SlideShare une entreprise Scribd logo
1  sur  27
Disaster Recovery
  7/6/2012

© 2012 MapR Technologies   Disaster Recovery 1
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
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
Definitions &
                           Considerations



© 2012 MapR Technologies     Disaster Recovery 4
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
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
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
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
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
Disaster Preparation




© 2012 MapR Technologies        Disaster Recovery 10
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
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
Exercise:
                           Create a Dump File



© 2012 MapR Technologies       Disaster Recovery 13
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
Exercise:
                   Create a Remote Mirror



© 2012 MapR Technologies   Disaster Recovery 15
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
Cluster Restoration




© 2012 MapR Technologies       Disaster Recovery 17
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
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
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
Exercise:
                      Restore from a Dump
                               File


© 2012 MapR Technologies   Disaster Recovery 21
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
Exercise:
                      Copy Read-Only Data



© 2012 MapR Technologies   Disaster Recovery 23
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
Final Thoughts




© 2012 MapR Technologies     Disaster Recovery 25
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
Questions




© 2012 MapR Technologies   Disaster Recovery 27

Contenu connexe

Tendances

NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
NYC Hadoop Meetup - MapR, Architecture, Philosophy and ApplicationsNYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
Jason Shao
 
Hadoop fault tolerance
Hadoop  fault toleranceHadoop  fault tolerance
Hadoop fault tolerance
Pallav Jha
 
Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...
Zbigniew Jerzak
 
Ambari Meetup: NameNode HA
Ambari Meetup: NameNode HAAmbari Meetup: NameNode HA
Ambari Meetup: NameNode HA
Hortonworks
 
MapReduce Container ReUse
MapReduce Container ReUseMapReduce Container ReUse
MapReduce Container ReUse
Hortonworks
 

Tendances (20)

20a installation
20a installation20a installation
20a installation
 
Introduction to Yarn
Introduction to YarnIntroduction to Yarn
Introduction to Yarn
 
Hadoop Internals
Hadoop InternalsHadoop Internals
Hadoop Internals
 
NYC Hadoop Meetup - MapR, Architecture, Philosophy and Applications
NYC Hadoop Meetup - MapR, Architecture, Philosophy and ApplicationsNYC 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 CassandraAutonomous 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 YARNAnatomy of Hadoop YARN
Anatomy of Hadoop YARN
 
Hadoop fault-tolerance
Hadoop fault-toleranceHadoop fault-tolerance
Hadoop fault-tolerance
 
Advanced Hadoop Tuning and Optimization
Advanced Hadoop Tuning and Optimization Advanced Hadoop Tuning and Optimization
Advanced Hadoop Tuning and Optimization
 
Hadoop Cluster With High Availability
Hadoop Cluster With High AvailabilityHadoop Cluster With High Availability
Hadoop Cluster With High Availability
 
Hadoop fault tolerance
Hadoop  fault toleranceHadoop  fault tolerance
Hadoop fault tolerance
 
MapReduce and Hadoop
MapReduce and HadoopMapReduce 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 systemAccelerate 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 KylinSpark 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 SystemWhat'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...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 ProcessingAdaptive Replication for Elastic Data Stream Processing
Adaptive Replication for Elastic Data Stream Processing
 
Ambari Meetup: NameNode HA
Ambari Meetup: NameNode HAAmbari Meetup: NameNode HA
Ambari Meetup: NameNode HA
 
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
Shift into High Gear: Dramatically Improve Hadoop & NoSQL PerformanceShift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
Shift into High Gear: Dramatically Improve Hadoop & NoSQL Performance
 
MapReduce Container ReUse
MapReduce Container ReUseMapReduce Container ReUse
MapReduce Container ReUse
 
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
HBaseCon 2015: DeathStar - Easy, Dynamic,  Multi-tenant HBase via YARNHBaseCon 2015: DeathStar - Easy, Dynamic,  Multi-tenant HBase via YARN
HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN
 

En vedette

Hurricane Katrina
Hurricane KatrinaHurricane Katrina
Hurricane Katrina
JTHartman
 
Disaster management ppt
Disaster management pptDisaster management ppt
Disaster management ppt
Aniket Pingale
 

En vedette (16)

Hurricane Katrina
Hurricane KatrinaHurricane Katrina
Hurricane Katrina
 
Defining Disaster
Defining DisasterDefining Disaster
Defining Disaster
 
Jeunesse Global présentation opportunités_(july2016)_francais
Jeunesse Global présentation opportunités_(july2016)_francaisJeunesse 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...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 CrisesLeadership At Times Of Crises
Leadership At Times Of Crises
 
Jeunesse Global : Fastest Growing Multi Level Marketing
Jeunesse Global : Fastest Growing Multi Level MarketingJeunesse Global : Fastest Growing Multi Level Marketing
Jeunesse Global : Fastest Growing Multi Level Marketing
 
Web 2.0 Disaster Management
Web 2.0 Disaster ManagementWeb 2.0 Disaster Management
Web 2.0 Disaster Management
 
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTERSOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
SOCIAL MEDIA: BEFORE, DURING AND AFTER A DISASTER
 
Jeunesse global compensation plan
Jeunesse global compensation planJeunesse global compensation plan
Jeunesse global compensation plan
 
Care Based Ethical Reasoning
Care Based Ethical ReasoningCare Based Ethical Reasoning
Care Based Ethical Reasoning
 
Hurricane Katrina
Hurricane KatrinaHurricane Katrina
Hurricane Katrina
 
Jeunesse Global Compensation Plan
Jeunesse Global Compensation PlanJeunesse Global Compensation Plan
Jeunesse Global Compensation Plan
 
Hurricane Katrina Adjustments & Responses
Hurricane Katrina Adjustments & ResponsesHurricane Katrina Adjustments & Responses
Hurricane Katrina Adjustments & Responses
 
Remembering Hurricane Katrina, 29-30 August 2005
Remembering Hurricane Katrina, 29-30 August 2005Remembering Hurricane Katrina, 29-30 August 2005
Remembering Hurricane Katrina, 29-30 August 2005
 
Alternative means of communication during disaster
Alternative means of communication during disasterAlternative means of communication during disaster
Alternative means of communication during disaster
 
Disaster management ppt
Disaster management pptDisaster management ppt
Disaster management ppt
 

Similaire à 80a 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...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
 

Similaire à 80a disaster recovery (20)

The Cloud as a means of Recovery
The Cloud as a means of RecoveryThe 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 mythsDB2 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...Shielding Data Assets: Exploring Data Protection and Disaster Recovery Strate...
Shielding Data Assets: Exploring Data Protection and Disaster Recovery Strate...
 
HADRFINAL13112016
HADRFINAL13112016HADRFINAL13112016
HADRFINAL13112016
 
Security drp on cloud
Security   drp on cloudSecurity   drp on cloud
Security drp on cloud
 
Dragon and cinder v brownbag
Dragon and cinder v brownbagDragon and cinder v brownbag
Dragon and cinder v brownbag
 
50a volumes
50a volumes50a volumes
50a volumes
 
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...
 
22 configuration
22 configuration22 configuration
22 configuration
 
DR hosting & cloud
DR hosting & cloudDR hosting & cloud
DR hosting & cloud
 
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and Resilience
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and ResilienceMastering 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_HondaTHBusiness_continuity_for_HondaTH
Business_continuity_for_HondaTH
 
IBM MQ Disaster Recovery
IBM MQ Disaster RecoveryIBM 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...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 StorageWebinar: 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#VirtualDesignMaster 3 Challenge 3 - Harshvardhan Gupta
#VirtualDesignMaster 3 Challenge 3 - Harshvardhan Gupta
 
HugNov14
HugNov14HugNov14
HugNov14
 
Dell emc back up solution in azure cloud
Dell emc back up solution in azure cloud Dell emc back up solution in azure cloud
Dell emc back up solution in azure cloud
 
Greenplum feature
Greenplum featureGreenplum feature
Greenplum feature
 
Using Docker For Development
Using Docker For DevelopmentUsing Docker For Development
Using Docker For Development
 

Plus de mapr-academy

Plus de mapr-academy (9)

53 lab-nfs
53 lab-nfs53 lab-nfs
53 lab-nfs
 
51 lab-volumes
51 lab-volumes51 lab-volumes
51 lab-volumes
 
48a tuning
48a tuning48a tuning
48a tuning
 
42 lab-managing services
42 lab-managing services42 lab-managing services
42 lab-managing services
 
41a managing services
41a managing services41a managing services
41a managing services
 
14 lab-planing
14 lab-planing14 lab-planing
14 lab-planing
 
12a architecture
12a architecture12a architecture
12a architecture
 
10c introduction
10c introduction10c introduction
10c introduction
 
3 map r installation & setup administration course description
3 map r installation & setup administration course description3 map r installation & setup administration course description
3 map r installation & setup administration course description
 

Dernier

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Dernier (20)

How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 

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

Notes de l'éditeur

  1. 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.