Distributed Database Management Systems: Lecture 33

Distributed Database Management Systems: Lecture 33. The main topics covered in this chapter include: data localization for hybrid fragmentation; query optimization; HyF contains both types of fragmentations; QO refers to producing a query execution plan (QEP) that represents execution strategy;...

Distributed Database Management Systems

Lecture 33

In the previous lecture

• Final phase of QD

• Data Localization: for HF,

VF and DF.

In today’s Lecture

• Data Localization for

Hybrid Fragmentation

• Query Optimization.

Reduction for HyF

• HyF contains both types of

Fragmentations

• EMP1= eNo ≤ E4 ( eNo, eName (EMP))

• EMP2= eNo > E4 ( eNo, eName (EMP))

• EMP3= eNo, title (EMP).

• Select eName from EMP

Summary of what we

have done so far

• Data Localization: applies

global query to fragments;

increases optimization

level-• So, next is the cost-based

• Components of Optimizer

• Search Space: set of eq

alternative exec plans

• Cost Model: predicts cost

of a execution plan

• Search Strategy:

produces best plan

Search Space

• Search space consists of

eq Query Trees

produced using Tr Rules

• Optimizer concentrates

on join trees, since join

cost is the most effective

• Example:

• Select eName, resp

From EMP, ASG, PROJ where EMP.eNo = ASG eNo and ASG.pNo =

PROJ.pNo

• Alternatives with N

relations are O(N!)

based on properties of relations

• So, restrictions are

applied

1- Heuristics

- Selection and

projection on base relations

- Avoid Cartesian

product

2- Shape of Tree

- Linear Tree: At least one

node for each operand is

a base relation

- Bushy tree: May have

operators with interm

tables only; allows

parallel execution

Search Strategy

• Most popular is Dynamic

Programming

• That starts with base

relations and keeps on

adding relations calculating cost

• DP is almost exhaustive

so produces best plan

• Too expensive with more

Cost Model

• Cost of operators, statistics

of base data to predict size

of intermediate tables

• Cost considered as Total

Time and Response Time.

• Total time = CPU time +

I/O time + tr time

• In WAN, major cost is tr

time

• Initially ratios were 20:1

for tr and I/O, for LAN it

is 1:1.6

• Response time = CPU

time + I/O time + tr

time

• Difference.?

• TCPU = time for a CPU inst

• TI/O = a disk I/O

• TMSG = fixed time for

initiating and recv a msg

• TTR = transmit a data unit from one site to another

• TT = 2TMSG + TTR*(x+y)

• RT = max{TMSG + TTR*X,

TMSG + TTR*Y}

Site 1 Site 2

Site 3

X units

Y units

Database Statistics

• Major factor is interm tabs

• If the interm results are to

• For each relation R[A1, A2, …, A n]

fragmented as R1, …, R r

1.length of each attribute: length(A i)

2 the number of distinct values for

each attribute in each fragment:

card( Ai (R j))

3 maximum and minimum values in

the domain of each attribute:

min(A i ), max(A i).

4.The cardinalities of each

domain: card(dom[A i])

and the cardinalities of

each fragment: card(R j)

5.Join selectivity factor for

some of the relations

SF J (R,S) = card(R ⋈

card(S))-Cardinalities of Intermediate Results

• SFS(A < value) = max(A) – value

/(max(A) – min(A))

• SFS(p(Ai) ^ p(Aj)) = SFS(p(Ai)) *

(SFSp(Aj))

• SFS(p(Ai) v p(Aj)) = SFS(p(Ai)) +

SFS(p(Aj))–(SFS(p(Ai))* SFS(p(Ai)))

Cardinality of Projection

• Hard to determine precisely

• Two cases when it is trivial

1- When a single attribute A,

card( A(R)) = card (A)

2- When PK is included

card( A(R)) = card (R)

• Semi Join:

SFSJ(R ⋉AS)= card( A(S))/ card(dom[A])

card(R ⋉AS) = SFSJ(S.A) *

card(R).

• Union: Hard to estimate

• Limits possible which are

card(R) + card(S) and

max{card (R) + card (S))

• Difference: Like Union,

card (R) for (R-S), and 0

Centralized Query

Optimization