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# 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 logging
import requests
import threading
from typing import 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'

    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 kafka_listener(self):
        listener for requests on Kafka topic.
        conusmer_configs = {
            'bootstrap.servers' : KAFKA_SERVER_IP,
            'group.id'          : 'consumer',
            'auto.offset.reset' : 'earliest'
        topic_request = "topic_request"

        consumerObj = KafkaConsumer(conusmer_configs)
        consumerObj.subscribe([topic_request])

        while True:
            receive_msg = consumerObj.poll(2.0)
            if receive_msg is None:
                print ("Telemetry backend listener is active:  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'))
            self.execute_process_kafka_request(kpi_id, duration, interval)
    def run_kafka_listener(self)->Empty: # type: ignore
        threading.Thread(target=self.kafka_listener).start()
        return True        
    def process_kafka_request(self, kpi_id, duration, interval
                        ): # type: ignore
        """
        Method to receive collector request attribues and initiates collecter backend.
        """
        start_time = time.time()
        while True:
            if time.time() - start_time >= duration: # type: ignore
                print("Timeout: consumer terminated", time.time() - start_time)
                break
            # print ("Received KPI: ", kpi_id, ", Duration: ", duration, ", Fetch Interval: ", interval)
            print ("Telemetry Backend running for KPI: ", kpi_id, "after FETCH INTERVAL: ", interval)
            time.sleep(interval)
    
    def execute_process_kafka_request(self, kpi_id: str, duration: int, interval: int):
        threading.Thread(target=self.process_kafka_request, args=(kpi_id, duration, interval)).start()
        

# ----------- 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"
        try:
            response = requests.get(self.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 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()}')

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

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

        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(self.kafka_topic, 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 -----------