Abstract: A query that is frequently processed to access an object storage is identified. Results from the query returned from the object storage is transformed into a relational database format as a materialized view. When the query is submitted a subsequent time, updated results are managed from the materialized view, other materialized views, and/or the object storage when needed.

Abstract: 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.

Abstract: In some examples, a database system includes a storage medium to store a materialized view (MV) that includes data satisfying an MV condition. At least one processor is to receive a query including a query condition, determine that the query condition partially matches the MV condition, and access a part of the data in the MV partially satisfy the query.

Abstract: 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.

Abstract: In some examples, a database system includes a plurality of processing engines to access data over a network from a data store. 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 the data store, and adds content of the retrieved tuples to an in-memory table, where the objects are range partitioned across the 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.

Abstract: 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.

Abstract: In some examples, a database system includes a storage medium to store a materialized view (MV) that includes data satisfying an MV condition. At least one processor is to receive a query including a query condition, determine that the query condition partially matches the MV condition, and access a part of the data in the MV to partially satisfy the query.

Abstract: 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.

Abstract: A method for spooling data for use in joining a small table with a large table in a relational database system. The method analyzes a join condition for combining records from the small and large tables, selects qualified rows from the large table, and writes the qualified rows to a spool file. The spool file includes a first partition containing hash values of all bind terms for the join condition; a second partition including a join column with a best selective bind term; and at least one additional partition including additional join columns used in bind terms. The partitions are grouped together within a container row in the spool file, and multiple container rows are written together within a super-container row in the spool file.

Abstract: A database system comprises a plurality of processing modules arranged to process data objects from a plurality of data object servers based upon a database query from a client computer system. A control task module is arranged to iteratively dynamically assign data objects from the plurality of data object servers to each of the plurality of processing modules based upon processing activity associated with the processing module during the database query from the client computer system. Alternatively, or in addition to, the control task module is arranged to iteratively dynamically assign data objects from the plurality of data object servers to each of the plurality of processing modules based upon a characteristic of the data objects to be dynamically assigned to the processing module during the database query from the client computer system.

Abstract: A query that is frequently processed to access an object storage is identified. Results from the query returned from the object storage is transformed into a relational database format as a materialized view. When the query is submitted a subsequent time, updated results are managed from the materialized view, other materialized views, and/or the object storage when needed.

Abstract: A method for spooling data for use in joining a small table with a large table in a relational database system. The method analyzes a join condition for combining records from the small and large tables, selects qualified rows from the large table, and writes the qualified rows to a spool file. The spool file includes a first partition containing hash values of all bind terms for the join condition; a second partition including a join column with a best selective bind term; and at least one additional partition including additional join columns used in bind terms. The partitions are grouped together within a container row in the spool file, and multiple container rows are written together within a super-container row in the spool file.

Abstract: A method, computer program, and system for optimizing the execution of a SQL request on a database system are disclosed. The database system has a state. The request has characteristics and the state has characteristics. The SQL request is parsed to create a logical plan. The logical plan is enumerated into a plurality of physical plans. The plurality of physical plans are costed using current environmental and data demographics of the database system to produce cost information. One of the plurality of physical plans is selected using the cost information and workload profile information to execute the request. Workload profile information includes one or more characteristics of the request and the state of the database system. The request is executed using the selected physical plan to produce results.