1. R*: An Overview of the Architecture
R. Williams, et al
IBM Almaden Research Center

2. Outline Environment and Data Definitions Object Naming
Distributed Catalogs
Transaction Management and Commit Protoctols
Query Preparation
Query Execution
SQL Additions and Changes

3. Environment and Data Definitions
CICS as the underlying communication model
Data distribuion:
Dispersed
Replicated
Partitioned
Horizontal
vertical
Snapshot

4. Figure 1 from paper

5. Figure 21.4 from CS 432 text

6. Object Naming System Wide Names (SWN):
BIRTH_SITE

7. Distributed Catalogs Local site maintains objects in its database
Catalog entry may be cached
Entries are versioned
SWN
Type
Format
Access path
Object ref (view)
Statistics

8. Transaction Management and Commit Protocol
Transaction number:
SITE.SEQ_NUM (or SITE.TIME)
Two phase commit (2PC)

9. Query Preparation Name resolution Authorization check
Distributed compilation
Global plan generation/optimization
Local access path selection
Local optimization
Local view materialization

10. Figure 2 from paper

11. Cost Model 3 weighted components: I/O CPU Message # of messages sent
# of bytes sent

12. Query Execution Synchronous vs asynchronous execution
Distributed concurrency control
Deadlock detection and resolution
Crash recovery

13. Figure 3 from paper

14. SQL Additions and Changes
DEFINE SYNONYM
DISTRIBUTE TABLE
HORIZONTALLY
VERTICALLY
REPLICATED
DEFINE SNAPSHOT
REFRESH SNAPSHOT
MIGRATE TABLE

15. Lothar F. Mackert Guy M. Lohman IBM Almaden Research Center
R* Optimizer Validation and Performance Evaluation for Distributed Queries
Lothar F. Mackert
Guy M. Lohman
IBM Almaden Research Center

16. Outline Distributed Compilation/Optimization Instrumentation
Experiments and Results

17. Distributed Compilation/Optimization
Issues:
Join site
Transfer methods:
ship whole
fetch matches
Cost model

18. Weights Estimation CPU: inverse of MIPS
I/O: avg seek, latency, transfer time
MSG: # of instruction per msg
BYTE: effective transmission speed of network

19. Figure 2 from paper

20. Instrumentation Distributed EXPLAIN Distributed COLLECT COUNTERS
Force optimizier

21. Experiment I Transfer method Merge-scan join of 2 tables:
500 tuples in each table
Project both table – 50%
100 different values for join attribute
Join result: 2477 tuples

22. Figure 4 from paper

23. Figure 3 from paper

24. Experiment II Distributed vs local join Join of 2 tables:
1000 tuples in each table
Project both table – 50%
3000 different values for join attribute

25. Figure 5 from paper

26. Figure 6 from paper

27. Experiment III
Relative importance of cost components

28. Figure 7, 8, 9, 10 from paper

29. Experiment IV Optimizer evaluation
Accurate estimates of # of msgs and bytes sent (<2% difference)
Better estimates when tables are more distributed

30. Experiment V Alternative distributed join methods: 2 tables:
Dynamically created indexes
Semijoins
Bloomjoins
2 tables:
1000 tuples for outer
Varies inner from 100 to 6000 tuples

31. Figure 11, 12 from paper

32. Other Experiments Clustered index: 50% Projection: Wider join column:
Bloomjoins < Semijoins < R*
50% Projection:
Site 1: Bloomjoins < Semijoins < R*
Site 2: Bloomjoins < R* << Semijoins
Wider join column:
Bloomjoins < R* << Semijoins