Managing large Data with SAS SPD Server

This paper provides the concepts behind demonstrations of how you can enhance query performance when you use the SAS SPD Server to manage large data tables. This paper does not cover the main concepts for Dynamic Clusters or Parallel Join. See white papers TW9593 and TW9594 for thorough discussions of the Dynamic Cluster and Parallel Join features. This paper focuses on managing data within standard SAS Scalable Performance Data Server (SPD Server) tables, and therefore Dynamic Cluster tables, to enhance data querying. There are three common types of queries for which data within a table can be optimized: ordered processing, subsetting, and a hybrid of ordered processing and subsetting. This paper presents two different ways of managing data at load time for a table that will enhance the performance of queries. One way focuses on ordered processing and one way focuses on subsetting of rows. The third hybrid situation that is a combination of both will be discussed. INTRODUCTION The SAS Scalable Performance Data Server (SPD Server) is designed to meet the storage and performance needs for processing large amounts of SAS data. As the size of data increases, the demands for processing that data quickly increase, and the storage architecture must fit the business needs. SPD Server is used at hundreds of sites worldwide and hosts some of the largest known SAS data warehouses. SAS SPD Server provides high-performance SAS data storage to customers in banking, credit-card, insurance, telecommunications, healthcare, pharmaceutical, transportation, and brokerage industries; and to government agencies.