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1. Extreme Database Management
New Features for Power DBAs

2. of DBAs admit doing
nothing to address
performance issues

3. 3
Over 50% of DBAs avoid making
changes to production because of
negatively impacting performance
90% experienced unplanned
downtime resulting from Database
changes NOT properly tested
CHANGE—AVOID OR EMBRACE

4. 4
DATA GROWTH
33% of DBAs handle close to 100
database instances each—with
data stores expanding by more
than 20% per year

5. 5

7. Unresponsive Database Problem
• How do I diagnose a slow or hung database?
– If the database is unresponsive, I can’t even
connect!
– Even if I can connect, I need a diagnosis quickly!
• Should I just bounce the database?
– All in-flight operations will be aborted, mid-tier
connections/states will be lost
– All diagnostic information will be lost
– “If I could only know which blocking session to kill!”

8. Real-Time ADDM
• Real-time analysis of hung or slow database systems
• Holistically identify global resource contentions and deadlocks
• Quantified performance impact
• Precise, actionable recommendations
• Provide cluster-wide analysis for RAC

9. 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 Real-Time ADDM
EM Agent
JDBC Connection
Diagnostic Connection
ADDM
Analysis
Database
Resource
Constraints
Hangs
Memory Issues
Resource Limits
Reached
Deadlocks
Top Issues Identified
by Real-Time ADDM

10. Comparative Performance Analysis
• Performance yesterday was good, today is
terrible, what happened?
• What changes were made?
• Is someone running a new batch job?
• RAC instance 1 is running much faster than
instance 2, what’s the difference?
• Is there a workload skew?
• Did someone make configuration changes?

12. 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?

13. Active Session History (ASH) Analytics
• Advanced analysis with graphicalASH
report
• 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

14. ASH Analytics Performance Dimensions
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
ASH
SQL
PL/SQL
Resource
Consump-
tion
Session
Identifiers
Session
Attributes

16. Advise
Act
Audit Core Analyze
Database Lifecycle Management
• Discover
• Hosts & Applications
• Dependencies and Relationships
• Inventory
• Collect
• Deep configuration data
• Parsed Configuration Files
• Patches installed• Real-Time Monitoring – Who/When
• Compliance Score
• Best Practices
• Oracle Recommendations
• Regulatory ( PCI,Cobit)
• Report
• Inventory &Trend
• Automatic Change Reconciliation
• Authorization vs Unauthorized
• Patch Advisories via MOS
• Upgrade Advisories from MOS
• Configuration Policy Violations
• Change/Patch Plans
• Mass deployment
• Schema Synchronization
• Settings, Drift & Policy Actions
• Configuration Changes
• Topology guided Impact Analysis
• Config Comparison for Drift Analysis
• To Gold & Baseline
• 1 to 1, 1 to N
• Target and System
• DB Change Management
• Data Comparison
• Change Plans
• Patch Conflict and PreReq Analysis

17. Capture Clone Mask Test
NAME SSN SALARY
AGUILAR 203-33-3234 40,000
BENSON 323-22-2943 60,000
NAME SSN SALARY
SMITH 111-22-3333 60,000
MILLER 112-23-4567 40,000
DB Replay capture files
SQL Tuning Set (STS)
AWR snapshots
Clone prod system
Consistent masking
across tables, capture
files, STS and AWR
snapshots
Securely
replay
workload &
STS
Production Stage Test
Secure Database Testing
Real Application Testing Integration with Data Masking

18. Test Data Management
Challenges
Error-prone,
manual process
Producing
relationally intact
subset is hard
but necessary
Cannot use
sensitive data in
test without
obfuscation
Full production
copies for test
systems not
cost effective

19. Data Modeling & Discovery
• Application Data Model (ADM)
– Scans application schemas to model relationships between tables and columns
– Extracts data relationships from Oracle Applications meta-data
– Stores referential relationships stored in repository
– Enables Data Masking & Subsetting operations to be application aware
• Sensitive Data Discovery & Data Masking
– Pattern-based sensitive data scanning
– Import from pre-built mask templates
– Pre-built Data Masking templates for Oracle applications
• Oracle eBusiness Suite
• Oracle FusionApplications

20. • Automatic data extraction rules from Application Data Model
• Understand different table types: transactional vs. reference vs. temp tables
• Subset by percentage of tables or columns (e.g. regions, year) at runtime
• Estimate subset size before execution
• Subset 100 GB  20 GB in 12 minutes using export method
Data Subsetting
Define new
Application
Data Model
Create
Data
Subset
Definition
Extract
Data
Subset
Export /
Import
In-Place
Delete
TestProduction

21. Database Upgrade Automation
Plan
• Detect new DB versions in My Oracle Support
• Suggest best upgrade path for patch compatibility
• In-context reference to Upgrade documentation
Analyze
• Check DB for upgradeability (space, version, etc.)
• Support upgrade from 10.2.0.4+ to 11.2
Deploy
• Mass deploy binaries to targets and create out-of-
place copies
• Upgrade process can be paused/resumed
Switch
• Switch instances to new installations
• Easy switchback if needed

23. Consolidation Planner
Determines candidates for consolidation
• Identifies under and over-utilized server
• Identifies ideal placements
• Works for physical and virtual environments
Benefits
• Maximizes server density
• Minimizes resource contention
• Maintains performance commitment
• Satisfies business, compliance, and technical constraints

24. Consolidation Planner
• Leverages resource utilization and
configuration data from Enterprise
Manager repository
– CPU, memory, storage, network
– Over a representative period
Administrator specifies servers and
constraints for workload migration
– Physical/virtual servers
– Existing/planned servers
– Business/technical constraints
Detailed analysis on different scenarios
of consolidated workloads

25. 27 Copyright © 2011, Oracle and/or itsaffiliates. All rights
reserved.
Oracle Exadata
Optimized for Database Consolidation
Exadata serves as cloud for databases
Large memory enables many
databases to be consolidated
Extreme performance for complex
workloads that mix OLTP, DW, batch,
reporting
I/O and CPU resource management
isolates workloads

26. Exadata Management
Integrated Management of Hardware and Software
• Hardware view
• Schematic of Storage cells, Database
Servers (compute nodes) & switches
• Hardware components alerts
• Software/system view
• Performance, availability, usage by
databases, services, clusters
• Software alerts from db, cluster, ASM
• Topology view of DB systems/clusters
• Configuration view
• Version summary of all components
along with patch recommendations

27. Storage Cell Performance
• Drill down from
database performance
page
• Composite cell health
indicators
• Helps triage
• Load imbalance
• ASM problems
• Cell configuration
issues
• Cell software or
hardware failures
• Network failures

28. Storage Cell Management
• Storage Cell monitoring and administration support
– Cell Home page and performance pages
– Actions supported: Start/stop Cell, verify connectivity, setup SSH, execute Cellcli on cells
– Setup IORM for database targets
• Management by Cell Group
– All cells used by a database automatically placed in a group
– Cell Group level administration operations (batch job monitoring)

29. Infiniband Network Monitoring
• Infiniband network and switches as EM
targets
• Network home page and performance page –
real time and historical
• Network topology view
• Perform admin tasks such as enable/disable
port, clear performance/error counters
Full monitoring
• Alerts (switch generated and EM
generated)
• Performance metrics
• Configuration metrics – detect and
notify configuration changes/best
practice violations

32. Database Cloud Setup and Resource Monitoring
• Manage zones and underlying
resources (databases, Server
Pools, VMs)
– Track resource flux, tenants,
policy violations, etc
– Drill down into individual
resources for deeper
monitoring
• Monitor requests and failure
rates and identify potential
bottlenecks to remediate

33. 35 Copyright © 2011, Oracle and/or itsaffiliates. All rights
reserved.
Database User Privileges
Challenge
Self Service works over shared
resources
SYS and SYSTEM are too powerful to
be shared with SSA user
SSA user should not be able to
change init parameters
Solution
Create a master account with limited
privileges for the SSA user
SSA user provides username and
password
This master account will be the admin
account for the database
Master Account Privileges
Grant DBA role and revoke the following privileges:
• Alter database
• Alter system
• Create any directory
• Drop any directory
• Grant any object
privilege
• Grant any privilege
• Grant any role

34. 36 Copyright © 2011, Oracle and/or itsaffiliates. All rights
reserved.
Locked values shown in
read only mode
Capturing Provisioning Profiles
• Capture database configuration via Provisioning Profiles
• Lock and Save deployment procedures by using values from profile or by overriding them
Database Provisioning Procedures

35. 37 Copyright © 2011, Oracle and/or itsaffiliates. All rights
reserved.
Catalog of Service Templates
09/05/2011
• Publish saved deployment procedures as service templates
• Build a large service catalog by changing database versions, configuration, and other params

36. Self-Service DB Provisioning by End User
Out-of-box console; no additional
set up required
Supports custom background
Rich service catalog:
Database service
OVM Templates and Assemblies
Java applications
Additional capabilities:
Backup and Restore VM/Database
Basic resource monitoring
Chargeback information
Quota monitoring
Cloud APIs
RESTFul APIs and CLIs ideal for
Cloud integrators

37. “British Telecom uses Oracle Enterprise Manager to provide database-
as-a-service and middleware-as cloud service offerings. We can now
deploy a database in 20 minutes whereas in the past it would have
taken us a couple of weeks. The business reaps the benefit in decreased
costs of hardware, being agile and being able to deliver services quickly
to market. BT are excited about the new features in Oracle Enterprise
Manager 12c such as customer self-service, templated provisioning,
agentless discovery, metering and chargeback—which we expect to
further help cut costs."
Surren Partabh
CTO Core Technologies
British Telecom
Source: BT Deploys Oracle Database as a Service, cutting provisioning time from weeks to minutes
http://streaming.oracle.com/ebn/podcasts/media/10957726_BT_110911.mp3

38. Metering and Chargeback
Charge UserDiscover & Plan Track Usage
Benefits
Align IT with business goals
Drive better decision making
Plan for IT budget requirements
Quicker return on IT investment
Key Features
Resource usage metering
Historical usage trends
Cost allocation and charge plan evaluation
Reporting for cloud self-service application

39. Database Metering and Chargeback Metrics
Metric Metric Type Aggregation
Dedicated
Base Charge Fixed sum
Backup Charge Fixed sum
CPU Utilization(SPECint®_rate_base2006) Usage avg
CPU Utilization(%)* Usage avg
Edition Config n/a
Version Config n/a
Storage Usage * Config avg
MemoryUsage * Config avg
Option Config n/a
Shared
(by Service)
Base Charge Fixed sum
DB Time Usage sum
CPU Time Usage sum
CPU Utilization(%) Usage avg
CPU Utilization(SPECint®_rate_base2006) Usage avg
SQL Executes Usage sum
User Transactions Usage sum
Disk Read Bytes (Physical) Usage sum
Disk Write Bytes (Physical) Usage sum
NetworkIO Usage avg