flytekit.extras.sqlite3.task.SQLite3Task#

class flytekit.extras.sqlite3.task.SQLite3Task(*args, **kwargs)[source]#

Run client side SQLite3 queries that optionally return a FlyteSchema object.

Note

This is a pre-built container task. That is, your user container will not be used at task execution time. Instead the image defined in this task definition will be used instead.

sql_task = SQLite3Task(
    "test",
    query_template="select TrackId, Name from tracks limit {{.inputs.limit}}",
    inputs=kwtypes(limit=int),
    output_schema_type=FlyteSchema[kwtypes(TrackId=int, Name=str)],
    task_config=SQLite3Config(
        uri=EXAMPLE_DB,
        compressed=True,
    ),
)

See the cookbook for additional usage examples and the base class flytekit.extend.PythonCustomizedContainerTask as well.

Parameters

name – unique name for the task, usually the function’s module and name.
task_config – Configuration object for Task. Should be a unique type for that specific Task
container_image – This is the external container image the task should run at platform-run-time.
executor – This is an executor which will actually provide the business logic.
task_resolver – Custom resolver - if you don’t make one, use the default task template resolver.
task_type – String task type to be associated with this Task.
requests – custom resource request settings.
limits – custom resource limit settings.
environment – Environment variables you want the task to have when run.
secret_requests (List[Secret]) –
Secrets that are requested by this container execution. These secrets will be mounted based on the configuration in the Secret and available through the SecretManager using the name of the secret as the group Ideally the secret keys should also be semi-descriptive. The key values will be available from runtime, if the backend is configured to provide secrets and if secrets are available in the configured secrets store. Possible options for secret stores are

Methods

compile(ctx, *args, **kwargs)#

Generates a node that encapsulates this task in a workflow definition.

Parameters: ctx (flytekit.core.context_manager.FlyteContext) –

construct_node_metadata()#

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

Return type: flytekit.models.core.workflow.NodeMetadata

dispatch_execute(ctx, input_literal_map)#

This function is largely similar to the base PythonTask, with the exception that we have to infer the Python interface before executing. Also, we refer to self.task_template rather than just self similar to task classes that derive from the base PythonTask.

Parameters

ctx (flytekit.core.context_manager.FlyteContext) –
input_literal_map (flytekit.models.literals.LiteralMap) –

Return type

Union[flytekit.models.literals.LiteralMap, flytekit.models.dynamic_job.DynamicJobSpec]

execute(**kwargs)#

Rather than running here, send everything to the executor.

Return type: Any

find_lhs()#

Return type: str

get_command(settings)#

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: List[str]

get_config(settings)#

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: Dict[str, str]

get_container(settings)#

Returns the container definition (if any) that is used to run the task on hosted Flyte.

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: flytekit.models.task.Container

get_custom(settings)[source]#

Return additional plugin-specific custom data (if any) as a serializable dictionary.

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: Dict[str, Any]

get_input_types()#

Returns the names and python types as a dictionary for the inputs of this task.

Return type: Dict[str, type]

get_k8s_pod(settings)#

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: Optional[flytekit.models.task.K8sPod]

get_query(**kwargs)#

Return type: str

get_sql(settings)#

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: Optional[flytekit.models.task.Sql]

get_type_for_input_var(k, v)#

Returns the python type for an input variable by name.

Parameters

k (str) –
v (Any) –

Return type

Type[Any]

get_type_for_output_var(k, v)#

Returns the python type for the specified output variable by name.

Parameters

k (str) –
v (Any) –

Return type

Type[Any]

classmethod interpolate_query(query_template, **kwargs)#

This function will fill in the query template with the provided kwargs and return the interpolated query. Please note that when SQL tasks run in Flyte, this step is done by the task executor.

Return type: Any

local_execute(ctx, **kwargs)#

This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).

Parameters: ctx (flytekit.core.context_manager.FlyteContext) –
Return type: Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise]]

post_execute(_, rval)#

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

Parameters

_ (Optional[flytekit.core.context_manager.ExecutionParameters]) –
rval (Any) –

Return type

Any

pre_execute(user_params)#

This function is a stub, just here to keep dispatch_execute compatibility between this class and PythonTask.

Parameters: user_params (Optional[flytekit.core.context_manager.ExecutionParameters]) –
Return type: Optional[flytekit.core.context_manager.ExecutionParameters]

sandbox_execute(ctx, input_literal_map)#

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Parameters

ctx (flytekit.core.context_manager.FlyteContext) –
input_literal_map (flytekit.models.literals.LiteralMap) –

Return type

flytekit.models.literals.LiteralMap

serialize_to_model(settings)#

Parameters: settings (flytekit.configuration.SerializationSettings) –
Return type: flytekit.models.task.TaskTemplate

Attributes

SERIALIZE_SETTINGS = SerializationSettings(image_config=ImageConfig(default_image=Image(name='custom_container_task', fqn='flyteorg.io/placeholder', tag='image'), images=None), project='PLACEHOLDER_PROJECT', domain='LOCAL', version='PLACEHOLDER_VERSION', env=None, git_repo=None, python_interpreter='/opt/venv/bin/python3', flytekit_virtualenv_root='/opt/venv', fast_serialization_settings=None, source_root=None)#

container_image#

disable_deck#: If true, this task will not output deck html file

docs#

environment#: Any environment variables that supplied during the execution of the task.

executor#

executor_type#

instantiated_in#

interface#

lhs#

location#

metadata#

name#: Return the name of the underlying task.

output_columns#

python_interface#: Returns this task’s python interface.

query_template#

resources#

security_context#

task_config#: Returns the user-specified task config which is used for plugin-specific handling of the task.

task_resolver#

task_template#: Override the base class implementation to serialize on first call.

task_type#

task_type_version#