Distributed Databases

@ KIDS Labs 1

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

@ KIDS Labs 2

Reasons for

Distributed Database • Business unit autonomy and distribution

• Data sharing

• Data communication costs

• Data communication reliability and costs

• Multiple application vendors

• Database recovery

• Transaction and analytic processing

@ KIDS Labs 3

Figure 13-1 – Distributed database environments (adapted

from Bell and Grimson, 1992)

@ KIDS Labs 4

Distributed Database Options

• Homogeneous - Same DBMS at each node – Autonomous - Independent DBMSs

– Non-autonomous - Central, coordinating DBMS

– Easy to manage, difficult to enforce

• Heterogeneous - Different DBMSs at different nodes – Systems – With full or partial DBMS functionality

– Gateways - Simple paths are created to other databases without the benefits of one logical database

– Difficult to manage, preferred by independent organizations

@ KIDS Labs 5

Distributed Database Options (cont.)

• Systems - Supports some or all functionality of

one logical database

– Full DBMS Functionality - All distributed DB functions

– Partial-Multi database - Some distributed DB functions

• Federated - Supports local databases for unique data requests

– Loose Integration - Local dbs have their own schemas

– Tight Integration - Local dbs use common schema

• Unfederated - Requires all access to go through a central,

coordinating module

@ KIDS Labs 6

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

@ KIDS Labs 7

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. @ KIDS Labs 8

Identical DBMSs

Figure 13-2: Homogeneous Database

Source: adapted from Bell and Grimson, 1992.

@ KIDS Labs 9

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.

@ KIDS Labs 10

Figure 13-3: Typical Heterogeneous Environment

Non-identical DBMSs

Source: adapted from Bell and Grimson, 1992.

@ KIDS Labs 11

Major Objectives

• Location Transparency

– User does not have to know the location of the

data

– Data requests automatically forwarded to

appropriate sites

• Local Autonomy

– Local site can operate with its database when

network connections fail

– Each site controls its own data, security, logging,

recovery

@ KIDS Labs 12

Significant Trade-Offs • Synchronous Distributed Database

– All copies of the same data are always identical

– Data updates are immediately applied to all copies throughout network

– Good for data integrity

– High overhead slow response times

• Asynchronous Distributed Database – Some data inconsistency is tolerated

– Data update propagation is delayed

– Lower data integrity

– Less overhead faster response time

@ KIDS Labs 13

Advantages of

Distributed Database over Centralized Databases

• Increased reliability/availability

• Local control over data

• Modular growth

• Lower communication costs

• Faster response for certain queries

@ KIDS Labs 14

Disadvantages of

Distributed Database Compared to

Centralized Databases

• Software cost and complexity

• Processing overhead

• Data integrity exposure

• Slower response for certain queries

@ KIDS Labs 15

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.

@ KIDS Labs 16

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.

@ KIDS Labs 17

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.

@ KIDS Labs 18

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

@ KIDS Labs 19

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

@ KIDS Labs 20

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

@ KIDS Labs 21

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

@ KIDS Labs 22

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.

@ KIDS Labs 23

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.

@ KIDS Labs 24

Types of Data Replication

• Push Replication –

–updating site sends changes to other sites

• Pull Replication –

–receiving sites control when update messages will be processed

@ KIDS Labs 25

Types of Push Replication

• Snapshot Replication -

– Changes periodically sent to master site

– Master collects updates in log

– Full or differential (incremental) snapshots

– Dynamic vs. shared update ownership

Near Real-Time Replication -

– Broadcast update orders without requiring

confirmation

– Done through use of triggers

– Update messages stored in message queue until

processed by receiving site

@ KIDS Labs 26

Issues for Data Replication

• Data timeliness – high tolerance for out-of-date data may be required

• DBMS capabilities – if DBMS cannot support multi-node queries, replication may be necessary

• Performance implications – refreshing may cause performance problems for busy nodes

• Network heterogeneity – complicates replication

• Network communication capabilities – complete refreshes place heavy demand on telecommunications

@ KIDS Labs 27

Horizontal Partitioning

• Different rows of a table at different sites

• Advantages - – Data stored close to where it is used efficiency

– Local access optimization better performance

– Only relevant data is available security

– Unions across partitions ease of query

• Disadvantages – Accessing data across partitions inconsistent

access speed

– No data replication backup vulnerability

@ KIDS Labs 28

Vertical Partitioning

• Different columns of a table at different

sites

• Advantages and disadvantages are the

same as for horizontal partitioning except

that combining data across partitions is

more difficult because it requires joins

(instead of unions)

@ KIDS Labs 29

Figure 13-6

Distributed processing system for a manufacturing company

@ KIDS Labs 30

Distributed DBMS

• Distributed database requires distributed DBMS

• Functions of a distributed DBMS:

– Locate data with a distributed data dictionary

– Determine location from which to retrieve data and process

query components

– DBMS translation between nodes with different local

DBMSs (using middleware)

– Data consistency (via multiphase commit protocols)

– Global primary key control

– Scalability

– Security, concurrency, query optimization, failure recovery

@ KIDS Labs 31

Figure 13-10: Distributed DBMS architecture

@ KIDS Labs 32

Local Transaction Steps

1. Application makes request to distributed DBMS

2. Distributed DBMS checks distributed data repository for location of data. Finds that it is local

3. Distributed DBMS sends request to local DBMS

4. Local DBMS processes request

5. Local DBMS sends results to application

@ KIDS Labs 33

Figure 13-10: Distributed DBMS Architecture (cont.)

(showing local transaction steps)

Local transaction – all

data stored locally

1

3

4

5

2

@ KIDS Labs 34

Global Transaction Steps

1. Application makes request to distributed DBMS

2. Distributed DBMS checks distributed data repository for location of data. Finds that it is remote

3. Distributed DBMS routes request to remote site

4. Distributed DBMS at remote site translates request for its local DBMS if necessary, and sends request to local DBMS

5. Local DBMS at remote site processes request

6. Local DBMS at remote site sends results to distributed DBMS at remote site

7. Remote distributed DBMS sends results back to originating site

8. Distributed DBMS at originating site sends results to application

@ KIDS Labs 35

Figure 13-10: Distributed DBMS architecture (cont.)

(showing global transaction steps)

Global transaction – some

data is at remote site(s)

1

2

4

5

6

3

7

8

@ KIDS Labs 36

Distributed DBMS

Transparency Objectives • Location Transparency

– User/application does not need to know where data resides

• Replication Transparency

– User/application does not need to know about duplication

• Failure Transparency

– Either all or none of the actions of a transaction are committed

– Each site has a transaction manager

• Logs transactions and before and after images

• Concurrency control scheme to ensure data integrity

– Requires special commit protocol

@ KIDS Labs 37

Two-Phase Commit

• Prepare Phase

– Coordinator receives a commit request

– Coordinator instructs all resource managers to get ready to “go either way” on the transaction. Each resource manager writes all updates from that transaction to its own physical log

– Coordinator receives replies from all resource managers. If all are ok, it writes commit to its own log; if not then it writes rollback to its log

@ KIDS Labs 38

Two-Phase Commit (cont.)

• Commit Phase

– Coordinator then informs each resource manager of

its decision and broadcasts a message to either

commit or rollback (abort). If the message is commit,

then each resource manager transfers the update

from its log to its database

– A failure during the commit phase puts a transaction

“in limbo.” This has to be tested for and handled with

timeouts or polling

@ KIDS Labs 39

Concurrency Control

Concurrency Transparency

– Design goal for distributed database

• Timestamping

– Concurrency control mechanism

– Alternative to locks in distributed databases

@ KIDS Labs 40

Query Optimization

• In a query involving a multi-site join and, possibly, a distributed database with replicated files, the distributed DBMS must decide where to access the data and how to proceed with the join. Three step process:

1 Query decomposition - rewritten and simplified

2 Data localization - query fragmented so that fragments reference data at only one site

3 Global optimization - • Order in which to execute query fragments

• Data movement between sites

• Where parts of the query will be executed

@ KIDS Labs 41

Evolution of Distributed DBMS

• “Unit of Work” - All of a transaction’s steps.

• Remote Unit of Work

– SQL statements originated at one location can

be executed as a single unit of work on a single

remote DBMS

@ KIDS Labs 42

Evolution of Distributed DBMS

(cont.)

• Distributed Unit of Work – Different statements in a unit of work may refer to

different remote sites

– All databases in a single SQL statement must be at a single site

• Distributed Request – A single SQL statement may refer to tables in

more than one remote site

– May not support replication transparency or failure transparency

@ KIDS Labs 43

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.

@ KIDS Labs 44

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:

@ KIDS Labs 45

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

@ KIDS Labs 46

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

@ KIDS Labs 47

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.

@ KIDS Labs 48

Two-Phase Commit Diagram

@ KIDS Labs 49

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.

@ KIDS Labs 50

@ KIDS Labs 51