomPool')
        get_workspace_custom_pool('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000', df=True)
        ```
    """
    workspace_id = resolve_workspace(workspace)

    workspace_custom_pool_id = resolve_workspace_custom_pool(
        workspace_id, workspace_custom_pool
    )

    return api_request(
        endpoint="/workspaces/"
        + workspace_id
        + "/spark/pools/"
        + workspace_custom_pool_id,
    )

get_workspace_custom_pool_id(workspace, workspace_custom_pool)

Retrieves the ID of a specific custom pool in the workspace.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
workspace_custom_pool	str	The name of the workspace custom pool.	required

Returns:

Type	Description
str | None	str|None: The ID of the workspace custom pool, or None if not found.

Examples:

get_workspace_custom_pool_id('MyProjectWorkspace', 'SmallPool')
get_workspace_custom_pool_id('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000')

Source code in src/pyfabricops/items/spark.py

def get_workspace_custom_pool_id(
    workspace: str, workspace_custom_pool: str
) -> str | None:
    """
    Retrieves the ID of a specific custom pool in the workspace.

    Args:
        workspace (str): The workspace name or ID.
        workspace_custom_pool (str): The name of the workspace custom pool.

    Returns:
        str|None: The ID of the workspace custom pool, or None if not found.

    Examples:
        ```python
        get_workspace_custom_pool_id('MyProjectWorkspace', 'SmallPool')
        get_workspace_custom_pool_id('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000')
        ```
    """
    workspace_custom_pools = list_workspace_custom_pools(
        workspace=resolve_workspace(workspace),
        df=False,
    )

    for _workspace_custom_pool in workspace_custom_pools:
        if _workspace_custom_pool["name"] == workspace_custom_pool:
            return _workspace_custom_pool["id"]
    logger.warning(
        f"Custom pool '{workspace_custom_pool}' not found in workspace '{workspace}'."
    )
    return None

get_workspace_spark_settings(workspace, *, df=True)

Get workspace Spark settings.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
df	Optional[bool]	If True or not provided, returns a DataFrame with flattened keys. If False, returns a list of dictionaries.	True

Returns:

Type	Description
str | None	str|None: The dict with workspace Spark settings.

Examples:

get_workspace_spark_settings('MyProjectWorkspace')
get_workspace_spark_settings('123e4567-e89b-12d3-a456-426614174000')

Source code in src/pyfabricops/items/spark.py

@df
def get_workspace_spark_settings(
    workspace: str,
    *,
    df: bool | None = True,
) -> str | None:
    """
    Get workspace Spark settings.

    Args:
        workspace (str): The workspace name or ID.
        df (Optional[bool]): If True or not provided, returns a DataFrame with flattened keys.
            If False, returns a list of dictionaries.

    Returns:
        str|None: The dict with workspace Spark settings.

    Examples:
        ```python
        get_workspace_spark_settings('MyProjectWorkspace')
        get_workspace_spark_settings('123e4567-e89b-12d3-a456-426614174000')
        ```
    """
    workspace_id = resolve_workspace(workspace)

    return api_request(
        endpoint="/workspaces/" + workspace_id + "/spark/settings"
    )

list_workspace_custom_pools(workspace, *, df=True)

Returns a list of custom pools from the specified workspace. This API supports pagination.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
df	Optional[bool]	If True or not provided, returns a DataFrame with flattened keys. If False, returns a list of dictionaries.	True

Returns:

Type	Description
Union[DataFrame, List[Dict[str, Any]], None]	A list of custom pools. If df=True, returns a DataFrame with flattened keys.

Examples:

list_workspace_custom_pools('MyProjectWorkspace')
list_workspace_custom_pools('MyProjectWorkspace', df=False)

Source code in src/pyfabricops/items/spark.py

@df
def list_workspace_custom_pools(
    workspace: str, *, df: bool | None = True
) -> DataFrame | list[dict[str, Any]] | None:
    """
    Returns a list of custom pools from the specified workspace.
    This API supports pagination.

    Args:
        workspace (str): The workspace name or ID.
        df (Optional[bool]): If True or not provided, returns a DataFrame with flattened keys.
            If False, returns a list of dictionaries.

    Returns:
        (Union[DataFrame, List[Dict[str, Any]], None]): A list of custom pools. If `df=True`, returns a DataFrame with flattened keys.

    Examples:
        ```python
        list_workspace_custom_pools('MyProjectWorkspace')
        list_workspace_custom_pools('MyProjectWorkspace', df=False)
        ```
    """
    workspace_id = resolve_workspace(workspace)
    return api_request(
        endpoint="/workspaces/" + workspace_id + "/spark/pools",
        support_pagination=True,
    )

resolve_workspace_custom_pool(workspace, workspace_custom_pool)

Resolves a workspace custom pool name to its ID.

Parameters:

Name	Type	Description	Default
workspace	str	The ID of the workspace.	required
workspace_custom_pool	str	The name of the workspace custom pool.	required

Returns:

Type	Description
Union[str, None]	The ID of the workspace custom pool, or None if not found.

Examples:

resolve_workspace_custom_pool('MyProjectWorkspace', 'MyCustoomPool')
resolve_workspace_custom_pool('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000')

Source code in src/pyfabricops/items/spark.py

def resolve_workspace_custom_pool(
    workspace: str,
    workspace_custom_pool: str,
) -> str | None:
    """
    Resolves a workspace custom pool name to its ID.

    Args:
        workspace (str): The ID of the workspace.
        workspace_custom_pool (str): The name of the workspace custom pool.

    Returns:
        (Union[str, None]): The ID of the workspace custom pool, or None if not found.

    Examples:
        ```python
        resolve_workspace_custom_pool('MyProjectWorkspace', 'MyCustoomPool')
        resolve_workspace_custom_pool('MyProjectWorkspace', '123e4567-e89b-12d3-a456-426614174000')
        ```
    """
    if is_valid_uuid(workspace_custom_pool):
        return workspace_custom_pool
    else:
        return get_workspace_custom_pool_id(
            resolve_workspace(workspace), workspace_custom_pool
        )

update_workspace_custom_pool(workspace, workspace_custom_pool, *, display_name=None, auto_scale_enabled=None, min_node_count=None, max_node_count=None, dynamic_executor_allocation_enabled=None, min_executors=None, max_executors=None, node_family='MemoryOptimized', node_size='Small', df=True)

Updates the properties of the specified workspace custom pool.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
workspace_custom_pool	str	The name or ID of the workspace custom pool to update.	required
display_name	str	The new display name for the workspace custom pool.	None
auto_scale_enabled	bool	Whether auto-scaling is enabled.	None
min_node_count	int	The minimum number of nodes.	None
max_node_count	int	The maximum number of nodes.	None
dynamic_executor_allocation_enabled	bool	Whether dynamic executor allocation is enabled.	None
min_executors	int	The minimum number of executors.	None
max_executors	int	The maximum number of executors. Always less than max_node_count.	None
node_family	str	The node family. Default is 'MemoryOptimized'.	'MemoryOptimized'
node_size	Literal['Small', 'Medium', 'Large']	The node size. Default is 'Small'.	'Small'
df	Optional[bool]	If True or not provided, returns a DataFrame with flattened keys. If False, returns a list of dictionaries.	True

Returns:

Type	Description
Union[DataFrame, Dict[str, Any], None]	The updated workspace custom pool details if successful, otherwise None.

Examples:

update_workspace_custom_pool(
    'MyProjectWorkspace',
    'MyCustomPool',
    display_name='MyCustomPoolRenamed',
    auto_scale_enabled=True,
    min_node_count=1,
    max_node_count=10,
    dynamic_executor_allocation_enabled=True,
    min_executors=1,
    max_executors=9, # Always less than max_node_count
    node_family='MemoryOptimized',
    node_size='Small'
)

Source code in src/pyfabricops/items/spark.py

@df
def update_workspace_custom_pool(
    workspace: str,
    workspace_custom_pool: str,
    *,
    display_name: str | None = None,
    auto_scale_enabled: bool | None = None,
    min_node_count: int | None = None,
    max_node_count: int | None = None,
    dynamic_executor_allocation_enabled: bool | None = None,
    min_executors: int | None = None,
    max_executors: int | None = None,
    node_family: str | None = "MemoryOptimized",
    node_size: Literal["Small", "Medium", "Large"] = "Small",
    df: bool | None = True,
) -> DataFrame | dict[str, Any] | None:
    """
    Updates the properties of the specified workspace custom pool.

    Args:
        workspace (str): The workspace name or ID.
        workspace_custom_pool (str): The name or ID of the workspace custom pool to update.
        display_name (str, optional): The new display name for the workspace custom pool.
        auto_scale_enabled (bool, optional): Whether auto-scaling is enabled.
        min_node_count (int, optional): The minimum number of nodes.
        max_node_count (int, optional): The maximum number of nodes.
        dynamic_executor_allocation_enabled (bool, optional): Whether dynamic executor allocation is enabled.
        min_executors (int, optional): The minimum number of executors.
        max_executors (int, optional): The maximum number of executors. Always less than max_node_count.
        node_family (str, optional): The node family. Default is 'MemoryOptimized'.
        node_size (Literal['Small', 'Medium', 'Large'], optional): The node size. Default is 'Small'.
        df (Optional[bool]): If True or not provided, returns a DataFrame with flattened keys.
            If False, returns a list of dictionaries.

    Returns:
        (Union[DataFrame, Dict[str, Any], None]): The updated workspace custom pool details if successful, otherwise None.

    Examples:
        ```python
        update_workspace_custom_pool(
            'MyProjectWorkspace',
            'MyCustomPool',
            display_name='MyCustomPoolRenamed',
            auto_scale_enabled=True,
            min_node_count=1,
            max_node_count=10,
            dynamic_executor_allocation_enabled=True,
            min_executors=1,
            max_executors=9, # Always less than max_node_count
            node_family='MemoryOptimized',
            node_size='Small'
        )
        ```
    """
    workspace_id = resolve_workspace(workspace)

    workspace_custom_pool_id = resolve_workspace_custom_pool(
        workspace_id, workspace_custom_pool
    )

    payload = {}

    if display_name:
        payload["name"] = display_name
    if auto_scale_enabled is not None:
        payload["autoScale"] = {}
        payload["autoScale"]["enabled"] = auto_scale_enabled
    if min_node_count is not None:
        payload["autoScale"]["minNodeCount"] = min_node_count
    if max_node_count is not None:
        payload["autoScale"]["maxNodeCount"] = max_node_count
    if dynamic_executor_allocation_enabled is not None:
        payload["dynamicExecutorAllocation"] = {}
        payload["dynamicExecutorAllocation"]["enabled"] = (
            dynamic_executor_allocation_enabled
        )
    if min_executors is not None:
        payload["dynamicExecutorAllocation"]["minExecutors"] = min_executors
    if max_executors is not None:
        payload["dynamicExecutorAllocation"]["maxExecutors"] = max_executors
    if node_family:
        payload["nodeFamily"] = node_family
    if node_size:
        payload["nodeSize"] = node_size

    return api_request(
        endpoint="/workspaces/"
        + workspace_id
        + "/spark/pools/"
        + workspace_custom_pool_id,
        method="patch",
        payload=payload,
    )

update_workspace_spark_settings(workspace, *, automatic_log_enabled=None, high_concurrency_notebook_interactive_run_enabled=None, high_concurrency_notebook_pipeline_run_enabled=None, pool_customize_compute_enabled=None, pool_default_name=None, pool_default_id=None, pool_default_type=None, starter_pool_max_node_count=None, starter_pool_max_executors=None, environment_name=None, environment_runtime_version=None, job_conservative_job_admission_enabled=None, job_session_timeout_in_minutes=None, df=True)

Update workspace Spark settings.

Parameters:

Name	Type	Description	Default
workspace	str	The workspace name or ID.	required
automatic_log_enabled	bool	Enable or disable automatic logging.	None
high_concurrency_notebook_interactive_run_enabled	bool	Enable or disable high concurrency for notebook interactive runs.	None
high_concurrency_notebook_pipeline_run_enabled	bool	Enable or disable high concurrency for notebook pipeline runs.	None
pool_customize_compute_enabled	bool	Enable or disable custom compute for pools.	None
pool_default_name	str	The default pool name.	None
pool_default_id	str	The default pool ID.	None
pool_default_type	str	The default pool type.	None
starter_pool_max_node_count	int	The maximum node count for the starter pool	None
starter_pool_max_executors	int	The maximum executors for the starter pool.	None
environment_name	str	The name of the environment.	None
environment_runtime_version	str	The runtime version for the environment.	None
job_conservative_job_admission_enabled	bool	Enable or disable conservative job admission	None
job_session_timeout_in_minutes	int	The session timeout in minutes for jobs.	None
df	Optional[bool]	If True or not provided, returns a DataFrame with flattened keys. If False, returns a list of dictionaries.	True

Returns:

Type	Description
str | None	str|None: The dict with updated workspace Spark settings.

Examples:

update_workspace_spark_settings(
    'MyProjectWorkspace',
    True,
    'mystorageaccount',
    'mysparklogs',
    'logs/'
)

Source code in src/pyfabricops/items/spark.py

@df
def update_workspace_spark_settings(
    workspace: str,
    *,
    automatic_log_enabled: bool = None,
    high_concurrency_notebook_interactive_run_enabled: bool = None,
    high_concurrency_notebook_pipeline_run_enabled: bool = None,
    pool_customize_compute_enabled: bool = None,
    pool_default_name: str = None,
    pool_default_id: str = None,
    pool_default_type: str = None,
    starter_pool_max_node_count: int = None,
    starter_pool_max_executors: int = None,
    environment_name: str = None,
    environment_runtime_version: str = None,
    job_conservative_job_admission_enabled: bool = None,
    job_session_timeout_in_minutes: int = None,
    df: bool | None = True,
) -> str | None:
    """
    Update workspace Spark settings.

    Args:
        workspace (str): The workspace name or ID.
        automatic_log_enabled (bool, optional): Enable or disable automatic logging.
        high_concurrency_notebook_interactive_run_enabled (bool, optional): Enable or disable high
            concurrency for notebook interactive runs.
        high_concurrency_notebook_pipeline_run_enabled (bool, optional): Enable or disable high
            concurrency for notebook pipeline runs.
        pool_customize_compute_enabled (bool, optional): Enable or disable custom compute for pools.
        pool_default_name (str, optional): The default pool name.
        pool_default_id (str, optional): The default pool ID.
        pool_default_type (str, optional): The default pool type.
        starter_pool_max_node_count (int, optional): The maximum node count for the starter pool
        starter_pool_max_executors (int, optional): The maximum executors for the starter pool.
        environment_name (str, optional): The name of the environment.
        environment_runtime_version (str, optional): The runtime version for the environment.
        job_conservative_job_admission_enabled (bool, optional): Enable or disable conservative job admission
        job_session_timeout_in_minutes (int, optional): The session timeout in minutes for jobs.
        df (Optional[bool]): If True or not provided, returns a DataFrame with flattened keys.
            If False, returns a list of dictionaries.

    Returns:
        str|None: The dict with updated workspace Spark settings.

    Examples:
        ```python
        update_workspace_spark_settings(
            'MyProjectWorkspace',
            True,
            'mystorageaccount',
            'mysparklogs',
            'logs/'
        )
        ```
    """
    workspace_id = resolve_workspace(workspace)

    payload = {}

    if automatic_log_enabled is not None:
        payload["automaticLog"] = {"enabled": automatic_log_enabled}

    if (high_concurrency_notebook_interactive_run_enabled is not None) or (
        high_concurrency_notebook_pipeline_run_enabled is not None
    ):
        payload["highConcurrency"] = {}
        if high_concurrency_notebook_interactive_run_enabled is not None:
            payload["highConcurrency"]["notebookInteractiveRunEnabled"] = (
                high_concurrency_notebook_interactive_run_enabled
            )
        if high_concurrency_notebook_pipeline_run_enabled is not None:
            payload["highConcurrency"]["notebookPipelineRunEnabled"] = (
                high_concurrency_notebook_pipeline_run_enabled
            )

    if (
        (pool_customize_compute_enabled is not None)
        or (pool_default_name is not None)
        or (pool_default_id is not None)
        or (pool_default_type is not None)
        or (starter_pool_max_node_count is not None)
        or (starter_pool_max_executors is not None)
    ):
        payload["pool"] = {}
        if pool_customize_compute_enabled is not None:
            payload["pool"]["customizeComputeEnabled"] = (
                pool_customize_compute_enabled
            )
        if (
            (pool_default_name is not None)
            or (pool_default_id is not None)
            or (pool_default_type is not None)
        ):
            payload["pool"]["defaultPool"] = {}
            if pool_default_name is not None:
                payload["pool"]["defaultPool"]["name"] = pool_default_name
            if pool_default_id is not None:
                payload["pool"]["defaultPool"]["id"] = pool_default_id
            if pool_default_type is not None:
                payload["pool"]["defaultPool"]["type"] = pool_default_type
        if (starter_pool_max_node_count is not None) or (
            starter_pool_max_executors is not None
        ):
            payload["pool"]["starterPool"] = {}
            if starter_pool_max_node_count is not None:
                payload["pool"]["starterPool"]["maxNodeCount"] = (
                    starter_pool_max_node_count
                )
            if starter_pool_max_executors is not None:
                payload["pool"]["starterPool"]["maxExecutors"] = (
                    starter_pool_max_executors
                )
    if environment_name is not None:
        payload["environment"] = {"name": environment_name}
    if environment_runtime_version is not None:
        payload["environment"] = {
            "runtimeVersion": environment_runtime_version
        }
    if (job_conservative_job_admission_enabled is not None) or (
        job_session_timeout_in_minutes is not None
    ):
        payload["job"] = {}
        if job_conservative_job_admission_enabled is not None:
            payload["job"]["conservativeJobAdmissionEnabled"] = (
                job_conservative_job_admission_enabled
            )
        if job_session_timeout_in_minutes is not None:
            payload["job"]["sessionTimeoutInMinutes"] = (
                job_session_timeout_in_minutes
            )

    return api_request(
        endpoint="/workspaces/" + workspace_id + "/spark/settings",
        method="patch",
        payload=payload,
    )