Teradata Us, Inc.

Hangzhou Alicloud Feitian Information Technology Co., Ltd.

Teredata, Us, Inc.

Congnan Luo

Heng Yuan

Yu Long

Michael Leon Reed

Judy Wu

Like Gao

Guilian Wang

Daniel Wong

Xian Liu

Gregory Howard Milby

Elizabeth Jacqueline Stone Brealey

Cheng Zhu

Guillian Wang

Fang Zheng

Xiaowei Zhu

Yuanfei Guo

The present application provides a data processing method, a system, a computing device and a computer readable storage medium, wherein the data processing method is applied to a data processing node of a distributed data processing system and includes: receiving a plurality of data processing requests sent by a client, determining a target processing amount based on the number of the plurality of data processing requests, and limiting a flow of the plurality of data processing requests in accordance with the target processing amount to obtain a target data processing request; forwarding the target data processing request to a global transaction manager, receiving a processing result from processing the target data processing request by the global transaction manager, and returning the processing result to the client corresponding to each data processing request.

Data processing method and system

A User-Defined Function (UDF) provided by a user and accessed through a user query is identified. A wrapper for the UDF is generated. The wrapper represents a customized instance of the UDF. The wrapper, during execution, selectively calls the UDF provided by the user. Any query that references the UDF is rewritten to process the wrapper instead of the UDF.

Transparent user-defined function (UDF) optimization

Database values and their associated indicators can be arranged in multiple 'buckets.' Adjacent buckets can be combined into a single bucket successively based one or more criteria associated with the indicators to effectively reduce the number of buckets until a desired number is reached.

Summarizing statistical data for database systems and/or environments

A data store system may include a persistent storage device that may store a plurality of data tables. The data store system may further include a processor array in communication with the persistent storage device. The processor array may receive a query comprising a call to a user-defined function (UDF) that may determine a plurality of aggregates for at least one data table from the plurality of data tables. The processor array may determine a number of the plurality of aggregates in the UDF. The processor array may perform a single read of rows associated with the plurality aggregates from the at least one data table. At least one row of the rows may be commonly associated with more than one of the plurality of aggregates. The processor array may generate each aggregate of the plurality of aggregates using the rows according to the UDF. A method and computer-readable medium may also be implemented.

Multiple aggregates in a single user-defined function

Relational data for a database table having multiple columns (dimensions) is represented as a histogram. The buckets of the histogram are clustered into clusters, the clusters are fewer in number than the buckets of the histogram. Each cluster is represented in hyper-space as a line segment (vector) with a thickness value. Average frequencies for the line segments (vectors) and their thickness values are computed. The average frequencies are processed by a query optimizer when processing a query predicate of a query to use as an estimation of selectivity or an estimation of a cardinality of a join operation.

Techniques for query processing using high dimension histograms

Statistics for a Relational Database Management System (RDBMS) set of tables are normalized and a grid computed. A particular grid cell having above threshold number of entries from the tables is determined and an R-Tree is dynamically constructed for that grid cell for use when performing a geospatial join operation against the tables.

Parallelizable gridded geospatial join (PGGJ)

Router threads read real-time stream data as that data is received at processing nodes along a processing path for the data. The data is initially parsed into workloads. Each router thread maintains real-time analytics for the workloads and reports deviations in the analytics to a workload coordinator. The workload coordinator dynamically and in real time provides updated data distribution maps based on the reported deviations, and the router threads use the data distribution maps to determine a next processing unit to forward the workloads, where the next processing unit includes an instance of a next processing node in the processing path for the workload. The next processing node performs additional processing on the workloads along the processing path.

Dynamic workload balancing for real-time stream data analytics

A prediction regarding one or more functions can be made for a database environment. In particular, a predication can be made with respect to values stored in at least one column of at least one table in a database, based on the evaluation of one or more functions for a subset of possible column values (i.e., resultant values derived from the evaluation of a subset of possible column values) without the need to calculate the function(s) for all of the actual entries in the column of the table(s). In effect, a functional predicate can be transformed (or translated) to a predicate that is dependent on the column values instead of the evaluation of one or more functions for the column values.

Making predictions regarding evaluation of functions for a database environment

Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Database partition management

Apparatus, systems, and methods may operate to receive requests to execute a plurality of compression and/or decompression mechanisms on one or more database objects; to execute each of the compression and/or decompression mechanisms, on a sampled basis, on the database objects; to determine comparative performance characteristics associated with each of the compression and/or decompression mechanisms; and to record at least some of the performance characteristics and/or derivative characteristics derived from the performance characteristics in a performance summary table. The table may be published to a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Database compression analyzer

Apparatus, systems, and methods may operate to receive a set of ordered user-selected compression rules as a compression rule set comprising at least one compression threshold condition, to create or transform a database object with rows to be selectively compressed according to the compression rules in the compression rule set (providing a transformed object), and to publish at least a portion of the transformed object to one of a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Database compression

A system, method, and computer-readable medium that facilitate in-database supervised discretisation mechanisms which improve data classification are provided. The disclosed mechanisms provide an efficient, automatic, and repeatable way to perform data discretisation without human intervention. Efficient processing of large and complex unknown data is provided that advantageously does not require the data being analyzed to be processed outside the database. The disclosed mechanisms may use an External Stored Procedure to avoid multiple joins of large tables and minimize the number of full table scans and, consequently, provide better performance than contemporary mechanisms. The disclosed system produces intermediate results in tables which may be conveyed to a visualization subsystem thereby providing users a better understanding of the data distribution in each category. Further, the disclosed system and method introduce a novel similarity-based solution to merge intervals when chi-square testing is not reliable and thereby improves the quality of the interval merge process.

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San Diego, CA, United States of America

Congnan Luo