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• CEO Fortified Data

• Working with SQL Server for 20yrs

• Author, speaker, and consultant focusing on Performance, Scalability, and Availability

• E-mail: [email protected]

• Twitter: @BBQSQL

• Web/Blog: http://www.fortifieddb.com

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mailto:[email protected]

http://www.fortifieddb.com/


About David Klee

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@kleegeek

davidklee.net

gplus.to/kleegeek

linked.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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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 caching

– Per vDisk

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SSD

SAN

LUN

LUN


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

• NUMA

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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

– Data files

– File groups

– Partitions

– Virtual disks

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Virtual disks

Paths

Controllers

LUNs


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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

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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

Data & Analytics

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