Rapid Development of Data Generators Using Meta Generators in PDGF

ABSTRACT Generating data sets for the performance testing of database systems on a particular hardware configuration and application domain is a very time consuming and tedious process. It is time consuming, because of the large amount of data that needs to be generated and tedious, because new data generators might need to be developed or existing once adjusted. The difficulty in generating this data is amplified by constant advances in hardware and software that allow the testing of ever larger and more complicated systems. In this paper, we present an approach for rapidly developing customized data generators. Our approach, which is based on the Parallel Data Generator Framework (PDGF), deploys a new concept of so called meta generators. Meta generators extend the concept of column-based generators in PDGF. Deploying meta generators in PDGF significantly reduces the development effort of customized data generators, it facilitates their debugging and eases their maintenance

Large scale data warehouses on grid: Oracle database 10g and HP ProLiant systems

Grid computing has the potential of drastically changing enterprise computing as we know it today. The main concept of Grid computing is to see computing as a utility. It should not matter where data resides, or what computer processes a task. This concept has been applied successfully to academic research. It also has many advantages for commercial data warehouse applications such as virtualization, flexible provisioning, reduced cost due to commodity hardware, high availability and high scale-out. In this paper we show how a large-scale, high performing and scalable Grid based data warehouse can be implemented using commodity hardware (industry standard x86based), Oracle Database 10G and Linux operating system. We further demonstrate this architecture in a recently published TPC-H benchmark. 1

Of Snowstorms and Bushy Trees

Many workloads for analytical processing in commercial RDBMSs are dominated by snowstorm queries, which are characterized by references to multiple large fact tables and their associated smaller dimension tables. This paper describes a technique for bushy join tree optimization for snowstorm queries in Oracle database system. This technique generates bushy join trees containing subtrees that produce substantially reduced sets of rows and, therefore, their joins with other subtrees are generally much more efficient than joins in the left-deep trees. The generation of bushy join trees within an existing commercial physical optimizer requires extensive changes to the optimizer. Further, the optimizer will have to consider a large join permutation search space to generate efficient bushy join trees. The novelty of the approach is that bushy join trees can be generated outside the physical optimizer using logical query transformation that explores a considerably pruned search space. The paper describes an algorithm for generating optimal bushy join trees for snowstorm queries using an existing query transformation framework. It also presents performance results for this optimization, which show significant execution time improvements. 1

Keeping the TPC Relevant!

The Transaction Processing Performance Council (TPC) is a nonprofit organization founded in 1988 to define transaction processing and database benchmarks. Since then, the TPC has played a crucial role in providing the industry with relevant standards for total system performance, price-performance, and energy-efficiency comparisons. TPC benchmarks are widely used by database researchers and academia. Historically known for database-centric standards, the TPC has developed a benchmark for virtualization and is currently developing a multisource data integration benchmark. The technology landscape is changing at a rapid pace, challenging industry experts and researchers to develop innovative techniques for evaluating, measuring, and characterizing the performance o