Data Warehousing CPS216 Notes 13 Shivnath Babu. 2 Warehousing l Growing industry: $8 billion way...

Data Warehousing CPS216Notes 13

Shivnath Babu

2

Warehousing

Growing industry: $8 billion way back in 1998

Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB

Teradata system Lots of buzzwords, hype

slice & dice, rollup, MOLAP, pivot, ...

3

Outline

What is a data warehouse? Why a warehouse? Models & operations Implementing a warehouse Future directions

4

What is a Warehouse?

Collection of diverse data subject oriented aimed at executive, decision maker often a copy of operational data with value-added data (e.g., summaries, history)

integrated time-varying non-volatile

more

5

What is a Warehouse?

Collection of tools gathering data cleansing, integrating, ... querying, reporting, analysis data mining monitoring, administering warehouse

6

Warehouse Architecture

Client Client

Warehouse

Source Source Source

Query & Analysis

Integration

Metadata

7

Motivating Examples

Forecasting Comparing performance of units Monitoring, detecting fraud Visualization

8

Why a Warehouse?

Two Approaches: Query-Driven (Lazy) Warehouse (Eager)

Source Source

?

9

Query-Driven Approach

Client Client

Wrapper Wrapper Wrapper

Mediator


10

Advantages of Warehousing

High query performance Queries not visible outside warehouse Local processing at sources unaffected Can operate when sources unavailable Can query data not stored in a DBMS Extra information at warehouse

Modify, summarize (store aggregates) Add historical information

11

Advantages of Query-Driven

No need to copy data less storage no need to purchase data

More up-to-date data Query needs can be unknown Only query interface needed at sources May be less draining on sources

12

OLTP vs. OLAP

OLTP: On Line Transaction Processing Describes processing at operational sites

OLAP: On Line Analytical Processing Describes processing at warehouse

13

OLTP vs. OLAP

Mostly updates Many small transactions Mb-Gb of data Raw data Clerical users Up-to-date data Consistency,

recoverability critical

Mostly reads Queries long, complex Gb-Tb of data Summarized,

consolidated data Decision-makers,

analysts as users

OLTP OLAP

14

Data Marts

Smaller warehouses Spans part of organization

e.g., marketing (customers, products, sales) Do not require enterprise-wide consensus

but long term integration problems?

15

Warehouse Models & Operators

Data Models relations stars & snowflakes cubes

Operators slice & dice roll-up, drill down pivoting other

16

Star

customer custId name address city53 joe 10 main sfo81 fred 12 main sfo

111 sally 80 willow la

product prodId name pricep1 bolt 10p2 nut 5

store storeId cityc1 nycc2 sfoc3 la

sale oderId date custId prodId storeId qty amto100 1/7/97 53 p1 c1 1 12o102 2/7/97 53 p2 c1 2 11105 3/8/97 111 p1 c3 5 50

17

Star Schema

saleorderId

datecustIdprodIdstoreId

qtyamt

customercustIdname

addresscity

productprodIdnameprice

storestoreId

city

18

Terms

Fact table Dimension tables Measures

saleorderId

datecustIdprodIdstoreId

qtyamt

customercustIdname

addresscity

productprodIdnameprice

storestoreId

city

19

Dimension Hierarchies

store storeId cityId tId mgrs5 sfo t1 joes7 sfo t2 freds9 la t1 nancy

city cityId pop regIdsfo 1M northla 5M south

region regId namenorth cold regionsouth warm region

sType tId size locationt1 small downtownt2 large suburbs

storesType

city region

snowflake schema constellations

20

Cube

sale prodId storeId amtp1 c1 12p2 c1 11p1 c3 50p2 c2 8

c1 c2 c3p1 12 50p2 11 8

Fact table view: Multi-dimensional cube:

dimensions = 2

21

3-D Cube

sale prodId storeId date amtp1 c1 1 12p2 c1 1 11p1 c3 1 50p2 c2 1 8p1 c1 2 44p1 c2 2 4

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

dimensions = 3

Multi-dimensional cube:Fact table view:

22

ROLAP vs. MOLAP

ROLAP:Relational On-Line Analytical Processing

MOLAP:Multi-Dimensional On-Line Analytical Processing

23

Aggregates


• Add up amounts for day 1• In SQL: SELECT sum(amt) FROM SALE WHERE date = 1

81

24

Aggregates


• Add up amounts by day• In SQL: SELECT date, sum(amt) FROM SALE GROUP BY date

ans date sum1 812 48

25

Another Example


• Add up amounts by day, product• In SQL: SELECT date, sum(amt) FROM SALE GROUP BY date, prodId

sale prodId date amtp1 1 62p2 1 19p1 2 48

drill-down

rollup

26

Aggregates

Operators: sum, count, max, min, median, ave

“Having” clause Using dimension hierarchy

average by region (within store) maximum by month (within date)

27

Cube Aggregation

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3sum 67 12 50

sump1 110p2 19

129

. . .

drill-down

rollup

Example: computing sums

28

Cube Operators

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3sum 67 12 50

sump1 110p2 19

129

. . .

sale(c1,*,*)

sale(*,*,*)sale(c2,p2,*)

29

c1 c2 c3 *p1 56 4 50 110p2 11 8 19* 67 12 50 129

Extended Cube

day 2 c1 c2 c3 *p1 44 4 48p2* 44 4 48

c1 c2 c3 *p1 12 50 62p2 11 8 19* 23 8 50 81

day 1

*

sale(*,p2,*)

30

Aggregation Using Hierarchies

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

region A region Bp1 56 54p2 11 8

customer

region

country

(customer c1 in Region A;customers c2, c3 in Region B)

31

Pivoting


day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

Multi-dimensional cube:Fact table view:

c1 c2 c3p1 56 4 50p2 11 8

Pivot turns unique values fromone column into unique columnsin the output

32

Derived Data

Derived Warehouse Data indexes aggregates materialized views (next slide)

When to update derived data? Incremental vs. refresh

33

Materialized Views

Define new warehouse relations using SQL expressions


product id name pricep1 bolt 10p2 nut 5

joinTb prodId name price storeId date amtp1 bolt 10 c1 1 12p2 nut 5 c1 1 11p1 bolt 10 c3 1 50p2 nut 5 c2 1 8p1 bolt 10 c1 2 44p1 bolt 10 c2 2 4

does not existat any source

34

Processing

ROLAP servers vs. MOLAP servers Index Structures What to Materialize? Algorithms

Client Client

Warehouse


Query & Analysis

Integration

Metadata

35

ROLAP Server

Relational OLAP Server

relationalDBMS

ROLAPserver

tools

utilities

sale prodId date sump1 1 62p2 1 19p1 2 48

Special indices, tuning;

Schema is “denormalized”

36

MOLAP Server

Multi-Dimensional OLAP Server

multi-dimensional

server

M.D. tools

utilitiescould also

sit onrelational

DBMS

Pro

du

ctCity

Date1 2 3 4

milk

soda

eggs

soap

AB

Sales

37

Index Structures

Traditional Access Methods B-trees, hash tables, R-trees, grids, …

Popular in Warehouses inverted lists bit map indexes join indexes text indexes

38

Inverted Lists

2023

1819

202122

232526

r4r18r34r35

r5r19r37r40

rId name ager4 joe 20

r18 fred 20r19 sally 21r34 nancy 20r35 tom 20r36 pat 25r5 dave 21

r41 jeff 26

. .

.

ageindex

invertedlists

datarecords

39

Using Inverted Lists

Query: Get people with age = 20 and name = “fred”

List for age = 20: r4, r18, r34, r35 List for name = “fred”: r18, r52 Answer is intersection: r18

40

Bit Maps

2023

1819

202122

232526

id name age1 joe 202 fred 203 sally 214 nancy 205 tom 206 pat 257 dave 218 jeff 26

. .

.

ageindex

bitmaps

datarecords

110110000

0010001011

41

Using Bit Maps

Query: Get people with age = 20 and name = “fred”

List for age = 20: 1101100000 List for name = “fred”: 0100000001 Answer is intersection: 010000000000

Good if domain cardinality small Bit vectors can be compressed

42

Join


• “Combine” SALE, PRODUCT relations• In SQL: SELECT * FROM SALE, PRODUCT WHERE ...

product id name pricep1 bolt 10p2 nut 5

joinTb prodId name price storeId date amtp1 bolt 10 c1 1 12p2 nut 5 c1 1 11p1 bolt 10 c3 1 50p2 nut 5 c2 1 8p1 bolt 10 c1 2 44p1 bolt 10 c2 2 4

43

Join Indexes

product id name price jIndexp1 bolt 10 r1,r3,r5,r6p2 nut 5 r2,r4

sale rId prodId storeId date amtr1 p1 c1 1 12r2 p2 c1 1 11r3 p1 c3 1 50r4 p2 c2 1 8r5 p1 c1 2 44r6 p1 c2 2 4

join index

44

What to Materialize?

Store in warehouse results useful for common queries

Example:day 2

c1 c2 c3p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3p1 67 12 50

c1p1 110p2 19

129

. . .

total sales

materialize

45

Materialization Factors

Type/frequency of queries Query response time Storage cost Update cost

46

Cube Aggregates Lattice

city, product, date

city, product city, date product, date

city product date

all

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

day 1

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3p1 67 12 50

129

use greedyalgorithm todecide whatto materialize

47


all

state

city

cities city statec1 CAc2 NY

48


city, product

city, product, date

city, date product, date

city product date

all

state, product, date

state, date

state, product

state

not all arcs shown...

49

Interesting Hierarchy

all

years

quarters

months

days

weeks

time day week month quarter year1 1 1 1 20002 1 1 1 20003 1 1 1 20004 1 1 1 20005 1 1 1 20006 1 1 1 20007 1 1 1 20008 2 1 1 2000

conceptualdimension table

Data Warehousing CPS216 Notes 13 Shivnath Babu. 2 Warehousing l Growing industry: $8 billion way...

Documents

Transcript of Data Warehousing CPS216 Notes 13 Shivnath Babu. 2 Warehousing l Growing industry: $8 billion way...