1. Distributed Databases
by Chien-Pin Hsu
CS157B Section 1
Nov 11, 2004
Dr. Sin-Min Lee

2. Distributed Database System
A distributed database system consists of loosely coupled sites that share no physical component
Appears to user as a single system
Database systems that run on each site are independent of each other
Processing maybe done at a site other than the initiator of request

3. Homogenous Distributed Database Systems
All sites have identical software
They are aware of each other and agree to cooperate in processing user requests
It appears to user as a single system

4. An Homogenous Distributed Database Systems example
A distributed system connects three databases: hq, mfg, and sales
An application can simultaneously access or modify the data in several databases in a single distributed environment.

5. What can we do?
A single query from a Manufacturing client on local database mfg can retrieve joined data from the products table on the local database and the dept table on the remote hq database.
For a client application, the location and platform of the databases are transparent.

6. Makes life easier!!
For example, if you are connected to database mfg but want to access data on database hq, creating a synonym on mfg for the remote dept table enables you to issue this query:
SELECT *
FROM dept
In this way, a distributed system gives the appearance of native data access.
Users on mfg do not have to know that the data they access resides on remote databases.

7. Heterogeneous Distributed Database System
In a heterogeneous distributed database system, at least one of the databases uses different schemas and software.
A database system having different schema may cause a major problem for query processing.
A database system having different software may cause a major problem for transaction processing.

8. Distributed Data Storage
Replication
System maintains multiple copies of data, stored in different sites, for faster retrieval and fault tolerance.
Fragmentation
Relation is partitioned into several fragments stored in distinct sites
Replication and fragmentation can be combined
Relation is partitioned into several fragments: system maintains several identical replicas of each such fragment.

9. Advantages of Replication
Availability: failure of site containing relation r does not result in unavailability of r is replicas exist.
Parallelism: queries on r may be processed by several nodes in parallel.
Reduced data transfer: relation r is available locally at each site containing a replica of r.

10. Disadvantages of Replication
Increased cost of updates: each replica of relation r must be updated.
Increased complexity of concurrency control: concurrent updates to distinct replicas may lead to inconsistent data unless special concurrency control mechanisms are implemented.
One solution: choose one copy as primary copy and apply concurrency control operations on primary copy.

11. Fragmentation
Data can be distributed by storing individual tables at different sites
Data can also be distributed by decomposing a table and storing portions at different sites – called Fragmentation
Fragmentation can be horizontal or vertical

12. Why use Fragmentation?
Usage - in general applications use views so it’s appropriate to work with subsets
Efficiency - data stored close to where it is most frequently used
Parallelism - a transaction can divided into several sub-queries to increase degree of concurrency
Security - data more secure - only stored where it is needed
Disadvantages:
Performance - may be slower
Integrity - more difficult

13. Horizontal Fragmentation
Each fragment, Ti , of table T contains a subset of the rows
Each tuple of T is assigned to one or more fragments.
Horizontal fragmentation is lossless

14. Horizontal Fragmentation Example
A bank account schema has a relation
Account-schema = (branch-name, account-number, balance).
It fragments the relation by location and stores each fragment locally: rows with branch-name = `Hillside` are stored in the Hillside in a fragment

15. Vertical Fragmentation
Each fragment, Ti, of T contains a subset of the columns, each column is in at least one fragment, and each fragment includes the key:
Ti = attr_listi (T)
T = T T2 … Tn
All schemas must contain a common candidate key (or superkey) to ensure lossless join property.
A special attribute, the tuple-id attribute may be added to each schema to serve as a candidate key.

16. Vertical Fragmentation Example
A employee-info schema has a relation
employee-info schema = (designation, name, Employee-id, salary).
It fragments the relation to put information in two tables for security concern.

17. Commit Protocols
Commit protocols are used to ensure atomicity across sites
Atomicity states that database modifications must follow an “all or nothing” rule.
a transaction which executes at multiple sites must either be committed at all the sites, or aborted at all the sites.

18. The Two-Phase Commit (2 PC) Protocol
What is this?
Two-phase commit is a transaction protocol designed for the complications that arise with distributed resource managers.
Two-phase commit technology is used for hotel and airline reservations, stock market transactions, banking applications, and credit card systems.
With a two-phase commit protocol, the distributed transaction manager employs a coordinator to manage the individual resource managers. The commit process proceeds as follows:

19. Phase1: Obtaining a Decision
Step 1  Coordinator asks all participants to prepare to commit transaction Ti.
Ci adds the records <prepare T> to the log and forces log to stable storage (a log is a file which maintains a record of all changes to the database)
sends prepare T messages to all sites where T executed

20. Phase1: Making a Decision
Step 2  Upon receiving message, transaction manager at site determines if it can commit the transaction
if not:
add a record <no T> to the log and send abort T message to Ci
if the transaction can be committed, then:
1). add the record <ready T> to the log
2). force all records for T to stable storage
3). send ready T message to Ci

21. Phase 2: Recording the Decision
Step 1  T can be committed of Ci received a ready T message from all the participating sites: otherwise T must be aborted.
Step 2  Coordinator adds a decision record, <commit T> or <abort T>, to the log and forces record onto stable storage. Once the record is in stable storage, it cannot be revoked (even if failures occur)
Step 3  Coordinator sends a message to each participant informing it of the decision (commit or abort)
Step 4  Participants take appropriate action locally.

22. Two-Phase Commit Diagram

23. There have been two performance issues with two phase commit:
Costs and Limitations
There have been two performance issues with two phase commit:
If one database server is unavailable, none of the servers gets the updates.
This is correctable through network tuning and correctly building the data distribution through database optimization techniques.