Storage and File Structure. Architecture of a DBMS.

Storage and File Structure

Architecture of a DBMS

Outline

• Overview of Physical Storage Media• Magnetic Disks• RAID• Tertiary Storage • Storage Access• File Organization• Organization of Records in Files

• DBMS stores information on (“hard”) disks.– A disk is a sequence of bytes, each has a disk

address.– READ: transfer data from disk to main memory (RAM).– WRITE: transfer data from RAM to disk.

• Data are stored and retrieved in units called disk blocks or pages.– Each page has a fixed size, say 512 bytes. It contains

a sequence of records.– Typically records in a page have the same size, say

100 bytes.– Typically records implement relational tuples.

Disks and Files

Cache

Main memory

Flash memory

Magnetic disk

Optical disk

Magnetic tapes

Storage-device hierarchy

Small capacityPower on

Small capacityPower on

medium capacityPower off

large capacityPower off

large capacityPower off

Very large capacityPower off

Very fastVery expensive

Very fastexpensive

SlowerCheap

Slowercheap

Slowercheap

Very slowVery cheap

Why Not Store Everything in Main Memory?

• Costs too much. RAM is much more expensive than disk.

• Main memory is volatile. We want data to be saved between runs.

• Typical storage hierarchy:– Main memory (RAM) for currently used data.– Disk for the main database (secondary storage).– Tapes for archiving older versions of the data (tertiary

storage).

Arranging Pages on Disk

• Next block concept: – blocks on same track,

followed by– blocks on same

cylinder, followed by– blocks on adjacent

cylinder

• If blocks in a file are arranged sequentially on disk (by `next’), we minimize seek and rotational delay.

Platters

Spindle

Disk head

Arm movement

Arm assembly

Tracks

Cylinder

Only one head reads/writes a block at any one time.

Accessing a Disk Page

• Time to access (read/write) a disk block:– seek time (moving arms to position disk head on

track)– rotational delay (waiting for block to rotate under

head)– transfer time (actually moving data to/from disk

surface)

• Seek time and rotational delay dominate.– Seek time varies from about 1 to 20msec– Rotational delay varies from 0 to 10msec– Transfer rate is about 1msec per 4KB page (Accessing main memory location – 60 nanoseconds)

• Key to lower I/O cost: reduce seek/rotation delays!

RAID

• Redundant Arrays of Independent Disks

Disk array – several disks organized to increase performance and reliability of a storage system.

Data striping - distributes data over several disks – data is partitioned and distributed in a round-robin manner.

Striping unit - e.g. bit (bit interleaved) or block (block interleaved)

• Data bits 1 1 1 0 0 1 1 0 1 …

1 1 1 0 0 1 1 0 1 ….

RAID 0: Non-Redundant Striping

C C C C

P P P

RAID 0: Non-Redundant Striping

RAID 1: Mirrored Disks

RAID 2: Memory Style Error Correcting Codes – Hamming Code

RAID Level

Hamming Code

• Use of extra parity bits to identify single error• Parity bits: All bit positions that are powers of two E.g. 1, 2, 4, 8, …

• Position 1: check 1 bit, skip 1 bit, check 1 bit, skip 1 bit, etc. (1,3,5,7,9,11,13,15,...)

• Position 2: check 2 bits, skip 2 bits, check 2 bits, skip 2 bits, etc. (2,3,6,7,10,11,14,15,...)

• Position 4: check 4 bits, skip 4 bits, check 4 bits, skip 4 bits, etc. (4,5,6,7,12,13,14,15,20,21,22,23,...)

• Position 8: check 8 bits, skip 8 bits, check 8 bits, skip 8 bits, etc. (8-15,24-31,40-47,...)

Set parity bit to 1 if the number of “ones” in the checked bits is odd, set parity bit to 0 if it is even.Given data: 10011010000 ---- ? ? 1 ? 0 0 1 ? 1 0 1 0 0 0 0

Position 1 checks bits 1,3,5,7,9,11,13,15: ? _ 1 _ 0 0 1 _ 1 0 1 0 0 0 0. Even parity so set position 1 to a 0

Position 2 checks bits 2,3,6,7,10,11, 14,15:0 ? 1 _ 0 0 1 _ 1 0 1 0 0 0 0. Odd parity so set position 2 to a 1

Position 4 checks bits 4,5,6,7,12,13,14,15:0 1 1 ? 0 0 1 _ 1 0 1 0 0 0 0. Odd parity so set position 4 to a 1

Position 8 checks bits 8,9,10,11,12,13,14,15:0 1 1 1 0 0 1 ? 1 0 1 0 0 0 0. Even parity so set position 8 to a 0

Code word: 011100101010000

Suppose bit 5 has error 011100101010000 011110101010000

Parity bit discrepencies indicates position of error : 1010

P P P

RAID 2: Memory Style Error Correcting Codes

P -- disks for the parity bits, with 7 bits,bits 1, 2, 4 are parity bits and 3,5,6,7 are data bits.

Costly -- we need several parity disks to locate the single disk failure (note that it does not handle > 1 failed disk)

RAID

• Disk controller can find out which disk has failed.• no need to locate failure by parity bits• only keep one parity bit for the 4 data bits.

• If the number of ones in checked bits and parity bit is odd, the data bit should be corrected.

• 0 1 0 0 0 -- change 0 to 1

• 1 1 0 1 1 -- no change

P

RAID 3: Bit Interleaved Parity

Disk controller knows this disk has failed

RAID

• RAID 3 : A single block is distributed to all disks.

• RAID 4 : a single block can be served by a single disk

• Both have a single parity disk, which is a bottleneck since each write operation accesses this disk.

• Hence we may distribute the parity blocks also

P


P

RAID 4: Block Interleaved Parity

P

P

P PP P

P P

P P

P P


RAID 4: Block Interleaved Parity

P P P

RAID 5: Block Interleaved Distributed Parity

RAID 6: P + Q Redundancy: for > 1 error

PP

Buffer Management in a DBMS

• Data must be in RAM for DBMS to operate on it!• Table of <frame#, pageid> pairs is maintained.

DB

MAIN MEMORY

DISK

disk page

free frame

Page Requests from Higher Levels

BUFFER POOL

choice of frame dictatedby replacement policy

• If requested page is not in pool:– Choose a frame for replacement– If frame is dirty (updated), write it to disk– Read requested page into chosen frame

• Pin the page and return its address to the requestor.

If requests can be predicted (e.g., sequential scans)

pages can be pre-fetched several pages at a time!


When a Page is Requested

• To release a page, requestor of a page must unpin it, and indicate whether the page has been modified: – dirty bit is used for this.

• Page in pool may be requested many times, – a pin count is used. A page is a candidate for

replacement iff pin count = 0.

• Concurrency control & recovery may entail additional I/O when a frame is chosen for replacement.

• Frame is chosen for replacement by a replacement policy: Least-recently-used (LRU), MRU etc.


Record Formats: Fixed Length

• Information about field types the same for all records in a file; stored in system catalogs.

Base address (B)

L1 L2 L3 L4

F1 F2 F3 F4

Address = B+L1+L2

Record Formats: Variable Length

Two alternative formats (# fields is fixed):

Second format offers direct access to the i’th field

4 $ $ $ $

FieldCount

Fields Delimited by Special Symbols

F1 F2 F3 F4

F1 F2 F3 F4

Array of Field Offsets

Fixed Length Records on a page

Record id = <page id, slot #>. In the PACKED alternative, moving records for free

space management changes rid; may not be acceptable.

Slot 1Slot 2

Slot N

. . . . . .

N M10. . .

M ... 3 2 1PACKED UNPACKED, BITMAP

Slot 1Slot 2

Slot N

FreeSpace

Slot M

11

number of records

numberof slots

Variable Length Records on a page

Can move records on a page without changing rid; so, attractive for fixed-length records too.

Page iRid = (i,N)

Rid = (i,2)

Rid = (i,1)

Pointerto startof freespace

SLOT DIRECTORY

N . . . 2 1 20 16 24 N

# slots

Files of Records

• Page or block is OK when doing I/O, but higher levels of DBMS operate on records, and files of records.

• FILE: A collection of pages, each containing a collection of records. Must support:

– Insert /delete /modify record– read a particular record (specified using record id)– scan all records (possibly with some conditions on the

records to be retrieved)

Heap File –(randomly orderd file)-Implemented as a List

• The header page id and Heap file name must be stored somewhere.

• Each page contains 2 `pointers’ plus data.

HeaderPage

DataPage

DataPage

DataPage

DataPage

DataPage

DataPage Pages with

Free Space

Full Pages

Heap File Using a Page Directory

• The entry for a page can include the number of free bytes on the page.

• The directory is a collection of pages• Can easily search for a page with enough space for a

record to be inserted.

Data Page 1

DataPage 2

DataPage N

HeaderPage

DIRECTORY

Alternative File Organizations

• Heap (random order) files: Suitable when typical access is a file scan retrieving all records.

• Sorted Files: Best if records must be retrieved in some order, or only a `range’ of records is needed.

• Indexes: Data structures to organize records via trees or hashing. – Like sorted files, they speed up searches for a subset of

records, based on values in certain (“search key”) fields– Updates are much faster than in sorted files.

Daniels,22,6003

Ashby,25,3000

Bristow,29,2007

Basu, 33, 4003

Jones, 40, 6003

Smith, 44, 3000

Tracy, 44, 5004

Cass, 50, 5004

Sorted file onAge field

Smith, 44, 3000

Jones, 40, 6003

Tracy, 44, 5004

Ashby, 25, 3000

Basu, 33, 4003

Bristow,29,2007

Cass,50, 5004

Daniels,22,6003

Heap file(randomlyOrdered)

Indexing

Indexing

• Indexing mechanisms speed up access to desired data.– E.g., author catalog in library

• Search Key - set of attributes used to look up records in a file.• An index file consists of records (called index entries) of the

form

• Index files are typically much smaller than the original file • Two basic kinds of indices:

– Ordered indices: search keys are stored in sorted order– Hash indices: search keys are distributed uniformly across

“buckets” using a “hash function”.

search-key pointer

Indexes• Any subset of the fields of a relation can be the search key for

an index on the relation.

Search key is not the same as candidate key or superkey (set of fields that uniquely identify a record in a relation).

• An index contains a collection of index entries

• An index entry is denoted as k* where k is a search key value and * tells where to find record(s) containing k

Given k, an index helps to retrieves all index entries k*

k1* k2*

k4* k5*

k3*

Employee

eid ename address sex dname

Index Entry k* in Index

1. <k, record id (rid) of data record with search key value k>

2. <k, list of rids of data records with search key value k>

assuming field ‘name’ is the search key

1. <“Lin Wang”, 10101> where 10101 is the rid of a record that contains “Lin Wang”

2. <“Lin Wang”, 10101, 10111, 11010> where 10101, 10111, 11010 are records which all contain “Lin Wang”.

Index Classification

• Unique index: Search key contains a candidate key.

• Clustered vs. unclustered: If order of data records is the same as, or `close to’, order

of index entries in the index, then it is called a clustered index.

• A file can be clustered on at most one search key.

• Cost of retrieving data records through index varies greatly based on whether index is clustered or not!

Clustered and unclustered Indexes

Smith, 44, 3000

Jones, 40, 6003

Tracy, 44, 5004

Ashby, 25, 3000

Basu, 33, 4003

Bristow,29,2007Cass,50, 5004

Daniels,22,6003

Heap file(randomlyOrdered)

Daniels,22,6003

Ashby,25,3000

Bristow,29,2007Basu, 33, 4003

Jones, 40, 6003

Smith, 44, 3000

Tracy, 44, 5004

Cass, 50, 5004

File sorted onAge field

Ashby

Basu

Bristow

Cass

Daniels

Jones

Smith

Tracy

Index on search key ‘name’, also the primary keyunclustered

22

33

44

Index on searchKey ‘age’clustered

Overflow page in Clustered index

Ashby,25,3000

Basu, 33, 4003

Bristow,29,2007

Cass, 50, 5004

Daniels,22,6003

Jones, 40, 6003

Smith, 44, 3000

Tracy, 44, 5004

Sorted file onname field

Ashby

Cass

Smith

Index on searchKey ‘name’clustered

We must use anoverflow page

Edward,22,2500

Insert record:(Edward, 22, 2500)

–Note: overflow pages may be needed for inserts. –(Thus, order of data records is ‘close to’, but not identical to, the sort order.)

Dense vs Sparse Index

• Dense vs Sparse:

• If there is at least one index entry in the index per search key value, then dense.

Sparse Index

Ashby, 25, 3000

Smith, 44, 3000

Ashby

Cass

Smith

22

25

30

40

44

44

50On name

Data FileDense Index

On Age

33

Bristow, 30, 2007

Basu, 33, 4003

Cass, 50, 5004

Tracy, 44, 5004

Daniels, 22, 6003

Jones, 40, 6003

Every sparse index is clustered!

Composite search keys

Composite Search Keys:

a combination of fields.

• Equality query: every field value is equal to a constant value. E.g. age=30 and sal =75

<age, sal> or <sal, age> allows efficient search.

sue 30 75

bob

cal

joe 20

10

20

8018

20

name age sal

<sal, age>

<age, sal> <age>

<sal>

20,20

20,10

18,80

30,75

20,20

10,20

75,30

80,18

18

20

20

30

10

20

75

80

Data recordssorted by name

index entries in indexsorted by <sal,age>

index entriessorted by <sal>

Composite search keys

Composite Search Keys: a combination of fields.

• Range query: Some field value is not a constant. E.g.:

• age =30; • age=30 and sal > 10

index entries in an index is sorted by the search key -- support range queries on the search key.

<age, sal> and <sal, age> allow 1 pointer search

<sal> requires 3 pointer searches –

for ages 20, 75, 80

sue 30 75

bob

cal

joe 20

10

20

8018

20

name age sal

<sal, age>

<age, sal> <age>

<sal>

20,20

20,10

18,80

30,75

20,20

10,20

75,30

80,18

18

20

20

30

10

20

75

80




Index only evaluation

Index only query evaluation– no need to retrieve data records

E.g. find the salaries of employees with

• age=30

– <age, sal> or <sal, age>

allows index only evaluation.

– <age, sal> allows faster search.

sue 30 75

bob

cal

joe 20

10

20

8018

20

name age sal

<sal, age>

<age, sal> <age>

<sal>

20,20

20,10

18,80

30,75

20,20

10,20

75,30

80,18

18

20

20

30

10

20

75

80




Choice of Indexes

• Consider the most important queries in turn. Consider the best query evaluation plan using the

current indexes, and see if a better plan is possible with an additional index.

• to do this we must understand how a DBMS evaluates queries and creates query evaluation plans!

• Before creating an index, must also consider the impact on the updates– Trade-off: Indexes can make queries go faster,

but updates slower. Require disk space, too.

Index Selection Guidelines

• Multi-attribute search keys should be considered when a WHERE clause contains several conditions.

– Order of attributes is important for range queries.

– can sometimes enable index-only strategies for important queries.

For index-only strategies, clustering is not important!

• Choose indexes that benefit as many queries as possible. Since only one index can be clustered per relation, choose it based on important queries that would benefit the most from clustering.

Storage and File Structure. Architecture of a DBMS.

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