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© 2017 IBM Corporation
Db2 Analytics Accelerator on IBM Integrated
Analytics System
Technical Overview
November 2017
Daniel Martin – danmartin@de.ibm.com
© 2017 IBM Corporation2
Disclaimer
IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal
without notice at IBM’s sole discretion. Information regarding potential future products is intended
to outline our general product direction and it should not be relied on in making a purchasing
decision. The information mentioned regarding potential future products is not a commitment,
promise, or legal obligation to deliver any material, code or functionality. Information about
potential future products may not be incorporated into any contract. The development, release,
and timing of any future features or functionality described for our products remains at our sole
discretion.
© 2017 IBM Corporation3
Agenda
• Analytics Accelerator Version 7
• New architecture
• New engine
• API Compatibility to previous generations
• Deployment on IBM Integrated Analytics System
• Sister deployment on IBM Z
• Platform Comparison
© 2017 IBM Corporation4
Db2 Analytics Accelerator Version 7.1 Deployment Options
In Version 7.1, Db2 acceleration can be implemented within
different operating environments:
• On an on-premises appliance
• On a software appliance installed on the z14 mainframe
Both new options will offer
• the same functionality
• the same API
• the same implementation
This provides:
• Coexistence and combination of deployment options, fully
transparent for Db2 applications
• Flexibility in moving data for query acceleration as
workload demands grow or change
• Consistency and efficiency in managing different Db2
Analytics Accelerator environments
Next Generation Technology:
Two deployment options
Current Technology:
Appliance
© 2017 IBM Corporation5
Hardware Appliance
Uniform experience, simultaneous use, and easy transition between different implementations
Common analytics engine across all the platforms: Db2 Warehouse
Summary: One API – One implementation – Two deployment options
Deployment on IBM Z
© 2017 IBM Corporation6
Db2 Analytics Accelerator Version 7.1:
Shared Container
© 2017 IBM Corporation7
Version 7.1 - New Architecture
Virtual or physical server CPU Memory
Docker Supported OS
Storage (local, SAN, NAS)
(Clustered) Filesystem
Docker container
IBM Analytics
Engine
Authentication
Accelerator
server
Workload
Monitoring
Systems
Manager
Additional
future
functionality
Infrastructure
Management
© 2017 IBM Corporation8
Powered by Db2 with BLU Acceleration
•Huge potential for faster ingest for incremental updates, and thereby less HTAP query delay!
•IBM’s premier analytics engine across many products
•Latest analytics technology innovations
•Improved SQL compatibility and performance
•Higher degree of concurrent users and queries
•Roadmap: Spark, ML, Notebooks, DSM, …
In-memory column processing with
dynamic movement of data from storage
Multi-core and SIMD parallelism
(Single instruction Multiple Data)
Patented compression technique
preserves order so data can be used without
decompressing
Skips unnecessary processing of
irrelevant data
The new engine is replaced internally – external interfaces will stay the same.
The same Db2 subsystem can be connected to the existing and new generation.
© 2017 IBM Corporation9
SQL Coverage: Comparing Accelerator V7 with existing V5 Technology
Data Type support Accelerator V7 (on IAS or Z) Accelerator V5 (on PDA)
EBCDIC MBCS, GRAPHIC Supported natively Converted to UTF 8
TIMESTAMP(12) Supported natively Truncated to precision 6
FOR BIT DATA subtype Supported natively for EBCDIC, UNICODE,
ASCII
Supported for EBCDIC only
DECFLOAT On the Roadmap Not supported
BINARY On the Roadmap Not supported
© 2017 IBM Corporation10
SQL Coverage: Comparing Accelerator V7 with existing V5 Technology
SQL support
Accelerator V7 (on IAS or Z) Accelerator V5 (on PDA)
correlated subqueries All types supported including table expressions with
sideway references
Only a small subset supported
Recursive SQL Supported Not supported
TIMESTAMP value 24:00:00 Supported natively Mapped to 23:59:59.999999
Scalar functions Improved support Some not supported when using specific
datatypes, e.g. MIN/MAX, DAY, LAST_DAY,
BIT*, TIMESTAMP_ISO, VARIANCE/STDDEV/…
with UNIQUE clause,
HEX() function Supported Not supported
Mixed Encodings Adding EBCDIC tables when UNICODE tables
already added is supported
(SQL joins with data in Unicode and EBCDIC not
possible)
Only supported to add UNICODE tables after
EBCDIC table has already been added (required
to set AQT_ENABLE_MULTIPLE_ENCODINGS
environment variable)
CURRENT_TIME,
CURRENT_TIMESTAMP,
CURRENT_DATE
Supported with improved accuracy (no longer
dependent on time synchronization)
Supported
Local Date Exit format Not supported Supported
© 2017 IBM Corporation11
Db2 Analytics Accelerator Version 7.1,
Deployment on IBM Integrated Analytics
System
© 2017 IBM Corporation12
NPS®
8000 Series
TwinFin™ with i-Class™
Advanced Analytics
NPS®
10000 Series
TwinFin™
2003
2006
2009
2010
PureData System for
Analytics N2000
PureData System for
Analytics N3000
IBM Integrated Analytics System
(PDA & PDOA Convergence)
Convergence - Combining the best of Netezza and DB2 Appliances
2012
2014
2017
BCU
for AIX
BW
7000
BW
7050
BW
7100
BW
7100
ISAS
7600
ISAS
7700
ISAS
7710
PDOA
1.0
PDOA
1.1
© 2017 IBM Corporation13
Db2 Analytics Accelerator for z/OS Version 7.1, deployment on IBM
Integrated Analytics System (IIAS)
• Next generation hardware appliance
• A full solution that provides all components out of the box –including optimized hardware and software
• All components provided by IBM in a balanced, performance-optimized configuration
• HW, which includes the rack, the physical servers and the storage
• SW stack including the Docker host operating system as well as the Docker container and the
infrastructure management
• IBM Power hardware for the appliance, balanced and optimized for price/performance
© 2017 IBM Corporation14
Hardware Building blocks (Server/Storage)
Compute Node
 Power8 S822L
 Murano Chipset
 Two 12-core Sockets @ 3.42 GHz
 512 Gigabytes of DDR3 memory
 Four dual port 16Gb FibreChannel HBAs
 Two Ethernet NICs, each with two 1Gb/s
two 10Gb/s ports
 Two 600GB SAS hard drives
 Flash Storage
 IBM FlashSystem 900
 Dual Flash Controllers
 5.7 Terabyte MicroLatency Flash Storage
Modules
 Two-Dimensional RAID 5 and spare for
extremely high reliability and availability
 One, Two, or Three units per rack
© 2017 IBM Corporation15
IBM Integrated Analytics System Configurations
M4001-003
1/3 Rack
M4001-006
2/3 Rack
M4001-010
Full Rack
Servers 3 5 7
Cores 72 120 168
Memory 1.5 TB 2.5 TB 3.5 TB
User capacity
(Assumes 4x
compression)
64 TB 128 TB 192 TB
IBM Power 8 S822L 24 core server
3.42GHz, IBM FlashSystem 900
Mellanox 10G Ethernet switches
© 2017 IBM Corporation16
Integrated Analytics System (1/3 rack) - Docker container
 One container per physical server
 Head Node vs Data Node
 3 sizes:
 1/3 Rack (3 servers)
 2/3 Rack (5 servers)
 Full Rack (7 servers)
Head node (node0101) node0102 node0103
 LDAP server and Accelerator server is active on one
node only at any given time
© 2017 IBM Corporation17
Scaling up/out with Accelerator on Z versus deployment on IIAS
CP
CP
CP
…4-35IFLs…
MLN
MLN
MLN
MLN
MLN
MLN
IDAA on Z: One dashDB Node dashDB Head Node
MLN
MLN
MLN
CP
CP
CP
dashDB Node 2
MLN
MLN
MLN
MLN
CP
CP
CP
dashDB Node 3
MLN
MLN
MLN
MLN
CP
CP
CP
…24CPcores…
…24CPcores…
…24CPcores…
IDAA on IIAS: 3 dashDB Nodes
Cluster Filesystem
© 2017 IBM Corporation18
Internal Network-Architecture
 Fully redundant topology
 Active-active technology
 Scales to 8 Racks
 Each node uses 4x 10GbE ports to form a
single bonded interface. (Linux mode-4 LACP
bonding)
1
0 fbond
Mlnx 2410 Switch
“fabsw01a”
1
0
1
0
1
0
node0102
Mlnx 2410 Switch
“fabsw01b”
1
0 fbond
1
0
1
0
1
0
node0103
1
0 fbond
1
0
1
0
1
0
node0101
1
0
1
0
1
0
1
0
node0105
fbond 1
0
1
0
1
0
1
0
node0107
fbond1
0
1
0
1
0
1
0
node0106
fbond
2x
40
1
0 fbond
1
0
1
0
1
0
node0104
 Fully redundant network using 1 GbE
 Active-active dual star topology
 Scales to 8 Racks
 Allows resilient management and monitoring
of all rack components
Management Network
Mlnx 8831 Switch
“mgtsw01a”
Mlnx 8831 Switch
“mgtsw01b”
1
mbond
1
node0101
Fab
Switch
1
mbond
1
node0102
1
mbond
1
node0103
1
mbond
1
node0104
1
mbond
1
node0105
1
mbond
1
node0106
1
mbond
1
node0107
1 1
TMS900
1 1
V5000
1 1
Term
Svr1 1
FC
Switch1
PDUs
1
 Fully redundant 16G FC
 Forms a Per Rack SAN and
defines a “Fault Domain”
 Allows high speed multi-pathed
access to all storage elements
from all processing nodes within
the “Fault Domain”
Fibre Channel SAN
Data Fabric Network
© 2017 IBM Corporation19
High Availability – Hardware Elements
Robust Hardware Elements
• Power8
• IBM Flash System 900
• Completely resilient storage subsystem for the appliance
• two load sharing power supplies, redundant fans, and two separate storage controllers
• RAID5 layout across Flash elements within each module, and then again RAID5 layout
across the Flash modules
Redundant Hardware Elements
• Data Fabric, Management Network and Storage Fibre Channel Network
• pairs of switches are used to provide complete failover redundancy
• Processing Nodes
• Partitions of failed node are reassigned evenly to the surviving nodes within the same rack
© 2017 IBM Corporation20
High Availability – Node Failover / Failback
 Processing Nodes – Organized into Highly Available clusters to provide continuous operation in the event of failure of one of the nodes
 First attempt to power recycle failed node and make it usable again
 If recycle failed, the data partitions of the failed node are reassigned to the surviving nodes within the same rack
 The system will experience an outage (up to 30 mins if reboot required) while a failover is performed
© 2017 IBM Corporation21
Encryption of „data at rest“
 Addresses risk of stolen flash cards / flash card replacements (e.g. after failures)
 Integrated Analytics System provides Encryption through IBM Flash 900 built-in encryption
 XTS-AES 256-bit symmetric used built-in
 Used by Accelerator on IAS w/o external key management.
Flash Controller is key-aware so system not protected in case of FlashSystem lost complete.
 Ext. key management leveraging separate key-management-server is on the roadmap
Attention: Data Studio does not display the encryption w/o external key management as encrypted disk although
there’s no data stored in clear. Starting using external key management, encryption will be detected by Data Studio
© 2017 IBM Corporation22
Db2 Analytics Accelerator Version 7.1,
deployment on IBM Z
© 2017 IBM Corporation23
• A software appliance running on IBM Z
• Packages the SW stack into an IBM Secure Service Container to deliver a fully self-managed
appliance running in a SSC LPAR that can be deployed in minutes
• Integrates seamlessly into the customer’s IBM Z environment and leverages known LPAR-,
memory and CPU management procedures, including call home support for enterprise hardware
components.
• Uses customer-provided storage to hold the Accelerator-side data
• Scales smoothly with the assignment of available processor cores, initially addressing sizes
comparable up to 1/2 rack PDA (N3001-005) appliances
Deployment on IBM Z
© 2017 IBM Corporation24
Accelerator on IBM Z – Optimized for Multiple Instances Usage
 Accelerator on IBM Z instance = 1 node = 1 LPAR
 Single instance typically 8 – 35 IFLs
 Single instance typically 0.5 – 8 TB memory
 Different workloads in different instances
 Optimize instance for workload
 Instance size determined by individual workload
requirements
• not “sum of all”
 IFLs may be shared – dev/test environments
 Isolation of independent / competing workloads
DB 1
Accelerator
on IBM Z 1
DB2
z/OS
DB2
z/OS
DB2
z/OS
Setup only symbolic
DB 3
Accelerator
on IBM Z 3
DB 2
Accelerator
on IBM Z 2
© 2017 IBM Corporation25
Key
advantages
• Out-of-the-box experience
• Workload Optimized System
• Wide set of Analytics use cases
• Proven technology with client
references cross-industry
• Out-of-the-box experience
• Workload Optimized System
• Optimized for True HTAP
• Evolving set of Analytics use
cases
• Download & Go experience
• Homogeneity within IBM Z:
common resources, operation
• Evolving set of Analytics use
cases
Workload
Size
• Very good scale-out • Very good scale-out
• Optimized for very large query
throughput and load
performance
• Good scale-up to full drawer
Db2 Analytics Accelerator
on Pure Data for Analytics
N3001
(Netezza Technology)
Db2 Analytics Accelerator on
Integrated Analytics System
M4001
Db2 Analytics Accelerator
on IBM Z
Key Characteristics of the deployment options
© 2017 IBM Corporation
Thank You

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Db2 analytics accelerator on ibm integrated analytics system technical overview

  • 1. © 2017 IBM Corporation Db2 Analytics Accelerator on IBM Integrated Analytics System Technical Overview November 2017 Daniel Martin – danmartin@de.ibm.com
  • 2. © 2017 IBM Corporation2 Disclaimer IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.
  • 3. © 2017 IBM Corporation3 Agenda • Analytics Accelerator Version 7 • New architecture • New engine • API Compatibility to previous generations • Deployment on IBM Integrated Analytics System • Sister deployment on IBM Z • Platform Comparison
  • 4. © 2017 IBM Corporation4 Db2 Analytics Accelerator Version 7.1 Deployment Options In Version 7.1, Db2 acceleration can be implemented within different operating environments: • On an on-premises appliance • On a software appliance installed on the z14 mainframe Both new options will offer • the same functionality • the same API • the same implementation This provides: • Coexistence and combination of deployment options, fully transparent for Db2 applications • Flexibility in moving data for query acceleration as workload demands grow or change • Consistency and efficiency in managing different Db2 Analytics Accelerator environments Next Generation Technology: Two deployment options Current Technology: Appliance
  • 5. © 2017 IBM Corporation5 Hardware Appliance Uniform experience, simultaneous use, and easy transition between different implementations Common analytics engine across all the platforms: Db2 Warehouse Summary: One API – One implementation – Two deployment options Deployment on IBM Z
  • 6. © 2017 IBM Corporation6 Db2 Analytics Accelerator Version 7.1: Shared Container
  • 7. © 2017 IBM Corporation7 Version 7.1 - New Architecture Virtual or physical server CPU Memory Docker Supported OS Storage (local, SAN, NAS) (Clustered) Filesystem Docker container IBM Analytics Engine Authentication Accelerator server Workload Monitoring Systems Manager Additional future functionality Infrastructure Management
  • 8. © 2017 IBM Corporation8 Powered by Db2 with BLU Acceleration •Huge potential for faster ingest for incremental updates, and thereby less HTAP query delay! •IBM’s premier analytics engine across many products •Latest analytics technology innovations •Improved SQL compatibility and performance •Higher degree of concurrent users and queries •Roadmap: Spark, ML, Notebooks, DSM, … In-memory column processing with dynamic movement of data from storage Multi-core and SIMD parallelism (Single instruction Multiple Data) Patented compression technique preserves order so data can be used without decompressing Skips unnecessary processing of irrelevant data The new engine is replaced internally – external interfaces will stay the same. The same Db2 subsystem can be connected to the existing and new generation.
  • 9. © 2017 IBM Corporation9 SQL Coverage: Comparing Accelerator V7 with existing V5 Technology Data Type support Accelerator V7 (on IAS or Z) Accelerator V5 (on PDA) EBCDIC MBCS, GRAPHIC Supported natively Converted to UTF 8 TIMESTAMP(12) Supported natively Truncated to precision 6 FOR BIT DATA subtype Supported natively for EBCDIC, UNICODE, ASCII Supported for EBCDIC only DECFLOAT On the Roadmap Not supported BINARY On the Roadmap Not supported
  • 10. © 2017 IBM Corporation10 SQL Coverage: Comparing Accelerator V7 with existing V5 Technology SQL support Accelerator V7 (on IAS or Z) Accelerator V5 (on PDA) correlated subqueries All types supported including table expressions with sideway references Only a small subset supported Recursive SQL Supported Not supported TIMESTAMP value 24:00:00 Supported natively Mapped to 23:59:59.999999 Scalar functions Improved support Some not supported when using specific datatypes, e.g. MIN/MAX, DAY, LAST_DAY, BIT*, TIMESTAMP_ISO, VARIANCE/STDDEV/… with UNIQUE clause, HEX() function Supported Not supported Mixed Encodings Adding EBCDIC tables when UNICODE tables already added is supported (SQL joins with data in Unicode and EBCDIC not possible) Only supported to add UNICODE tables after EBCDIC table has already been added (required to set AQT_ENABLE_MULTIPLE_ENCODINGS environment variable) CURRENT_TIME, CURRENT_TIMESTAMP, CURRENT_DATE Supported with improved accuracy (no longer dependent on time synchronization) Supported Local Date Exit format Not supported Supported
  • 11. © 2017 IBM Corporation11 Db2 Analytics Accelerator Version 7.1, Deployment on IBM Integrated Analytics System
  • 12. © 2017 IBM Corporation12 NPS® 8000 Series TwinFin™ with i-Class™ Advanced Analytics NPS® 10000 Series TwinFin™ 2003 2006 2009 2010 PureData System for Analytics N2000 PureData System for Analytics N3000 IBM Integrated Analytics System (PDA & PDOA Convergence) Convergence - Combining the best of Netezza and DB2 Appliances 2012 2014 2017 BCU for AIX BW 7000 BW 7050 BW 7100 BW 7100 ISAS 7600 ISAS 7700 ISAS 7710 PDOA 1.0 PDOA 1.1
  • 13. © 2017 IBM Corporation13 Db2 Analytics Accelerator for z/OS Version 7.1, deployment on IBM Integrated Analytics System (IIAS) • Next generation hardware appliance • A full solution that provides all components out of the box –including optimized hardware and software • All components provided by IBM in a balanced, performance-optimized configuration • HW, which includes the rack, the physical servers and the storage • SW stack including the Docker host operating system as well as the Docker container and the infrastructure management • IBM Power hardware for the appliance, balanced and optimized for price/performance
  • 14. © 2017 IBM Corporation14 Hardware Building blocks (Server/Storage) Compute Node  Power8 S822L  Murano Chipset  Two 12-core Sockets @ 3.42 GHz  512 Gigabytes of DDR3 memory  Four dual port 16Gb FibreChannel HBAs  Two Ethernet NICs, each with two 1Gb/s two 10Gb/s ports  Two 600GB SAS hard drives  Flash Storage  IBM FlashSystem 900  Dual Flash Controllers  5.7 Terabyte MicroLatency Flash Storage Modules  Two-Dimensional RAID 5 and spare for extremely high reliability and availability  One, Two, or Three units per rack
  • 15. © 2017 IBM Corporation15 IBM Integrated Analytics System Configurations M4001-003 1/3 Rack M4001-006 2/3 Rack M4001-010 Full Rack Servers 3 5 7 Cores 72 120 168 Memory 1.5 TB 2.5 TB 3.5 TB User capacity (Assumes 4x compression) 64 TB 128 TB 192 TB IBM Power 8 S822L 24 core server 3.42GHz, IBM FlashSystem 900 Mellanox 10G Ethernet switches
  • 16. © 2017 IBM Corporation16 Integrated Analytics System (1/3 rack) - Docker container  One container per physical server  Head Node vs Data Node  3 sizes:  1/3 Rack (3 servers)  2/3 Rack (5 servers)  Full Rack (7 servers) Head node (node0101) node0102 node0103  LDAP server and Accelerator server is active on one node only at any given time
  • 17. © 2017 IBM Corporation17 Scaling up/out with Accelerator on Z versus deployment on IIAS CP CP CP …4-35IFLs… MLN MLN MLN MLN MLN MLN IDAA on Z: One dashDB Node dashDB Head Node MLN MLN MLN CP CP CP dashDB Node 2 MLN MLN MLN MLN CP CP CP dashDB Node 3 MLN MLN MLN MLN CP CP CP …24CPcores… …24CPcores… …24CPcores… IDAA on IIAS: 3 dashDB Nodes Cluster Filesystem
  • 18. © 2017 IBM Corporation18 Internal Network-Architecture  Fully redundant topology  Active-active technology  Scales to 8 Racks  Each node uses 4x 10GbE ports to form a single bonded interface. (Linux mode-4 LACP bonding) 1 0 fbond Mlnx 2410 Switch “fabsw01a” 1 0 1 0 1 0 node0102 Mlnx 2410 Switch “fabsw01b” 1 0 fbond 1 0 1 0 1 0 node0103 1 0 fbond 1 0 1 0 1 0 node0101 1 0 1 0 1 0 1 0 node0105 fbond 1 0 1 0 1 0 1 0 node0107 fbond1 0 1 0 1 0 1 0 node0106 fbond 2x 40 1 0 fbond 1 0 1 0 1 0 node0104  Fully redundant network using 1 GbE  Active-active dual star topology  Scales to 8 Racks  Allows resilient management and monitoring of all rack components Management Network Mlnx 8831 Switch “mgtsw01a” Mlnx 8831 Switch “mgtsw01b” 1 mbond 1 node0101 Fab Switch 1 mbond 1 node0102 1 mbond 1 node0103 1 mbond 1 node0104 1 mbond 1 node0105 1 mbond 1 node0106 1 mbond 1 node0107 1 1 TMS900 1 1 V5000 1 1 Term Svr1 1 FC Switch1 PDUs 1  Fully redundant 16G FC  Forms a Per Rack SAN and defines a “Fault Domain”  Allows high speed multi-pathed access to all storage elements from all processing nodes within the “Fault Domain” Fibre Channel SAN Data Fabric Network
  • 19. © 2017 IBM Corporation19 High Availability – Hardware Elements Robust Hardware Elements • Power8 • IBM Flash System 900 • Completely resilient storage subsystem for the appliance • two load sharing power supplies, redundant fans, and two separate storage controllers • RAID5 layout across Flash elements within each module, and then again RAID5 layout across the Flash modules Redundant Hardware Elements • Data Fabric, Management Network and Storage Fibre Channel Network • pairs of switches are used to provide complete failover redundancy • Processing Nodes • Partitions of failed node are reassigned evenly to the surviving nodes within the same rack
  • 20. © 2017 IBM Corporation20 High Availability – Node Failover / Failback  Processing Nodes – Organized into Highly Available clusters to provide continuous operation in the event of failure of one of the nodes  First attempt to power recycle failed node and make it usable again  If recycle failed, the data partitions of the failed node are reassigned to the surviving nodes within the same rack  The system will experience an outage (up to 30 mins if reboot required) while a failover is performed
  • 21. © 2017 IBM Corporation21 Encryption of „data at rest“  Addresses risk of stolen flash cards / flash card replacements (e.g. after failures)  Integrated Analytics System provides Encryption through IBM Flash 900 built-in encryption  XTS-AES 256-bit symmetric used built-in  Used by Accelerator on IAS w/o external key management. Flash Controller is key-aware so system not protected in case of FlashSystem lost complete.  Ext. key management leveraging separate key-management-server is on the roadmap Attention: Data Studio does not display the encryption w/o external key management as encrypted disk although there’s no data stored in clear. Starting using external key management, encryption will be detected by Data Studio
  • 22. © 2017 IBM Corporation22 Db2 Analytics Accelerator Version 7.1, deployment on IBM Z
  • 23. © 2017 IBM Corporation23 • A software appliance running on IBM Z • Packages the SW stack into an IBM Secure Service Container to deliver a fully self-managed appliance running in a SSC LPAR that can be deployed in minutes • Integrates seamlessly into the customer’s IBM Z environment and leverages known LPAR-, memory and CPU management procedures, including call home support for enterprise hardware components. • Uses customer-provided storage to hold the Accelerator-side data • Scales smoothly with the assignment of available processor cores, initially addressing sizes comparable up to 1/2 rack PDA (N3001-005) appliances Deployment on IBM Z
  • 24. © 2017 IBM Corporation24 Accelerator on IBM Z – Optimized for Multiple Instances Usage  Accelerator on IBM Z instance = 1 node = 1 LPAR  Single instance typically 8 – 35 IFLs  Single instance typically 0.5 – 8 TB memory  Different workloads in different instances  Optimize instance for workload  Instance size determined by individual workload requirements • not “sum of all”  IFLs may be shared – dev/test environments  Isolation of independent / competing workloads DB 1 Accelerator on IBM Z 1 DB2 z/OS DB2 z/OS DB2 z/OS Setup only symbolic DB 3 Accelerator on IBM Z 3 DB 2 Accelerator on IBM Z 2
  • 25. © 2017 IBM Corporation25 Key advantages • Out-of-the-box experience • Workload Optimized System • Wide set of Analytics use cases • Proven technology with client references cross-industry • Out-of-the-box experience • Workload Optimized System • Optimized for True HTAP • Evolving set of Analytics use cases • Download & Go experience • Homogeneity within IBM Z: common resources, operation • Evolving set of Analytics use cases Workload Size • Very good scale-out • Very good scale-out • Optimized for very large query throughput and load performance • Good scale-up to full drawer Db2 Analytics Accelerator on Pure Data for Analytics N3001 (Netezza Technology) Db2 Analytics Accelerator on Integrated Analytics System M4001 Db2 Analytics Accelerator on IBM Z Key Characteristics of the deployment options
  • 26. © 2017 IBM Corporation Thank You

Editor's Notes

  1. The different deployment options provide you with maximum flexibility: You can continue to use the appliance model for ultimate price/performance. You can also go to the public or private cloud to provision additional capacity on demand – or you can deploy a SW appliance on zLinux to avoid managing a separate environment. The important point is that all these form factors come with one API, one database engine – so from the outside you have a uniform experience. This allows you to invest once in enabling your applications and workloads for the accelerator and then transition easily between on-premises, private cloud and public cloud implementations.
  2. [[Aus: Integrated Analytics System Enablement – Session 1.pptx]]
  3. This slides shows the two main components of the IBM Integrated Analytics System (IIAS), One Data Module consists of 2 Compute Nodes combined with 1 Flash system. Power architecture Higher performance across fewer nodes CPU acceleration with multi-core and Single Instruction Multiple Data (SIMD) parallelism Increased reliability and availability Flash storage, standard Near real-time latency for higher transfer speeds 99.999% reliability and operational efficiency Why First Tier Storage? (DK) The current IIAS version is a OLAP optimized version like PDA. Different from PDOA systems which were optimized for mixed workload the PDA like IIAS requires less storage than a PDOA like system. It‘s using fast Flash Storage for the hot data. For the PDOA like IIAS system, it‘s planned to add a Second Tier Storage system based of high speed HDDs later to allow storing cooling data on less expensive hard drives. Open questions: what is a Two Dimensional RAID5 ? The combination of IBM® Variable Stripe RAID and system-level RAID 5 protection across IBM MicroLatency® modules (within a flash enclosure) is called two-dimensional (2D) flash RAID. Two-dimensional (2D) flash RAID consists of IBM Variable Stripe RAID and system-wide RAID 5. Variable Stripe RAID technology helps reduce downtime and maintain performance and capacity in the event of partial or full flash chip failures. System-wide RAID 5, with easily accessed hot swappable flash modules, helps prevent data loss and promote availability. RAID 5 configurations provide a high degree of redundancy with Variable Stripe RAID and RAID 5 protection. RAID 5 data protection includes one IBM MicroLatency module dedicated as parity and another as a dedicated hot spare. The maximum capacity utilization for RAID 5 is provided by using 12 IBM MicroLatency modules. (see: https://www.ibm.com/support/knowledgecenter/en/STJKN5_12.0.3/fs_a9x_po_flash_raid.html)
  4. For performance: Full Mako compares to 2/3 Acc on IIAS (we expect 2-5x performance gain) For User capacity: Full Mako compares to full Acc on IIAS
  5. GPFS = General Parallel File System => IBM Spectrum Scale
  6. MLN = multiple logical nodes (each logical node works on a defined subset of the data)
  7. LACP-> Link Aggregation Control Protocol
  8. Outage of up to 30 min is related to the „power-recycling policy“ in Sailfish: they try to re-enable a server by reboot. This requires up to 20 min which might increase overall cycle to 30 min.
  9. Problem Statement: Compliance rules and regulation require Accelerator customers to encrypt sensitive data like patient information in healthcare at rest. At rest is defined as persistent user data stored on any node of the appliance and should protect this data at rest so that if one or more disks are lost then whoever gets hold of the disks will not be able to read the data. See RRC#96 Note: this includes "temporary data" stored on host disks like query spill to disk data or CDC staging data and sensitive Accelerator traces. Some customers also consider schema information (like stored in the Accelerator catalog) as sensitive - so this should be encrypted, too. Since Mako already „data-at-rest-encryption“ available due to the ise of encryption enabled HDDs [Copied from V51_Operation.ppt NEEDS VERIFICATION and ADAPTION] As of Flash Storage PPT / Sailfish enablement: IBM FlashSystem 900 Encrytion: Key encryption is protected by an Advanced Encryption Standard (XTS-AES) algorithm key wrap using the 256-bit symmetric option in XTS mode, as defined in the IEEE1619-2007 standard. FlashSystem 900 Encryption for data at rest will not be implemented for the Beta Systems, but will be implemented in code releases beginning with 1.5.1.x on the Maverick array but not on Texan array DB2 Native Encryption will not be used for Data at rest for Accelerator on IAS See AcceleratorWIKI: https://Acceleratorwiki.dst.ibm.com/index.php/Encryption_of_data_at_rest_(Db2/Sailfish)
  10. Envision a different usage pattern
  11. V5 support until 2023