Microsoft Technology Licensing, LLC

Other

Oracle America, Inc.

Sreenivasa R Viswanadha

David Kryze

Patrick An Phu Nguyen

Theodore Vassilakis

Apostolos Lerios

Eric Karlson

Prakash Narayan

Laurent An Minh Nguyen

Scalable implementations of exact distinct counts and multiple exact distinct counts in distributed query processing systems are implemented via systems and devices. Distinct counts and multiple exact distinct counts for identifiers/values are performed based on keys. For distinct counts, datasets including data fields are sorted by values of fields and divided into balanced partitions in distributed servers. Subsets of fields with the same value are partitioned together. Key presence is determined for subsets on each partition, and the number of instances for the key are aggregated for exact distinct counts of values. For multiple distinct counts, fields of a dataset are combined by un-pivoting field columns. Compound keys are generated for combined fields from field identifiers of the combined fields and values of another field. Totals of unique values of the combined fields are determined for values in the counted field based on the compound keys.

Scalable implementations of exact distinct counts and multiple exact distinct counts in distributed query processing systems

A distributed join is performed with two large data sets that are shuffled on different keys without shuffling the larger data set, even when the distributed join is performed on the key of the smaller data set. A third data set is generated that is shuffled on the key of the smaller data set and includes data associated with the key of the larger data set. The third data set and the smaller data set are joined on the shuffle key of the smaller data set to create a fourth data set that includes the first and second key. The fourth data set is shuffled on the key of the larger data set. The fourth data set and the larger data set are joined on the key of the larger data set to generate a fifth data set that can be shuffled on the key of the smaller data set.

Efficient distributed joining of two large data sets

Systems and devices implement scalable implementations of multi-dimensional aggregations with input blending in distributed query processing systems. Multi-dimensional aggregations for identifiers/values designated fields in datasets are performed based on keys. Datasets are sorted by identifier/value and divided into first partitions. Each row of data with a specific sorted-by-identifier/value is only present in one of the first partitions. Keys are generated from each combination of two or more dataset fields, and a blended table of data is generated over the partitions based on each different key combination. Designated data field characteristics are determined for the blended table based on the different key combinations. The characteristics are divided into second partitions based on the keys, where each key is present in only one of the second partitions. A final designated data field characteristic is determined for each row of data in each of the second partitions as the multi-dimensional aggregation.

Scalable implementations of multi-dimensional aggregations with input blending in distributed query processing systems

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to receive at least one database query to be executed. At least one computation graph corresponding to the at least one database query is generated. The computation graph is transformed to an optimized computation graph. The respective portions of the optimized computation graph are distributed to a plurality of distributed computing systems for execution. A result for the at least one database query is provided.

Optimization of database queries via transformations of computation graph

Various embodiments can include systems, methods, and non-transitory computer readable media configured to receive at least one operation to be performed using (i) first data that is managed by a first computing system and (ii) second data that is managed by a second computing system, the operation being received through an interface provided by the computing system, and wherein the operation is based at least in part on a Structured Query Language (SQL). At least one optimization can be performed based at least in part on the operation. The operation can be executed using at least the first data and the second data. A result generated can be provided upon executing the operation through the interface provided by the computing system. The computing system, the first computing system, and the second computing system are each able to concurrently process, access, and create at least a portion of the generated result.

Systems and methods for an SQL-driven distributed operating system

A method for displaying a user-defined artifact in a debugger view is provided. The user-defined artifact is defined in a software application that is developed by a computer software application developer. The software application is included in a packaged application that includes instructions, with each instruction being either a user-defined instruction or a non-user defined instruction. A breakpoint is defined at a desired instruction of the packaged application. The method includes initiating debugging of the packaged application and hitting the breakpoint defined at the desired instruction. Also included is identifying the user-defined artifact in the software application and obtaining data to populate the debugger view. Further included is filtering-in data associated with the user-defined artifact and displaying the debugger view showing the filtered-in data. The method also includes marking data not associated with the user-defined artifact and stepping through the user-defined instruction.

Growing community of inventors

Cupertino, CA, United States of America

Sreenivasa R Viswanadha