Virtualizing your business-critical SQL Servers should not imply that they will run slower than if they were physical. When properly architected and managed, virtual SQL Servers should be equally as fast as their physical counterparts, if not faster. However, if not properly constructed, silent and seemingly random performance killers can strike and significantly hurt your database performance. This session is packed with many tips and tricks gained from years of experience for getting the most performance from your virtual SQL Servers. The major roadblocks to performance will be discussed and the knowledge gained will help you work with your infrastructure engineers so you can optimize the system stack for performance. Tools, techniques, and processes will be demonstrated to help you measure and validate the system performance of the key components underneath your data.

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@kleegeek
davidklee.net
heraflux.com
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Specialties / Focus Areas / Passions:
• Performance Tuning & Troubleshooting
• Virtualization
• Cloud Enablement
• Infrastructure Architecture
• Health & Efficiency
• Capacity Management Founder & Chief Architect

3.  What is Virtualization?
 Resources and queues
 Physical servers
 Storage and interconnects
 VM architecture
 Inside the VM
© Heraflux Technologies® 3

4.  Added layer
 Physical resources
 Resource queues
 Very small queue delays
 When done right…
 Shared everything architecture
© Heraflux Technologies® 4

5. 150 GHz
CPU 4 TB
Memory
4x10GbE
Network
20 TB
Tier 1
Storage
40 TB
Tier 2
Storage
VM
16 vCPU
128 GB vRAM
VM
8 vCPU
64 GB vRAM
VM
2 vCPU
16 GB
vRAM
VM
2 vCPU
16 GB
vRAM
VM
2 vCPU
16 GB
vRAM
VM
2 vCPU
16 GB
vRAM
VM
2 vCPU
16 GB
vRAM
VM
2 vCPU
16 GB
vRAM
V I R T U A L I Z A T I O N
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6. Hypervisor
CPU Scheduler
CPU
Execution
CPU Scheduling Queue
Memory Allocator
Mem
R / W
Mem Allocation Queue
Disk Scheduler
Disk
R / W
Disk Scheduling Queue
Network Scheduler
Network
Tran / Rec
Network Scheduling Queue
VM TASK
VM TASK
VM TASK
VM TASK
VM TASK
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7. • Finite!
• Single compute node hardware
• Total cluster compute capacity
• Storage speed (IOPs, throughput)
• VM maximums
• Interconnect path speed
Hard Limits (Resources) Soft Limits (Queues)
• Measurable but not finite
• Memory oversubscription
• CPU scheduler contention
• Shared resource utilization
• “Noisy Neighbors”
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Networking
Interconnects
Storage
Physical Server
Virtualization
Operating System
SQL Server Instance
SQL Server DB
Application

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10.  Most shared
 Most critical
 Most complex
 Most problematic
 Slowest piece of the stack
 Many individual points of contention
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Controller
Controller
LUN
LUN
LUN
LUN
Disk Pool
T1
T2
T3

12.  Test raw performance
 SQLIO
Deprecated!
 DiskSpd Batch
heraflux.com/go/diskspd
 Collect metrics:
 I/Os per second (IOPs)
 Latency (ms)
 Throughput (MB/s) 0.00
10000.00
20000.00
30000.00
40000.00
50000.00
60000.00
70000.00
1 2 4 8 16 32 64 128
IOps Thread Intensity
IOps Per Operations per Thread
Sequential Read
Random Read
Sequential Write
Random Write
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(Img src: http://www.asacomputers.com/2U-Server-Sandy-Bridge.html)

15.  Manufacturer
 Intel
 AMD
 Sockets
 Cores
 Speed vs. GHz
 Logical vs. Physical
 Hyperthreading
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(Img src: http://frankdenneman.nl/2011/01/05/amd-magny-cours-and-esx/)
CPU CPU CPU CPU
Memory

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(Img src: http://www.learnyourtech.com/hardware/)

18.  Sample Cisco UCS
memory config
 Slot & chip
placement
© Heraflux Technologies® 18
(Src: http://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-b-series-blade-servers/b200m4-specsheet.pdf page 44)

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20.  Resource limits are easy to detect / work around
 Queue contention much harder
 Time in queue = time lost from VM
 Silent performance killer
 Everything in a VM must be scheduled
 … including idle resources
 Queue processing is not always FIFO
© Heraflux Technologies® 20

21.  Get physical machine configuration
 Try to fit VM inside one NUMA node
 Otherwise, balance across number
of NUMA nodes
 Test configurations for
best results
© Heraflux Technologies® 21

22.  Example: 16 vCPU VM
 What’s better?
 2 vSocket x 8 vCore?
 4 vSocket x 4 vCore?
 8 vSocket x 2 vCore?
 Varies by workload,
hardware
 Test it for yourself!
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0
100000
200000
300000
400000
500000
600000
700000
800000
900000
8 16 64 256
Transactions/min Concurrent HammerDB Users
vNUMA SQL Server Scalability - 16 vCPUs - HammerDB
4socket x 4CPU 8socket x 2CPU 2socket x 8CPU

23.  1 vCPU to 1 pCPU is
a poor recommendation
 Queues
 Ready Time
 Co-Stop
 Ex: Client environment
 74 running VMs
 VMs @ 2-8 vCPU
 72 total pCores
 Fantastic performance
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27.  “Right” amount of vCPU and vRAM resources
 Physical world = Size for end of life
 Virtual world = Size for right now
 Idle vCPUs will slow application VMs performance
 Repeat “right-sizing” analysis routinely
© Heraflux Technologies® 27

28.  Virtual disks whenever possible
 Multiple SCSI controllers
 VMware – PVSCSI
 Spread out the workload
© Heraflux Technologies® 28

29.  C: - Operating System
 D: - SQL Server Instance Home
 E: - System Databases (master, model, msdb) *
 F: - User Database Data (1 of X)…
 G: - User Database Log (1 of Y)…
 H: - TempDB…
 Y: - Windows Page file **
 Z: - Backups Adjust as necessary
(but stay standardized)
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30.  64KB NTFS block sizes
 Set power settings to “High Performance” (CPU-Z)
 Set antivirus exclusions for SQL Server (tinyurl.com/sqlav)
 Ongoing OS-level performance metric collection
 No greater than five minute interval
 Windows Perfmon, Microsoft SCOM,
or other third-party utility
 heraflux.com/go/perfmon
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31.  Goal: Maximize performance while reducing resource scheduling
 Parallelizable workloads
 Determine how parallel the workload is
 Set MaxDOP = vNUMA node core count (?)
 Cost threshold for parallelism = Not default
 Jonathan Kehayias – Tuning CTOP from Plan Cache
 bit.ly/1rTs9UX
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32.  Spread out the I/O
 File groups, data files, partitions
 Parallelism with multiple active storage paths
 Reduce I/O
 Table / index compression vs. SAN compression
 In-memory constructs
 More RAM
 SSD read / write caching
 Faster I/O – All flash SAN
 And then clean up bad schemas & queries!
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33.  Virtualization works.
 Equivalent performance if done right
 Efficiency in data handling
© Heraflux Technologies® 33

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@kleegeek
davidklee.net
heraflux.com
linkedin.com/in/davidaklee