Michael W. Watzke

Toward a progress indicator for database queries

Many modern software systems provide progress indicators for long-running tasks. These progress indicators make systems more user-friendly by helping the user quickly estimate how much of the task has been completed and when the task will finish. However, none of the existing commercial RDBMSs provides a non-trival progress indicator for long-running queries. In this paper, we consider the problem of supporting such progress indicators. After discussing the goals and challenges inherent in this problem, we present a set of techniques sufficient for implementing a simple yet useful progress indicator for a large subset of RDBMS queries. We report an initial implementation of these techniques in PostgreSQL.

Increasing the accuracy and coverage of SQL progress indicators

Recently, progress indicators have been proposed for long-running SQL queries in RDBMSs. Although the proposed techniques work well for a subset of SQL queries, they are preliminary in the sense that (1) they cannot provide non-trivial estimates for some SQL queries, and (2) the provided estimates can be rather imprecise in certain cases. In this paper, we consider the problem of supporting non-trivial progress indicators for a wider class of SQL queries with more precise estimates. We present a set of techniques in achieving this goal. We report an initial implementation of these techniques in PostgreSQL.

A comparison of three methods for join view maintenance in parallel RDBMS

In a typical data warehouse, materialized views are used to speed up query execution. Upon updates to the base relations in the warehouse, these materialized views must also be maintained. The need to maintain these materialized views can have a negative impact on performance that is exacerbated in parallel RDBMSs, since simple single-node updates to base relations can give rise to expensive all-node operations for materialized view maintenance. We present a comparison of three materialized join view maintenance methods in a parallel RDBMS, which we refer to as the naive, auxiliary relation, and global index methods. The last two methods improve performance at the cost of using more space. The results of this study show that the method of choice depends on the environment, in particular, the update activity on base relations and the amount of available storage space.

Transaction reordering

Traditional workload management methods mainly focus on the current system status while information about the interaction between queued and running transactions is largely ignored. This paper proposes using transaction reordering, a workload management method that considers both the current system status and information about the interaction between queued and running transactions, to improve the transaction throughput in an RDBMS. Our main idea is to reorder the transaction sequence submitted to the RDBMS to minimize resource contention and to maximize resource sharing. The advantages of the transaction reordering method are demonstrated through experiments with three commercial RDBMSs.