DatabricksFunctionTask

Package: flyteplugins.connectors.databricks.task

Actual Plugin that transforms the local python code for execution within a spark context

class DatabricksFunctionTask(
    args,
    kwargs,
)

Properties

Methods

aio()

def aio(
    args: *args,
    kwargs: **kwargs,
) -> Coroutine[Any, Any, R] | R

The aio function allows executing “sync” tasks, in an async context. This helps with migrating v1 defined sync tasks to be used within an asyncio parent task. This function will also re-raise exceptions from the underlying task.

Example:

@env.task
def my_legacy_task(x: int) -> int:
    return x

@env.task
async def my_new_parent_task(n: int) -> List[int]:
    collect = []
    for x in range(n):
        collect.append(my_legacy_task.aio(x))
    return asyncio.gather(*collect)

config()

def config(
    sctx: SerializationContext,
) -> Dict[str, str]

Returns additional configuration for the task. This is a set of key-value pairs that can be used to configure the task execution environment at runtime. This is usually used by plugins.

container_args()

def container_args(
    serialize_context: SerializationContext,
) -> List[str]

Returns the container args for the task. This is a set of key-value pairs that can be used to configure the task execution environment at runtime. This is usually used by plugins.

custom_config()

def custom_config(
    sctx: flyte.models.SerializationContext,
) -> typing.Dict[str, typing.Any]

Returns additional configuration for the task. This is a set of key-value pairs that can be used to configure the task execution environment at runtime. This is usually used by plugins.

data_loading_config()

def data_loading_config(
    sctx: SerializationContext,
) -> DataLoadingConfig

This configuration allows executing raw containers in Flyte using the Flyte CoPilot system Flyte CoPilot, eliminates the needs of sdk inside the container. Any inputs required by the users container are side-loaded in the input_path Any outputs generated by the user container - within output_path are automatically uploaded

execute()

def execute(
    kwargs,
) -> typing.Any

forward()

def forward(
    args: *args,
    kwargs: **kwargs,
) -> Coroutine[Any, Any, R] | R

Think of this as a local execute method for your task. This function will be invoked by the call method when not in a Flyte task execution context. See the implementation below for an example.

override()

def override(
    short_name: Optional[str],
    resources: Optional[Resources],
    cache: Optional[CacheRequest],
    retries: Union[int, RetryStrategy],
    timeout: Optional[TimeoutType],
    reusable: Union[ReusePolicy, Literal['off'], None],
    env_vars: Optional[Dict[str, str]],
    secrets: Optional[SecretRequest],
    max_inline_io_bytes: int | None,
    pod_template: Optional[Union[str, PodTemplate]],
    queue: Optional[str],
    interruptible: Optional[bool],
    links: Tuple[Link, ...],
    kwargs: **kwargs,
) -> TaskTemplate

Override various parameters of the task template. This allows for dynamic configuration of the task when it is called, such as changing the image, resources, cache policy, etc.

post()

def post(
    return_vals: typing.Any,
) -> typing.Any

This is the postexecute function that will be called after the task is executed

pre()

def pre(
    args,
    kwargs,
) -> typing.Dict[str, typing.Any]

This is the preexecute function that will be called before the task is executed

sql()

def sql(
    sctx: SerializationContext,
) -> Optional[str]

Returns the SQL for the task. This is a set of key-value pairs that can be used to configure the task execution environment at runtime. This is usually used by plugins.