The Gamma database machine project

Abstract

This paper describes the design of the Gamma database machine and the techniques employed in its implementation. Gamma is a relational database machine currently operating on an Intel iPSC/2 hypercube with 32 processors and 32 disk drives. Gamma employs three key technical ideas which enable the architecture to be scaled to 100s of processors. First, all relations are horizontally partitioned across multiple disk drives enabling relations to be scanned in parallel. Second, novel parallel algorithms based on hashing are used to implement the complex relational operators such as join and aggregate functions. Third, dataflow scheduling techniques are used to coordinate multioperator queries. By using these techniques it is possible to control the execution of very complex queries with minimal coordination -a necessity for configurations involving a very large number of processors.In addition to describing the design of the Gamma software, a thorough performance evaluation of the iPSC/2 hypercube version of Gamma is also presented. In addition to measuring the effect of relation size and indices on the response time for selection, join, aggregation, and update queries, we also analyze the performance of Gamma relative to the number of processors employed when the sizes of the input relations are kept constant (speedup) and when the sizes of the input relations are increased proportionally to the number of processors (scaleup). The speedup results obtained for both selection and join queries are linear; thus, doubling the number of processors halves the response time for a query. The scaleup results obtained are also quite encouraging. They reveal that a nearly constant response time can be maintained for both selection and join queries as the workload is increased by adding a proportional number of processors and disks.

Parallel algorithms for computing temporal aggregates

“…Another algorithm, Project by list, exploits the ability of the parallel system architecture to broadcast tuples to multiple processors. The Gamma database machine project [4] implemented similar scalar aggregates and aggregate functions on a shared-nothing architecture. More recently, parallel algorithms for handling temporal aggregates were presented [11], but for a shared-memory architecture.…”

Adding a Performance-Oriented Perspective to Data Warehouse Design

“…However, the answers obtained from a reduced data set are only approximate and in most cases the error is large [11], which greatly limits the applicability of data reduction in the data warehouse context. Many parallel databases systems have appeared both as research prototypes [12], [13], [14], [15] and as commercial products such as NonStop SQL fromTandem [16] or Oracle. However, even these "brute force" approaches have several difficulties when used in data warehouses such as the well-known problems of finding effective solutions for parallel data placement and parallel joins.…”

Adaptive parallel query execution in DBS3

“…This approach is very popular in research prototypes, e.g. Bubba [Boral90], Gamma [Dewitt90] and Volcano [Graefe94], and commercial products, e.g. DB2, Informix, Tandem and Teradata.…”