Why new hardware may not make SQL Server faster

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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Why new hardware may not make SQL Server faster

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