Remove deprecated files and configurations, including .env, Dockerfile, docker-compose.yml, and client-schedule.py. Update README.md to reflect new architecture and features, enhancing clarity on system capabilities and workflows. Adjust values.yaml for image tag and replica count, and improve code documentation across various modules for better maintainability.
204 lines
6.9 KiB
Python
204 lines
6.9 KiB
Python
from temporalio import workflow, activity
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with workflow.unsafe.imports_passed_through():
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from typing import Any
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import traceback
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from datetime import datetime, timezone
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from pymongo import MongoClient
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from pandas import DataFrame
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.notifications.models import NotificationLevel
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from sientia_do.temporal.activities.base import BaseActivity
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from sientia_do.observability.logger import Logger
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from sientia_do.temporal.constants import DATETIME_FORMAT_MS_WITH_TZ
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def clear_mongo_id(docs: list) -> list:
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"""
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Remove MongoDB internal `_id` fields from documents.
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This utility function recursively removes the MongoDB `_id` field from
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documents and nested structures. It's used to clean data before
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processing or export operations.
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Args:
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docs (list): List of documents to clean
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Returns:
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list: Documents with `_id` fields removed
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Note:
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This function modifies the input list in-place and returns the same reference
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"""
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for doc in docs:
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if isinstance(doc, list):
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clear_mongo_id(doc)
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elif isinstance(doc, dict):
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if "_id" in doc:
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del doc["_id"]
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for key, value in doc.items():
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if isinstance(value, list):
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clear_mongo_id(value)
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elif isinstance(value, dict):
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clear_mongo_id([value])
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return docs
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class MongoDB(BaseActivity):
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"""
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MongoDB operations for data retrieval and storage.
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This class provides MongoDB connectivity and operations for the Scouter system.
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It handles:
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- Connection management with automatic reconnection
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- Data retrieval with timestamp-based filtering
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- Document cleaning and preprocessing
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- Error handling and notification integration
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The class implements Temporal activities for MongoDB operations, enabling
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distributed data processing with fault tolerance and monitoring.
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"""
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def __init__(self, connection_string: str, database_name: str,
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logger: Logger,
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notification_handler: NotificationHandler):
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"""
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Initialize MongoDB connection and services.
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Args:
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connection_string (str): MongoDB connection URI string
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database_name (str): Name of the target database
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logger (Logger): Logger instance for operation logging
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notification_handler (NotificationHandler): Handler for system notifications
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Raises:
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ConnectionError: If MongoDB connection fails
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"""
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self.connection_string = connection_string
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self.database_name = database_name
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self.client = MongoClient(
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self.connection_string, serverSelectionTimeoutMS=5000)
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self.client.server_info() # Trigger an exception if connection fails
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self.database = self.client[self.database_name]
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# Initialize MongoDB client here (omitted for brevity)
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logger.info("MongoDB connection initialized")
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BaseActivity.__init__(self,
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logger=logger,
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notification_handler=notification_handler,
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set_error_counter=True)
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def shutdown(self):
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"""
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Gracefully close MongoDB client connection.
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This method ensures proper cleanup of MongoDB connections to prevent
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connection leaks and ensure graceful application termination.
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"""
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try:
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if self.client:
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self.logger.info("Closing MongoDB connection...")
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self.client.close()
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self.logger.info("MongoDB connection closed successfully")
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except Exception as e:
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self.logger.error(f"Failed to close MongoDB connection: {e}")
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def __del__(self):
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"""
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Destructor to ensure MongoDB client is closed.
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This destructor ensures that MongoDB connections are properly closed
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when the object is garbage collected, preventing resource leaks.
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"""
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self.shutdown()
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@activity.defn(name="load_latest_data")
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async def load_latest_data(self, input_data: dict[str, Any]) -> dict[str, Any]:
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"""
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Load the latest data from MongoDB collection since a specified timestamp.
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This activity retrieves data from a MongoDB collection, optionally
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filtering by timestamp to enable incremental data processing. It
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handles connection management and provides comprehensive error reporting.
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Args:
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input_data (dict[str, Any]): Activity input parameters.
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Required fields:
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- metadata (dict[str, Any]): Workflow execution metadata
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- collection_name (str): Name of the MongoDB collection
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- last_data_timestamp (str | None): Last processed timestamp for filtering
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Returns:
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dict[str, Any]: Retrieved data, or empty dict if no data found
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Raises:
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Exception: If MongoDB operation fails
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"""
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metadata = input_data['metadata']
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collection_name = input_data['collection_name']
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last_data_timestamp = input_data['last_data_timestamp']
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self.info(
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f"Loading data from MongoDB: {input_data}",
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metadata=metadata
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)
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try:
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if last_data_timestamp is None:
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data_filter = {}
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else:
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data_filter = {
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"inserted_at": {
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"$gt": datetime.strptime(last_data_timestamp, DATETIME_FORMAT_MS_WITH_TZ)
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}
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}
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self.debug(
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f"Data filter: {data_filter}",
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metadata=metadata
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)
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data = list(self.database[collection_name].find(
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data_filter, {"_id": 0}))
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data = clear_mongo_id(data)
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self.debug(
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f"Collected: {data}",
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metadata=metadata
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)
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for item in data:
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item['inserted_at'] = item['inserted_at'].replace(
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tzinfo=timezone.utc).strftime(DATETIME_FORMAT_MS_WITH_TZ)
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self.info(
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f"Loaded {len(data)} documents from MongoDB",
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metadata=metadata
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)
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self.debug(
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f"Loaded data: {data}",
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metadata=metadata
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)
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return DataFrame(data).to_dict()
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except Exception as e:
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trace = traceback.format_exc()
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self.send_notification(
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metadata=metadata,
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notification_id="MONGO_LOAD_ERROR",
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message=f"Error loading data from MongoDB: {e}",
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block="load_latest_data",
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level=NotificationLevel.ERROR,
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attachment_content=trace
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)
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raise e
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