Teradata Us, Inc.

Teredata, Us, Inc.

Heng Yuan

Congnan Luo

Gregory Howard Milby

Yu Long

Guilian Wang

Daniel Wong

Michael Leon Reed

Mark Sirek

Judy Wu

Elizabeth Jacqueline Stone Brealey

Guillian Wang

Kranthi Kiran Reddy Patil

Improved techniques for performing Spatial Joins multi-processing computing systems and environments are disclosed. One or more intersection of bounds (or limits) of data sets is determined as a join bounding space. The join bounding space is in a space (Global space or Global universe) where a spatial join between (or for) the data can be performed. The determined join bounding space can be partitioned into sub-partitions of the join bounding space. The sub-partitions of the join bounding space can assigned respectively to multiple processing unit for processing in parallel in. In addition, distribution cost information associated with the cost of distribution of the datasets (and/or their components) to the processing units of a multi-processing system can be provided and/or used to effectively distribute and/or redistribute processing of the Spatial Join between the processing units of a multi-processing system.

Spatial joins in multi-processing computing systems including massively parallel processing database systems

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

A database system comprises a processing module, and a storage device communicatively coupled with the processing module and allocated thereto. The storage device stores a base table allocated to the processing module. The processing module receives a database query from a client computer system, and generates a spool based upon a number of columns of a table, and partitions at least one column of the spool based upon the database query received from the client computer system.

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Heng Yuan