Monitor Spark jobs and Snowpark Connect sessions in Snowsight

The new Spark Monitoring UI in Snowsight centralizes usage and history for Snowpark Connect for Spark across your Snowflake account. It allows you to monitor and troubleshoot batch executions and interactive sessions in one place.### Use casesImagine you need to investigate why a batch Spark job took longer than usual or why an interactive session failed. With this interface, you can list executions, view logs, track queries, and access execution profiles visually.- Track status, duration, and owner of batch jobs and Spark sessions.- Investigate execution logs and OpenTelemetry traces for DataFrame actions.- Identify slow or failed queries using direct links to the Query Profile.### How to useTo open the monitoring interface in Snowsight, navigate to the monitoring section in the user interface.To set a custom name for your Spark session in code, use the app_name parameter during initialization:pythonfrom snowflake.snowpark_connect import init_spark_sessioninit_spark_session(app_name="my_app_name")What happens in this code:1. Imports the initialization function from Snowpark Connect.2. Initializes the session by passing the app_name argument with your chosen identifier.3. The specified name appears as the session name in the Spark sessions tab in Snowsight.### Limitations and requirementsPublic Preview feature, available to all accounts with a gradual rollout. The logs section requires the LOG_LEVEL parameter to be set to a level other than OFF at the appropriate account or database scope.