Teradata Us, Inc.

Haiyan Chen

Rama Krishna Korlapati

Grace K Au

Bhashyam Ramesh

Grace Kwan-On Au

Tri Minh Tran

James Ronald Carlson

A data store system includes a storage device and a processor in communication with the storage device. The processor may receive data from a source and generate a plurality of rows from the data. The processor may further apply row reduction criteria to the buffered plurality of rows. The processor may further, in response to application of the row reduction criteria, determine at least one resultant row. A number of the at least one resultant row is less than a number of the plurality of rows. The processor may further store the at least one resultant row in the storage device. A method and computer-readable medium is also disclosed.

Time series table compression

Techniques for query statistics inheritance are provided. Statistics for a database are used to determine selectivity estimates for sparse joins and tables being joined together within a given query. These statistics are inherited up to the given query along with the selectivity estimates and provided to a database optimizer to use when developing query plans and selecting an optimal query plan for the given query.

Techniques for query statistics inheritance

A system may include a storage device configured to store a data store comprising data. The database system may further include a processor in communication with the storage device. The processor may receive a query that includes at least one predicate associated with the data. The processor may further determine that the at least one predicate is eligible to be converted to a range set predicate. The processor may further convert the at least one predicate to a range set predicate. The processor may further process the query with the range set predicate. A method and computer-readable medium may also be implemented.

Predicate-based range set generation

A method for estimating the selectivity of a database base table predicate, the cardinality of a join, and the cardinality of an aggregation. The method includes receiving a database query, the query comprising one or more query predicates and referencing one or more database tables. One or more join indexes are identified, the join index(es) defined on respective database tables referenced by the database query. The join index(es) comprises one or more join index predicates, and includes one or more join columns in its select list. The row count selected by the query predicates is calculated at least partly using the row count or statistics of the one or more join indexes. The selectivity of the base table predicate is calculated at least partly from the calculated row count. The cardinality of the join is estimated at least partly from the row count and statistics of the identified join index(es).

Cardinality and selectivity estimation using a single table join index

Techniques for independent column detection in selectivity estimations are provided. Values for database statistics are sampled and two independent tests are performed against the values for a first column and a second column. When each test determines that the first and second columns are independent, the first and second columns are said to be independent columns for selectivity estimation.

Independent column detection in selectivity estimation

A SQL query that includes an IN-List is optimized by utilizing an array insert to insert the IN-List into a spool and piggybacking IN-List rows into a join step.

Optimizing the processing of in-list rows

A SQL query that includes an IN-List is optimized by (1) performing an evaluation to determine whether access to a table can be performed as a join operation, (2) converting the IN-List to an IN-LIST relation, and (3) joining the IN-List relation with the table to access the data in the table.

Optimizing search trees by increasing success size parameter

A database query is optimized for a star database schema that includes at least one fact table and at least one dimension table, where the database query includes an IN-List condition. During optimization, the IN-List is transformed into a relation and the relation is joined with the dimension table to produce a join result. The join result is then joined to the fact table.

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Yorktown Heights, NY, United States of America

Haiyan Chen