Microsoft Technology Licensing, LLC

Erik Ismert

Craig Steven Freedman

Srikumar Rangarajan

Aleksandras Surna

Nimish S Khanolkar

Michael James Zwilling

Cesar Alejandro Galindo-Legaria

Raul Garcia

Ganapathy Subramaniam Krishnamoorthy

Alazel Acheson

Panagiotis Antonopoulos

Marc Todd Friedman

Conor J Cunningham

Jose A Blakeley

Milind Madhukar Joshi

Naveen Prakash

Peter Alan Carlin

Sameer Arun Verkhedkar

Eric N Hanson

Christian Kleinerman

Jack Smith Richins

Evgueni Zabokritski

Shrinivas Ashwin

Adrian Sorin Baras

Lubor Kollar

Craig Alan Gick

Marko Zivanović

Milos Andelković

Jiazhen Wu

Frank Huber

Nikola Vujic

Girkumar Nair

Nikola Vujić

A data system in which a parent transaction that has multiple nested transactions has isolation between the nested transactions. Each of at least some of the plurality of operations are included within a nested transaction within the parent transaction. For each of at least some of the nested transactions, a begin transaction identifier is assigned to the nested transaction; and on or around completion of the nested transaction, a completion transaction identifier is assigned as a statement identifier at least one object version operated upon by the nested transaction. Visibility of a particular version of an object to an active nested transaction may be verified by comparing the begin transaction identifier of the active nested transaction to the statement identifier of the particular version of the object. The comparison is done such that visibility achieves the proper isolation between nested transactions.

Multi-version data system nested transactions isolation

Running a transaction against a database that allows the transaction to be automatically retried when the transaction fails such that certain transaction failures that cause the transaction to be automatically retried are not visible to an entity that receives data as a result of the execution of the transaction. A first set of statements of a transaction is identified by identifying statements that will cause a change in the database state. A second set of statements is identified, that result in data being generated to be sent to the entity. The method further includes executing the first set of statements to change the database. The method further includes running the second set of statements separately from the first set of statements to generate data to be sent to the entity.

Automatically retrying transactions with split procedure execution

A system and method of enabling row level security through security policies is disclosed herein. In this system and method, a computing device may be communicatively coupled to a storage device. The computing device may further be activated and maintain data that comprises a plurality of rows. When executed by the computing device, the system and method may process a data definition language statement comprising a security policy definition. Further, the system and method may receive a query language statement comprising a request to access a first column of a row from the plurality of rows. The system and method may process the request and determine if access may be granted to a user based on the security policy definition in the system.

Row level security

Performing database queries. A method includes receiving a particular database query. The method further includes accessing a query plan based on the particular database query. The query plan has operators and specific operational parameters associated with each of the operators. The association of operators and specific operational parameters is specific to the particular database query. From the query plan, the method further includes instantiating a plurality of compiled code templates. Each code template includes executable code that when executed performs functionality of one of the operators from the query plan with the specific operational parameters applied in the compilation. The method further includes binding the code templates together using programmatic control flow to create a functioning program.

Method of converting query plans to native code

A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.

Normalizing data for fast superscalar processing

The subject disclosure is directed towards technology by which an expression in a database engine is executed against stacks of data. Each instruction of the expression is evaluated against the data stacks until completed against each data stack, such as by iterating to execute an instruction through the data stacks before executing the next instruction. The data may be arranged in the data stacks (in memory) in various ways, such as to have each data stack contain the data of one database row, (e.g., with the data stack elements comprising column data. Data may be grouped, such as to put the data from different rows into the same data stack.

Vector extensions to an interpreted general expression evaluator in a database system

Various embodiments of the present invention enable functions, procedures, and triggers to be written in any of the .NET languages and executed by the RDBMS. User code can access data from the local or other SQL servers using the SQL Programming Model and both the SqlServer or SqlClient implementations respectively. Moreover, to improve upon the previous extensibility mechanism, a set of APIs (commonly known as 'the in-process provider' or 'inproc provider') is utilized to provide efficient and easy to use data access while running inproc, a data access API that is an implementation of the ADO.net programming model.

Systems and methods for a database engine in-process data provider

Systems and methods that incorporate fuzzing capabilities within an SQL server to facilitate penetration testing. A fuzzing component associated with the SQL server provides an entry point for accessing the fuzzing system to update explicit user specified parameters associated with SQL, wherein the server's in depth knowledge regarding semantics of the language code (e.g., manner of parsing) can be employed to determine vulnerabilities thereof.

Fuzzing system and method for exhaustive security fuzzing within an SQL server

Systems and methods that generate specialized plans for compiling SQL queries. A plan generator component scans the query representation for parameter sensitive predicates and evaluates each predicate individually based on the parameter values. Accordingly, queries can be identified not only based on their structures, but also based on their parameter conditions. The specialized plans are more efficient for particular values, wherein queries that employ such values are optimally executed.

Parameter-sensitive plans for structural scenarios

A system and method for creation, modification and/or use of plan guide(s) is provided. Plan guide(s) can be employed to optimize the performance of query(ies) when it is not possible and/or desirable to change the text of the query directly. Plan guides can be useful, for example, when a small subset of queries in a database application deployed from a third-party vendor is not performing as expected. Plan guides influence optimization of queries by attaching query hints to them. In accordance with an aspect of the subject invention, a query plan execution system is provided. The system includes an execution component, a query optimizer, and, a plan cache. Plan guides and other associated metadata are stored in a plan guide metadata store.

Query plan selection control using run-time association mechanism

Growing community of inventors

Shoreline, WA, United States of America

Erik Ismert