Deploying CDP RTS MS

Prerequisites:

Make sure to create rts-ms secret with required data in the AWS secret manager before deploying CDP RTS MS.

To create the rts-ms secret in the AWS secret manager, follow the steps below:

1. Create a rts-ms secret sample key and value in the rts-ms secret, and update ConfigMaps data with actual values.

   {
     "MongoDbDatabase": "<MongoDbDatabase>",
     "MongoDbUri": "mongodb://<user>:<password>@<ip>:<port>/?directConnection=true",
     "TsdbHost": "<TsdbHost>",
     "VrmDbPassword": "<TsdbHost>",
     "VrmDbUrl": "jdbc:mariadb://<ip>:<port>/<dbName>?autoReconnect=true",
     "VrmDbUsername": "<VrmDbUsername>",
     "serverPort": "<serverPort>"
   }

Deploying CDP RTS MS

To deploy the CDP RTS MS, follow the steps below:

1. Navigate to the Devtron Chart Store, and select the cdp-rts-ms chart to deploy.

   

2. Now, configure and deploy the CDP RTS MS charts.

   

3. In the YAML section, update the ConfigMap using below details, and deploy the charts.

   countCollectionSuffix: <COUNT_COLLECTION_SUFFIX>
   countQueryIdKey: <COUNT_QUERY_ID_KEY>
   countUpdateThreshold: "<COUNT_UPDATE_THRESHOLD>"
   countUpdateWaitTimeout: "<COUNT_UPDATE_WAIT_TIMEOUT>"
   
   suggestionCollectionSuffix: <SUGGESTION_COLLECTION_SUFFIX>
   suggestionQueryIdKey: <SUGGESTION_QUERY_ID_KEY>
   suggestionUpdateThreshold: "<SUGGESTION_UPDATE_THRESHOLD>"
   suggestionUpdateWaitTimeout: "<SUGGESTION_UPDATE_WAIT_TIMEOUT>"
   
   statsCollectionSuffix: <STATS_COLLECTION_SUFFIX>
   statsQueryIdKey: <STATS_QUERY_ID_KEY>
   statsUpdateThreshold: "<STATS_UPDATE_THRESHOLD>"
   statsUpdateWaitTimeout: "<STATS_UPDATE_WAIT_TIMEOUT>"
   
   maxPoolSize: "<MAX_POOL_SIZE>"
   minIdle: "<MIN_IDLE>"
   idleTimeout: "<IDLE_TIMEOUT>"
   
   fromEmailId: "<FROM_EMAIL_ID>"
   toEmailId: "<TO_EMAIL_ID>"
   
   VrmDbDriver: <DB_DRIVER_CLASS>
   physicalStrategy: <HIBERNATE_PHYSICAL_STRATEGY>
   dialect: <HIBERNATE_DIALECT>
   
   dbRefreshInterval: "<DB_REFRESH_INTERVAL>"
   serverPort: "<SERVER_PORT>"
   TsdbPort: "<TSDB_PORT>"
   
   jobStore: <JOB_STORE>
   quartzSchema: <QUARTZ_SCHEMA>
   quartzDataSource: "<QUARTZ_DATASOURCE>"
   quartzTablePrefix: <QUARTZ_TABLE_PREFIX>
   instanceName: <INSTANCE_NAME>
   instanceId: <INSTANCE_ID>
   isClustered: "<IS_CLUSTERED>"
   clusterCheckinInterval: "<CLUSTER_CHECKIN_INTERVAL>"
   
   AWS_REGION: <AWS_REGION>
   AWS_BUCKET_NAME: <AWS_BUCKET_NAME>
   BucketType: <BUCKET_TYPE>
   
   LOG_LEVEL_APP: <LOG_LEVEL_APP>
   LOG_LEVEL_ROOT: <LOG_LEVEL_ROOT>
   
   threadPoolSize: "<THREAD_POOL_SIZE>"
   batchSize: "<BATCH_SIZE>"
   maxBatchRecords: "<MAX_BATCH_RECORDS>"
   
   athenaBuckets: <ATHENA_BUCKET_MAPPINGS>
   athenaDatabase: <ATHENA_DATABASE_MAPPINGS>
   athenaQueryOutputBucket: <ATHENA_QUERY_OUTPUT_BUCKET>
   
   glueJobName: <GLUE_JOB_NAME>
   glueJobFunnelName: <GLUE_JOB_FUNNEL_NAME>
   trendsGluejob: <TRENDS_GLUE_JOB_NAME>
   
   baseUrl_coreApi: "<CORE_API_BASE_URL>"
   queryEngine: <QUERY_ENGINE>
   
   trinoUrl: <TRINO_JDBC_URL>
   trinoUsername: <TRINO_USERNAME>
   trinoDriveName: <TRINO_DRIVER_CLASS>
   
   airflowUrl: <AIRFLOW_API_URL>
   airflowUsername: <AIRFLOW_USERNAME>
   airflowPassword: <AIRFLOW_PASSWORD>
   
   ACCESS_KEY: "<ACCESS_KEY>"
   ACCESS_SECRET: "<ACCESS_SECRET>"
   MINIO_ENDPOINT_URL: "<MINIO_ENDPOINT_URL>"
   
   airflowSegmentExportDAGName: <AIRFLOW_SEGMENT_EXPORT_DAG>
   nightlyRefreshJobName: <NIGHTLY_REFRESH_JOB_NAME>
   
   dashbackend_auth: <DASHBACKEND_AUTH>
   dashbackend_baseurl: "<DASHBACKEND_BASEURL>"
   exportType: Table

   

4. On successful deployment, validate the deployment as shown below.