Microsoft Technology Licensing, LLC

Vasileios Papadimos

Cristian Diaconu

Craig Steven Freedman

Hanumantha Rao Kodavalla

Cesar Alejandro Galindo-Legaria

Xin Zhang

Yi Fang

Campbell Bryce Fraser

Adrian Birka

Andrew Scott Richardson

Chandrashekhar Kadiam

Ankit Mahajan

Prashanth Purnananda

In-Jerng Choe

Per-Åke Larson

Gjorgji Gjeorgjievski

Devin Leo Rider

Weiyun Huang

Joel L Redman, Jr

Nan Xing

Alexey Eksarevskiy

Ciprian G Clinciu

Philip Shao

Brian Andrew Kirby

Pedro M Lopes

Joseph I Sack

Chaitanya Sreenivas Ravella

Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating a qualified timestamp corresponding to execution of a first operation of a first transaction over a database and determining, based on the qualified timestamp, that a row of a table is not locked by a second operation of a second transaction over the database. In addition, the techniques may include determining that the row meets a condition of the first operation. Further, the techniques may include updating, based on the qualified timestamp, the row in response to the row meeting the condition, the updating including acquiring a lock on the row of the table.

Method and system for lock after qualification for update queries

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 database system includes a query optimizer which applies transformations to a logical representation of an input query in a declarative query language to generate expressions for a query execution plan. The query optimizer stores information, for each rule application, indicating a transformation applied and bindings for the transformation, the bindings indicating expressions input to the transformation. When a new query execution plan is to be generated for the input query, the query optimizer uses this stored information, for expressions in an original query execution plan, to guide the query optimizer to produce a current query execution plan while avoiding transformations not used to generate expressions in the original query execution plan.

Recording lineage in query optimization

The processing of transaction oriented data tends to be row-oriented, while the processing of analytical operations tends to be column-oriented. Various systems, sometimes referred to as operational data warehouses, may comprise mechanisms adapted for use in scenarios where both transactional data processing and analytical queries are to be performed efficiently. The operational data warehouse (ODW) may perform and update data efficiently by maintaining a table in structures comprising a column store, a delta store, a delete bitmap, and a delete buffer. In this environment, key values may be associated for each row such that the ODW may more efficiently seek rows. Further, rows may also be excluded from a column store based at least in part on a filter criterion. The filtering criterion may be used to filter out rows based on a created predicate set by a user or the system.

Efficient updates in non-clustered column stores

Systems and methods are described that generate an execution plan for a query in a relational database system. The systems and methods generate the execution plan by generating one or more initial logical representations of the query, performing an exploration process around each of the one or more initial logical representations of the query, the performing of the exploration process around a particular initial logical representation of the query comprising applying transformation rules to generate one or more additional logical representations of the query that are logically equivalent to the particular initial logical representation of the query and that are within a maximum allowable transformation distance of the particular initial logical representation of the query, generating one or more execution plans for each initial logical representation of the query and each additional logical representation of the query, and selecting an execution plan from among the generated execution plans.

Distance-based logical exploration in a relational database query optimizer

A database management system that manages data in a database includes a query optimizer that performs query optimization based on statistics related to data in the database. The query optimizer incrementally updates the statistics. The query optimizer organizes the statistics related to the data in the database into a statistics tree structure having a root node corresponding to global statistics, internal nodes corresponding to summary statistics of descendant nodes, and leaf nodes corresponding to disjoint data ranges in the database. The query optimizer performs statistics tree transformation operations on the statistics tree structure. The transformation operations transform the statistics tree structure into a form that at least partially minimizes the system resources needed to update the statistics. The query optimizer updates the statistics corresponding to those nodes of the statistics tree structure where a change has occurred in the disjoint data ranges.

Incremental maintenance of range-partitioned statistics for query optimization

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Vasileios Papadimos