distributed databases by chien-pin hsu cs157b section 1 nov 11, 2004 dr. sin-min lee

Distributed Databases

by Chien-Pin HsuCS157B 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 component

Appears to user as a single systemDatabase systems that run on each site are

independent of each otherProcessing maybe done at a site other than the

initiator of request

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

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.

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:

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.

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.

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.

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.

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.

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

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

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

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

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 = 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, Employee-id, salary).

It fragments the relation to put information in two tables for security concern.

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.

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:

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

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

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.

Two-Phase Commit Diagram

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.