Abstract:
At least one implementation, described herein, detects fuzzy duplicates and eliminates such duplicates. Fuzzy duplicates are multiple, seemingly distinct tuples (i.e., records) in a database that represent the same real-world entity or phenomenon.
Abstract:
Index structures and query processing framework that enforces a given threshold on the overhead of computing conjunctive keyword queries. This includes a keyword processing algorithm, logic to determine which indexes to materialize, and a probabilistic approach to reducing the overhead for determining which indexes to build. The index structures leverage the fact that the frequency distribution of natural-language text follows a power law. Given a document collection, a set of indexes is proposed for materialization so that the time for intersecting keywords does not exceed a given threshold Δ. When considering the associated space requirement, the additional indexes are limited. Materialization of such a set of indexes for reasonable values of Δ (e.g., the time required to scan 20% of the largest inverted index), at least for a collection of short documents is distributed by the power law.
Abstract:
A method of estimating results of a database query, the results are estimated by performing a sampling of weighted tuples in a database based on a probability of usage of tuples required in executing a workload. A probability is associated with each tuple sampled. An aggregate is computed over values in each sampled tuple while multiplying by the inverses of the probabilities associated with each tuple sampled.
Abstract:
A monitoring component of a database server collects a subset of a query workload along with related statistics. A remote index tuning component uses the workload subset and related statistics to determine a physical design that minimizes the cost of executing queries in the workload subset while ensuring that queries omitted from the subset do not degrade in performance.
Abstract:
Integrating the partitioning of physical design structures with the physical design process can result in more efficient query execution. When candidate structures are evaluated for their relative benefit, one or more partitioning methods is associated with each structure so that the benefits of various partitioning methods are taken into consideration when the structures are selected for use by the database. A pool of partitioned candidate structures is formed by proposing and evaluating the benefit of candidate structures with associated partitioning on a per query basis. The selected partitioned candidates are then used to construct generalized structures with associated partitioning methods that are evaluated for their benefit over the workload. Those generalized structures are added to the pool of partitioned candidate structures. From this augmented pool of partitioned candidate structures, an optimal set of partitioned structures is enumerated for use by the database system.
Abstract:
A lightweight physical design alerter can analyze a workload and determine whether a comprehensive tuning session would result in a configuration improvement over the current configuration. The alerter provides a low-overhead procedure that can run during normal operation of a database management system and produce a notification if a current configuration is less than optimal. The alerter can report lower and upper bounds on the improvements that could be obtained if a comprehensive tuning tool is launched. A lower bound can be justified by generating feasible configurations. The disclosed embodiments can be extended to query updates, materialized views, and other physical design features (e.g., partitioning).
Abstract:
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.
Abstract:
Layout in a database system is performed using workload information. Execution information for a workload is obtained. Cumulative access and co-access information for database objects is then assembled. A cost model is developed for quantitatively capturing the value of different layouts, and a search is performed for a recommended database layout. In one embodiment, a greedy search is performed which initially attempts provide a layout that minimizes co-location of objects on storage objects, and then attempts to improve that layout via a greedy search.
Abstract:
An outlier index for a database and a given workload is generated by identifying sub-relations of tuples in the database induced by selection and group by conditions in queries in the workload. A variance is then generated for values in each sub-relation. Sub-relations having higher variances are selected, and outliers from such sub-relations having higher variances are generated.
Abstract:
Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data.