Enviar pesquisa
Carregar
80a disaster recovery
•
1 gostou
•
939 visualizações
M
mapr-academy
Seguir
Tecnologia
Negócios
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 27
Recomendados
58a migration
58a migration
mapr-academy
55a remote cluster
55a remote cluster
mapr-academy
52 nfs
52 nfs
mapr-academy
70a monitoring & troubleshooting
70a monitoring & troubleshooting
mapr-academy
Hands on MapR -- Viadea
Hands on MapR -- Viadea
viadea
30a accessing your cluster
30a accessing your cluster
mapr-academy
13c planning
13c planning
mapr-academy
MapR Tutorial Series
MapR Tutorial Series
selvaraaju
Recomendados
58a migration
58a migration
mapr-academy
55a remote cluster
55a remote cluster
mapr-academy
52 nfs
52 nfs
mapr-academy
70a monitoring & troubleshooting
70a monitoring & troubleshooting
mapr-academy
Hands on MapR -- Viadea
Hands on MapR -- Viadea
viadea
30a accessing your cluster
30a accessing your cluster
mapr-academy
13c planning
13c planning
mapr-academy
MapR Tutorial Series
MapR Tutorial Series
selvaraaju
20a installation
20a installation
mapr-academy
Introduction to Yarn
Introduction to Yarn
Omid Vahdaty
Hadoop Internals
Hadoop Internals
Pietro Michiardi
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
Jason Shao
Autonomous control in Big Data platforms: and experience with Cassandra
Autonomous control in Big Data platforms: and experience with Cassandra
Emiliano
Anatomy of Hadoop YARN
Anatomy of Hadoop YARN
Rajesh Ananda Kumar
Hadoop fault-tolerance
Hadoop fault-tolerance
Ravindra Bandara
Advanced Hadoop Tuning and Optimization
Advanced Hadoop Tuning and Optimization
Shivkumar Babshetty
Hadoop Cluster With High Availability
Hadoop Cluster With High Availability
Edureka!
Hadoop fault tolerance
Hadoop fault tolerance
Pallav Jha
MapReduce and Hadoop
MapReduce and Hadoop
Nicola Cadenelli
Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system
Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system
Shuai Yuan
Spark tunning in Apache Kylin
Spark tunning in Apache Kylin
Shi Shao Feng
What's New and Upcoming in HDFS - the Hadoop Distributed File System
What's New and Upcoming in HDFS - the Hadoop Distributed File System
Cloudera, Inc.
Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...
Zbigniew Jerzak
Adaptive Replication for Elastic Data Stream Processing
Adaptive Replication for Elastic Data Stream Processing
Zbigniew Jerzak
Ambari Meetup: NameNode HA
Ambari Meetup: NameNode HA
Hortonworks
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
MapR Technologies
MapReduce Container ReUse
MapReduce Container ReUse
Hortonworks
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
HBaseCon
Hurricane Katrina
Hurricane Katrina
JTHartman
Defining Disaster
Defining Disaster
Matt Dove
Mais conteúdo relacionado
Mais procurados
20a installation
20a installation
mapr-academy
Introduction to Yarn
Introduction to Yarn
Omid Vahdaty
Hadoop Internals
Hadoop Internals
Pietro Michiardi
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
Jason Shao
Autonomous control in Big Data platforms: and experience with Cassandra
Autonomous control in Big Data platforms: and experience with Cassandra
Emiliano
Anatomy of Hadoop YARN
Anatomy of Hadoop YARN
Rajesh Ananda Kumar
Hadoop fault-tolerance
Hadoop fault-tolerance
Ravindra Bandara
Advanced Hadoop Tuning and Optimization
Advanced Hadoop Tuning and Optimization
Shivkumar Babshetty
Hadoop Cluster With High Availability
Hadoop Cluster With High Availability
Edureka!
Hadoop fault tolerance
Hadoop fault tolerance
Pallav Jha
MapReduce and Hadoop
MapReduce and Hadoop
Nicola Cadenelli
Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system
Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system
Shuai Yuan
Spark tunning in Apache Kylin
Spark tunning in Apache Kylin
Shi Shao Feng
What's New and Upcoming in HDFS - the Hadoop Distributed File System
What's New and Upcoming in HDFS - the Hadoop Distributed File System
Cloudera, Inc.
Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...
Zbigniew Jerzak
Adaptive Replication for Elastic Data Stream Processing
Adaptive Replication for Elastic Data Stream Processing
Zbigniew Jerzak
Ambari Meetup: NameNode HA
Ambari Meetup: NameNode HA
Hortonworks
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
MapR Technologies
MapReduce Container ReUse
MapReduce Container ReUse
Hortonworks
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
HBaseCon
Mais procurados
(20)
20a installation
20a installation
Introduction to Yarn
Introduction to Yarn
Hadoop Internals
Hadoop Internals
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
Autonomous control in Big Data platforms: and experience with Cassandra
Autonomous control in Big Data platforms: and experience with Cassandra
Anatomy of Hadoop YARN
Anatomy of Hadoop YARN
Hadoop fault-tolerance
Hadoop fault-tolerance
Advanced Hadoop Tuning and Optimization
Advanced Hadoop Tuning and Optimization
Hadoop Cluster With High Availability
Hadoop Cluster With High Availability
Hadoop fault tolerance
Hadoop fault tolerance
MapReduce and Hadoop
MapReduce and Hadoop
Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system
Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system
Spark tunning in Apache Kylin
Spark tunning in Apache Kylin
What's New and Upcoming in HDFS - the Hadoop Distributed File System
What's New and Upcoming in HDFS - the Hadoop Distributed File System
Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...
Adaptive Replication for Elastic Data Stream Processing
Adaptive Replication for Elastic Data Stream Processing
Ambari Meetup: NameNode HA
Ambari Meetup: NameNode HA
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
MapReduce Container ReUse
MapReduce Container ReUse
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
Destaque
Hurricane Katrina
Hurricane Katrina
JTHartman
Defining Disaster
Defining Disaster
Matt Dove
Jeunesse Global présentation opportunités_(july2016)_francais
Jeunesse Global présentation opportunités_(july2016)_francais
Vincent Lasnier (文森特)
Planning for Healthcare Facility Evacuations: Developing County-wide Nursing...
Planning for Healthcare Facility Evacuations: Developing County-wide Nursing...
Partners in Emergency Preparedness Conference
Leadership At Times Of Crises
Leadership At Times Of Crises
ReginaPhelps
Jeunesse Global : Fastest Growing Multi Level Marketing
Jeunesse Global : Fastest Growing Multi Level Marketing
Titis Jatmiko
Web 2.0 Disaster Management
Web 2.0 Disaster Management
NH Division of Economic Development
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
TheProjectNZ
Jeunesse global compensation plan
Jeunesse global compensation plan
JoinJuenesseglobal
Care Based Ethical Reasoning
Care Based Ethical Reasoning
MQuinn59
Hurricane Katrina
Hurricane Katrina
kaltham
Jeunesse Global Compensation Plan
Jeunesse Global Compensation Plan
Alex Delgado
Hurricane Katrina Adjustments & Responses
Hurricane Katrina Adjustments & Responses
Tom McLean
Remembering Hurricane Katrina, 29-30 August 2005
Remembering Hurricane Katrina, 29-30 August 2005
Professor Eric K. Noji, M.D., MPH, DTMH(Lon), FRCP(UK)hon
Alternative means of communication during disaster
Alternative means of communication during disaster
Dr.Sharon Abdul Jameela
Disaster management ppt
Disaster management ppt
Aniket Pingale
Destaque
(16)
Hurricane Katrina
Hurricane Katrina
Defining Disaster
Defining Disaster
Jeunesse Global présentation opportunités_(july2016)_francais
Jeunesse Global présentation opportunités_(july2016)_francais
Planning for Healthcare Facility Evacuations: Developing County-wide Nursing...
Planning for Healthcare Facility Evacuations: Developing County-wide Nursing...
Leadership At Times Of Crises
Leadership At Times Of Crises
Jeunesse Global : Fastest Growing Multi Level Marketing
Jeunesse Global : Fastest Growing Multi Level Marketing
Web 2.0 Disaster Management
Web 2.0 Disaster Management
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
Jeunesse global compensation plan
Jeunesse global compensation plan
Care Based Ethical Reasoning
Care Based Ethical Reasoning
Hurricane Katrina
Hurricane Katrina
Jeunesse Global Compensation Plan
Jeunesse Global Compensation Plan
Hurricane Katrina Adjustments & Responses
Hurricane Katrina Adjustments & Responses
Remembering Hurricane Katrina, 29-30 August 2005
Remembering Hurricane Katrina, 29-30 August 2005
Alternative means of communication during disaster
Alternative means of communication during disaster
Disaster management ppt
Disaster management ppt
Semelhante a 80a disaster recovery
The Cloud as a means of Recovery
The Cloud as a means of Recovery
santiagocuellar1
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
Florence Dubois
Shielding Data Assets: Exploring Data Protection and Disaster Recovery Strate...
Shielding Data Assets: Exploring Data Protection and Disaster Recovery Strate...
MaryJWilliams2
HADRFINAL13112016
HADRFINAL13112016
Thevapriyan Shanmugam
Security drp on cloud
Security drp on cloud
Andrea Cirulli
Dragon and cinder v brownbag
Dragon and cinder v brownbag
Alon Marx
50a volumes
50a volumes
mapr-academy
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
DataWorks Summit
22 configuration
22 configuration
mapr-academy
DR hosting & cloud
DR hosting & cloud
NetGains Technologies Pvt. Ltd.
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and Resilience
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and Resilience
MaryJWilliams2
Business_continuity_for_HondaTH
Business_continuity_for_HondaTH
Predee Kajonpai
IBM MQ Disaster Recovery
IBM MQ Disaster Recovery
MarkTaylorIBM
Track 2, session 3, business continuity and disaster recovery in the virtuali...
Track 2, session 3, business continuity and disaster recovery in the virtuali...
EMC Forum India
Webinar: Eliminate Backups and Simplify DR with Hybrid Cloud Storage
Webinar: Eliminate Backups and Simplify DR with Hybrid Cloud Storage
Storage Switzerland
#VirtualDesignMaster 3 Challenge 3 - Harshvardhan Gupta
#VirtualDesignMaster 3 Challenge 3 - Harshvardhan Gupta
vdmchallenge
HugNov14
HugNov14
Adam Faris
Dell emc back up solution in azure cloud
Dell emc back up solution in azure cloud
vipinvips
Greenplum feature
Greenplum feature
Ahmad Yani Emrizal
Using Docker For Development
Using Docker For Development
Laura Frank Tacho
Semelhante a 80a disaster recovery
(20)
The Cloud as a means of Recovery
The Cloud as a means of Recovery
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
Shielding Data Assets: Exploring Data Protection and Disaster Recovery Strate...
Shielding Data Assets: Exploring Data Protection and Disaster Recovery Strate...
HADRFINAL13112016
HADRFINAL13112016
Security drp on cloud
Security drp on cloud
Dragon and cinder v brownbag
Dragon and cinder v brownbag
50a volumes
50a volumes
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
22 configuration
22 configuration
DR hosting & cloud
DR hosting & cloud
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and Resilience
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and Resilience
Business_continuity_for_HondaTH
Business_continuity_for_HondaTH
IBM MQ Disaster Recovery
IBM MQ Disaster Recovery
Track 2, session 3, business continuity and disaster recovery in the virtuali...
Track 2, session 3, business continuity and disaster recovery in the virtuali...
Webinar: Eliminate Backups and Simplify DR with Hybrid Cloud Storage
Webinar: Eliminate Backups and Simplify DR with Hybrid Cloud Storage
#VirtualDesignMaster 3 Challenge 3 - Harshvardhan Gupta
#VirtualDesignMaster 3 Challenge 3 - Harshvardhan Gupta
HugNov14
HugNov14
Dell emc back up solution in azure cloud
Dell emc back up solution in azure cloud
Greenplum feature
Greenplum feature
Using Docker For Development
Using Docker For Development
Mais de mapr-academy
53 lab-nfs
53 lab-nfs
mapr-academy
51 lab-volumes
51 lab-volumes
mapr-academy
48a tuning
48a tuning
mapr-academy
42 lab-managing services
42 lab-managing services
mapr-academy
41a managing services
41a managing services
mapr-academy
14 lab-planing
14 lab-planing
mapr-academy
12a architecture
12a architecture
mapr-academy
10c introduction
10c introduction
mapr-academy
3 map r installation & setup administration course description
3 map r installation & setup administration course description
mapr-academy
Mais de mapr-academy
(9)
53 lab-nfs
53 lab-nfs
51 lab-volumes
51 lab-volumes
48a tuning
48a tuning
42 lab-managing services
42 lab-managing services
41a managing services
41a managing services
14 lab-planing
14 lab-planing
12a architecture
12a architecture
10c introduction
10c introduction
3 map r installation & setup administration course description
3 map r installation & setup administration course description
Último
Artificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning era
Deakin University
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
Rizwan Syed
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
shyamraj55
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
naman860154
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
Malak Abu Hammad
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
Scott Keck-Warren
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
Mattias Andersson
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
comworks
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
ThousandEyes
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Delhi Call girls
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
BookNet Canada
Unlocking the Potential of the Cloud for IBM Power Systems
Unlocking the Potential of the Cloud for IBM Power Systems
Precisely
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
Mark Billinghurst
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
Scott Keck-Warren
The transition to renewables in India.pdf
The transition to renewables in India.pdf
Competition Advisory Services (India) LLP
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
soniya singh
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Alan Dix
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024
Neo4j
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
Florian Wilhelm
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
Enterprise Knowledge
Último
(20)
Artificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning era
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Unlocking the Potential of the Cloud for IBM Power Systems
Unlocking the Potential of the Cloud for IBM Power Systems
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
The transition to renewables in India.pdf
The transition to renewables in India.pdf
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
80a disaster recovery
1.
Disaster Recovery
7/6/2012 © 2012 MapR Technologies Disaster Recovery 1
2.
Disaster Recovery
Agenda • Definitions & Considerations • Disaster Preparation • Exercise: Create a Dump File • Exercise: Create a Remote Mirror • Cluster Restoration • Exercise: Restore from a Dump File • Exercise: Copy Read-Only Data • Final Thoughts © 2012 MapR Technologies Disaster Recovery 2
3.
Disaster Recovery
Objectives At the end of this module you will be able to: • Describe the important considerations involved in disaster recovery • Identify the different approaches to disaster recovery • Explain how to prepare for a disaster • Create a dump file and remote mirror • Describe the different ways of recovering from a disaster • Restore data from a dump file © 2012 MapR Technologies Disaster Recovery 3
4.
Definitions &
Considerations © 2012 MapR Technologies Disaster Recovery 4
5.
What is Disaster
Recovery? Disaster Recovery is a large and complex topic – You must consider a variety of objectives and tradeoffs that involve complex business and technical issues – This presentation is not a tutorial on Disaster Recovery One key aspect of Disaster Recovery is preservation of data – If one data center is lost, we need to ensure that there is a consistent copy of the data available elsewhere in order to restore service – MapR provides a solution to this problem using volume mirroring • Data can be dumped to local storage and managed manually, or • One cluster can replicate its data to another cluster automatically © 2012 MapR Technologies Disaster Recovery 5
6.
Considerations
When designing your DR solution you must take into account two key factors – RPO – Recovery Point Objective • Essentially this is how much data you are prepared to lose in a disaster • This impacts how frequently you update the backup – RTO – Recovery Time Objective • This defines how quickly you want to restore service after a disaster • Lower RTOs imply more automation and often pre-built backup systems There are two fundamental tradeoffs you must consider – An operational cluster for recovery vs. manual rebuild – Mirror dumps to local storage vs. automatic mirroring © 2012 MapR Technologies Disaster Recovery 6
7.
Cluster Recovery Approaches
Your primary cluster is gone for whatever reason. You need to restore service elsewhere Option #1: have an available and running cluster already prepared – RTO will be relatively low – of course data has to be restored first Option #2: create a new cluster when needed – RTO will be significantly higher © 2012 MapR Technologies Disaster Recovery 7
8.
Data Mirroring Approaches
Option #1: dump volumes – Dumps of course need to be moved off site – After a disaster, obtain dumps and manually restore to cluster • Note that if you have done incremental mirror dumps to reduce the size of the dumps, multiple dumps will have to be restored for each volume – Places less demand on network but • Has high manual operational cost • RTO is likely very high – RPO can also involve significant data loss depending on how frequently you move data off site Option #2: Mirror to a remote cluster – Frequency of incrementals is configurable frequency implies your RPO – Restoration may require copying data from read only to read/write volume – Requires operational cluster as target of mirrors © 2012 MapR Technologies Disaster Recovery 8
9.
Mirroring Key Behaviors
Mirror data is pulled by the destination cluster Destination/mirror volume is read only Mirrors are snapshot based and thus time consistent – Meaning that the replica mirror will have an exact copy of the data that was present at the time of the mirror start – There is no danger of one file being changed while the mirror is occurring Mirroring operation is smart enough to only replicate what has changed since the last mirroring operation – At the block level - changing one byte of a 1TB file triggers an 8K update, not 1TB Data is compressed on the wire when transmitting Mirroring consumes significant network bandwidth over the WAN – More network capacity improves rate you can mirror and thus RPO – Plan taking into account data change rates and network bandwidth © 2012 MapR Technologies Disaster Recovery 9
10.
Disaster Preparation © 2012
MapR Technologies Disaster Recovery 10
11.
Preparation – no
backup cluster Execute initial backup by dumping volume Move dump files to remote location Execute periodic dumps and move off site Dumps can be full or incremental Incremental dumps are faster but complicate restore © 2012 MapR Technologies Disaster Recovery 11
12.
Preparation – active
backup cluster Create mirror volume on backup cluster Execute initial backup by dumping volume or initiate mirroring If the volume is large relative to network bandwidth, a dump is likely a better choice Restore dump files to mirror volume if needed Commence remote scheduled mirroring If network bandwidth is scarce you can dump locally and restore manually at other site © 2012 MapR Technologies Disaster Recovery 12
13.
Exercise:
Create a Dump File © 2012 MapR Technologies Disaster Recovery 13
14.
Exercise: Create a
Dump File Full dump: maprcli volume dump create -name volume -dumpfile name -e statefile1 Incremental dump maprcli volume dump create -s statefile1 -e statefile2 -name volume -dumpfile name Note: you can limit the number of incremental dumps by using different statefiles © 2012 MapR Technologies Disaster Recovery 14
15.
Exercise:
Create a Remote Mirror © 2012 MapR Technologies Disaster Recovery 15
16.
Exercise: Create a
Remote Mirror Create Mirror (on backup cluster): maprcli volume create -name volume_mirror -source volume@cluster –type 1 Initiate mirroring: maprcli volume mirror start –name volume_mirror © 2012 MapR Technologies Disaster Recovery 16
17.
Cluster Restoration © 2012
MapR Technologies Disaster Recovery 17
18.
Restoration
X Build the cluster if needed Use offsite dumps or read only mirrors as data source Restore data to the cluster Activate cluster operations – schedule jobs, inform users, update dependent systems, etc. © 2012 MapR Technologies Disaster Recovery 18
19.
Restoration from Dump
Files Restore dump files to new mirror volumes Restore each incremental dump! If volumes need to be writable Create volumes to receive data Copy data from mirror volumes © 2012 MapR Technologies Disaster Recovery 19
20.
Restoration from Remote
Mirrors Data is already there! If not mounted, you can just mount the mirrors If volumes need to be writable Create volumes to receive data Copy data from mirror volumes © 2012 MapR Technologies Disaster Recovery 20
21.
Exercise:
Restore from a Dump File © 2012 MapR Technologies Disaster Recovery 21
22.
Exercise: Restore from
a Dump File Full dump: maprcli volume dump restore -dumpfile name -name volume_mirror –n Incremental dump maprcli volume dump restore -dumpfile name -name volume_mirror © 2012 MapR Technologies Disaster Recovery 22
23.
Exercise:
Copy Read-Only Data © 2012 MapR Technologies Disaster Recovery 23
24.
Exercise: Copy Read-Only
Data Mount mirror maprcli volume mount –name volume_mirror -path pathToRO Create Read/Write Volume: maprcli volume create -name volume -mount 1 -path pathToRW Copy data using NFS cp –r -p /mapr/cluster/pathToRO /mapr/cluster/pathToRW © 2012 MapR Technologies Disaster Recovery 24
25.
Final Thoughts © 2012
MapR Technologies Disaster Recovery 25
26.
Final Thoughts
Active cluster with mirroring – best RPO and RTO – Faster mirroring -> more network bandwidth but more currency (better RPO) – Full initial dump useful if lots of data already in primary cluster • Sneaker net to backup cluster – Incremental dumps with sneaker net appropriate if bandwidth constrained Manual dumps with offsite storage – RPO and RTO will be worse – Frequency of incremental dumps defines RPO – Full vs. incremental dumps • Full consume significantly more storage space and are slower • Incremental will take significantly longer to restore © 2012 MapR Technologies Disaster Recovery 26
27.
Questions © 2012 MapR
Technologies Disaster Recovery 27
Notas do Editor
KDB: This information really belongs in the earlier deck on volumes in the mirroring section, but the information is not there, so I put it here.