1. Distributed Database Systems
Dr. Mohamed Osman Hegazi

2. Dr. Mohamed Osman Hegazi
Definitions:
Distributed Database : is a collection of multiple logically interrelated databases distributed over a computer network.
Distributed database management systems (DDBMS): The software that permits the management of DDBS and makes the distribution transparent to the users.
Distributed database system (DDBS) = DDB + D–DBMS
The two important terms in this definitions are:
Logically interrelated. (The Application)
Distributed over a network.
Dr. Mohamed Osman Hegazi

3. Dr. Mohamed Osman Hegazi
Motivation for Distributed Database
The development of computer network promotes de-centralization
In a company, the database organization might reflect the organizational structure, which is distributed into units. Each unit maintains its own database
Sharing of data can be achieved by developing a distributed database system which:
Makes data accessible by all units
Stores data close to where it is most frequently used
Dr. Mohamed Osman Hegazi

4. Dr. Mohamed Osman Hegazi
DDBMS Advantages:
Data are located near “greatest demand” site
Faster data access
Faster data processing
Growth facilitation
Improved communications
Reduced operating costs
User-friendly interface
Less danger of a single-point failure
Processor independence
Dr. Mohamed Osman Hegazi

5. Dr. Mohamed Osman Hegazi
DDBMS Disadvantages
Complexity of management and control
Security
Lack of standards
Increased storage requirements
Greater difficulty in managing the data environment
Increased training cost
Dr. Mohamed Osman Hegazi

6. Dr. Mohamed Osman Hegazi
The concept of DDB:
A DDBS is not a collection of files that can be individually stored at each node of computer network. To form a DDBS, files should not only be logically related, but there should be structure among the files, and access should be via a common interface.
Dr. Mohamed Osman Hegazi

7. Distributed Database Management Systems
Dr. Mohamed Osman Hegazi

8. Dr. Mohamed Osman Hegazi
An Example
EMP(ENO, ENAME, TITLE)
ASG(ENO, PNO, DUR, RESP)
PROJ(PNO, PNAME, BUDGET)
PAY(TITLE,SAL)
Dr. Mohamed Osman Hegazi

9. Dr. Mohamed Osman Hegazi
Distributed Query
If these table is stored in one place then we can “for example” using the following query to get the name and the salary of the employee who works more than 12 months.
SELECT ENAME, SAL
FROM EMP, ASG, PAY
WHERE ASG. DUR >12
AND EMP.ENO=ASG.ENO
AND PAY.TITLE=EMP.TITLE
 But if these table are distributed over deferent site then the execution of this query needs allot of process to be done , DDMS do this process and let the end user feel like database’s only user (transparence)
Dr. Mohamed Osman Hegazi

10. Dr. Mohamed Osman Hegazi
Distributed Database Transparency
The concepts of DDB is to fragment the data and store each fragment on its site.
Data may be replicated on different site (replication)
DDBMS hide these details from the user and makes the distribution transparent to the users. Distributed Database Transparency Features
Distribution transparency
Transaction transparency
Failure transparency
Performance transparency
Heterogeneity transparency
Dr. Mohamed Osman Hegazi

11. Dr. Mohamed Osman Hegazi
Distributed DB Design
Top-down approach:
have a database
how to split and allocate to individual sites
Two issues in top-down design
Fragmentation
Allocation
Multi-databases (or bottom-up):
combine existing databases
how to deal with heterogeneity & autonomy
Dr. Mohamed Osman Hegazi

12. Dr. Mohamed Osman Hegazi
Fragmentation
Horizontal Primary
depends on local attributes
R Derived
depends on foreign relation
Vertical R
Dr. Mohamed Osman Hegazi

13. Dr. Mohamed Osman Hegazi
Example
Employee relation E (#,name,loc,sal,…)
40% of queries: % of queries:
Qa: select * Qb: select *
from E from E
where loc=Sa where loc=Sb
and… and ...
Motivation: Two sites: Sa, Sb
Qa   Qb Sa Sb
Dr. Mohamed Osman Hegazi

14. Dr. Mohamed Osman Hegazi
# Name Loc Sal 5
Joe Sa 10 E 7
Sally Sb 25 8
Tom Sa 15 .. ..
F = {F1,F2} 5
Joe Sa 10 7
Sally Sb 25 ..
At Sb 8
Tom Sa 15
At Sa ..
F1 = loc=Sa(E)
F2 = loc=Sb(E)
 primary horizontal fragmentation
Dr. Mohamed Osman Hegazi

15. Dr. Mohamed Osman Hegazi
Loc=SA 
sal < 10
Qa: Select … loc = SA ...
Qb: Select … loc = SB ...
Loc=SA 
sal  10 F3 F2
Prefer F2 to F1 and F3 F1
Loc=SB 
sal < 10
Loc=SB 
sal  10
Dr. Mohamed Osman Hegazi

16. Dr. Mohamed Osman Hegazi
Horizontal Fragmentation :
Peer to peer relationship – brothers
Dr. Mohamed Osman Hegazi

17. Vertical fragmentation
Example: E E2 E1
R[T]  R1[T1], R2[T2],…, Rn[Tn] Ti  T
 Just like normalization of relations
Dr. Mohamed Osman Hegazi

18. Vertical Fragmentation example
New York
PROJ
PNO
PNAME
BUDGET
LOC P1
Instrumentation
150000
Montreal P3
CAD/CAM
250000 P2
Database Develop.
135000 P4
Maintenance
310000
Paris P5
500000
Boston
PROJ1: information about project budgets
PROJ2: information about project names and locations
PROJ1
PROJ2
PNO
BUDGET
PNO
PNAME
LOC P1
Instrumentation
Montreal P3
CAD/CAM
New York P2
Database Develop. P4
Maintenance
Paris P5
Boston P1
150000 P2
135000 P3
250000 P4
310000 P5
500000
Dr. Mohamed Osman Hegazi

19. Grouping Attributes E1(#,NM,LOC) E2(#,SAL) Example:
E(#,NM,LOC,SAL) E1(#,NM)
E2(#,LOC)
E3(#,SAL)
Which is the right vertical fragmentation?
…..
Dr. Mohamed Osman Hegazi

20. Dr. Mohamed Osman Hegazi
Vertical Fragmentation :
branch relationship – parents and son
Dr. Mohamed Osman Hegazi

21. Dr. Mohamed Osman Hegazi
Hybrid Fragmentation R  HF HF R1 R2   VF VF VF VF VF     
R11
R12
R21
R22
R23
Dr. Mohamed Osman Hegazi

22. Dr. Mohamed Osman Hegazi
Allocation
Example: E  F1 = loc=Sa(E); F2 = loc=Sb(E)
Fragment E F1 F1
Site c
Site a F2
Site b
Do we replicate fragments?
Where do we place each copy of each fragment?
Dr. Mohamed Osman Hegazi

23. Allocation Alternatives
Non-replicated
partitioned : each fragment resides at only one site
Replicated
fully replicated : each fragment at each site
partially replicated : each fragment at some of the sites
Rule :
If replication is advantageous,
otherwise replication may cause problems
read - only queries
update queries
1
Dr. Mohamed Osman Hegazi

24. Dr. Mohamed Osman Hegazi
Optimization problem
What is the best placement of fragments and/or best number of copies to:
minimize query response time
maximize throughput
minimize “some cost”
...
Subject to constraints
Available storage
Available bandwidth, processing power,…
Keep 90% of response time below X
Very hard problem
Dr. Mohamed Osman Hegazi

25. Dr. Mohamed Osman Hegazi
Static data allocation & Dynamic data allocation
Static data allocation : No change on allocation sites or no need for extra storage space (no expanding on the size . No increasing on data )
Dynamic data allocation: dynamically changed the location of the data as a result of expansion in the data, which usually results because of the nature of the systems producing data. Problems of data sites can be treated through two types of models:
Adaptive Models: models that apply when the reason for the change of location due to system activity,( online systems- data storage on line( Example airline bookings). These models saves the additional temporary copies of data and then dealing with these copies by processors duplicate copies (replication).
non-adaptive models: These models solve dynamically allocation at the stage of establishing the system or at the stage of reorganization the system
Dr. Mohamed Osman Hegazi

26. Dr. Mohamed Osman Hegazi
Replication
Replication is to store copies of the same data in more than one location (site) and then these copies must be consistency updated "Despite the distance from each other"
Controlling the updating of these copies is done by one of two techniques:
Lazy replication: it is to update the data after the completion of work on one of the copies (master copy). This means that update is done outside the boundaries of transaction Eager replication: is to update the replicated data within the transaction boundaries while working on one of the copies.
central update(initial copy primary copy): update the primary copy first and then update the secondary copy. This method leads to lack of synchronization of the update, which facilitates control of consistency, but may lead to the problems of the bottleneck
Or update everywhere: ​​updating the copies in all places make all the copies of equal opportunities for the update.
Dr. Mohamed Osman Hegazi

27. Dr. Mohamed Osman Hegazi
Where/when
Eager
Lazy
Primary Copy
Early Solutions in Ingres
Sybase/IBM/Oracle Placement Strat.
Serialization- Graph Based
Update Everywhere
ROWA/ROWAA Quorum based Oracle Synchr. Repl.
Oracle Advanced Repl. Weak consistency Strat
Dr. Mohamed Osman Hegazi

28. Distributed Query Processing
The aim of queries processing in distributed data is to let the work on distributed data appear like a single database system. The problem of query processing in distributed data can be fragmented into several levels according to the problems of data
The query processing takes SQL statements or OQL as input and then process it through several stages until it is executing the query.
Query Decomposition & Data Localization:
The first stages of the distributed query processing is to analyze the query to the relation algebra, then the second stages localize the data by distribute the query.
Query Optimization:
The third stages is to achieve optimal implementation of the query by making the executive be as little as possible and delete the unneeded expression.
The query optimization is one of the important aspects in dealing with queries, there are many algorithms used in the investigation of this aspect .
Dr. Mohamed Osman Hegazi

29. Dr. Mohamed Osman Hegazi

30. Dr. Mohamed Osman Hegazi
Concurrency Control in distributed database
Concurrency control in databases is the activities that make the transactions consistence among all the system data.
DDBMS take care of synchronized the data that distributed over the network side, each of these sites are running programs dealing with it is own data. In this situation the process of controlling the concurrency of the distributed data is one of the more complex issues.
There are four techniques used to control the concurrence on distributed database:
locking techniques
Timestamp.
Optimistic algorithm: make all operations on the data performed except for the operation that updates the data in this case operation updates the local data first.
Complex algorithm for timestamps.
Dr. Mohamed Osman Hegazi

31. Distributed Concurrency Control
Nonreplicated Scheme
Each site maintains a local lock manager to administer lock and unlock requests for local data
Deadlock handling is more complex
Single-Coordinator Approach
The system maintains a single lock manager that resides in a single chosen site
Can be used with replicated data
Advantages
simple implementation
simple deadlock handling
Disadvantages
bottleneck
vulnerability
Dr. Mohamed Osman Hegazi

32. . . . Distributed Concurrency Control
Majority Protocol
A lock manager at each site
When a transaction wishes to lock a data item Q, which is replicated in n different sites, it must send a lock request to more than half of the n sites in which Q is stored
complex to implement
difficult to handle deadlocks
Dr. Mohamed Osman Hegazi