KPI Computation for Network Slicing

This page covers the steps to view new PDUSessionEstSR KPI within a given time range (a KPI that is computed by KPI MS) via UUI

Manual Configuration:

 1. UUI:

Update the images of the UUI components -

• image: onap/usecase-ui-server: 5.1.1
• image: onap/usecase-ui: 5.1.0

Deployment Prerequisite/dependencies for DCAE:

1. VES, PM mapper and DataFile Collector, dcae-mongodb, Datalake Extraction Service(DES) and Datalake-feeder services should be running.

Datalake Extraction Service(DES):

1. Build presto image and push the images to a exsting repository
   1. Download and extract presto package version v0.0.2: presto-v0.0.2.tar.gz
   2. docker build -t presto:v0.0.2 .
   3. docker tag presto:v0.0.2 registry.baidubce.com/onap/presto:v0.0.2
   4. docker push registry.baidubce.com/onap/presto:v0.0.2
   5. Note： Replace the repository path with your own repository.
2. Install presto service
   1. kubectl -n onap run dl-presto --image=presto:v0.0.2 --env="MongoDB_IP=dcae-mongohost" --env="MongoDB_PORT=27017" --image-pull-policy=IfNotPresent
   2. kubectl expose pod dl-presto --name=dl-presto --port=9000 --target-port=9000 --type=NodePort -n onap

3. Login to the dev-dcae-datalake-postgres-primary container and execute following sql commands:

    datalake-db init_data

   delete from map_db_topic;
delete from map_db_design;
delete from db;
delete from data_exposure ;
insert into db (id, db_type_id, enabled, encrypt, name,host,port,database_name) values (2, 'MONGO', true, true, 'MongoDB 1','dcae-mongohost',27017,'datalake');
insert into map_db_topic(db_id,topic_id) select db.id, topic.id from db_type, db, topic where db.db_type_id=db_type.id and db_type.tool=false;
update kafka set secure=true, pass='admin_secret' where id=1;
insert into topic_name (id) values ('unauthenticated.PERFORMANCE_MEASUREMENTS');
insert into topic(id, topic_name_id,correlate_cleared_message,enabled, message_id_path,data_format) values (6, 'unauthenticated.PERFORMANCE_MEASUREMENTS',true,true,'/event/commonEventHeader','JSON');
insert into data_exposure(id, note,sql_template,db_id) values ('pm_data','pm_message', 'SELECT json_format(CAST( event.perf3gppFields.measDataCollection.measInfoList[1].measTypes.sMeasTypesList AS JSON)) AS sMeasTypesList, json_format(CAST( event.perf3gppFields.measDataCollection.measInfoList[1].measValuesList AS JSON)) AS measValuesList FROM unauthenticatedperformancemeasurements WHERE any_match(event.perf3gppFields.measDataCollection.measInfoList[1].measTypes.sMeasTypesList, e -> e like ''${snssai}'') AND event.commonEventHeader.startEpochMicrosec >= ${time}',2); //db_id refers to mongodb in table db, this template filters PM data based on snssai and startEpochMicrosec;