SlideShare una empresa de Scribd logo
1 de 5
Descargar para leer sin conexión
Non-Stop Service System
 Minimizing Downtime and Maintaining the system availability up to 100%
 Planed Downtime – Regular inspection & System upgrade/patch
 Executing Switch-Over : Main modification of normal service
 Unplanned Downtime – Failure in some systems
 Executing Fail-Over : Main modification of urgent service
 High Availability / HA
 Non-stop system or the availability of its contents
 five 9 (99.999%)
 Various techniques exist at the level of S/W and H/W
HA of DBMS
 Activates by synchronizing the databases through nodes
 Techniques are different depending on the architecture of parallel database
Categories Shared Nothing Architecture Shared Disk Architecture
Shared resources There is no shared resources Disk
Data
synchronization
Replication through network Sharing disk
Performance*
Fast performance as there is no shared
resources
Performance reduced by complicated
Processing(2PC/3PC) of shared resources
System costs* Low costs (Local disk & Network) High costs(Shared storage facilities)
Distance*
There are not too much harsh in a long distance
as the general TCP based network is used
There is a restriction of distance as the dedicated
network of high cost for sharing disk is required
Data conformity*
The extra consideration is required to control
the data inconsistency in each node for the
features of network replication
The data conformity is guaranteed in each nodes as
the data is shared
Critical Failures
Unable to synchronize nodes when there is a
network failure
Entire services in system do not work when there is a
disk failure
Appropriate system
Faster performance is more required than
data conformity
Data conformity is more required than
performance
Relevant DBMS
technique
Replication RAC (Real Application Cluster)
Relevant DBMS
ALTIBASE HDB, DB2, MS-SQL, ORACLE,
SYBASE
ORACLE, DB2
▣Trade-off between “performance, system establishment costs, distance” and “ data conformity”
DB
SERVER main
DB
SERVER sub
replication
AP 1 AP n
Fail-Over
AP 1 APm+1AP m AP n
DB
SERVER main
DB
SERVER sub
replication
[Figure 1, High Availability secured in a 2-Way Replication System] [Figure 2, Scalability improved in a 2-Way Replication System]
What is Replication?
Replication is a technique for sending information about the changes to the contents of
a single database over a network to one or more other databases.
The Purpose of Replication
♦ Secures High Availability
♦ Improves performance and scalability by Load-balancing
♦ Minimizes Data Loss in the Event of a Physical Outage or Disaster
Main Features Description
TCP/IP Network-Based
Because the only facility that is required for replication is a network connection, no
additional expenses are incurred. Replication over long distances is possible, depending on
network performance (a Gigabit LAN is recommended)
Heterogeneous OS Support
Replication is possible between heterogeneous operating systems, and regardless of the
number of OS bits (32 or 64) or CPU endian
Integrated Replication
High Performance as the replication module is completely integrated with DBMS
No additional ALTIBASE HDB packages are required for replication
ALTIBASE HDB can be flexibly used depending on the user’s requirements
Redo Log-based Redo logs are sent in real time by records
Table-Based Management
Replication is managed by table
Table can be added to replication or deleted from replication while database is running
Two Modes: LAZY and EAGER Supports both LAZY(Async) and EAGER(Sync) replication modes
High-Speed Replication
In LAZY mode, the replication is executed with at least 95% of master transactions speed
while not affecting the master transaction
(Measured on a UNIX system in a Gigabit LAN environment)
Up to 32-Way Replication
A single ALTIBASE HDB node can have up to 32 replication objects
Load distribution across heterogeneous systems is supported
Main Features Description
Point-To-Point Replication Replicating 1:1 only between nodes that it does not transfer to other nodes
Network Fault Detection ALTIBASE HDB provides dedicated threads to detect physical network faults
Automatic Recovery
The time point at which replication was most recently performed is recorded. In the event
of network failure, replication resumes automatically once the network connection is
restored.
Support for Multiple IPs
If two or more IP addresses are assigned to a single replication, replication can
automatically switch to the other IP address in the event of a network fault, thus increasing
the availability of replication.
Control via SQL Interface
All commands required in order to use and manage replication have an SQL interface thus
it is convenient to use.
Data Conflict Resolution Methods Three (3) schemes and one (1) utility are provided to resolve data conflicts.
Additional Functions
Replication can be used to clone (i.e. copy the entire contents of) tables.
If the active node fails, offline replication can be conducted to access the redo log files on
the node that failed and resume replication on the standby node.

Más contenido relacionado

La actualidad más candente

PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...IJCNCJournal
 
A load balancing model based on cloud partitioning
A load balancing model based on cloud partitioningA load balancing model based on cloud partitioning
A load balancing model based on cloud partitioningLavanya Vigrahala
 
A load balancing model based on cloud partitioning for the public cloud. ppt
A  load balancing model based on cloud partitioning for the public cloud. ppt A  load balancing model based on cloud partitioning for the public cloud. ppt
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
 
load balancing in public cloud ppt
load balancing in public cloud pptload balancing in public cloud ppt
load balancing in public cloud pptKrishna Kumar
 
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingPerformance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
 
Cloud computing Module 2 First Part
Cloud computing Module 2 First PartCloud computing Module 2 First Part
Cloud computing Module 2 First PartSoumee Maschatak
 
Server Load Balancing
Server Load BalancingServer Load Balancing
Server Load Balancingalluwanted
 
Database replication
Database replicationDatabase replication
Database replicationArslan111
 
Scheduling in distributed systems - Andrii Vozniuk
Scheduling in distributed systems - Andrii VozniukScheduling in distributed systems - Andrii Vozniuk
Scheduling in distributed systems - Andrii VozniukAndrii Vozniuk
 
Load balancing in Distributed Systems
Load balancing in Distributed SystemsLoad balancing in Distributed Systems
Load balancing in Distributed SystemsRicha Singh
 
Load Balancing In Distributed Computing
Load Balancing In Distributed ComputingLoad Balancing In Distributed Computing
Load Balancing In Distributed ComputingRicha Singh
 
Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...
Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...
Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...Continuent
 
Sql disaster recovery
Sql disaster recoverySql disaster recovery
Sql disaster recoverySqlperfomance
 
Challenges in Cloud Computing – VM Migration
Challenges in Cloud Computing – VM MigrationChallenges in Cloud Computing – VM Migration
Challenges in Cloud Computing – VM MigrationSarmad Makhdoom
 

La actualidad más candente (20)

XenApp Load Balancing
XenApp Load BalancingXenApp Load Balancing
XenApp Load Balancing
 
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...
 
A load balancing model based on cloud partitioning
A load balancing model based on cloud partitioningA load balancing model based on cloud partitioning
A load balancing model based on cloud partitioning
 
A load balancing model based on cloud partitioning for the public cloud. ppt
A  load balancing model based on cloud partitioning for the public cloud. ppt A  load balancing model based on cloud partitioning for the public cloud. ppt
A load balancing model based on cloud partitioning for the public cloud. ppt
 
Load balancing
Load balancingLoad balancing
Load balancing
 
load balancing in public cloud ppt
load balancing in public cloud pptload balancing in public cloud ppt
load balancing in public cloud ppt
 
Web Server Load Balancing
Web Server Load BalancingWeb Server Load Balancing
Web Server Load Balancing
 
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingPerformance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
 
Cloud computing Module 2 First Part
Cloud computing Module 2 First PartCloud computing Module 2 First Part
Cloud computing Module 2 First Part
 
Server Load Balancing
Server Load BalancingServer Load Balancing
Server Load Balancing
 
Database replication
Database replicationDatabase replication
Database replication
 
Microsoft Clustering
Microsoft ClusteringMicrosoft Clustering
Microsoft Clustering
 
Scheduling in distributed systems - Andrii Vozniuk
Scheduling in distributed systems - Andrii VozniukScheduling in distributed systems - Andrii Vozniuk
Scheduling in distributed systems - Andrii Vozniuk
 
Load balancing in Distributed Systems
Load balancing in Distributed SystemsLoad balancing in Distributed Systems
Load balancing in Distributed Systems
 
Load Balancing In Distributed Computing
Load Balancing In Distributed ComputingLoad Balancing In Distributed Computing
Load Balancing In Distributed Computing
 
Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...
Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...
Webinar Slides: Real-Time Replication vs. ETL - How Analytics Requires New Te...
 
Cluster Computing
Cluster ComputingCluster Computing
Cluster Computing
 
Sql disaster recovery
Sql disaster recoverySql disaster recovery
Sql disaster recovery
 
Challenges in Cloud Computing – VM Migration
Challenges in Cloud Computing – VM MigrationChallenges in Cloud Computing – VM Migration
Challenges in Cloud Computing – VM Migration
 
Deco1
Deco1Deco1
Deco1
 

Destacado

Salesfactory NL
Salesfactory NLSalesfactory NL
Salesfactory NLjfbodart
 
[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS
[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS
[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHSChallenge:Future
 
REDES DE COMPUTADORES
REDES DE COMPUTADORESREDES DE COMPUTADORES
REDES DE COMPUTADORESdaniel279
 
[Altibase] 7 how the buffer is managed in altibase
[Altibase] 7 how the buffer is managed in altibase[Altibase] 7 how the buffer is managed in altibase
[Altibase] 7 how the buffer is managed in altibasealtistory
 
Evolución de la natación
Evolución de la nataciónEvolución de la natación
Evolución de la nataciónHydra Ordoñez
 
Brahyan echeverri red de computadores
Brahyan echeverri red de computadoresBrahyan echeverri red de computadores
Brahyan echeverri red de computadoresbrahyanecheverri
 
[Altibase] 6 what is the mvcc
[Altibase] 6 what is the mvcc[Altibase] 6 what is the mvcc
[Altibase] 6 what is the mvccaltistory
 

Destacado (13)

3. Clientele Rev01
3. Clientele Rev013. Clientele Rev01
3. Clientele Rev01
 
2 дахь хөнгөлөлт
2 дахь хөнгөлөлт2 дахь хөнгөлөлт
2 дахь хөнгөлөлт
 
Articles
ArticlesArticles
Articles
 
Surat ijin pelatihan blog
Surat ijin pelatihan blogSurat ijin pelatihan blog
Surat ijin pelatihan blog
 
Salesfactory NL
Salesfactory NLSalesfactory NL
Salesfactory NL
 
[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS
[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS
[Challenge:Future] Semi finals - HOW TO CREATE JOBS FOR THE KENYAN YOUTHS
 
08 Ticket_Approved
08 Ticket_Approved08 Ticket_Approved
08 Ticket_Approved
 
REDES DE COMPUTADORES
REDES DE COMPUTADORESREDES DE COMPUTADORES
REDES DE COMPUTADORES
 
[Altibase] 7 how the buffer is managed in altibase
[Altibase] 7 how the buffer is managed in altibase[Altibase] 7 how the buffer is managed in altibase
[Altibase] 7 how the buffer is managed in altibase
 
Evolución de la natación
Evolución de la nataciónEvolución de la natación
Evolución de la natación
 
Brahyan echeverri red de computadores
Brahyan echeverri red de computadoresBrahyan echeverri red de computadores
Brahyan echeverri red de computadores
 
[Altibase] 6 what is the mvcc
[Altibase] 6 what is the mvcc[Altibase] 6 what is the mvcc
[Altibase] 6 what is the mvcc
 
PAGINA 16
 PAGINA 16 PAGINA 16
PAGINA 16
 

Similar a [Altibase] 8 replication part1 (overview)

System models for distributed and cloud computing
System models for distributed and cloud computingSystem models for distributed and cloud computing
System models for distributed and cloud computingpurplesea
 
Designing High Availability Networks, Systems, and Software for the Universit...
Designing High Availability Networks, Systems, and Softwarefor the Universit...Designing High Availability Networks, Systems, and Softwarefor the Universit...
Designing High Availability Networks, Systems, and Software for the Universit...Shumon Huque
 
Distributed systems and scalability rules
Distributed systems and scalability rulesDistributed systems and scalability rules
Distributed systems and scalability rulesOleg Tsal-Tsalko
 
Clusters (Distributed computing)
Clusters (Distributed computing)Clusters (Distributed computing)
Clusters (Distributed computing)Sri Prasanna
 
Topic : X.25, Frame relay and ATM
Topic :  X.25, Frame relay and ATMTopic :  X.25, Frame relay and ATM
Topic : X.25, Frame relay and ATMDr Rajiv Srivastava
 
Network-aware Data Management for Large Scale Distributed Applications, IBM R...
Network-aware Data Management for Large Scale Distributed Applications, IBM R...Network-aware Data Management for Large Scale Distributed Applications, IBM R...
Network-aware Data Management for Large Scale Distributed Applications, IBM R...balmanme
 
Network-aware Data Management for High Throughput Flows Akamai, Cambridge, ...
Network-aware Data Management for High Throughput Flows   Akamai, Cambridge, ...Network-aware Data Management for High Throughput Flows   Akamai, Cambridge, ...
Network-aware Data Management for High Throughput Flows Akamai, Cambridge, ...balmanme
 
Cloud computing system models for distributed and cloud computing
Cloud computing system models for distributed and cloud computingCloud computing system models for distributed and cloud computing
Cloud computing system models for distributed and cloud computinghrmalik20
 
Cloud Computing System models for Distributed and cloud computing & Performan...
Cloud Computing System models for Distributed and cloud computing & Performan...Cloud Computing System models for Distributed and cloud computing & Performan...
Cloud Computing System models for Distributed and cloud computing & Performan...hrmalik20
 
Distribute Storage System May-2014
Distribute Storage System May-2014Distribute Storage System May-2014
Distribute Storage System May-2014Công Lợi Dương
 
Network and distributed systems
Network and distributed systemsNetwork and distributed systems
Network and distributed systemsSri Prasanna
 
Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...
Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...
Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...DataStax Academy
 

Similar a [Altibase] 8 replication part1 (overview) (20)

MYSQL
MYSQLMYSQL
MYSQL
 
System models for distributed and cloud computing
System models for distributed and cloud computingSystem models for distributed and cloud computing
System models for distributed and cloud computing
 
NoSQL
NoSQLNoSQL
NoSQL
 
linuxcluster.ppt
linuxcluster.pptlinuxcluster.ppt
linuxcluster.ppt
 
Accordion - VLDB 2014
Accordion - VLDB 2014Accordion - VLDB 2014
Accordion - VLDB 2014
 
Designing High Availability Networks, Systems, and Software for the Universit...
Designing High Availability Networks, Systems, and Softwarefor the Universit...Designing High Availability Networks, Systems, and Softwarefor the Universit...
Designing High Availability Networks, Systems, and Software for the Universit...
 
Distributed systems and scalability rules
Distributed systems and scalability rulesDistributed systems and scalability rules
Distributed systems and scalability rules
 
Clusters (Distributed computing)
Clusters (Distributed computing)Clusters (Distributed computing)
Clusters (Distributed computing)
 
Cluster computing
Cluster computingCluster computing
Cluster computing
 
Topic : X.25, Frame relay and ATM
Topic :  X.25, Frame relay and ATMTopic :  X.25, Frame relay and ATM
Topic : X.25, Frame relay and ATM
 
Network-aware Data Management for Large Scale Distributed Applications, IBM R...
Network-aware Data Management for Large Scale Distributed Applications, IBM R...Network-aware Data Management for Large Scale Distributed Applications, IBM R...
Network-aware Data Management for Large Scale Distributed Applications, IBM R...
 
Network-aware Data Management for High Throughput Flows Akamai, Cambridge, ...
Network-aware Data Management for High Throughput Flows   Akamai, Cambridge, ...Network-aware Data Management for High Throughput Flows   Akamai, Cambridge, ...
Network-aware Data Management for High Throughput Flows Akamai, Cambridge, ...
 
Database System Architectures
Database System ArchitecturesDatabase System Architectures
Database System Architectures
 
Cloud computing system models for distributed and cloud computing
Cloud computing system models for distributed and cloud computingCloud computing system models for distributed and cloud computing
Cloud computing system models for distributed and cloud computing
 
Cloud Computing System models for Distributed and cloud computing & Performan...
Cloud Computing System models for Distributed and cloud computing & Performan...Cloud Computing System models for Distributed and cloud computing & Performan...
Cloud Computing System models for Distributed and cloud computing & Performan...
 
Distribute Storage System May-2014
Distribute Storage System May-2014Distribute Storage System May-2014
Distribute Storage System May-2014
 
Network and distributed systems
Network and distributed systemsNetwork and distributed systems
Network and distributed systems
 
cluster computing
cluster computingcluster computing
cluster computing
 
Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...
Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...
Tales From The Front: An Architecture For Multi-Data Center Scalable Applicat...
 
GDPS and System Complex
GDPS and System ComplexGDPS and System Complex
GDPS and System Complex
 

Más de altistory

[Altibase] 4-5 summary of tablespace management
[Altibase] 4-5 summary of tablespace management[Altibase] 4-5 summary of tablespace management
[Altibase] 4-5 summary of tablespace managementaltistory
 
[Altibase] 13 backup and recovery
[Altibase] 13 backup and recovery[Altibase] 13 backup and recovery
[Altibase] 13 backup and recoveryaltistory
 
[Altibase] 12 replication part5 (optimization and monitoring)
[Altibase] 12 replication part5 (optimization and monitoring)[Altibase] 12 replication part5 (optimization and monitoring)
[Altibase] 12 replication part5 (optimization and monitoring)altistory
 
[Altibase] 11 replication part4 (conflict resolution)
[Altibase] 11 replication part4 (conflict resolution)[Altibase] 11 replication part4 (conflict resolution)
[Altibase] 11 replication part4 (conflict resolution)altistory
 
[Altibase] 10 replication part3 (system design)
[Altibase] 10 replication part3 (system design)[Altibase] 10 replication part3 (system design)
[Altibase] 10 replication part3 (system design)altistory
 
[Altibase] 9 replication part2 (methods and controls)
[Altibase] 9 replication part2 (methods and controls)[Altibase] 9 replication part2 (methods and controls)
[Altibase] 9 replication part2 (methods and controls)altistory
 
[Altibase] 5 durability
[Altibase] 5 durability[Altibase] 5 durability
[Altibase] 5 durabilityaltistory
 
[Altibase] 4-4 disk tablespace
[Altibase] 4-4 disk tablespace[Altibase] 4-4 disk tablespace
[Altibase] 4-4 disk tablespacealtistory
 
[Altibase] 4-3 volatile tablespace
[Altibase] 4-3 volatile tablespace[Altibase] 4-3 volatile tablespace
[Altibase] 4-3 volatile tablespacealtistory
 
[Altibase] 4-2 memory tablespace
[Altibase] 4-2 memory tablespace[Altibase] 4-2 memory tablespace
[Altibase] 4-2 memory tablespacealtistory
 
[Altibase] 4-1 tablespace concept
[Altibase] 4-1 tablespace concept[Altibase] 4-1 tablespace concept
[Altibase] 4-1 tablespace conceptaltistory
 
[Altibase] 3-3 directory contents
[Altibase] 3-3 directory contents[Altibase] 3-3 directory contents
[Altibase] 3-3 directory contentsaltistory
 
[Altibase] 3-1 architecture
[Altibase] 3-1 architecture[Altibase] 3-1 architecture
[Altibase] 3-1 architecturealtistory
 
[Altibase] 2-4 sql functions
[Altibase] 2-4 sql functions[Altibase] 2-4 sql functions
[Altibase] 2-4 sql functionsaltistory
 
[Altibase] 2-3 general functions
[Altibase] 2-3 general functions[Altibase] 2-3 general functions
[Altibase] 2-3 general functionsaltistory
 
[Altibase] 2-2 Altibase storage
[Altibase] 2-2 Altibase storage[Altibase] 2-2 Altibase storage
[Altibase] 2-2 Altibase storagealtistory
 
[Altibase] 2-1 features
[Altibase] 2-1 features[Altibase] 2-1 features
[Altibase] 2-1 featuresaltistory
 
[Altibase] 1-5 hybrid dbms
[Altibase] 1-5 hybrid dbms[Altibase] 1-5 hybrid dbms
[Altibase] 1-5 hybrid dbmsaltistory
 
[Altibase] 1-4 data differentiation
[Altibase] 1-4 data differentiation[Altibase] 1-4 data differentiation
[Altibase] 1-4 data differentiationaltistory
 
[Altibase] 1-3 history of the hybrid dbms
[Altibase] 1-3 history of the hybrid dbms[Altibase] 1-3 history of the hybrid dbms
[Altibase] 1-3 history of the hybrid dbmsaltistory
 

Más de altistory (20)

[Altibase] 4-5 summary of tablespace management
[Altibase] 4-5 summary of tablespace management[Altibase] 4-5 summary of tablespace management
[Altibase] 4-5 summary of tablespace management
 
[Altibase] 13 backup and recovery
[Altibase] 13 backup and recovery[Altibase] 13 backup and recovery
[Altibase] 13 backup and recovery
 
[Altibase] 12 replication part5 (optimization and monitoring)
[Altibase] 12 replication part5 (optimization and monitoring)[Altibase] 12 replication part5 (optimization and monitoring)
[Altibase] 12 replication part5 (optimization and monitoring)
 
[Altibase] 11 replication part4 (conflict resolution)
[Altibase] 11 replication part4 (conflict resolution)[Altibase] 11 replication part4 (conflict resolution)
[Altibase] 11 replication part4 (conflict resolution)
 
[Altibase] 10 replication part3 (system design)
[Altibase] 10 replication part3 (system design)[Altibase] 10 replication part3 (system design)
[Altibase] 10 replication part3 (system design)
 
[Altibase] 9 replication part2 (methods and controls)
[Altibase] 9 replication part2 (methods and controls)[Altibase] 9 replication part2 (methods and controls)
[Altibase] 9 replication part2 (methods and controls)
 
[Altibase] 5 durability
[Altibase] 5 durability[Altibase] 5 durability
[Altibase] 5 durability
 
[Altibase] 4-4 disk tablespace
[Altibase] 4-4 disk tablespace[Altibase] 4-4 disk tablespace
[Altibase] 4-4 disk tablespace
 
[Altibase] 4-3 volatile tablespace
[Altibase] 4-3 volatile tablespace[Altibase] 4-3 volatile tablespace
[Altibase] 4-3 volatile tablespace
 
[Altibase] 4-2 memory tablespace
[Altibase] 4-2 memory tablespace[Altibase] 4-2 memory tablespace
[Altibase] 4-2 memory tablespace
 
[Altibase] 4-1 tablespace concept
[Altibase] 4-1 tablespace concept[Altibase] 4-1 tablespace concept
[Altibase] 4-1 tablespace concept
 
[Altibase] 3-3 directory contents
[Altibase] 3-3 directory contents[Altibase] 3-3 directory contents
[Altibase] 3-3 directory contents
 
[Altibase] 3-1 architecture
[Altibase] 3-1 architecture[Altibase] 3-1 architecture
[Altibase] 3-1 architecture
 
[Altibase] 2-4 sql functions
[Altibase] 2-4 sql functions[Altibase] 2-4 sql functions
[Altibase] 2-4 sql functions
 
[Altibase] 2-3 general functions
[Altibase] 2-3 general functions[Altibase] 2-3 general functions
[Altibase] 2-3 general functions
 
[Altibase] 2-2 Altibase storage
[Altibase] 2-2 Altibase storage[Altibase] 2-2 Altibase storage
[Altibase] 2-2 Altibase storage
 
[Altibase] 2-1 features
[Altibase] 2-1 features[Altibase] 2-1 features
[Altibase] 2-1 features
 
[Altibase] 1-5 hybrid dbms
[Altibase] 1-5 hybrid dbms[Altibase] 1-5 hybrid dbms
[Altibase] 1-5 hybrid dbms
 
[Altibase] 1-4 data differentiation
[Altibase] 1-4 data differentiation[Altibase] 1-4 data differentiation
[Altibase] 1-4 data differentiation
 
[Altibase] 1-3 history of the hybrid dbms
[Altibase] 1-3 history of the hybrid dbms[Altibase] 1-3 history of the hybrid dbms
[Altibase] 1-3 history of the hybrid dbms
 

Último

Enterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze IncEnterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze Incrobinwilliams8624
 
Kawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in TrivandrumKawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in TrivandrumKawika Technologies
 
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfTobias Schneck
 
Fields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxFields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxJoão Esperancinha
 
AI Embracing Every Shade of Human Beauty
AI Embracing Every Shade of Human BeautyAI Embracing Every Shade of Human Beauty
AI Embracing Every Shade of Human BeautyRaymond Okyere-Forson
 
ERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptxERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptxAutus Cyber Tech
 
Deep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampDeep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampVICTOR MAESTRE RAMIREZ
 
Why Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdfWhy Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdfBrain Inventory
 
IA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG timeIA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG timeNeo4j
 
Introduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptxIntroduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptxIntelliSource Technologies
 
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.Sharon Liu
 
Webinar_050417_LeClair12345666777889.ppt
Webinar_050417_LeClair12345666777889.pptWebinar_050417_LeClair12345666777889.ppt
Webinar_050417_LeClair12345666777889.pptkinjal48
 
Cybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadCybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadIvo Andreev
 
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Jaydeep Chhasatia
 
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLBig Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLAlluxio, Inc.
 
online pdf editor software solutions.pdf
online pdf editor software solutions.pdfonline pdf editor software solutions.pdf
online pdf editor software solutions.pdfMeon Technology
 
Top Software Development Trends in 2024
Top Software Development Trends in  2024Top Software Development Trends in  2024
Top Software Development Trends in 2024Mind IT Systems
 
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsYour Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsJaydeep Chhasatia
 
eAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionseAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionsNirav Modi
 

Último (20)

Enterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze IncEnterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze Inc
 
Kawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in TrivandrumKawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in Trivandrum
 
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
 
Fields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxFields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptx
 
AI Embracing Every Shade of Human Beauty
AI Embracing Every Shade of Human BeautyAI Embracing Every Shade of Human Beauty
AI Embracing Every Shade of Human Beauty
 
ERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptxERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptx
 
Deep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampDeep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - Datacamp
 
Salesforce AI Associate Certification.pptx
Salesforce AI Associate Certification.pptxSalesforce AI Associate Certification.pptx
Salesforce AI Associate Certification.pptx
 
Why Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdfWhy Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdf
 
IA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG timeIA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG time
 
Introduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptxIntroduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptx
 
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
 
Webinar_050417_LeClair12345666777889.ppt
Webinar_050417_LeClair12345666777889.pptWebinar_050417_LeClair12345666777889.ppt
Webinar_050417_LeClair12345666777889.ppt
 
Cybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadCybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and Bad
 
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
 
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLBig Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
 
online pdf editor software solutions.pdf
online pdf editor software solutions.pdfonline pdf editor software solutions.pdf
online pdf editor software solutions.pdf
 
Top Software Development Trends in 2024
Top Software Development Trends in  2024Top Software Development Trends in  2024
Top Software Development Trends in 2024
 
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsYour Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
 
eAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionseAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspections
 

[Altibase] 8 replication part1 (overview)

  • 1. Non-Stop Service System  Minimizing Downtime and Maintaining the system availability up to 100%  Planed Downtime – Regular inspection & System upgrade/patch  Executing Switch-Over : Main modification of normal service  Unplanned Downtime – Failure in some systems  Executing Fail-Over : Main modification of urgent service  High Availability / HA  Non-stop system or the availability of its contents  five 9 (99.999%)  Various techniques exist at the level of S/W and H/W HA of DBMS  Activates by synchronizing the databases through nodes  Techniques are different depending on the architecture of parallel database
  • 2. Categories Shared Nothing Architecture Shared Disk Architecture Shared resources There is no shared resources Disk Data synchronization Replication through network Sharing disk Performance* Fast performance as there is no shared resources Performance reduced by complicated Processing(2PC/3PC) of shared resources System costs* Low costs (Local disk & Network) High costs(Shared storage facilities) Distance* There are not too much harsh in a long distance as the general TCP based network is used There is a restriction of distance as the dedicated network of high cost for sharing disk is required Data conformity* The extra consideration is required to control the data inconsistency in each node for the features of network replication The data conformity is guaranteed in each nodes as the data is shared Critical Failures Unable to synchronize nodes when there is a network failure Entire services in system do not work when there is a disk failure Appropriate system Faster performance is more required than data conformity Data conformity is more required than performance Relevant DBMS technique Replication RAC (Real Application Cluster) Relevant DBMS ALTIBASE HDB, DB2, MS-SQL, ORACLE, SYBASE ORACLE, DB2 ▣Trade-off between “performance, system establishment costs, distance” and “ data conformity”
  • 3. DB SERVER main DB SERVER sub replication AP 1 AP n Fail-Over AP 1 APm+1AP m AP n DB SERVER main DB SERVER sub replication [Figure 1, High Availability secured in a 2-Way Replication System] [Figure 2, Scalability improved in a 2-Way Replication System] What is Replication? Replication is a technique for sending information about the changes to the contents of a single database over a network to one or more other databases. The Purpose of Replication ♦ Secures High Availability ♦ Improves performance and scalability by Load-balancing ♦ Minimizes Data Loss in the Event of a Physical Outage or Disaster
  • 4. Main Features Description TCP/IP Network-Based Because the only facility that is required for replication is a network connection, no additional expenses are incurred. Replication over long distances is possible, depending on network performance (a Gigabit LAN is recommended) Heterogeneous OS Support Replication is possible between heterogeneous operating systems, and regardless of the number of OS bits (32 or 64) or CPU endian Integrated Replication High Performance as the replication module is completely integrated with DBMS No additional ALTIBASE HDB packages are required for replication ALTIBASE HDB can be flexibly used depending on the user’s requirements Redo Log-based Redo logs are sent in real time by records Table-Based Management Replication is managed by table Table can be added to replication or deleted from replication while database is running Two Modes: LAZY and EAGER Supports both LAZY(Async) and EAGER(Sync) replication modes High-Speed Replication In LAZY mode, the replication is executed with at least 95% of master transactions speed while not affecting the master transaction (Measured on a UNIX system in a Gigabit LAN environment) Up to 32-Way Replication A single ALTIBASE HDB node can have up to 32 replication objects Load distribution across heterogeneous systems is supported
  • 5. Main Features Description Point-To-Point Replication Replicating 1:1 only between nodes that it does not transfer to other nodes Network Fault Detection ALTIBASE HDB provides dedicated threads to detect physical network faults Automatic Recovery The time point at which replication was most recently performed is recorded. In the event of network failure, replication resumes automatically once the network connection is restored. Support for Multiple IPs If two or more IP addresses are assigned to a single replication, replication can automatically switch to the other IP address in the event of a network fault, thus increasing the availability of replication. Control via SQL Interface All commands required in order to use and manage replication have an SQL interface thus it is convenient to use. Data Conflict Resolution Methods Three (3) schemes and one (1) utility are provided to resolve data conflicts. Additional Functions Replication can be used to clone (i.e. copy the entire contents of) tables. If the active node fails, offline replication can be conducted to access the redo log files on the node that failed and resume replication on the standby node.