flytekit.SQLTask#
- class flytekit.SQLTask(*args, **kwargs)[source]#
Base task types for all SQL tasks. See
flytekit.extras.sqlite3.task.SQLite3Task
andflytekitplugins.athena.task.AthenaTask
for examples of how to use it as a base class.- class SQLite3Task(*args, **kwargs)
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 integrations guide 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
This SQLTask should mostly just be used as a base class for other SQL task types and should not be used directly. See
flytekit.extras.sqlite3.task.SQLite3Task
Methods
- compile(ctx, *args, **kwargs)#
Generates a node that encapsulates this task in a workflow definition.
- Parameters
- Return type
Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise]]
- 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 method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.
VoidPromise
is returned in the case when the task itself declares no outputs.Literal Map
is returned when the task returns either one more outputs in the declaration. Individual outputs may be noneDynamicJobSpec
is returned when a dynamic workflow is executed
- 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
- 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
Optional[flytekit.models.task.Container]
- get_custom(settings)#
Return additional plugin-specific custom data (if any) as a serializable dictionary.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_extended_resources(settings)#
Returns the extended resources to allocate to the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
Optional[flyteidl.core.tasks_pb2.ExtendedResources]
- get_input_types()#
Returns the names and python types as a dictionary for the inputs of this task.
- 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_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.
- get_type_for_output_var(k, v)#
Returns the python type for the specified output variable by name.
- classmethod interpolate_query(query_template, **kwargs)[source]#
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
- 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
- Return type
Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, Coroutine]]
- local_execution_mode()#
- post_execute(user_params, rval)#
Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op
- Parameters
execute (rval is returned value from call to) –
user_params (Optional[flytekit.core.context_manager.ExecutionParameters]) – are the modified user params as created during the pre_execute step
rval (Any) –
- Return type
- pre_execute(user_params)#
This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called
This should return either the same context of the mutated context
- Parameters
user_params (Optional[flytekit.core.context_manager.ExecutionParameters]) –
- Return type
- sandbox_execute(ctx, input_literal_map)#
Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.
- Parameters
input_literal_map (flytekit.models.literals.LiteralMap) –
- Return type
flytekit.models.literals.LiteralMap
Attributes
- 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.
- instantiated_in
- interface
- lhs
- location
- metadata
- name
- python_interface
Returns this task’s python interface.
- query_template
- security_context
- task_config
Returns the user-specified task config which is used for plugin-specific handling of the task.
- task_type
- task_type_version