Extreme Database Management New Features for Power DBAs

of DBAs admit doing nothing to address performance issues

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

DATA GROWTH

33% of DBAs handle close to 100

database instances each—with

data stores expanding by more

than 20% per year

5

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!”

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

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

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?

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?

Active Session History (ASH) Analytics

• Advanced analysis with graphical ASH

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

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

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

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

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

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 Fusion Applications

• 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

Test Production

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

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

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

27 Copyright © 2011, Oracle and/or its affi l iates. 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

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

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

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)

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

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

35 Copyright © 2011, Oracle and/or its affi l iates. 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

36 Copyright © 2011, Oracle and/or its affi l iates. 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

37 Copyright © 2011, Oracle and/or its affi l iates. 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

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

“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

Metering and Chargeback

Charge User Discover & 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

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

Memory Usage * 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

Network IO Usage avg