872 results about "Execution plan" patented technology

A method for generating an interactive profile of a structure, such as a building, employing an interactive profile system that preferably utilizes an Internet web browser to interface with a user. The interactive profile system includes an application engine embodied in a computer program that is preferably based within a server. A plan set, usually in a CAD format, is received into the interactive profile system, typically submitted by the user or client. The building can be any structure, such as a home, office or warehouse, and can also include the property that the structure occupies. The plan set is converted to a profile data set by the profiling engine. In compliance with an enhanced data protocol, which is a specific format for organizing the profile data set in a standardized array. The profiling engine parses, or extracts, the profile data set to develop and link a plurality of potentially interrelated building. The profiling engine performs a systematic enhancement of the plan set, building upon the elemental physical descriptions of the plan set. Each element of the physical description is functionally analyzed for relational attributes and then expanded and tagged. The user directs a profile query to the application engine of the interactive profile system. The profile query is relatable to the enhanced profile and more specifically relatable to at least one of the plurality of interrelated elements of the building. Typical profile requests can include proposed or actual changes to the building, requests for material listings, and project assessments.

A method, system, and computer program product to make query processing more robust in the face of optimization errors. The invention validates the statistics and assumptions used for compiling a query as the query is executed and, when necessary, progressively re-optimizes the query in mid-execution based on the knowledge learned during its partial execution. The invention selectively places a number of CHECK operators in a query execution plan to validate the optimizer's cardinality estimates against actual cardinalities. Errors beyond a threshold trigger re-optimization, and the optimizer decides whether the old plan is still optimal and whether to re-use previously computed results. The invention addresses arbitrary SQL queries whose plans can contain sub-queries, updates, trigger checking, and view maintenance operations. The invention can handle concurrent update transactions or updates of common sub-expressions in a query execution plan without compromising consistency and isolation as locking information is tied to the record ID.

A method, apparatus, and article of manufacture for optimizing database queries using automatic summary tables. Query execution plans derived from an automatic summary table can be used to generate results for the query if a comparison of the query requirements with an automatic summary table definition determines that the automatic summary table overlaps the query, and if an optimization process determines that using the summary table will lower the cost of the query. The optimization process involves enumerating a plurality of query execution plans for the query, wherein the query execution plans enumerated include those that access combinations of query and summary tables. Each such query execution plan is assigned a cost representing an estimation of its execution characteristics, and the least costly query execution plan is selected for the query.

A method for automatically and adaptively determining query execution plans for parametric queries. A first classifier trained by an initial set of training points is generated using a set of random decision trees (RDTs). A query workload and/or database statistics are dynamically updated. A new set of training points collected off-line is used to modify the first classifier into a second classifier. A database query is received at a runtime subsequent to the off line phase. The query includes predicates having parameter markers bound to actual values. The predicates are associated with selectivities. The query execution plan is determined by identifying an optimal average of posterior probabilities obtained across a set of RDTs and mapping the selectivities to a plan. The determined query execution plan is included in an augmented set of training points that includes the initial set and the new set.

A database query optimizer constructs a graph comprising nodes, relations, and expressions. The query optimizer then constructs execution plans for sub-parts of the graph. The combination of execution plans make up the overall execution plan for the query. The execution plan information is appended to the graph itself, allowing changing an execution plan in one portion of the graph without necessarily changing execution plans in other portions of the graph. By representing a query using the graph of the preferred embodiments that includes execution plan information, the query optimizer is able to evaluate the execution plans of different options quickly and efficiently, thereby enhancing the performance of the query optimizer.

A method of optimizing an execution of a query on data may include steps of creating schema object(s) in a database schema, enable monitoring of the schema object(s) to monitor the amount of change of a predetermined characteristic of the schema object(s) over time and loading the data into the created schema object(s). It is then determined whether the amount of change of the predetermined characteristic exceeds a selectable threshold. Updated statistical information may be obtained only when the amount of change exceeds the threshold. The updated statistical information may then be provided to a query execution optimizer, which selects an execution plan based upon the updated statistical information when the amount of change exceeds the threshold or based upon previously provided statistical information when the amount of change does not exceed the threshold. The query may then be executed using the selected execution plan.

A method for evaluating a user query on a relational database having records stored therein, a workload made up of a set of queries that have been executed on the database, and a query optimizer that generates a query execution plan for the user query. Each query plan includes a plurality of intermediate query plan components that verify a subset of records from the database meeting query criteria. The method accesses the query plan and a set of stored intermediate statistics for records verified by query components, such as histograms that summarize the cardinality of the records that verify the query component. The method forms a transformed query plan based on the selected intermediate statistics (possibly by rewriting the query plan) and estimates the cardinality of the transformed query plan to arrive at a more accurate cardinality estimate for the query. If additional intermediate statistics are necessary, a pool of intermediate statistics may be generated based on the queries in the workload by evaluating the benefit of a given statistic over the workload and adding intermediate statistics to the pool that provide relatively great benefit.

Disclosed is a multi-dimensional selectivity estimation method using compressed histogram information which the database query optimizer in a database management system uses to find the most efficient execution plan among all possible plans. The method includes the several steps to generate a large number of small-sized multi-dimensional histogram buckets, sampling DCT coefficients which have high values with high probability, compressing information from the multi-dimensional histogram buckets using a multi-dimensional discrete cosine transform(DCT) and storing compressed information, and estimating the query selectivity by using compressed and stored histogram information as the statistics.

A computer-implemented method, apparatus and article of manufacture for performing an automatic error recovery in a database system. Automatic error recovery is performed for a query execution plan, following errors, problems or failures that occur during execution, by automatically or manually deactivating and/or activating components, features or code paths, and then re-submitting the query execution plan for execution in the computer system.

A database system with methodology for generating bushy nested loop join trees is described. In one embodiment, for example, an improved method is described for optimization of a query requesting data from a database, the method comprises steps of: generating a left deep operator tree for returning data requested by the query based on traversing a left deep operator tree search space; transforming the left deep operator tree into a semantically correct structure for returning data requested by the query; and building a query execution plan for returning data requested by the query based on the semantically correct structure.

Queries are optimized according to a first optimization mode by generating execution plans and selecting the lowest cost plan. Inputs optimized according to the first optimization mode, to database operations with input parameters that are inconsistent with the first optimization mode, are replaced with equivalent inputs optimized according to a second optimization mode, the second optimization mode being consistent with the input parameter. Blocking operations are eliminated from queries using a cost-based approach.

A system and method for optimizing a database query with improved performance enhancements is herein disclosed. The database query consists of one or more logical expressions. Through the repeated application of one or more rules, the logical expressions are transformed into execution plans. The query optimizer partitions the database query into one or more subproblems with each subproblem consisting of one or more logical expressions. A plan is obtained for each subproblem with the plan for the database query including the plans for each subproblem. The query optimizer is cost-based and uses rules including transformation and implementation rules that are used to perform transformations on the logical expressions in a subproblem in order to produce a plan. The rules are classified into context-free and context-sensitive in order to avoid generating duplicate expressions. Context-free rules are applied once for each logical expression and context-sensitive rules are applied once for each logical expression for a particular optimization goal. In a preferred embodiment, the query optimizer performs several optimization passes over the database query in order to obtain an optimal plan. For each pass, if no optimal plan exists for the requested optimization goal, existing plans having the same optimization goal are utilized with each input reoptimized for a more cost effective plan.

Execution of a restartable sub-tree of a query execution plan comprises determining whether use of parallel processes is a preferred or optimal mode of executing the sub-tree. The determination is based, at least in part, on how long it takes to restart the sub-tree using two or more parallel processes and/or how long it takes to probe the sub-tree, i.e., to fetch a row that meets one or more conditions or correlations associated with the sub-query, using the two or more parallel processes. Thus, a dynamic computational cost-based operation is described, which determines at query runtime whether to execute the restartable sub-tree using a single server process or multiple parallel server processes.

A system, apparatus, and program storage device implementing a method of optimizing queries used for searching a computerized database, wherein the method comprises providing a query comprising a sequence of inner joins and outerjoins; and rewriting the query by producing a sequence of outer Cartesian products for the query; producing a sequence of nullification operations on the query; and producing a sequence of best match operations on the query. The method further comprises optimizing the query using a query execution plan for processing the rewritten query, wherein the query execution plan expands a search space in the database for which the rewritten query may be run.

A computer implemented method, apparatus, and computer usable program code for generating an execution plan graph from a data flow. A metadata representation of the data flow is generated in response to receiving the data flow. A set of code units is generated from the metadata representation. Each code unit in the set of code units is executable on multiple different types of runtime engines. The set of code units is processed to produce the execution plan graph.

Approaches for indexing and retrieving spatial data with a quadtree index in database management systems are described. In an embodiment, data objects are stored without decomposition within a linearized quadtree stored within a B-tree index. In another embodiment, a method determines an optimal execution plan for a spatial query by parsing it to determine a query type and geometry object associated with the query. The method tessellates the query object by recursively decomposing the quadtree blocks that cover it. Cost-based decomposition decisions are made by consulting a cost model furnished by the database management system to minimize the cost of the resulting index range plan on the B-tree storage. Thus, data-directed query decomposition enacted by the method results in the optimal cost index range plan for the current data distribution and system context. In another embodiment, a system identifies and displays an optimal index range plan in a user interface.

A self-healing database engine and optimizer framework support automatically responding to execution errors to allow continued execution of a query plan. Upon encountering an execution error, the database engine automatically initiates a rebuilding of the query plan and executes the rebuilt execution plan. If an error is encountered in the rebuilt query plan then the query implementation methods are analyzed. If a query function is identified for which an alternative implementation method is available, then this alternative implementation method is substituted to create a new query plan. The new query plan is then executed to determine if the error is corrected.

The subject invention leverages threshold-based strategies applied to relational data to facilitate in determining an optimal execution plan for top-k selection queries. These strategies utilize a given query and relational data metadata to identify possible execution plans. This allows alternatives to scanning techniques to be considered in order to further enhance the overall efficiency of the optimal execution plan. A query optimizer can prune, for example, the alternative execution plans during enumeration of the plan space and/or during cost evaluations of the possible alternative execution plans. A cost model for the query optimizer can utilize a cost function based on an approximation of the number of iterations required to complete a threshold-based strategy.

This invention relates generally to a system for processing database queries, and more particularly to a method for generating high level language or machine code to implement query execution plans. The present invention provides a method for generating executable machine code for query execution plans, that is adaptive to dynamic runtime conditions, that is compiled just in time for execution and most importantly, that avoids the bounds checking, pointer indirection, materialization and other similar kinds of overhead that are typical in interpretive runtime execution engines.

Systems and methods for optimizing database queries involving aggregates identify instantiations of rules in an internal language representation of the original query, and then applies the rules to those instantiations to form an optimized query execution plan.

A computer implemented method, apparatus, and computer usable program code for generating code for an integrated data system. A mixed data flow is received. The mixed data flow contains mixed data flow operators, which are associated with multiple runtime environments. A graph is generated containing logical operators based on the mixed data flow in response to receiving the mixed data flow. The logical operators are independent of the plurality of runtime environments. The graph is converted to a model. The logical operators are converted to model operators associated with the multiple runtime environments. The model operators allow for analysis of operations for the mixed data flow. The model is converted into an execution plan graph. The execution plan graph is executable on different runtime environments.