Abstract: An index and materialized view selection wizard produces a fast and reasonable recommendation for a configuration of indexes, materialized views, and indexes on materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes indexes, materialized views and indexes on materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.

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 in one of many known ways 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. Further methods involve the use of weighted sampling and weighted selection of outlier values for low selectivity queries, or queries having group by.

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 in one of many known ways 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.

Abstract: An index and materialized view selection wizard produces a fast and reasonable recommendation of indexes and materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes both indexes and materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.

Abstract: An index and materialized view selection wizard produces a fast and reasonable recommendation for a configuration of indexes, materialized views, and indexes on materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes indexes, materialized views and indexes on materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.

Abstract: An index and materialized view selection wizard produces a fast and reasonable recommendation for a configuration of indexes, materialized views, and indexes on materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes indexes, materialized views and indexes on materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.

Abstract: An index tuning wizard produces a fast and reasonable recommendation identifying database indexes to use given a specified workload. A query optimizer is used to determine the expected usefulness of potential indexes for the specified workload by taking cost of queries in the workload into account. A cost based pruning of indexes is then performed to provide an intermediate set of proposed indexes. Indexes having most benefit based on storage constraints are then selected. The optimizer is then used again, and further pruning is done on a benefits basis. An index is not recommended unless it has a significant impact on the workload.

Abstract: A system and a method is disclosed for analyzing, benchmarking and tuning an object oriented database (OODB) and applications with a trace based technique. Code associated with the application is augmented with a plurality of instructions that define the production of an application execution trace and a database layout trace during execution of the application with a database. A precompiler or manual or automatic techniques are used to augment source code associated with the application. Alternatively, an instrumentor can be employed to augment object code associated with the application. A simulator employs the database layout trace and the application execution trace to simulate the execution of the application with an OODB selected by the user. The simulator may be ported to different OODBs or platforms. The simulator produces results associated with the simulation for input to a data analyzer.