Query Optimization

Dr. Karen C. Davis

Professor School of Electronic and Computing Systems

School of Computing Sciences and Informatics

Outline

• overview of relational query optimization• logical optimization

– algebraic equivalences– transformation of trees

• physical optimization– selection algorithms– join algorithms

• cost-based optimization• research example using relational algebra

Relational Query Optimization

query optimizer

logical physicalSQL query

relational algebraquery tree

access plan(executable)

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

SQL is declarative

• describes what data, not how to retrieve it

select distinct …

from …

where …

• helpful for users, not necessarily good for efficient execution

Relational Algebra is procedural

• specifies operators and the order of evaluation

• steps for query evaluation:

1. translate SQL to RA operators (query tree)

2. perform heuristic optimizations:a. push RA select operators down the tree

b. convert select and cross product to join

c. others based on algebraic transformations

Relational Algebra Operators

name symbolically evaluation

select σcR applies condition c to R

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

cross product

R X S all possible combinations of tuples of R are appended with tuples from S

join R ⋈c S πl (σc (R X S)), where l is a list of attributes of R and S with duplicate columns removed and c is a join condition

SQL to RA

select distinct … πl

from … x

where … σc

πl

|

σc

|

X

/ \

R S

πl

|

σc

|

X

/ \

X S

/ \ R T

πl

|

σc

|

X

/ \

X S

/ \ X T

/ \ R U

two relations

three relations

four relations ↓

SQL to RA Tree Example

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

from A, B

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

πA.x, A.y, B.z

|

σA.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

SQL to RA Tree Example

select lname

from employee, works_on, projects

where pname = ‘Aquarius’ and

pnumber = pno and

essn = ssn and

bdate = ‘1985-12-03’

πlname

|

σpname = ‘Aquarius’ and

pnumber = pno and

essn = ssn and

bdate = ‘1985-12-03’

|

X

/ \

X projects

/ \ employee works_on

Simple Heuristic Optimization

1. cascade selects (split them up)

πl

|

σc1 and c2 and c3

|

X

/ \

R S

πl

|

σc1

|

σc2

|

σc3

|

X

/ \

R S

2. Push any single attribute selects down the tree to be just above their relation

πl

|

σc1

|

σc2

|

σc3

|

X

/ \

R S

πl

|

σc2

|

X

/ \ σc1

σc3

| |

R S

3. Convert 2-attribute select and cross product to join

πl

|

σc2

|

X

/ \ σc1

σc3

| |

R S

πl

|

⋈c2

/ \ σc1

σc3

| |

R S

smaller intermediate

results

efficient join algorithms

Practice problem: optimize RA tree

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

from projects P, department D, employee E

where D.dnumber = P.dnum and // c1

D.mgrssn = E.ssn and // c2

P.plocation = ‘Stafford’; // c3

RA tree to RA expression

πl

|

⋈c2

/ \ σc1

σc3

| |

R S

σc1 R

σc3 S

⋈c

2

πl( )

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 = ssn

and lname = 'Smith');

RA:π pnumber (σ lname = ‘Smith’ employee ⋈ssn=mgrssn department

⋈ dnumber = dnum projects)⋃

π pnumber (σ lname = ‘Smith’ employee ⋈ssn=essn works_on ⋈ pnumber = pno projects)

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

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-merge using indexes

example execution plan

Multiple View Processing Plan (MVPP)

view chromosome: 101100010100001

index chromosome: 1100110

Fitness: sum of query processing costs of individual queries using the views and indexes selected

⋈orderkey

(v7)

Customer (C)

Orders (O)

Lineitem (L)

Nation (N)

Part (P)

Q1Q2 Q3

πO.orderkey,

O.shippriority

(v9)

πC.custkey, C.name,

C.acctbal, N.name,

C.address, C.phone

(v12)

πP.type,

L.extendedprice

(v15)

σ C.mktsegment =

“building”

and L.shipdate = “1995-03-

15” (v8)

σ O.orderdate = “1994-10-

01”

(v11)

σ L.shipdate = “1995-

09-01”

(v14)⋈nationkey

(v10)

⋈custkey

(v6)⋈partkey

(v13)πname, address,

phone, acctbal,

nationkey, custkey,

mktsegment (v1)

πorderkey, orderdate,

custkey, shippriority

(v2)

πpartkey, orderkey,

shipdate,

extendedprice

(v3)

πnationkey,

name

(v4)

πpartkey,

type(v5)

thesis defense of Sirisha Machiraju: Space Allocation for Materialized Views and Indexes Using Genetic Algorithms, June 2002