CS263 Lecture 19 Query Optimisation

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CS263Lecture 19 Query Optimisation
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LECTURE PLAN

• Motivation for Query Optimisation
• Phases of Query Processing
• Query Trees
• RA Transformation Rules
• Heuristic Processing Strategies
• Cost Estimation for RA Operations

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Motivation for Query Optimisation

• List all the managers that work in the sales
  department.
• SELECT
• FROM emp, dept
• WHERE emp.deptno dept.deptno
• AND emp.job Manager
• AND dept.name Sales

There are at least three alternative ways of
representing this query as a Relational Algebra
expression.
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Motivation for Query Optimisation
Metrics 1000 tuples in the EMP relation 50
tuples in the DEPT relation 50 employees are
Managers (one per department) 5 separate Sales
departments (across the country)
Cost of processing the following query alternate
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Motivation for Query Optimisation
Metrics 1000 tuples in the EMP relation 50
tuples in the DEPT relation 50 employees are
Managers (one per department) 5 separate Sales
departments (across the country)
Cost of processing the following query alternate
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Motivation for Query Optimisation
Cost of processing the following query
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Phases of Query Processing
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Query Processing Stage - 1

• Cast the query into internal form
• This involves the conversion of the original
  (SQL) query into some internal representation
  more suitable for machine manipulation.
• The internal representation typically chosen is
  either some kind of abstract syntax tree, or a
  relational algebra query tree.

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Relational Algebra Query Trees

• A Relational Algebra query can be represented as
  a query tree. For example the query to list all
  the managers that work in the sales department
  could be described as one of the following

Root
Intermediate operations
Leaves
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Relational Algebra Query Trees

• A Relational Algebra query can be represented as
  a query tree. For example the query to list all
  the managers that work in the sales department
  could be described as one of the following

Root
Intermediate operations
Leaves
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Relational Algebra Query Trees
Alternativequery tree for the query to list all
the managers that work in the sales department
?(job Manager) Ù (nameSales) (EMP
emp.deptno dept.deptno DEPT)
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Relational Algebra Query Trees

• Alternativequery tree for the query to list all
  the managers that work in the sales department

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Query Processing Stage - 2

• Convert to canonical form
• Find a more efficient representation of the
  query by converting the internal representation
  into some equivalent (canonical) form through the
  application of a set of well-defined
  transformation rules.
• The set of transformation rules to apply will
  generally be the result of the application of
  specific heuristic processing strategies
  associated with particular DBMSs.

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Transformation Rules for RA Operations

• Conjunctive selection operations can cascade into
  individual selection operations (and vice versa).
• Sometimes referred to as cascade of selection.
• spÙqÙr(R) sp(sq(sr(R)))
• Example
• sdeptno10 Ù salgt1000(Emp)
  sdeptno10(ssalgt1000(Emp))

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Transformation Rules for RA Operations

• Commutativity of selection
• sp(sq(R)) sq(sp(R))
• Example
• ssalgt1000(sdeptno10(Emp))
  sdeptno10(ssalgt1000(Emp))

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Transformation Rules for RA Operations

• In a sequence of projection operations, only the
  last in the sequence is required.
• PLPM PN(R) PL (R)
• Example
• PdeptnoPname(Dept) Pdeptno (Dept))

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Transformation Rules for RA Operations

• Commutativity of selection and projection.
• PAi, , Am(sp(R)) sp(PAi, , Am(R))
• where p ÎA1, A2, , Am
• Example
• Pname, job(snameSmith(Emp))
  snameSmith'(Pname, job(Staff))

Selection predicate (p) is only made up of
projected attributes
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Transformation Rules for RA Operations

• Commutativity of theta-join (and Cartesian
  product).
• R p S S p R

NOTE Theta-join is a generalisation of both the
equi-join and natural-join
R X S S X R
Example
EMP emp.deptno dept.deptno DEPT
DEPT emp.deptno dept.deptno EMP
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Transformation Rules for RA Operations

• Commutativity of selection and theta-join
• (or Cartesian product).

Selection predicate (p) is only made up of join
attributes
Example
(semp.deptno10 (EMP)) emp.deptno
dept.deptno DEPT
semp.deptno10 (EMP emp.deptno
dept.deptno DEPT)
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Transformation Rules for RA Operations

• Commutativity of projection and theta-join
• (or Cartesian product).

PL(R r S) (PL1(R)) r (PL2(S))
Project attributes L L1 ? L2, where L1 are
attributes of R, and L2 are attributes of S. L
will also contain the join attributes
Example
P job, location, deptno (EMP emp.deptno
dept.deptno DEPT)
(P job, deptno (EMP)) emp.deptno
dept.deptno (P location, deptno (DEPT))
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Transformation Rules for RA Operations

• Commutativity of union and intersection
• (but not set difference).
• R È S S È R
• R Ç S S Ç R

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Transformation Rules for RA Operations

• Commutativity of selection and set operations
  (union, intersection, and set difference).
• Union
• sp(R È S) sp(S) È sp(R)
• Intersection
• sp(R Ç S) sp(S) Ç sp(R)
• Set Difference
• sp(R - S) sp(S) - sp(R)

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Transformation Rules for RA Operations

• 10 Commutativity of projection and union
• PL(R È S) PL(S) È PL(R)

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Transformation Rules for RA Operations

• 11 Associativity of natural join (and Cartesian
  product)
• Natural Join
• (R S) T R (S T)
• Cartesian Product
• (R X S) X T R X (S X T)

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Transformation Rules for RA Operations

• 12 Associativity of union and intersection (but
  not set difference)
• Union
• (R È S) È T S È (R È T)
• Intersection
• (R Ç S) Ç T S Ç (R Ç T)

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Heuristic Processing Strategies

• Perform selection operations as early as possible
• Translate a Cartesian product and subsequent
  selection (whose predicate represents a join
  condition) into a join operation.
• Use associativity of binary operations to ensure
  that the most restrictive selection operations
  are executed first
• Perform projections as early as possible.
• Compute common expressions once

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Heuristic Processing - Example
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Query Processing Stage - 3

• Choose candidate low-level procedures
• Consider the (optimised canonical) query as a
  series of low-level operations (join, restrict,
  etc).
• For each of these operations generate alternative
  execution strategies and calculate the cost of
  such strategies on the basis of statistical
  information held about the database tables
  (files).

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Query Processing Stage - 4

• Generate query plans and choose the cheapest
• Construct a set of candidate Query Execution
  Plans (QEPs).
• Each QEP is constructed by selecting a candidate
  implementation procedure for each operation in
  the canonical query and then combining them to
  form a string of associated operations.
• Each QEP will have an (estimated) cost associated
  with it the sum of the cost of each of its
  operations.
• Choose the QEP with the least cost.

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Cost Based Optimisation

• Cost Based Optimisation (stages 3 4)
• A good declarative query optimiser does not rely
  solely on heuristic processing strategies.
• It chooses the QEP with the lowest estimated
  cost.
• After heuristic rules are applied to a query,
  there still remains a number of alternative ways
  to execute it .
• The Query Optimiser estimates the cost of
  executing each one (or at least a number) of
  these alternatives, and selects the cheapest one.

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Costs associated with query execution

• Secondary storage access costs
• Searching for data blocks on disk,
• Reading data blocks from disk
• Writing data block to disk
• Storage costs
• Cost of storing intermediate (temp) files
• Computation costs
• Cost of CPU usage
• Main memory usage costs
• Cost of buffering data
• Communication costs
• Cost of moving data across

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Database statistics used in cost estimation

• Information held on each relation
• number of tuples
• number of blocks
• blocking factor
• primary access method
• primary access attributes
• secondary indexes
• secondary indexing attributes
• number of levels for each index
• number of distinct values of each attribute

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Physical Data Structures File Types

• Heap (Sequential, Unordered)
• no key columns
• queries, other than appends, scan every page
• rows are appended at the end
• duplicate rows are allowed
• Ordered
• physically sorted data file with no index
• Hash (Random, Direct)
• data is located based on the (calculated) value
  of a hash field (key)
• Indexed Sequential (ISAM)
• sorted data file with a primary index
• BTree
• dynamic multilevel index
• reuses deleted space on associated data pages

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Strategies for implementing the RESTRICT operation

• Different access strategies dependant upon the
  structure of the file in which the relation is
  stored, and whether the predicate attribute(s)
  have been indexed/hashed Each uses a different
  cost algorithm (which refers to specific database
  statistics).
• Linear Search (Heap)
• Binary Search (Ordered)
• Equality on Hash Key
• Equality condition on primary key
• Inequality condition on primary key
• Equality condition on secondary index
• Inequality condition on secondary BTree index
• If the selection predicate is a composite (AND
  OR) then there are additional cost
  considerations!

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Strategies for implementing the JOIN operation

• Different access strategies dependant upon the
  structure of the files in which the relations to
  be joined are stored, and whether the join
  attributes have been indexed/hashed Each uses
  its own cost algorithm (which refers to specific
  database statistics).
• Block nested loop join
• Indexed nested loop join
• Sort-merge join
• Hash join

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Query Optimisation Summary

• The aims of query processing are to transform a
  query written in a high-level language (SQL),
  into a correct and efficient execution strategy
  expressed in a low-level language (Relational
  Algebra), and to execute the strategy to retrieve
  the required data.
• There are many equivalent transformations of the
  same high-level query, the DBMS has to choose the
  one that minimises resource usage.
• There are two main techniques for query
  optimisation. The first uses heuristic rules that
  order the operations in a query. The second
  compares different execution strategies for those
  operations, based on their relative costs, and
  selects the least resource intensive (cheapest)
  ones.