b718801290
Update get_last_data_timestamp method in Redis activity to return a string or None, improving type clarity for timestamp retrieval.
119 lines
4.0 KiB
Python
119 lines
4.0 KiB
Python
from temporalio import workflow, activity
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with workflow.unsafe.imports_passed_through():
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from logging import Logger
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from sientia_do.notifications.handlers import NotificationHandler
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from sientia_do.temporal.activities.redis_base import Redis as RedisBase
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from sientia_do.temporal.utils.logger import Logger
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from typing import Any
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from pandas import DataFrame
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from datetime import datetime
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class Redis(RedisBase):
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def __init__(self, host: str, port: int,
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username: str, password: str,
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logger: Logger, notification_handler: NotificationHandler):
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RedisBase.__init__(self, host, port, username,
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password, logger, notification_handler)
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@activity.defn(name="get_last_data_timestamp")
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async def get_last_data_timestamp(self, input_data: dict[str, Any]) -> str | None:
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"""
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Gets the last data timestamp from redis.
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"""
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metadata = input_data['metadata']
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key = f"{input_data['workflow_name']}_{input_data['schedule_name']}"
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data_hold = self.get(key)
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self.debug(
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f"Last collected timestamp: {data_hold}",
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metadata=metadata
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)
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if not data_hold:
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return None
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return data_hold
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@activity.defn(name="put_last_data_timestamp")
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async def put_last_data_timestamp(self, input_data: dict[str, Any]):
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"""
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Puts the last data timestamp into redis.
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"""
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metadata = input_data['metadata']
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key = f"last_data_timestamp_{input_data['workflow_name']}_{input_data['schedule_name']}"
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data = DataFrame(input_data['data'])
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last_data_timestamp = data['inserted_at'].max()
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self.debug(
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f"Last collected timestamp to insert: {last_data_timestamp}",
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metadata=metadata
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)
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self.set(key, last_data_timestamp)
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return last_data_timestamp
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@activity.defn(name="group_and_hold_data")
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async def group_and_hold_data(self, input_data: dict[str, Any]):
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"""
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Groups and holds data in redis. Keep a copy of the most recent
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received data for a given pipeline and schedule. This activity updates
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the data in redis and return the full keeped data.
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Args:
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input_data (dict[str, Any]): The data to group and hold.
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workflow_name (str): The name of the workflow.
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schedule_name (str): The name of the schedule.
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data (dict[str, Any]): The data to group and hold.
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retention_time (int): The retention time for data in redis in seconds.
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"""
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metadata = input_data['metadata']
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self.debug("Grouping and holding data...",
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metadata=metadata
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)
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data = DataFrame(input_data['data'])
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retention_time = input_data['retention_time']
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key = f"held_data_{input_data['workflow_name']}_{input_data['schedule_name']}"
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data_hold = self.get(key)
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if not data_hold:
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data_hold = {}
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if data.empty:
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self.warning("No data to export",
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metadata=metadata
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)
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return data_hold
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for _, row in data.iterrows():
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value = row['value']
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data_hold[row['name']] = value
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data_hold['timestamp'] = data['timestamp'].max() if not data.empty else \
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datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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self.set(key, data_hold, ttl=retention_time)
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data_hold_df = DataFrame(data_hold, index=[0])
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data_hold_melted = data_hold_df.melt(
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id_vars='timestamp', var_name='variable', value_name='value')
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data_hold_melted['model_id'] = input_data['model_id']
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data_hold_melted.reset_index(drop=True, inplace=True)
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self.debug(
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f"Data grouped and held successfully:\n {data_hold_melted.to_string()}",
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metadata=metadata
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)
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return data_hold_melted.to_dict()
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