Abstract: In an example embodiment, rate limiting is performed at the instance level (i.e., locally), but utilizing throughput statistics of other instances. These statistics may be measured locally by each instance and then transmitted to a central store, where they are aggregated. Each instance is then able to asynchronously request the aggregated statistics from the central store and use this information to manage the parameters of its own local rate limiter.

Abstract: In an example embodiment, rate limiting is performed at the instance level (i.e., locally), but utilizing throughput statistics of other instances. These statistics may be measured locally by each instance and then transmitted to a central store, where they are aggregated. Each instance is then able to asynchronously request the aggregated statistics from the central store and use this information to manage the parameters of its own local rate limiter.

Abstract: In an example embodiment, rate limiting is performed at the instance level (i.e., locally), but utilizing throughput statistics of other instances. These statistics may be measured locally by each instance and then transmitted to a central store, where they are aggregated. Each instance is then able to asynchronously request the aggregated statistics from the central store and use this information to manage the parameters of its own local rate limiter.

Abstract: Data may be stored using hybrid multidimensional and column-centric storage techniques. A hierarchy of regions of a multidimensional space may be maintained on one or more storage devices. Range information for the hierarchy may be maintained in a column-centric storage. Leaf nodes of the hierarchy may comprise tuple data stored in a column-centric storage. Tuples may be located by identifying candidate regions encompassing the tuple and scanning column-centric stores at the leaf level. Region splitting may be deferred to favor column-centric search characteristics.