Distributed Database Management Systems: Lecture 30

Distributed Database Management Systems: Lecture 30. The main topics covered in this chapter include: basic concepts of query optimization; QP in centralized and distributed DBs; query processor transforms complex queries into concise and simple ones;...

Distributed Database Management Systems

Lecture 30

In the previous lecture

based CC

• Query processing is

critical performance issue

problem specially in DDBS environment

• Main function of QP is to

transform an SQL query into equivalent relational algebra one (low level

language)

• Transformation must

achieve correctness and efficiency

• Considering the tables

• EMP(eNo, eName, title)

• ASG(eNo, pNo, resp, dur)

• PROJ(pNo, pName,

budget, loc)

• Query: Get the names of

employees who are

managing a project

• SELECT eName

FROM EMP, ASG

WHERE EMP.eNo = ASG.eNo

AND resp = ‘Manager’

eName ( resp=‘Manager’ ^ EMP.eNo =

ASG.eNo) (EMPxASG)

eName (EMP ⋈ ( resp=‘Manager’

(ASG)))

• Obviously second one needs

less computing resources

since avoids Cartesian product

• Same query in DDBS

• Suppose EMP and ASG

are HF as

• EMP1 = eNo ≤ ‘E3’ (EMP)

• EMP2 = eNo > ‘E3’ (EMP)

• ASG1 = eNo ≤ ‘E3’ (ASG)

• ASG2 = eNo > ‘E3’ (ASG)

• Further suppose these

fragments are stored

at site 1, 2, 3 and 4

and result at site 5

ASC1’= resp = ‘Manager(ASG1)

EMP1’=EMP1 ⋈ (ASG1’)

Site 1

Site 3

ASC2’= resp = ‘Manager(ASG2)

EMP2’=EMP2 ⋈ (ASG2’)

result = (EMP1 U EMP2) ⋈ eNo

resp = ‘Manager’ (ASG1 U ASG2)

Site 1 Site 2 Site 3 Site 4

ASG1 ASG2 EMP1 EMP2

Lets Assume

• size(EMP)

• size(ASG)

400 1000

• tuple access cost

• tuple transfer cost

1 unit

10 units

• There are 20 Managers

• Data distributed evenly at all

sites

• Communication Cost will

dominate in WAN

• Not that dominant in

LANs, so total cost

should be considered in LANs

• QO can also maximize

throughput

Operators’ Complexity

• Select, Project (without

duplicate elimination) O(n)

• Project (with duplicate

elimination), Group O(nlogn)

• Join, Semi-Join,

Division, Set Operators O(nlog n)

• Cartesian Product O(n2 )

Characterization of Query Processors

• Types of Optimization

–Exhaustive search for the

cost of each strategy to find the most optimal one

–May be very costly in case of

multiple options and more

fragments

–Heuristics

• Optimization Timing

• Size of intermediate tables not

known always

• Cost justified with repeated

execution

• Intermediate tables’ size known

• Re-optimzation may be required

• Statistics

–Relation/Fragment:

Cardinality, size of a tuple,

fraction of tuples participating

in a join with another relation

–Attribute: cardinality of

domain, actual number of

distinct values

• Decision Sites

–Centralized: simple, need

knowledge about the entire

distributed database

–Distributed: cooperation among

sites to determine the schedule, need only local information

–Hybrid: one site determines the

global schedule, each site

optimizes the local subqueries

• Other factors like:

SQL Query on Distributed Relations

QUERY DECOMPOSITION GLOBALSCHEMAAlgebraic Query on Distributed

Relations

DATA LOCALIZATION FRAGMENTSCHEMAFragment Query

GLOBAL OPTIMIZATION STAT OFFRAGMENTSOptimized Fragment Query with

Communication Operations LOCAL

OPTIMIZATION SCHEMALOCAL

Optimized Local Query