SlideShare una empresa de Scribd logo
1 de 34
2018 © Trivadis
BASEL BERN BRUGG LAUSANNE ZUERICH DUESSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG KOPENHAGEN MUNICH STUTTGART VIENNA
2018 © Trivadis
Exadata X7-2 POC with OVM
Jacques Kostic
Principal Consultant IMS Lausanne
Emiliano Fusaglia
Principal Consultant IMS Lausanne
TechEvent September 2018
Exadata X7-2 POC with OVM
1
2018 © Trivadis
Exadata X7-2 POC with OVM
2
TechEvent September 2018
Experience:
• Initially C/C++ developer
• In touch with Oracle since 1990 from version 4 on SCO Unix!
• High Availability and Backup & Recovery Architect
• SQL and Instance Performance & Tuning
• License Audit and Consolidation
Certifications:
• Oracle Certified Master 11g & 12c
• Oracle 11g Performance Tuning Certified Expert
• Oracle RAC 11g and Grid Infrastructure Administration
• Oracle Exadata Administrator Certified Expert
• Oracle Certified SQL Expert 11g
Teaching Courses at Trivadis:
• Oracle 11g & 12c Grid Infrastructure & RAC
• Oracle 11g & 12c Data Guard
• Oracle 11g & 12c Performance & Tuning
• Oracle 11g & 12c Administration
• SQL & PL-SQL
• OEM – 12 & 13
About me…
@JKOFR
2018 © Trivadis
Exadata X7-2 POC with OVM
3
TechEvent September 2018
Specialties:
• Database Cloud computing (DBaaS)
• Oracle RAC
• Grid Infrastructure (CRS, ASM)
• Data Guard
• Instance and SQL Performance & Tuning
• Linux & Virtualization
Certifications:
• Oracle Certified Professional 9i, 10g, 11g & 12c
• Oracle Exadata Administrator X3 –X4 Certified Expert
Teaching Courses at Trivadis:
• Oracle 11g & 12c Grid Infrastructure & RAC
• Oracle 11g & 12c Data Guard
• Oracle Exadata
• Oracle 12c New Features
About me…
@EFusaglia
2018 © Trivadis
AGENDA
1. Customer Introduction
2. Trivadis Proposal
3. POC Execution
4. Conclusion
5. Q&A
TechEvent September 2018
Exadata X7-2 POC with OVM
6
2018 © Trivadis
Customer Introduction
TechEvent September 2018
Exadata X7-2 POC with OVM
7
2018 © Trivadis
Customer Overview
The name will not be disclosed but the most relevant
characteristics to the project are reported below.
 Major player of the banking sector
 In the process to choose the next DWH platform able to guarantee:
 Optimal Performance
 Scalability
 Licensing Optimization
 Consolidation
Customer
Environment
TechEvent September 2018
Exadata X7-2 POC with OVM
8
2018 © Trivadis
Additional Information & Requirements
 Current Production database size of 18TB, annually increasing of 15%.
 Guarantee an RTO of 24h and an RPO 6h.
 Increase the number of DWH environments from 3 to 6.
 QA database should be a full production copy, while the remaining environments a
data subset.
 The sub-setting procedure should be developed by the supplier.
 The new architecture should offer fast cloning procedure.
TechEvent September 2018
Exadata X7-2 POC with OVM
9
2018 © Trivadis
Current Oracle architecture
 IBM AIX P7, two Production LPAR and one QA all with capped CPUs
 PROD: 9 VCPU, 148 GB of RAM
 QA: 6 VCPU, 148 GB of RAM
 SMT4
 Distributed on two data centers, maintained by IBM Storage SVC replication
 Oracle Licenses 10 CPU Enterprise Edition with:
 Partitioning
 Diagnostic Pack
 Tuning packs
 Major Performance problems:
 Poor IO performances
 CPU bound
TechEvent September 2018
Exadata X7-2 POC with OVM
10
2018 © Trivadis
Trivadis proposal
TechEvent September 2018
Exadata X7-2 POC with OVM
11
2018 © Trivadis
Trivadis Proposal
After a careful evaluation Trivadis was convinced that the Exadata X7-2 was the best option in
term of:
 Customer satisfaction.
 Agility to integrate a new DWH platform inside the customer’s ecosystem.
Than the next question was: Bare Metal or Virtualized?
TechEvent September 2018
Exadata X7-2 POC with OVM
12
2018 © Trivadis
Exadata X7-2: Bare Metal
Pros.
 Use the entire machine capacity
 Less environments to manage
 Pay-as-you-grow approach (COD) for software licensing
 Minimum 14 cores per DB nodes (8 for Eighth Rack)
 All Oracle options are licensed on all cores
 https://docs.oracle.com/cd/E80920_01/DBMLI/exadata-capacity-on-demand.htm#DBMLI147
 IO Resource Management
Cons.
 No physical isolation between environments
 License costs
TechEvent September 2018
Exadata X7-2 POC with OVM
13
2018 © Trivadis
Exadata X7-2: Virtualized
Pros.
 Physical isolation between environments
 OVM Hard partitioning facilitate licensing optimization
 Minimum 14 cores per DB nodes (8 cores for Eighth Rack) must be licensed for
Enterprise Edition
 For other options, it’s linked to the CPU allocation of each VM
 Two cores per database node reserved to dom0 (no license required)
 Flexible and dynamic vCPU allocation
 IO Resource Management between databases accros all VMs.
Db_unique_name must be unique across the entire Exadata
Cons.
 More complex to manage
TechEvent September 2018
Exadata X7-2 POC with OVM
14
2018 © Trivadis
Trivadis Architecture based on Exadata X7-2 Virtualized
TechEvent September 2018
Exadata X7-2 POC with OVM
15
PRD PRD’
passive
Cell 1 Cell 2 Cell 3
NAS Backup
STB STB’
passive
INT’
passive
INT
Cell 1 Cell 2 Cell 3
NAS Backup
QA’
passive
QA
Site 1 Site 2
Data Gard
Replication
AD’
passive
AD
HM’
passive
HM
Trivadis Intelligent Backup
2018 © Trivadis
24 vCPU PRD Passive24 vCPU PRD Active
Our Go Live Proposal Exadata 1
data
fra
data
fra
free
StorageServer1 StorageServer2 StorageServer3
HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6
DBServer1
8 vCPU AD Passive 8 vCPU AD Active
IO Resource Manager: Category, Inter-Database, Intra-Database (db_unique_name unique across all VClusters)
DBServer2
TechEvent September 2018
Exadata X7-2 POC with OVM
16
4 vCPU HM Passive 4 vCPU HM Active
data
fra
2018 © Trivadis
8 vCPU STB Passive8 vCPU STB Active
Our Go Live Proposal Exadata 2
data
fra
data
fra
data
fra
free
StorageServer1 StorageServer2 StorageServer3
HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6
DBServer1
24 vCPU INT Passive 24 vCPU INT Active
4 vCPU QA Passive 4 vCPU QA Active
IO Resource Manager: Category, Inter-Database, Intra-Database (db_unique_name unique across all VClusters)
DBServer2
TechEvent September 2018
Exadata X7-2 POC with OVM
17
2018 © Trivadis
 Dynamic host cpu reconfiguration using: xm vcpu-set
 Dynamic oracle CPU_COUNT adjustment as of Oracle Oracle 12c
 Dynamic resource management update
Exadata X7-2: Elastic Capacity on Demand
Elasticity on demand: up to 34 vCPUs per VM
DEVPRD
34 vCPUs
18 vCPUs
2 vCPUs
TechEvent September 2018
Exadata X7-2 POC with OVM
18
2018 © Trivadis
Database Fast Clone
 Use ASM Sparse Disk Group
 Suitable for NON-Production database
 Smart Scan is supported!
 It requires a TestMaster database open in Read Only
 The Test Master Database can not be modified or deleted as long the latest
child snapshot is in use, due to Exadata Snapshot technology which uses
“allocate on first write”, and not “copy on write” snapshot.
 IO Performance degradation:
 100 time slower - 35 microsecond vs 3.5 millisecond
 More info's here: https://emilianofusaglia.net/tag/asm-sparse-disk-group/
TechEvent September 2018
Exadata X7-2 POC with OVM
19
2018 © Trivadis
Licensing Optimization
 Cold Failover mode
 Oracle Active/Passive 10-days-per-year
 http://www.oracle.com/us/corporate/pricing/data-recovery-licensing-070587.pdf
 18 CPU Licenses required including:
 Enterprise Edition
 Partitioning
 Diagnostic and Tuning Packs
 Single instance databases on Oracle 12.2.0.1
TechEvent September 2018
Exadata X7-2 POC with OVM
20
2018 © Trivadis
POC Execution and Result
TechEvent September 2018
Exadata X7-2 POC with OVM
21
2018 © Trivadis
POC Execution: the context
Our Competitor
 IBM P8
 Full Flash Storage
 Max 16 Cores with SMT8
 Tests done with 10, 12,14 cores SMT8
 Corresponding CPU licenses: 10, 12, 14
 1 database 18 TB with 90 GB of SGA
 Oracle 12.2.0.1
TechEvent September 2018
Exadata X7-2 POC with OVM
22
2018 © Trivadis
POC Execution: the context
Our Environment
 Exadata X7-2 ¼ rack
 OVM Configuration
 Single instance mode
 Two-node cluster with various vCPUs configurations
 36, 28, 24, 20, 16
 Corresponding CPU licenses: 9, 7, 6, 5, 4
 1 database 18 TB with 90 GB of SGA
 Oracle 12.2.0.1
TechEvent September 2018
Exadata X7-2 POC with OVM
23
2018 © Trivadis
POC Execution: the context
In Summary
 Trivadis is proposing a complete change of architecture
 IBM is just replacing P7 by P8 and adding Full Flash Storage
TechEvent September 2018
Exadata X7-2 POC with OVM
24
2018 © Trivadis
POC Execution: the setup
 We had problems to setup the stuff
 We had to use October 2017 Image
 Thanks a lot to Arrow for the help!
TechEvent September 2018
Exadata X7-2 POC with OVM
25
2018 © Trivadis
POC Execution : the setup
TechEvent September 2018
Exadata X7-2 POC with OVM
26
We had network problems with
the management switch!
In reality the step was failing
because the Switch was OFF
2018 © Trivadis
POC Execution: the initial load
 Import took more than 54 hours for IBM
 It took around 48 hours on Exadata
 We used Multitenant to facilitate iterations during the POC
 Pluggable database snapshots
TechEvent September 2018
Exadata X7-2 POC with OVM
27
We get finally
ready to start!
2018 © Trivadis
POC Execution: the result
 IBM was able to increase the load speed by a factor of four.
 But it was achieved by:
 using the 14 cores (SMT8) configuration
 Setting the optimizer to 11.2.0.4 features!
- Many ORA-00600 on stats export/imports during the load processing
 High CPU usage during the processing
 Runs with 12 and 10 cores were CPU bound
 But still performing around 2.5 better than the current state
 Because run using 14 cores was not CPU bond, they stopped at that level.
 But they did not tried to run using optimizer_feature=’12.2.0.1’!
TechEvent September 2018
Exadata X7-2 POC with OVM
28
2018 © Trivadis
POC Execution: the result
 We started our first run with 36 vCPUs and we achieved a speed increased by
factor two:
 Leaving the optimize to the default 12.2.0.1 value.
 Low CPU usage
 Average IO wait time of 35 microseconds!
 Some jobs were running very badly and we discovered that the optimizer setting
was not the same used by our competitor 
 We decided to fix the underlying queries!
TechEvent September 2018
Exadata X7-2 POC with OVM
29
2018 © Trivadis
POC Execution: the result
 Some queries were hinted to use optimizer_feature=‘12.1.0.2’
 Some queries were hinted to use optimizer_feature=’11.2.0.4’
 Some queries were hinted to avoid view merge
 Some queries where hinted to avoid materialize of a particular factoring
clause
TechEvent September 2018
Exadata X7-2 POC with OVM
30
2018 © Trivadis
POC Execution: the result
 We were finally able to achieve the same performance result obtain by
IBM!
 We decided then to start downsizing the vCPU configuration to see what
we can get from this beast!
 Runs with 28 and 24 did not change the performances at all!
 We got 3% less performance with 20 vCPU and around 8% less with 16
vCPU!
 CPU usage was high but acceptable with the 16 vCPU configuration
TechEvent September 2018
Exadata X7-2 POC with OVM
31
2018 © Trivadis
POC Execution : Conclusions
Following our different runs
 We decide to adjust our final offer to 24 vCPU
 There are still lot of optimizations to be done!
TechEvent September 2018
Exadata X7-2 POC with OVM
32
2018 © Trivadis
Conclusion
TechEvent September 2018
Exadata X7-2 POC with OVM
33
2018 © Trivadis
Conclusion
 We fully addressed all customer needs
 The scalability of our platform (measured with the ratio
between the number of vCPUs and the jobs execution time)
was a key success
TechEvent September 2018
Exadata X7-2 POC with OVM
34
Jacques Kostic, Principal Consultant
Tel. +41-79-909 7263 Jacques.Kostic@trivadis.com
Emilian Fusaglia, Principal Consultant
Tel. +41-79-909 7213 Emiliano.Fusaglia@trivadis.com
35 TechEvent September 201814.09.2018
Session Feedback – now
TechEvent September 201836 14.09.2018
Please use the Trivadis Events mobile app to give feedback on each session
Use "My schedule" if you have registered for a session
Otherwise use "Agenda" and the search function
If the mobile app does not work (or if you have a Windows smartphone), use your
smartphone browser
– URL: http://trivadis.quickmobileplatform.eu/
– User name: <your_loginname> (such as "svv")
– Password: sent by e-mail...

Más contenido relacionado

La actualidad más candente

PGConf.ASIA 2019 - The Future of TDEforPG - Taiki Kondo
PGConf.ASIA 2019 - The Future of TDEforPG - Taiki KondoPGConf.ASIA 2019 - The Future of TDEforPG - Taiki Kondo
PGConf.ASIA 2019 - The Future of TDEforPG - Taiki KondoEqunix Business Solutions
 
PGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores Finnoto
PGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores FinnotoPGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores Finnoto
PGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores FinnotoEqunix Business Solutions
 
Deep Learning on the SaturnV Cluster
Deep Learning on the SaturnV ClusterDeep Learning on the SaturnV Cluster
Deep Learning on the SaturnV Clusterinside-BigData.com
 
SPACK: A Package Manager for Supercomputers, Linux, and MacOS
SPACK: A Package Manager for Supercomputers, Linux, and MacOSSPACK: A Package Manager for Supercomputers, Linux, and MacOS
SPACK: A Package Manager for Supercomputers, Linux, and MacOSinside-BigData.com
 
Symmetric Crypto for DPDK - Declan Doherty
Symmetric Crypto for DPDK - Declan DohertySymmetric Crypto for DPDK - Declan Doherty
Symmetric Crypto for DPDK - Declan Dohertyharryvanhaaren
 
02 ai inference acceleration with components all in open hardware: opencapi a...
02 ai inference acceleration with components all in open hardware: opencapi a...02 ai inference acceleration with components all in open hardware: opencapi a...
02 ai inference acceleration with components all in open hardware: opencapi a...Yutaka Kawai
 
Poc exadata pres_doag_2015
Poc exadata pres_doag_2015Poc exadata pres_doag_2015
Poc exadata pres_doag_2015Jacques Kostic
 
PGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGai
PGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGaiPGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGai
PGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGaiEqunix Business Solutions
 
OLTP+OLAP=HTAP
 OLTP+OLAP=HTAP OLTP+OLAP=HTAP
OLTP+OLAP=HTAPEDB
 
Energy Efficient Computing using Dynamic Tuning
Energy Efficient Computing using Dynamic TuningEnergy Efficient Computing using Dynamic Tuning
Energy Efficient Computing using Dynamic Tuninginside-BigData.com
 
Hardware & Software Platforms for HPC, AI and ML
Hardware & Software Platforms for HPC, AI and MLHardware & Software Platforms for HPC, AI and ML
Hardware & Software Platforms for HPC, AI and MLinside-BigData.com
 
Learning from ZFS to Scale Storage on and under Containers
Learning from ZFS to Scale Storage on and under ContainersLearning from ZFS to Scale Storage on and under Containers
Learning from ZFS to Scale Storage on and under Containersinside-BigData.com
 
The Data Center and Hadoop
The Data Center and HadoopThe Data Center and Hadoop
The Data Center and HadoopDataWorks Summit
 
Host Data Plane Acceleration: SmartNIC Deployment Models
Host Data Plane Acceleration: SmartNIC Deployment ModelsHost Data Plane Acceleration: SmartNIC Deployment Models
Host Data Plane Acceleration: SmartNIC Deployment ModelsNetronome
 
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...inside-BigData.com
 
dCUDA: Distributed GPU Computing with Hardware Overlap
 dCUDA: Distributed GPU Computing with Hardware Overlap dCUDA: Distributed GPU Computing with Hardware Overlap
dCUDA: Distributed GPU Computing with Hardware Overlapinside-BigData.com
 
A Fresh Look at HPC from Huawei Enterprise
A Fresh Look at HPC from Huawei EnterpriseA Fresh Look at HPC from Huawei Enterprise
A Fresh Look at HPC from Huawei Enterpriseinside-BigData.com
 
NVIDIA GTC 2018: Enabling GPU-as-a-Service Providers with Red Hat OpenShift
NVIDIA GTC 2018:  Enabling GPU-as-a-Service Providers with Red Hat OpenShiftNVIDIA GTC 2018:  Enabling GPU-as-a-Service Providers with Red Hat OpenShift
NVIDIA GTC 2018: Enabling GPU-as-a-Service Providers with Red Hat OpenShiftJeremy Eder
 
Deep Dive - Usage of on premises data gateway for hybrid integration scenarios
Deep Dive - Usage of on premises data gateway for hybrid integration scenariosDeep Dive - Usage of on premises data gateway for hybrid integration scenarios
Deep Dive - Usage of on premises data gateway for hybrid integration scenariosSajith C P Nair
 
NNSA Explorations: ARM for Supercomputing
NNSA Explorations: ARM for SupercomputingNNSA Explorations: ARM for Supercomputing
NNSA Explorations: ARM for Supercomputinginside-BigData.com
 

La actualidad más candente (20)

PGConf.ASIA 2019 - The Future of TDEforPG - Taiki Kondo
PGConf.ASIA 2019 - The Future of TDEforPG - Taiki KondoPGConf.ASIA 2019 - The Future of TDEforPG - Taiki Kondo
PGConf.ASIA 2019 - The Future of TDEforPG - Taiki Kondo
 
PGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores Finnoto
PGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores FinnotoPGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores Finnoto
PGConf.ASIA 2019 Bali - Patroni on GitLab.com - Jose Cores Finnoto
 
Deep Learning on the SaturnV Cluster
Deep Learning on the SaturnV ClusterDeep Learning on the SaturnV Cluster
Deep Learning on the SaturnV Cluster
 
SPACK: A Package Manager for Supercomputers, Linux, and MacOS
SPACK: A Package Manager for Supercomputers, Linux, and MacOSSPACK: A Package Manager for Supercomputers, Linux, and MacOS
SPACK: A Package Manager for Supercomputers, Linux, and MacOS
 
Symmetric Crypto for DPDK - Declan Doherty
Symmetric Crypto for DPDK - Declan DohertySymmetric Crypto for DPDK - Declan Doherty
Symmetric Crypto for DPDK - Declan Doherty
 
02 ai inference acceleration with components all in open hardware: opencapi a...
02 ai inference acceleration with components all in open hardware: opencapi a...02 ai inference acceleration with components all in open hardware: opencapi a...
02 ai inference acceleration with components all in open hardware: opencapi a...
 
Poc exadata pres_doag_2015
Poc exadata pres_doag_2015Poc exadata pres_doag_2015
Poc exadata pres_doag_2015
 
PGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGai
PGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGaiPGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGai
PGConf.ASIA 2019 Bali - Keynote Speech 3 - Kohei KaiGai
 
OLTP+OLAP=HTAP
 OLTP+OLAP=HTAP OLTP+OLAP=HTAP
OLTP+OLAP=HTAP
 
Energy Efficient Computing using Dynamic Tuning
Energy Efficient Computing using Dynamic TuningEnergy Efficient Computing using Dynamic Tuning
Energy Efficient Computing using Dynamic Tuning
 
Hardware & Software Platforms for HPC, AI and ML
Hardware & Software Platforms for HPC, AI and MLHardware & Software Platforms for HPC, AI and ML
Hardware & Software Platforms for HPC, AI and ML
 
Learning from ZFS to Scale Storage on and under Containers
Learning from ZFS to Scale Storage on and under ContainersLearning from ZFS to Scale Storage on and under Containers
Learning from ZFS to Scale Storage on and under Containers
 
The Data Center and Hadoop
The Data Center and HadoopThe Data Center and Hadoop
The Data Center and Hadoop
 
Host Data Plane Acceleration: SmartNIC Deployment Models
Host Data Plane Acceleration: SmartNIC Deployment ModelsHost Data Plane Acceleration: SmartNIC Deployment Models
Host Data Plane Acceleration: SmartNIC Deployment Models
 
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator S...
 
dCUDA: Distributed GPU Computing with Hardware Overlap
 dCUDA: Distributed GPU Computing with Hardware Overlap dCUDA: Distributed GPU Computing with Hardware Overlap
dCUDA: Distributed GPU Computing with Hardware Overlap
 
A Fresh Look at HPC from Huawei Enterprise
A Fresh Look at HPC from Huawei EnterpriseA Fresh Look at HPC from Huawei Enterprise
A Fresh Look at HPC from Huawei Enterprise
 
NVIDIA GTC 2018: Enabling GPU-as-a-Service Providers with Red Hat OpenShift
NVIDIA GTC 2018:  Enabling GPU-as-a-Service Providers with Red Hat OpenShiftNVIDIA GTC 2018:  Enabling GPU-as-a-Service Providers with Red Hat OpenShift
NVIDIA GTC 2018: Enabling GPU-as-a-Service Providers with Red Hat OpenShift
 
Deep Dive - Usage of on premises data gateway for hybrid integration scenarios
Deep Dive - Usage of on premises data gateway for hybrid integration scenariosDeep Dive - Usage of on premises data gateway for hybrid integration scenarios
Deep Dive - Usage of on premises data gateway for hybrid integration scenarios
 
NNSA Explorations: ARM for Supercomputing
NNSA Explorations: ARM for SupercomputingNNSA Explorations: ARM for Supercomputing
NNSA Explorations: ARM for Supercomputing
 

Similar a Poc Exadata X7-2 OVM

Iperconvergenza come migliora gli economics del tuo IT
Iperconvergenza come migliora gli economics del tuo ITIperconvergenza come migliora gli economics del tuo IT
Iperconvergenza come migliora gli economics del tuo ITNetApp
 
Plan with confidence: Route to a successful Do178c multicore certification
Plan with confidence: Route to a successful Do178c multicore certificationPlan with confidence: Route to a successful Do178c multicore certification
Plan with confidence: Route to a successful Do178c multicore certificationMassimo Talia
 
Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...
Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...
Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...Embarcados
 
Advanced Networking: The Critical Path for HPC, Cloud, Machine Learning and more
Advanced Networking: The Critical Path for HPC, Cloud, Machine Learning and moreAdvanced Networking: The Critical Path for HPC, Cloud, Machine Learning and more
Advanced Networking: The Critical Path for HPC, Cloud, Machine Learning and moreinside-BigData.com
 
How We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdfHow We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdfRakuten Group, Inc.
 
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...Linaro
 
Evolution of Supermicro GPU Server Solution
Evolution of Supermicro GPU Server SolutionEvolution of Supermicro GPU Server Solution
Evolution of Supermicro GPU Server SolutionNVIDIA Taiwan
 
Xilinx Edge Compute using Power 9 /OpenPOWER systems
Xilinx Edge Compute using Power 9 /OpenPOWER systemsXilinx Edge Compute using Power 9 /OpenPOWER systems
Xilinx Edge Compute using Power 9 /OpenPOWER systemsGanesan Narayanasamy
 
22by7 and DellEMC Tech Day July 20 2017 - Power Edge
22by7 and DellEMC Tech Day July 20 2017 - Power Edge22by7 and DellEMC Tech Day July 20 2017 - Power Edge
22by7 and DellEMC Tech Day July 20 2017 - Power EdgeSashikris
 
High availability microsoftvsoracle
High availability microsoftvsoracleHigh availability microsoftvsoracle
High availability microsoftvsoracleJacques Kostic
 
Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...
Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...
Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...Cloud Native Day Tel Aviv
 
Cisco connect winnipeg 2018 gain insight and programmability with cisco dc ...
Cisco connect winnipeg 2018   gain insight and programmability with cisco dc ...Cisco connect winnipeg 2018   gain insight and programmability with cisco dc ...
Cisco connect winnipeg 2018 gain insight and programmability with cisco dc ...Cisco Canada
 
Aleksejs Nemirovskis - Manage your data using oracle BDA
Aleksejs Nemirovskis - Manage your data using oracle BDAAleksejs Nemirovskis - Manage your data using oracle BDA
Aleksejs Nemirovskis - Manage your data using oracle BDAAndrejs Vorobjovs
 
Netronome Corporate Brochure
Netronome Corporate BrochureNetronome Corporate Brochure
Netronome Corporate BrochureNetronome
 
Sven Vogel: Running CloudStack and OpenShift with NetApp on KVM
Sven Vogel: Running CloudStack and OpenShift with NetApp on KVMSven Vogel: Running CloudStack and OpenShift with NetApp on KVM
Sven Vogel: Running CloudStack and OpenShift with NetApp on KVMShapeBlue
 
Cisco connect montreal 2018 compute v final
Cisco connect montreal 2018   compute v finalCisco connect montreal 2018   compute v final
Cisco connect montreal 2018 compute v finalCisco Canada
 
IBM Power Systems at FIS InFocus 2019
IBM Power Systems at FIS InFocus 2019IBM Power Systems at FIS InFocus 2019
IBM Power Systems at FIS InFocus 2019Paula Koziol
 
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28Amazon Web Services
 
“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...
“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...
“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...Edge AI and Vision Alliance
 

Similar a Poc Exadata X7-2 OVM (20)

Iperconvergenza come migliora gli economics del tuo IT
Iperconvergenza come migliora gli economics del tuo ITIperconvergenza come migliora gli economics del tuo IT
Iperconvergenza come migliora gli economics del tuo IT
 
Plan with confidence: Route to a successful Do178c multicore certification
Plan with confidence: Route to a successful Do178c multicore certificationPlan with confidence: Route to a successful Do178c multicore certification
Plan with confidence: Route to a successful Do178c multicore certification
 
Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...
Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...
Webinar Renesas - IoT é Segura? Com Renesas Synergy sim! E o SSP 1.5 tornou a...
 
Advanced Networking: The Critical Path for HPC, Cloud, Machine Learning and more
Advanced Networking: The Critical Path for HPC, Cloud, Machine Learning and moreAdvanced Networking: The Critical Path for HPC, Cloud, Machine Learning and more
Advanced Networking: The Critical Path for HPC, Cloud, Machine Learning and more
 
How We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdfHow We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdf
 
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
 
Evolution of Supermicro GPU Server Solution
Evolution of Supermicro GPU Server SolutionEvolution of Supermicro GPU Server Solution
Evolution of Supermicro GPU Server Solution
 
Xilinx Edge Compute using Power 9 /OpenPOWER systems
Xilinx Edge Compute using Power 9 /OpenPOWER systemsXilinx Edge Compute using Power 9 /OpenPOWER systems
Xilinx Edge Compute using Power 9 /OpenPOWER systems
 
22by7 and DellEMC Tech Day July 20 2017 - Power Edge
22by7 and DellEMC Tech Day July 20 2017 - Power Edge22by7 and DellEMC Tech Day July 20 2017 - Power Edge
22by7 and DellEMC Tech Day July 20 2017 - Power Edge
 
E3MV - Embedded Vision - Sundance
E3MV - Embedded Vision - SundanceE3MV - Embedded Vision - Sundance
E3MV - Embedded Vision - Sundance
 
High availability microsoftvsoracle
High availability microsoftvsoracleHigh availability microsoftvsoracle
High availability microsoftvsoracle
 
Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...
Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...
Brain in the Cloud: Machine Learning on OpenStack & Kubernetes Done Right - E...
 
Cisco connect winnipeg 2018 gain insight and programmability with cisco dc ...
Cisco connect winnipeg 2018   gain insight and programmability with cisco dc ...Cisco connect winnipeg 2018   gain insight and programmability with cisco dc ...
Cisco connect winnipeg 2018 gain insight and programmability with cisco dc ...
 
Aleksejs Nemirovskis - Manage your data using oracle BDA
Aleksejs Nemirovskis - Manage your data using oracle BDAAleksejs Nemirovskis - Manage your data using oracle BDA
Aleksejs Nemirovskis - Manage your data using oracle BDA
 
Netronome Corporate Brochure
Netronome Corporate BrochureNetronome Corporate Brochure
Netronome Corporate Brochure
 
Sven Vogel: Running CloudStack and OpenShift with NetApp on KVM
Sven Vogel: Running CloudStack and OpenShift with NetApp on KVMSven Vogel: Running CloudStack and OpenShift with NetApp on KVM
Sven Vogel: Running CloudStack and OpenShift with NetApp on KVM
 
Cisco connect montreal 2018 compute v final
Cisco connect montreal 2018   compute v finalCisco connect montreal 2018   compute v final
Cisco connect montreal 2018 compute v final
 
IBM Power Systems at FIS InFocus 2019
IBM Power Systems at FIS InFocus 2019IBM Power Systems at FIS InFocus 2019
IBM Power Systems at FIS InFocus 2019
 
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28
 
“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...
“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...
“Making Edge AI Inference Programming Easier and Flexible,” a Presentation fr...
 

Último

Cyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataCyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataTecnoIncentive
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxSimranPal17
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectBoston Institute of Analytics
 
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxThe Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxTasha Penwell
 
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024Susanna-Assunta Sansone
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfblazblazml
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our WorldEduminds Learning
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...Dr Arash Najmaei ( Phd., MBA, BSc)
 
SMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptxSMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptxHaritikaChhatwal1
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Boston Institute of Analytics
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonTimothy Spann
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBoston Institute of Analytics
 
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdfWorld Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdfsimulationsindia
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Thomas Poetter
 
Digital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksDigital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksdeepakthakur548787
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Seán Kennedy
 

Último (20)

Cyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataCyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded data
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptx
 
Data Analysis Project: Stroke Prediction
Data Analysis Project: Stroke PredictionData Analysis Project: Stroke Prediction
Data Analysis Project: Stroke Prediction
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis Project
 
Insurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis ProjectInsurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis Project
 
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxThe Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
 
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our World
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
 
SMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptxSMOTE and K-Fold Cross Validation-Presentation.pptx
SMOTE and K-Fold Cross Validation-Presentation.pptx
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max Princeton
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
 
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdfWorld Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
 
Digital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksDigital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing works
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...
 

Poc Exadata X7-2 OVM

  • 1. 2018 © Trivadis BASEL BERN BRUGG LAUSANNE ZUERICH DUESSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG KOPENHAGEN MUNICH STUTTGART VIENNA 2018 © Trivadis Exadata X7-2 POC with OVM Jacques Kostic Principal Consultant IMS Lausanne Emiliano Fusaglia Principal Consultant IMS Lausanne TechEvent September 2018 Exadata X7-2 POC with OVM 1
  • 2. 2018 © Trivadis Exadata X7-2 POC with OVM 2 TechEvent September 2018 Experience: • Initially C/C++ developer • In touch with Oracle since 1990 from version 4 on SCO Unix! • High Availability and Backup & Recovery Architect • SQL and Instance Performance & Tuning • License Audit and Consolidation Certifications: • Oracle Certified Master 11g & 12c • Oracle 11g Performance Tuning Certified Expert • Oracle RAC 11g and Grid Infrastructure Administration • Oracle Exadata Administrator Certified Expert • Oracle Certified SQL Expert 11g Teaching Courses at Trivadis: • Oracle 11g & 12c Grid Infrastructure & RAC • Oracle 11g & 12c Data Guard • Oracle 11g & 12c Performance & Tuning • Oracle 11g & 12c Administration • SQL & PL-SQL • OEM – 12 & 13 About me… @JKOFR
  • 3. 2018 © Trivadis Exadata X7-2 POC with OVM 3 TechEvent September 2018 Specialties: • Database Cloud computing (DBaaS) • Oracle RAC • Grid Infrastructure (CRS, ASM) • Data Guard • Instance and SQL Performance & Tuning • Linux & Virtualization Certifications: • Oracle Certified Professional 9i, 10g, 11g & 12c • Oracle Exadata Administrator X3 –X4 Certified Expert Teaching Courses at Trivadis: • Oracle 11g & 12c Grid Infrastructure & RAC • Oracle 11g & 12c Data Guard • Oracle Exadata • Oracle 12c New Features About me… @EFusaglia
  • 4. 2018 © Trivadis AGENDA 1. Customer Introduction 2. Trivadis Proposal 3. POC Execution 4. Conclusion 5. Q&A TechEvent September 2018 Exadata X7-2 POC with OVM 6
  • 5. 2018 © Trivadis Customer Introduction TechEvent September 2018 Exadata X7-2 POC with OVM 7
  • 6. 2018 © Trivadis Customer Overview The name will not be disclosed but the most relevant characteristics to the project are reported below.  Major player of the banking sector  In the process to choose the next DWH platform able to guarantee:  Optimal Performance  Scalability  Licensing Optimization  Consolidation Customer Environment TechEvent September 2018 Exadata X7-2 POC with OVM 8
  • 7. 2018 © Trivadis Additional Information & Requirements  Current Production database size of 18TB, annually increasing of 15%.  Guarantee an RTO of 24h and an RPO 6h.  Increase the number of DWH environments from 3 to 6.  QA database should be a full production copy, while the remaining environments a data subset.  The sub-setting procedure should be developed by the supplier.  The new architecture should offer fast cloning procedure. TechEvent September 2018 Exadata X7-2 POC with OVM 9
  • 8. 2018 © Trivadis Current Oracle architecture  IBM AIX P7, two Production LPAR and one QA all with capped CPUs  PROD: 9 VCPU, 148 GB of RAM  QA: 6 VCPU, 148 GB of RAM  SMT4  Distributed on two data centers, maintained by IBM Storage SVC replication  Oracle Licenses 10 CPU Enterprise Edition with:  Partitioning  Diagnostic Pack  Tuning packs  Major Performance problems:  Poor IO performances  CPU bound TechEvent September 2018 Exadata X7-2 POC with OVM 10
  • 9. 2018 © Trivadis Trivadis proposal TechEvent September 2018 Exadata X7-2 POC with OVM 11
  • 10. 2018 © Trivadis Trivadis Proposal After a careful evaluation Trivadis was convinced that the Exadata X7-2 was the best option in term of:  Customer satisfaction.  Agility to integrate a new DWH platform inside the customer’s ecosystem. Than the next question was: Bare Metal or Virtualized? TechEvent September 2018 Exadata X7-2 POC with OVM 12
  • 11. 2018 © Trivadis Exadata X7-2: Bare Metal Pros.  Use the entire machine capacity  Less environments to manage  Pay-as-you-grow approach (COD) for software licensing  Minimum 14 cores per DB nodes (8 for Eighth Rack)  All Oracle options are licensed on all cores  https://docs.oracle.com/cd/E80920_01/DBMLI/exadata-capacity-on-demand.htm#DBMLI147  IO Resource Management Cons.  No physical isolation between environments  License costs TechEvent September 2018 Exadata X7-2 POC with OVM 13
  • 12. 2018 © Trivadis Exadata X7-2: Virtualized Pros.  Physical isolation between environments  OVM Hard partitioning facilitate licensing optimization  Minimum 14 cores per DB nodes (8 cores for Eighth Rack) must be licensed for Enterprise Edition  For other options, it’s linked to the CPU allocation of each VM  Two cores per database node reserved to dom0 (no license required)  Flexible and dynamic vCPU allocation  IO Resource Management between databases accros all VMs. Db_unique_name must be unique across the entire Exadata Cons.  More complex to manage TechEvent September 2018 Exadata X7-2 POC with OVM 14
  • 13. 2018 © Trivadis Trivadis Architecture based on Exadata X7-2 Virtualized TechEvent September 2018 Exadata X7-2 POC with OVM 15 PRD PRD’ passive Cell 1 Cell 2 Cell 3 NAS Backup STB STB’ passive INT’ passive INT Cell 1 Cell 2 Cell 3 NAS Backup QA’ passive QA Site 1 Site 2 Data Gard Replication AD’ passive AD HM’ passive HM Trivadis Intelligent Backup
  • 14. 2018 © Trivadis 24 vCPU PRD Passive24 vCPU PRD Active Our Go Live Proposal Exadata 1 data fra data fra free StorageServer1 StorageServer2 StorageServer3 HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6 DBServer1 8 vCPU AD Passive 8 vCPU AD Active IO Resource Manager: Category, Inter-Database, Intra-Database (db_unique_name unique across all VClusters) DBServer2 TechEvent September 2018 Exadata X7-2 POC with OVM 16 4 vCPU HM Passive 4 vCPU HM Active data fra
  • 15. 2018 © Trivadis 8 vCPU STB Passive8 vCPU STB Active Our Go Live Proposal Exadata 2 data fra data fra data fra free StorageServer1 StorageServer2 StorageServer3 HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6 HD1 HD2 HD3 HD4 HD5 HD6 DBServer1 24 vCPU INT Passive 24 vCPU INT Active 4 vCPU QA Passive 4 vCPU QA Active IO Resource Manager: Category, Inter-Database, Intra-Database (db_unique_name unique across all VClusters) DBServer2 TechEvent September 2018 Exadata X7-2 POC with OVM 17
  • 16. 2018 © Trivadis  Dynamic host cpu reconfiguration using: xm vcpu-set  Dynamic oracle CPU_COUNT adjustment as of Oracle Oracle 12c  Dynamic resource management update Exadata X7-2: Elastic Capacity on Demand Elasticity on demand: up to 34 vCPUs per VM DEVPRD 34 vCPUs 18 vCPUs 2 vCPUs TechEvent September 2018 Exadata X7-2 POC with OVM 18
  • 17. 2018 © Trivadis Database Fast Clone  Use ASM Sparse Disk Group  Suitable for NON-Production database  Smart Scan is supported!  It requires a TestMaster database open in Read Only  The Test Master Database can not be modified or deleted as long the latest child snapshot is in use, due to Exadata Snapshot technology which uses “allocate on first write”, and not “copy on write” snapshot.  IO Performance degradation:  100 time slower - 35 microsecond vs 3.5 millisecond  More info's here: https://emilianofusaglia.net/tag/asm-sparse-disk-group/ TechEvent September 2018 Exadata X7-2 POC with OVM 19
  • 18. 2018 © Trivadis Licensing Optimization  Cold Failover mode  Oracle Active/Passive 10-days-per-year  http://www.oracle.com/us/corporate/pricing/data-recovery-licensing-070587.pdf  18 CPU Licenses required including:  Enterprise Edition  Partitioning  Diagnostic and Tuning Packs  Single instance databases on Oracle 12.2.0.1 TechEvent September 2018 Exadata X7-2 POC with OVM 20
  • 19. 2018 © Trivadis POC Execution and Result TechEvent September 2018 Exadata X7-2 POC with OVM 21
  • 20. 2018 © Trivadis POC Execution: the context Our Competitor  IBM P8  Full Flash Storage  Max 16 Cores with SMT8  Tests done with 10, 12,14 cores SMT8  Corresponding CPU licenses: 10, 12, 14  1 database 18 TB with 90 GB of SGA  Oracle 12.2.0.1 TechEvent September 2018 Exadata X7-2 POC with OVM 22
  • 21. 2018 © Trivadis POC Execution: the context Our Environment  Exadata X7-2 ¼ rack  OVM Configuration  Single instance mode  Two-node cluster with various vCPUs configurations  36, 28, 24, 20, 16  Corresponding CPU licenses: 9, 7, 6, 5, 4  1 database 18 TB with 90 GB of SGA  Oracle 12.2.0.1 TechEvent September 2018 Exadata X7-2 POC with OVM 23
  • 22. 2018 © Trivadis POC Execution: the context In Summary  Trivadis is proposing a complete change of architecture  IBM is just replacing P7 by P8 and adding Full Flash Storage TechEvent September 2018 Exadata X7-2 POC with OVM 24
  • 23. 2018 © Trivadis POC Execution: the setup  We had problems to setup the stuff  We had to use October 2017 Image  Thanks a lot to Arrow for the help! TechEvent September 2018 Exadata X7-2 POC with OVM 25
  • 24. 2018 © Trivadis POC Execution : the setup TechEvent September 2018 Exadata X7-2 POC with OVM 26 We had network problems with the management switch! In reality the step was failing because the Switch was OFF
  • 25. 2018 © Trivadis POC Execution: the initial load  Import took more than 54 hours for IBM  It took around 48 hours on Exadata  We used Multitenant to facilitate iterations during the POC  Pluggable database snapshots TechEvent September 2018 Exadata X7-2 POC with OVM 27 We get finally ready to start!
  • 26. 2018 © Trivadis POC Execution: the result  IBM was able to increase the load speed by a factor of four.  But it was achieved by:  using the 14 cores (SMT8) configuration  Setting the optimizer to 11.2.0.4 features! - Many ORA-00600 on stats export/imports during the load processing  High CPU usage during the processing  Runs with 12 and 10 cores were CPU bound  But still performing around 2.5 better than the current state  Because run using 14 cores was not CPU bond, they stopped at that level.  But they did not tried to run using optimizer_feature=’12.2.0.1’! TechEvent September 2018 Exadata X7-2 POC with OVM 28
  • 27. 2018 © Trivadis POC Execution: the result  We started our first run with 36 vCPUs and we achieved a speed increased by factor two:  Leaving the optimize to the default 12.2.0.1 value.  Low CPU usage  Average IO wait time of 35 microseconds!  Some jobs were running very badly and we discovered that the optimizer setting was not the same used by our competitor   We decided to fix the underlying queries! TechEvent September 2018 Exadata X7-2 POC with OVM 29
  • 28. 2018 © Trivadis POC Execution: the result  Some queries were hinted to use optimizer_feature=‘12.1.0.2’  Some queries were hinted to use optimizer_feature=’11.2.0.4’  Some queries were hinted to avoid view merge  Some queries where hinted to avoid materialize of a particular factoring clause TechEvent September 2018 Exadata X7-2 POC with OVM 30
  • 29. 2018 © Trivadis POC Execution: the result  We were finally able to achieve the same performance result obtain by IBM!  We decided then to start downsizing the vCPU configuration to see what we can get from this beast!  Runs with 28 and 24 did not change the performances at all!  We got 3% less performance with 20 vCPU and around 8% less with 16 vCPU!  CPU usage was high but acceptable with the 16 vCPU configuration TechEvent September 2018 Exadata X7-2 POC with OVM 31
  • 30. 2018 © Trivadis POC Execution : Conclusions Following our different runs  We decide to adjust our final offer to 24 vCPU  There are still lot of optimizations to be done! TechEvent September 2018 Exadata X7-2 POC with OVM 32
  • 31. 2018 © Trivadis Conclusion TechEvent September 2018 Exadata X7-2 POC with OVM 33
  • 32. 2018 © Trivadis Conclusion  We fully addressed all customer needs  The scalability of our platform (measured with the ratio between the number of vCPUs and the jobs execution time) was a key success TechEvent September 2018 Exadata X7-2 POC with OVM 34
  • 33. Jacques Kostic, Principal Consultant Tel. +41-79-909 7263 Jacques.Kostic@trivadis.com Emilian Fusaglia, Principal Consultant Tel. +41-79-909 7213 Emiliano.Fusaglia@trivadis.com 35 TechEvent September 201814.09.2018
  • 34. Session Feedback – now TechEvent September 201836 14.09.2018 Please use the Trivadis Events mobile app to give feedback on each session Use "My schedule" if you have registered for a session Otherwise use "Agenda" and the search function If the mobile app does not work (or if you have a Windows smartphone), use your smartphone browser – URL: http://trivadis.quickmobileplatform.eu/ – User name: <your_loginname> (such as "svv") – Password: sent by e-mail...