TelemetryBackendService.py

# Copyright 2022-2023 ETSI TeraFlowSDN - TFS OSG (https://tfs.etsi.org/)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import ast
import time
import random
import logging
import requests
import threading
from typing import Any, Tuple
from common.proto.context_pb2 import Empty
from confluent_kafka import Producer as KafkaProducer
from confluent_kafka import Consumer as KafkaConsumer
from confluent_kafka import KafkaException
from confluent_kafka import KafkaError
from confluent_kafka.admin import AdminClient, NewTopic
from common.proto.telemetry_frontend_pb2 import Collector, CollectorId
from common.method_wrappers.Decorator import MetricsPool, safe_and_metered_rpc_method

LOGGER = logging.getLogger(__name__)
METRICS_POOL = MetricsPool('Telemetry', 'TelemetryBackend')
KAFKA_SERVER_IP = '127.0.0.1:9092'
ADMIN_KAFKA_CLIENT = AdminClient({'bootstrap.servers': KAFKA_SERVER_IP})
ACTIVE_COLLECTORS = []

class TelemetryBackendService:
    """
    Class to listens for request on Kafka topic, fetches metrics and produces measured values to another Kafka topic.
    """

    def __init__(self):
        LOGGER.info('Init TelemetryBackendService')
    
    def run_kafka_listener(self)->bool: # type: ignore
        threading.Thread(target=self.kafka_listener).start()
        return True        
    
    def kafka_listener(self):
        """
        listener for requests on Kafka topic.
        """
        conusmer_configs = {
            'bootstrap.servers' : KAFKA_SERVER_IP,
            'group.id'          : 'backend',
            'auto.offset.reset' : 'latest'
        }
        topic_request = "topic_request"
        if (self.create_topic_if_not_exists([topic_request])):
            consumerObj = KafkaConsumer(conusmer_configs)
            consumerObj.subscribe([topic_request])

            while True:
                receive_msg = consumerObj.poll(2.0)
                if receive_msg is None:
                    print (time.time(), " - Telemetry backend is listening on Kafka Topic: ", topic_request)     # added for debugging purposes
                    continue
                elif receive_msg.error():
                    if receive_msg.error().code() == KafkaError._PARTITION_EOF:
                        continue
                    else:
                        print("Consumer error: {}".format(receive_msg.error()))
                        break
                (kpi_id, duration, interval) = ast.literal_eval(receive_msg.value().decode('utf-8'))
                collector_id = receive_msg.key().decode('utf-8')
                self.run_initiate_collector_backend(collector_id, kpi_id, duration, interval)



    def run_initiate_collector_backend(self, collector_id: str, kpi_id: str, duration: int, interval: int):
        threading.Thread(target=self.initiate_collector_backend, args=(collector_id, kpi_id, duration, interval)).start()

    def initiate_collector_backend(self, collector_id, kpi_id, duration, interval
                        ): # type: ignore
        """
        Method to receive collector request attribues and initiates collecter backend.
        """
        start_time = time.time()
        while True:
            ACTIVE_COLLECTORS.append(collector_id)
            if time.time() - start_time >= duration: # type: ignore
                print("Requested Execution Time Completed: \n --- Consumer terminating: KPI ID: ", kpi_id, " - ", time.time() - start_time)
                break
            # print ("Received KPI: ", kpi_id, ", Duration: ", duration, ", Fetch Interval: ", interval)
            self.extract_kpi_value(collector_id, kpi_id)
            # print ("Telemetry Backend running for KPI: ", kpi_id, "after FETCH INTERVAL: ", interval)
            time.sleep(interval)

    def extract_kpi_value(self, collector_id: str, kpi_id: str):
        """
        Method to extract kpi value.
        """
        measured_kpi_value = random.randint(1,100)
        self.generate_kafka_response(collector_id, kpi_id , measured_kpi_value)

    def generate_kafka_response(self, collector_id: str, kpi_id: str, kpi_value: Any):
        """
        Method to write response on Kafka topic
        """
        producer_configs = {
            'bootstrap.servers': KAFKA_SERVER_IP,
        }
        topic_response = "topic_response"
        msg_value : Tuple [str, Any] = (kpi_id, kpi_value)
        msg_key    = collector_id
        producerObj = KafkaProducer(producer_configs)
        producerObj.produce(topic_response, key=msg_key, value= str(msg_value), callback=self.delivery_callback)
        producerObj.flush()

    def create_topic_if_not_exists(self, new_topics: list):
        """
        Method to create Kafka topic if it does not exist.
        Args:
            admin_client (AdminClient): Kafka admin client.
        """
        for topic in new_topics:
            try:
                topic_metadata = ADMIN_KAFKA_CLIENT.list_topics(timeout=5)
                if topic not in topic_metadata.topics:
                    # If the topic does not exist, create a new topic
                    print(f"Topic '{topic}' does not exist. Creating...")
                    new_topic = NewTopic(topic, num_partitions=1, replication_factor=1)
                    ADMIN_KAFKA_CLIENT.create_topics([new_topic])
                return True
            except KafkaException as e:
                print(f"Failed to create topic: {e}")
                return False
        
        self.verify_required_kafka_topics()

    def delivery_callback(self, err, msg):
        """
        Callback function to handle message delivery status.
        Args:
            err (KafkaError): Kafka error object.
            msg (Message): Kafka message object.
        """
        if err:
            print(f'Message delivery failed: {err}')
        else:
            print(f'Message delivered to topic {msg.topic()}')

        # Function to create a list of topics

    # Function to list all topics in the Kafka cluster
    def verify_required_kafka_topics(self) -> list:
        """List all topics in the Kafka cluster."""
        try:
            # Fetch metadata from the broker
            metadata = ADMIN_KAFKA_CLIENT.list_topics(timeout=10)
            topics = list(metadata.topics.keys())
            print("Topics in the cluster:", topics)
            return topics
        except Exception as e:
            print(f"Failed to list topics: {e}")
            return []
    

# ----------- BELOW: Actual Implementation of Kafka Producer with Node Exporter -----------

    def fetch_node_exporter_metrics(self):
        """
        Method to fetch metrics from Node Exporter.
        Returns:
            str: Metrics fetched from Node Exporter.
        """
        KPI = "node_network_receive_packets_total"
        EXPORTER_ENDPOINT = "http://node-exporter-7465c69b87-b6ks5.telebackend:9100/metrics"
        try:
            response = requests.get(EXPORTER_ENDPOINT) # type: ignore
            if response.status_code == 200:
                # print(f"Metrics fetched sucessfully...")
                metrics = response.text
                # Check if the desired metric is available in the response
                if KPI in metrics:
                    KPI_VALUE = self.extract_metric_value(metrics, KPI)
                    # Extract the metric value
                    if KPI_VALUE is not None:
                        print(f"KPI value: {KPI_VALUE}")
                        return KPI_VALUE
            else:
                print(f"Failed to fetch metrics. Status code: {response.status_code}")
                return None
        except Exception as e:
            print(f"Failed to fetch metrics: {str(e)}")
            return None

    def extract_metric_value(self, metrics, metric_name):
        """
        Method to extract the value of a metric from the metrics string.
        Args:
            metrics (str): Metrics string fetched from Node Exporter.
            metric_name (str): Name of the metric to extract.
        Returns:
            float: Value of the extracted metric, or None if not found.
        """
        try:
            # Find the metric line containing the desired metric name
            metric_line = next(line for line in metrics.split('\n') if line.startswith(metric_name))
            # Split the line to extract the metric value
            metric_value = float(metric_line.split()[1])
            return metric_value
        except StopIteration:
            print(f"Metric '{metric_name}' not found in the metrics.")
            return None

    def produce_metrics(self):
        """
        Method to produce metrics to Kafka topic as per Kafka configs.
        """
        conf = {
            'bootstrap.servers': KAFKA_SERVER_IP,
        }

        admin_client = AdminClient(conf)
        self.create_topic_if_not_exists(admin_client)

        kafka_producer = KafkaProducer(conf)

        try:
            start_time = time.time()
            while True:
                metrics = self.fetch_node_exporter_metrics()  # select the function name based on the provided requirements

                if metrics:
                    kafka_producer.produce("topic_raw", str(metrics), callback=self.delivery_callback)
                    kafka_producer.flush()
                    # print("Metrics produced to Kafka topic")

                # Check if the specified run duration has elapsed
                if time.time() - start_time >= self.run_duration: # type: ignore
                    break

                # waiting time until next fetch 
                time.sleep(self.fetch_interval) # type: ignore
        except KeyboardInterrupt:
            print("Keyboard interrupt detected. Exiting...")
        finally:
            kafka_producer.flush()
            # kafka_producer.close()        # this command generates ERROR

# ----------- ABOVE: Actual Implementation of Kafka Producer with Node Exporter -----------