Considers a version of the database physical design problem in which the input is a sequence of queries and updates. The goal is to recommend a target physical design for each query or update in the sequence, taking into account both the effect of the physical design on the cost of executing the query or update and the cost of changing the physical design.

Assumes that the optimizer requests indexes that it thinks might be useful for a particular query. These requested indexes form the initial configuration, which is then modified to meet a space constraint.

Uses random queries that are generated from templates. Templates constrain generated queries to ensure that they are reasonable and that they can benefit from indexing.

General approach is to generate candidate MVs and indexes based on the workload, and then filter to meet a space constraint. Multiquery optimization is used when generating candidate MVs.

Automatic hash-based relation partitioning in shared nothing systems.

This is a one-page description of a SIGMOD demo.

Hardcopy on file. This is the journal version of [chna00]. A variety of heuristic techniques for choosing minimal sets of heuristics in such a way that the quality of plans produced by the optimizer is not reduced.

The journal version of this paper is [chna01].

Adaptive declustering in shared-nothing systems using a two-level, tree-structured index.

General recommendation method is to add virtual indexes to the schema, optimize the query, and check whether any virtual statistics are used in the optimal plan. Statistics for virtual indexes are inferred from existing column statistics. To recommend indexes for a workload, recommend for each query in the workload in sequence and then greedily select a subset of the recommended indexes.

How to implement hypothetic database configurations, so that workload costs can be estimated under those configurations. Configuration includes hypothetic indexes and statistics that allow the optimizer to decide whether such an index should be used. Proposes that sampling be used to collect the statistics. Allows specification of scale factors so configurations with larger/smaller databases can be simulated. Presents an analysis interface that supports workload analysis and configuration analysis for current and hypothetical configurations.

Assumes that an upper bound is given on the number of indexes. Workload is specified as a set of SQL DML statements, including insert, delete and update. Search space includes both single and multi-attribute indexes. Index configurations are evaluated by the DBMS optimizer, and several techniques are used to reduce the number of configurations for which optimizer evaluation is required. To generate a set of candidate indexes, this method determines an optimal index configuration independently for each query in the workload. The initial candidate set is then taken as the union of the indexes in the single-query optimal configurations. A hybrid exhaustive/greedy approach is used to control search. To find a k-index configuration, first find the optimal m-index configuration (m <= k) using exhaustive search, then add k-m indexes greedily. Multi-column indexes are handled by first finding an good configuration with single-column indexes, then generating and adding a set of candidate two-column indexes, and then rerunning the optimizer on the new candidate set. This is repeated to handle indexes with more than two columns.

Special Issue on Data Placement for Parallelism