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 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: 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: 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: 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: 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: 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: 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 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: 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 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: 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.

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: In some examples, a system performs a delimiter identification process that includes identifying candidate record delimiters and candidate field delimiters in the input data, and providing different pairs of candidate record delimiters and candidate field delimiters. For each respective pair of the different pairs, the system identifies records using the corresponding candidate record delimiter of the respective pair, and computes a collection of measures including a measure indicating a quantity of unique fields observed in the records identified using the corresponding field delimiter of the respective pair. The system selects, based on values of the collection of measures computed for corresponding pairs of the different pairs, a record delimiter and a field delimiter in a pair of the different pairs.

Abstract: A data engine request is received on a local data system. The data engine request includes a portion of the request that is to be processed on an external data engine system. The portion is forwarded to the external data engine system and statistics for accessing external objects of the external data engine system is acquired. The statistics are evaluated for compliance with a Service Level Goal (SLG) associated with the request. Rules-based processing permits optimization and planning of the request on the local data engine system to be modified in view of the statistics received from the external data engine system to comply with the SLG. In an embodiment, actual resource utilization metrics noted during execution of the portion on the external data engine system is provided as feedback to the local data engine system for re-planning and re-optimizing the request with a modified execution plan.

Abstract: A database system receives a query. The database system retrieves an old query execution plan (QEP), OldPlan, for the query. The database system submits the query to an optimizer. The optimizer returns a new QEP, NewPlan, for the query. The database system submits the OldPlan and the NewPlan to a machine learning classifier (ML classifier). The ML classifier predicts that executing the NewPlan will result in a performance regression as compared to executing the OldPlan. The database system executes the OldPlan instead of the NewPlan.

Abstract: 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.