parallel star join dataindexes efficient query processing in data warehousing and olap
Download
Skip this Video
Download Presentation
Parallel Star Join + DataIndexes : Efficient Query Processing in Data Warehousing and OLAP

Loading in 2 Seconds...

play fullscreen
1 / 21

Parallel Star Join + DataIndexes : Efficient Query Processing in Data Warehousing and OLAP - PowerPoint PPT Presentation


  • 128 Views
  • Uploaded on

Parallel Star Join + DataIndexes : Efficient Query Processing in Data Warehousing and OLAP. Anindya Datta Debra VanderMeer Krithi Ramamritham Presented by – Ashutosh Joshi. Motivation. OLAP involves efficient retrieval of data from data warehouses for decision-support purposes

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Parallel Star Join + DataIndexes : Efficient Query Processing in Data Warehousing and OLAP' - carina


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
parallel star join dataindexes efficient query processing in data warehousing and olap

Parallel Star Join + DataIndexes : Efficient Query Processing in Data Warehousing and OLAP

Anindya Datta

Debra VanderMeer

Krithi Ramamritham

Presented by –

Ashutosh Joshi

motivation
Motivation
  • OLAP involves efficient retrieval of data from data warehouses for decision-support purposes
  • Data Warehouses are extremely large and queries are highly computationally expensive
  • DataIndex is a storage structure serving as both index and data
  • Parallel Star Join (PSJ) is an efficient algorithm for performing star join in parallel
the road map
The Road Map
  • A physical design principle for exploiting parallelism
  • Parallel Star Join algorithm
  • Experiment results
the star schema
The Star Schema

Dimension Table

PART

CUSTOMER

Fact Table

PartKey4

Name 55

Mfgr 25

Brand 10

Type 25

Size 4

Others... 41

164

CustKey 4

Name 25

Address 40

Nation 25

Region 25

Phone 15

AcctBal 8

MktSegment 10

Comment 117

269

SALES

PartKey 4

SuppKey 4

CustKey 4

Quantity 8

ExtPrice 8

Discount 8

Tax 8

RetFlag 1

Status 1

ShipDate 2

CommitDate 2

ReceiptDate 2

ShipInstruct 25

ShipMode 10

Comment 44

137

200,000

SUPPLIER

150,000

SuppKey 4

Name 25

Address 40

Nation 25

Region 25

Phone 15

AcctBal 8

Comment 101

243

TIME

TimeKey 2

Alpha 10

Year 4

Month 4

Week 4

Day 4

28

6,000,000

2,557

10,000

a physical design principle
A Physical Design Principle
  • DataIndexes
    • Serve as both index as well as data
    • Based on vertical partitioning of tables
    • Two types
      • Projection Index (PI)
      • Join Index (JI)
projection index

CustKey

CK1

CK2

CK3

CK4

Projection Index

Base Table

CustKey

Qty

ExtPrice

Discount

CK1

Q1

E1

D1

CK2

Q2

E2

D2

CK3

Q3

E3

D3

CK4

Q4

E4

D4

PI

PI

PI

Qty

ExtPrice

Discount

Q1

E1

D1

Q2

E2

D2

Q3

E3

D3

Q4

E4

D4

join index

RIDs

RID1

RID2

RID3

RID3

Join Index

Base Dimension Table

Base Fact Table

Name

Address

CustKey

CustKey

Tax

ExtPrice

Discount

N1

A1

CK1

CK1

T1

E1

D1

N2

A2

CK2

CK2

T2

E2

D2

N3

A3

CK3

CK3

T3

E3

D3

CK3

T4

E4

D4

PI

PI

PI

JI

PI

PI

Name

Address

CustKey

Tax

ExtPrice

Discount

N1

A1

CK1

T1

E1

D1

N2

A2

CK2

T2

E2

D2

N3

A3

CK3

T3

E3

D3

T4

E4

D4

the principle
The Principle
  • Each foreign key column in the fact table is stored as Join Index (JI)
  • Rest of the columns (for both dimension as well as fact table) are stored as Projection Index (PI)
parallel star join
Parallel Star Join
  • Data placement strategy
    • Based on shared nothing architecture with N processors
    • Assume a d dimensional data warehouse
    • Partition N processors into d+1 groups
    • Assign to each group j, dimension table Djand Jj , the fact table join index
    • Assign metric PIs to the group d+1
processor group partitioning
Processor Group Partitioning
  • Number of processors is governed by the size of dimension table Dj
  • Size of jth processor group
  • Size of metric group
physical data placement
Physical Data Placement
  • Horizontally partition JI’s across all processors
  • Replicate PI’s on all processors
  • Use round-robin strategy for partitioning JI’s
the parallel star join algorithm
The Parallel Star Join Algorithm
  • A general k- dimensional star join query
      • Select AdP, AmP

from F, D1, … , Dk

where Pjoin and Pselect

  • The algorithm has three phases
    • Local rowset generation
    • Global rowset synthesis
    • Output preparation
local rowset generation

1

25

0

5

0

7

15

1

Local Rowset generation
  • Load PI fragment

Pc

P1

P2

PI fragment

PI fragment

PI fragment

Qty > 10

PI fragment

Rowset fragment

local rowset generation contd
Local Rowset Generation (contd)
  • Merge dimension rowset fragments
  • Distribute dimension rowset

Rowset

fragment

P1

P2

P3

P4

OR

Rdim,i

local rowset generation contd1

RIDs

RID1

1

1

RID2

0

0

RID3

0

0

RID3

0

1

Local Rowset Generation (contd)
  • Load JI fragment
  • Merge partial fact rowsets

Rfact,i

Rdim,i

JIi

global rowset synthesis
Global Rowset Synthesis
  • Merge local fact rowsets
  • Distribute global rowset to groups participating in the output phase

Rfact,2

G1

G2

Rfact,1

G3

G4

AND

Rglobal

output preparation

Output

CustKey

RIDs

CK1

CK1

RID1

1

CK2

CK2

RID2

1

CK3

RID3

0

CK4

RID3

0

Output Preparation
  • Distribute global rowset to individual processors
  • Load PI columns necessary for output
  • Merge output

JIi

Rglobal

PIi

performance comparison
Performance Comparison
  • The PSJ algorithm was compared with Bitmapped Join Index algorithm and the Pipelined Hash join algorithm
  • Two performance metrics used
    • Response time in block access (RTBA)
    • Aggregate Data Transmission (ADT)
scalability experiments
Scalability Experiments
  • The curves rise as the scale factor and number of processors increase
  • PSJ cost is much lower than BJI and HASH costs
  • At large memory sizes, PSJ approaches “near-perfect” scalability
scalability experiments contd
Scalability Experiments(contd)
  • Transmission costs for PSJ and BJI are the same
  • Both curves exhibit imperfect scalability
  • HASH has substantially higher transmission costs than PSJ
conclusion
Conclusion
  • DataIndex is a physical design strategy which provides efficient partitioning of the schema
  • Parallel Star Join algorithm provides a means to perform star join in parallel
  • PSJ algorithm performs better than BJI and HASH algorithms in terms of I/O and transmission costs
ad