by Chien-Pin Hsu
CS157B Section 1
Nov 11, 2004
Dr. Sin-Min Lee
Distributed Database System
A distributed database system consists of
loosely coupled sites that share no physical
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
Homogenous Distributed Database
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
An Homogenous Distributed Database
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.
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.
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:
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.
Heterogeneous Distributed Database
In a heterogeneous distributed
database system, at least one of the
databases uses different schemas and
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.
Distributed Data Storage
– System maintains multiple copies of data, stored in
different sites, for faster retrieval and fault tolerance.
– Relation is partitioned into several fragments stored in
Replication and fragmentation can be combined
• Relation is partitioned into several fragments: system
maintains several identical replicas of each such
Advantages of Replication
Availability: failure of site containing relation
r does not result in unavailability of r is
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.
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
• One solution: choose one copy as primary copy
and apply concurrency control operations on
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 –
Fragmentation can be horizontal or vertical
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
Performance - may be slower
Integrity - more difficult
Each fragment, Ti , of table T contains a
subset of the rows
Each tuple of T is assigned to one or more
Horizontal fragmentation is lossless
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
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 = Πattr_listi
T = T1 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.
Vertical Fragmentation Example
A employee-info schema has a relation
employee-info schema = (designation, name,
It fragments the relation to put information in two
tables for security concern.
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 Two-Phase Commit (2 PC) Protocol
What is this?
Two-phase commit is a transaction protocol designed
for the complications that arise with distributed
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:
Phase1: Obtaining a Decision
Step 1 Coordinator asks all
participants to prepare to commit
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
sends prepare T messages to all sites where
Phase1: Making a Decision
Step 2 Upon receiving message, transaction
manager at site determines if it can commit the
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
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.
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.