Disaster Recovery for SAP HANA with SUSE Linux

Disaster Recovery for
SAP HANA
with SUSE Linux
Dan Lahl, SAP
VP Database Products
Dan.lahl@sap.com
Markus Guertler, SUSE
Senior Architect SAP Linux Lab
mguertler@suse.com

Agenda
• SAP HANA typical implementations
• Outlook for the next 12 – 18 months
• Disaster Recovery Capabilities of SAP HANA
• Automate SAP HANA System Replication
• Setup and Implementation
• Outlook: Scale Out Scenario
• Our Community

For decades, a complex landscape of core, yet
disparate, technologies have been cobbled together
to solve business problems
DATA STORAGE
& RETRIEVAL ANALYSIS FORECASTING
SENSING &
UNDERSTANDING
HOLDING YOU BACK
REAL-TIME
SHARING
These technologies are critical to
success but the infrastructure is

Re-think Applications with SAP HANA in-memory computing
Don’t just Speed Up IT, rather transform your applications and business
An innovative data
management and
application approach for
transactions, analytics and
custom development using
an in-memory platform
One in-memory atomic copy of data for
Transactions + Analysis
SAP HANA
(DRAM)
 Eliminate unnecessary complexity and latency
 Less hardware to manage
 Accelerate through innovation and simplification
Transact
“In-memory computing will have
a long-term, disruptive impact by
radically reducing overall total
cost of ownership – lower power,
cooling, floor space, resources
and fewer servers.”
Separated Transactions + Analysis
ETL
+ Acceleration processes
Analyze
ETL
Cache
Accelerate
 3 copies of data in different data models
 Inherent data latency
 Poor innovation leading to wastage
VS
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 4

SAP HANA Platform – More than just a database
Any Apps
Any App Server
SAP Business Suite
and BW ABAP App Server
SQL MDX R JSON Open Connectivity
SAP HANA Platform
Extended Application Services
App Server| UI Integration Services | Web Server
Processing Engine
OLTP | OLAP | Search | Text Analysis |Predictive | Events | Spatial | Rules | Planning | Calculators
Database Services
Application Function Libraries & Data Models
Predictive Analysis Libraries | Business Function Libraries | Data Models & Stored Procedures
Integration Services
Data Virtualization | Replication | ETL/ELT | Mobile Synch | Streaming
Deployment:
Development
On-Premise | Hybrid | On-Demand
Supports any Device
SAP HANA platform converges Database, Data Processing and Application
Platform capabilities & provides Libraries for predictive, planning, text, spatial,
and business analytics so businesses can operate in real-time.
Administration

Renovate existing systems while enabling future
breakthroughs
Operational
Datamarts
EDW on HANA
(BWonH)
Industry
Platforms
(Healthcare)
REAL-TIME ANALYTICS
Operational
Analytics
Big Data
Warehousing
Predictive, Spatial &
Text Analytics
SAP
BusinessSuite
& SAP
BusinessOne
30+ HANA Apps,
Accelerators
& RDS
StartUp & ISV
Apps
REAL-TIME APPLICATIONS
Planning &
Optimization
SAP HANA PLATFORM
Consumer
Engagement
Database &
Data Processing
Services
Application
Platform Services
Integration &
Data Virtualization
Services
Mission-Critical
Deployment
Services
(Appliance, Cloud)
Sense &
Respond

Disaster Recovery for SAP HANA with SUSE Linux

See how Unilever uses SAP HANA to transform
supply chain decision making processes.
Hear about itelligence’s easy and smooth
migration process with SAP HANA and SAP
HANA Enterprise Cloud.
Hear how Joskin is accelerating global
expansion with SAP Business Suite powered by
SAP HANA.
Hear how automotive supplier Faurecia
validated SAP HANA in a proof of concept to do
complex MRP runs much faster
Hear how Usha is using SAP ERP powered by
SAP HANA to expand manufacturing capacity
and margins.
City of Boston uses SAP HANA to improve
citizens’ experience.
MOLSON Coors Transforms Reporting with
SAP HANA
Learn how Centerpoint Energy utilized SAP
HANA to build three game changing applications
for Centerpoint and its customers.
Hear how NCT is using HANA to provide better
treatments and outcomes for cancer patients

CenterPoint Call Center Predictive Analytics Engine (PAE)
‘Likeliness for the Customer’s Call’
Customer Vision
Program
The CRM PAE was developed to understand the ‘likeliness of the
customer’s call’ and empower the Call Center Agents to analyze ~40
business scenarios (High Bill, Disconnect, Outages, Pay my Bill, etc)
by predicting why the customer might be calling – potentially reducing
the Agent’s Call Handling Time (AHT).
PAE analyzes data across 7 systems, for ‘Super 8’ processes (40
sub-processes- High Bill/ Re-connect/ Outage/Move-in Move-out/
Street Light Enquiry / Home Service Plus and more), fetches 116
dynamic data points to generate 55 dynamic messages within 5
seconds of the call, and recommends agents with a likely reason for
the customer’s call.
HANA has significantly improved the performance of PAE, with run
times reduced from 90 seconds to just one second—a 9,000%
improvement in many cases. Output of PAE is also shared across IVR,
WEB and Power Alert Services
The PAE process dramatically reduces the average 14 clicks to 2
clicks and thereby reduces the handling time (AHT) and improves
customer satisfaction. What’s more, the system can now use the HANA-supplied
data to proactively communicate with customers or deflect a call.
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 9 9

CenterPoint ‘High Volume’ Customer
Segmentation for Marketing’
Customer Vision
Program
• Using CRM segment builder helps teams develop more
personalized, targeted campaigns based on analytics,
helping ensure that the right message is sent to the right
customer through the right channel.
• CenterPoint’s marketing team couldn’t easily identify key
audiences to contact, create targeted communications or analyze
the results of their marketing programs, compromising the
success of their efforts.
• For the first time, our teams now are using the same single
source of truth to discuss and report sales opportunities,
activities, marketing campaigns, marketing effectiveness
and results using Segment builder on HANA, we provide
CNP Marketing with relevant information that they can act upon
and help us build more productive relationships with customers.
 Using HANA, Marketing is able to create targeted
campaigns with up to date information in minutes. Used to
take weeks (with historical data) and now takes minutes
 Segments ~ 5 million customers (~10 Million Business
Partners )

CenterPoint Smart Meter Analytics
Forecasting Model Engine
• Prior to FME, Load Studies based upon IDR data were
routine from the 1980’s through September 1999 . . . (then
dormant)
Jointly, ERCOT Load Research Samples of residential
and small commercial customers were discontinued
effective JAN 2012.
• Load Studies incorporating SMART METER data
significantly improves upon traditional methods
• solid estimates of Unbilled Revenues & weather
impacts, timely Load Studies and an improved
understanding of changes in usage
• Applications built on SAP HANA to handle BIG DATA (15
minutes Interval data for ~2.2 million premises >10
billion records)
• Speed of analysis on large volumes of data
• Insight into their business process information for
better decision making
• Manage multiple data sources and deliver a single
version of truth
• Platform for driving innovation and advanced analytics
use cases for different business units
• Leverage SAP HANA technology such to address the
Big Data challenge and do what was not possible till
now
• Empower business users to access data quickly and
seamlessly to drive self-service BI and analysis

High Availability & Disaster Recovery
Customer
http://www.saphana.com/docs/DOC-2010

High Availability – Disaster Recovery
Overview
Business Continuity
High Availability
per Data Center
Disaster recovery
between Data Centers
SAP HANA Host Auto-Failover
(Scale-Out with Standby)
SAP HANA Storage Replication
SAP HANA System Replication
● Performance Optimized
● Cost Optimized
SAP HANA System Replication
● Performance Optimized
● Cost Optimized
1
2
3
4
© 2014 SAP AG or an SAP affiliate company. All rights reserved. Customer 13

HA & DR Concepts in general
Classification of solutions
…system operational
RPO RTO
operation resumed…
time
Sync or
backup
design & prepare detect recover perf. ramp
KPIs:
• Recovery Point Objective (RPO) = worst-case data-loss
• Recovery Time Objective (RTO) = time to recover from outage
Solution Used for Cost RPO RTO Perf. ramp
Backup & Recovery HA & DR $ high high med
SAP HANA Host Auto-Failover HA $ 0 med long
SAP HANA Storage Replication w/ QA, Dev. DR $$ 0* med long
SAP HANA System Replication HA & DR $$$ 0* low short
SAP HANA System Replication w/ QA, Dev. HA & DR $**/$$ 0* med long
*synchronous solution
** single host installations

High Availability Options
1 Scale-Out or Host Auto-Failover

SAP HANA High Availability: Host Auto-Failover
High Availability configuration
•N active servers in one cluster
•M standby server(s) in one cluster
•Shared file system for all servers
Services
•Name and index server on all nodes
•Statistics server (only on one active server)
•Name server active on Standby
Failover
•Server X fails
•Server N+1 reads indexes from shared storage and connects to logical
connection of server X
•Storage Connector API ensures remount of necessary disk areas
(Note 1900823 - Storage Connector API  Attachments)
Shared Storage
SAN Storage
Storage Connector API
Server 1
Server 2
Server 3
Server 4
Server 5
Server 6
Standby Server

HANA High Availability
Host Auto-Failover (standby)
Different implementation of High Availability by HW partners
Using storage solution inside Using internal disk
Name
Server
Index
Server
Standby
Name
Server
Index
Server
Name
Server
Index
Server
Data
Disks
Log
Disks
Data
Disks
Log
Disks
Data
Disks
Log
Disks
GPFS
GPFS

High Availability Options
2 SAP HANA System Replication

SAP HANA High Availability: System Replication
Performance Optimized
Performance optimized option
Secondary system
completely used for the
preparation of a possible
take-over
Resources used for data pre-load
on Secondary
Take-overs and Performance
Ramp shortened maximally
Data Center 1
Clients Application Servers
OS: DNS, hostnames, virt. IPs
Primary
(active)
Name Server
Index server
Secondary
(active, data pre-loaded)
Name Server
Index server
HA Solution Partner
HA Solution Partner
Transfer
by
HANA
database
Internal kernel
Disks
Internal
Disks
Data
Disks
Log
Disks
Data
Disks
Log
Disks

SAP HANA High Availability: System Replication
Cost Optimized
Cost optimized with
Operating non-prod systems
on Secondary
Resources freed (no data
pre-load) to be offered to one
or more non-prod installations
During take-over the non-prod
operation has to be
ended
Take-over performance
similar to cold start-up
Data Center 1
Primary
(active)
Name Server
Index server
Secondary
PRD
shadow
operation
Name Server
Index server
HA Solution Partner
HA Solution Partner
Transfer
by
HANA
database
Internal kernel
Disks
Data
Disks
Log
Disks
Internal
QA/DEV
running
Data Disks
Disks
PRD QA/DEV
Log
Disks
Data
Disks
Log
Disks

SAP HANA High Availability
Comparison of minimal setups for available solutions
Scale-Out 1+1
(Host Auto-Failover)
active standby
Master
Name
Server
Index
Server
Data
Disks
Log
Disks
Name
Server
Index
Server
Storage
System
System Replication with both alternatives (1+1)
Primary
(active)
Name Server
Index server
Secondary
Name Server
Index server
Transfer
by
HANA
database
Internal kernel
Disks
Internal
Disks
Data
Disks
Log
Disks
Data
Disks
Log
Disks
Performance optimized
(data pre-loaded)
or
Cost optimized
Secondary
PRD
shadow
operation
Name Server
Index server
QA/DEV
running
Data
Disks
PRD QA/DEV
Log
Disks
Data
Disks
Log
Disks

Disaster Recovery Options
3 SAP HANA Storage Replication

SAP HANA Disaster Recovery: Storage Replication
Cluster across Data Centers with non-prod on 2nd site
Arrangement usually offered with a
strong part of hardware partners
involvement
 Support issues handled
by/routed to HW partners
TCO reduction by combined
operation with non-prod on
Secondary
 Needs another disk stack for
non-prod usage load
Cluster management often included
and delivered as a whole package
Data Center 1 Data Center 2
OS: DNS, hostnames
Primary
Name
Serve
r
Index
server
Name
Serve
r
Index
server
OS: Mounts
Data
Volumes
Log
Volume
Name
Serve
r
Index
server
Secondary
Prod. (inactive), QA&DEV (active)
Name
Server
Index
server
Name
Server
Index
server
Name
Server
Index
server
HA Solution Partner
Storage
Mirroring
HA Solution Partner
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume

HA & DR solutions from hardware vendors
with certified HANA offering – May 2014*
(only China)
Scale Out (BW) S - L M M, L M M S - L M M M
High Availability X X X X X X X X X
DR – Storage Repl.:
Async
X X,X X planned
DR – Storage Repl.:
Sync
X X X X X X
All certified synchronous and asynchronous solutions for SAP HANA Storage Replication are
listed in SAP note 1755396.
* For most up to date list please go to the SAP Product Availability Matrix (Westmere) or http://scn.sap.com/docs/DOC-52522 (IvyBridge)

Disaster Recovery Options
4 SAP HANA System Replication

SAP HANA Disaster Recovery: System Replication
Cluster across Data Centers with DB controlled transfer
Performance optimized option
 Faster Take-Over
 Shortened Performance Ramp
(seconds to less minutes)
 SYNC & ASYNC possible
Several cluster options
 Some HW Partners offer pre-packaged
options
Step-by-Step Implementation Guide
(updated recently to SPS8):
 https://scn.sap.com/docs/DOC-
47702
Primary
(active)
Name
Serve
r
Index
serve
r
Name
Serve
r
Index
serve
r
OS: Mounts
Data
Volumes
Log
Volume
Name
Serve
r
Index
serve
r
Secondary
Name
Server
Index
server
Name
Server
Index
server
Name
Server
Index
server
HA Solution Partner
HA Solution Partner
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume
Transfer
by
HANA
database
kernel

SAP HANA Disaster Recovery: System Replication
Cluster across Data Centers with non-prod operation on 2nd site
Cost Optimized option
SYNC & ASYNC possible
TCO reduction by combined
operation with non-prod on
Secondary
Needs another disk stack for
non-prod usage load
Step-by-Step Implementation
Guide (updated recently to SPS8):
 https://scn.sap.com/docs/DO
C-47702
Primary
(active)
Name
Serve
r
Index
serve
r
Name
Serve
r
Index
serve
r
OS: Mounts
Data
Volumes
Log
Volumes
Name
Serve
r
Index
serve
r
Secondary
(active,)
Name
Server
shadow
operation
Index
server
PRD
Name
Server
Index
server
Name
Server
Index
server
HA Solution Partner
HA Solution Partner
Data
Volumes
Log
Volumes
Data
QA/DEV
running
PRD QA/DEV
Volumes
Log
Volumes
Data
Volumes
Log
Volumes
Transfer
by
HANA
database
kernel
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume

SAP HANA in Data Centers
Cluster Manager with direct SAP HANA System Replication Support
SUSE Cluster
 Included with “SUSE SLES for SAP Applications”
 Developed in the SAP Linux Lab
 Blog: Fail-Safe Operation of SAP HANA®: SUSE Extends Its High-Availability Solution
HP Service Guard
 Directly available from HP and HP drives the implementation process
 Link for further information:
o Reference Guide: HP ServiceGuard Extensions, March 2014
o User Guide: Managing HP ServiceGuard Extensions for SAP for Linux, December 2013
SAP Landscape Virtualization Manager (SAP LVM)
 Consulting package to create a cluster manager with SAP LVM available
Other cluster managers
 In pipeline or can be adapted with individual consulting packages
Note: Solution certification is not offered for cluster managers
Support is handled by solution partner directly

High Availability for SAP with SUSE
Around 8 years of experience with High Availability for
SAP NetWeaver Systems
Starting around 1 year ago to implement the HANA
SR Automation
Solutions are jointly developed between SUSE, SAP,
customers and partners in the SAP Linux Lab in
Walldorf

HANA in a SUSE® Linux Enterprise
High Availability Extension Cluster
HANA Single Box – System Replication / Scale-UP

SAP HANA SR and SUSE Linux Enterprise
HANA Single Box
node 1 node 2
Pacemaker
System Replication
SAP HANA
PR1
primary
SAP HANA
PR1
secondary
System
PR1
vIP
System
PR1

HANA Single Box
node 1 node 2
Pacemaker
System Replication
SAP HANA
PR1
primary
SAP HANA
PR1
secondary
System
PR1
System
PR1

HANA Single Box
node 1 node 2
Pacemaker
System Replication
SAP HANA
PR1
[primary]
SAP HANA
PR1
primary
System
PR1
System
PR1
vIP

HANA Single Box
node 1 node 2
Pacemaker
System Replication
SAP HANA
PR1
secondary
SAP HANA
PR1
primary
vIP
System
PR1
System
PR1
Direction of the system replication will only be changed if the
parameter AUTOMATED_REGISTER is been changed to “true”

From Concept to Implementation
suse01 suse02
SAP HANA
Primary
SAP HANA
Secondary
vIP
Cluster Communication
SAPHana
Master/Slave Resource Master Slave
SAPHanaTopology
Clone Clone
Clone Resource
Fencing

Setup and Implementation
Install package SAPHanaSR with two resource agents:
SAPHanaTopology and SAPHana
Setup Guide SAPHanaSR HAWK Wizard
and

Four Steps to Install and Configure
Install HANA
Configure System Replication
Install and initialize SUSE Cluster
Configure SR Automation using HAWK wizard

SAPHanaSR HAWK Wizard
Technical preview included
in the shipping.

HANA System Replication in HAWK

The Five Interfaces
HANA Startframework: sapstartsrv / sapcontrol / HDB
(calls, output format “GetProcessList”)
HANA-Topology: landscapeHostConfiguration.py
(rc, output format)
SR-Topology: hdbnsutil
(calls, output format “-sr_state --sapcontrol=1”)
SAP Hostagent: saphostctrl
(call, output format “ListInstances”)
SR-Status: hdbsql (now) / systemReplicationStatus.py
(future) (now; rc, calls, output format)

Outlook: HANA in a SUSE Linux Enterprise
HANA Multi Node – System Replication / Scale-OUT
site 1 site 2
System
Replication
This scenario is currently in development
N M
A B
N M
A B
HANA
Database
HANA
memory-preload A B
HANA PR1
primary
HANA PR1
syncmem
resource failover
active / active

Our Community
Developed jointly in the SAP Linux Lab
Integration of the solution in partner products
Upstream open-source project
Scoping, discussing and implementing Scale-Out
You are invited to join
our community :-)
Visit our booth (#180) or contact us via email
sap@suse.com

HANA SR Automation using SUSE
Reduces complexity
- by automating the sr-takeover and IP failover (unbind / bind)
Reduces risk
- by having always a consistent picture of the HANA topology
Increases reliability
- due to short takeover times

Find our Best Practices at:
www.suse.com/products/sles-for-sap/resource-library/
Thank you.

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Public
KAESER: Transforming Operations with
SAP® Business Suite powered by SAP HANA®
Company
KAESER KOMPRESSOREN SE
Headquarters
Coburg, Germany
Industry
Industrial machinery and components
Products and Services
Compressed air systems and compressed
air consulting services
Employees
4,400
Revenue
€600 million (2012)
Web Site
www.kaeser.com
Partner
SAP Consulting organization
Objectives
 Create an innovative IT environment that supports the move toward a solution-provider
business model
 Enhance existing business processes and leverage the power of Big Data and
predictive maintenance to become more proactive, customer oriented, and competitive
 Leverage the SAP HANA® platform to transform and simplify the entire SAP® solution
landscape
Technical implementation
 Successful migration of the SAP Customer Relationship Management (SAP CRM)
application to SAP HANA in just 2.5 months and with just 1.5 days of downtime
 Great collaboration with SAP during all phases of the project
Future plans
 Launch predictive maintenance capabilities with a custom solution based on SAP
CRM powered by SAP HANA to step up customer service
 Migrate all SAP Business Suite applications, such as the SAP ERP, SAP Supply
Chain Management, and SAP NetWeaver® Business Warehouse applications, to SAP
HANA
 Deploy SAP CRM powered by SAP HANA in the cloud with other cloud offerings like
“We will leverage the full power of SAP HANA to enhance existing
the SAP JAM social software platform to enable a mobile and social CRM strategy
business processes, introduce entirely new ones, and reduce total cost
of ownership. We are off to a very good start with the smooth and fast
migration of SAP CRM to SAP HANA, which will be followed by other
SAP Business Suite applications and custom solutions.”
Falko Lameter , CIO, KAESER KOMPRESSOREN SE
Successful
Successful and smooth production
launch of SAP CRM powered by SAP
HANA
Faster
Five times faster database response
times
Simpler
Simpler and more agile IT landscape
and business processes
Solid
Foundation for predictive maintenance
30112 (14/03) This content is approved by the customer and may not be altered under any circumstances.

Further options for System Replication

SAP HANA Zero Downtime maintenance
Featured by SAP NetWeaver ABAP stack
As an evolution of “Near Zero Downtime
Maintenance”, SPS7 now offers
Zero Downtime Maintenance
 Based on connectivity suspend feature of the SAP
NetWeaver ABAP stack (SAP note 1913302)
o DBSL of the database interface decouples transaction
management between ABAP and HANA database
o This keeps transaction on ABAP layer alive and allows to
change components (software versions) on the layers
below on secondary (shadow) HANA instance
 Further information also in Step-by-Step Implementation
Guide for SAP HANA System Replication:
https://scn.sap.com/docs/DOC-47702
 Hardware mix
(SAP note 1984882 - Using HANA System Replication for
Hardware Exchange with minimum Downtime)
SAP NetWeaver
ABAP Server
DBSL

Software
Upgrade
Order
SAP HANA Version SAP HANA Version +1
Sync/Async
mirrored
redo log writing
Transport
incremental data
Walldorf
Primary
Name
Server
Index
server
Name
Server
Index
server
Name
Server
Index
server
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume
Rot
Secondary
Name
Server
Index
server
Name
Server
Index
server
Name
Server
Index
server
Data
Volumes
Log
Volume
Data
Volumes
Log
Volume

Data Center Setups with Pre-load Option
Multi Tier System Replication – Cascading Systems
Data Center Data Center
Sync
Production Local standby
with data preload
Remote standby system
with or without preload
(mixed usage with non-prod.)
Async
Available with SAP HANA (Three cascading systems with SPS7)

SAP Business Systems @ SAP
SAP HANA Usage in Production

SAP Business Systems @ SAP
HANA usage in production
SAP’s financial accounting system running
SAP Business Suite on HANA
 Scale-Out 2+1 (local High Availability)
 Secured with SAP HANA System Replication
SAP’s CRM system running
SAP CRM on HANA
 Scale-out 2+1(local High Availability)
 Secured with SAP HANA System Replication
SAP’s BW system running
SAP CRM on HANA
 Scale-Out 8+1(local High Availability)
 No DR
Data Center:
St. Leon-Rot
Data Center:
Walldorf
Distance: about 10 km
Redundant connections: 3

Technical details
Business SAP Application SAP Business Suite on HANA
Number of named Users 65000
Transactions per day tbc
Server Hardware Manufacturer IBM
CPU Model 8 CPUs with 10 core , Xeon E7-8870 @2.4GHz
Memory 2 x 4096 GB
Operating System SUSE Linux Enterprise Server 11.2
Scale-out Scale-out plus standby (2+1)
Storage IBM GPFS file system
Restart Restart Time (min)/Takeover 5 – 15 min
Performance Ramp 1 h – 1,5 h
Loading RowStore 3 min

Database DB Version HANA 1.0 Revision 73
DB Usage Custom
DR Solution SAP HANA System Replication
Replication mode syncmem
Distance between Data Centers 10 km
Bandwidth for System Replication 10 Gbit
Total database size 1,7 TB
Column Store size 1460 GB
Row Store size 140 GB
Avg local log write wait time 210 μs (for avg. log buffer size of 9,5 MB)
Avg remote log write wait time 448 μs (for avg. log buffer size of 9,5 MB)
Delta data shipping throughput 108 MB/sec
Log backup (log volume) per day 165 GB
Transferred data volume between data centers per day 1,7 TB
Backup Storage IBM GPFS file system backup (local disks)
Avg. size of full data backup 1,7 TB
Throughput 775 MB/s (2800 GB/h)
Avg. runtime of backup (to FS) 41 min
Estimated total recovery time 340 min
Estimated data recovery time 70 min (~1,7 TB)
Estimated log recovery time 270 min (~3,7 TB – log backups of 3 weeks)
[1] In a similar productive SAP system the recovery was done with a three weeks old backup (1,8 TB) and the log backups of these three weeks (7 TB). The data recovery took 70 minutes and the log recovery took 9 hours – makes
roughly 10 hours recovery time for this system.

SAP HANA Disaster Recovery
News with SAP HANA SPS07 and Beyond
System Replication extension
 Multi Tier System Replication allowing
first 1:n setups:
Three cascading systems in a row
 Zero Downtime maintenance
Compressed log transfer
 Basic encryption for internal
communication
Planned beyond
 System Replication extension
oPure Log-based transfer
oBackup on shadow instance
oOnline Add Host & Remove Host on
Secondary
oActive/Active Operation (r/o reporting on
Sec.)
oMore 1:n relationships for shadow
instances
oTime travel via internal snapshots on
shadow instance to handle logical errors
oMore asymmetric options (nm)
oTime delay option between sites
 Log Shipping
oBased on backup files (initial data, sub
sequential log, steady roll forward)

Disaster Recovery for SAP HANA with SUSE Linux

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