TelemetryFrontendServiceServicerImpl.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 threading
import time
from typing import Tuple, Any
import grpc
import logging

from confluent_kafka import Consumer as KafkaConsumer
from common.proto.context_pb2 import Empty
from monitoring.service.NameMapping import NameMapping
from confluent_kafka import Producer as KafkaProducer
from confluent_kafka import KafkaException
from confluent_kafka import KafkaError
from common.proto.telemetry_frontend_pb2 import CollectorId, Collector, CollectorFilter, CollectorList
from common.method_wrappers.Decorator import MetricsPool, safe_and_metered_rpc_method
from common.proto.telemetry_frontend_pb2_grpc import TelemetryFrontendServiceServicer

from telemetry.database.TelemetryModel import Collector as CollectorModel
from telemetry.database.managementDB import managementDB

LOGGER            = logging.getLogger(__name__)
METRICS_POOL      = MetricsPool('Monitoring', 'TelemetryFrontend')
KAFKA_SERVER_IP   = '127.0.0.1:9092'
ACTIVE_COLLECTORS = []
KAFKA_TOPICS      = {'request' : 'topic_request', 
                     'response': 'topic_response'}


class TelemetryFrontendServiceServicerImpl(TelemetryFrontendServiceServicer):
    def __init__(self, name_mapping : NameMapping):
        LOGGER.info('Init TelemetryFrontendService')
        self.managementDBobj = managementDB()
        self.kafka_producer = KafkaProducer({'bootstrap.servers': KAFKA_SERVER_IP,})
        self.kafka_consumer = KafkaConsumer({'bootstrap.servers' : KAFKA_SERVER_IP,
                                            'group.id'          : 'frontend',
                                            'auto.offset.reset' : 'latest'})

    def add_collector_to_db(self, request: Collector ): # type: ignore
        try:
            # Create a new Collector instance
            collector_to_insert                     = CollectorModel()
            collector_to_insert.collector_id        = request.collector_id.collector_id.uuid
            collector_to_insert.kpi_id              = request.kpi_id.kpi_id.uuid
            # collector_to_insert.collector_decription= request.collector
            collector_to_insert.sampling_duration_s = request.duration_s
            collector_to_insert.sampling_interval_s = request.interval_s
            collector_to_insert.start_timestamp     = time.time()
            collector_to_insert.end_timestamp       = time.time()
            managementDB.add_row_to_db(collector_to_insert)
        except Exception as e:
            LOGGER.info("Unable to create collectorModel class object. {:}".format(e))

    # @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    def StartCollector(self, 
                       request : Collector, grpc_context: grpc.ServicerContext # type: ignore
                      ) -> CollectorId: # type: ignore
        # push info to frontend db
        LOGGER.info ("gRPC message: {:}".format(request))
        response = CollectorId()
        _collector_id       = str(request.collector_id.collector_id.uuid)
        _collector_kpi_id   = str(request.kpi_id.kpi_id.uuid)
        _collector_duration = int(request.duration_s)
        _collector_interval = int(request.interval_s)
        # pushing Collector to DB
        self.add_collector_to_db(request)
        self.publish_to_kafka_request_topic(_collector_id, _collector_kpi_id, _collector_duration, _collector_interval)
        # self.run_publish_to_kafka_request_topic(_collector_id, _collector_kpi_id, _collector_duration, _collector_interval)
        response.collector_id.uuid = request.collector_id.collector_id.uuid # type: ignore
        return response
    
    def run_publish_to_kafka_request_topic(self, msg_key: str, kpi: str, duration : int, interval: int):
        # Add threading.Thread() response to dictonary and call start() in the next statement
        threading.Thread(target=self.publish_to_kafka_request_topic, args=(msg_key, kpi, duration, interval)).start()

    def publish_to_kafka_request_topic(self, 
                             collector_id: str, kpi: str, duration : int, interval: int
                             ):
        """
        Method to generate collector request to Kafka topic.
        """
        # time.sleep(5)
        # producer_configs = {
        #     'bootstrap.servers': KAFKA_SERVER_IP,
        # }
        # topic_request = "topic_request"
        msg_value : Tuple [str, int, int] = (kpi, duration, interval)
        # print ("Request generated: ", "Colletcor Id: ", collector_id, \
        #         ", \nKPI: ", kpi, ", Duration: ", duration, ", Interval: ", interval)
        # producerObj = KafkaProducer(producer_configs)
        self.kafka_producer.produce(KAFKA_TOPICS['request'], key=collector_id, value= str(msg_value), callback=self.delivery_callback)
        # producerObj.produce(KAFKA_TOPICS['request'], key=collector_id, value= str(msg_value), callback=self.delivery_callback)
        LOGGER.info("Collector Request Generated: {:}, {:}, {:}, {:}".format(collector_id, kpi, duration, interval))
        # producerObj.produce(topic_request, key=collector_id, value= str(msg_value), callback=self.delivery_callback)
        ACTIVE_COLLECTORS.append(collector_id)
        self.kafka_producer.flush()

    def run_kafka_listener(self):
        # print ("--- STARTED: run_kafka_listener ---")
        threading.Thread(target=self.kafka_listener).start()
        return True

    def kafka_listener(self):
        """
        listener for response on Kafka topic.
        """
        # # print ("--- STARTED: kafka_listener ---")
        # conusmer_configs = {
        #     'bootstrap.servers' : KAFKA_SERVER_IP,
        #     'group.id'          : 'frontend',
        #     'auto.offset.reset' : 'latest'
        # }
        # # topic_response = "topic_response"

        # consumerObj = KafkaConsumer(conusmer_configs)
        self.kafka_consumer.subscribe([KAFKA_TOPICS['response']])
        # print (time.time())
        while True:
            receive_msg = self.kafka_consumer.poll(2.0)
            if receive_msg is None:
                # print (" - Telemetry frontend listening on Kafka Topic: ", KAFKA_TOPICS['response'])     # 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
            try:
                collector_id = receive_msg.key().decode('utf-8')
                if collector_id in ACTIVE_COLLECTORS:
                    (kpi_id, kpi_value) = ast.literal_eval(receive_msg.value().decode('utf-8'))
                    self.process_response(collector_id, kpi_id, kpi_value)
                else:
                    print(f"collector id does not match.\nRespone ID: '{collector_id}' --- Active IDs: '{ACTIVE_COLLECTORS}' ")
            except Exception as e:
                print(f"No message key found: {str(e)}")
                continue
                # return None

    def process_response(self, collector_id: str, kpi_id: str, kpi_value: Any):
        if kpi_id == "-1" and kpi_value == -1:
            # LOGGER.info("Sucessfully terminated Collector: {:}".format(collector_id))
            print ("Sucessfully terminated Collector: ", collector_id)
        else:
            print ("Frontend-Received values Collector Id:", collector_id, "-KPI:", kpi_id, "-VALUE:", kpi_value)

    @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    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()}')

    @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    def StopCollector(self, 
                      request : CollectorId, grpc_context: grpc.ServicerContext # type: ignore
                     ) -> Empty:  # type: ignore
        LOGGER.info ("gRPC message: {:}".format(request))
        _collector_id = request.collector_id.uuid
        self.publish_to_kafka_request_topic(_collector_id, "", -1, -1)
        return Empty()

    @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    def SelectCollectors(self, 
                         request : CollectorFilter, contextgrpc_context: grpc.ServicerContext # type: ignore
                        ) -> CollectorList:  # type: ignore
        LOGGER.info("gRPC message: {:}".format(request))
        response = CollectorList()
        filter_to_apply = dict()
        filter_to_apply['kpi_id']       = request.kpi_id[0].kpi_id.uuid
        # filter_to_apply['duration_s'] = request.duration_s[0]
        try:
            rows = self.managementDBobj.select_with_filter(CollectorModel, **filter_to_apply)
        except Exception as e:
            LOGGER.info('Unable to apply filter on kpi descriptor. {:}'.format(e))
        try:
            if len(rows) != 0:
                for row in rows:
                    collector_obj = Collector()
                    collector_obj.collector_id.collector_id.uuid = row.collector_id
                    response.collector_list.append(collector_obj)
            return response
        except Exception as e:
            LOGGER.info('Unable to process response {:}'.format(e))