parallel databases pres

8/8/2019 Parallel Databases Pres

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04/25/2005 Yan Huang - CSCI5330 DatabaseImplementation Parallel Database

Parallel Databases


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Introduction

A variety of hardware architectures allow multiple

computers to share and access to data. A parallel

database can allow access to a single database by

users on multiple machines, with increased

performance.


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Purpose

Databases are growing increasingly large

Large volumes of transaction data are collected and stored for

later analysis.

Multimedia objects like images are increasingly stored indatabases


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Large-scale parallel database

Large-scale parallel database systems increasingly

used for:

storing large volumes of data

providing high throughput for transaction processing


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Queries

Queries are expressed in high level language (SQL)

makes parallelization easier.

Different queries can be run in parallel with each other.Concurrency control takes care of conflicts.

Thus, databases naturally provide themselves to

parallelism.


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Parallelism in Databases

Data can be partitioned across multiple disks for

parallel I/O.

Individual relational operations

(e.g., sort, join, aggregation)

can be executed in parallel

database.


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Parallel Database Architectures


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Shared memory

Shared disk

Shared nothing

Hierarchical


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Shared memory -- processors share a common

memory (Extremely efficient communication between

processors).

Shared disk -- processors share a common disk

(Shared-disk systems can scale to a somewhat larger

number of processors, but communication between

processors is slower.)


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Shared nothing -- Each processor has exclusive

access to its main memory.

Hierarchical -- hybrid of the above architectures(Combines characteristics of shared-memory, shared-

disk, and shared-nothing architectures.)


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Parallel Level

A coarse-grain parallel machine

consists of a small number of

powerful processors


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Massively Parallel

A massively parallel orfine grain parallel machineutilizes thousands of smaller processors.


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04/25/2005 Yan Huang - CSCI5330 Database

Implementation Parallel Database

Parallel System Performance Measure

Speedup: = small system elapsed time

large system elapsed time

Scaleup: = small system small problem elapsed time

big system big problem elapsed time


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Database Performance Measures

throughput --- the number of tasks that can becompleted in a given time interval.

response time --- the amount of time it takes to

complete a single task from the time it is submitted.


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Factors Limiting Speedup and Scaleup

Speedup and scaleup are often sublinear due to:

Startup costs: Cost of starting up multiple processes

may dominate computation time, if the degree of

parallelism is high.

Interference: Processes accessing shared resources(e.g.,system bus, disks, or locks) compete with each

other

Skew: Overall execution time determined by slowest of

parallely executing tasks.


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Parallel Database Issues

Data Partitioning

Parallel Query Processing


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I/O Parallelism

Horizontal partitioning tuples of a relation are dividedamong many disks such that each tuple resides on one

disk.

Partitioning techniques (number of disks = n):

Round-robin Hash partitioning

Range partitioning


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Typical Database Query Types

Sequential scan

Point query

Range query


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Comparison of Partitioning Techniques

Round

Robin

Hashing Range

Sequential

Scan

Best/good

parallelism

Good Good

Point Query Difficult Good for hash key Good for range

vector

Range Query Difficult Difficult Good for range

vector

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