Query Optimization From KCD

8/13/2019 Query Optimization From KCD

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Query Optimization

Dr. Karen C. Davis

Professor

School of Electronic and Computing SystemsSchool of Computing Sciences and Informatics


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Outline

overview of relational query optimization

logicaloptimization algebraic equivalences

transformation of trees physicaloptimization

selection algorithms

join algorithms

cost-based optimization research example using relational algebra


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Relational Query Optimization

query optimizer

logical physicalSQL query

relational algebra

query tree

access plan(executable)


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Learning Outcomes

translate basic SQL to RA query tree

perform heuristic optimizations to tree

use cost-based optimization to select algorithms

for tree operators to generate an execution plan


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SQL is declarative

describes what data, not how to retrieve it

select distinct from

where

helpful for users, not necessarily good for

efficient execution


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Relational Algebra is procedural

specifies operatorsand the order of evaluation

steps for query evaluation:

1. translate SQL to RA operators (query tree)

2. perform heuristic optimizations:

a. push RA selectoperators down the tree

b. convert selectand cross product tojoinc. others based on algebraic transformations


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Relational Algebra Operators

name symbolically evaluation

select cR applies condition c to R

project lR keeps a list (l) of attributes of R

crossproduct

R XS all possible combinations of tuplesof Rare appended with tuples

from S

join RcS l(c (R X S)), where lis a list ofattributes of Rand Swith duplicate

columns removed and cis a joincondition


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SQL to RA

select distinct lfrom x

where c

l|

c|X

/ \

R S

l

|

c|

X

/ \X S/ \

R T

l|

c|

X

/ \

X S

/ \X T

/ \R U

two relations

three relations

four relations


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SQL to RA Tree Example

select A.x, A.y, B.z

from A, B

where A.a = B.z and A.x > 10

A.x, A.y, B.z|

A.a = B.z and A.z > 10

|X

/ \

A B

evaluated bottom-up left to right;intermediate values are passed up

the tree to the next operator


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SQL to RA Tree Example

select lname

from employee, works_on, projects

where pname = Aquarius and

pnumber = pno andessn = ssn and

bdate = 1985-12-03

lname|

pname = Aquarius andpnumber = pno and

essn = ssn andbdate = 1985-12-03

|

X

/ \

X projects/ \

employee works_on


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Simple Heuristic Optimization

1. cascade selects (split them up)

l

|

c1andc2and c3|

X/ \

R S

l|

c1

|c2|

c3

|X

/ \

R S


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2. Push any single attribute selects down the

tree to be just above their relation

l|

c1|

c2|

c3|

X

/ \

R S

l|

c2|

X

/ \

c1 c3| |R S


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3. Convert 2-attribute select and cross product

to join

l|

c2|

X

/ \

c1 c3| |R S

l|

c2

/ \

c1 c3

| |R Ssmaller

intermediate

results

efficient join

algorithms


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Practice problem: optimize RA tree

select P.pnumber, P.dnum, E.lname, E.bdate

from projects P, department D, employee Ewhere D.dnumber = P.dnum and // c1

D.mgrssn = E.ssn and // c2

P.plocation = Stafford; // c3


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RA tree to RA expression

l|

c2

/ \

c1 c3| |

R S

c1R c3Sc2l( )


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Other Operators in Relational Algebra

SQL:(select pnumber from projects, department, employee

where dnum = dnumber and mgrssn = ssn

and lname = 'Smith)

union

(select pnumber from projects, works_on, employee

where pnumber = pno and essn = ssnand lname = 'Smith');

RA:

pnumber(lname = Smith employee

ssn=mgrssn departmentdnumber = dnumprojects)

pnumber(lname = Smith employee ssn=essn works_on

pnumber = pnoprojects)


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Selection

Algorithms linear search

binary search

primary index or hash for point query

primary index for range query

clustering index

secondary index

conjunctives

individual index composite index or hash

intersection of record pointers for multiple indexes


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Join Algorithms

nested loop join

single-scan join

sort-merge join

hash join

http://docs.oracle.com/cd/E13085_01/doc/timesten.1121/e14261/query.htm

sort-mergeusing

indexes

example execution plan


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Multiple View Processing Plan (MVPP)

view chromosome:101100010100001

index chromosome:

1100110

Fitness: sum of queryprocessing costs ofindividual queriesusing the views and

indexes selected

orderkey

(v7)

Customer (C) Orders (O) Lineitem (L) Nation (N) Part (P)

Q1 Q2 Q3

O.orderkey,

O.shippriority

(v9)

C.custkey, C.name,

C.acctbal, N.name,C.address, C.phone

(v12)

P.type,

L.extendedprice

(v15)

C.mktsegment =

building

and L.shipdate = 1995-

03-15 (v8)

O.orderdate = 1994-10-

01

(v11)

L.shipdate = 1995-

09-01

(v14)

nationkey

(v10)

custkey

(v6)partkey

(v13)

name, address,phone, acctbal,nationkey, custkey,

mktsegment (v1)

orderkey,

orderdate, custkey,shippriority

(v2)

partkey, orderkey,

shipdate,extendedprice

(v3)

nationkey,

name

(v4)

partkey,

type(v5)

thesis defense of Sirisha Machiraju: Space Allocation for Materialized Views

and Indexes Using Genetic Algorithms, June 2002

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Query Optimization From KCD