Abstract: In a cloud database system employing multiple types of storage, such as external object store, managed object store. block storage, and compute node memory, each type of storage having different kinds of file organization, different types of data organization, different forms of storage access, and different latency and throughput costs, a system and method for caching different data transformations created during query executions involving different data stores. Transformed versions of data read from external object storage are saved to a multi-layered warehouse cache for use in subsequent query executions.

Abstract: A query is received. It is determined that the query does not fit a profile for a run-the-business set of queries, where the profile for the run-the-business set of queries excludes queries that are not routine parts of running a business and that do not require priority processing. The query is executed with a dynamically-created compute capacity that is not part of a compute capacity used to run the run-the-business set of queries.

Abstract: Improved techniques for management of memory (or memory management) for computing systems and environments are disclosed. The improved techniques are especially well suited for computing systems that operate in highly complex and/or demanding computing environments (e.g., massively parallel database systems that may be required to process many complex database queries in parallel. Memory can be managed dynamically at run time to determine and designate one of multiple memories that are available for execution of executable components (e.g., database queries, Opcodes of a Virtual Machine). In addition, memory can be managed dynamically at run time to effectively reuse memory locations of a memory (e.g., stack memory) being used for execution of one or more executable components (e.g., Opcodes of a Virtual Machine) at run time when the memory is being actively used to execute the one or more executable components.

Abstract: A SQL query performs a function. The SQL query includes a SQL operator that has two input relations. The first input relation is a script relation having a plurality of script records. Each script record includes a transformation field, the contents of which specify a transformation to be performed by the SQL operator. The second input relation is a parameter relation having a plurality of parameter records. Each parameter record includes a data-to-process field that identifies data to be processed by the transformation specified in the transformation field of a selected script record. The selected script record is determined by a mapping. The SQL operator has one output relation having a plurality of output records. Each output record contains the result of transformation specified in a respective selected script record using the data to be processed identified in the data-to-be-processed field in a respective selected parameter record.

Abstract: A computer system executes a database management system (DBMS). The DBMS manages a database comprised of DBMS resources. The DBMS receives a request to be executed. The request is a DBMS action to be executed using the DBMS resources. The request includes a predicate specifying a maximum cost for executing the request, and a deadline, specifying a deadline by which the request is to be completed in its execution. The DBMS determines a plurality of workloads under which the request is qualified to execute. Each workload of the plurality of workloads includes a respective set of requests that have common characteristics. Each workload of the plurality of workloads includes a respective cost criterion and a respective elapsed time criterion. The DBMS selects a selected workload from among the plurality of workloads. The selected workload has a selected cost criterion and a selected elapsed time criterion. The DBMS begins execution of the request using the selected workload.

Abstract: A database system enumerates one-column candidate referential integrities (1CRIs) from a plurality of input columns in one or more relations. The database system applies one or more disqualification tests to the 1CRIs to eliminate illegitimate 1CRIs resulting in a list of non-disqualified 1CRIs, wherein the disqualification tests are applied to an 1CRI being tested (hereinafter (A*,B*), A* representing a set of values of a referenced column or columns and B* representing a set of values of a referencing column or columns) until (A*,B*) is disqualified or until all of the disqualification tests have been executed and (A*,B*) has not been disqualified, in which case (A*,B*) is added to the list of non-disqualified 1CRIs, wherein each of the disqualification tests reduces the likelihood of incorrectly adding (A*,B*) to the list of non-disqualified 1CRIs.

Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER) storing planning and execution information for QEs from previous queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous queries. Additionally, dynamic join indexes representing QE results are created for high-value QEs selected from the QER and maintained within a DJI repository. During query plan creation for a current or subsequent query, the optimizer searches the QER and DJI repository for DJIs created for high-value QEs corresponding to QEs contained in the current or subsequent query. DJIs corresponding to the matching QEs are used in the query planning phase to rewrite the current or subsequent user query so that stored QE results are used to answer QEs contained in the current or subsequent query.

Abstract: A data store system may include a storage device configured to store a plurality of data store tables and may include a processor in communication with the storage device. The processor may receive a plurality of requests. For each request, the processor may: (1) determine an associated workload type for the request; (2) determine a first respective rate at which the request is to be released for scheduling of execution; and (3) release the request for scheduling of execution based on the first respective rate. For each released request, the processor may: (1) determine a second respective rate based on the associated workload type at which each released request is scheduled to be executed; and (2) in response to execution being scheduled for a released request, execute the released request. A method and computer-readable medium are also disclosed.

Abstract: 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.

Abstract: In some examples, a system receives an input graph representation of one or more query plans for one or more database queries, and generates, by an embedding machine learning model based on the input graph representation, a feature vector that provides a distributed representation of the one or more query plans. The system determines, using the feature vector, one or more user behaviors and/or workload characteristics of one or more workloads in one or more database systems.

Abstract: A database system receives a query that includes a reference to a foreign table. The foreign table is used to access an Object Store (OS) outside the database system. The OS stores objects. The objects have path names, which are pointers to the objects. When the foreign table was created one or more wildcards were used to specify the path names for the objects in the OS to be accessed by the query. The database system directing the OS to provide a list containing the path names of the objects in the OS. The database system receiving the list and applying the one or more wildcards to identify the path names of the objects to be accessed by the query. The database system producing a result by executing the query, accessing the objects in the OS identified by the path names of the objects to be accessed by the query.

Abstract: 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.

Abstract: Artificial Intelligence-based (AI-based) modeling can be used to predict “Critical Times” when “bottlenecks” in a processing of data would occur. Moreover, for each one of the predicted Critical Times, it can be determined which one of multiple Computing Resources would cause the bottleneck, so that more precise measures can be taken and taken before a Critical Time, in an effort to prevent bottlenecks from happening in computing systems, especially more complex database systems with more demeaning service needs and requirements.

Abstract: A system and method for caching data objects retrieved from a network object store or cloud storage remotely accessible by a database management node. Retrieved data objects are stored within the database management node in a cache memory having multiple cache zones providing different input/output (I/O) latencies with respect to cache data access. Retrieved data objects are placed within the cache zones in accordance with access and storage costs associated with the retrieved data objects, wherein data objects having higher associated costs are placed in cache zones having lower I/O latencies. The costs associated a data object may be determined from object store vendor costs, object store storage tier levels, locations of the data management node and the object store, method of connection to the object store, or read from a pricing matrix containing predetermined object costs associated with stored data objects.

Abstract: A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database comprised of one or more tables storing data. The RDBMS executes a query with a semi-join operation comprising an inclusion join and/or an exclusion join performed against at least an outer table and an inner table, wherein the inclusion join returns a row from the outer table when there is a match with a row in the inner table, and the exclusion join returns a row from the outer table when there is no match with a row in the inner table. The RDBMS performs a rewrite of the query to avoid spooling and/or sorting of the inner table, when the inner table is larger than the outer table and a cost after the rewrite is lower than before the rewrite.

Abstract: In some examples, a database system accesses a plurality of objects in a remote object store. In response to a query to change data in a first object of the plurality of objects, the database system specifies the first object prior to the change as a first version of the first object, and creates a second version of the first object after the change. The database system maintains metadata identifying unmodified objects of the plurality of objects, and during a garbage collection process when deciding whether to remove a given object of the plurality of objects, accesses the metadata to determine whether the given object has been modified, and prevents removal of the given object in response to determining that the given object is unmodified.

Abstract: 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.

Abstract: In some examples, a system sends a transaction to a database server to cause storing of data of the transaction in a cache of the database server, where the data in the cache is for inclusion in a backup of data from the database server to a remote data store (e.g., the backup may be in a cloud and may be a snapshot). The system detects a failure associated with the database server, and in response to detecting the failure, requests, from the database server or a replacement database server, transaction information of at least one transaction that was successfully applied to the remote data store, the transaction information based on the backup of data. The system causes replay one or more transactions to recover data at the database server or the replacement database server, to perform recovery of the database server or the replacement database server to a current state.

Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER) storing planning and execution information for QEs from previous queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous queries. Additionally, dynamic join indexes representing QE results are created for high-value QEs selected from the QER and maintained within a DJI repository. During query plan creation for a current or subsequent query, the optimizer searches the QER and DJI repository for DJIs created for high-value QEs corresponding to QEs contained in the current or subsequent query. DJIs corresponding to the matching QEs are used in the query planning phase to rewrite the current or subsequent user query so that stored QE results are used to answer QEs contained in the current or subsequent query.

Abstract: In a cloud database system, a system and method for analyzing query workloads on installed customer systems and generating tiered offers promoting higher query execution speeds in the form of better response times for a selected portion of queries in exchange for a higher price. Upon selecting an offer, the cloud database system is automatically configured to include additional compute resources as required to execute future instances of the selected queries to take advantage of the performance improvements provided with the selected offer.