Microsoft Technology Licensing, LLC

Karthik Saligrama Ramachandra

Alan D Halverson

Willis Lang

Cesar Alejandro Galindo-Legaria

Ajay Kalhan

Conor J Cunningham

Hideaki Kimura

Peter Alan Carlin

Nikhil Teletia

Srinath Shankar

Rathijit Sen

Qun Guo

Shize Xu

Kwanghyun Park

Surabhi Gupta

Sanket Jayant Purandare

Kameswara Venkatesh Emani

David Johns DeWitt

Distributed query processing is often performed by a set of nodes that apply MapReduce to a data set and materialize partial results to storage, which are then aggregated to produce the query result. However, this architecture requires a preconfigured set of database nodes; can only fulfill queries that utilize MapReduce processing; and may be slowed down by materializing partial results to storage. Instead, distributed query processing can be achieved by choosing a node for various portions of the query, and generating customized code for the node that only performs the query portion that is allocated to the node. The node executes the code to perform the query portion, and rather than materializing partial results to storage, streams intermediate query results to a next selected node in the distributed query. Nodes selection may be involve matching the details of the query portion with the characteristics and capabilities of the available nodes.

Per-node custom code engine for distributed query processing

Methods and systems are provided for converting a loop (e.g., a cursor loop) to a declarative Structured Query Language (SQL) query that invokes a custom aggregate function. The loop includes a select query and a loop body that includes a program fragment that can be evaluated over a result set of the select query one row at a time. The system verifies that the loop body does not modify a persistent state of the database. A custom aggregate function that expresses the loop body is automatically constructed according to a contract. An aggregate class comprising aggregation methods of the contract are used to construct the aggregate function based on results of static analysis. The select query is automatically rewritten to form a declarative SQL query that invokes the custom aggregate function. The declarative SQL query may be executed by a database management system (DBMS) SQL server.

Optimizing cursor loops in relational database systems using custom aggregates

According to examples, an apparatus may include a machine readable medium on which is stored machine readable instructions that may cause a processor to, for each of a plurality of resource setting levels, determine resource usage characteristics and execution times of executed workloads, assign, based on the resource usage characteristics of the executed workloads, each of the executed workloads into one of a plurality of resource bins, determine, for each of the resource bins, an average execution time of the executed workloads in the resource bin, determine a total average execution time of the executed workloads from the determined average execution times, identify a lowest total average execution time of the determined total average execution times, determine the resource setting level corresponding to the identified lowest total average execution time, and tune a resource setting to the determined resource setting level.

Tune resource setting levels for query execution

Processing a database query. A method includes receiving a database query from a user. The database query includes one or more imperative functions. The one or more imperative functions are converted to one or more declarative query representations. The one or more declarative query representations include standardized relational operators included in a relational query language. Further, the one or more declarative query representations are optimizable by a query optimizer of the database. The database query is optimized at the query optimizer to create a query plan by evaluating any declarative query representation originally in the database query received from the user and the one or more declarative query representations.

Method for optimization of imperative code executing inside a relational database engine

Managing cluster resources for a cluster. The method includes identifying a plurality of quiescence policies. Each of the quiescence policies define one or more conditions for cluster entities which when met, identify that cluster resources should be reclaimed from cluster entities by quiescing cluster entities. The method further includes, for each quiescence policy, identifying an amount of reclaimed capacity defining the amount of cluster resources that will be reclaimed by implementing the quiescence policy. The method further includes, for each quiescence policy, identifying an amount of reserve capacity defining the amount of cluster resources that should be reserved for resuming quiesced cluster entities. The method further includes; selecting a quiescence policy based on comparing the reclaimed capacities and reserve capacities for the quiescence policies. The method further includes, booking cluster resources for cluster entities on the cluster according to the selected quiescence policy.

Modeling and forecasting reserve capacity for overbooked clusters

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Madison, WI, United States of America

Karthik Saligrama Ramachandra