Microsoft Technology Licensing, LLC

Gjorgji Gjeorgjievski

Vasileios Papadimos

Sudin Bhat

Edgars Sedols

Chandrashekhar Kadiam

Ankit Mahajan

Parag Nandan Paul

In-Jerng Choe

Nan Xing

Joel L Redman, Jr

Alexey Eksarevskiy

Pedro M Lopes

Joseph I Sack

Methods for cardinality estimation feedback loops in query processing are performed by systems and devices. A query host executes queries against data sources via an engine based on estimated cardinalities, and query monitors generate event signals during and at completion of execution. Event signals include indicia of actual data cardinality, runtime statistics, and query parameters in query plans, and are routed to analyzers of a feedback optimizer where event signal information is analyzed. The feedback optimizer utilizes analysis results to generate change recommendations as feedback for later executions of the queries, or similar queries, performed by a query optimizer of the query host. The query host stores change recommendations, and subsequent queries are monitored for the same or similar queries to which change recommendations are applied to query plans for execution and observance by the query monitors. Change recommendations are optionally viewed and selected via a user interface.

System and method for cardinality estimation feedback loops in query processing

A method for optimizing memory usage for a hyperloglog (HLL) estimator is disclosed. When a multiset is applied to an HLL estimator, a sparse representation of a sketch may be created. The sparse representation may be sorted whenever a total number of buckets in the sparse representation is evenly divisible by a sorting threshold and is also less than a transition threshold. The sparse representation may be converted to a dense representation when the number of buckets in the sparse representation reaches the transition threshold.

Optimizing memory usage for a HyperLogLog estimator

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Kirkland, WA, United States of America

Gjorgji Gjeorgjievski