Scaling Up and Out your Virtualized SQL Servers

Scaling Up and Out Your Virtualized

SQL Servers

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SCALING UP AND OUT YOUR VIRTUALIZED SQL SERVERS

About Ben DeBow• Partner - SQLHA• Working with SQL Server for 20 years• Author, speaker, and consultant focusing on Performance,

Scalability, and Availability

[email protected] @BBQSQL sqlha.com

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About David Klee

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@kleegeekdavidklee.netgplus.to/kleegeeklinked.com/a/davidaklee

Specialties / Focus Areas / Passions:

• Performance Tuning• Virtualization• Infrastructure• Troubleshooting

• High Availability• Disaster Recovery• Capacity Management• Health & Efficiency


Agenda• Scale Out vs. Scale Up• Scale In• “Right-Sizing”• Efficiency• Workload characteristics

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Scalability• Scale up = vertical scaling• Vertical scaling = single instance faster• Horizontal– Data dependent routing– Read Only Replicas– Peer to Peer Replication

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Scale Out vs. Scale Up• Age-old problem• One server to thousands• Now introduce virtualization

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Scale Up

PROS• Single system / app

distribution• Faster & quicker

CONS• Single server bottlenecks• Scalability limits• Internal SQL Server

contention• More expensive

hardware

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Scale Out

PROS• Separation of

workloads• Smaller VMs• More agile VMs• Load balance• Outages impact less

CONS• Licensing• Management• Day-to-day operations• Complexity

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Scale IN First• Efficiency, health, and bottleneck

analysis• Limited upsides to more hardware• Performance gains without more

resources

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Bottleneck Analysis• Underneath the VM• VM config and placement• SQL Server instance• SQL Server database• Determine tipping points

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Bottleneck Example - Storage

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ControllerController

ControllerController

LUNLUN

LUNLUN

LUNLUN

LUNLUN

Disk PoolDisk Pool

VM

VM

VM

VM


VM Configuration• Start with gold certified template• Adjust resources• One size does not fit all

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“Right-Sizing”• What? How?• Resource allocations–What is needed now

• Type of workload• vNUMA

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Leverage VM Infrastructure• VM proximity

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Leverage VM Infrastructure• Transparent SSD read / write caching– Per vDisk

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SSD

SANSAN

LUNLUN

LUNLUN


Priority Tiers• Mission-critical production vs.

everything else• Tier / prioritize resource queues• Separation of tiers

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Add Hardware• Faster CPUs / more cores• More RAM• Faster storage (IOps / latency)• Faster / more interconnects– Storage fabric– Networking

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Efficiency - Concurrency• Locking & blocking• Read Committed Snapshot Isolation• Asynchronous handling– Service Broker– SSIS– PoSH

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Efficiency - Code Review• Schema design• Strongly typed data types• Queries & procedures• App data handling• …. And the list continues forever…

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Efficiency - CPU• Cores & schedulers• Hyper-Threading• vNUMA

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CPU Sizing – vNUMA Results31% Improvement

31% Improvement

• 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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Efficiency - Memory• Use memory to reduce I/O• Use less memory with better efficiency• Schema & index strategies• Data & index compression• Buffer pool extensions

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Efficiency - Storage• Goal: reduce I/O consumption• Better distribution of workload– Tables– Data Files – File groups– Partitions

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Virtual disksPathsControllersLUNs


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SAN

DB

E:

FG1

FG2

DF4

DF3

DF2

DF1

G:

F:


Efficiency - Storage• Data / index compression• Indexing strategies• Datatype analysis• In-memory constructs• Buffer pool extensions

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Segregating Workloads• One VM per purpose• Offload read-only activities– Availability Groups– Replication– Backups

• Sharding / data dependent routing#devconnections


Workload Characteristics• Monitoring• Workload performance stat collection– At all layers

• Aggregation by host & cluster• Analysis

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Ongoing Review• Routine “right-sizing” analysis• Capacity analysis & planning• Baselines• What to capture and analyze?

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Conclusions• Efficient virtualization• Workload characterization• Bottleneck relief• Long-term management

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QUESTIONS?

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THANK YOU!

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Scaling Up and Out your Virtualized SQL Servers

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