Performance analysis of a relational data base management system

Hawthorn, Paula

doi:10.1145/582095.582097

Cited by 24 publications

(14 citation statements)

References 14 publications

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“…Suppose queries types can be partitioned into overhead-intensite and data.intensite, under the condition that queries are overhead-intensive, the database will not be costeffective [Ha79]. It is further shown that data-intensive queries can be performed efficiently on database machines if the function performed on the data is a function the database machine provides [Ha82].…”

Section: Discussionmentioning

confidence: 99%

Design Methodology for Back-End Database Machines in Distributed Environments.

Ramamoorthy¹

1984

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Section: Discussionmentioning

confidence: 99%

Design Methodology for Back-End Database Machines in Distributed Environments.

Ramamoorthy¹

1984

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“…Specializing the operating system functions leads to performance gains [HAWT79] , but even better performance is possible with the use of especially designed, custom-built In the IDM the data manipulation processor is called a "database accelerator" [BRIT80] ; in RAP it is a "cell processer" [SAD078] ; in DBC it is a "mass memory processor"…”

Section: Design Differences Of Database Machinesmentioning

confidence: 99%

“…A measured query in the INGRES data management system [HAWT79] that corresponds to queries in the business application performed data manipulation tasks 8.1% of the time.…”

Section: Business Applicationsmentioning

confidence: 99%

The effect of target applications on the design of database machines

Hawthorn¹

1981

Proceedings of the 1981 ACM SIGMOD International Conference on Management of Data - SIGMOD '81

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LBL-12412Specialized, single function processors can be built to be faster and cheaper than general purpose processors. Most database machines use such special purpose processors to manipulate data, with a general purpose managing processor to control the special purpose processors and perform utility functions.In this paper, the organization and use of these data manipulation processors is explored. Database machines are classified into single data manipulation processor systems, multiple disk-associated data manipulation processor systems, and multiple cache-associated processor systems.Examples of actual database machines are given for each category.Application types are classified into business, bibliographic search, and statistical analysis systems. A metric is developed to compare the performance of the categories of database machines with respect to the application types. The metric is the effective instruction rate, which is cornparable to the instruction rate (millions of instructions per second) for conventional computers.It is shown that the effective instruction rate is highly ~ensitive to the proportion of work performed in the database machine's data manipulation processors.Therefore, determining the work that must be performed in the machine's managing processor is found to be important to determining the performance of the machine.Database machine performance for each category of database machines is compared for each type of application. It is shown that single processor systems are best for business applications; that disk-based multiple processor systems are best for bibliographic search applications; and that hybrid systems are best for statistical analysis applications. Therefore, the design of the organization of data manipulation processors is shown to be application-dependent.

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“…In addition, within both short transactions and long queries some of the pages are rereferenced, and are called locality sets (Chou and Dewitt, 1985). Viewed as a whole, the combination of transaction and query accesses generates all possib]Le access patterns (RodriguezRosell, 1976;Smith, 1978;Hawthorn and Stonebraker, 1979;Effelsberg and Loomis, 1984;Chou and Dewitt, 1985;Verkamo, 1985;Sacco and Schkolnick, 1986;Kearns and Defazio, 1989). Traditional buffer management policies (e.g., strict LRU policy that does not exploit the knowledge of access components such as sequential vs random accesses) may not provide good buffer hit probability (Smith, 1978;Effelsberg and Haerder, 1984;Teng and Gumaer, 1984;Chou and Dewitt, 1985;Sacco and Schkolnick, 1986;IBM, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, our characterization method first distinguishes three types of access patterns from a trace: (1) locality within a transaction, (2) random accesses by transactions, and (3) sequential accesses by long queries. The presence of these types of access behavior has been shown (Rodriguez-Rosell, 1976;Hawthorn and Stonebraker, 1979;Effelsberg and Loomis, 1984;Verkamo, 1985;Kearns and Defazio, 1989). Each access component can be accounted for separately, both in terms of buffer management as well as buffer hit prediction.…”

Section: Introductionmentioning

confidence: 99%

Characterization of database access pattern for analytic prediction of buffer hit probability

Dan

Chung

1995

VLDB Journal

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Abstract. The analytic prediction of buffer hit probability, based on the characterization of database accesses from real reference traces, is extremely useful for workload management and system capacity planning. The knowledge can be helpful for proper allocation of buffer space to various database relations, as well as for the management of buffer space for a mixed transaction and query environment. Access characterization can also be used to predict the buffer invalidation effect in a multi-node environment which, in turn, can influence transaction routing strategies. However, it is a challenge to characterize the database access pattern of a real workload reference trace in a simple manner that can easily be used to compute buffer hit probability. In this article, we use a characterization method that distinguishes three types of access patterns from a trace: (1) locality within a transaction, (2) random accesses by transactions, and (3) sequential accesses by long queries. We then propose a concise way to characterize the access skew across randomly accessed pages by logically grouping the large number of data pages into a small number of partitions such that the frequency of accessing each page within a partition can be treated as equal. Based on this approach, we present a recursive binary partitioning algorithm that can infer the access skew characterization from the buffer hit probabilities for a subset of the buffer sizes. We validate the buffer hit predictions for single and multiple node systems using production database traces. We further show that the proposed approach can predict the buffer hit probability of a composite workload from those of its component files.

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Performance analysis of a relational data base management system

Cited by 24 publications

References 14 publications

Design Methodology for Back-End Database Machines in Distributed Environments.

Design Methodology for Back-End Database Machines in Distributed Environments.

The effect of target applications on the design of database machines

Characterization of database access pattern for analytic prediction of buffer hit probability

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