# Copyright (c) 2019, MD2K Center of Excellence
# - Nasir Ali <nasir.ali08@gmail.com>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import uuid
import warnings
from datetime import datetime
from typing import List
from cerebralcortex.core.util.spark_helper import get_or_create_sc
from cerebralcortex.core.config_manager.config import Configuration
from cerebralcortex.core.data_manager.object.data import ObjectData
from cerebralcortex.core.data_manager.raw.data import RawData
from cerebralcortex.core.data_manager.raw.stream_handler import DataSet
from cerebralcortex.core.data_manager.sql.data import SqlData
from cerebralcortex.core.data_manager.time_series.data import TimeSeriesData
from cerebralcortex.core.datatypes import DataStream
from cerebralcortex.core.log_manager.log_handler import LogTypes
from cerebralcortex.core.log_manager.logging import CCLogging
from cerebralcortex.core.messaging_manager.messaging_queue import MessagingQueue
from cerebralcortex.core.metadata_manager.stream.metadata import Metadata
[docs]class Kernel:
def __init__(self, configs_dir_path: str = None, auto_offset_reset: str = "largest", enable_spark:bool=True, enable_spark_ui=False):
"""
CerebralCortex constructor
Args:
configuration_filepath (str): Directory path of cerebralcortex configurations.
auto_offset_reset (str): Kafka offset. Acceptable parameters are smallest or largest (default=largest)
Raises:
ValueError: If configuration_filepath is None or empty.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
"""
if configs_dir_path is None or configs_dir_path == "":
raise ValueError("config_file path cannot be None or blank.")
if enable_spark:
self.sparkContext = get_or_create_sc(enable_spark_ui=enable_spark_ui)
self.sqlContext = get_or_create_sc(type="sqlContext", enable_spark_ui=enable_spark_ui)
self.sparkSession = get_or_create_sc(type="sparkSession", enable_spark_ui=enable_spark_ui)
else:
self.sparkContext = None
self.sqlContext = None
self.sparkSession = None
self.config_filepath = configs_dir_path
self.config = Configuration(configs_dir_path).config
self.debug = self.config["cc"]["debug"]
self.logging = CCLogging(self)
self.logtypes = LogTypes()
self.SqlData = SqlData(self)
self.RawData = RawData(self)
self.ObjectData = ObjectData(self)
self.MessagingQueue = None
self.TimeSeriesData = None
warnings.simplefilter('always', DeprecationWarning)
if self.config["visualization_storage"] != "none":
self.TimeSeriesData = TimeSeriesData(self)
if enable_spark and self.config["messaging_service"] != "none":
self.MessagingQueue = MessagingQueue(self, auto_offset_reset)
###########################################################################
# RAW DATA MANAGER METHODS #
###########################################################################
[docs] def save_stream(self, datastream: DataStream, ingestInfluxDB: bool = False)->bool:
"""
Saves datastream raw data in selected NoSQL storage and metadata in MySQL.
Args:
datastream (DataStream): a DataStream object
ingestInfluxDB (bool): Setting this to True will ingest the raw data in InfluxDB as well that could be used to visualize data in Grafana
Returns:
bool: True if stream is successfully stored or throws an exception
Raises:
Exception: log or throws exception if stream is not stored
Todo:
Add functionality to store data in influxdb.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> ds = DataStream(dataframe, MetaData)
>>> CC.save_stream(ds)
"""
return self.RawData.save_stream(datastream=datastream, ingestInfluxDB=ingestInfluxDB)
[docs] def get_stream(self, stream_name: str, version: str = "all", data_type=DataSet.COMPLETE) -> DataStream:
"""
Retrieve a data-stream with it's metadata.
Args:
stream_name (str): name of a stream
version (str): version of a stream. Acceptable parameters are all, latest, or a specific version of a stream (e.g., 2.0) (Default="all")
data_type (DataSet): DataSet.COMPLETE returns both Data and Metadata. DataSet.ONLY_DATA returns only Data. DataSet.ONLY_METADATA returns only metadata of a stream. (Default=DataSet.COMPLETE)
Returns:
DataStream: contains Data and/or metadata
Raises:
ValueError: if stream name is empty or None
Note:
Please specify a version if you know the exact version of a stream. Getting all the stream data and then filtering versions won't be efficient.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> ds = CC.get_stream("ACCELEROMETER--org.md2k.motionsense--MOTION_SENSE_HRV--RIGHT_WRIST")
>>> ds.data # an object of a dataframe
>>> ds.metadata # an object of MetaData class
>>> ds.get_metadata(version=1) # get the specific version metadata of a stream
"""
return self.RawData.get_stream(stream_name=stream_name, version=version, data_type=data_type)
###########################################################################
# TIME SERIES DATA MANAGER METHODS #
###########################################################################
[docs] def save_data_to_influxdb(self, datastream: DataStream):
"""
Save data stream to influxdb only for visualization purposes.
Args:
datastream (DataStream): a DataStream object
Returns:
bool: True if data is ingested successfully or False otherwise
Todo:
This needs to be updated with the new structure. Should metadata be stored or not?
Example:
>>> CC = Kernel("/directory/path/of/configs/")
>>> ds = DataStream(dataframe, MetaData)
>>> CC.save_data_to_influxdb(ds)
"""
self.TimeSeriesData.save_data_to_influxdb(datastream)
###########################################################################
# SQL DATA MANAGER METHODS #
###########################################################################
################### STREAM RELATED METHODS ################################
[docs] def is_stream(self, stream_name: str) -> bool:
"""
Returns true if provided stream exists.
Args:
stream_name (str): name of a stream
Returns:
bool: True if stream_name exist False otherwise
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.is_stream("ACCELEROMETER--org.md2k.motionsense--MOTION_SENSE_HRV--RIGHT_WRIST")
>>> True
"""
return self.SqlData.is_stream(stream_name)
[docs] def get_stream_versions(self, stream_name: str) -> list:
"""
Returns a list of versions available for a stream
Args:
stream_name (str): name of a stream
Returns:
list: list of int
Raises:
ValueError: if stream_name is empty or None
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_stream_versions("ACCELEROMETER--org.md2k.motionsense--MOTION_SENSE_HRV--RIGHT_WRIST")
>>> [1, 2, 4]
"""
return self.SqlData.get_stream_versions(stream_name)
[docs] def get_stream_name(self, metadata_hash: uuid) -> str:
"""
metadata_hash are unique to each stream version. This reverse look can return the stream name of a metadata_hash.
Args:
metadata_hash (uuid): This could be an actual uuid object or a string form of uuid.
Returns:
str: name of a stream
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_stream_name("00ab666c-afb8-476e-9872-6472b4e66b68")
>>> ACCELEROMETER--org.md2k.motionsense--MOTION_SENSE_HRV--RIGHT_WRIST
"""
return self.SqlData.get_stream_name(metadata_hash)
[docs] def get_stream_info_by_hash(self, metadata_hash: uuid) -> str:
"""
metadata_hash are unique to each stream version. This reverse look can return the stream name of a metadata_hash.
Args:
metadata_hash (uuid): This could be an actual uuid object or a string form of uuid.
Returns:
dict: stream metadata and other info related to a stream
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_stream_name("00ab666c-afb8-476e-9872-6472b4e66b68")
>>> {"name": .....} # stream metadata and other information
"""
return self.SqlData.get_stream_info_by_hash(metadata_hash=metadata_hash)
[docs] def list_streams(self)->List[Metadata]:
"""
Get all the available stream names with metadata
Returns:
List[Metadata]: list of available streams metadata
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.list_streams()
"""
return self.SqlData.list_streams()
[docs] def search_stream(self, stream_name):
"""
Find all the stream names similar to stream_name arg. For example, passing "location"
argument will return all stream names that contain the word location
Returns:
List[str]: list of stream names similar to stream_name arg
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.search_stream("battery")
>>> ["BATTERY--org.md2k.motionsense--MOTION_SENSE_HRV--LEFT_WRIST", "BATTERY--org.md2k.phonesensor--PHONE".....]
"""
return self.SqlData.search_stream(stream_name=stream_name)
################### USER RELATED METHODS ##################################
[docs] def create_user(self, username:str, user_password:str, user_role:str, user_metadata:dict, user_settings:dict)->bool:
"""
Create a user in SQL storage if it doesn't exist
Args:
username (str): Only alphanumeric usernames are allowed with the max length of 25 chars.
user_password (str): no size limit on password
user_role (str): role of a user
user_metadata (dict): metadata of a user
user_settings (dict): user settings, mCerebrum configurations of a user
Returns:
bool: True if user is successfully registered or throws any error in case of failure
Raises:
ValueError: if selected username is not available
Exception: if sql query fails
"""
return self.SqlData.create_user( username, user_password, user_role, user_metadata, user_settings)
[docs] def delete_user(self, username:str)->bool:
"""
Delete a user record in SQL table
Args:
username: username of a user that needs to be deleted
Returns:
bool: if user is successfully removed
Raises:
ValueError: if username param is empty or None
Exception: if sql query fails
"""
return self.SqlData.delete_user(username)
[docs] def is_user(self, user_id: str = None, user_name: str = None) -> bool:
"""
Checks whether a user exists in the system. One of both parameters could be set to verify whether user exist.
Args:
user_id (str): id (uuid) of a user
user_name (str): username of a user
Returns:
bool: True if a user exists in the system or False otherwise.
Raises:
ValueError: Both user_id and user_name cannot be None or empty.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.is_user(user_id="76cc444c-4fb8-776e-2872-9472b4e66b16")
>>> True
"""
return self.SqlData.is_user(user_id, user_name)
[docs] def get_user_id(self, user_name: str) -> str:
"""
Get the user id linked to user_name.
Args:
user_name (str): username of a user
Returns:
str: user id associated to user_name
Raises:
ValueError: User name is a required field.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_user_id("nasir_ali")
>>> '76cc444c-4fb8-776e-2872-9472b4e66b16'
"""
return self.SqlData.get_user_id(user_name)
[docs] def get_user_name(self, user_id: str) -> str:
"""
Get the user name linked to a user id.
Args:
user_name (str): username of a user
Returns:
bool: user_id associated to username
Raises:
ValueError: User ID is a required field.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_user_name("76cc444c-4fb8-776e-2872-9472b4e66b16")
>>> 'nasir_ali'
"""
return self.SqlData.get_user_name(user_id)
[docs] def get_all_users(self, study_name: str) -> List[dict]:
"""
Get a list of all users part of a study.
Args:
study_name (str): name of a study
Raises:
ValueError: Study name is a requied field.
Returns:
list[dict]: Returns empty list if there is no user associated to the study_name and/or study_name does not exist.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_all_users("mperf")
>>> [{"76cc444c-4fb8-776e-2872-9472b4e66b16": "nasir_ali"}] # [{user_id, user_name}]
"""
return self.SqlData.get_all_users(study_name)
[docs] def get_user_settings(self, username: str=None, auth_token: str = None) -> dict:
"""
Get user settings by auth-token or by username. These are user's mCerebrum settings
Args:
username (str): username of a user
auth_token (str): auth-token
Returns:
list[dict]: List of dictionaries of user metadata
Todo:
Return list of User class object
Raises:
ValueError: User ID/name cannot be empty.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_user_settings(username="nasir_ali")
>>> [{"mcerebrum":"some-conf"........}]
"""
return self.SqlData.get_user_settings(username, auth_token)
[docs] def connect(self, username: str, password: str, encrypted_password:bool=False) -> dict:
"""
Authenticate a user based on username and password and return an auth token
Args:
username (str): username of a user
password (str): password of a user
encrypted_password (str): is password encrypted or not. mCerebrum sends encrypted passwords
Raises:
ValueError: User name and password cannot be empty/None.
Returns:
dict: return eturn {"status":bool, "auth_token": str, "msg": str}
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.connect("nasir_ali", "2ksdfhoi2r2ljndf823hlkf8234hohwef0234hlkjwer98u234", True)
>>> True
"""
return self.SqlData.login_user(username, password, encrypted_password)
[docs] def is_auth_token_valid(self, username: str, auth_token: str, checktime:bool=False) -> bool:
"""
Validate whether a token is valid or expired based on the token expiry datetime stored in SQL
Args:
username (str): username of a user
auth_token (str): token generated by API-Server
checktime (bool): setting this to False will only check if the token is available in system. Setting this to true will check if the token is expired based on the token expiry date.
Raises:
ValueError: Auth token and auth-token expiry time cannot be null/empty.
Returns:
bool: returns True if token is valid or False otherwise.
"""
return self.SqlData.is_auth_token_valid(username, auth_token, checktime)
[docs] def update_auth_token(self, username: str, auth_token: str, auth_token_issued_time: datetime,
auth_token_expiry_time: datetime) -> bool:
"""
Update an auth token in SQL database to keep user stay logged in. Auth token valid duration can be changed in configuration files.
Notes:
This method is used by API-server to store newly created auth-token
Args:
username (str): username of a user
auth_token (str): issued new auth token
auth_token_issued_time (datetime): datetime when the old auth token was issue
auth_token_expiry_time (datetime): datetime when the token will get expired
Raises:
ValueError: Auth token and auth-token issue/expiry time cannot be None/empty.
Returns:
bool: Returns True if the new auth token is set or False otherwise.
"""
return self.SqlData.update_auth_token(username, auth_token, auth_token_issued_time, auth_token_expiry_time)
[docs] def gen_random_pass(self, string_type: str="varchar", size: int = 8) -> str:
"""
Generate a random password
Args:
string_type: Accepted parameters are "varchar" and "char". (Default="varchar")
size: password length (default=8)
Returns:
str: random password
"""
return self.SqlData.gen_random_pass(string_type, size)
[docs] def encrypt_user_password(self, user_password: str) -> str:
"""
Encrypt password
Args:
user_password (str): unencrypted password
Raises:
ValueError: password cannot be None or empty.
Returns:
str: encrypted password
"""
return self.SqlData.encrypt_user_password(user_password)
################### KAFKA RELATED METHODS ##################################
[docs] def store_or_update_Kafka_offset(self, topic: str, topic_partition: str, offset_start: str, offset_until: str)->bool:
"""
Store or Update kafka topic offsets. Offsets are used to track what messages have been processed.
Args:
topic (str): name of the kafka topic
topic_partition (str): partition number
offset_start (str): starting of offset
offset_until (str): last processed offset
Raises:
ValueError: All params are required.
Exception: Cannot add/update kafka offsets because ERROR-MESSAGE
Returns:
bool: returns True if offsets are add/updated or throws an exception.
"""
self.SqlData.store_or_update_Kafka_offset(topic, topic_partition, offset_start, offset_until)
[docs] def get_kafka_offsets(self, topic: str) -> dict:
"""
Get last stored kafka offsets
Args:
topic (str): kafka topic name
Returns:
list[dict]: list of kafka offsets. This method will return empty list if topic does not exist and/or no offset is stored for the topic.
Raises:
ValueError: Topic name cannot be empty/None
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.get_kafka_offsets("live-data")
>>> [{"id","topic", "topic_partition", "offset_start", "offset_until", "offset_update_time"}]
"""
return self.SqlData.get_kafka_offsets(topic)
###########################################################################
# OBJECTS DATA MANAGER METHODS #
###########################################################################
[docs] def create_bucket(self, bucket_name: str) -> bool:
"""
creates a bucket aka folder in object storage system.
Args:
bucket_name (str): name of the bucket
Returns:
bool: True if bucket was successfully created. On failure, returns an error with dict {"error":"error-message"}
Raises:
ValueError: Bucket name cannot be empty/None.
Examples:
>>> CC = Kernel("/directory/path/of/configs/")
>>> CC.create_bucket("live_data_folder")
>>> True
"""
return self.ObjectData.create_bucket(bucket_name)
[docs] def upload_object(self, bucket_name: str, object_name: str, object_filepath:str) -> bool:
"""
Upload an object in a bucket aka folder of object storage system.
Args:
bucket_name (str): name of the bucket
object_name (str): name of the object to be uploaded
object_filepath (str): it shall contain full path of a file with file name (e.g., /home/nasir/obj.zip)
Returns:
bool: True if object successfully uploaded. On failure, returns an error with dict {"error":"error-message"}
Raises:
ValueError: Bucket name cannot be empty/None.
"""
return self.ObjectData.upload_object(bucket_name, object_name, object_filepath)
[docs] def get_buckets(self) -> dict:
"""
returns all available buckets in an object storage
Returns:
dict: {bucket-name: str, [{"key":"value"}]}, in case of an error {"error": str}
"""
return self.ObjectData.get_buckets()
[docs] def get_bucket_objects(self, bucket_name: str) -> dict:
"""
returns a list of all objects stored in the specified Minio bucket
Args:
bucket_name (str): name of the bucket aka folder
Returns:
dict: {bucket-objects: [{"object_name":"", "metadata": {}}...], in case of an error {"error": str}
"""
return self.ObjectData.get_bucket_objects(bucket_name)
[docs] def get_object_stats(self, bucket_name: str, object_name: str) -> dict:
"""
Returns properties (e.g., object type, last modified etc.) of an object stored in a specified bucket
Args:
bucket_name (str): name of a bucket aka folder
object_name (str): name of an object
Returns:
dict: information of an object (e.g., creation_date, object_size etc.). In case of an error {"error": str}
Raises:
ValueError: Missing bucket_name and object_name params.
Exception: {"error": "error-message"}
"""
return self.ObjectData.get_object_stats(bucket_name, object_name)
[docs] def get_object(self, bucket_name: str, object_name: str) -> dict:
"""
Returns stored object (HttpResponse)
Args:
bucket_name (str): name of a bucket aka folder
object_name (str): name of an object that needs to be downloaded
Returns:
file-object: object that needs to be downloaded. If file does not exists then it returns an error {"error": "File does not exist."}
Raises:
ValueError: Missing bucket_name and object_name params.
Exception: {"error": "error-message"}
"""
return self.ObjectData.get_object(bucket_name, object_name)
[docs] def is_bucket(self, bucket_name: str) -> bool:
"""
checks whether a bucket exist
Args:
bucket_name (str): name of the bucket aka folder
Returns:
bool: True if bucket exist or False otherwise. In case an error {"error": str}
Raises:
ValueError: bucket_name cannot be None or empty.
"""
return self.ObjectData.is_bucket(bucket_name)
[docs] def is_object(self, bucket_name: str, object_name: str) -> bool:
"""
checks whether an object exist in a bucket
Args:
bucket_name (str): name of the bucket aka folder
object_name (str): name of the object
Returns:
bool: True if object exist or False otherwise. In case an error {"error": str}
Raises:
Excecption: if bucket_name and object_name are empty or None
"""
return self.ObjectData.is_object(bucket_name, object_name)
###########################################################################
# Kafka consumer producer #
###########################################################################
[docs] def kafka_produce_message(self, topic: str, msg: dict):
"""
Publish a message on kafka message queue
Args:
topic (str): name of the kafka topic
msg (dict): message that needs to published on kafka
Returns:
bool: True if successful. In case of failure, it returns an Exception message.
Raises:
ValueError: topic and message parameters cannot be empty or None.
Exception: Error publishing message. Topic: topic_name - error-message
"""
self.MessagingQueue.produce_message(topic, msg)
[docs] def kafka_subscribe_to_topic(self, topic: str):
"""
Subscribe to kafka topic as a consumer
Args:
topic (str): name of the kafka topic
Yields:
dict: kafka message
Raises:
ValueError: Topic parameter is missing.
"""
return self.MessagingQueue.subscribe_to_topic(topic)
################### CACHE RELATED METHODS ##################################
[docs] def set_cache_value(self, key: str, value: str) -> bool:
"""
Creates a new cache entry in the cache. Values are overwritten for existing keys.
Args:
key: key in the cache
value: value associated with the key
Returns:
bool: True on successful insert or False otherwise.
Raises:
ValueError: if key is None or empty
"""
return self.SqlData.set_cache_value(key, value)
[docs] def get_cache_value(self, key: str) -> str:
"""
Retrieves value from the cache for the given key.
Args:
key: key in the cache
Returns:
str: The value in the cache
Raises:
ValueError: if key is None or empty
"""
return self.SqlData.get_cache_value(key)
###########################################################################
# mProve Data Provenance #
###########################################################################
# def store_activity(self, activity: str, start: int, end: int, location: int):
# """
# Create an entity node for an activity (a stream operator computation)
#
# Args:
# activity: Name of the operation
# start: Start time
# end: End time
# location: Index position etc
# """
# self.MProvConnection.store_activity(activity=activity, start=start, end=end, location=location)
#
# def store_stream_tuple(self, stream_name: str, stream_index: int, input_tuple: BasicTuple):
# """
# Create an entity node for a stream tuple
#
# Args:
# stream_name: The name of the stream itself
# stream_index: The index position (count) or timestamp (if unique)
# input_tuple: The actual stream value
# Returns:
# token for the new node
# """
# self.MProvConnection.store_stream_tuple(stream_name=stream_name, stream_index=stream_index, input_tuple=input_tuple)
#
# def store_annotation(self, stream_name: str, stream_index: int, annotation_name: str, annotation_value):
# """
# Create a node for an annotation to an entity / tuple
#
# Args:
# stream_name: The name of the stream itself
# stream_index: The index position (count) or timestamp (if unique) of the stream element we are annotating
# annotation_name: The name of the annotation
# annotation_value: The value of the annotation
# """
# self.MProvConnection.store_annotation(stream_name=stream_name, stream_index=stream_index, annotation_name=annotation_name, annotation_value=annotation_value)
#
# def store_window_and_inputs(self, output_stream_name: str, output_stream_index: int, input_tokens_list: list ):
# """
# Store a mapping between an operator window, from
# which a stream is to be derived, and the input
# nodes
#
# Args:
# output_stream_name:
# output_stream_index:
# input_tokens_list:
# """
# self.MProvConnection.store_window_and_inputs(output_stream_name=output_stream_name, output_stream_index=output_stream_index, input_tokens_list=input_tokens_list)
#
# def store_windowed_result(self, output_stream_name: str, output_stream_index: int, output_tuple: BasicTuple, input_tokens_list: list, activity: str, start: int, end: int ):
# """
# When we have a windowed computation, this creates a complex derivation subgraph
# in one operation.
#
# Args:
# output_stream_name: The name of the stream our operator produces
# output_stream_index: The position of the outgoing tuple in the stream
# output_tuple: The tuple itself
# input_tokens_list: IDs of the inputs to the computation
# activity: The computation name
# start: Start time
# end: End time
# """
# self.MProvConnection.store_windowed_result(output_stream_name=output_stream_name, output_stream_index=output_stream_index, output_tuple=output_tuple, input_tokens_list=input_tokens_list, activity=activity, start=start, end=end)