1. Distributed Database Management Systems
Lecture 30

2. In the previous lecture
Locking based CC
Timestamp ordering based CC
Concluded TM.

3. In this Lecture Basic Concepts of Query Optimization
QP in centralized and Distributed DBs.

4. Introduction SQL one of the success factors of RDBMS
Query processor transforms complex queries into concise and simple ones

5. Query processing is critical performance issue
QP a complex problem specially in DDBS environment

6. Main function of QP is to transform an SQL query into equivalent relational algebra one (low level language)
Transformation must achieve correctness and efficiency

7. Correctness is straightforward since rules exist
An SQL query can have many equivalents in R Algebra

8. Considering the tables
EMP(eNo, eName, title)
ASG(eNo, pNo, resp, dur)
PROJ(pNo, pName, budget, loc)
Query: Get the names of employees who are managing a project

9. SELECT eName
FROM EMP, ASG
WHERE EMP.eNo = ASG.eNo
AND resp = ‘Manager’

10. eName(resp=‘Manager’ ^ EMP.eNo = ASG.eNo) (EMPxASG)
eName(EMP ⋈ (resp=‘Manager’ (ASG)))
Obviously second one needs less computing resources since avoids Cartesian product

11. Centralized QP is to choose best query execution plan
Distributed is more complex; it also involves the selection of site to execute query

12. Same query in DDBS
Suppose EMP and ASG are HF as
EMP1 = eNo ≤ ‘E3’ (EMP)
EMP2 = eNo > ‘E3’ (EMP)
ASG1 = eNo ≤ ‘E3’ (ASG)
ASG2 = eNo > ‘E3’ (ASG)

13. Further suppose these fragments are stored at site 1, 2, 3 and 4 and result at site 5

14. Site 5 Site 4 Site 3 Site 2 Site 1 EMP1’ EMP2’ ASG1’ ASG2’
ASC1’=resp = ‘Manager(ASG1)
EMP1’=EMP1 ⋈(ASG1’)
Site 1
Site 3
ASC2’=resp = ‘Manager(ASG2)
EMP2’=EMP2 ⋈(ASG2’)
Site 2
Site 4
ASG1’
ASG2’
result = EMP1’ U EMP2’
Site 5
EMP1’
EMP2’

15.  resp = ‘Manager’ (ASG1 U ASG2)
result = (EMP1 U EMP2) ⋈ eNo
 resp = ‘Manager’ (ASG1 U ASG2)
Site 1
Site 2
Site 3
Site 4
ASG1
ASG2
EMP1
EMP2

16. Lets Assume size(EMP) size(ASG) 400 1000 tuple access cost
tuple transfer cost
1 unit
10 units
There are 20 Managers
Data distributed evenly at all sites

17. Strategy 1 produce ASG': 20*1 20
transfer ASG' to the sites of E: 20 * 10
200
produce EMP': (10+10) *1*2 40
transfer EMP' to result site: 20*10
Total
460

18. Strategy 2 Transfer EMP to site 5: 400 * 10 4000
Transfer ASG to the site * 10
10000
Produce ASG‘ by selecting ASG
1000
Join EMP and ASG’
8000
Total
23000

19. Query Optimization An important aspect of QP
Minimize resource consumption
I/O cost + CPU cost + communication cost
First two in Centralized DB

20. Communication Cost will dominate in WAN
Not that dominant in LANs, so total cost should be considered in LANs
QO can also maximize throughput

21. Operators’ Complexity
Select, Project (without duplicate elimination)
O(n)
Project (with duplicate elimination), Group
O(nlogn)
Join, Semi-Join, Division, Set Operators
O(nlog n)
Cartesian Product
O(n2)

22. Characterization of Query Processors

23. Types of Optimization
Exhaustive search for the cost of each strategy to find the most optimal one
May be very costly in case of multiple options and more fragments
Heuristics

24. Optimization Timing Static: during compilation
Size of intermediate tables not known always
Cost justified with repeated execution
Dynamic: during execution
Intermediate tables’ size known
Re-optimzation may be required

25. Statistics
Relation/Fragment: Cardinality, size of a tuple, fraction of tuples participating in a join with another relation
Attribute: cardinality of domain, actual number of distinct values

26. Decision Sites
Centralized: simple, need knowledge about the entire distributed database
Distributed: cooperation among sites to determine the schedule, need only local information
Hybrid: one site determines the global schedule, each site optimizes the local subqueries

27. Other factors like: Network topology Replicated fragments
Use of semijoins.

28. Optimized Local Query SQL Query on Distributed Relations QUERY GLOBAL
DECOMPOSITION
GLOBAL
SCHEMA
Algebraic Query on Distributed
Relations
DATA
LOCALIZATION
FRAGMENT
Fragment Query
OPTIMIZATION
STAT OF
FRAGMENTS
Optimized Fragment Query with
Communication Operations
LOCAL
Optimized
Local Query