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SQL Tuning for Smarties, Dummies and Everyone in Between Novices
Jagan AthreyaDirector, Database Manageability, Oracle
Arup Nanda
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Senior Director, Database Architecture, Starwood Hotels and Resorts
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver anycontract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions.The development release and timing of anyThe development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
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Outline
• SQL Tuning Challengesg g
• SQL Tuning Solutions – New Feature Overview
• Problem Root Causes and their Solutions
• Preventing SQL Problems
• Q & A
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SQL Tuning ChallengesReal-world DBA and Development TeamsReal-world DBA and Development Teams
• DBA team– Mostly average, some superstars– Superstars take most of the burden – over-stretched
• Development staff– Mostly non-Oracle skills – Java, C++y ,– Usually considers the DB as a “black box”– Writing efficient queries, troubleshooting performance issues
is delegated to DBAsis delegated to DBAs
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SQL Tuning ChallengesProduction PerformanceProduction Performance
• Situation:– Query from hell pops up– Brings the database to its knees
DBA is blamed for the failure– DBA is blamed for the failure
• Response– DBA: “Developer should be taking care of this.”– Developer: “Why is the DBA not aware of this problem?”– Manager: “DBA will review all queries and approve them.”
• Challenge• Challenge– What is the most efficient way to manage this process?
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SQL Tuning ChallengesChange Causing ProblemsChange Causing Problems
• Situation– New SQL statements added as part of application patch
deployment– Database upgradesDatabase upgrades– Database patching
• Response– Users: “How will the application perform after the changes?”– DBA: “How do I ensure that our SLA remains intact after the
changes are rolled out?”
• Challenge– How to reduce business risk while absorbing new
technologies?
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technologies?
SQL Tuning ChallengesOptimizer Statistics ManagementOptimizer Statistics Management
• Situation– Data in Production has evolved over time. Have the optimizer
statistics stayed current?
• Response• ResponseDBA:– Will statistics refresh break something?– What will happen if we don’t collect?– How often should I collect the statistics ?– What happens when you collect a new set?pp y
• Challenge– What is the recommended strategy for managing optimizer
statistics to ensure the best performance?
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statistics to ensure the best performance?
SQL Tuning ChallengesBad Plans – Diagnosis and ResolutionBad Plans – Diagnosis and Resolution
• No time to find the root cause. How to prevent this from recurring?
• Bind variables: How do you prevent bad plans based on choice of bind variables?on choice of bind variables?
• How to diagnose a bad plan– 10053 trace, endless pouring over traces, p g– Wrongly constructed predicates
• How to fix a bad plan Hi t ? h f d ?– Hints? change of code?
– Baselines vs. SQL Profiles– Pick out a single SQL or a bunch from the shared pool
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Outline
• SQL Tuning Challengesg g
• SQL Tuning Solutions – New Feature Overview
• Problem Root Causes and their Solutions
• Preventing SQL Problems
• Q & A
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Real-Time SQL MonitoringLooking Inside SQL Execution
• Automatically monitors long running SQL
g
• Enabled out-of-the-box with no performance overhead
• Monitors each SQL execution
• Exposes monitoring statisticsp g– Global execution level– Plan operation level– Parallel Execution level
• Guides tuning efforts
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New capabilities in SQL MonitoringNew in Oracle Database 11g Release 2 g
• PL/SQL monitoring including associated high load SQL monitored recursively• Exadata aware I/O performance monitoring and associated metric datap g• Capture rich metadata such as bind values, session details e.g. user,
program, client_id and error codes and error messages• Save as Active Report for rich interactive offline analysis
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DEMO
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Application TuningAutomatic SQL TuningAutomatic SQL Tuning
High-Load
Packaged Apps +SQL Profile
C t i bl A
Well-Tuned SQL
Customizable Apps + SQL Advice
Customizable Apps + Indexes & MVs + Partitions
Applications
Automatic Tuning Optimizer
• Automatic SQL Tuning• Identifies high-load SQL from AWR• Tunes SQL using SQL Profilesg• Implements greatly improved SQL plans (optional)
• Performance benefit of advice provided• SQL Profiling tunes execution plan without changing SQL text
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• Enables transparent tuning for packaged applications
Automatic SQL TuningNew in Oracle Database 11g Release 2New in Oracle Database 11g Release 2
Gather Missing or Stale Statistics
C t SQL P filSQL ProfilingStatistics Analysis
Access Path AnalysisSQL Restructure Analysis
Create a SQL Profile
Add Missing Access Structures
Modify SQL Constructs
Adopt Alternati eAlternative Plan AnalysisParallel Query Analysis
Automatic Tuning
Administrator
Comprehensive
Adopt AlternativeExecution Plan
Create Parallel SQL Profile
SQL Tuning Advisor
• SQL Tuning Advisor • NEW: Identifies alternate execution plans using real-time and historical
Automatic Tuning Optimizer SQL Tuning
Recommendations
NEW: Identifies alternate execution plans using real time and historical performance data
• NEW: Recommends parallel profile if it will improve SQL performance significantly (2x or more)
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SQL Tuning for DevelopersIntegration with Visual Studio
• Introduced in Oracle Developer Tools for Visual Studio Release 11.1.0.7.20• Oracle Performance Analyzer
– Tune running applications with the help of ADDM• Query Window
– Tune individual SQL statements with STA• Server Explorer
– Manage AWR snapshots and ADDM tasksManage AWR snapshots and ADDM tasks
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Agenda
• SQL Tuning Challengesg g
• SQL Tuning Solutions – New Feature Overview
• Problem Root Causes and their Solutions
• Preventing SQL Problems
• Q & A
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What makes SQL go bad?Root Causes of Poor SQL Performance1. Optimizer statistics issues
a. Stale/Missing statisticsb. Incomplete statisticsc. Improper optimizer configurationd. Upgraded database: new optimizerpg pe. Changing statisticsf. Rapidly changing data
2. Application IssuesMi i t ta. Missing access structures
b. Poorly written SQL statements3. Cursor sharing issues
a. Bind-sensitive SQL with bind peekingp gb. Literal usage
4. Resource and contention issuesa. Hardware resource crunchb C t ti ( l k t ti bl k d t t ti )b. Contention (row lock contention, block update contention)c. Data fragmentation
5. Parallelism issuesa. Not parallelized (no scaling to large data)
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p ( g g )b. Improperly parallelized (partially parallelized, skews)
What makes SQL go bad?Root Causes of Poor SQL Performance
1. Optimizer statistics issuesa. Stale/Missing statisticsb. Incomplete statisticsc. Improper optimizer configurationd. Upgraded database: new optimizere. Changing statisticsg gf. Rapidly changing data
2. Application Issues3. Cursor sharing issues 4. Resource and contention issues5. Parallelism issues
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Oracle Optimizer Statistics
Inaccurate statistics Suboptimal Plans
Optimizer Statistics
CBO
Optimizer Statistics• Table Statistics
CBO• Column Statistics
I d S i i• Index Statistics
• Partition Statistics• Partition Statistics
• System Statistics
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System Statistics
Oracle Optimizer StatisticsPreventing SQL Regressions
Novice Mode
• Automatic Statistics Collection Job (stale or missing)g)• Out-of-the box, runs in maintenance window• Configuration can be changed (at table
level)G th t ti ti d di ti• Gathers statistics on user and dictionary objects
• Uses new collection algorithm with accuracy of compute and speed faster than
Nightlyaccuracy of compute and speed faster than sampling of 10%
• Incrementally maintains statistics for partitioned tables very efficientpartitioned tables – very efficient • Set DBMS_STATS.SET_GLOBAL_PREFS
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Oracle Optimizer StatisticsPreventing SQL Regressions
Expert Mode
• Extended Statistics• Extended Optimizer Statistics provides a mechanism
to collect statistics on a group of related columns:• Function-Based Statistics• Multi-Column Statistics
• Full integration into existing statistics frameworkFull integration into existing statistics framework• Automatically maintained with column statisticsDBMS_STATS.CREATE_EXTENDED_STATS
• Pending StatisticsPending Statistics• Allows validation of statistics before publishing• Disabled by default• To enable, set table/schema PUBLISH setting to FALSE, gDBMS_STATS.SET_TABLE_PREFS('SH','CUSTOMERS','PUBLISH','false')
• To use for validationALTER SESSION SET optimizer_pending_statistics = TRUE;
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• Publish after successful verification
What makes SQL go bad?Root Causes of Poor SQL Performance
1. Optimizer statistics issues2. Application Issues
a Missing access structuresa. Missing access structuresb. Poorly written SQL statements
3. Cursor sharing issues 4 Resource and contention issues4. Resource and contention issues5. Parallelism issues
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Identify performance problems using ADDMAutomatic Database Diagnostic Monitor Novice
M d
• Provides database and cluster-wide performance
Mode
cluster-wide performance diagnostic
• Throughput centric - Focus on reducing time ‘DB time’g
• Identifies top SQL:• Shows SQL impact• Frequency of
occurrence• Pinpoints root cause:
– SQL stmts waiting for Row Lock waitsRow Lock waits
– SQL stmts not shared
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Identify High Load SQL Using Top ActivityNovice M dPerformance Page
• Identify Top SQL by DB Time:• CPU
Mode
• I/O• Non-idle waits
• Different Levels of Analysisy• Historical analysis
• AWR data• Performance Page
Top Activity
g• Real-time analysis
• ASH data• More granular analysisg y• Enables identification of
transient problem SQL• Top Activity Page
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• Tune using SQL Tuning Advisor
Advanced SQL TuningUniverse of Access Structures
Novice+Mode
• Indexes: B-tree indexes, B-tree cluster indexes, Hash cluster indexes, Global and local indexes, Reverse key indexes, Bitmap indexes, Function-based indexes, Domain indexes
• Materialized Views: Primary Key materialized views, Object materialized viewsObject materialized views ROWID materialized views Complex materialized views
• Partitioned Tables: Range partitioning, Hash partitioning,List partitioning, Composite partitioning,Interval Partitioning REF partitioningInterval Partitioning, REF partitioning,Virtual Column Based partitioning
B-tree index
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SQL Access Advisor: Partition Advisor Novice+Mode
Indexes
MaterializedviewsSQL Access
AdvisorRepresentative
Workload
Materializedviews logs
Automatic Tuning Optimizer
Access Path
Partitionedobjects
Access Path Analysis
objects
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SQL Access AdvisorAdvanced Options
Expert Mode
• Workload filtering• Limited vs. advanced mode• Tablespaces for access structuresTablespaces for access structures• Hypothetical workload tuning• Factoring in the cost of creation• Space limitations for indexes and MVs• Space limitations for indexes and MVs
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1 O ti i t ti ti i
What makes SQL go bad?Root Causes of Poor SQL Performance
1. Optimizer statistics issues2. Application Issues3. Cursor sharing issues
a Literal usagea. Literal usageb. Bind-sensitive SQL with bind peeking
4. Resource and contention issues5. Parallelism issues5. Parallelism issues
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What makes SQL go bad?a. Literal Usage Issue
Expert Mode
SELECT * FROM jobs WHERE min_salary > 12000;
SELECT * FROM jobs WHERE min_salary > 15000;
SELECT * FROM jobs WHERE min_salary > 10000;
SELECT * FROM …SELECT * FROM …SELECT * FROM
cursor_sharing ={exact, force, similar}SELECT FROM …
Sh i
Library Cache
Sharing Cursors is good!
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Library Cache
What makes SQL go bad?b. Bind Peeking Issue
NMode
Processed_Flag
YY Full Table ScanYYY CBO10g FTS
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Two different optimal plans for different bind values
Index Range Scan
NIRS
1
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Problem: Binds will affect optimality in any subsequent uses of the stored plan
Fixing problems with Adaptive Cursor SharingAdaptive Cursor Sharing Expert
ModeSELECT * FROM emp WHERE wage := wage_value
Selectivity Ranges:
Mode
Selectivity Ranges:
1
2
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Same Plan
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Different Plan
4
30 35Same Plan,
Expand Interval
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34 43Interval
Agenda
• SQL Tuning Challengesg g
• SQL Tuning Solutions – New Feature Overview
• Problem Root Causes and their Solutions
• Preventing SQL Problems
• Q & A
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Preventing problems with SQL Plan Management
• Problem: changes in the environment cause plans to change
NL
GB
Parse
• Plan baseline is establishedNL
NL
Statement log
Plan history
• SQL statement is parsed again and a different plan is generated
HJ
GB
Plan baselineGB
NL
NL
• New plan is not executed but k d f ifi ti HJmarked for verification
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SQL Plan ManagementMigration of Stored Outlines to Plan Baselines
Oracle Database 11g
GB 5. Migrate Stored Outlines into SPM
Plan History
GB
OH Schema
GB
No planregressions
HJ
HJ
into SPMHJ
HJ HJ
GB
HJ
4. Upgrade to 11g
Oracle Database 11g
1 B iOH Schema
HJ
GB
CREATE_STORED_OUTLINES=true1. Begin with
2. Run all SQL in the Application and auto
t St d O tli
Oracle Database 9 or 10g
HJ
CREATE_STORED_OUTLINES=false
create a Stored Outline for each one
3. After Store Outlines are
captured
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captured
SQL Performance Analyzer (SPA) Validate statistics refresh with SPA
• Steps:1. Capture SQL workload in STS
using automatic cursor cache
Validating upgrade with SPASQL Workload
using automatic cursor cache capture capability
2. Execute SPA pre-change trial3. Refresh statistics using
PENDING i
SQL plans + stats SQL plans + stats
Pre change Trial Post change TrialPENDING option4. Execute SPA post-change trial5. Run SPA report comparing SQL
execution statistics Compare
Pre-change Trial Post-change Trial
execution statistics
• Before PUBLISHing stats:• Remediate individual few SQL Analysis Report
SQL Performance
Qfor plan regressions: SPM, STA
• Revert to old statistics if too many regressions observed
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Conclusion Identify, Resolve, Prevent
PreventSPASPM
1. Production Performance2. Change Causing Problems3. Optimizer Statistics Management4 B d l Di i d R l ti
Resolve
4. Bad plans – Diagnosis and Resolution
ADDM, Top Activity, SQL MonitoringResolve
Tuning Advisor
Identify
Tuning Advisor,Access Advisor,
Auto Stat CollectionTop Activity,
ADDM, SQL Monitoring
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