File Organizations and Indexing

Lecture 4R&G Chapter 8

"If you don't find it in the index, look very carefully through the entire catalogue."

-- Sears, Roebuck, and Co., Consumer's Guide, 1897

Review: Memory, Disks, & Buffer Mgt

• Everything won’t fit in RAM (usually)• Hierarchy of storage, RAM, disk, tape• “Block” - unit of storage on disk• “Frame” – a block-sized chunk of memory• Allocate space on disk for fast access• Buffer pool management

– Frames in RAM to hold blocks– Policy to move blocks between RAM & disk

Context

Query Optimizationand Execution

Relational Operators

Files and Access Methods

Buffer Management

Disk Space Management

DB

Files of Records

• Blocks interface for 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– fetch a particular record (specified using

record id)– scan all records (possibly with some conditions

on the records to be retrieved)• Note: typically

page size = block size = frame size.

Record Formats: Fixed Length

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

• Finding i’th field done via arithmetic.

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 offers direct access to i’th field, efficient storage of nulls (special don’t know value); small directory overhead.

$ $ $ $

Fields Delimited by Special Symbols

F1 F2 F3 F4

F1 F2 F3 F4

Array of Field Offsets

Page Formats: Fixed Length Records

Record id = <page id, slot #>. In first 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

“Slotted Page” for Variable Length Records

• Record id = <page id, slot #>• Can move records on page without changing rid; so,

attractive for fixed-length records too.• Page is full when data space and slot array meet.

Page iRid = (i,N)

Rid = (i,2)

Rid = (i,1)

Pointerto startof freespaceSLOT DIRECTORY

N . . . 2 120 16 24 N

# slotsSlot Array

Data

• Q: What if a record grows too big to fit in the page???

System Catalogs• For each relation:

– name, file location, file structure (e.g., Heap file)– attribute name and type, for each attribute– index name, for each index– integrity constraints

• For each index:– structure (e.g., B+ tree) and search key fields

• For each view:– view name and definition

• Plus statistics, authorization, buffer pool size, etc.

Catalogs are themselves stored as relations!

Attr_Cat(attr_name, rel_name, type, position)

attr_name rel_name type position

attr_name Attribute_Cat string 1

rel_name Attribute_Cat string 2

type Attribute_Cat string 3

position Attribute_Cat integer 4

sid Students string 1

name Students string 2

login Students string 3

age Students integer 4

gpa Students real 5

fid Faculty string 1

fname Faculty string 2

sal Faculty real 3

Alternative File Organizations

Many alternatives exist, each good for some situations, and not so good in others:– Heap files: Suitable when typical access is a

file scan retrieving all records.– Sorted Files: Best for retrieval in search key

order, or only a `range’ of records is needed.– Clustered Files (with Indexes): A compromise

between the above two extremes.

Unordered (Heap) Files

• Simplest file structure contains records in no particular order.

• As file grows and shrinks, disk pages are allocated and de-allocated.

• To support record level operations, we must:– keep track of the pages in a file– keep track of free space on pages– keep track of the records on a page

• There are many alternatives for keeping track of this.– We’ll consider 2

Heap File Implemented as a List

• The header page id and Heap file name must be stored someplace.– Database “catalog”

• 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; linked list implementation is just one alternative.– Much smaller than linked list of all HF pages!

DataPage 1

DataPage 2

DataPage N

HeaderPage

DIRECTORY

• Q: How to find a particular record in a Heap file???

Cost Model for AnalysisWe ignore CPU costs, for simplicity:

– B: The number of data blocks– R: Number of records per block– D: (Average) time to read or write disk block

• Measuring number of block I/O’s ignores gains of pre-fetching and sequential access; thus, even I/O cost is only loosely approximated.

• Average-case analysis; based on several simplistic assumptions.– Often called a “back of the envelope”

calculation. Good enough to show the overall trends!

Some Assumptions in the Analysis

• Single record insert and delete.• Equality selection - exactly one match

(what if more or less???).• Heap Files:

– Insert always appends to end of file.– Delete just leaves free space in the page.

• Sorted Files:– Files compacted after deletions.– Selections on search key.

Cost of Operations

B: The number of data pagesR: Number of records per pageD: (Average) time to read or write disk page

Heap File Sorted File Clustered File

Scan all records

Equality Search(unique key)

Range Search

Insert

Delete

BD

0.5 BD

BD

2D

(0.5B+1)D

Sorted Files

• Q: When do Heap files perform well? When don’t they?

• Heap files are lazy on update - you end up paying on searches.

• Sorted files eagerly maintain the file on update.– The opposite choice in the trade-off

• Let’s consider an extreme version– No gaps allowed, pages fully packed always– Q: How might you relax these assumptions?

Cost of Operations

B: The number of data pagesR: Number of records per pageD: (Average) time to read or write disk page

Heap File Sorted File Clustered File

Scan all records

Equality Search(unique key)

Range Search

Insert

Delete

BD

(log2 B) * D

[(log2 B) + #match pg]*D

((log2B)+B)D(because rd,w0.5 File)

Same cost as Insert

BD

0.5 BD

BD

2D

(0.5B+1)D

Indexes: avoiding the extremes

• Hash files are great for lots of updates and scans.

• Sorted files are great for lots of “rifle-shot” look ups on one particular search key.– Q: How do they do on look ups on other fields??

• Q: Is there a “goldilocks”“goldilocks” solution????

• Clustered files are “sort of sorted”.– Need additional structure to help find things.

• A Primary Index provides such structure.

Index Overview

• An Index is a collection of “data entries” plus a way to quickly find entries with given key values.

• Two main families of indexes: Hash and Tree– Hash-based indexes only good for equality search.– Tree-based indexes best for range search; also good

for equality search. (Files rarely kept sorted in practice; B+ tree index is better.)

• Primary index is associated with file structure.– can have at most one per file

• Can have any number of additional Secondary Indexes.– These can speed up other “Access Paths”.

File Structure Summary• File Layer manages access to records in pages.

– Record and page formats depend on fixed vs. variable-length.

– Free space management is an important issue.– Slotted page format supports variable length records

and allows records to move on page.• Many alternative file organizations exist, each

appropriate in some situation.– We looked at Heap and Sorted so far.– If selection queries are frequent, sorting the file or

building an index is important.• Back of the envelope calculations are imprecise,

but can expose fundamental systems tradeoffs.– A technique that you should become comfortable

with!

• Next up: Indexes.