Teradata Us, Inc.

Sai Pavan Kumar Pakala

Bhashyam Ramesh

Jaiprakash G Chimanchode

Pratik Patodi

Dhrubajyoti Roy

Stephen Andrew Brobst

Stephen Molini

Muthukumaran Raveendiran

Jalprakash G Chimanchode

Todd Walter

Control versioning of records in a temporal table is provided to reduce data redundancy. New Data Definition Language (DDL) syntax is provided to make individual columns within a table sensitive or insensitive to whether new row versions are generated when Database Manipulation Language (DML) statements operate on the table. The database parser and back-end data processors are configured to create the table with the user-defined versioning attributes and to manage versioning of the rows without requiring additional programming.

Control versioning of temporal tables to reduce data redundancy

An improved hash table structure compatible with in-memory processing for increasing cache efficiency during hash join processing of a small and large table in a relational database system. The hash table, residing in processor memory, includes a first partition containing a join condition column providing best selectivity for joining the small table with the large table, at least one additional partition containing additional join condition columns for joining the small table with the large table; and an array of hash values, the array of hash values providing an index into the hash table partitions.

Hash table structure for optimizing hash join operations in a relational database system

A database system receives a query and determines that the query includes an inner join between a parent table and a child table. The database system determines that the following relationships exists between the parent table and the child table: referential integrity ('RI') between a primary key attribute (pk) in the parent table and a foreign key attribute (fk) in the child table and a temporal relationship constraint ('TRC') between a period attribute in the parent table and a TRC-attribute in the child table. The database system determines that the query satisfies non-temporal join elimination conditions and temporal join elimination conditions and that the query contains no other qualification conditions on the parent table's period attribute and eliminates the inner join when planning execution of the query.

Join elimination enhancement for real world temporal applications

A method for performing in-memory hash join processing. This method utilizes bulk processing within the hash join steps, such as performing bulk reads of hash values from tables to be joined, and performing bulk probes of hash values in tables to be joined, thereby providing more efficient utilization of memory bandwidth and CPU throughput, reducing memory accesses in the execution path, and reducing CPU cycles per instruction. Data movement is reduced by reducing load-stores to memory and by performing more operations in CPU cache.

Method for performing in-memory hash join processing in relational database systems

A computer receives a query for optimization. The query includes a SEQUENCED join between a temporal parent table and a temporal child table. The parent table has one or more rows. The child table has one or more rows. The parent table has a Primary Key ('PK') column having a value for each row. The parent table has a temporal dimension having a value for each row. The child table has a Foreign Key ('FK') column having a value for each row. The child table has a temporal dimension. The child table's temporal dimension has a value for each row. The computer determines that the query, the parent table, the child table, and the join satisfy a set of CURRENT temporal join elimination criteria. The computer determines that the query, the parent table, the child table, and the join satisfy a set of SEQUENCED temporal join elimination criteria. In response, the computer eliminates the join from the query to produce an optimized form of the query. In further response, the computer executes the optimized form of the query to produce a result. The result has one or more rows. The result has one or more projected columns. The projected columns have values. The result has a temporal dimension. The result's temporal dimension has a value. In further response, the computer stores the result in a memory.

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Secunderabad, India

Sai Pavan Kumar Pakala