Traditional workload management methods mainly focus on the current system status while information about the interaction between queued and running transactions is largely ignored. An exception to this is the transaction reordering method, which reorders the transaction sequence submitted to the RDBMS and improves the transaction throughput by considering both the current system status and information about the interaction between queued and running transactions. The existing transaction reordering method only considers the reordering opportunities provided by analyzing the lock conflict information among multiple transactions. This significantly limits the applicability of the transaction reordering method. In this paper, we extend the existing transaction reordering method into a general transaction reordering framework that can incorporate various factors as the reordering criteria. We show that by analyzing the resource utilization information of transactions, the transaction reordering method can also improve the system throughput by increasing the resource sharing opportunities among multiple transactions. We provide a concrete example on synchronized scans and demonstrate the advantages of our method through experiments with a commercial parallel RDBMS.

Traditional workload management methods mainly focus on the current system status while information about the interaction between queued and running transactions is largely ignored. An exception to this is the transaction reordering method, which reorders the transaction sequence submitted to the RDBMS and improves the transaction throughput by considering both the current system status and information about the interaction between queued and running transactions. The existing transaction reordering method only considers the reordering opportunities provided by analyzing the lock conflict information among multiple transactions. This significantly limits the applicability of the transaction reordering method. In this paper, we extend the existing transaction reordering method into a general transaction reordering framework that can incorporate various factors as the reordering criteria. We show that by analyzing the resource utilization information of transactions, the transaction reordering method can also improve the system throughput by increasing the resource sharing opportunities among multiple transactions. We provide a concrete example on synchronized scans and demonstrate the advantages of our method through experiments with a commercial parallel RDBMS.

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.

The maintenance of materialized aggregate join views is a well-studied problem. However, to date the published literature has largely ignored the issue of concurrency control. Clearly, immediate materialized view maintenance with transactional consistency, if enforced by generic concurrency control mechanisms, can result in low levels of concurrency and high rates of deadlock. While this problem is superficially amenable to well-known techniques, such as fine-granularity locking and special lock modes for updates that are associative and commutative, we show that these previous high concurrency locking techniques do not fully solve the problem, but a combination of a "value-based" latch pool and these previous high concurrency locking techniques can solve the problem.

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-trivial 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. 1.

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.

Interest in incremental and adaptive query processing has led to the investigation of equijoin evaluation algorithms that are non-blocking. This investigation has yielded a number of algorithms, including the symmetric hash join, the XJoin, the Ripple Join, and their variants. However, to our knowledge no one has proposed a nonblocking spatial join algorithm. In this paper, we propose a parallel non-blocking spatial join algorithm that uses duplicate avoidance rather than duplicate elimination. Results from a prototype implementation in a commercial parallel object-relational DBMS show that it generates answer tuples steadily even in the presence of memory overflow, and that its rate of producing answer tuples scales with the number of processors. Also, when allowed to run to completion, its performance is comparable with the state-of-the-art blocking parallel spatial join algorithm.

Recently, Haas and Hellerstein proposed the hash ripple join algorithm in the context of online aggregation. Although the algorithm rapidly gives a good estimate for many join-aggregate problem instances, the convergence can be slow if the number of tuples that satisfy the join predicate is small or if there are many groups in the output. Furthermore, if memory overflows (for example, because the user allows the algorithm to run to completion for an exact answer), the algorithm degenerates to block ripple join and performance suffers. In this paper, we build on the work of Haas and Hellerstein and propose a new algorithm that (a) combines parallelism with sampling to speed convergence, and (b) maintains good performance in the presence of memory overflow. Results from a prototype implementation in a parallel DBMS show that its rate of convergence scales with the number of processors, and that when allowed to run to completion, even in the presence of memory overflow, it is competitive with the traditional parallel hybrid hash join algorithm.

This paper presents a number of new techniques for parallelizing geo-spatial database systems and discusses their implementation in the Paradise object-relational database system. The effectiveness of these techniques is demonstrated using a variety of complex geo-spatial queries over a 120 GB global geo-spatial data set.