Distributed Database Systems

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Outline
◼ Introduction
◼ Distributed Database Design
◼ Distributed Data Control
◼ Distributed Query Processing
◼ Distributed Transaction Processing
◼ Data Replication
◼ Database Integration – Multi-database Systems
◼ Web Data Management

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Outline
◼ Introduction
❑ What is a distributed DBMS
❑ History
❑ Distributed DBMS promises
❑ Design issues
❑ Distributed DBMS architecture

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Distributed Computing

◼ A number of autonomous processing elements (not

necessarily homogeneous) that are interconnected by a
computer network and that cooperate in performing their
assigned tasks.
◼ What is being distributed?
❑ Processing logic
❑ Function
❑ Data
❑ Control

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Current Distribution – Geographically
Distributed Data Centers

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What is a Distributed Database System?

A distributed database is a collection of multiple, logically

interrelated databases distributed over a computer network

A distributed database management system (Distributed

DBMS) is the software that manages the DDB and provides
an access mechanism that makes this distribution
transparent to the users

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What is not a DDBS?

◼ A timesharing computer system

◼ A loosely or tightly coupled multiprocessor system
◼ A database system which resides at one of the nodes of
a network of computers - this is a centralized database
on a network node

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Distributed DBMS Environment

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Implicit Assumptions

◼ Data stored at a number of sites → each site logically

consists of a single processor
◼ Processors at different sites are interconnected by a
computer network → not a multiprocessor system
❑ Parallel database systems
◼ Distributed database is a database, not a collection of
files → data logically related as exhibited in the users’
access patterns
❑ Relational data model
◼ Distributed DBMS is a full-fledged DBMS
❑ Not remote file system, not a TP system

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Important Point

Logically integrated
but
Physically distributed

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Outline
◼ Introduction
❑

❑ History
❑

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History – File Systems

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History – Database Management

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History – Early Distribution
Peer-to-Peer (P2P)

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History – Client/Server

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History – Data Integration

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History – Cloud Computing

On-demand, reliable services provided over the Internet in

a cost-efficient manner
◼ Cost savings: no need to maintain dedicated compute
power
◼ Elasticity: better adaptivity to changing workload

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Data Delivery Alternatives

◼ Delivery modes
❑ Pull-only
❑ Push-only
❑ Hybrid
◼ Frequency
❑ Periodic
❑ Conditional
❑ Ad-hoc or irregular
◼ Communication Methods
❑ Unicast
❑ One-to-many
◼ Note: not all combinations make sense
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Outline
◼ Introduction
❑

❑ Distributed DBMS promises

❑

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Distributed DBMS Promises

 Transparent management of distributed, fragmented,

and replicated data

 Improved reliability/availability through distributed

transactions

 Improved performance

 Easier and more economical system expansion

Transparency

◼ Transparency is the separation of the higher-level

semantics of a system from the lower level
implementation issues.
◼ Fundamental issue is to provide
data independence
in the distributed environment
❑ Network (distribution) transparency
❑ Replication transparency
❑ Fragmentation transparency
◼ horizontal fragmentation: selection
◼ vertical fragmentation: projection
◼ hybrid
Example

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Transparent Access

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Distributed Database - User View

Distributed Database

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Distributed DBMS - Reality
User
Query

User
DBMS
Application
Software
DBMS
Software

DBMS Communication
Software Subsystem

User
DBMS User Application
Software Query
DBMS
Software

User
Query

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Types of Transparency

◼ Data independence
◼ Network transparency (or distribution transparency)
❑ Location transparency
❑ Fragmentation transparency
◼ Fragmentation transparency
◼ Replication transparency

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Reliability Through Transactions

◼ Replicated components and data should make distributed

DBMS more reliable.
◼ Distributed transactions provide
❑Concurrency transparency
❑ Failure atomicity

• Distributed transaction support requires implementation of

❑ Distributed concurrency control protocols

❑ Commit protocols

◼ Data replication
❑ Great for read-intensive workloads, problematic for updates
❑ Replication protocols

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Potentially Improved Performance

◼ Proximity of data to its points of use

❑ Requires some support for fragmentation and replication

◼ Parallelism in execution

❑ Inter-query parallelism

❑ Intra-query parallelism

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Scalability

◼ Issue is database scaling and workload scaling

◼ Adding processing and storage power

◼ Scale-out: add more servers

❑ Scale-up: increase the capacity of one server → has limits

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Outline
◼ Introduction
❑

❑ Design issues
❑

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Distributed DBMS Issues

◼ Distributed database design

❑ How to distribute the database
❑ Replicated & non-replicated database distribution
❑ A related problem in directory management

◼ Distributed query processing

❑ Convert user transactions to data manipulation instructions
❑ Optimization problem
◼ min{cost = data transmission + local processing}
❑ General formulation is NP-hard

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Distributed DBMS Issues

◼ Distributed concurrency control

❑ Synchronization of concurrent accesses
❑ Consistency and isolation of transactions' effects
❑ Deadlock management

◼ Reliability
❑ How to make the system resilient to failures
❑ Atomicity and durability

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Distributed DBMS Issues

◼ Replication
❑ Mutual consistency
❑ Freshness of copies
❑ Eager vs lazy
❑ Centralized vs distributed
◼ Parallel DBMS
❑ Objectives: high scalability and performance
❑ Not geo-distributed
❑ Cluster computing

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Related Issues

◼ Alternative distribution approaches

❑ Modern P2P
❑ World Wide Web (WWW or Web)
◼ Big data processing
❑ 4V: volume, variety, velocity, veracity
❑ MapReduce & Spark
❑ Stream data
❑ Graph analytics
❑ NoSQL
❑ NewSQL
❑ Polystores

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Outline
◼ Introduction
❑

❑ Distributed DBMS architecture

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DBMS Implementation Alternatives

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Dimensions of the Problem

◼ Distribution
❑ Whether the components of the system are located on the same machine or
not
◼ Heterogeneity
❑ Various levels (hardware, communications, operating system)
❑ DBMS important one
◼ data model, query language,transaction management algorithms
◼ Autonomy
❑ Not well understood and most troublesome
❑ Various versions
◼ Design autonomy: Ability of a component DBMS to decide on issues related to its
own design.
◼ Communication autonomy: Ability of a component DBMS to decide whether and
how to communicate with other DBMSs.
◼ Execution autonomy: Ability of a component DBMS to execute local operations in
any manner it wants to.

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Client/Server Architecture

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Advantages of Client-Server
Architectures
◼ More efficient division of labor
◼ Horizontal and vertical scaling of resources
◼ Better price/performance on client machines
◼ Ability to use familiar tools on client machines
◼ Client access to remote data (via standards)
◼ Full DBMS functionality provided to client workstations
◼ Overall better system price/performance

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Database Server

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Distributed Database Servers

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Peer-to-Peer Component Architecture

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MDBS Components & Execution

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Mediator/Wrapper Architecture

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Cloud Computing

On-demand, reliable services provided over the Internet in

a cost-efficient manner
◼ IaaS – Infrastructure-as-a-Service

◼ PaaS – Platform-as-a-Service

◼ SaaS – Software-as-a-Service

◼ DaaS – Database-as-a-Service

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Simplified Cloud Architecture

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