systém riadenia bázy dát (database management system)

Ján GENČI

PDT

2009

Systém riadenia bázy dát(Database Management System)

2

Obsah

• RAID

• 2-phase multiway sort-merge

• Fyzická organizácia dát

• Indexovanie

• Systémový katalóg

• Operácie relačnej algebry (krátko)

• Implementácia operácií relačnej algebry

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Obsah (nestihneme)

• Transakčné spracovanie

• Paralelné spracovanie

• Zotavenie po chybách

4

Literatúra [1]

• Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer D. Widom: Database System Implementation, Prentice Hall, 1999. ISBN-10: 0130402648,

pp.653

• Database Systems: The Complete Book, 2001

5

Literatúra [2]

• Elmasri R., Navathe S. B. : Fundamentals of database systems. 4th ed., Pearson Education, 2001. 5th ed. – 2006, pp. 1030 (ch. 13-15 -19; 120 resp. 220 str.)

6

Literatúra [3]

• Ramakrishnan R., Gehrke J.: Database Management Systems. McGraw-Hill Science/Engineering/Math; 3rd ed., 2002, pp. 906 (ch. 7-14; 220 str.)

7

Literatúra [4]

• Abraham Silberschatz, Henry Korth, S. Sudarshan: Database System Concepts. McGraw-Hill Science/Engineering/Math; 5th ed., 2005. pp.~920 (ch. 11-14-17; 170 resp. 290 str.

RAID

Obrázky (väčšina) z [2]

9

RAID

• Originally - Redundant Arrays of Inexpensive Disks.

• Currently - Redundant Array of Independent Disks

• Chen, Lee, Gibson, Katz, and Patterson (1994), ACM Computing Survey, Vol. 26, No.2 (June 1994).

• http://sk.wikipedia.org/wiki/RAID (pekne názorne spracované)

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RAID 0

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RAID 1, 2

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RAID 3, 4, 5, 6

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RAID – ďalšie kombinácie

• 10, 01 - Kombinácie základných RAIDov

• Performance:– Block-interleaved distributed-parity disk arrays

(RAID 5) have the best small read, large read, and large write performance of any redundant disk array.

– Small write requests are somewhat inefficient compared with redundancy schemes such as mirroring.

Two phase, multiway sort-merge

Partially based on presentation of Simonas Šaltenis - Advanced Algorithm Design and Analysis

15

Purpose of Algorithm

• Sorting of very large collection of data (Data>Memory)

• Classic algorithm – With’s sort-merge algorithm (Wirth C.: Algoritmy a dátové štruktúry.)

16

Princíp – 1. fáza

1. Vytvoriť maximálne možné veľké „behy“ (utriedené postupnosti elementov) – najlepšie načítaním do dostupnej pamäte a zotriedením napr. quick-sortom

2. Spájanie behov (mergovanie)

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Princíp – 2. fáza

File Y:

File X:

Run 1 Run 2

Current page

Current page

EOF

Bf1p1

Bf2p2 Bfo

po

min(Bf1[p1], Bf2[p2], …, Bfk[pk])

Read, when pi = B

Write, when Bfo full

Run k=n/m

Current page

Bfkpk

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Zhodnotenie

• Phase 1: O(n), Phase 2: O(n)

• Total: O(n) I/Os!

• Files only of “limited” size can be sorted– Phase 2 can merge a maximum of m-1 runs

(m – number of buffers).– Which means: N/M (number of runs) < m-1

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Triedenie veľmi veľkých súborov

(m-1)2M

(m-1)3M = N

Phase 2

Phase 1

…

M M

(m-1)M

M M

M

M

… M M

(m-1)M

M M

M

M

… M M

(m-1)M

M M

M

M

…

… …. . .

. . .

. . .

. . . . . .

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Otázky

SRBD – štruktúry a algoritmy

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Primárne (fyzické) organizácie

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O čom budeme hovoriť

• Podporované dátové typy

• Formovanie záznamov

• Organizácia (radenie) záznamov– fyzická – logická

• „Umiestnenie“ DBMS v rámci OS

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Podporované dátové typy

• Tzv. built-in dátové typy

• Pre účely ukladania dát, je pre nás zaujímavá veľkosť dátového typu (sizeof(typ))

• „Sémantika“ typu je podporená implementáciou (HW alebo SW) relevantných operácií (out of scope)

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Storage Record Formats

• A fixed-length record

• A record with variable-length fields

• A variable-field record with separator characters.

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Storage Record Formats [2]

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Fixed length record

• Size of items is recorded in the system catalog

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Variable length records

• Result of item(s) of variable length

F 1 F 2 F 3 F 4$ $ $ $ Fi = po lo žka i

je dno tlivé po lia sú o dde le né o dde ľo vač m i

F 1 F 2 F 3 F 4

po le ukazo vate ľo v na po lo žky záznam u

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NULL value representation

• Prakticky väčšina zdrojov o spôsobe implementácie „mlčí“

• Pri záznamoch premenlivej dĺžky sa dá využiť null pointer na prvok záznamu

• ORACLE v dokumentácii pre ORA7 prezentoval ukladanie NULL hodnoty cez bitmapový prefix záznamu

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Fyzická organizácia záznamov

s lo t 1

s lo t 2

s lo t N

N

s lo t 1

s lo t 2

s lo t M

M

s lot 3

01 11

M 3 2 1

po č e t záznam o v po č e t s lo to vhlavič kas tránky

vo ľném ie s to

" p ac k e d " o r g an i zác i a " u n p ac k e d " ( b i t m ap o vá) o r g an i zác i a

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Fyzická organizácia záznamov 2

r id = ( i,N )

r id = ( i,2)

r id = ( i,1)

s t r án k a i

dĺžka 24

vo ľ n é m i e s t o

N241620

ad r e s ár s l o t o v( s l o t d i r e c t o r y )

N 2 1

poč et položiekv adres ár i s lo tov

d át o váo b l as ť

adres ár s lo tov obs ahujeokrem dĺžky každého záznam uaj ukazovateľ na zač iatokkaždého záznam u

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Umiestňovanie záznamov do fyzických blokov

• Spanned

• Unspanned

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Logické organizácie záznamov

• Sekvenčná

• Hašovaná

• Heap (hromada)

• Zhodnotenie z pohľadu operácií insert, find a delete

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Sekvenčná organizácia

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Zhodnotenie – sekvenčná org.

• Insert – drahá operácia (potreba posunúť priemerne N/2 záznamov) – oblasti pretečenia (overflow areas)

• Find – možnosť binárneho vyhľadávania podľa usporiadavajúceho atribútu - O(log2N), ináč O(N) = N/2 alebo N

• Delete – drahá operácia (potreba posunúť priemerne N/2 záznamov) – možnosť označovať záznamy ako zmazané pack

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Interné Hashovanie

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Zhodnotenie – hashovanie

• Insert – O(1) ak neuvažujeme konflikty; ak uvažujeme = najhorší prípad O(N)

• Find – O(1) – hashovací atribút, O(N) ostatné atribúty

• Delete – O(1)

• Štruktúra musí byť dimenzovaná na maximálny počet záznamov

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Externé hashovanie

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Zhodnotenie - externé hashovanie

• Ako interné hashovanie

• Konflikty sa riešia blokmi pretečenia (viď ďalší slajd )

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Ext. Hashovanie – overflow bloky

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Extendible hashing

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Zhodnotenie – ext. hashing

• Ako externé hashovanie

• Plusom je možnosť dynamického rozširovania „veľkosti hashovacieho poľa“

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Heap (hromada)

• Záznamy sú neusporiadané – nie je usporiadavací atrubút

• Strácame možnosť - binárne vyhľadávanie; primárny index (ale iba pre usporiad. atr.)

• Veľmi efektívna operácia INSERT

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Miesto DBMS v rámci OS

Cooked files Raw devices

• NTFS

DBMS

Služby OS

Filesystem

Driver

DBMS

Služby OS

-

Driver

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Otázky

Indexovanie

Z podstatnej časti podľa [2]

Všetky obrázky z [2]

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Index

• Alternatívny spôsob prístupu k dátam

• Lokalizácia záznamu podľa obsahu

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Kategorizácia indexov

• Podľa počtu úrovní:– Jedno-úrovňové– Viac-úrovňové

• Podľa indexovaného atribútu:– Primárne– Klastrovacie (clustering)– Sekundárne

• Podľa počtu indexovaných záznamov:– Hustý (dense) – všetky záznamy v indexe– Riedky (sparse) – len časť záznamov v indexe

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Primárny index

• Indexuje „usporiadavajúci“ (ordering) atribút

• Riedky (sparse) index

• „Kotviaci“ záznam

• INSERT problém

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Clustering index

• Aj nad „neusporia-davajúcim“ atribú-tom

• Primárna organizá-cia sa usporiada podľa daného atri-bútu – pri budovaní indexu

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Clustering index

• Pri bežnej práci sa primárna organizácia nemodifikuje, ale používajú sa overflow bloky

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Sekundárny index

• Index nad neusporiada-vajúcim atribútom (ale kľúčovým)

• Hustý (dense) index

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Sekundárny index

• Nad nekľúčovým atribútom (opakujúce sa hodnoty)

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Priebežné zhodnotenie

• Zatiaľ iba jednoúrovňové indexy• Prínos (N – počet záznamov, r – záznamov v bloku)

– Vyhľadávanie nad „ordered“ kľúčom – log2N

– Vyhľadávanie nad „non-ordered“ kľúčom – N/2– Vyhľadávanie nad nekľúčovým atribútom – N

– Primárny index log2(N/r)

– Sekundárny index log2N (počet čítaných blokov – podstatne

menší, kvôli vyššiemu blokovaciemu faktoru)

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Príklad – sekvenčný súbor (ordering attribute)

• Ordered file with r = 30,000 records • Block size B = 1024 bytes. • Records are of fixed size and are unspanned• Record length R = 100 bytes. • The blocking factor

bfr = floor(B/R) = floor(1024/100) = 10 records per block.

• The number of blocks b = (r/bfr) = r (30,000/1O)l = 3000 blocks.

• A binary search would need approximately – floor(log2 b) = floor(log2 3000) = 12 block accesses.

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Primárny index

• Na osvieženie pamäti

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Príklad – primárny index

• Key field of the file is V = 9 bytes long, a block pointer is P = 6 bytes

• size of index entry R = (9 + 6) = 15 bytes, blocking factor

bfri = floor(B/Ri ) = floor(1024/15) = 68 entries per block.

• The total number of index entries ri is equal to the number of blocks in the data file - 3000.

• The number of index blocks is hence bi = ceiling(r/bfri) = ceiling(3000/68) = 45 blocks.

• To perform a binary search on the index file would need ceiling (log2 bi)l = ceiling (log245) = 6 (block accesses).

• To search for a record using the index, we need one additional block access to the data file - total of 6 + 1 = 7 block accesses

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Príklad – sekundárny index

• As example 1: r = 30,000 ,R = 100 bytes, B = 1024 bytes.• To do a linear search, we would require

b/2 = 3000/2 = 1500 block accesses (on the average, 3000 in the worst case)

• Supppose V = 9 and P = 6 bfri = 68– secondary index is dense the total number of index entries ri

is equal to the number of records = 30,000.– The number of blocks needed for the index is

bi = ceiling(r/bfr) = 1(30,000/68) l = 442 blocks.– A binary search on this secondary index needs

ceiling(log2bi ) = ceiling (log2442) = 9 block accesses.

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Porovnanie (single-level) indexov

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Multi-Level Indexes

• Because a single-level index is an ordered file, we can create a primary index to the index itself ; in this case, the original index file is called the first-level index and the index to the index is called the second-level index.

• We can repeat the process, creating a third, fourth, ..., top level until all entries of the top level fit in one disk block

• A multi-level index can be created for any type of first-level index (primary, secondary, clustering) as long as the first-level index consists of more than one disk block

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Multilevel indexy

• Prvá úroveň - dense alebo sparse

• Ďalšie úrovne už iba sparse

• Top level – iba jeden blok

• Vyhľadávanie vyžaduje pribl. (logbfribi) „block accesses“

• INSERT problém !!!

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Dynamic Multilevel Indexes Using B-Trees and B+-Trees

• Because of the insertion and deletion problem, most multi-level indexes use B-tree or B+-tree data structures, which leave space in each tree node (disk block) to allow for new index entries

• These data structures are variations of search trees that allow efficient insertion and deletion of new search values.

• In B-Tree and B+-Tree data structures, each node corresponds to a disk block

• Each node is kept between half-full and completely full

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Dynamic Multilevel Indexes Using B-Trees and B+-Trees (contd.)

• An insertion into a node that is not full is quite efficient; if a node is full the insertion causes a split into two nodes

• Splitting may propagate to other tree levels

• A deletion is quite efficient if a node does not become less than half full

• If a deletion causes a node to become less than half full, it must be merged with neighboring nodes

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Difference between B-tree and B+-tree

• In a B-tree, pointers to data records exist at all levels of the tree

• In a B+-tree, all pointers to data records exists at the leaf-level nodes

• A B+-tree can have less levels (or higher capacity of search values) than the corresponding B-tree

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B-tree structure

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B+-tree structure

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B+-tree example

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B-tree example - numbers

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B+-tree example - numbers

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B-tree – duplicate keys

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Otázky

Systémový katalóg

Na základe prezentácie

Ľubomíra Miškoviča

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Čo je systémový katalóg

• Systémový katalóg uchováva dáta ktoré popisujú každú databázu (metadata)

• Obsahuje popis:– Položiek, viet, súborov a vzťahov medzi nimi– Konceptuálnej schémy, externých schém a

internú schému. Je tu popísané aj mapovanie medzi schémami na rôznych úrovniach

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Zjednodušený model prostredia databázového systému

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Obsah systémového katalógu

• Katalógy pre relačné SRBD obsahujú – Názvy relácií – Názvy atribútov– Domény atribútov– Primárne kľúče– Sekundárne kľúčové atribúty– Cudzie kľúče– Podmienky

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Obsah systémového katalógu

• Ďalej obsahujú popisy– Externých pohľadov– Uloženie štruktúr a indexov pre internú úroveň– Informácie o bezpečnosti a autorizácií, ktoré

definujú prístup používateľa k databázovým pohľadom

– Prihlasovacie mená tvorcov alebo vlastníkov každej relácie

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Obsah systémového katalógu

• Uchovávajú informácie ako– Veľkosť záznamu – Aktuálny počet záznamov– Počet indexov– Meno tvorcu každej relácie

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Spôsoby implementácie systémového katalógu

• Systémový katalóg môže byť vytváraný pre každú databázu v systéme, alebo môže byť spoločný pre všetky databázy

• Systémový katalóg môže byť tvorený tabuľkami, ktorých štruktúra je totožná s tabuľkou databázy alebo špeciálnou štruktúrou

80

Príklad systémových katalógov pre Informix

• Systables – opisuje každú tabuľku v databáze. Obsahuje jeden riadok pre každú tabuľku v databáze, pohľad alebo synonymum definované v databáze. Zahŕňa všetky tabuľky v databáze aj tabuľku systémového katalógu

• Syscolumns – definuje každý stĺpec v databáze. Pre každý stĺpec definovaný v tabuľke alebo pohľade existuje jeden riadok

• Sysindex – popisuje indexy v databáze. Obsahuje jeden riadok pre každý index definovaný v databáze

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Systables

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syscolumns

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Vzťah medzi tabuľkami

84

Oracle

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Postgres

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Otázky

Relačná algebra (RA) a implementácia operácií RA

Podľa [2]

88

Relačná algebra

• Relácia - podmnožina karteziánskeho súčinu

R D1 ... Dn

• Relačná algebra:– Formálny jazyk pre relačný model– Základný súbor operácií pre vyhľadávacie

dotazy

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• Selekcia • Projekcia • Kartézsky súčin • Spojenie (join) (theta-, equi-, natural- )

• Množinové (union kompatibilné):– Prienik (intersection) – Zjednotenie (union)– Rozdiel (difference) \

Operácie relačnej algebry

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Elementary conditionEC and condition C

• Definition:Elementary (simple) condition EC is clause of the form:

<Attribute> <Operator> <Value>

where operator is from the set of relational operators {=,<,>,<=,>=,≠}.

• Definition: Condition C is clause of the form :

[NOT] EC1 [{OR | AND } [ [NOT] EC2] …]

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Examples

• (O1): SSN='123456789'(EMPLOYEE)

• (O2): DNUMBER>5(DEPARTMENT)

• (O3): DNO=5(EMPLOYEE)

• (O4):

DNO=5 AND SALARY>30000 AND SEX=' F' (EMPLOYEE)

• (O5):

ESSN='123456789' AND PNO=10 (WORKS_ON)

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SELECT operation

• Definition:c = { tiR | c(ti)} (3-value

logic)Implementation:

– Linear search– Binary search– Using a primary index (or hash key)– Using a primary index to retrieve multiple records– Using a clustering index to retrieve multiple records– Using a secondary (B+-tree) index on an equality

comparison– ...

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S1:Linear search (brute force)

Retrieve every record in the file, and test whether its attribute values satisfy the

selection condition.

for every ti

if (c(ti) == TRUE)

output(ti)

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S2:Binary search

If the selection condition involves an equality comparison on a key attribute on which the

file is ordered.

SSN='123456789'(EMPLOYEE)

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S3: Using a primary index (or hash key)

If the selection condition involves an equality comparison on a key attribute with a primary index (or hash key), use the primary index (or hash key) to retrieve the record. Note

that this condition retrieves a single record (at most).

SSN='123456789'(EMPLOYEE)

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S4: Using a primary index to retrieve multiple records

If the comparison condition is >, >=, <', or <= on a key field with a primary index, use the

index to find the record satisfying the corresponding condition

DNUMBER>5(DEPARTMENT) (selectivity, distribution)

DNO=5 AND SALARY>30000 AND SEX=' F' (EMPLOYEE)

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S5: Using a clustering index to retrieve multiple records

If the selection condition involves an equality comparison on a non (key attribute with a

clustering index for example, DNO = 5 in S3) use the index to retrieve all the records

satisfying the condition.

DNO=5(EMPLOYEE) (if clusterred on DNO)

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S6: Using a secondary (B+-tree) index on an equality comparison

This search method can be used to retrieve a single record if the indexing field is a key (has unique values) or to retrieve multiple

records if the indexing field is not a key. This can also be used for comparisons involving

>, >=, <, or <=.

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S7: Conjunctive selection using an individual index

If an attribute involved in any single simple condition in the conjunctive condition has an access path that permits the use of one of the Methods S2 (binary search) to S6 (B-

tree), use that condition to retrieve the records and then check whether each retrieved record satisfies the remaining

simple conditions in the conjunctive condition.

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S8:Conjunctive selection using a composite index

If two or more attributes are involved in equality conditions in the conjunctive

condition and a composite index (or hash structure) exists on the combined fields-for example, if an index has been created on the composite key (ESSN, PNO) of the WORKS_ON file for O5-we can use the

index directly.

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JOIN operation

• R ⋈c S = {tiR,tjS| c(ti,tj) == TRUE }

• Implementácia– Nested-loop join (brute force)– Single-loop join (using an access structure to

retrieve the matching records)– Sort-merge join– Hash-join

102

J1. Nested-loop join (brute force)

For each record t in R (outer loop), retrieve every record s from S (inner loop) and test

whether the two records satisfy the join condition c (incl. theta-join).

for each ti

for each sj

if( c(ti,sj) == TRUE )

output(ti.sj)

Improvement - nested-block join

103

J2. Single-loop join (using an access structure to retrieve the matching

records)If an index (or hash key) exists for one of the

two join attributes-say, B of S,

retrieve each record t in R, one at a time (single loop), and then use the access

structure to retrieve directly all matching records s from S that satisfy

t[B] =t[A] (equi-join).

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J3. Sort-merge join

If the records of R and S are physically sorted (ordered) by value of the join attributes A and B,

respectively, we can implement the join in the most efficient way possible.

Both files are scanned concurrently in order of the join attributes, matching the records that have the same values for A and B. If the files are not sorted, they may be sorted first by using external sorting.

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J4. Hash-join

• The records of files R and S are both hashed to the same hash file, using the same hashing function on the join attributes A of R and B of S as hash keys.

• First, a single pass through the file with fewer records (say, R) hashes its records to the hash file buckets (partitioning phase - records of R are partitioned into the hash buckets).

• In the second phase (probing phase), a single pass through the other file (S) then hashes each of its records to probe the appropriate bucket, and that record is combined with all matching records from R in that bucket.

106

PROJECT operation

<attribute list>(R)

• Implementation:– straightforward to implement if <attribute list> includes

a key of relation R – the same number of records.– If <attribute list> does not include a key of R,

duplicate tuples must be eliminated (sorting, hashing).– Index can be used in some cases.

107

SET operation

• CARTESIAN PRODUCT operation R S is quite expensive, because its result includes a record for each combination of records from R and S.

• Can be improved by processing at the block level

• UNION, INTERSECTION, and SET DIFFERENCE apply only to union-compatible relations (that have the same number of attributes and the same attribute domains).

• Implementation - sort-merge technique and hashing

108

Sort-merge technique (for the SET operation)

• The two relations are sorted on the same attributes.

• After sorting, a single scan through each relation is sufficient to produce the result.

• For example, we can implement the UNION operation, R S, by scanning and merging both sorted files concurrently, and whenever the same tuple exists in both relations, only one is kept in the merged result.

• For the INTERSECTION operation, R S, we keep in the merged result only those tuples that appear in both relations.

109

Hashing (for the SET operation)

• One table is partitioned and the other is used to probe the appropriate partition.

• For example, to implement R S, first hash (partition) the records of R; then, hash (probe) the records of S, but do not insert duplicate records in the buckets.

• To implement R S, first partition the records of R to the hash file. Then, while hashing each record of S, probe to check if an identical record from R is found in the bucket, and if so add the record to the result file.

• To implement R - S, first hash the records of R to the hash file buckets. While hashing (probing) each record of S, if an identical record is found in the bucket, remove that record from the bucket.

110

Implementing Aggregate Operations

• The aggregate operators (MIN, MAX, COUNT, AVERAGE, SUM), when applied to an entire table, can be computed by a table scan or by using an appropriate index, if available.

• For example, consider the following SQL query:SELECT MAX(SALARY)FROM EMPLOYEE;

• If an (ascending) index on SALARY exists for the EMPLOYEE relation, then the optimizer can decide on using the index to search for the largest value by following the rightmost pointer in each index node from the root to the rightmost leaf.

111

Implementing Aggregate Operations

• The dense index can be used for the COUNT, AVERAGE, and SUM aggregates.

• The associated computation would be applied to the values in the index.

112

GROUP BY clause

• When a GROUP BY clause is used in a query, the aggregate operator must be applied separately to each group of tuples.

• In this case, the computation is more complex - the table must first be partitioned into subsets of tuples, where each partition (group) has the same value for the grouping attributes.

• Sorting or hashing are used to partition the file into the appropriate groups

• If a clustering index exists on the grouping attributes, then the records are already partitioned (grouped) into the appropriate subsets.

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• Otázky