Abstract: Embodiments relate to efficiently performing an inner join on segments from a data source, where the segments have wildcards in join columns. An algorithm for an inner segment join operator may build a location map that describes locations, by segment and column, of values in the join columns of rows in the segments. A map of wildcard locations, by segment and column, may also be constructed. The location map may be supplemented with information from the wildcard map. The data source is scanned and contents of rows in the data source are queried against the location map to join rows with segments that match due to either matching values or wildcard presence. A user interface may be provided to facilitate construction of joins using the inner segment join operator.

Abstract: A compute cloud hosts a distributed application and is configured to add or remove instances of the application at datacenters at disperse geographic regions. Operations of the application are captured in a telemetry stream. Each record in the telemetry stream indicates a time, client location, and performance measure for a corresponding client request. After pre-processing such as rounding the times, the telemetry records are passed to a frequent itemset mining algorithm that identifies frequent time-location pairs in the telemetry stream. The frequent time-location pairs are consolidated into encompassing frequent region time-range pairs. An aggregate performance measure is computed from the performance measures of the telemetry records that match a frequent region time-range pair. A recommended region and time for adding or removing instances of the application is computed based on the aggregate performance measure and the region time-range pair.

Abstract: A compute cloud hosts a distributed application and is configured to add or remove instances of the application at datacenters at disperse geographic regions. Operations of the application are captured in a telemetry stream. Each record in the telemetry stream indicates a time, client location, and performance measure for a corresponding client request. After pre-processing such as rounding the times, the telemetry records are passed to a frequent itemset mining algorithm that identifies frequent time-location pairs in the telemetry stream. The frequent time-location pairs are consolidated into encompassing frequent region time-range pairs. An aggregate performance measure is computed from the performance measures of the telemetry records that match a frequent region time-range pair. A recommended region and time for adding or removing instances of the application is computed based on the aggregate performance measure and the region time-range pair.

Abstract: Embodiments relate to efficiently performing an inner join on segments from a data source, where the segments have wildcards in join columns. An algorithm for an inner segment join operator may build a location map that describes locations, by segment and column, of values in the join columns of rows in the segments. A map of wildcard locations, by segment and column, may also be constructed. The location map may be supplemented with information from the wildcard map. The data source is scanned and contents of rows in the data source are queried against the location map to join rows with segments that match due to either matching values or wildcard presence. A user interface may be provided to facilitate construction of joins using the inner segment join operator.