07.Overview of Query Processing

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Distributed Database SystemsAutumn, 2008

Chapter 7

Overview of Query

Processing

1Distributed Database Systems


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SQL: Non-Procedural Language of RDB

Tuple calculus

{ t|F(t) } where:

t: tuple variable

F(t) : well formed formula

Example

Get the No. and name of all managers


""|, MANAGERTITLEtEMPtENAMEENOt


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Domain calculus

where:

xi : domain variables

: well formed formula

Example

{x,y |E(x,y, "manager") }


,,,|,,, 2121 nn xxxFxxx

nxxxF ,,, 21

Variables are position sensitive!


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SQL is a tuple calculus language

SELECT ENO,ENAME

FROM EMP

WHERE TITLE=manager


End user uses non-procedural languages

to express queries.


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Query Processor

Query processor transforms queries into

procedural operations to access data



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Query Processor

Distributed query processor has to deal

with

query decomposition, and

data localization



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7.1 Query Processing Problems

Distributed Database Systems 7


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Centralized query processor must transform calculus query intoalgebra operation, and

choose the best execution plan Example:

SELECT ENAME

FROME,G

WHERE E.ENO = G.ENO

AND RESP=manager



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Relational Algebra 1

Relational Algebra 2


GE ManagerRESPENOENAME ""

GEENOGENOEManagerRESPENAME ..""

Execution plan 2 is better for consumingless resources!


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In DDB, the query processor mustconsider the communication cost and

select the best site!

Same query as last example, but G and Eare distributed.

Simple plan:

To transport all segments to query site andexecute there. This causes too much network

traffic, very costly.



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Distributed Query Example

Distribution of E and G



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Query


GE ManagerREPSPENOENAME ""


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Optimized Processing



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7.2 Objectives of Query Processing



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Two-fold objectives:

Transformation, and

Optimization



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Cost to be considered for optimization:

CPU time

I/O time, and

Communication time


WAN: the last cost is dominant

LAN: all three are equal


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7.3 Complexity of Relational Algebra Operations



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7.3 Complexity of Relational Algebra Operations

Measured by n (cardinality) and tuples aresorted on comparison attributes


O(n)

O(nlogn)O(nlogn)

O(n2)

)duplicates(with,

GROUP),duplicates(with

,,,,


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7.4 Characterization of Query Processor



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7.4.1 Languages

For users:

calculus or algebra based languages.

For query processor:

map the input into internal form of

algebra augmented with

communication primitives.



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7.4.2 Types of Optimization

Exhaustive search

Workable for small solution space

Heuristics

Perform first,semi-join, etc. for largesolution space


,


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7.4.3 Optimization Timing

Static

Do it at compiling time by using statistics,

appropriate for exhaustive search, optimized

once, but executed many times. Dynamic

Do it at execution time, accurate, repeated

for every execution, expensive.



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7.4.4 Statistics

Facts of

Cardinalities

Attribute value distribution

Size of relation, etc.

Provided to query optimizer and

periodically updated.



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7.4.5 Decision Site

For query optimization, it may be done by Single site centralizedapproach, or

All the sites involved distributed, or

Hybrid

one site makes major decision incooperation with other sites making local

decisions



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7.4.6 Exploration of the Network Topology

WAN communication cost is dominant

LAN

communication cost is comparable to I/Ocost. Broadcasting capability, star network,

satellite network should be considered.



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7.4.7 Exploration of Replicated Fragments

Use replications to minimize

communication costs.



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7.4.8 Use of Semi-joins

Reduce the size of operand

relations to cut down

communication costs whenoverhead is not significant.



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7.5 Layers of Query Processing



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7.5.1 Query Decomposition

Decompose calculus query into algebraquery using global conceptual schema

information.


Step 1 calculus normalization

Step 2 semantic analysis to rejectincorrect queries

Step 3 simplification to eliminateredundant components

Step 4 translation of calculus queryinto optimized algebra query.


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7.5.2 Data Localization

Distributed query is mapped intoa fragment query and simplified

to produce agoodone.



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7.5.3 Global Query Optimization

Find an execution strategy close tooptimal.

Find the best ordering of operations in

the fragment query, includingcommunication operations.

Cost function defined in time is required.



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7.5.4 Local Query Optimization

Centralized system algorithms

(to be discussed in chapter 9)



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7.6 Conclusions

Query processor

must be able to findgood execution plan for a calculus query, s.

t. CPU time, I/O time and communication

time are minimized. Method: laying of

decomposition

localization global query optimization

local query optimization

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07.Overview of Query Processing

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