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.

Abstract: A multi-parameter data type framework can, among other things, provide a more comprehensive, systematic, and/or formal mechanisms for determining an appropriate data type for a data set. For example, the multi-parameter data type framework can be used to allow analytic tools to virtually automatically figure out an appropriate data type for a set of data values.

Abstract: A system and method for extending compression-aware aggregation logic to column partitioned database sources when an SQL query involves simple or complex aggregate expressions. The logic can be applied when there are multiple fields specified in a Group By clause, when a Group By clause includes an expression involving multiple columns from a column partitioned table, or when there is no Group By clause in the query. This logic extends the benefits of push-down aggregation to complex aggregate queries to build partially aggregated rows that can be directly added into an intermediate cache. For cases where the fields within aggregate expressions are themselves compressed, the aggregation techniques leverage the compression information of the aggregate fields. This aggregation mechanism can be applicable to compression techniques including run-length encoding (RLE), value list compression (VLC) and Presence, Delta on Mean (PDM) on columnar source tables such as Column Partitioned (CP) or Parquet tables.

Abstract: 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.

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 at least one column-partitioned base table storing data. Column values from at least one column of the column-partitioned base table are stored in one or more containers spread across one or more data blocks. Metadata comprising summarized information about the column values in the containers is stored in a metadata index subtable. A query with a filtering condition on the column is applied to the metadata index subtable before the column-partitioned base table is accessed, so that only qualified containers and data blocks are accessed, and unqualified containers and data blocks are eliminated, when responding to the query.

Abstract: An apparatus, method and computer program product for estimating as-a-Service (aaS) query prices in a relational database management system (RDBMS). An optimizer of the RDBMS inserts an EXPLAIN modifier into a query, wherein the EXPLAIN modifier results in the optimizer generating a summary of a query execution plan for the query that includes one or more cost estimates for the RDBMS to perform the query. A price estimate for the query is then generated based on the cost estimates, wherein the price estimate is generated using one or more configurable pricing formulae. The price estimate is merged into the summary of the query execution plan for the query. Moreover, a price guarantee may be generated for the price estimate, wherein the price guarantee is honored when the query is subsequently invoked for execution by the RDBMS.

Abstract: A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

Abstract: A system may include a storage device configured to persistently store a plurality of data elements. The system may further include a processor in communication with the storage device. The processor may receive a data element. The processor may further identify contents of the data element. The processor may further create a data structure indicative of the contents of the data element. The processor may further store the data structure in the storage device. A method and computer-readable medium are also disclosed.

Abstract: In some examples, the database system maintains metadata for a plurality of data objects, the metadata containing ranges of values of an attribute for the plurality of data objects, where the ranges of values of the attribute comprise a respective range of values of the attribute for each corresponding data object of the plurality of data objects. The database system generates a data structure tracking quantities of ranges of values of the attribute that have a specified relationship with respect to corresponding different values of the attribute. The database system receives a database query comprising a predicate specifying a condition on a given value of the attribute, and computes, for the database query, a selectivity of filtering based on the metadata, the selectivity computed based on the data structure.

Abstract: A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

Abstract: 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.

Abstract: In some examples, a system receives function descriptors for different types of functions to be used when processing database queries, each function descriptor of the function descriptors comprising information relating to a respective function of the different types of functions. The system computes, based on a first function descriptor for a first function of the different types of functions, an estimate of a runtime metric associated with execution of the first function for processing a database query.

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: Improved techniques for management of access in computing environments and systems are disclosed. An object-level data access mechanism can be provided. to effectively provide an object-level locking mechanism for locking data objects of database tables, individually, as individual data objects. Furthermore, the object-level data access mechanism can be provided as a safe and efficient filtering mechanism (e.g., cuckoo filter) that effectively provide an object-level locking mechanisms for locking data objects of a database table, individually (i.e., as individual locks placed on individual data objects). For example, a set of filters (e.g., write cuckoo and read cuckoo) can be provided for a database table to facilitate concurrent database operations in a safe but efficient manner.

Abstract: Techniques for ordering predicates in column partitioned databases for query optimization. Predicates on a single CP table within a query are organized to predicate-CP nodes with various sets of column partitions. The predicates within each predicate-CP node and the predicate-CP nodes as a whole are ordered in ascending order of cost which is determined by CPU/IO cost and predicate selectivity. The reorganized query is then executed.

Abstract: 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.

Abstract: A data store system may include a storage device configured to store a plurality of data store tables. The data store may further include a plurality of processing units. At least one processing unit from the plurality of processing units may receive an analytic function call. The at least one processing unit may further identify, in the analytic function call, at least one column of a data store table on which to execute an analytic function in the analytic function call and may further identify, in the analytic function call, an identifier column of the data store table. Each row of the at least one column may be associated with a common row value of the identifier column. The at least one processing unit may further identify, in the analytic function call, at least one index column of the data store table. Each value in each at the least one index column may identify an index value on which to index each value of the at least one column with respect to each value of the identifier column.

Abstract: In some examples, a database system receives data relating to plural micro-models that apply respective analytics, and distributes a plurality of data segments of the received data across the plurality of processing engines based on values of a segmentation key included in the received data. A plurality of processing engines, performs in parallel, operations associated with the plural micro-models using respective data segments of the plurality of data segments, where different processing engines of the plurality of processing engines perform operations associated with respective micro-models of the plural micro-models.

Abstract: 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.