SlideShare a Scribd company logo
Introduction to Distributed
Database
Submitted By :
• Sonia Flora Panesar
Parul University
Vadodara
Distributed Database
(DDB):
It is comprises of two terms - Distributed Computing
and Database.
Distributed Computing means when multiple
computers are interconnected via some network works
as a single unit to perform the certain task. i.e
Distributed computing systems partitions a big,
unmanageable problem in to smaller pieces and solve
it efficiently in a coordinated manner. They are loosely
coupled. They do not have shared memory concept.
Database is an organised collection of data.
Overview
 Definition
 Difference between parallel and distributed DB
 Types of distributed DB
 Distributed Data Storage
Replication
Fragmentation
Allocation
 Advantages ( Objectives ) of DDB
 Disadvantages
Definition
Distributed Database(DDB) is defined as a
collection of multiple Logically interrelated database
over a computer network.
Distributed Database Management
System(DDBMS) is defined as a software that
manages a distributed database while making the
distribution transparent to the user. i.e A collection of
files stored at different nodes of a network and the
maintaining of interrelationships among them via
hyperlinks has become a common organisation on the
internet
4
Parallel database Distributed Database
It is a system where multiple
processors or machines are used to
excecute and run queries in parallel.
It is a collection of multiple logically
interrelated database distributed over
a network
The nodes are located at
geographical same location
The nodes are usually located at
geographically different locations.
These are based on shared memory
or shared disk architecture i.e they
either shared a primary or a
secondary (disk)memory or both.
These are based on shared nothing
architecture.i.e every machine has its
own primary or secondary
(disk)memory, no common memory
exists in the mode of operation.
Their excution speed is quiet fast Their execution speed is slow.
They are difficult to expand They are easier to expand.
Types of Distributed Database Systems:
Distributed database
systems
Homogeneous Heterogeneous
Homogeneous distributed
database system
 All sites have identical database management
system software.
 All sites are aware of one another, and agree to
cooperate in processing users’ requests.
 The operating system, the data structures and the
database application used at each location must be
same or compatible.
 This system appears to the user as a single system
and it is much easier to design and manage
Heterogeneous distributed
database system
 Different sites may use different database
management system software.
 The sites may not be aware of one another.
 They may provide only limited facilities for
cooperation in transaction processing.
 The operating system, the data structures and the
database application used at each location are
incompatible
Distributed Data Storage
Consider a relation r that is to be stored in the database. There
are several approaches to storing this relation in the
distributed database.
 Replication: The system maintains several identical
replicas (copies) of the relation, and stores each replica at a
different site.
 Fragmentation: The system partitions the relation into
several fragments and stores each fragment at a different
site.
 Allocation: Each fragment – or each (replicas)copy of
fragment – must be assigned to a particular site in the
distributed system. This is called data allocation
Replication
 If relation r is replicated, a copy of relation r is stored in
two or more sites. In the most extreme case, we have full
replication, in which a copy is stored in every site in the
system. There are number of advantages and
Disadvantages to this replication :
 Availability : If one of the sites containing relation r fails,
then the relation r can be found in another site.
 Increased Parallelism : The more replicas of r there are,
the greater the chance that the needed data will be found in
the site where the transaction is executing.
 Increased Overhead on Update : The system must ensure
that all replicas of a relation r are consistent , otherwise
erroneous computation may result. Thus , whenever r is
updated, the update must be propagated to all sites
containing replicas.
 Replication enhances the performance of read operation
and increase the availability of data to read only
transaction.
Fragmentation
 If relation r is fragmented, r is divided into a
number of fragments r1, r2, . . . , rn. These
fragments contain sufficient information to allow
reconstruction of the original relation r using
Union or Join operation on various fragments.
 There are two different schemes for fragmenting a
relation:
 Horizontal fragmentation
 Vertical fragmentation
8
Horizontal Fragmentation
This splits the relation by assigning each tuple of r to one or more fragments
Branch-name Account-
number
Balance
Hillside A-305 500
Hillside A-226 336
Valleyview A-177 205
Valleyview A-402 10000
Hillside A-155 62
Branch-
name
Account-
no
Balance
Valleyview A-177 205
Valleyview A-402 10000
Branch-
name
Account-no Balance
Hillside A-305 500
Hillside A-226 336
Hillside A-155 62
Vertical fragmentation
This splits the relation into subrelation where each sub relation is defined by a subset of the
columns of the original relation.
Branch
no
Accountn
o
Cu_name Balance Tup_id
Hillside A-305 Lowman 500 1
Hillside A-226 Camp 336 2
Valleyview A-177 Camp 205 3
Valleyview A-402 Khan 10000 4
Hillside A-155 Khan 62 5
Valleyview A-408 Khan 1123 6
Branchno Cu_name Tup_id
Hillside Lowman 1
Hillside Camp 2
Valleyview Camp 3
Valleyview Khan 4
Hillside Khan 5
Valleyview Khan 6
Accountn
o
Balance Tup_id
A-305 500 1
A-226 336 2
A-177 205 3
A-402 10000 4
A-155 62 5
Allocation
 The choice of sites and the degree of replication depend on the
performance and availability goals of the system.
 It also depends on the types and frequencies of transactions
submitted at each site
 For example
 If high availability is required and transaction can be
submitted at any site.
 If most transactions are retrieval only, fully replicated
database is a good choice.
 If certain transactions that access particular parts of the
database are mostly submitted at a particular site,the
corresponding set of fragments can be allocated at that site only.
Advantages ( Objectives of
Distributed Databases )
1. Management of Distributed Data with different levels of
transparency : Distribution transparent in the sense of
hiding the details of where each file(table ,relation) is
physically stored within the system. Types of
transparencies are
 Distribution or Network Transparency : Freedom for the
user from the operational details of the network. It is
further divided into Location Transparency (independent
of location of data and the location of the system) and
Naming Transparency ( named object can be accessed
without additional information).
 Replication Transparency : It makes the user unaware of
the existence of copies.
 Fragmentation Transparency : It makes the user unaware
of the existence of Horizontal or Vertical Fragments
2. Increased reliability and
availability
 These are potential advantages cited
for distributed data base.
 Reliability is broadly defined as the
probability that the system is running at
a certain time point
 Availability is the probability that the
system is continuously available during
the time interval.
3. Improved Performance
 The distributed DBMS fragments the
data base by keeping the data closer to
where it is needed most.
 Data localisation reduced the contention
for CPU and input output services and
simultaneously reduced excess delays
involved in wide area networks.
4. Easier
Expansion
 Expansion of the system
in terms of adding more
data increasing data base
size or adding more
processors is much easier
5. Keeping Track
of Data
 The ability to keep track
of the data distribution,
fragmentation and
replication by expanding
the DDBMS catalogue
6. Distributed
Query
Processing
The ability to access remote
sites and transmit querries
and data amongst the
various sites via
communication network.
7. Replicated data
management
The ability to decide which
copy of replicated data item
to access and to maintain
the consistency of copies of
a replicated data item.
8. Distributed Transaction
Management
 The ability to devise execution strategy for
querries and transactions that access data
from more than one site and to synchronise
the access to distributed data and maintain
the integrity of the overall data base.
9. Distributed data base recovery
 The ability to recover from individual
site crashes and from new types of
failures such as failure of the
communication link.
10. Security
Distributed transaction must
be executed with the proper
management of the security
of the data and the access
privilege of users.
11. Distributed
Directory (
Catalogue )
Management
Directory contains
information ( meta data )
about data in the data base.
The directory may be global
for entire DDB
Disadvantages
 Complexity of management and control
 Increased storage and infrastructure
requirement
 Multiple copies of data has to be at
different sites thus an additional disc
storage space will be required
 The probability of security lapse
increases when data are located at
multiple sites.
 Difficult to maintain integrity
THANKYOU
Ad

More Related Content

What's hot (20)

Distributed Database System
Distributed Database SystemDistributed Database System
Distributed Database System
Sulemang
 
Distributed Database Management System
Distributed Database Management SystemDistributed Database Management System
Distributed Database Management System
AAKANKSHA JAIN
 
Distributed DBMS - Unit 6 - Query Processing
Distributed DBMS - Unit 6 - Query ProcessingDistributed DBMS - Unit 6 - Query Processing
Distributed DBMS - Unit 6 - Query Processing
Gyanmanjari Institute Of Technology
 
DDBMS Paper with Solution
DDBMS Paper with SolutionDDBMS Paper with Solution
DDBMS Paper with Solution
Gyanmanjari Institute Of Technology
 
Ddbms1
Ddbms1Ddbms1
Ddbms1
pranjal_das
 
Lec 7 query processing
Lec 7 query processingLec 7 query processing
Lec 7 query processing
Md. Mashiur Rahman
 
Distributed Query Processing
Distributed Query ProcessingDistributed Query Processing
Distributed Query Processing
Mythili Kannan
 
Distributed System ppt
Distributed System pptDistributed System ppt
Distributed System ppt
OECLIB Odisha Electronics Control Library
 
Ddb 1.6-design issues
Ddb 1.6-design issuesDdb 1.6-design issues
Ddb 1.6-design issues
Esar Qasmi
 
Distributed dbms architectures
Distributed dbms architecturesDistributed dbms architectures
Distributed dbms architectures
Pooja Dixit
 
Locks In Disributed Systems
Locks In Disributed SystemsLocks In Disributed Systems
Locks In Disributed Systems
mridul mishra
 
Distributed DBMS - Unit 1 - Introduction
Distributed DBMS - Unit 1 - IntroductionDistributed DBMS - Unit 1 - Introduction
Distributed DBMS - Unit 1 - Introduction
Gyanmanjari Institute Of Technology
 
Distribution transparency and Distributed transaction
Distribution transparency and Distributed transactionDistribution transparency and Distributed transaction
Distribution transparency and Distributed transaction
shraddha mane
 
Distributed Database Management System(DDMS)
Distributed Database Management System(DDMS)Distributed Database Management System(DDMS)
Distributed Database Management System(DDMS)
mobeen.laws
 
Map reduce in BIG DATA
Map reduce in BIG DATAMap reduce in BIG DATA
Map reduce in BIG DATA
GauravBiswas9
 
Database , 12 Reliability
Database , 12 ReliabilityDatabase , 12 Reliability
Database , 12 Reliability
Ali Usman
 
Server system architecture
Server system architectureServer system architecture
Server system architecture
Faiza Hafeez
 
Database System Architectures
Database System ArchitecturesDatabase System Architectures
Database System Architectures
Information Technology
 
Distributed file system
Distributed file systemDistributed file system
Distributed file system
Anamika Singh
 
Directory structure
Directory structureDirectory structure
Directory structure
sangrampatil81
 
Distributed Database System
Distributed Database SystemDistributed Database System
Distributed Database System
Sulemang
 
Distributed Database Management System
Distributed Database Management SystemDistributed Database Management System
Distributed Database Management System
AAKANKSHA JAIN
 
Distributed Query Processing
Distributed Query ProcessingDistributed Query Processing
Distributed Query Processing
Mythili Kannan
 
Ddb 1.6-design issues
Ddb 1.6-design issuesDdb 1.6-design issues
Ddb 1.6-design issues
Esar Qasmi
 
Distributed dbms architectures
Distributed dbms architecturesDistributed dbms architectures
Distributed dbms architectures
Pooja Dixit
 
Locks In Disributed Systems
Locks In Disributed SystemsLocks In Disributed Systems
Locks In Disributed Systems
mridul mishra
 
Distribution transparency and Distributed transaction
Distribution transparency and Distributed transactionDistribution transparency and Distributed transaction
Distribution transparency and Distributed transaction
shraddha mane
 
Distributed Database Management System(DDMS)
Distributed Database Management System(DDMS)Distributed Database Management System(DDMS)
Distributed Database Management System(DDMS)
mobeen.laws
 
Map reduce in BIG DATA
Map reduce in BIG DATAMap reduce in BIG DATA
Map reduce in BIG DATA
GauravBiswas9
 
Database , 12 Reliability
Database , 12 ReliabilityDatabase , 12 Reliability
Database , 12 Reliability
Ali Usman
 
Server system architecture
Server system architectureServer system architecture
Server system architecture
Faiza Hafeez
 
Distributed file system
Distributed file systemDistributed file system
Distributed file system
Anamika Singh
 

Similar to Introduction to distributed database (20)

1 ddbms jan 2011_u
1 ddbms jan 2011_u1 ddbms jan 2011_u
1 ddbms jan 2011_u
betheperformer
 
Lec 8 (distributed database)
Lec 8 (distributed database)Lec 8 (distributed database)
Lec 8 (distributed database)
Sudarshan Mondal
 
DDBS PPT (1).pptx
DDBS PPT (1).pptxDDBS PPT (1).pptx
DDBS PPT (1).pptx
HarshitSingh334328
 
Distributed database
Distributed databaseDistributed database
Distributed database
sanjay joshi
 
Distributed databases
Distributed databasesDistributed databases
Distributed databases
Suneel Dogra
 
Distributed database
Distributed databaseDistributed database
Distributed database
sanjay joshi
 
DBMS - Distributed Databases
DBMS - Distributed DatabasesDBMS - Distributed Databases
DBMS - Distributed Databases
MythiliMurugan3
 
Chapter-6 Distribute Database system (3).ppt
Chapter-6 Distribute Database system (3).pptChapter-6 Distribute Database system (3).ppt
Chapter-6 Distribute Database system (3).ppt
latigudata
 
Distributed Database
Distributed DatabaseDistributed Database
Distributed Database
Amity University | FMS - DU | IMT | Stratford University | KKMI International Institute | AIMA | DTU
 
Advance DBMS
Advance DBMSAdvance DBMS
Advance DBMS
Md. Mashiur Rahman
 
ADBMS Presentation1weerfbhytffdghg1.pptx
ADBMS Presentation1weerfbhytffdghg1.pptxADBMS Presentation1weerfbhytffdghg1.pptx
ADBMS Presentation1weerfbhytffdghg1.pptx
deshmukhvishakha2003
 
Distributed Storage in advanced database.pptx
Distributed Storage in advanced database.pptxDistributed Storage in advanced database.pptx
Distributed Storage in advanced database.pptx
rojansebastian1
 
DDBMS.pptx
DDBMS.pptxDDBMS.pptx
DDBMS.pptx
ManishBej1
 
ADBMS Presentation_new.pdtttttttttttttttttttttttttttttt
ADBMS Presentation_new.pdttttttttttttttttttttttttttttttADBMS Presentation_new.pdtttttttttttttttttttttttttttttt
ADBMS Presentation_new.pdtttttttttttttttttttttttttttttt
RajeshPawar832767
 
ADBMS Presentation_new1234567878765.pptx
ADBMS Presentation_new1234567878765.pptxADBMS Presentation_new1234567878765.pptx
ADBMS Presentation_new1234567878765.pptx
deshmukhvishakha2003
 
Unit-1 Introduction to Big Data.pptx
Unit-1 Introduction to Big Data.pptxUnit-1 Introduction to Big Data.pptx
Unit-1 Introduction to Big Data.pptx
AnkitChauhan817826
 
Distributed database detailed version by jh
Distributed database detailed version by jhDistributed database detailed version by jh
Distributed database detailed version by jh
rockop6k
 
nnnn.pptx
nnnn.pptxnnnn.pptx
nnnn.pptx
NANDHINIS900805
 
DBMS.pptx
DBMS.pptxDBMS.pptx
DBMS.pptx
NANDHINIS900805
 
Pptofdistributeddb
PptofdistributeddbPptofdistributeddb
Pptofdistributeddb
Mahavir Devmane
 
Ad

Recently uploaded (20)

Time series for yotube_1_data anlysis.pdf
Time series for yotube_1_data anlysis.pdfTime series for yotube_1_data anlysis.pdf
Time series for yotube_1_data anlysis.pdf
asmaamahmoudsaeed
 
Lesson 6-Interviewing in SHRM_updated.pdf
Lesson 6-Interviewing in SHRM_updated.pdfLesson 6-Interviewing in SHRM_updated.pdf
Lesson 6-Interviewing in SHRM_updated.pdf
hemelali11
 
HershAggregator (2).pdf musicretaildistribution
HershAggregator (2).pdf musicretaildistributionHershAggregator (2).pdf musicretaildistribution
HershAggregator (2).pdf musicretaildistribution
hershtara1
 
Automated Melanoma Detection via Image Processing.pptx
Automated Melanoma Detection via Image Processing.pptxAutomated Melanoma Detection via Image Processing.pptx
Automated Melanoma Detection via Image Processing.pptx
handrymaharjan23
 
Automation Platforms and Process Mining - success story
Automation Platforms and Process Mining - success storyAutomation Platforms and Process Mining - success story
Automation Platforms and Process Mining - success story
Process mining Evangelist
 
2024 Digital Equity Accelerator Report.pdf
2024 Digital Equity Accelerator Report.pdf2024 Digital Equity Accelerator Report.pdf
2024 Digital Equity Accelerator Report.pdf
dominikamizerska1
 
Oral Malodor.pptx jsjshdhushehsidjjeiejdhfj
Oral Malodor.pptx jsjshdhushehsidjjeiejdhfjOral Malodor.pptx jsjshdhushehsidjjeiejdhfj
Oral Malodor.pptx jsjshdhushehsidjjeiejdhfj
maitripatel5301
 
Sets theories and applications that can used to imporve knowledge
Sets theories and applications that can used to imporve knowledgeSets theories and applications that can used to imporve knowledge
Sets theories and applications that can used to imporve knowledge
saumyasl2020
 
Fundamentals of Data Analysis, its types, tools, algorithms
Fundamentals of Data Analysis, its types, tools, algorithmsFundamentals of Data Analysis, its types, tools, algorithms
Fundamentals of Data Analysis, its types, tools, algorithms
priyaiyerkbcsc
 
TOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdf
TOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdfTOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdf
TOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdf
NhiV747372
 
L1_Slides_Foundational Concepts_508.pptx
L1_Slides_Foundational Concepts_508.pptxL1_Slides_Foundational Concepts_508.pptx
L1_Slides_Foundational Concepts_508.pptx
38NoopurPatel
 
AWS-Certified-ML-Engineer-Associate-Slides.pdf
AWS-Certified-ML-Engineer-Associate-Slides.pdfAWS-Certified-ML-Engineer-Associate-Slides.pdf
AWS-Certified-ML-Engineer-Associate-Slides.pdf
philsparkshome
 
real illuminati Uganda agent 0782561496/0756664682
real illuminati Uganda agent 0782561496/0756664682real illuminati Uganda agent 0782561496/0756664682
real illuminati Uganda agent 0782561496/0756664682
way to join real illuminati Agent In Kampala Call/WhatsApp+256782561496/0756664682
 
Dr. Robert Krug - Expert In Artificial Intelligence
Dr. Robert Krug - Expert In Artificial IntelligenceDr. Robert Krug - Expert In Artificial Intelligence
Dr. Robert Krug - Expert In Artificial Intelligence
Dr. Robert Krug
 
50_questions_full.pptxdddddddddddddddddd
50_questions_full.pptxdddddddddddddddddd50_questions_full.pptxdddddddddddddddddd
50_questions_full.pptxdddddddddddddddddd
emir73065
 
Analysis of Billboards hot 100 toop five hit makers on the chart.docx
Analysis of Billboards hot 100 toop five hit makers on the chart.docxAnalysis of Billboards hot 100 toop five hit makers on the chart.docx
Analysis of Billboards hot 100 toop five hit makers on the chart.docx
hershtara1
 
RAG Chatbot using AWS Bedrock and Streamlit Framework
RAG Chatbot using AWS Bedrock and Streamlit FrameworkRAG Chatbot using AWS Bedrock and Streamlit Framework
RAG Chatbot using AWS Bedrock and Streamlit Framework
apanneer
 
Process Mining as Enabler for Digital Transformations
Process Mining as Enabler for Digital TransformationsProcess Mining as Enabler for Digital Transformations
Process Mining as Enabler for Digital Transformations
Process mining Evangelist
 
CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™
CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™
CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™
muhammed84essa
 
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...
disnakertransjabarda
 
Time series for yotube_1_data anlysis.pdf
Time series for yotube_1_data anlysis.pdfTime series for yotube_1_data anlysis.pdf
Time series for yotube_1_data anlysis.pdf
asmaamahmoudsaeed
 
Lesson 6-Interviewing in SHRM_updated.pdf
Lesson 6-Interviewing in SHRM_updated.pdfLesson 6-Interviewing in SHRM_updated.pdf
Lesson 6-Interviewing in SHRM_updated.pdf
hemelali11
 
HershAggregator (2).pdf musicretaildistribution
HershAggregator (2).pdf musicretaildistributionHershAggregator (2).pdf musicretaildistribution
HershAggregator (2).pdf musicretaildistribution
hershtara1
 
Automated Melanoma Detection via Image Processing.pptx
Automated Melanoma Detection via Image Processing.pptxAutomated Melanoma Detection via Image Processing.pptx
Automated Melanoma Detection via Image Processing.pptx
handrymaharjan23
 
Automation Platforms and Process Mining - success story
Automation Platforms and Process Mining - success storyAutomation Platforms and Process Mining - success story
Automation Platforms and Process Mining - success story
Process mining Evangelist
 
2024 Digital Equity Accelerator Report.pdf
2024 Digital Equity Accelerator Report.pdf2024 Digital Equity Accelerator Report.pdf
2024 Digital Equity Accelerator Report.pdf
dominikamizerska1
 
Oral Malodor.pptx jsjshdhushehsidjjeiejdhfj
Oral Malodor.pptx jsjshdhushehsidjjeiejdhfjOral Malodor.pptx jsjshdhushehsidjjeiejdhfj
Oral Malodor.pptx jsjshdhushehsidjjeiejdhfj
maitripatel5301
 
Sets theories and applications that can used to imporve knowledge
Sets theories and applications that can used to imporve knowledgeSets theories and applications that can used to imporve knowledge
Sets theories and applications that can used to imporve knowledge
saumyasl2020
 
Fundamentals of Data Analysis, its types, tools, algorithms
Fundamentals of Data Analysis, its types, tools, algorithmsFundamentals of Data Analysis, its types, tools, algorithms
Fundamentals of Data Analysis, its types, tools, algorithms
priyaiyerkbcsc
 
TOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdf
TOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdfTOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdf
TOAE201-Slides-Chapter 4. Sample theoretical basis (1).pdf
NhiV747372
 
L1_Slides_Foundational Concepts_508.pptx
L1_Slides_Foundational Concepts_508.pptxL1_Slides_Foundational Concepts_508.pptx
L1_Slides_Foundational Concepts_508.pptx
38NoopurPatel
 
AWS-Certified-ML-Engineer-Associate-Slides.pdf
AWS-Certified-ML-Engineer-Associate-Slides.pdfAWS-Certified-ML-Engineer-Associate-Slides.pdf
AWS-Certified-ML-Engineer-Associate-Slides.pdf
philsparkshome
 
Dr. Robert Krug - Expert In Artificial Intelligence
Dr. Robert Krug - Expert In Artificial IntelligenceDr. Robert Krug - Expert In Artificial Intelligence
Dr. Robert Krug - Expert In Artificial Intelligence
Dr. Robert Krug
 
50_questions_full.pptxdddddddddddddddddd
50_questions_full.pptxdddddddddddddddddd50_questions_full.pptxdddddddddddddddddd
50_questions_full.pptxdddddddddddddddddd
emir73065
 
Analysis of Billboards hot 100 toop five hit makers on the chart.docx
Analysis of Billboards hot 100 toop five hit makers on the chart.docxAnalysis of Billboards hot 100 toop five hit makers on the chart.docx
Analysis of Billboards hot 100 toop five hit makers on the chart.docx
hershtara1
 
RAG Chatbot using AWS Bedrock and Streamlit Framework
RAG Chatbot using AWS Bedrock and Streamlit FrameworkRAG Chatbot using AWS Bedrock and Streamlit Framework
RAG Chatbot using AWS Bedrock and Streamlit Framework
apanneer
 
Process Mining as Enabler for Digital Transformations
Process Mining as Enabler for Digital TransformationsProcess Mining as Enabler for Digital Transformations
Process Mining as Enabler for Digital Transformations
Process mining Evangelist
 
CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™
CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™
CERTIFIED BUSINESS ANALYSIS PROFESSIONAL™
muhammed84essa
 
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...
disnakertransjabarda
 
Ad

Introduction to distributed database

  • 1. Introduction to Distributed Database Submitted By : • Sonia Flora Panesar Parul University Vadodara
  • 2. Distributed Database (DDB): It is comprises of two terms - Distributed Computing and Database. Distributed Computing means when multiple computers are interconnected via some network works as a single unit to perform the certain task. i.e Distributed computing systems partitions a big, unmanageable problem in to smaller pieces and solve it efficiently in a coordinated manner. They are loosely coupled. They do not have shared memory concept. Database is an organised collection of data.
  • 3. Overview  Definition  Difference between parallel and distributed DB  Types of distributed DB  Distributed Data Storage Replication Fragmentation Allocation  Advantages ( Objectives ) of DDB  Disadvantages
  • 4. Definition Distributed Database(DDB) is defined as a collection of multiple Logically interrelated database over a computer network. Distributed Database Management System(DDBMS) is defined as a software that manages a distributed database while making the distribution transparent to the user. i.e A collection of files stored at different nodes of a network and the maintaining of interrelationships among them via hyperlinks has become a common organisation on the internet
  • 5. 4 Parallel database Distributed Database It is a system where multiple processors or machines are used to excecute and run queries in parallel. It is a collection of multiple logically interrelated database distributed over a network The nodes are located at geographical same location The nodes are usually located at geographically different locations. These are based on shared memory or shared disk architecture i.e they either shared a primary or a secondary (disk)memory or both. These are based on shared nothing architecture.i.e every machine has its own primary or secondary (disk)memory, no common memory exists in the mode of operation. Their excution speed is quiet fast Their execution speed is slow. They are difficult to expand They are easier to expand.
  • 6. Types of Distributed Database Systems: Distributed database systems Homogeneous Heterogeneous
  • 7. Homogeneous distributed database system  All sites have identical database management system software.  All sites are aware of one another, and agree to cooperate in processing users’ requests.  The operating system, the data structures and the database application used at each location must be same or compatible.  This system appears to the user as a single system and it is much easier to design and manage
  • 8. Heterogeneous distributed database system  Different sites may use different database management system software.  The sites may not be aware of one another.  They may provide only limited facilities for cooperation in transaction processing.  The operating system, the data structures and the database application used at each location are incompatible
  • 9. Distributed Data Storage Consider a relation r that is to be stored in the database. There are several approaches to storing this relation in the distributed database.  Replication: The system maintains several identical replicas (copies) of the relation, and stores each replica at a different site.  Fragmentation: The system partitions the relation into several fragments and stores each fragment at a different site.  Allocation: Each fragment – or each (replicas)copy of fragment – must be assigned to a particular site in the distributed system. This is called data allocation
  • 10. Replication  If relation r is replicated, a copy of relation r is stored in two or more sites. In the most extreme case, we have full replication, in which a copy is stored in every site in the system. There are number of advantages and Disadvantages to this replication :  Availability : If one of the sites containing relation r fails, then the relation r can be found in another site.  Increased Parallelism : The more replicas of r there are, the greater the chance that the needed data will be found in the site where the transaction is executing.
  • 11.  Increased Overhead on Update : The system must ensure that all replicas of a relation r are consistent , otherwise erroneous computation may result. Thus , whenever r is updated, the update must be propagated to all sites containing replicas.  Replication enhances the performance of read operation and increase the availability of data to read only transaction.
  • 12. Fragmentation  If relation r is fragmented, r is divided into a number of fragments r1, r2, . . . , rn. These fragments contain sufficient information to allow reconstruction of the original relation r using Union or Join operation on various fragments.  There are two different schemes for fragmenting a relation:  Horizontal fragmentation  Vertical fragmentation
  • 13. 8 Horizontal Fragmentation This splits the relation by assigning each tuple of r to one or more fragments Branch-name Account- number Balance Hillside A-305 500 Hillside A-226 336 Valleyview A-177 205 Valleyview A-402 10000 Hillside A-155 62 Branch- name Account- no Balance Valleyview A-177 205 Valleyview A-402 10000 Branch- name Account-no Balance Hillside A-305 500 Hillside A-226 336 Hillside A-155 62
  • 14. Vertical fragmentation This splits the relation into subrelation where each sub relation is defined by a subset of the columns of the original relation. Branch no Accountn o Cu_name Balance Tup_id Hillside A-305 Lowman 500 1 Hillside A-226 Camp 336 2 Valleyview A-177 Camp 205 3 Valleyview A-402 Khan 10000 4 Hillside A-155 Khan 62 5 Valleyview A-408 Khan 1123 6 Branchno Cu_name Tup_id Hillside Lowman 1 Hillside Camp 2 Valleyview Camp 3 Valleyview Khan 4 Hillside Khan 5 Valleyview Khan 6 Accountn o Balance Tup_id A-305 500 1 A-226 336 2 A-177 205 3 A-402 10000 4 A-155 62 5
  • 15. Allocation  The choice of sites and the degree of replication depend on the performance and availability goals of the system.  It also depends on the types and frequencies of transactions submitted at each site  For example  If high availability is required and transaction can be submitted at any site.  If most transactions are retrieval only, fully replicated database is a good choice.  If certain transactions that access particular parts of the database are mostly submitted at a particular site,the corresponding set of fragments can be allocated at that site only.
  • 16. Advantages ( Objectives of Distributed Databases ) 1. Management of Distributed Data with different levels of transparency : Distribution transparent in the sense of hiding the details of where each file(table ,relation) is physically stored within the system. Types of transparencies are  Distribution or Network Transparency : Freedom for the user from the operational details of the network. It is further divided into Location Transparency (independent of location of data and the location of the system) and Naming Transparency ( named object can be accessed without additional information).
  • 17.  Replication Transparency : It makes the user unaware of the existence of copies.  Fragmentation Transparency : It makes the user unaware of the existence of Horizontal or Vertical Fragments
  • 18. 2. Increased reliability and availability  These are potential advantages cited for distributed data base.  Reliability is broadly defined as the probability that the system is running at a certain time point  Availability is the probability that the system is continuously available during the time interval.
  • 19. 3. Improved Performance  The distributed DBMS fragments the data base by keeping the data closer to where it is needed most.  Data localisation reduced the contention for CPU and input output services and simultaneously reduced excess delays involved in wide area networks.
  • 20. 4. Easier Expansion  Expansion of the system in terms of adding more data increasing data base size or adding more processors is much easier 5. Keeping Track of Data  The ability to keep track of the data distribution, fragmentation and replication by expanding the DDBMS catalogue
  • 21. 6. Distributed Query Processing The ability to access remote sites and transmit querries and data amongst the various sites via communication network. 7. Replicated data management The ability to decide which copy of replicated data item to access and to maintain the consistency of copies of a replicated data item.
  • 22. 8. Distributed Transaction Management  The ability to devise execution strategy for querries and transactions that access data from more than one site and to synchronise the access to distributed data and maintain the integrity of the overall data base.
  • 23. 9. Distributed data base recovery  The ability to recover from individual site crashes and from new types of failures such as failure of the communication link.
  • 24. 10. Security Distributed transaction must be executed with the proper management of the security of the data and the access privilege of users. 11. Distributed Directory ( Catalogue ) Management Directory contains information ( meta data ) about data in the data base. The directory may be global for entire DDB
  • 25. Disadvantages  Complexity of management and control  Increased storage and infrastructure requirement  Multiple copies of data has to be at different sites thus an additional disc storage space will be required
  • 26.  The probability of security lapse increases when data are located at multiple sites.  Difficult to maintain integrity

Editor's Notes

  翻译: