89278827e7
Enhance release workflow to trigger only on merged pull requests and streamline data insertion in DataManager - Updated the release workflow to execute only when a pull request is merged. - Simplified the document insertion logic in DataManager by consolidating the dictionary construction. - Refactored OPC Manager tests to utilize asynchronous mocks for improved accuracy in testing.
289 lines
11 KiB
Python
289 lines
11 KiB
Python
import json
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import os
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import traceback
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from time import sleep
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from kafka import KafkaProducer
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from kafka.errors import NoBrokersAvailable
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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.observability.logger import Logger
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from sientia_do.observability.metrics_controller import MetricsController
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from sientia_do.observability.sientia_monitoring import SientiaMonitoring
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from sientia_do.repository.mongodb_repository import MongoDBRepository
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from sientia_do.temporal.constants import now
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import ingestor.metrics as metrics
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class DataManager(SientiaMonitoring):
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"""
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Manages data persistence and export operations for the OPC Ingestor.
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The DataManager is responsible for:
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- Storing OPC data in MongoDB for historical analysis and persistence
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- Exporting data to Kafka for real-time streaming and downstream processing
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- Managing database connections and ensuring data integrity
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- Providing data access interfaces for other components
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The manager supports both MongoDB and Kafka operations, with Kafka export
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being optional and configurable. It implements retry logic for connection
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failures and provides comprehensive error handling and notification.
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Args:
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kafka_servers (str): Comma-separated string of Kafka server addresses
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mongo_connection_string (str): MongoDB connection string
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mongo_database (str): MongoDB database name
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export_to_kafka (bool): Whether to enable Kafka export functionality
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metadata (dict): Application metadata for notifications and tracking
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logger (Logger): Logger instance for application logging
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notification_handler (NotificationHandler): Handler for sending notifications
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Attributes:
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pod_id (str): Pod identifier for metrics labeling
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kafka_producer (KafkaProducer): Kafka producer instance for data export
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export_to_kafka (bool): Whether Kafka export is enabled
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connection_string (str): MongoDB connection string
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database (str): MongoDB database name
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mongo_client (MongoClient): MongoDB client instance
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metadata (dict): Application metadata
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"""
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def __init__(
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self,
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kafka_servers: str,
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mongo_connection_string: str,
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mongo_database: str,
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export_to_kafka: bool,
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metadata: dict,
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logger: Logger,
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notification_handler: NotificationHandler,
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metrics_controller: MetricsController,
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) -> None:
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"""
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Initializes the DataManager instance with Kafka and MongoDB connections.
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This constructor attempts to establish connections to the specified services:
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1. Kafka: Initializes producer with retry logic (up to 3 attempts)
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2. MongoDB: Establishes connection and verifies server availability
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The initialization process includes:
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- Kafka producer setup with JSON serialization
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- MongoDB client initialization and connection testing
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- Metrics recording for connection status
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- Error handling with notifications
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Args:
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kafka_servers (str): Comma-separated string of Kafka server addresses
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mongo_connection_string (str): MongoDB connection string
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mongo_database (str): MongoDB database name
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export_to_kafka (bool): Whether to enable Kafka export
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metadata (dict): Application metadata
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logger (Logger): Logger instance
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notification_handler (NotificationHandler): Notification handler
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Raises:
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NoBrokersAvailable: If the connection to Kafka servers fails after 3 attempts.
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Metrics:
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- KAFKA_CONNECTION_STATUS: Set to 1 on successful connection, 0 on failure
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"""
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self.pod_id = os.getenv('HOSTNAME', 'localhost')
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self.kafka_producer = None
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self.export_to_kafka = export_to_kafka
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SientiaMonitoring.__init__(
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self,
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logger=logger,
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metrics_controller=metrics_controller,
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notification_handler=notification_handler,
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)
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if self.export_to_kafka:
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for i in range(0, 3):
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logger.info(
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f'Trying ({i}) to initializing DataManager with Kafka servers: {kafka_servers}'
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)
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try:
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self.kafka_producer = KafkaProducer(
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bootstrap_servers=kafka_servers,
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value_serializer=lambda v: json.dumps(v).encode(
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'utf-8'
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), # Serialize JSON messages
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key_serializer=lambda k: str(k).encode('utf-8') if k else None,
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)
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# Kafka connected
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metrics.KAFKA_CONNECTION_STATUS.labels(pod_id=self.pod_id).set(1)
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break
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except NoBrokersAvailable:
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logger.error(f'Kafka servers {kafka_servers} are not available. Retrying...')
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sleep(5)
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else:
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# Kafka not connected
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metrics.KAFKA_CONNECTION_STATUS.labels(pod_id=self.pod_id).set(0)
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logger.error(
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f'Failed to connect to Kafka servers {kafka_servers} after 3 attempts.'
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)
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raise NoBrokersAvailable(
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f'Failed to connect to Kafka servers {kafka_servers} after 3 attempts.'
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)
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logger.info(f'DataManager initialized with Kafka servers: {kafka_servers}')
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logger.info(
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f'Trying to initializing DataManager with MongoDB servers: {mongo_connection_string}'
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)
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self.connection_string = mongo_connection_string
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self.database = mongo_database
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self.mongo_repository = MongoDBRepository(
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connection_string=self.connection_string,
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database_name=self.database,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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self.metadata = metadata
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logger.info(f'DataManager initialized with MongoDB servers: {self.connection_string}')
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def shutdown(self):
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"""
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Gracefully shuts down the DataManager and closes all connections.
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This method ensures proper cleanup of:
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- Kafka producer connection with message flushing
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- MongoDB client connection
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- Metrics recording for connection status
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The method handles connection closure gracefully, logging any errors
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that occur during shutdown while ensuring all resources are properly released.
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"""
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if self.kafka_producer:
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try:
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self.kafka_producer.flush(timeout=10)
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self.kafka_producer.close()
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# Mark as disconnected
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metrics.KAFKA_CONNECTION_STATUS.labels(pod_id=self.pod_id).set(0)
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except Exception as e:
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self.logger.error(f'Error closing Kafka producer: {e}')
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else:
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self.logger.warning('Kafka producer is already closed or not initialized.')
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try:
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self.mongo_repository.close()
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except Exception as e:
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self.logger.error(f'Error closing MongoDB client: {e}')
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def __del__(self):
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self.shutdown()
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def delivery_report(self, msg):
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"""
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Callback for successful Kafka message delivery reports.
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This method is called by the Kafka producer when a message is successfully
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delivered to a topic. It logs the delivery details including topic, partition,
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and offset information for debugging and monitoring purposes.
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Args:
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msg: Kafka message object containing delivery details
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"""
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self.logger.debug(
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f'Record successfully produced to {msg.topic} [{msg.partition}] at offset {msg.offset}'
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)
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def delivery_error(self, err):
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"""
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Callback for Kafka message delivery error reports.
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This method is called by the Kafka producer when a message delivery fails.
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It logs the error details for debugging and monitoring purposes.
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Args:
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err: Error information from the failed delivery attempt
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"""
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self.logger.error(f'Delivery failed for record : {err}')
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async def publish(self, topic: str, data: dict) -> None:
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"""
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Publishes a message to a specified Kafka topic.
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Args:
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topic (str): The name of the Kafka topic to which the message will be published.
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data (dict): The message data to be sent to the Kafka topic.
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Returns:
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None
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Raises:
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Exception: If there is an error during message delivery, it will be handled by the `delivery_error` callback.
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"""
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if self.export_to_kafka and self.kafka_producer:
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try:
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self.logger.debug(f'Publishing message to topic {topic}: {data}')
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self.kafka_producer.send(topic=topic, value=data).add_callback(
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self.delivery_report
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).add_errback(self.delivery_error)
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self.kafka_producer.flush(timeout=10)
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await self.emit_metric(
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metric_object=metrics.KAFKA_MESSAGES_SENT,
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tags={
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'pod_id': self.pod_id,
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'topic': topic,
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},
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)
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except Exception as e:
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await self.emit_metric(
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metric_object=metrics.KAFKA_MESSAGES_ERRORS,
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tags={
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'pod_id': self.pod_id,
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'topic': topic,
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},
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)
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trace = traceback.format_exc()
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await self.send_notification_async(
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metadata=self.metadata,
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notification_id=f'KAFKA_PRODUCER_ERROR_{topic}',
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message=f'Error publishing message to topic {topic}: {e}',
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block='kafka_producer',
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level=NotificationLevel.ERROR,
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attachment_content=trace,
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)
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self.logger.error(trace)
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try:
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await self.mongo_repository.insert(
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collection_name=topic,
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document={**data, 'inserted_at': now()},
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metadata=self.metadata,
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)
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self.logger.debug(f'Message inserted into MongoDB collection {topic}: {data}')
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await self.emit_metric(
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metric_object=metrics.TAG_WRITTEN_COUNT,
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tags={
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'pod_id': self.pod_id,
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'tag_name': data['name'],
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'collection_name': topic,
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},
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)
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except Exception as e:
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trace = traceback.format_exc()
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await self.send_notification_async(
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metadata=self.metadata,
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notification_id=f'MONGO_PRODUCER_ERROR_{topic}',
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message=f'Error inserting message to MongoDB: {e}',
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block='mongo_producer',
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level=NotificationLevel.ERROR,
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attachment_content=trace,
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)
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self.logger.error(trace)
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