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# 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
import enum
from sqlalchemy import Column, String, Float, Enum
from sqlalchemy.orm import registry
from common.proto import analytics_frontend_pb2
from common.proto import kpi_manager_pb2
from sqlalchemy.dialects.postgresql import UUID, ARRAY
logging.basicConfig(level=logging.INFO)
LOGGER = logging.getLogger(__name__)
# Create a base class for declarative models
Base = registry().generate_base()
class AnalyzerOperationMode (enum.Enum):
BATCH = analytics_frontend_pb2.AnalyzerOperationMode.ANALYZEROPERATIONMODE_BATCH
STREAMING = analytics_frontend_pb2.AnalyzerOperationMode.ANALYZEROPERATIONMODE_STREAMING
class Analyzer(Base):
__tablename__ = 'analyzer'
analyzer_id = Column(UUID(as_uuid=False) , primary_key=True)
algorithm_name = Column(String , nullable=False)
input_kpi_ids = Column(ARRAY(UUID(as_uuid=False)) , nullable=False)
output_kpi_ids = Column(ARRAY(UUID(as_uuid=False)) , nullable=False)
operation_mode = Column(Enum(AnalyzerOperationMode), nullable=False)
batch_min_duration_s = Column(Float , nullable=False)
batch_max_duration_s = Column(Float , nullable=False)
bacth_min_size = Column(Float , nullable=False)
bacth_max_size = Column(Float , nullable=False)
# helps in logging the information
def __repr__(self):
return (f"<Analyzer(analyzer_id='{self.analyzer_id}', algorithm_name='{self.algorithm_name}', "
f"input_kpi_ids={self.input_kpi_ids}, output_kpi_ids={self.output_kpi_ids}, "
f"operation_mode='{self.operation_mode}', batch_min_duration_s={self.batch_min_duration_s}, "
f"batch_max_duration_s={self.batch_max_duration_s}, bacth_min_size={self.bacth_min_size}, "
f"bacth_max_size={self.bacth_max_size})>")
@classmethod
def ConvertAnalyzerToRow(cls, request):
"""
Create an instance of Analyzer table rows from a request object.
Args: request: The request object containing analyzer gRPC message.
Returns: A row (an instance of Analyzer table) initialized with content of the request.
"""
return cls(
analyzer_id = request.analyzer_id.analyzer_id.uuid,
algorithm_name = request.algorithm_name,
input_kpi_ids = [k.kpi_id.uuid for k in request.input_kpi_ids],
output_kpi_ids = [k.kpi_id.uuid for k in request.output_kpi_ids],
operation_mode = AnalyzerOperationMode(request.operation_mode), # converts integer to coresponding Enum class member
batch_min_duration_s = request.batch_min_duration_s,
batch_max_duration_s = request.batch_max_duration_s,
bacth_min_size = request.batch_min_size,
bacth_max_size = request.batch_max_size
)
@classmethod
def ConvertRowToAnalyzer(cls, row):
"""
Create and return an Analyzer gRPC message initialized with the content of a row.
Args: row: The Analyzer table instance (row) containing the data.
Returns: An Analyzer gRPC message initialized with the content of the row.
"""
# Create an instance of the Analyzer message
response = analytics_frontend_pb2.Analyzer()
response.analyzer_id.analyzer_id.uuid = row.analyzer_id
response.algorithm_name = row.algorithm_name
response.operation_mode = row.operation_mode
_kpi_id = kpi_manager_pb2.KpiId()
for input_kpi_id in row.input_kpi_ids:
_kpi_id.kpi_id.uuid = input_kpi_id
response.input_kpi_ids.append(_kpi_id)
for output_kpi_id in row.output_kpi_ids:
_kpi_id.kpi_id.uuid = output_kpi_id
response.output_kpi_ids.append(_kpi_id)
response.batch_min_duration_s = row.batch_min_duration_s
response.batch_max_duration_s = row.batch_max_duration_s
response.batch_min_size = row.bacth_min_size
response.batch_max_size = row.bacth_max_size
return response