AnalyticsFrontendServiceServicerImpl.py

# Copyright 2022-2024 ETSI OSG/SDG TeraFlowSDN (TFS) (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 logging, grpc, json

from typing          import Dict
from confluent_kafka import Consumer as KafkaConsumer
from confluent_kafka import Producer as KafkaProducer
from confluent_kafka import KafkaError

from common.tools.kafka.Variables             import KafkaConfig, KafkaTopic
from common.proto.context_pb2                 import Empty
from common.method_wrappers.Decorator         import MetricsPool, safe_and_metered_rpc_method
from common.proto.analytics_frontend_pb2      import Analyzer, AnalyzerId, AnalyzerFilter, AnalyzerList
from common.proto.analytics_frontend_pb2_grpc import AnalyticsFrontendServiceServicer
from analytics.database.Analyzer_DB           import AnalyzerDB
from analytics.database.AnalyzerModel         import Analyzer as AnalyzerModel


LOGGER           = logging.getLogger(__name__)
METRICS_POOL     = MetricsPool('AnalyticsFrontend', 'NBIgRPC')
ACTIVE_ANALYZERS = []   # In case of sevice restarts, the list can be populated from the DB.

class AnalyticsFrontendServiceServicerImpl(AnalyticsFrontendServiceServicer):
    def __init__(self):
        LOGGER.info('Init AnalyticsFrontendService')
        self.db_obj         = AnalyzerDB()
        self.kafka_producer = KafkaProducer({'bootstrap.servers' : KafkaConfig.get_kafka_address()})
        self.kafka_consumer = KafkaConsumer({'bootstrap.servers' : KafkaConfig.get_kafka_address(),
                                            'group.id'           : 'analytics-frontend',
                                            'auto.offset.reset'  : 'latest'})


    @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    def StartAnalyzer(self, 
                       request : Analyzer, grpc_context: grpc.ServicerContext # type: ignore
                      ) -> AnalyzerId: # type: ignore
        LOGGER.info ("At Service gRPC message: {:}".format(request))
        response = AnalyzerId()

        self.db_obj.add_row_to_db(
            AnalyzerModel.ConvertAnalyzerToRow(request)
        )
        self.PublishStartRequestOnKafka(request)
        
        response.analyzer_id.uuid = request.analyzer_id.analyzer_id.uuid
        return response

    def PublishStartRequestOnKafka(self, analyzer_obj):
        """
        Method to generate analyzer request on Kafka.
        """
        analyzer_uuid = analyzer_obj.analyzer_id.analyzer_id.uuid
        analyzer_to_generate : Dict = {
            "algo_name"       : analyzer_obj.algorithm_name,
            "input_kpis"      : [k.kpi_id.uuid for k in analyzer_obj.input_kpi_ids],
            "output_kpis"     : [k.kpi_id.uuid for k in analyzer_obj.output_kpi_ids],
            "oper_mode"       : analyzer_obj.operation_mode,
            "thresholds"      : json.loads(analyzer_obj.parameters["thresholds"]),
            "window_size"     : analyzer_obj.parameters["window_size"],
            "window_slider"   : analyzer_obj.parameters["window_slider"],
            # "store_aggregate" : analyzer_obj.parameters["store_aggregate"] 
        }
        self.kafka_producer.produce(
            KafkaTopic.ANALYTICS_REQUEST.value,
            key      = analyzer_uuid,
            value    = json.dumps(analyzer_to_generate),
            callback = self.delivery_callback
        )
        LOGGER.info("Analyzer Start Request Generated: Analyzer Id: {:}, Value: {:}".format(analyzer_uuid, analyzer_to_generate))
        ACTIVE_ANALYZERS.append(analyzer_uuid)
        self.kafka_producer.flush()


    @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    def StopAnalyzer(self, 
                      request : AnalyzerId, grpc_context: grpc.ServicerContext # type: ignore
                     ) -> Empty:  # type: ignore
        LOGGER.info ("At Service gRPC message: {:}".format(request))
        try:
            analyzer_id_to_delete = request.analyzer_id.uuid
            self.db_obj.delete_db_row_by_id(
                AnalyzerModel, "analyzer_id", analyzer_id_to_delete
            )
            self.PublishStopRequestOnKafka(analyzer_id_to_delete)
        except Exception as e:
            LOGGER.error('Unable to delete analyzer. Error: {:}'.format(e))
        return Empty()

    def PublishStopRequestOnKafka(self, analyzer_uuid):
        """
        Method to generate stop analyzer request on Kafka.
        """
        # analyzer_uuid = analyzer_id.analyzer_id.uuid
        analyzer_to_stop :  Dict = {
            "algo_name"   : None,
            "input_kpis"  : [],
            "output_kpis" : [],
            "oper_mode"   : None
        }
        self.kafka_producer.produce(
            KafkaTopic.ANALYTICS_REQUEST.value,
            key      = analyzer_uuid,
            value    = json.dumps(analyzer_to_stop),
            callback = self.delivery_callback
        )
        LOGGER.info("Analyzer Stop Request Generated: Analyzer Id: {:}".format(analyzer_uuid))
        self.kafka_producer.flush()
        try:
            ACTIVE_ANALYZERS.remove(analyzer_uuid)
        except ValueError:
            LOGGER.warning('Analyzer ID {:} not found in active analyzers'.format(analyzer_uuid))


    @safe_and_metered_rpc_method(METRICS_POOL, LOGGER)
    def SelectAnalyzers(self, 
                         request : AnalyzerFilter, contextgrpc_context: grpc.ServicerContext # type: ignore
                        ) -> AnalyzerList:  # type: ignore
        LOGGER.info("At Service gRPC message: {:}".format(request))
        response = AnalyzerList()

        return response

    def delivery_callback(self, err, msg):
        if err:
            LOGGER.debug('Message delivery failed: {:}'.format(err))
            print('Message delivery failed: {:}'.format(err))
        # else:
        #     LOGGER.debug('Message delivered to topic {:}'.format(msg.topic()))
        #     print('Message delivered to topic {:}'.format(msg.topic()))