Throwing Hardware at SQL Server Performance problems?

Think again, there’s a better way!

SQL Server Performance

Approaches to Performance Improvement

» Buy bigger, faster hardware Sometimes the only answer

Often the easy way out

Can be high risk

» New SQL Server features May help if your problem is one they address

» Make better use of existing resources Start by understanding the issues

Gather and analyze information

Make informed decisions

Be proactive

Performance information

» Many sources Dynamic Management views

System Tables

WMI

Performance counters

» Needs to be stored and correlated History

Baselines

Microsoft tools• Often a “science project”

Commercial tools• History and correlation sometimes built in

• Help with analysis and resolution

• Database Performance Advisor (DPA) does both

Hardware Settings

» Power Saver Mode

Defaults to Balanced

SQL Server works better on High Power

https://support.microsoft.com/kb/2207548?wa=wsignin1.0

» HBA Queue Depth

Defaults to 16 or 32

SQL Server needs 64 or 128

Storage Issues

» Data, Logs, and Tempdb on separate volumes

» RAID 10 for faster writes than RAID 5

» MPIO – load balanced

» Compression reduces IO by increasing rows per page

» Table and index scans increase IO Indexes

Query tuning

» Disk Performance Seconds Per Read - < 10 ms

Seconds Per Write < 5 ms

Parallelism

» Limits on speed of a core

» Adding cores to improve performance Multiple queries in parallel

Multiple cores for a single query

• Scan in parallel

• Sort/filter in parallel

Hyper-threading works in current versions

» CXPACKET wait type indicates parallelism is being used Not necessarily a bad thing

Many large queries can produce high CXPACKET waits

MAXDOP

» Limits the number of schedulers for a query Set at instance level or query hint

Defaults to 0 – all schedulers on system

Too much parallelism can be bad

• Competition for processor resources

• Splitting and combining overhead

• Crossing NUMA boundaries

• Servers may have > 64 cores which is more than most queries can use effectively

MAXDOP <= NUMA node size

MAXDOP = 8 good place to start

Cost Threshold for Parallelism

» Optimizer creates serial plan first

» If the cost of the serial plan is greater than the Cost Threshold for Parallelism it creates a parallel plan

» The lowest cost plan is used

» Cost Threshold default is 5

» With modern servers, 5 is much to small Waste time and processor cycles computing parallel plans

May use parallel plan for very small queries

» Start with 25-30 and if it helps try 40 or 50

Buffer Cache

» Cache pages so query engine doesn’t have to go to disk

» Low buffer cache = increased IO

» Measure memory stress Page Life Expectancy (PLE) – estimated time page will stay in memory

• Larger the better

• Look for sudden drops in PLE• Large table scans

• Lots of IO to reload cache

Lazy Writes• Short on memory – dirty pages flushed to disk between checkpoints

» Fix buffer cache issues Reduce table scans

Increase memory

Plan Cache

» Compiled plans are stored in cache for possible reuse Lookup by hash of query text

• Must be exact match

Plan not reused are gradually aged out of cache

» Single use plans Usually non-parameterized queries

Waste cache space – slow search for plans

OPTIMIZE FOR ADHOC QUERIES instance parameter• Caches queries on second use

• Saves cache space

• Use routinely

FORCED PARAMETERIZATION• Makes optimizer more aggressive about parameterizing queries

• Use only after testing

• Only when rewriting queries isn’t possible

Table and Index Scans

» Query needs to access all rows Aggregates

Reports

» Scan is the best way to execute the query Not many rows

Most of the rows required

» No index on the column

» There is an index but it can’t be used Implicit datatype conversions

Scalar functions

Wait Analysis

» SQL tasks are either executing or waiting

» Understanding what tasks are waiting for can help you find performance issues

» DPA offers several ways to analyze waits System wide waits – what does the system wait for

Waits by query – what waits make up the response time of a query

» DPA helps analyze query performance Which queries take the most time

Which queries run most often

Which queries use the most resources

» DPA analyzes tends Resource usage changing

Queries running slower

Database Performance Analyzer Demo

Janis Griffin

» Database Performance Evangelist

» SolarWinds – The Power to Manage IT

First steps

» Check Power Saver and HBA settings

» Implement data compression

» Set MAXDOP to 9 and Cost Threshold for Parallelism to 30

» SET OPTIMIZE FOR ADHOC QUERIES option

» Set up monitoring for (at least) Tables Scans

PLE

Lazy Writes

Seconds per Write

Seconds per Read

» Check for missing indexes and tune most expensive queries

Summary

» Investing in hardware without first understanding performance issues is a high risk option

» Gathering performance information is key to understanding and resolving performance issues

» Baselines and history are important to performance analysis

» Wait analysis can help diagnose performance issues

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© 2014 SOLARWINDS WORLDWIDE, LLC. ALL RIGHTS RESERVED.

© 2014 SOLARWINDS WORLDWIDE, LLC. ALL RIGHTS RESERVED.

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