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Chapter 13: Query Processing
Database System Concepts, 5th Ed.
©Silberschatz, Korth and Sudarshan
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Database System Concepts


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
Chapter 1: Introduction
Part 1: Relational databases

Chapter 2: Relational Model

Chapter 3: SQL

Chapter 4: Advanced SQL

Chapter 5: Other Relational Languages
Part 2: Database Design

Chapter 6: Database Design and the E-R Model

Chapter 7: Relational Database Design

Chapter 8: Application Design and Development
Part 3: Object-based databases and XML

Chapter 9: Object-Based Databases

Chapter 10: XML
Part 4: Data storage and querying

Chapter 11: Storage and File Structure

Chapter 12: Indexing and Hashing

Chapter 13: Query Processing

Chapter 14: Query Optimization
Part 5: Transaction management

Chapter 15: Transactions

Chapter 16: Concurrency control

Chapter 17: Recovery System
Database System Concepts - 5th Edition, Aug 27, 2005.
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Part 6: Data Mining and Information Retrieval

Chapter 18: Data Analysis and Mining

Chapter 19: Information Retreival
Part 7: Database system architecture

Chapter 20: Database-System Architecture

Chapter 21: Parallel Databases

Chapter 22: Distributed Databases
Part 8: Other topics

Chapter 23: Advanced Application Development

Chapter 24: Advanced Data Types and New Applications

Chapter 25: Advanced Transaction Processing
Part 9: Case studies

Chapter 26: PostgreSQL

Chapter 27: Oracle

Chapter 28: IBM DB2

Chapter 29: Microsoft SQL Server
Online Appendices

Appendix A: Network Model

Appendix B: Hierarchical Model

Appendix C: Advanced Relational Database Model
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©Silberschatz, Korth and Sudarshan
Part 4: Data storage and querying
(Chapters 11 through 14).
 Chapter 11: Storage and File Structure

deals with disk, file, and file-system structure.
 Chapter 12: Indexing and Hashing

A variety of data-access techniques are presented including hashing and
B+ tree indices.
 Chapters 13: Query Processing and Chapter 14: Query Optimization

address query-evaluation algorithms, and query optimization techniques.
These chapters provide an understanding of the internals of the storage
and retrieval components of a database which are necessary for query
processing and optimization
Database System Concepts - 5th Edition, Aug 27, 2005.
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Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
Database System Concepts - 5th Edition, Aug 27, 2005.
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Basic Steps in Query Processing
1. Parsing and translation
2. Optimization
3. Evaluation
Database System Concepts - 5th Edition, Aug 27, 2005.
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Basic Steps in Query Processing (Cont.)
 Parsing and translation
First, translate the query into its internal form.
 This is then translated into relational algebra.
 Parser checks syntax, verifies relations
 Optimization
 Enumerate all possible query-evaluation plans
 Compute the cost for the plans
 Pick up the plan having the minimum cost
 Evaluation
 The query-execution engine takes a query-evaluation plan,
 executes that plan,
 and returns the answers to the query.

Database System Concepts - 5th Edition, Aug 27, 2005.
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Basic Steps in QP: Optimization
 A relational algebra expression may have many equivalent expressions

E.g., balance2500(balance(account)) is equivalent to balance(balance2500(account))
 Each relational algebra operation can be evaluated using one of several different
algorithms

Correspondingly, a relational-algebra expression can be evaluated in many ways.
 Query evaluation-plan.



annotated expression specifying detailed evaluation strategy
Ex1: can use an index on balance to find accounts with balance < 2500,
Ex2: can perform complete relation scan and discard accounts with balance 
2500
Database System Concepts - 5th Edition, Aug 27, 2005.
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Basic Steps: Optimization (Cont.)
 Query Optimization

Amongst all equivalent evaluation plans, choose the one with lowest cost.

Cost is estimated using statistical information from the database catalog

e.g. number of tuples in each relation, size of tuples, etc.
 In this chapter we study

How to measure query costs

Algorithms for evaluating relational algebra operations

How to combine algorithms for individual operations in order to evaluate a
complete expression
 In Chapter 14

We study how to optimize queries, that is, how to find an evaluation plan with
lowest estimated cost
Database System Concepts - 5th Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
Database System Concepts - 5th Edition, Aug 27, 2005.
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Measures of Query Cost
 Cost is generally measured as total elapsed time for answering query

Many factors contribute to time cost

disk accesses, CPU, or even network communication
 Typically disk access is the predominant cost, and is also relatively easy to
estimate. Measured by taking into account

Number of seeks
X average-seek-cost

Number of blocks read
X average-block-read-cost

Number of blocks written X average-block-write-cost

Cost to write a block is greater than cost to read a block
– data is read back after being written to ensure that the write was
successful
Database System Concepts - 5th Edition, Aug 27, 2005.
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Measures of Query Cost (Cont.)
 For simplicity we just use number of block transfers from disk as the cost
measure

We ignore the difference in cost between sequential and random I/O for
simplicity

We also ignore CPU costs for simplicity
 Costs depends on the size of the buffer in main memory

Having more memory reduces need for disk access

Amount of real memory available to buffer depends on other concurrent
OS processes, and hard to determine ahead of actual execution

We often use worst case estimates, assuming only the minimum amount
of memory needed for the operation is available
 Real systems take CPU cost into account, differentiate between sequential
and random I/O, and take buffer size into account
 We do not include cost to writing output (query result) to disk in our cost
formulae
Database System Concepts - 5th Edition, Aug 27, 2005.
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Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
Database System Concepts - 5th Edition, Aug 27, 2005.
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Selection Operation
 File scan

search algorithms that locate and retrieve records that fulfill a selection condition.
 Algorithm A1 (linear search):

Scan each file block and test all records to see whether they satisfy the selection
condition.

Cost estimate (number of disk blocks scanned) = br
 br denotes number of blocks containing records from relation r

If selection is on a key attribute, cost = (br /2)


stop on finding record
Linear search can be applied regardless of

selection condition or

ordering of records in the file, or

availability of indices
Database System Concepts - 5th Edition, Aug 27, 2005.
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Selection Operation (Cont.)
 Algorithm A2 (binary search):

Applicable if selection is an equality comparison on the attribute on which file is
ordered.

Assume that the blocks of a relation are stored contiguously

Cost estimate (number of disk blocks to be scanned):

log2(br) — cost of locating the first tuple by a binary search on the blocks

Plus number of blocks containing records that satisfy selection condition
– Will see how to estimate this cost in Chapter 14
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Selections Using Indices
 Index scan – search algorithms that use an index

selection condition must be on search-key of index.
 HT: height of index
 A3 (primary index on candidate key, equality).


 A4

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Retrieve a single record that satisfies the corresponding equality condition
Cost = HTi + 1
(primary index on nonkey, equality) Retrieve multiple records.
Records will be on consecutive blocks
Cost = HTi + number of blocks containing retrieved records
 A5 (equality on search-key of secondary index).

Retrieve a single record if the search-key is a candidate key
 Cost = HTi + 1
 Retrieve multiple records if search-key is not a candidate key

Cost = HTi + number of records retrieved
– can be very expensive!
 each record may be on a different block
– one block access for each retrieved record
Database System Concepts - 5th Edition, Aug 27, 2005.
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Selections Involving Comparisons
 Can implement selections of the form AV (r) or A  V(r) by using


a linear file scan or binary search,
or by using indices in the following ways:
 A6 (primary index, comparison). (Relation is sorted on A)

For A  V(r) use index to find first tuple  v and scan relation
sequentially from there
For AV (r) just scan relation sequentially till first tuple > v; do not use
index
 A7 (secondary index, comparison).
 For A  V(r) use index to find first index entry  v and scan index
sequentially from there, to find pointers to records.

For AV (r) just scan leaf pages of index finding pointers to records, till
first entry > v
 In either case, retrieve records that are pointed to
– requires an I/O for each record
– Linear file scan may be cheaper if many records are to be fetched!

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Complex Selections
 Conjunction:
1 2. . . n(r)
 A8 (conjunctive selection using one index).

Select a combination of i and algorithms A1 through A7 that results in the
least cost fori (r).

Test other conditions on tuple after fetching it into memory buffer.
 A9 (conjunctive selection using multiple-key index).

Use appropriate composite (multiple-key) index if available.
 A10 (conjunctive selection by intersection of identifiers).

Requires indices with record pointers.

Use corresponding index for each condition, and take intersection of all the
obtained sets of record pointers.

Then fetch records from file

If some conditions do not have appropriate indices, apply test in memory.
Database System Concepts - 5th Edition, Aug 27, 2005.
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Complex Selections (Cont’)
 Disjunction:1 2 .
. . n (r).
 A11 (disjunctive selection by union of identifiers).

Applicable if all conditions have available indices.

Otherwise use linear scan.

Use corresponding index for each condition, and take union of all the
obtained sets of record pointers.

Then fetch records from file
 Negation:
(r)

Use linear scan on file

If very few records satisfy , and an index is applicable to 
 Find satisfying records using index and fetch from file
Database System Concepts - 5th Edition, Aug 27, 2005.
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Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
Database System Concepts - 5th Edition, Aug 27, 2005.
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Sorting
 We may build an index on the relation, and then use the index to read the
relation in sorted order.

This may lead to one disk block access for each tuple.
 For relations that fit in memory, techniques like quicksort can be used.

Pick a middle element P in an array A

Push the elements having less value than P to the left array LA

Push the elements having bigger value than P to the right array RA

Apply the same idea recursively on LA and RA
 For relations that don’t fit in memory, external sort-merge is a good choice.
Database System Concepts - 5th Edition, Aug 27, 2005.
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External Sort-Merge
Let M denote memory size (in pages).
1.
Create sorted runs. Let i be 0 initially.
Repeatedly do the following till the end of the relation:
(a) Read M blocks of relation into memory
(b) Sort the in-memory blocks
(c) Write sorted data to run Ri; increment i.
Let the final value of I be N
2.
Merge the runs (N-way merge). We assume (for now) that N < M.
1. Use N blocks of memory to buffer input runs, and 1 block to buffer
output. Read the first block of each run into its buffer page
2. repeat



Select the first record (in sort order) among all buffer pages
Write the record to the output buffer. If the output buffer is full
write it to disk.
Delete the record from its input buffer page.
If the buffer page becomes empty then
read the next block (if any) of the run into the buffer.
3. until all input buffer pages are empty:
Database System Concepts - 5th Edition, Aug 27, 2005.
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External Sort-Merge (Cont.)
 If N  M, several merge passes are required.

In each pass, contiguous groups of M - 1 runs are merged.

A pass reduces the number of runs by a factor of M -1, and creates runs
longer by the same factor.


E.g. If M=11, and there are 90 runs, one pass reduces the number of
runs to 9, each 10 times the size of the initial runs
Repeated passes are performed till all runs have been merged into one.
Database System Concepts - 5th Edition, Aug 27, 2005.
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External Merge Sort (Cont.)
 Cost analysis:

br
: the number of blocks in R

M
: the number of blocks in each run

br / M : the initial number of runs

Total number of merge passes required: logM–1(br/M)

Disk accesses for initial run creation as well as in each pass is 2br

for final pass, we don’t count write cost
– we ignore final write cost for all operations since the output of an
operation may be sent to the parent operation without being written
to disk
Thus total number of disk accesses for external sorting:
br ( 2 logM–1(br / M) + 1)
Database System Concepts - 5th Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
Database System Concepts - 5th Edition, Aug 27, 2005.
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Join Operation
 Several different algorithms to implement joins

Nested-loop join

Block nested-loop join

Indexed nested-loop join

Merge-join

Hash-join
 Choice based on cost estimate
 Examples use the following information

Number of records


customer: 10,000
depositor: 5000
Number of blocks

customer:
400
Database System Concepts - 5th Edition, Aug 27, 2005.
depositor: 100
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©Silberschatz, Korth and Sudarshan
Nested-Loop Join
 To compute the theta join
r

s
for each tuple tr in r do begin
for each tuple ts in s do begin
test pair (tr,ts) to see if they satisfy the join condition 
if they do, add tr • ts to the result.
end
end
 r is called the outer relation and s the inner relation of the join.
 Requires no indices and can be used with any kind of join condition.
 Expensive since it examines every pair of tuples in the two relations.
Database System Concepts - 5th Edition, Aug 27, 2005.
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보조자료
Nested-loop join
Borrower relation: N = 16, B = 4
Customer
name
Loan
number
Loan relation : N = 12, B = 3
Jones
L - 170
Loan number
Branch name
amount
Bahn
L – 82
L- 170
Downtown
300
Kim
L – 42
L – 42
Redwood
400
Lee
L – 48
L – 48
Redwood
1500
Jane
L – 112
L – 112
Perryridge
2300
Smith
L – 34
L – 321
Redwood
3100
Hwang
L – 321
L – 90
Downtown
800
Choi
L – 109
L – 112
Perryridge
2300
Pedro
L – 90
L – 31
Redwood
200
Sammy
L – 112
L - 70
Perryridge
600
Jun
L – 31
L - 221
Downtown
1000
Jung
L – 62
L – 155
Redwood
800
Shin
L – 99
L - 320
Downtown
2500
Koh
L – 70
Mark
L – 221
Harry
Database System Concepts -
L - 116
5th
Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
보조자료
Nested-loop join
Borrower relation
Customer name
Loan number
Jones
L - 170
Bahn
L – 82
Kim
L – 42
Lee
Loan relation
Loan number
Branch name
amount
L- 170
Downtown
300
L – 42
Redwood
400
L – 48
L – 48
Redwood
1500
Jane
L – 112
L – 112
Perryridge
2300
Smith
L – 34
L – 321
Redwood
3100
Hwang
L – 321
L – 90
Downtown
800
Choi
L – 109
L – 112
Perryridge
2300
Pedro
L – 90
L – 112
L – 31
Redwood
200
Sammy
Jun
L – 31
L - 70
Perryridge
600
Jung
L – 62
L - 221
Downtown
1000
Shin
L – 99
L – 155
Redwood
800
Koh
L – 70
L - 320
Downtown
2500
Mark
L – 221
Harry
L - 116
Database System Concepts - 5th Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
보조자료
Nested-loop join
Borrower relation
Customer name
Loan number
Jones
L - 170
Bahn
Loan relation
Loan number
Branch name
amount
L – 82
L- 170
Downtown
300
Kim
L – 42
L – 42
Redwood
400
Lee
L – 48
L – 48
Redwood
1500
Jane
L – 112
L – 112
Perryridge
2300
Smith
L – 34
L – 321
Redwood
3100
Hwang
L – 321
L – 90
Downtown
800
Choi
L – 109
L – 90
L – 112
Perryridge
2300
Pedro
Sammy
L – 112
L – 31
Redwood
200
Jun
L – 31
L - 70
Perryridge
600
Jung
L – 62
L - 221
Downtown
1000
Shin
L – 99
L – 155
Redwood
800
Koh
L – 70
L - 320
Downtown
2500
Mark
L – 221
Harry
L - 116
Database System Concepts - 5th Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
보조자료
Nested-loop join
Borrower relation
Customer name
Loan number
Jones
L - 170
Bahn
L – 82
Kim
L – 42
Lee
L – 48
Jane
L – 112
Smith
L – 34
Hwang
L – 321
Choi
Loan relation
Loan number
Branch name
amount
L- 170
Downtown
300
L – 42
Redwood
400
L – 48
Redwood
1500
L – 112
Perryridge
2300
L – 109
L – 321
Redwood
3100
Pedro
L – 90
L – 90
Downtown
800
Sammy
L – 112
L – 112
Perryridge
2300
Jun
L – 31
L – 31
Redwood
200
Jung
L – 62
L – 99
L - 70
Perryridge
600
Shin
Koh
L – 70
L - 221
Downtown
1000
Mark
L – 221
L – 155
Redwood
800
Harry
L - 116
L - 320
Downtown
2500
Database System Concepts - 5th Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
보조자료
Nested-loop join
Loan relation
Borrower relation
Customer name
Loan number
Loan number
Branch name
amount
Jones
L - 170
L- 170
Downtown
300
Bahn
L – 82
L – 42
Redwood
400
Kim
L – 42
L – 48
L – 48
Redwood
1500
Lee
Jane
L – 112
L – 112
Perryridge
2300
Smith
L – 34
L – 321
Redwood
3100
Hwang
L – 321
L – 90
Downtown
800
Choi
L – 109
L – 112
Perryridge
2300
Pedro
L – 90
L – 31
Redwood
200
Sammy
L – 112
L - 70
Perryridge
600
Jun
L – 31
L - 221
Downtown
1000
Jung
L – 62
Shin
L – 99
L – 155
Redwood
800
Koh
L – 70
L - 320
Downtown
2500
Mark
L – 221
Harry
L - 116

borrower relation을 outer relation으로 하면


Loan relation을 outer relation으로 했을 경우

Database System Concepts - 5th Edition, Aug 27, 2005.
16*3 + 4 = 52회의 disk access가 필요
12*4 + 3 = 51회의 disk access가 필요
13.31
©Silberschatz, Korth and Sudarshan
Nested-Loop Join (Cont.)
 In the worst case, if there is enough memory only to hold one block of each
relation, the estimated cost is
nr  bs + br disk accesses
where nr is number of record in R and bs and br are number of disk
blocks in S and R
 If the smaller relation fits entirely in memory, use that as the inner relation.

Reduces cost to br + bs disk accesses.
 Assuming worst case memory availability cost estimate is

5000  400 + 100 = 2,000,100 disk accesses with depositor as outer
relation, and

1000  100 + 400 = 1,000,400 disk accesses with customer as the outer
relation.
 If smaller relation (depositor) fits entirely in memory, the cost estimate will be
500 disk accesses.
 Block nested-loops algorithm (next slide) is preferable.
Database System Concepts - 5th Edition, Aug 27, 2005.
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Block Nested-Loop Join
 Variant of nested-loop join in which every block of inner relation is paired with
every block of outer relation.
for each block Br of r do begin
for each block Bs of s do begin
for each tuple tr in Br do begin
for each tuple ts in Bs do begin
Check if (tr,ts) satisfy the join condition
if they do, add tr • ts to the result.
end
end
end
end
 Won Kim’s Join Method

One chapter of ’80 PhD Thesis at Univ of Illinois at Urbana-Champaign
Database System Concepts - 5th Edition, Aug 27, 2005.
13.33
©Silberschatz, Korth and Sudarshan
보조자료
Block nested-loop join
Borrower relation
Loan relation
Customer name
Loan number
Jones
L - 170
Loan number
Branch name
amount
Bahn
L – 82
L- 170
Downtown
300
Kim
L – 42
L – 42
Redwood
400
Lee
L – 48
L – 112
L – 48
Redwood
1500
Jane
Smith
L – 34
L – 112
Perryridge
2300
Hwang
L – 321
L – 321
Redwood
3100
Choi
L – 109
L – 90
Downtown
800
Pedro
L – 90
L – 112
Perryridge
2300
Sammy
L – 112
L – 31
Redwood
200
Jun
L – 31
L - 70
Perryridge
600
Jung
L – 62
L – 99
L - 221
Downtown
1000
Shin
Koh
L – 70
L – 155
Redwood
800
Mark
L – 221
L - 320
Downtown
2500
Harry
L - 116
Database System Concepts - 5th Edition, Aug 27, 2005.
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©Silberschatz, Korth and Sudarshan
Block nested-loop join
보조자료
Borrower relation
Customer name
Loan number
Jones
L - 170
Bahn
L – 82
Kim
Loan relation
Loan number
Branch name
amount
L – 42
L- 170
Downtown
300
Lee
L – 48
L – 42
Redwood
400
Jane
L – 112
L – 48
Redwood
1500
Smith
L – 34
L – 112
Perryridge
2300
Hwang
L – 321
L – 321
Redwood
3100
Choi
L – 109
Downtown
800
Pedro
L – 90
L – 90
Sammy
L – 112
L – 112
Perryridge
2300
Jun
L – 31
L – 31
Redwood
200
Jung
L – 62
L - 70
Perryridge
600
Shin
L – 99
L - 221
Downtown
1000
Koh
L – 70
L – 155
Redwood
800
Mark
L – 221
L - 320
Downtown
2500
Harry
L - 116
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Block nested-loop join
보조자료
Borrower relation
Customer name
Loan number
Jones
L - 170
Bahn
L – 82
Kim
L – 42
Lee
L – 48
Jane
L – 112
Smith
L – 34
Hwang
L – 321
Choi
L – 109
Pedro
L – 90
Sammy
L – 112
Jun
L – 31
Jung
L – 62
Shin
L – 99
Koh
L – 70
Mark
L – 221
Harry
Database System Concepts -
Loan relation
Loan
number
Branch
name
amount
L- 170
Downtown
300
L – 42
Redwood
400
L – 48
Redwood
1500
L – 112
Perryridge
2300
L – 321
Redwood
3100
L – 90
Downtown
800
L – 112
Perryridge
2300
L – 31
Redwood
200
L - 70
Perryridge
600
L - 221
Downtown
1000
L – 155
Redwood
800
L - 320
Downtown
2500
L - 116
5th
Edition, Aug 27, 2005.
13.36
©Silberschatz, Korth and Sudarshan
보조자료
Block nested-loop join
Borrower relation
Loan relation
Loan number
Branch name
amount
L- 170
Downtown
300
L – 42
Redwood
400
L – 48
Redwood
1500
Customer name
Loan number
Jones
L - 170
Bahn
L – 82
Kim
L – 42
Lee
L – 48
L – 112
Perryridge
2300
Jane
L – 112
L – 321
Redwood
3100
Smith
L – 34
L – 90
Downtown
800
Hwang
L – 321
L – 112
Perryridge
2300
Choi
L – 109
L – 31
Redwood
200
Pedro
L – 90
Sammy
L – 112
L - 70
Perryridge
600
Jun
L – 31
L - 221
Downtown
1000
Jung
L – 62
L – 155
Redwood
800
Shin
L – 99
L - 320
Downtown
2500
Koh
L – 70
Mark
L – 221
Harry
L - 116
Database System Concepts - 5th Edition, Aug 27, 2005.


borrower relation을 outer relation으로 하면
 4*3 + 4 = 16회의 disk access가 필요
Loan relation을 outer relation으로 했을 경우
 3*4 + 3 = 15회의 disk access가 필요
13.37
©Silberschatz, Korth and Sudarshan
Block Nested-Loop Join (Cont.)
 Worst case estimate: br  bs + br block accesses.

Each block in the inner relation s is read once for each block in the outer
relation (instead of once for each tuple in the outer relation
 Best case: br + bs block accesses.
 Improvements to nested loop and block nested loop algorithms:

In block nested-loop, use M - 2 disk blocks as blocking unit for outer
relations, where M = memory size in blocks; use remaining two blocks to
buffer inner relation and output
Cost = br / (M-2)  bs + br
 If equi-join attribute forms a key of inner relation, stop inner loop on first
match
 Scan inner loop forward and backward alternately, to make use of the
blocks remaining in buffer (with LRU replacement)


Use index on inner relation if available (next slide)
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Indexed Nested-Loop Join
 Index lookups can replace file scans if

join is an equi-join or natural join and
 an index is available on the inner relation’s join attribute
 Can construct an index just to compute a join.
 For each tuple tr in the outer relation r, use the index (B+ tree) to look up tuples in
s that satisfy the join condition with tuple tr.
 Worst case: buffer has space for only one page of r, and for each tuple in r, we
perform an index lookup on s.
 Cost of the join: br + nr  c

Where c is the cost of traversing index and fetching all matching s tuples for
one tuple or r
 c can be estimated as cost of a single selection on s using the join condition.
 If indices are available on join attributes of both r and s, use the relation with
fewer tuples as the outer relation.
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Indexed nested-loop join

Customer name
Loan
number
Jones
L - 170
Bahn
Loan number
Branch name
amount
L – 82
L- 170
Downtown
300
Kim
L – 42
L – 42
Redwood
400
Lee
L – 48
L – 48
Redwood
1500
Jane
L – 112
L – 112
Perryridge
2300
Smith
L – 34
L – 321
Redwood
3100
Hwang
L – 321
Downtown
800
L – 109
L – 90
Choi
Pedro
L – 90
L – 112
Perryridge
2300
Sammy
L – 112
L – 31
Redwood
200
Jun
L – 31
L - 70
Perryridge
600
Jung
L – 62
L - 221
Downtown
1000
Shin
L – 99
L – 155
Redwood
800
Koh
L – 70
L - 320
Downtown
2500
Mark
L – 221
Harry
L - 116
보조자료
B+ tree
Index
Borrower relation의 16개의 tuple에 대해 각각 그 tuple과 join할 수 있는 loan relation의 tuple을 B+tree로
검색 (3회의 disk access)
 Tree의 height가 2, 그리고, 실제의 data access를 위한 1회, 따라서, 3회의 disk access 필요
 따라서, 총 cost는 4 block access +16*3 block access = 43회의 disk access필요
Database System Concepts - 5th Edition, Aug 27, 2005.
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Example of Nested-Loop Join Costs
 Compute depositor
customer, with depositor as the outer relation.

Let customer have a primary B+-tree index on the join attribute customername, which contains 20 entries in each index node.

Then, since customer has 10,000 tuples, the height of the tree is 4, and
one more access is needed to find the actual data

depositor has 5000 tuples
 Cost of block nested-loop join


400*100 + 100 = 40,100 disk accesses assuming worst case memory
(may be significantly less with more memory)
Cost of indexed nested-loop join

100 + 5000 * 5 = 25,100 disk accesses


For each depositor record, search B+ tree of customer
CPU cost likely to be less than that for block nested loops join
Database System Concepts - 5th Edition, Aug 27, 2005.
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Merge-Join
1.
Sort both relations on their join attribute (if not already sorted on the join attributes).
2.
Merge the sorted relations to join them
1.
Join step is similar to the merge stage of the sort-merge algorithm.
2.
Main difference is handling of duplicate values in join attribute — every pair with
same value on join attribute must be matched
3.
Detailed algorithm in book
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Merge Join
보조자료
on The borrower and loan Relation

Loan number
Loannumber
Branchname
amount
Smith
L-11
L-11
Round Hill
900
Jackson
L-14
L-14
Downtown
1500
Hayes
L-15
L-15
Perryridge
1500
Adams
L-16
L-16
Perryridge
1300
Jones
L-17
L-17
Downtown
1000
Williams
L-17
Smith
L-23
L-23
Redwood
2000
Curry
L-93
L-93
Mianus
500
Customname
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Merge-Join (Cont.)
 Can be used only for equi-joins and natural joins
 Each block needs to be read only once (assuming all tuples for any given value of
the join attributes fit in memory
 Thus number of block accesses for merge-join is
br + bs
+
the cost of sorting if relations are unsorted.
 hybrid merge-join: If one relation is sorted, and the other has a secondary B+-tree
index on the join attribute

Merge the sorted relation with the leaf entries of the B+- tree

Using the order property of the leaf entries of the B+- tree

Sort the result on the addresses of the unsorted relation’s tuples

Scan the unsorted relation in physical address order and merge with previous
result, to replace addresses by the actual tuples

Sequential scan more efficient than random lookup
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Hash-Join
 Applicable for equi-joins and natural joins.
 A hash function h is used to partition tuples of both relations
 h maps JoinAttrs values to {0, 1, ..., n}, where JoinAttrs denotes the common
attributes of r and s used in the natural join.
 r0, r1, . . ., rn denote partitions of r tuples
 Each tuple tr  r is put in partition ri where i = h (tr [JoinAttrs]).
 s0,, s1. . ., sn denotes partitions of s tuples

Each tuple ts s is put in partition si, where i = h (ts [JoinAttrs]).
 Note: In book, ri is denoted as Hri, si is denoted as Hsi and n is denoted as nh
 r tuples in ri need only to be compared with s tuples in si

Need not be compared with s tuples in any other partition, since:

an r tuple and an s tuple that satisfy the join condition will have the same
value for the join attributes..
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Hash-Join (Cont.)
JOIN
JOIN
JOIN
JOIN
JOIN
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보조자료
Hash Join
on The borrower and loan Relation

Customname
해시
집합
해시
집합
(R)
(S)
Loannumber
Branch-name
amou
nt
Adams
L-16
3
0
L-11
Round Hill
900
Curry
L-93
6
1
L-14
Downtown
1500
Hayes
L-15
2
2
L-15
Perryridge
1500
Jackson
L-14
1
3
L-16
Perryridge
1300
Jones
L-17
4
4
L-17
Downtown
1000
Smith
L-11
0
5
L-23
Redwood
2000
Smith
L-23
5
6
L-93
Mianus
500
Williams
L-17
4
Customname
Database System Concepts - 5th Edition, Aug 27, 2005.
Loan number
Loan number
해시
집합
해시
집합
(R)
(S)
Loannumber
Branch-name
amou
nt
Smith
L-11
0
0
L-11
Round Hill
900
Jackson
L-14
1
1
L-14
Downtown
1500
Hayes
L-15
2
2
L-15
Perryridge
1500
Adams
L-16
3
3
L-16
Perryridge
1300
Jones
L-17
4
4
L-17
Downtown
1000
Williams
L-17
4
Smith
L-23
5
5
L-23
Redwood
2000
Curry
L-93
13.47
6
6
L-93
Mianus
500
©Silberschatz, Korth and Sudarshan
Hash-Join Algorithm
The hash-join of r and s is computed as follows.
1. Partition the relation s using hashing function h.
When partitioning a relation, one block of memory is reserved as the output
buffer for each partition.
2. Partition r similarly.
3. For each i:
(a)
Load si into memory and build an in-memory hash index on it using the
join attribute.
This hash index uses a different hash function than the earlier one h.
(b)
Read the tuples in ri from the disk one by one.
For each tuple tr locate each matching tuple ts in si using the in-memory
hash index.
Output the concatenation of their attributes.
Relation s is called the build input and
r is called the probe input.
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Hash-Join Algorithm Analysis
 The value n and the hash function h is chosen such that each si should fit in
memory.

Typically n is chosen as bs/M * f where f is a “fudge factor”, typically
around 1.2

The probe relation partitions si need not fit in memory
 Recursive partitioning required if number of partitions n is greater than
number of pages M of memory.

instead of partitioning n ways, use M – 1 partitions for s

Further partition the M – 1 partitions using a different hash function

Use same partitioning method on r

Rarely required: e.g., recursive partitioning not needed for relations of
1GB or less with memory size of 2MB, with block size of 4KB.
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Handling of Overflows in Hash-Join
 Hash-table overflow occurs in partition si if si does not fit in memory.
Reasons could be

Many tuples in s with same value for join attributes

Bad hash function
 Partitioning is said to be skewed if some partitions have significantly more
tuples than some others
 Overflow resolution can be done in build phase

Partition si is further partitioned using different hash function.

Partition ri must be similarly partitioned.
 Overflow avoidance performs partitioning carefully to avoid overflows during
build phase

E.g. partition build relation into many partitions, then combine them
 Both approaches fail with large numbers of duplicates

Fallback option: use block nested loops join on overflowed partitions
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Cost of Hash-Join
 If recursive partitioning is not required: cost of hash join is
3(br + bs) +2  nh
 If recursive partitioning required, number of passes required for partitioning s is
logM–1(bs) – 1. This is because each final partition of s should fit in memory.
 The number of partitions of probe relation r is the same as that for build relation
s; the number of passes for partitioning of r is also the same as for s.
 Therefore it is best to choose the smaller relation as the build relation.
 Total cost estimate with recursive partitioning is:
2(br + bs logM–1(bs) – 1 + br + bs
 If the entire build input can be kept in main memory, n can be set to 0 and the
algorithm does not partition the relations into temporary files.

Cost estimate goes down to br + bs.
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Example of Cost of Hash-Join
customer
depositor
 Assume that memory size is 20 blocks
 bdepositor= 100 and bcustomer = 400.
 depositor is to be used as build input.

Partition it into five partitions, each of size 20 blocks.

This partitioning can be done in one pass.
 Similarly, partition customer into five partitions, each of size 80.

This is also done in one pass.
 Therefore total cost: 3(100 + 400) = 1500 block transfers

ignores cost of writing partially filled blocks
Database System Concepts - 5th Edition, Aug 27, 2005.
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Hybrid Hash–Join
 Useful when memory sized are relatively large, and the build input is bigger than
memory.
 Main feature of hybrid hash join:
Keep the first partition of the build relation in memory.
 E.g. With memory size of 25 blocks, depositor can be partitioned into five
partitions, each of size 20 blocks.
 Division of memory:
 The first partition occupies 20 blocks of memory

1 block is used for input, and 1 block each for buffering the other 4 partitions.
 customer is similarly partitioned into five partitions each of size 80;
 the first is used right away for probing, instead of being written out and read
back.
 Cost of 3(80 + 320) + 20 +80 = 1300 block transfers for
hybrid hash join, instead of 1500 with plain hash-join.
 Hybrid hash-join most useful if M >>
Database System Concepts - 5th Edition, Aug 27, 2005.
bs
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Complex Joins
 Join with a conjunctive condition:
r
1  2...   n
s

Either use nested loops/block nested loops, or

Compute the result of one of the simpler joins r

i
s
final result comprises those tuples in the intermediate result that satisfy
the remaining conditions
1  . . .  i –1  i +1  . . .  n
 Join with a disjunctive condition
r
1  2 ...  n s

Either use nested loops/block nested loops, or

Compute as the union of the records in individual joins r
(r
1 s)
 (r
2
s)  . . .  (r
Database System Concepts - 5th Edition, Aug 27, 2005.
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n
 i s:
s)
©Silberschatz, Korth and Sudarshan
Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
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Other Operations: Duplicate elimination
 Duplicate elimination can be implemented via hashing or sorting.

On sorting duplicates will come adjacent to each other, and all but one set
of duplicates can be deleted.

Optimization: duplicates can be deleted during run generation as well as
at intermediate merge steps in external sort-merge.

Hashing is similar – duplicates will come into the same bucket.
 Projection is implemented by performing projection on each tuple followed by
duplicate elimination.
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Other Operations : Aggregation
 Aggregation can be implemented in a manner similar to duplicate elimination.

Sorting or hashing can be used to bring tuples in the same group together,
and then the aggregate functions can be applied on each group.

Optimization: combine tuples in the same group during run generation and
intermediate merges, by computing partial aggregate values

For count, min, max, sum:
– keep aggregate values on tuples found so far in the group.
– When combining partial aggregate for count, add up the aggregates

For avg:
– keep sum and count,
– and divide sum by count at the end
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Other Operations : Set Operations
 Set operations (,  and ):

can either use variant of merge-join after sorting, or variant of hash-join.
 E.g., Set operations using hashing:

Partition both relations using the same hash function, thereby creating, r0,,
r1, .., rn and s0 , s1, .., sn

Process each partition i as follows.


Using a different hashing function, build an in-memory hash index on ri
after it is brought into memory.
r  s: Add tuples in si to the hash index if they are not already in it.

At end of si add the tuples in the hash index to the result.
r  s: Output tuples in si to the result if they are already there in the hash
index.
r – s: For each tuple in si, if it is there in the hash index, delete it from the
index.

At end of si add remaining tuples in the hash index to the result.
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Other Operations : Outer Join
 Outer join can be computed either as

A join followed by addition of null-padded non-participating tuples.

by modifying the join algorithms.
 Modifying merge join to compute r
s
s, non participating tuples are those in r – R(r

In r

Modify merge-join to compute r
s: During merging, for every tuple tr
from r that do not match any tuple in s, output tr padded with nulls.

Right outer-join and full outer-join can be computed similarly.
 Modifying hash join to compute r
s)
s

If r is probe relation, output non-matching r tuples padded with nulls

If r is build relation, when probing keep track of which
r tuples matched s tuples. At end of si output
non-matched r tuples padded with nulls
Database System Concepts - 5th Edition, Aug 27, 2005.
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Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
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Evaluation of Expressions
 So far, we have seen algorithms for individual operations
 Need to consider alternatives for evaluating an entire expression tree


Materialization:

generate results of an expression whose inputs are relations or are
already computed,

materialize (store) it on disk. Repeat.
Pipelining:

pass on tuples to parent operations even as an operation is being
executed
 Alternatives for evaluating an entire expression tree may have different costs
 We study above alternatives in more detail
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Materialization
 Materialized evaluation:

evaluate one operation at a time, starting at the lowest-level.
 Use intermediate results materialized into temporary relations to evaluate nextlevel operations.
 E.g., in figure below,
 balance2500 (account )
 compute and store (materialize)

then compute the store its join with customer,
 and finally compute the projections on customer-name.
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Materialization (Cont.)
 Materialized evaluation is always applicable
 Cost of writing results to disk and reading them back can be quite high

Our cost formulas for operations ignore cost of writing final results to disk,
so

Overall cost = sum of costs of individual operations +
cost of writing intermediate results to disk
 Double buffering: (use two output buffers for each operation)

When one buffer is full, write it to disk while the other buffer is getting filled

Allows overlap of disk writes with computation

Reduces the total execution time
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Pipelining
 Pipelined evaluation :

evaluate several operations simultaneously,

passing the results of one operation on to the next.
 E.g., in previous expression tree, don’t store the result of
 balance 2500 (account )

instead, pass tuples directly to the join.

Similarly, don’t store result of join, pass tuples directly to projection.
 Much cheaper than materialization: no need to store a temporary relation to disk.
 Pipelining may not always be possible – e.g., sort, hash-join.
 For pipelining to be effective, use evaluation algorithms that generate output
tuples even as tuples are received for inputs to the operation.
 Pipelines can be executed in two ways

demand driven and producer driven
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Pipelining (Cont.)
 In demand driven or
lazy evaluation

System repeatedly requests next tuple from top level operation

Each operation requests next tuple from children operations as required, in order
to output its next tuple

In between calls, operation has to maintain “state” so it knows what to return
next

Each operation is implemented as an iterator implementing the following
operations

open()
– E.g. file scan: initialize file scan, store pointer to beginning of file as state
– E.g.merge join: sort relations and store pointers to beginning of sorted
relations as state

next()
– E.g. for file scan: Output next tuple, and advance and store file pointer
– E.g. for merge join: continue with merge from earlier state till
next output tuple is found. Save pointers as iterator state.

close()
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Pipelining (Cont.)
 In produce-driven or eager pipelining


Operators produce tuples eagerly and pass them up to their parents

Buffer maintained between operators, child puts tuples in buffer, parent
removes tuples from buffer

if buffer is full, child waits till there is space in the buffer, and then
generates more tuples
System schedules operations that have space in output buffer and can
process more input tuples
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Evaluation Algorithms for Pipelining
 Some algorithms are not able to output results even as they get input tuples

E.g. merge join, or hash join

These result in intermediate results being written to disk and then read back
always
 Algorithm variants are possible to generate (at least some) results on the fly, as
input tuples are read in

E.g. hybrid hash join generates output tuples even as probe relation tuples
in the in-memory partition (partition 0) are read in

Pipelined join technique: Hybrid hash join, modified to buffer partition 0
tuples of both relations in-memory, reading them as they become available,
and output results of any matches between partition 0 tuples

When a new r0 tuple is found, match it with existing s0 tuples, output
matches, and save it in r0

Symmetrically for s0 tuples
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Multiple Joins
 Join involving three relations: loan
depositor
customer
 Strategy 1.

Compute depositor

use result to compute loan
customer first
(depositor
customer)
 Strategy 2.

Computer loan

and then join the result with customer.
depositor first,
 Strategy 3.

Perform the pair of joins at once.

Build an index on loan for loan-number, and on customer for customer-name.

For each tuple t in depositor, look up the corresponding tuples in customer
and the corresponding tuples in loan.

Each tuple of deposit is examined exactly once.
 Strategy 1 & 2 may use materialization or pipelining
 Strategy 3 combines two operations into one special-purpose operation that is
more efficient than implementing two joins of two relations.
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Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
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Chapter 13. Summary (1)
 The first action that the system must perform on a query is to translate the
query into its internal form, which (for relational database systems) is usually
based on the relational algebra.

In the process of generating the internal form of the query, the parser
checks the syntax of the user’s query, verifies that the relation names
appearing in the query are names of relations in the database, and so on.

If the query was expressed in terms of a view, the parser replaces all
references to the view name with the relational-algebra expression to
compute the view.
 Given a query, there are generally a variety of methods for computing the
answer.

It is the responsibility of the query optimizer to transform the query as
entered by the user into an equivalent query that can be computed more
efficiently.

Chapter 14 covers query optimization.
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Chapter 13. Summary (2)
 We can process simple selection operations by performing a linear scan, by doing
a binary search, or by making use of indices.

We can handle complex selections by computing unions and intersections of
the results of simple selections.
 We can sort relations larger than memory by the external merge-sort algorithm.
 Queries involving a natural join may be processed in several ways, depending on
the availability of indices and the form of physical storage for the relations.

If the join result is almost as large as the Cartesian product of the two relations,
a block nested-loop join strategy may be advantageous.

If indices are available, the indexed nested-loop join can be used.

If the relations are sorted, a merge join may be desirable. It may be
advantageous to sort a relation prior to join computation (so as to allow use of
the merge join strategy).

The hash join algorithm partitions the relations into several pieces, such that
each piece of one of the relations fits in memory. The partitioning is carried out
with a hash function on the join attributes, so that corresponding pairs of
partitions can be joined independently.
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Chapter 13. Summary (3)
 Duplicate elimination, projection, set operations (union, intersection and
difference), and aggregation can be done by sorting or by hashing.
 Outer join operations can be implemented by simple extensions of join
algorithms.
 Hashing and sorting are dual, in the sense that any operation such as duplicate
elimination, projection, aggregation, join, and outer join that can be
implemented by hashing can also be implemented by sorting, and vice versa

that is, any operation that can be implemented by sorting can also be
implemented by hashing.
 An expression can be evaluated by means of materialization, where the system
computes the result of each subexpression and stores it on disk, and then uses
it to compute the result of the parent expression.
 Pipelining helps to avoid writing the results of many subexpressions to disk, by
using the results in the parent expression even as they are being generated.
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Ch13. Bibliographical Notes (1)
 A query processor must parse statements in the query language, and must
translate them into an internal form.
 Parsing of query languages differs little from parsing of traditional programming
languages.
 Most compiler texts, such as Aho et al. [1986], cover the main parsing techniques,
and present optimization from a programming-language point of view.
 Knuth [1973] presents an excellent description of external sorting algorithms,
including an optimization that can create initial runs that are (on the average) twice
the size of memory.
 Based on performance studies conducted in the mid-1970s, database systems
of that period used only nested-loop join and merge join.
 These studies, which were related to the development of System R, determined
that either the nested-loop join or merge join nearly always provided the optimal
join method(Blasgen and Eswaran [1976]); hence, these two wer the only join
algorithms implemented in System R.
 The System R study, however, did not include an analysis of hash join
algorithms. Today, hash joins are considered to be highly efficient.
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Ch13. Bibliographical Notes (2)
 Hash join algorithms were initially developed for parallel database systems.
 Hash join techniques are described in Kitsuregawa et al. [1983], and
extensions including hybrid hash join are described in Shapiro [1986].
 Zeller and Gray [1990] and Davison and Graefe [1994] describe hash join
techniques that can adapt to the available memory, which is important in
systems where multiple queries may be running at the same time.
 Graefe et al. [1998] describes the use of hash joins and hash teams, which
allow pipelining of hash-joins by using the same partitioning for all hash-joins in
a pipeline sequence, in the Microsoft SQL Server.
 Graefe [1993] presents an excellent survey of query-evaluation techniques.
 An earlier survey of query-processing techniques appears in Jarke and Koch
[1984].
 Query processing in main memory database is covered by DeWitt et al. [1984]
and Whang and Krishnamurthy [1990].
 Kim [1982] and Kim [1984] describe join strategies and the optimal use of
available main memory.
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Chapter 13: Query Processing
 13.1 Overview
 13.2 Measures of Query Cost
 13.3 Selection Operation
 13.4 Sorting
 13.5 Join Operation
 13.6 Other Operations
 13.7 Evaluation of Expressions
 13.8 Summary
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End of Chapter
Database System Concepts, 5th Ed.
©Silberschatz, Korth and Sudarshan
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