Abstract: Techniques and structures to facilitate anomaly detection within a networking system, including receiving a plurality of performance metric messages at a database system, extracting a plurality of anomaly detection messages included in the performance metric messages, storing the plurality of anomaly detection messages in an in-memory database and executing a machine learning model to process the plurality of anomaly detection messages in the in-memory database to detect whether anomalous usage of the networking system has been detected.

Abstract: Methods and systems are described for creating and/or processing a definition for a dashboard with manually input code and user selections in a graphical user interface. In some implementations, user interface elements are displayed and selections from a user accepted. Responsive to the selections, definitions for one or more visualizations and datasources are automatically generated and displayed in a user interface element that accepts manually input customizations and code from the user. The resulting combination of code and definitions is stored as a definition for the dashboard, which when processed will transform data received from the at least one of the datasources, combine the transformed data with data from another of the datasources, and cause the display of a visualization based on the combined data.

Abstract: Methods and systems are described for creating and/or processing a definition for a dashboard with manually input code and user selections in a graphical user interface. In some implementations, user interface elements are displayed and selections from a user accepted. Responsive to the selections, definitions for one or more visualizations and datasources are automatically generated and displayed in a user interface element that accepts manually input customizations and code from the user. The resulting combination of code and definitions is stored as a definition for the dashboard, which when processed will transform data received from the at least one of the datasources, combine the transformed data with data from another of the datasources, and cause the display of a visualization based on the combined data.

Abstract: System and methods are described for a monitoring system to monitor operations of a distributed computing system and provide feedback to subscribers. The monitoring system includes a data collection engine to receive data items comprising events, metrics, or one or more configurations about a distributed computing system being monitored, and to store selected data items into a message queue. The monitoring system also includes a data processing engine to receive the selected data items from the queue, transform the selected data items into transformed data items based at least in part on one or more settings for a subscriber and the one or more configurations of the distributed computing system, and store the transformed data items into one or more of a database and a cache.

Abstract: Techniques and structures to facilitate anomaly detection within a networking system, including receiving a plurality of performance metric messages at a database system, extracting a plurality of anomaly detection messages included in the performance metric messages, storing the plurality of anomaly detection messages in an in-memory database and executing a machine learning model to process the plurality of anomaly detection messages in the in-memory database to detect whether anomalous usage of the networking system has been detected