1. Parallel Star Join + DataIndexes : Efficient Query Processing in Data Warehousing and OLAP
Anindya Datta
Debra VanderMeer
Krithi Ramamritham
Presented by –
Ashutosh Joshi

2. 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

3. The Road Map A physical design principle for exploiting parallelism
Parallel Star Join algorithm
Experiment results

4. The Star Schema Dimension Table Fact Table PART CUSTOMER PartKey 4
Name 55
Mfgr 25
Brand
Type
Size
Others... 41
164
CustKey
Name
Address
Nation
Region
Phone
AcctBal
MktSegment 10
Comment
269
SALES
PartKey 4
SuppKey 4
CustKey
Quantity
ExtPrice
Discount
Tax
RetFlag
Status
ShipDate
CommitDate 2
ReceiptDate 2
ShipInstruct 25
ShipMode
Comment
137
200,000
SUPPLIER
150,000
SuppKey 4
Name 25
Address 40
Nation 25
Region 25
Phone
AcctBal 8
Comment 101
243
TIME
TimeKey 2
Alpha 10
Year 4
Month 4
Week 4
Day 28
6,000,000
2,557
10,000

5. 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)

6. Projection Index Base Table PI PI PI CustKey Qty ExtPrice Discount CK1

7. Join Index Base Dimension Table Base Fact Table PI PI PI JI PI PI 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
RIDs
RID1
RID2
RID3
Tax
ExtPrice
Discount N1 A1
CK1 T1 E1 D1 N2 A2
CK2 T2 E2 D2 N3 A3
CK3 T3 E3 D3 T4 E4 D4

8. 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)

9. 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 Dj and Jj , the fact table join index
Assign metric PIs to the group d+1

10. Processor Group Partitioning
Number of processors is governed by the size of dimension table Dj
Size of jth processor group
Size of metric group

11. Physical Data Placement
Horizontally partition JI’s across all processors
Replicate PI’s on all processors
Use round-robin strategy for partitioning JI’s

12. 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

13. Local Rowset generation
Load PI fragment Pc P1 P2
PI fragment
PI fragment
PI fragment 25 5 7 15 1
Qty > 10
PI fragment
Rowset fragment

14. Local Rowset Generation (contd)
Merge dimension rowset fragments
Distribute dimension rowset
Rowset
fragment P1 P2 P3 P4 OR
Rdim,i

15. Local Rowset Generation (contd)
Load JI fragment
Merge partial fact rowsets 1 1
RIDs
RID1
RID2
RID3
Rfact,i
Rdim,i
JIi

16. Global Rowset Synthesis
Merge local fact rowsets
Distribute global rowset to groups participating in the output phase
Rfact,1 G1 G2
Rfact,2 G3 G4
AND
Rglobal

17. Output Preparation Distribute global rowset to individual processors
Load PI columns necessary for output
Merge output
CustKey
CK1
CK2
CK3
CK4 1
Output
CK1
CK2
RIDs
RID1
RID2
RID3
PIi
JIi
Rglobal

18. 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)

19. 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

20. 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

21. 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