Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) identifies a query with low performance. The program code generates a small data environment for use in optimizing the query. The program code identifies table(s) and field(s) related to the query with low performance. The program code samples a portion of each table of the one or more tables based on, for each table, parameters of the one or more fields in each table, where the portion sampled comprises records from each table with common data traits to a whole of each table. The program code generates a small data environment comprising the portion of each table. The program code performance tests an optimized version of the query by executing it on the small data environment.

Abstract: A computer-implemented method improves the performance of write ahead logging. The method includes generating a set of query data and a set of log data, where the set of log data is configured to create a write ahead log, and the set of query data is configured to make changes to data in a database. The method also includes writing the set of query data to a virtual file system. The method further includes separating the set of log data into a set of control intervals, where each control interval includes an entry. The method includes writing, each entry into a log buffer, where the writing the set of query data to the virtual file system and the writing each entry into the log buffer are performed in parallel. The method also includes combining each entry into a complete log.

Abstract: A computer implemented method for managing buffer pool usage includes identifying a database comprising one or more buffer pools and a set of database objects, adding a virtual layer between the set of database objects and the one or more buffer pools, wherein the virtual layer is configured to manage buffer pool resource utilization, managing, using the added virtual layer, buffer pool resource utilization with respect to the set of database objects, wherein the managing comprises defining buffer pool usage corresponding to the set of database objects, dynamically mapping database objects to the buffer pools based, at least in part, on the defined buffer pool usage, analyzing, by one or more processors, queues to identify updated buffer pool usage, and adjusting database object allocation according to the identified updated resource usage. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) to: obtain queries (each query including workload(s)); obtain execution statistics for the one or more workloads; normalize one or more queries. Normalizing includes generating cost analytics, identifying similar cost analytics for query blocks across the queries, and applying a cost model to identify at least one query block for result reuse across. The program code distributes the queries to system resources by assigning the at least one query block (in more than one query/workload) to a first resource to execute (as part of a first workload) against database tables and assigning the at least one query block to a second resource to execute (as part of a first workload) against a shared cache of a heterogenous database system.

Abstract: A system collects statistical data for a data page, divides the data page into parts, analyzes the data page and the statistical data, based on compression efficiency of one or more compression methods for each part of each page, to determine a compression method for each part of page, and compresses, based on the analyzing, the parts of the data page.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains queries from sources via a query interface. The program code analyzes the queries to obtain attributes of the queries. The program code applies a trained classifier to the attributes to classify the queries into finite categories based on a predicted usage of a given resource type exceeding a pre-determined threshold and an anticipated execution duration for each query. The program code determines, based on the finite categories of the one or more queries and resource configurations of resources comprising the database service, which resource of the resources can efficiently execute each query of the one or more queries. The program code schedules each query of the one or more queries for execution by the determined resources.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that executes a query in the database that includes a SELECT statement that targets a base table. The program code generates an intermediate pending table with a table structure based on the SELECT statement. The program code determines that an INSERT operation inserted a record into the base table behind the fetch on the base table performed based on the SELECT statement. The program code inserts the record into the intermediate pending table in the table structure. The program code performs, based on the SELECT statement, a fetch on the intermediate pending table. The program code generates output for the SELECT statement by merging results of the fetch on the base table and the fetch on the intermediate pending table.

Abstract: Embodiments receive historical application data from at least one historical application, receive incoming application data about at least one incoming application, extract a first set of features from the historical application data and a second set of features from the incoming application data using at least one machine learning model, convert the first set of features to a first set of feature vectors and the second set of features to a second set of feature vectors, perform a vector similarity search by comparing the first set of feature vectors to the second set of feature vectors, determine a matching vector based on the vector similarity search, and schedule execution of the at least one incoming application using a node based on the matching vector.

Abstract: A method for improving a query performance of a query of a database application using rewrite includes introducing a database query into a database application to obtain a query result, analyzing the database query to identify index columns and a predicate column; identifying associated columns by determining if any associations exist between the index columns and the predicate column, calculating a confidence score value for each of the associated columns responsive to the associated columns and the predicate column, generating a list of associated columns with the confidence score values, generating a rewritten query by rewriting the database query based on the list of associated columns and the confidence score values and validating the rewritten query.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) to: obtain queries (each query including workload(s)); obtain execution statistics for the one or more workloads; normalize one or more queries. Normalizing includes generating cost analytics, identifying similar cost analytics for query blocks across the queries, and applying a cost model to identify at least one query block for result reuse across. The program code distributes the queries to system resources by assigning the at least one query block (in more than one query/workload) to a first resource to execute (as part of a first workload) against database tables and assigning the at least one query block to a second resource to execute (as part of a first workload) against a shared cache of a heterogenous database system.

Abstract: Embodiments analyze an incoming query for a database, train a query model using machine learning based on the analyzing of the incoming query, determine a model result based on the trained query model; extract a similar information query based on the model result, generate a recommendation for query tuning and a visual impact of the recommendation for the query tuning based on the extracting of the similar information query, and output the recommendation for the query turning and the visual impact of the recommendation for the query tuning.

Abstract: A system, method, and computer program product are configured to: provide a tablespace comprising an original partition having a lower boundary L, an upper boundary U, a split percentage P, and a split ratio R; and in response to the original partition reaching a fullness of P, automatically split the original partition to a first progeny partition and a second progeny partition, wherein the first progeny partition has a lower boundary L1 that is the same as the lower boundary L of the original partition and an upper boundary U1 that is determined based on whether the insert is a sequential or random insert, and wherein the second progeny partition has an upper boundaries U2 that is the same as the upper boundary U of the original partition and a lower boundary L2 that is determined based on whether the insert is a sequential or random insert.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains queries from sources via a query interface. The program code analyzes the queries to obtain attributes of the queries. The program code applies a trained classifier to the attributes to classify the queries into finite categories based on a predicted usage of a given resource type exceeding a pre-determined threshold and an anticipated execution duration for each query. The program code determines, based on the finite categories of the one or more queries and resource configurations of resources comprising the database service, which resource of the resources can efficiently execute each query of the one or more queries. The program code schedules each query of the one or more queries for execution by the determined resources.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: generating database logging data of a database, wherein the database includes a certain leaf page associated to a certain row interval; examining logging data of the database logging data, and based on the examining the logging data, determining that a contention risk condition is present with respect to the certain row interval; and in response to the determining that the contention risk condition is present with respect to the certain row interval, storing incoming subsequent INSERT query statement transaction data for the certain row interval to an external indexing structure external to the certain leaf page.

Abstract: Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

Abstract: A system and method for dynamically visualize and modify user authority and security resource edge based on access behaviors in a cloud computing environment. The method provides a new access control perspective to ensure the credibility of database object usage patterns, build clearer and explicit security data consumption mode for data usage requests, and guide tenants to have a more comprehensive understanding and concise use. Processing components and method are configured to gather data and analytics including collecting data resource and database object usage tagging both resources and users with security level by machine learning and building an access model for each role. The method uses graph modeling with created, trained access patterns for users protected resources and produces in-memory implicit sub-groups for users with similar access requirements and limits their resource accesses according to their history behaviors.

Abstract: Embodiments analyze an incoming query for a database, train a query model using machine learning based on the analyzing of the incoming query, determine a model result based on the trained query model; extract a similar information query based on the model result, generate a recommendation for query tuning and a visual impact of the recommendation for the query tuning based on the extracting of the similar information query, and output the recommendation for the query turning and the visual impact of the recommendation for the query tuning.

Abstract: One or more computer processors generate a debug chain from one or more similar resource bound breakpoints, wherein the debug chain provides dynamic code flow. The one or more computer processors distribute the generated debug chain to one or more tenants.

Abstract: Increasing index search efficiency and availability in a database is provided. An index space matrix corresponding to a plurality of mini-indices of the database is built using ordered boundary values that correspond to key value ranges of each respective mini-index to increase index search efficiency by removing redundant mini-index accesses. Mini-indices of the plurality of mini-indices are consolidated asynchronously without suspending access to the mini-indices using a particular consolidation policy of a plurality of consolidation policies that corresponds to an amount of key value overlap identified between the mini-indices. Data operations corresponding to transactions are performed using the index space matrix during consolidation of the mini-indices without suspending access to the mini-indices.

Abstract: Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.