Dina Bitton

Enterprise information integration: successes, challenges and controversies

The goal of EII systems is to provide uniform access to multiple data sources without having to first load them into a data warehouse. Since the late 1990s, several EII products have appeared in the marketplace and significant experience has been accumulated from fielding such systems. This collection of articles, by individuals who were involved in this industry in various ways, describes some of these experiences and points to the challenges ahead.

Performance of complex queries in Main Memory Database Systems

Memory residence can buy both functionality and performance for a database management system. In this paper, we present a description and a benchmark of an experimental implementation of a Main Memory Database System (MMDBS) that was designed to support complex interactive queries. We describe and evaluate the main memory database structures and query processing algorithms implemented in this prototype. Our measurements and analysis, focused on aggregates and joins, include both memory requirements and response time, since there is a clear trade-off between space and time in the design of a MMDBS. In contrast to conventional Disk-based Database Systems (DDBSs), we found that an MMDBS can efficiently execute complex relational queries. We identify strategies that exploit memory residence effectively. We also identified a number of performance problems related to query optimization in main memory and memory management for MMDBSs.

A general framework for computing block accesses

A physical database system design should take account of skewed block access distributions, nonuniformly distributed attribute domains, and dependent attributes. In this paper we derive general formulas for the number of blocks accessed under these assumptions by considering a class of related occupancy problems. We then proceed to develop robust and accurate approximations for these formulas. We investigate three clases of approximation methods, respectively based on generating functions, Taylor series expansions, and majorization. These approximations are as simple to use and far more accurate than the cost estimate formulas generated by making independence and uniformity assumptions.

Panel: The effect of large main memory on database systems

The avallablhty of mexpenslve, large mam memories coupled with the demand for faster response time are brmgmg a new perspective to database technology Designers of database systems are reconsldermg the assumption that databases must reside on disk during transactlon processmg Subatantlal performance gams can be achieved by makmg a large portlon of, or the entlre database, reside m mam memory One approach 1s to use very large buffers to Improve conventional disk access methods A more radical approach 1s to view the database as part of the user’s address space m mam memory, rather than as a collection of mass-storage files This leads to the advent of Main Memory Database Systems (MMDBS’s), which exploit memory residency of the database m their schemes for physical data organlzatlon, query optnnlzatlon, concurrency control and crash recovery

Elastic Pipelining in an In-Memory Database Cluster

An in-memory database cluster consists of multiple interconnected nodes with a large capacity of RAM and modern multi-core CPUs. As a conventional query processing strategy, pipelining remains a promising solution for in-memory parallel database systems, as it avoids expensive intermediate result materialization and parallelizes the data processing among nodes. However, to fully unleash the power of pipelining in a cluster with multi-core nodes, it is crucial for the query optimizer to generate good query plans with appropriate intra-node parallelism, in order to maximize CPU and network bandwidth utilization. A suboptimal plan, on the contrary, causes load imbalance in the pipelines and consequently degrades the query performance. Parallelism assignment optimization at compile time is nearly impossible, as the workload in each node is affected by numerous factors and is highly dynamic during query evaluation. To tackle this problem, we propose elastic pipelining, which makes it possible to optimize intra-node parallelism assignments in the pipelines based on the actual workload at runtime. It is achieved with the adoption of new elastic iterator model and a fully optimized dynamic scheduler. The elastic iterator model generally upgrades traditional iterator model with new dynamic multi-core execution adjustment capability. And the dynamic scheduler efficiently provisions CPU cores to query execution segments in the pipelines based on the light-weight measurements on the operators. Extensive experiments on real and synthetic (TPC-H) data show that our proposal achieves almost full CPU utilization on typical decision-making analytical queries, outperforming state-of-the-art open-source systems by a huge margin.

AS/sup 3/AP-a comparative relational database benchmark

The authors describe and motivate the design of a scalable and portable benchmark for database systems, the AS/sup 3/AP benchmark (Ansi SQL Standard Scalable and Portable). The benchmark is designed to provide meaningful measures of database processing power, to be portable between different architectures, and to be scalable to facilitate comparisons between systems with different capabilities. The authors introduce a performance metric, namely, the equivalent database ratio, to be used in comparing systems.<<ETX>>