Teradata Us, Inc.

Ncr Corporation

Teradota Us, Inc.

Teredata, Us, Inc.

Bhashyam Ramesh

Michael Warren Watzke

Douglas Paul Brown

Anita Richards

Manjula Koppuravuri

Jaiprakash G Chimanchode

Sai Pavan Kumar Pakala

Donald Raymond Pederson

Grace K Au

Stephen Andrew Brobst

J Mark Morris

Haiyan Chen

John Mark Morris

Steven Bart Cohen

John R Catozzi

Pekka Kostamaa

Chandrasekhar Tekur

Tirupathi Prabhu Bellapukonda

Pratik Patodi

Dhrubajyoti Roy

Naveen Thaliyil Sankaran

Mohan Kumar Kj

Muthukumaran Raveendiran

Michael Leon Reed

Olli Pekka Kostamaa

Kuorong Chiang

Like Gao

Philip Jason Benton

Stephen Molini

Richard Glick

James Ronald Carlson

Rangavajjula K N Sai Krishna

Caroline Ballinger

Srikant S Sharma

Lovlean Arora

Sourabh Maity

Ramachandran Gopalakrishnan

Vamshi Krishna Vangapalli

Todd Walter

Vinupriya Selvamanee

Jitendra Yasaswi Bharadwaj Katta

Karan Kaur Phull

J Venkata Ramana

John A Greene

Anitha G

Vijayasaradhi Indurthi

Venkata Ramana Jyothula

Jalprakash G Chimanchode

Jaya Saxena

Suresh Kumar Jami

Debashis Mahata

In some examples, in response to a join query to join a plurality of tables, a first processing engine retrieves tuples of a first table from a subset of objects of a data store, and adds content of the retrieved tuples to an in-memory table, where the objects are range partitioned across a plurality of processing engines based on respective ranges of values of at least one join attribute in the join query. The first processing engine retrieves, from the data store, tuples of a second table of the plurality of tables based on a range of values of the at least one join attribute in the retrieved tuples of the first table. The first processing engine performs an in-memory join of the plurality of tables based on the retrieved tuples of the second table and the in-memory table.

Range partitioned in-memory joins

A method, apparatus and computer program product for estimating resource consumption for steps in a query execution plan for a query performed by a relational database management system (RDBMS) in a computer system. Past execution data for the steps are used to train a machine learning (ML) model and its model parameters to predict execution times for the steps. A prediction module comprised of the ML model configured by the model parameters predicts an execution time for a current step of the query execution plan for the query, based on current step information and current system load. A boosting module boosts the current step either up or down for processing by the RDBMS to meet a service level goal (SLG) for the query, based on the predicted execution time for the current step, as well as an elapsed query time, a query SLG time, and/or a query CPU time.

Estimator of resource consumption by query execution plan steps

Techniques for improving the execution of database queries in a multi-processor system or distributed processing system environment are provided. In a database system including multiple parsing engines (PEs) for parsing database queries, or requests, received by the system and generating execution plans for the requests, execution plans generated for requests can be saved in a global request cache accessible to each of the parsing engines. Requests which have been parsed and cached by a PE can be retrieved for use by other PEs, thereby avoiding unnecessarily parsing the same database request in multiple PEs. The global request cache may be a distributed cache consisting of request caches local to each parsing engine, with execution plans allocated to the local request caches using hashing techniques applied to the database requests associated with the execution plans.

System and method for sharing database query execution plans between multiple parsing engines

A query is preprocessed for features identified by a Data Manipulation Language (DML) in the text of the query. A machine-learning algorithm uses the features as input and provides as output a predicted query parsing execution time needed by a query parser to parse the query. The predicted query parsing time is provided as input to a query optimizer. The query optimizer uses the predicted query parsing time as a factor in optimizing a query execution plan for the query. Subsequently, the query execution plan is executed against a database as the query.

Predictive query parsing time and optimization

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 function reference for a function is identified in a query. A plurality of processing environments that can provide the function is identified. Function costs for the function to process in the processing environments are obtained. Input data transfer costs are acquired for providing input data identified in the query to each of the functions. A specific one of the functions from a specific processing environment is selected based on the function costs and the input data transfer costs. A query execution plan for executing the query with the specific function is generated. The query execution plan is provided to a database engine for execution.

Cost based function optimization for coupled systems

In some examples, a database system includes a memory to store a predicate-object name cache, where the predicate-object name cache contains predicates mapped to respective object names. The database system further includes at least one processor to receive a query containing a given predicate, identify, based on accessing the predicate-object name cache, one or more object names indicated by the predicate-object name cache as being relevant for the given predicate, retrieve one or more objects identified by the one or more object names from a remote data store, and process the query with respect to data records of the one or more objects retrieved from the remote data store.

Query processing using a predicate-object name cache

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 data store system may include at least one storage device to store a plurality of data and at least one processor with access to the storage device. The at least one processor may receive a plurality of features associated with an environment. The at least one processor may further generate a state representation of the environment based on the plurality of features. The at least one processor may further generate a plurality of predicted future states of the environment based on the state representation. The at least one processor may further generate at least one action to be performed by the environment based on the plurality of predicted future states. The at least one processor may provide the at least one action to the environment to be performed. A method and computer-readable medium are also disclosed.

Autonomous workload management in an analytic platform

In some examples, a database system includes a plurality of processing engines to process data for database operations, and instructions executable on at least one processor to insert first data into first objects stored in a remote data store coupled to the database system over a network, and select, based on a size of the first data, a first partition level from a plurality of different partition levels to associate with the first objects. Different partition levels define different quantities of hash buckets that correspond to different distributions of objects across the plurality of processing engines. The first partition level is associated with the first objects.

Multilevel partitioning of objects

In some examples, a system stores data in a logically disconnected data store. In response to a query for data in the data store, the system accesses metadata of objects stored in the data store, the metadata including information of a respective range of values of at least one clustering attribute in data contained in each respective object of the objects. The system partitions the objects across the plurality of processing engines based on the information of the respective ranges of values of the at least one clustering attribute in the data contained in the objects. The system assigns, based on the partitioning, the objects to respective processing engines of the plurality of processing engines.

Assignment of objects to processing engines for efficient database operations

A database includes a Value List Compression (VLC) predicate evaluator. A table identified in a query that is being processed is identified as having compressed data values. The predicate evaluator compares a query predicate of the query against actual decompressed values noted in a dictionary for the table and the predicate evaluator maintains a bitmap for selective ones of the actual values that satisfy the query predicate. The matched bitmap positions are processed against an index maintained in the table for the actual values to provide selective decompressed table entries as results for the query.

Value list compression (VLC) aware qualification

A method for performing row qualification in database table retrieval and join operations. This method, referred to as bulk qualification, evaluates conditions on multiple rows in a database table at the same time, providing more efficient utilization of memory bandwidth and CPU throughput.

Method for predicate evaluation in relational database systems

Search spaces for obtaining query execution plans for a query are identified. The search spaces are subdivided into sub-search spaces. Searches are initiated within the sub search spaces and plan costs for competing query execution plans are noted along with search costs associated with continuing to search the sub-search spaces. A decision is made based on the plan costs and search costs for utilizing search resources as to when to terminate the searching and to select the then-existing lowest cost query execution plan as an optimal query execution plan for executing the query.

Query plan searching and optimization

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Secunderabad, India

Bhashyam Ramesh