1.16.15

flytekitplugins.great_expectations.task

Directory

Classes

Class Description
BatchRequestConfig Use this configuration to configure Batch Request.
GreatExpectationsTask This task can be used to validate your data.

flytekitplugins.great_expectations.task.BatchRequestConfig

Use this configuration to configure Batch Request. A BatchRequest can either be a simple BatchRequest or a RuntimeBatchRequest.

class BatchRequestConfig(
    data_connector_query: typing.Optional[typing.Dict[str, typing.Any]],
    runtime_parameters: typing.Optional[typing.Dict[str, typing.Any]],
    batch_identifiers: typing.Optional[typing.Dict[str, str]],
    batch_spec_passthrough: typing.Optional[typing.Dict[str, typing.Any]],
)
Parameter Type Description
data_connector_query typing.Optional[typing.Dict[str, typing.Any]] query to request data batch
runtime_parameters typing.Optional[typing.Dict[str, typing.Any]] parameters to be passed at runtime
batch_identifiers typing.Optional[typing.Dict[str, str]] identifiers to identify the data batch
batch_spec_passthrough typing.Optional[typing.Dict[str, typing.Any]] reader method if your file doesn’t have an extension

Methods

Method Description
from_dict()
from_json()
schema()
to_dict()
to_json()

from_dict() 

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
) -> ~A
Parameter Type Description
kvs typing.Union[dict, list, str, int, float, bool, NoneType]
infer_missing

from_json() 

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
) -> ~A
Parameter Type Description
s typing.Union[str, bytes, bytearray]
parse_float
parse_int
parse_constant
infer_missing
kw

schema() 

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
) -> SchemaType[A]
Parameter Type Description
infer_missing bool
only
exclude
many bool
context
load_only
dump_only
partial bool
unknown

to_dict() 

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]
Parameter Type Description
encode_json

to_json() 

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
) -> str
Parameter Type Description
skipkeys bool
ensure_ascii bool
check_circular bool
allow_nan bool
indent typing.Union[int, str, NoneType]
separators typing.Tuple[str, str]
default typing.Callable
sort_keys bool
kw

flytekitplugins.great_expectations.task.GreatExpectationsTask

This task can be used to validate your data. You can use this when you want to validate your data within the task or workflow. If you want to validate your data as and when the type is given, use the GreatExpectationsType.

class GreatExpectationsTask(
    name: str,
    datasource_name: str,
    expectation_suite_name: str,
    data_connector_name: str,
    inputs: typing.Dict[str, typing.Type],
    data_asset_name: typing.Optional[str],
    outputs: typing.Optional[typing.Dict[str, typing.Type]],
    local_file_path: typing.Optional[str],
    checkpoint_params: typing.Optional[typing.Dict[str, typing.Union[str, typing.List[str]]]],
    task_config: flytekitplugins.great_expectations.task.BatchRequestConfig,
    context_root_dir: str,
    kwargs,
)

Please see class level documentation.

Parameter Type Description
name str name of the task
datasource_name str tell where your data lives and how to get it
expectation_suite_name str suite which consists of the data expectations
data_connector_name str connector to identify data batches
inputs typing.Dict[str, typing.Type] inputs to pass to the execute() method
data_asset_name typing.Optional[str] name of the data asset (to be used for RuntimeBatchRequest)
outputs typing.Optional[typing.Dict[str, typing.Type]]
local_file_path typing.Optional[str] dataset file path useful for FlyteFile and FlyteSchema
checkpoint_params typing.Optional[typing.Dict[str, typing.Union[str, typing.List[str]]]] optional SimpleCheckpoint parameters
task_config flytekitplugins.great_expectations.task.BatchRequestConfig batchrequest config
context_root_dir str directory in which GreatExpectations’ configuration resides
kwargs **kwargs

Properties

Property Type Description
container_image None
deck_fields None If not empty, this task will output deck html file for the specified decks
disable_deck None If true, this task will not output deck html file
docs None
enable_deck None If true, this task will output deck html file
environment None Any environment variables that supplied during the execution of the task.
instantiated_in None
interface None
lhs None
location None
metadata None
name None
python_interface None Returns this task’s python interface.
resources None
security_context None
task_config None Returns the user-specified task config which is used for plugin-specific handling of the task.
task_resolver None
task_type None
task_type_version None

Methods

Method Description
compile() Generates a node that encapsulates this task in a workflow definition.
construct_node_metadata() Used when constructing the node that encapsulates this task as part of a broader workflow definition.
dispatch_execute() This method translates Flyte’s Type system based input values and invokes the actual call to the executor.
execute() This method will be invoked to execute the task.
find_lhs()
get_command() Returns the command which should be used in the container definition for the serialized version of this task.
get_config() Returns the task config as a serializable dictionary.
get_container() Returns the container definition (if any) that is used to run the task on hosted Flyte.
get_custom() Return additional plugin-specific custom data (if any) as a serializable dictionary.
get_default_command() Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.
get_extended_resources() Returns the extended resources to allocate to the task on hosted Flyte.
get_image() Update image spec based on fast registration usage, and return string representing the image.
get_input_types() Returns the names and python types as a dictionary for the inputs of this task.
get_k8s_pod() Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.
get_sql() Returns the Sql definition (if any) that is used to run the task on hosted Flyte.
get_type_for_input_var() Returns the python type for an input variable by name.
get_type_for_output_var() Returns the python type for the specified output variable by name.
local_execute() This function is used only in the local execution path and is responsible for calling dispatch execute.
local_execution_mode()
post_execute() Post execute is called after the execution has completed, with the user_params and can be used to clean-up,.
pre_execute() This is the method that will be invoked directly before executing the task method and before all the inputs.
reset_command_fn() Resets the command which should be used in the container definition of this task to the default arguments.
sandbox_execute() Call dispatch_execute, in the context of a local sandbox execution.
set_command_fn() By default, the task will run on the Flyte platform using the pyflyte-execute command.
set_resolver() By default, flytekit uses the DefaultTaskResolver to resolve the task.

compile() 

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]

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

Parameter Type Description
ctx flytekit.core.context_manager.FlyteContext
args *args
kwargs **kwargs

construct_node_metadata() 

def construct_node_metadata()

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

dispatch_execute() 

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> typing.Union[flytekit.models.literals.LiteralMap, flytekit.models.dynamic_job.DynamicJobSpec, typing.Coroutine]

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 none
  • DynamicJobSpec is returned when a dynamic workflow is executed
Parameter Type Description
ctx flytekit.core.context_manager.FlyteContext
input_literal_map flytekit.models.literals.LiteralMap

execute() 

def execute(
    kwargs,
) -> typing.Any

This method will be invoked to execute the task.

Parameter Type Description
kwargs **kwargs

find_lhs() 

def find_lhs()

get_command() 

def get_command(
    settings: SerializationSettings,
) -> List[str]

Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.

Parameter Type Description
settings SerializationSettings

get_config() 

def get_config(
    settings: SerializationSettings,
) -> Optional[Dict[str, str]]

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

Parameter Type Description
settings SerializationSettings

get_container() 

def get_container(
    settings: SerializationSettings,
) -> _task_model.Container

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

Parameter Type Description
settings SerializationSettings

get_custom() 

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, typing.Any]]

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

Parameter Type Description
settings flytekit.configuration.SerializationSettings

get_default_command() 

def get_default_command(
    settings: SerializationSettings,
) -> List[str]

Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.

Parameter Type Description
settings SerializationSettings

get_extended_resources() 

def get_extended_resources(
    settings: SerializationSettings,
) -> Optional[tasks_pb2.ExtendedResources]

Returns the extended resources to allocate to the task on hosted Flyte.

Parameter Type Description
settings SerializationSettings

get_image() 

def get_image(
    settings: SerializationSettings,
) -> str

Update image spec based on fast registration usage, and return string representing the image

Parameter Type Description
settings SerializationSettings

get_input_types() 

def get_input_types()

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

get_k8s_pod() 

def get_k8s_pod(
    settings: SerializationSettings,
) -> _task_model.K8sPod

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

Parameter Type Description
settings SerializationSettings

get_sql() 

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]

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

Parameter Type Description
settings flytekit.configuration.SerializationSettings

get_type_for_input_var() 

def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for an input variable by name.

Parameter Type Description
k str
v typing.Any

get_type_for_output_var() 

def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

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

Parameter Type Description
k str
v typing.Any

local_execute() 

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

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).

Parameter Type Description
ctx flytekit.core.context_manager.FlyteContext
kwargs **kwargs

local_execution_mode() 

def local_execution_mode()

post_execute() 

def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
) -> typing.Any

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

Parameter Type Description
user_params typing.Optional[flytekit.core.context_manager.ExecutionParameters] are the modified user params as created during the pre_execute step
rval typing.Any

pre_execute() 

def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
) -> typing.Optional[flytekit.core.context_manager.ExecutionParameters]

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

Parameter Type Description
user_params typing.Optional[flytekit.core.context_manager.ExecutionParameters]

reset_command_fn() 

def reset_command_fn()

Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.

sandbox_execute() 

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap

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

Parameter Type Description
ctx flytekit.core.context_manager.FlyteContext
input_literal_map flytekit.models.literals.LiteralMap

set_command_fn() 

def set_command_fn(
    get_command_fn: Optional[Callable[[SerializationSettings], List[str]]],
)

By default, the task will run on the Flyte platform using the pyflyte-execute command. However, it can be useful to update the command with which the task is serialized for specific cases like running map tasks (“pyflyte-map-execute”) or for fast-executed tasks.

Parameter Type Description
get_command_fn Optional[Callable[[SerializationSettings], List[str]]]

set_resolver() 

def set_resolver(
    resolver: TaskResolverMixin,
)

By default, flytekit uses the DefaultTaskResolver to resolve the task. This method allows the user to set a custom task resolver. It can be useful to override the task resolver for specific cases like running tasks in the jupyter notebook.

Parameter Type Description
resolver TaskResolverMixin