The like regular expression predicate has been part of the SQL standard since at least 1989. However, despite its popularity and wide usage, database vendors provide only limited indexing support for regular expression queries which almost always require a full table scan. In this paper we propose a rigorous and robust approach for providing indexing support for regular expression queries. Our approach consists of formulating the indexing problem as a combinatorial optimization problem. We begin with a database, abstracted as a collection of strings. From this data set we generate a query workload. The input to the optimization problem is the database and the workload. The output is a set of multigrams (substrings) which can be used as keys to records which satisfy the query workload. The multigrams can then be integrated with the data structure (like B+ trees) to provide indexing support for the queries. We provide a deterministic and a randomized approximation algorithm (with provable guarantees) to solve the optimization problem. Extensive experiments on synthetic data sets demonstrate that our approach is accurate and efficient. We also present a case study on PROSITE patterns - which are complex regular expression signatures for classes of proteins. Again, we are able to demonstrate the utility of our indexing approach in terms of accuracy and efficiency. Thus, perhaps for the first time, there is a robust and practical indexing mechanism for an important class of database queries.
Hore formalizes the multigram selection problem as an instance of the Budgeted Maximum Coverage (BMC) @cite_5 problem. Specifically the cover set forms the Budgeted' part while the utility of the index is captured in the Coverage' component. The main principle of the BEST algorithm is to select multigrams which increase the index hit rate so more queries can utilize the algorithm. However, a major weakness of the BEST algorithm is that it neither scales to large datasets nor large query workloads because it uses a cartesian product of query workload and database as the search space.
@cite_5
2080379754
In this paper we describe novel techniques that make it possible to build an industrial-strength tool for automating the choice of indexes in the physical design of a SQL database. The tool takes as input a workload of SQL queries, and suggests a set of suitable indexes. We ensure that the indexes chosen are effective in reducing the cost of the workload by keeping the index selection tool and the query optimizer "in step". The number of index sets that must be evaluated to find the optimal configuration is very large. We reduce the complexity of this problem using three techniques. First, we remove a large number of spurious indexes from consideration by taking into account both query syntax and cost information. Second, we introduce optimizations that make it possible to cheaply evaluate the goodness of an index set. Third, we describe an iterative approach to handle the complexity arising from multicolumn indexes. The tool has been implemented on Microsoft SQL Server 7.0. We performed extensive experiments over a range of workloads, including TPC-D. The results indicate that the tool is efficient and its choices are close to optimal.