Overview of Storage and Indexing (based on slides from Wisconsin) - - PDF document

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Murali Mani

Overview of Storage and Indexing

(based on slides from Wisconsin)

Murali Mani

Data on External Storage

Disks: Can retrieve random page at fixed cost

But reading several consecutive pages is much cheaper

than reading them in random order

Tapes: Can only read pages in sequence

Cheaper than disks; used for archival storage

File organization: Method of arranging a file of

records on external storage.

Record id (rid) is sufficient to physically locate record Indexes are data structures that allow us to find the record

ids of records with given values in index search key fields

Architecture: Buffer manager stages pages from

external storage to main memory buffer pool. File and index layers make calls to the buffer manager.

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Why Not Store Everything in Main Memory?

• Cost. Disks always cheaper than memory.

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).

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Disks

Secondary storage device of choice. Main advantage over tapes: random

access vs. sequential.

Data is stored and retrieved in units called

disk blocks or pages.

Unlike RAM, time to retrieve a disk page

varies depending upon location on disk.

Therefore, relative placement of pages on disk

has major impact on DBMS performance!

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Components of a Disk

The platters spin (say, 90rps). The arm assembly is moved

in or out to position a head on a desired track. Tracks under heads make a cylinder (imaginary!).

Only one head

reads/writes at any one time.

Platters Spindle Disk head Arm movement Arm assembly Tracks Sector

Block size is a multiple of

sector size (which is fixed).

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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 Key to lower I/O cost: reduce seek/rotation

delays! Hardware vs. software solutions?

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Arranging Pages on Disk

`Next’ block concept: blocks on same track, followed by blocks on same cylinder, followed by blocks on adjacent cylinder Blocks in a file should be arranged

sequentially on disk (by `next’), to minimize seek and rotational delay.

For a sequential scan, pre-fetching several

pages at a time is a big win!

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RAID

Disk Array: Arrangement of several disks that

gives abstraction of a single, large disk.

Goals: Increase performance and reliability. Two main techniques: Data striping: Data is partitioned; size of a

partition is called the striping unit. Partitions are distributed over several disks.

Redundancy: More disks => can handle more

• failures. Redundant information allows

reconstruction of data if a disk fails.

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Disk Space Management

Lowest layer of DBMS software manages space on

disk.

Higher levels call upon this layer to: allocate/de-allocate a page read/write a page Request for a sequence of pages must be satisfied

by allocating the pages sequentially on disk! Higher levels don’t need to know how this is done, or how free space is managed.

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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 dictated by replacement policy

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DBMS vs. OS File System

OS does disk space & buffer mgmt: why not let OS manage these tasks?

Differences in OS support: portability issues Some limitations, e.g., files can’t span disks.

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Record Formats: Fixed Length

Information about field types same for all

records in a file; stored in system catalogs.

Finding i’th field does not require scan of

record.

Base address (B)

L1 L2 L3 L4 F1 F2 F3 F4

Address = B+L1+L2

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Record Formats: Variable Length

Two alternative formats (# fields is fixed): Second offers direct access to i’th field, efficient storage

• f nulls (special don’t know value); small directory overhead.

4 $ $ $ $

Field Count

Fields Delimited by Special Symbols

F1 F2 F3 F4 F1 F2 F3 F4

Array of Field Offsets

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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 1 Slot 2 Slot N

. . . . . .

N M 1 . . . M ... 3 2 1 PACKED UNPACKED, BITMAP Slot 1 Slot 2 Slot N Free Space Slot M 1 1 number

• f records

number

• f slots

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Page Formats: Variable Length Records

Can move records on page without changing rid;

so, attractive for fixed-length records too.

Page i Rid = (i,N) Rid = (i,2) Rid = (i,1)

Pointer to start

• f free

space

SLOT DIRECTORY

N . . . 2 1 20 16 24

N # slots

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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)

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Alternative File Organizations

Many alternatives exist, each ideal for some situations, and not so good in others:

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

• rder, 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.

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Indexes

An index on a file speeds up selections on

the search key fields for the index.

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 key (minimal

set of fields that uniquely identify a record in a relation).

An index contains a collection of data

entries, and supports efficient retrieval of all data entries k* with a given key value k.

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B+ Tree Indexes

Leaf pages contain data entries, and are chained (prev & next) Non-leaf pages have index entries; only used to direct searches:

P0 K 1 P 1 K 2 P 2 K m P m

index entry

Non-leaf Pages Pages (Sorted by search key) Leaf

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Example B+ Tree

Find 28*? 29*? All > 15* and < 30* Insert/delete: Find data entry in leaf, then change

• it. Need to adjust parent sometimes.

And change sometimes bubbles up the tree

2* 3*

Root

17

30 14* 16* 33* 34*38* 39* 13 5 7* 5* 8* 22*24* 27 27* 29*

Entries <= 17 Entries > 17

Note how data entries in leaf level are sorted

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Hash-Based Indexes

Good for equality selections. Index is a collection of buckets.

Bucket = primary page plus zero or more

• verflow pages.

Buckets contain data entries.

Hashing function h: h(r) = bucket in which

(data entry for) record r belongs. h looks at the search key fields of r.

No need for “index entries” in this scheme.

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Choice of Indexes

What indexes should we create? Which relations should have indexes? What

field(s) should be the search key? Should we build several indexes?

For each index, what kind of an index

should it be?

Hash/tree?

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Choice of Indexes (Contd.)

One approach: Consider the most important queries

in turn. Consider the best plan using the current indexes, and see if a better plan is possible with an additional index. If so, create it.

Obviously, this implies that we must understand how a

DBMS evaluates queries and creates query evaluation plans!

For now, we discuss simple 1-table queries.

Before creating an index, must also consider the

impact on updates in the workload!

Trade-off: Indexes can make queries go faster, updates

• slower. Require disk space, too.

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Index Selection Guidelines

Attributes in WHERE clause are candidates for index keys. Exact match condition suggests hash index. Range query suggests tree index. Multi-attribute search keys should be considered when a

WHERE clause contains several conditions.

Order of attributes is important for range queries. Such indexes can sometimes enable index-only strategies for

important queries.

Try to 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.

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Examples of Indexes

B+ tree index on E.age can

be used to get qualifying tuples.

How selective is the condition? Consider the GROUP BY

query.

If many tuples have E.age >

10, using E.age index and sorting the retrieved tuples may be costly.

Equality queries and

duplicates:

SELECT E.dno FROM Emp E WHERE E.age>40 SELECT E.dno, COUNT (*) FROM Emp E WHERE E.age>10 GROUP BY E.dno SELECT E.dno FROM Emp E WHERE E.hobby=Stamps

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Indexes with Composite Search Keys

Composite Search Keys:

Search on a combination of fields.

• Equality query: Every field value

is equal to a constant value. E.g. wrt <sal,age> index:

age=20 and sal =75

• Range query: Some field value

is not a constant. E.g.:

age =20; or age=20 and sal >

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Data entries in index sorted by

search key to support range queries.

sue 13 75 bob cal joe 12 10 20 80 11 12 name age sal <sal, age> <age, sal> <age> <sal> 12,20 12,10 11,80 13,75 20,12 10,12 75,13 80,11 11 12 12 13 10 20 75 80

Data records sorted by name Data entries in index sorted by <sal,age> Data entries sorted by <sal>

Examples of composite key indexes using lexicographic order.

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Composite Search Keys

To retrieve Emp records with age=30 AND sal=4000, an

index on <age,sal> would be better than an index on age or an index on sal.

Choice of index key orthogonal to clustering etc. If condition is: 20<age<30 AND 3000<sal<5000: Clustered tree index on <age,sal> or <sal,age> is best. If condition is: age=30 AND 3000<sal<5000: Clustered <age,sal> index much better than <sal,age> index! Composite indexes are larger, updated more often.