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Workshop

Oracle Database Performance Management Best Practices

2


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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 any material, code, or functionality, and should not be relied upon in making purchasing decisions. The 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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Agenda

4

• Introduction

• Oracle Database Performance Tuning Fundamentals

• Find, Fix and Validate: Methodology and Toolset

• Find Fix and Validate: An Example

• Real-World Best Practices & Tips

• New Features and Enhancements (DB 12.1.0.2)


Top Challenges

Database Management

39% Handle more than 50 DBs each

Data stores growing by 20%+ 46%

Source: From Database Clouds to Big Data: 2013 IOUG Survey On Database Manageability

?

78% Downtime resulting from untested changes

48%

Use SQL tuning to address performance issues

Key Takeaway:

Improve & Ensure Higher Quality of Service

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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 6

Continuous Innovation


Agenda

7

• Introduction







Database Time (DB Time)

• Total time in database calls by foreground sessions

• Includes CPU time, IO time and non-idle wait time

• DB Time <> response time

• New metric for Oracle performance analysis

Database time is total time spent by user processes either actively working or actively

waiting in a database call.

8


Active Session = Session currently spending time in a database call

Database Time (DB Time) = Total time session spent in all database calls

Average Activity of the Session (% Activity) = The ratio of time active to total wall-clock time

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TIME = time spent in database

Fundamental Concepts


DB Time Theory to Practice: EM Performance page

• Active Sessions by wait class over time

• Colored area = Amount of DB time , More the DB time , More the Problem

• “Click on the big stuff…”

10


ASH Theory to Practice: EM ASH Analytics Page

• Graphical ASH report for advanced analysis

• Select any time period for analysis

• Analyze performance across many dimensions

• Provides visual filtering for recursive drill-downs

• Different visualizations: Stacked chart or Tree Map

• Collaborate with others using Active Reports


Agenda

12

• Introduction







Past DB Tuning Process

• Inefficient, Inaccurate, Manual

The “trial and error” method can consume more than 50% of the DBA time

Default value is 100, …so let’s set it to 50…

Will optimizer_index_cost_adj

improving my sequential reads…

Find a problematic

metric

Implement a possible Fix

Wait for end-user

feedback Did it work? Are customers complaining?


Future Generation DB Tuning Process • Effective, Accurate, Automated

Find Fix

Validate

Built-in, self-diagnostics

engine:

Automatic Database

Diagnostics Monitor (ADDM) – (Diagnostics Pack)

Diagnostics Automates complex and time consuming

task of application tuning

(Tuning Pack)

Tuning

Validates

tuning activities

(Real Application Testing)

SQL

Performance Analyzer

(SPA)


Find Fix Validate

STEP 1 - Find Issues STEP 2 - Fix Issues

STEP 3 - Validate Fix

Tuning Diagnostics

SQL Performance Analyzer (SPA)


Diagnostics


Find-Fix-Validate Methodology and Toolset Find:

– Gather Data: Performance and workload data capture

• System statistics, wait information, SQL Statistics, etc.

– Data Analysis and Problem Identification

• What types of operations are consuming most time?

• Which resources is the bottleneck?

• What is the root cause?

• How can problem be remediated?

– If multiple problems identified, which is most critical?

– Which solution will give me most benefit?

– For SQL Related problems, ADDM points recommends running SQL Tuning Advisor

AWR

ADDM

ADDM

Rich feature-set provides comprehensive support for diagnosing problems: System Session, Targeted or Transient, Hung or slow moving databases, etc. Built into the Oracle kernel and highly optimized (already part of the SGA)

17


Gathering Data Automatic Workload Repository (AWR)

• Built-in workload and performance statistics repository in the database

• Automatically captures workload data

– Every 60 minutes, or manually, saves data for 8 days by default

• Resides in SYSAUX tablespace

• Space requirements automatically managed

– Old data is automatically purged nightly based on retention interval

• Stores different classes of data:

– BASE STATISTICS e.g., physical reads

– SQL STATISTICS e.g., disk reads (per sql stmt)

– METRICS e.g., physical reads / sec

– SQL Monitor Report Data

– ACTIVE SESSION HISTORY (ASH)

• Support for Exadata and In-Memory Database statistics

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Active Session History (ASH)

• Samples active sessions every one second into memory (v$active_session_history)

• Helps answers questions like “has this SQL gotten slower over time?”

• Direct access to kernel structures

• One of ten samples flushed to AWR at every snapshot

• Data captured includes:

– SID

– SQL ID

– Program, Module, Action

– Wait event#

– Object, File, Block

– actual wait time (if captured while waiting)

Enables targeted performance analysis of transient problems

Performance

Time

*

Range

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DB Time

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Active Session History (ASH)

WAITING

State

db file sequential read qa324jffritcf 213 7:38:26

Event SQL ID Module SID Time

CPU aferv5desfzs5 Get review id 213 7:42:35

WAITING log file sync abngldf95f4de One click 213 7:52:33

WAITING buffer busy wait hk32pekfcbdfr Add to cart 213 7:50:59

Book by author

20


User Session Performance Dimensions

ASH

SQL

PL/SQL

Resources Session

Identifiers

Session Attributes

SQL

SQL ID

Plan Hash

Operation

OpCode

PL/SQL

PL/SQL

Top Level PL/SQL

Resources

Wait Class

Wait Event

Object

Blocking Session

Identifiers

Instance

Services

User Session

Parallel Process

Program

Session Type

Attributes

Consumer Group

Module

Action

Client

Trans. ID


Application users are complaining of poor performance. How do we diagnose the issue and resolve it?

• Several factors can impact SQL Response time – Increased or unusual load on system

– Hardware Issues

– Runaway queries consuming system resources

– Changes in execution plans

– Missing or stale object statistics

• Need a mechanism to quickly analyze in-memory performance data

Case Study 1: Slow Application Response Problem - Correlating AWR & ASH Data


Application Response Time Analysis - AWR

Foreground Wait Class section in AWR to see distribution of DB waits over Waits classes

AWR top 5 section shows the Wait Class which contributes most to DB wait time

1 Wait class application constitutes 55% of DB time

2 Wait event enq TX contributes to this DB time

Objects involved in TX row lock contention can be identified in Segment Statistics section of AWR

3 Identify segments that contribute to this wait


Analyze ASH: Correlate the findings from AWR

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1 Analyze ASH Report for the period of increase of applications waits, it shows the same as AWR

2 Identify the module that suffered from this type of contention from the top service/module section


Identify the Impacted Sessions and SQL’s

3 Identify the top sessions impacted from this type of contention/wait from the Top sessions section

4 Identify the SQL’s associated with this type of

contention/wait from the Top SQL section


Identify the Blockers and the Objects Causing the Contention

5 Identify the blocking sessions associated with this

wait event from the Top Blocking Sessions section

6 Nail down the objects that is subject to this wait

from the Top DB Objects Section

So how do you resolve this issue?


Case Study 2: Understanding I/O Bottle Neck with ASH Analytics

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• Graphical ASH report for advanced analysis

• Provides visual filtering for recursive drill-downs

• Select any time period for analysis

• Analyze performance across many dimensions

• Different visualizations: Stacked chart or Tree Map

• Collaborate with others using Active Reports

Understand the Plan Operation that causes the I/O Identify the Object subject to Full Table Scan Identify SQL’s that are subject to User I/O


• Runs proactively out of the box, reactively when required

• Top-down analysis using Automatic Workload Repository snapshots

• Real-time performance and historic analysis with Automatic Database Diagnostic Monitor

• Resolve performance issues faster with drill-down root-cause analysis

• Classification tree based on Oracle performance tuning expertise

• Performance expert; now a RAC specialist too in Oracle Database 11g

Boost Administrator Productivity

Eliminate Performance Bottlenecks

Lower IT Management Costs

Deliver Higher Quality of Service

Automatic Performance Diagnostics Monitor (ADDM)

Automatic Workload Repository Snapshots

Performance & Management

Advisors

High Load SQL

RAC Issues

IO / CPU Issues

Self-Diagnostic Engine


Proactive Performance Management: ADDM

29


Automatic Performance Diagnostics Continuous Evolution in Database Performance Management

Real-Time ADDM

Compare Period ADDM ADDM

Enhanced Real-Time

ADDM

• Diagnose persistent performance issues

• Uses AWR snapshots

• Regular interval

• Automatic / Manual

• In-depth performance comparison across two periods

• Relies on AWR data

• Manual

• Hung or extremely slow databases

• Uses a normal and diagnostic mode connection

• Manual

• Proactively detect & diagnose transient high-impact problems

• Built inside the DB

• Automatically runs every 3 seconds


Comparative Performance Analysis with AWR Baselines and Reports

• Automatic creation and management of reference baselines

• Compare Period ADDM: Enables performance comparison of two periods with AWR Reports

• Makes analysis of workload variations and performance diagnosis easier

• Moving window baseline created out-of-the-box

• Enables proactive monitoring with adaptive thresholds

• Baseline is key for performance tuning to

– Set alert thresholds

– Monitor performance

– Compare performance variations

time

Performance

Baseline

Actual

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Compare Period ADDM

• Full ADDM analysis across two AWR snapshot periods

• Detects causes, measure effects, then correlates them

– Causes: workload changes, configuration changes

– Effects: regressed SQL, reach resource limits (CPU, I/O, memory, interconnect)

• Makes actionable recommendations along with quantified impact

AWR Snapshot Period 1

AWR Snapshot Period 2

Analysis Report Compare Period ADDM

SQL Commonality

Regressed SQL

I/O Bound

Undersized SGA

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Compare Period ADDM: Method STEP 1: • Identify what changed

• DB configurations, workload changes

STEP 2: • Quantify performance differences

• Uses DB Time as basis for measuring performance

STEP 3: • Identify root cause

• Correlate performance differences with changes

Did the Buffer cache get smaller?

Why is there 10% new SQL?

How come Top SQL impact increased by 45%?

Read I/O are up by 55%, why?

Did a buffer cache reduction cause a read I/O increase?

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Compare Period: Report

34


Enhanced Real-Time

ADDM

Real-Time ADDM—Architecture

• Makes a lightweight connection without acquiring additional locks and resources, bypassing the SQL layer through the agent

• Also attempts to initiate standard JDBC connection

• Data returned by either connection is analyzed by ADDM

EM Agent

JDBC Connection

Diagnostic Connection

Database

ADDM Analysis

Hangs

Latches

Deadlocks


Real-Time ADDM Enhancements Oracle Database 12c

• Automatic real-time problem detection and analysis

• Database self-monitors for serious performance issues

• Recognize bad performance trends and trigger analysis:

• High CPU, I/O spikes, memory, interconnect, hangs, deadlocks

• Identify a problem before it threatens application performance

• Short duration (5 min spikes) ADDM analysis

• Actionable advice for critical issues

• Richer data set available for analysis

• Reports (analysis and data) stored in AWR for historical analysis

• ADDM, SQL Monitoring reports


Triggering Conditions

# Rule Condition

1 High Load Average active sessions greater than 3 times the number of CPU cores

2 I/O bound Impact on active sessions based on single block read performance

3 CPU bound Active sessions greater than 10% of total load and CPU utilization great

than 50%

4 Over-allocated

memory

Allocation over 95% of physical memory

5 Interconnect bound Single block interconnect transfer time based

6 Session Limit Session limit close to 100%

7 Process Limit Process limit close to 100%

8 Hung Session Significant number of hung sessions. If this number is greater than 10%

of total sessions

9 Deadlock Detected Any deadlock detected by hang analyzer


Find: Performance Diagnostics Summary

• Database Time

• AWR

• Enterprise Manager ASH: Transient / Targeted Performance Analysis

• ADDM: Proactive Performance Management and Diagnosis

• The above toolset helps identify the potential problems and recommends appropriate solutions

• For SQL related issues, ADDM recommends SQL Tuning Advisors…

Next Steps: Tune the identified SQL problems

Topics Covered


Tuning


SQL Performance Issues: Broad Categories

Hardware resource crunch

Contention (row lock contention, block contention)

Data fragmentation

Resource & Contention Issues

RESOURCES

Stale/Missing statistics

Incomplete statistics

Improper optimizer configuration

Upgraded Database: new optimizer

Changing statistics

Rapidly changing data

Optimizer

Missing access structures

Poorly written SQL statements

Bind-sensitive SQL with bind peeking (Cursor Sharing)

Literal usage

Application Issues

APPLICATIONS

Not parallelized (no scaling to large data)

Improperly parallelized (partially parallelized, skews)

Rapidly changing data


Automatic SQL Tuning

• SQL Tuning Advisor

• Gives suggestions on the various problems identified during the diagnosis phase

• Uses the same CBO but has more time budget to perform comprehensive analysis

• Identifies alternate execution plans using real-time and historical performance data

• Recommends parallel profile if it will improve SQL performance significantly (2x or more)

SQL Profiling

Statistics Analysis

Access Path Analysis

SQL Restructure Analysis

Alternative Plan Analysis

Parallel Query Analysis

Automatic Tuning Optimizer

Administrator

Comprehensive

SQL Tuning

Recommendations

Gather Missing or Stale Statistics

Create a SQL Profile

Add Missing Access Structures

Modify SQL Constructs

Adopt Alternative

Execution Plan

Create Parallel SQL Profile

SQL Tuning Advisor


What is SQL Profile?

• Contains auxiliary information collected by the ATO for a SQL statement

– Customized optimizer settings

• Based on past execution history (e.g., first_rows vs. all_rows)

– Compensation for missing or stale statistics

– Compensation for errors in optimizer estimates

• Estimation errors occur due to data skews and correlations, complex filters and joins

• Doesn’t require any change to the SQL text

– Ideal for Packaged Apps

• Persistent: Works across shutdowns & upgrades

• Transportable across databases (10.2)

• Force Matching for Literals: This setting may be useful for queries that use only literal values

• SQLTUNE_CATEGORY: Enables you to test a profile in a restricted environment before making it available to other sessions. Can be enforced at session level or system level, this defaults to system level

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SQL Profiling Flow

Optimizer (Tuning Mode)

create submit

SQL Profiling

Optimizer (Normal Mode)

output submit

SQL Profile

SQL Tuning Advisor

Database Users

Well-Tuned

Plan

After …

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SQL Tuning in Oracle Database 10g End-to-End Workflow

Workload

SQL Tuning Candidates

SQL Tuning Advisor

ADDM

AWR

one hour

Generate

Recommendations

DBA

Invoke Advisor

Implement

DBA

A good end-to-end solution, but manual intervention required

Evaluate

Recommendations

DBA

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Automatic SQL Tuning in Oracle Database 11g/12c

• Complete automation of SQL tuning

• Automatically captures high-load SQL

• Automatically tunes SQL without changing application by creating SQL Profiles

• Automatically implements (optional) greatly improved SQL plans

• Automatically reports analysis

• Automatically runs during maintenance window

Packaged Apps

Custom Apps

Automatic SQL Tuning

Auto Capture High-Load SQL

SQL Profiles

Nightly

Well-tuned SQL

Automatic implement

Manually implement

SQL Analysis

Report


Automatic SQL Tuning in Oracle 11g/12c

It’s Automatic!

Choose Candidate SQL

one week

Workload

SQL Tuning Candidates

Test SQL Profiles Implement

SQL Profiles

Generate

Recommendations

AWR DBA

View Reports / Control Process

46


Workload Tuning: SQL Access Advisor

Partitions (11g +)

MV and MV Logs

Bit-map indexes

Automatic Tuning

Optimizer Access Path Analysis

B*-tree indexes

DBA

Recommendations

SQL Access Advisor

Partition Advisor: • Range Partition • Interval Partition • Hash Partition • List Partition

Significant reduction in analysis time for very large workloads (DB12c)

• Provides List Partition recommendations for 12c Database • Analyzes already partitioned tables for further optimization

48


SQL Access Advisor: Partition Advisor Analysis Run Times Oracle Database 11.2 versus 12.1

1

10

100

1000

10000

1 2 3 4 5 6 7 8 9 10

Ru

n T

ime

(se

con

ds)

SQL Workload Size (in thousands)

Partition Advisor analyses large workloads 40x faster than before

DB 11.2

DB 12.1

40X

20X

2X


SQL Access Advisor Features

• Recommends indexes, partitions (hash and interval only) materialized views, and materialized view logs to create and/or drop for faster performance

• Analyzes entire workload and not just independent SQL statements

• Takes into account impact of new access structures on DML operations

• Considers storage, creation and maintenance costs

• Simultaneously considers

– index solutions

– materialized view solutions

– combinations of both

• Optimizes materialized views for

– maximum query rewrite usage

– fast refresh

50


Real-Time SQL Monitoring Looking Inside SQL Execution

• Automatically monitors long running SQL

• Enabled out-of-the-box with no performance impact

• Monitors each SQL execution

• Exposes monitoring statistics – Global execution level

– Plan operation level

– Parallel Execution level

• Guides tuning efforts

• Bind values shown

• SQL level metrics – CPU, I/O requests, throughput, PGA, temp space

• Graphical explain plan

• I/O statistics for each operation

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Real-Time PL/SQL Monitoring

• PL/SQL execution no longer a “black box”

– Answers questions like “why did my DBMS_STATS job take twice as long this time?”

• Shows global (PL/SQL) and SQL level statistics

• Each SQL called by PL/SQL recursively monitored

• Drill-down to slow SQL for diagnosing unexpected PL/SQL behavior

52


Real-Time SQL Monitoring: Case Study 1

My simple query is taking longer than before. What could have gone wrong?

• SQL Performance Problems – Statistics – Resources

– Application issues – Parallelism

– Initialization parameters


Real-Time SQL Monitoring SQL with count and Group by


Real-Time SQL Monitoring PGA Size increased


Real-Time SQL Monitoring: Case Study 2

I enabled parallel query, yet this query is taking so long. How do we approach this problem?

• Parallel Downgrades – Uncontrolled parallel execution – Parallel Server availability

–Object level settings – Session level settings


Real-Time SQL Monitoring Parallel Tab

• Parallel Coordinator busy for the entire duration !!


Real-Time SQL Monitoring Enabled Parallel DML

• Parallel Slaves busy for the entire duration !!!


Real-Time Database Monitoring What’s Really Happening in the Database

Challenge:

• SQL & PL/SQL Monitoring only monitors a single execution

• How does a DBA monitor a composite operation such as a batch job?

Solution:

• Real-Time Database Operations Monitoring

• Benefit: Allows DBAs to look into complex composite DB operations for analysis and tuning purposes


Monitor Complex Database Operations

• Oracle Database 11g: Support for simple DB operations

• SQL statements (e.g., SQL for DSS, batch/report SQL, runaway SQL)

• PL/SQL procedures/functions

• Oracle Database 12c: Support for composite operations

• Session(s) activity between 2 points of time defined by application code / DBA

• For example; SQL*Plus script, batch job, or ETL processing

• At most one DBOP per DB session



65


SPA Motivation

• Businesses need to adapt to changes to stay competitive, compliant and evolve

– SQL performance regressions: #1 cause of poor system performance.

– DB upgrades, schema, optimizer statistics refresh

• Current testing landscape and limitations

– Expensive capture, partial workload, non-production optimizer context, binds

– Large workloads (100Ks SQL stmts are common)

– Manual and time consuming testing and regression tuning

– No end-to-end testing solution

– Test In Production is not too uncommon


Proactively detects ALL SQL regressions, BEFORE actual change is deployed

Integrated comprehensive solution for end-to-end SQL workload testing

66


SPA Overview

• Helps users predict the impact of system changes on SQL workload response time

• Low overhead capture of SQL workload to SQL Tuning Set (STS) on production system

• Build different SQL trials (experiments) of SQL statements performance by test execution

• Analyzes performance differences

• Offers fine-grained performance analysis on individual SQL

• Integrated with STS, SQL Plan Baselines, & SQL Tuning Advisor to form an end-to-end solution

Analysis Report

Compare SQL Performance

SQL plans + stats SQL plans + stats

Pre-change Trial Post-change Trial

SQL Workload

STS


When to use SPA?

• SPA supports testing in Oracle Database Releases 10.2 , 11g and 12c

– Optimizer statistics refresh

– Validate and Implement of tuning recommendations from ATO

– Database parameter changes, schema changes (e.g., add/drop indexes)

– I/O subsystem changes (e.g., ASM, Database Machine)

• SPA handles trials in a manner that does not change database data

– Hence can be used for testing in production/standby environments

• For supported releases, test database upgrades and patch-set releases*

*MOS Note: 560977.1

• Across many releases of Oracle and for upgrades

• On test, standby, and production environments

• Extended to home-grown scripts, third-party testing tools, etc.

• Supports most applications - EBS, SAP, Siebel, home-grown, etc.

SPA Provides Broad Testing Coverage

68


• Rich GUI through Enterprise Manager

• DBMS_SQLPA package PL/SQL API

SPA: Enterprise Manager Interface

69


SPA Report

1

2

3

4

5

70


SPA Report: Regressed SQL Statements

71


SPA Challenges

Production Database

Test Database

DBA

Production System: Easier but…

• Could be resource intensive and impact production performance

• Changes needs to be manually scoped to private session

• Could take a long time to finish

• No resource control by default

Running SPA on:

Test System: Safe but…

• Requires separate HW

• Data in test system should be same as production

• Lengthy, error-prone task


SPA Quick Check

• Optimized for use on prod systems

• Optimal Trial or Explain Plan mode

• Disable multi-executions, full DML execute disabled Optimized

• Per SQL time limits

• Testing scoped to private session

• Associate with Resource Consumer Group Controlled

• Context-aware change testing workflows, such as,

• Optimizer gather statistics

• Init.ora parameter changes

Change-Aware

SPA Quick Check

Production Database

Optimized Controlled/S

coped

Change-Aware

Optimal Trial

Mode, no DML

execute

Pre-selected

STS and

default SPA

settings

Per SQL Time Limits,

Limits testing scope to

private session

Context-aware

change testing

DBA


Identifies subset SQL workload with plan changes first

Test-executes only SQLs with plan

changes

Minimizes use of production resources

dramatically – up to 10x reduction

Multiple executions disabled

No full DML (execute Select

part of workload)

SPA Quick Check Optimized


Per-SQL time limit – protects from runaway SQL

Resource throttling - Associate with Resource Consumer Group

Testing scope limited to private session

SPA Quick Check Controlled


SPA Quick Check Change-aware

Change-aware: Knows what change is being tested

In-line with routine DBA tasks such as statistics gathering, init.ora parameter changes

Intelligently limits impact to private test session


SPA Quick Check


Agenda

79

• Introduction







Find Fix and Validate: An Example

80



Tuning Diagnostics



Diagnostics


Step 1 – The Find • ADDM automatically detects performance bottlenecks

• Database Home Page gives a quick high level overview of current status • Database found to be running high load (Active Sessions) • ADDM has already detected some performance issues (5 findings)


Step 1 – The Find • Average Active Sessions

• For further investigation, look at the DB Perf. Page and “Click on the Big Stuff” • DB Perf. Page shows significant resource being utilized on CPU and User I/O • Clicking on the snapshot icon will take us to the ADDM Home Page for that time


Step 1 – The Find • ADDM Home Page

• ADDM Home Page: Database activity gives quick performance overview • Looking at the ADDM performance analysis, SQL statements are impacting the system

significantly • Let’s drill down on the Top SQL Findings


Step 1 – The Find Automatic Diagnosis and Recommendations

• 91.2% of the impact is from the SQL statements in the report • Performance diagnostics data provided for SQL causing high DB resource usage


Tuning


Step 2 – The Fix SQL Tuning Advisor

• Run the problematic performing statements through Tuning Advisor • We will automatically capture the problematic statement • Top SQL statements are automatically captured in AWR • Seamless integration between Diagnostics and Tuning


Step 2 – The Fix

• Comprehensive analysis and multiple alternatives to improve performance • SQL Profiles can be implemented transparently & non-intrusively to tune SQL statements

Comprehensive Recommendations

• As an example, let’s gather statistics in pending mode…


Step 2 – The Fix Set Publish to False for Scoped Change Testing


Step 2 – The Fix Gather Optimizer Statistics


Validate with SPA Quick Check

92


Step 3 – Validate Optimizer Statistics Refresh Validation

DB Home Page -> Performance -> SPA Link


Step 3 – Validate

Take the Guess Work Out!


Find Fix Validate


Diagnostics Tuning




Summary

96


Conclusion: Find-Fix-Validate Improved Quality Of Service

Manage and Automate

• MORE databases

• MORE risk free administration on production

• MORE effort less testing on production

• MORE applications support: OLTP, DW, COTS

• MORE users, larger databases

• MORE mission-critical applications

Become

• MORE proactive and strategic

• MORE important and valuable!

Get

• MORE uptime

• MORE Quality Of Service

• MORE sleep at night!

• MORE weekends off!

97


Real-World Best Practices & Tips

1


AWR • Use AWR Warehouse for longer retention period:

– Allows long term analysis

– Zero overhead and impact on production

• Global AWR reports:

– Use Global AWR reports for RAC

– Configured using $OH/rdbms/admin/awrgrpt.sql

• Adaptive thresholds:

– The significance level thresholds give users a way to specify alerting in terms of "how unusual" rather than "how much“

• Performance tuning metrics:

– Use DB Time, CPU Time IO time , not just Elapsed time for performance tuning

– DB Time encompasses multiple metrics and provides better insight into the performance of the system

2


Tips: Configuring EM to display Top N Activity

By default, EM is configured to display Top 10 items in the ASH Analytics Page

More than 10 SQL More than 10 sessions

We can change the Top N SQL or sessions to display in Top Activity. Here is how to do it

$emctl set property [–sysman_pwd <sysman_pwd>] -name oracle.sysman.db.ashPurgeSize -value <value>

restart the OMS

Default value is 10 , you can set this to 20 or 30 depending on the memory available for the OMS server. Setting to higher values would require higher memory and hence not recommended.

3


Diagnostic connection

– In this mode, Real-Time ADDM performs a latch-less connection to the database.

– This mode is intended for extreme hang situations when a normal JDBC connection is not possible.

– Precise, actionable recommendations

– Provides cluster-wide analysis for RAC

Real-Time ADDM

4


Use Real-time SQL Monitoring to Understand SQL Execution

SQL Details Active Report

– Shows what’s happening inside SQL execution

– Automatically monitors long running SQL

– Shows the statistics and resources consumed at each step of the plan.

– Shows actual cardinality at each step, helps resolve problems with poor cardinality estimates

Exposes monitoring statistics

– Plan operation level

– Parallel Execution level

– I/O, CPU, memory, network

– Exadata Smart Scans

SQL Monitoring

5


Use Active Reports where available: SQL details, SQL Monitor, SPA , ASH, ADDM

• Composite Active Report

– Single HTML file that embeds a set of correlated active reports

– Allows the user to navigate from one active report to another without connecting to the database

– Examples: Performance Hub, SQL Details, Session Details and SQL Monitor List

• Performance Hub

– Provides a new consolidated view of all performance data for a given time range

– The Performance Hub can also be saved as a Composite Active Report

• Real Time Performance Hub, Historical Performance Hub, Real-time Performance Hub (RAC)

DB 12c update

• In addition to the composite active reports -- there is now also an active report for SQL Tuning and Automatic Real-time ADDM

• The SPA and SQL Monitor Details active reports, which were introduced in 11g, have also been enhanced

SQL Monitoring

6


Tips: How to generate Active report without EM • SQL Details Active Report : Use dbms_perf.report_session() function

• SQL Monitor Active Report: Use dbms_sqltune.report_sql_monitor_list() function

• SQL Performance Analyzer Active Report: Use dbms_sqlpa.report_analysis_task() function

• Performance Hub Active Report: Use dbms_perf.report_perfhub () function

• For example, the following SQL*Plus script shows you how to generate a Real-time PerfHub Active Report: SQL>set pages 0 SQL>linesize 32767 SQL>trimspool on SQL>trim on SQL>long 1000000 SQL>longchunksize 10000000 SQL>spool perfhub_active.html SQL>select dbms_perf.report_perfhub(is_realtime=>1,type=>'active') from dual;

The resulting file perfhub_active.html should be edited to remove the header (first line in the file) and the last line (spool off).

7


SQL Tuning

• Use Automatic Tuning Advisor to tune SQL, one of the most underutilized feature by customers

– Automatically captures high-load SQL

– Automatically tunes SQL without changing application by creating SQL Profiles (If auto implement is enabled)

– Automatically runs during maintenance window

– Set task parameters to 1-2 yrs (default 30) for historical tuning information

• SPA Quick check for easy validation directly on production

– Validate optimizer statistics refresh/gather before publishing

– Validate recommendations from the SQL Tuning Advisor (SQL Profiles, Index recommendations )

8


SQL Profiles Vs SQL Plan Baselines: Use Cases, Pros/Cons

• Use case: Reactive, SQL performance is sub-optimal, invoke SQL Tuning Advisor on high load or business critical SQL

Pros:

• A SQL Profile contains auxiliary information that corrects the cardinality estimated in a sub-optimal Plan.

• Flexible and better approach with volatile data, not locked in by table sizes or existing access structures

• SQL Profiles with Indexes (recommendations) can significantly improve performance

SQL Profiles

• Use case: Proactive, you already know SQL performance is good and would like to seed the known plan to the optimizer

Pros:

• Preserves the known plans, useful in a very predictable environment

Cons:

• In a volatile environment, hard to know a good plan.

• Application SQL should be executed more than once to capture the baseline

• If access structures change or dropped, may not be able to use the required plan

SQL Plan Baselines


SQL Profiles Vs SQL Plan Baselines

• Contains auxiliary information that mitigates the defects in a sub-optimal plan.

• Profiles provide additional information to the optimizer to help select the best plan

• Doesn't constrain the optimizer to any specific plan, which is why they can be shared.

• Are more like extended statistics, better information for the optimizer to decide about how many rows will flow out of a step in the plan.

• Useful when you want the system to adapt immediately to changes like new object statistics.

SQL Profiles

• Consists of a set of accepted plans, a set of existing plans known to be efficient.

• Aides the optimizer to select the best plan from among the accepted plans.

• Constrain the optimizer to only select from a set of accepted plans.

• A preventive mechanism that records and evaluates the execution plans of SQL statements over time.

• Useful if you are more conservative and want to control which plans are used.

SQL Plan Baselines


New Features and Enhancements (DB 12.1.0.2)

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New Features

• AWR Warehouse

• EM Express

• In Memory Advisor

• Adaptive Query Optimization

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AWR Warehouse

3


AWR Warehouse - Business Drivers

• AWR is the de facto performance repository for Oracle databases since 10g

• Default retention period of 8 days prevents diagnosis of long term performance problems (“Compare performance during this quarter’s books close with last quarter’s”)

• Increasing AWR retention period increases storage overhead and cost in critical production environments

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AWR Warehouse - Architecture

• Central warehouse configured for long term AWR data retention

• Historical and ongoing AWR snapshots collected from databases enabled for AWR warehouse

• ETL jobs moves snapshots from source databases into AWR warehouse efficiently using Oracle Data Pump

• Retention period configurable for weeks, months, years or forever (default)

5


AWR Warehouse Features

• Warehouse dashboard tracking ETL jobs

• All AWR features available on long term AWR data

– Performance page

– AWR report

– ASH analytics

– Compare Period ADDM

– Compare Period Report

• Integrated seamlessly into EM UI

• Zero runtime overhead on source Production databases

6


EM Express

8


Oracle Enterprise Manager Database Express 12c Integrated, GUI Management Tool for Database Administration

Integrated Install

• Pre-configured & installed with the database

• Runs inside database

• No extra MW components

• Leverages XDB server inside db for web services

• Supports SI, RAC, SE, EE

Small Footprint

• Disk: 20 MB

• Runtime: zero when idle

• Minimal CPU, memory overhead

• 100% of UI rendering performed in browser

• DB Server only runs SQL

Comprehensive Administration

• Basic admin support for:

• Storage management

• Security management

• Configuration management

• Advanced performance diagnostics and tuning

9


Enterprise Manager Database Express Menus

10


Database Performance Hub

Unified Performance Monitoring

• Single view of DB performance ADDM, SQL Tuning, Real-Time SQL

Monitoring, ASH Analytics

• Switch between ASH analytics, workload view, ADDM findings and SQL monitoring seamlessly

• Supports both real-time & historical mode

• Dedicated tab for RAC

• Historical view of SQL Monitoring reports

11


Oracle Database In-Memory Central

12


In-Memory Central


Oracle Database In-Memory Advisor

14


Oracle Database In-Memory Advisor: Challenges

• Optimizing database In-Memory Column Store configuration for maximum performance

• Actionable recommendations

–Workload based cost/benefit analysis

– In-memory area population plan

15


Features

• Assist with IM Column Store size selection

• Recommend tables, partitions and sub-partitions for a given IM Column Store size

• Use workload and performance data to prioritize objects

• Take into account differences in disk and memory footprint, as well as compression ratios

16


Features

• Recommendation based on cost/benefit calculations

– Cost: Estimated memory size with various compression options

– Benefit: Estimated DB time reduction of processing analytic workload

• Report

– Vary IM Column Store size to receive specific loading plan

– Generate DDL script with all the tables/partitions/sub-partitions recommended

– Top SQL benefits from given configuration

17


Future

• Recommend reconfiguration of existing In-Memory Column Store

– Suggest objects to evict

• RAC advice

– In-Memory Column Store configuration can vary by instance

• Column specific recommendations

– Recommend specific columns to be loaded in memory instead of the entire object. Useful for wide tables.

18


Adaptive Query Optimization

21


Adaptive Query Optimization

Set of capabilities that enable optimizer to • Make run time adjustments execution plans

• Discover additional information that can lead to better statistics

22


Adaptive Plan Types

• Join : Changes join method from Nested Loops to Hash Join, and vice versa, based on cardinality.

• Parallel Distribution : Changes data distribution method among parallel processes from Hybrid Hash to Broadcast, based on cardinality.

23


Adaptive Statistics – Dynamic Statistics

• When OPTIMIZER_DYNAMIC_SAMPLING parameter is set to 11, statistics are gathered before statement execution and cached as dynamic statistics.

• Even when enabled, factors like predicate complexity, existing base statistics and total execution time expected determines whether to gather dynamics statistics or not.

24


Adaptive Statistics – Automatic Reoptimization

In contrast to adaptive plans, automatic reoptimization changes a plan on subsequent executions after the initial execution. • Statistics feedback : When SQL statement is executed (with statistics feedback monitoring enabled) for the first time, optimizer compares its original cardinality estimates to the actual cardinalities observed during execution and, if estimates differ significantly from actual cardinalities, it stores the correct estimates for subsequent use. After the first execution, the optimizer disables monitoring for statistics feedback.

25


Adaptive Statistics – Automatic Reoptimization

• Performance feedback : The parallel degree chosen by the optimizer is compared to the parallel degree computed base on the actual performance statistics (e.g. CPU-time) gathered during the initial execution of the statement. If the two values vary significantly then the statement is marked for reoptimization and the initial execution performance statistics are stored as feedback to help compute a more appropriate degree of parallelism for subsequent executions.

26


Adaptive Statistics – SQL Plan Directives

• SQL plan directives, created automatically as a result of Automatic Reoptimzation, can direct the optimizer to use dynamic statistics to obtain more accurate cardinality estimate.

• Created whenever there is a mismatch between optimizer's cardinality estimates and the actual number of rows returned after execution.

• Created on query expressions, not at statement or object level.

27


Resources


References 2 Day DBA : http://docs.oracle.com/cd/E16655_01/server.121/e17643/toc.htm 2 Day + Performance Tuning Guide: http://docs.oracle.com/cd/E16655_01/server.121/e17635/toc.htm Oracle Database 12c - EM Express Performance Hub: http://www.oracle.com/technetwork/database/manageability/emx-perfhub-1970118.html Oracle Database 12c - EM Express Active Reports: http://www.oracle.com/technetwork/database/manageability/emx-activerep-1970119.html SQL Performance Analyzer Active Report: http://www.oracle.com/technetwork/database/manageability/sqlpa-094776.html Database Manageability and Real Application Testing: http://www.oracle.com/technetwork/database/manageability/index.html

http://docs.oracle.com/cd/E16655_01/server.121/e17643/toc.htm




http://www.oracle.com/technetwork/database/manageability/emx-perfhub-1970118.html





http://www.oracle.com/technetwork/database/manageability/emx-activerep-1970119.html





http://www.oracle.com/technetwork/database/manageability/sqlpa-094776.html



http://www.oracle.com/technetwork/database/manageability/index.html

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