Exadata and Database Machine Administration Workshop

Traditional Enterprise Database Storage Deployment 2-3 Exadata Storage Deployment 2-4 Exadata Implementation Architecture Overview 2-6 Introducing Exadata 2-7 Exadata Hardware Details Su

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Exadata and Database Machine Administration Workshop

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Robert Pastijn Marshall Presser Georg Schmidt Akshay Shah Kam Shergill Tim Shelter Eric Siglin Sundararaman Sridharan Vijay Sridharan

Mahesh Subramaniam Lawrence To

Alex Tsukerman Kodi Umamageswaran Douglas Utzig

Harald van Breederode Mark Van de Wiel Dave Winter

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Traditional Enterprise Database Storage Deployment 2-3

Exadata Storage Deployment 2-4

Exadata Implementation Architecture Overview 2-6

Introducing Exadata 2-7

Exadata Hardware Details (Sun Fire X4270 M2) 2-8

Exadata Specifications 2-9

InfiniBand Network 2-10

Classic Database I/O and SQL Processing Model 2-11

Exadata Smart Scan Model 2-12

Exadata Smart Storage Capabilities 2-13

Exadata Smart Scan Scale-Out Example 2-16

Exadata Hybrid Columnar Compression 2-19

Exadata Hybrid Columnar Compression Architecture Overview 2-20

Exadata Smart Flash Cache 2-21

Exadata Storage Index 2-23

Storage Index with Partitions Example 2-25

Database File System 2-26

I/O Resource Management 2-27

Benefits Multiply 2-28

Exadata Key Benefits for Data Warehousing 2-29

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Additional Resources 2-35

Practice 2 Overview: Introducing Exadata Features 2-36

3 Exadata Architecture

Objectives 3-2

Exadata Software Architecture Overview 3-3

Exadata Software Architecture Details 3-5

Exadata Smart Flash Cache Architecture 3-7

Exadata Monitoring Architecture 3-9

Disk Storage Entities and Relationships 3-10

Interleaved Grid Disks 3-12

Flash Storage Entities and Relationships 3-13

Disk Group Configuration 3-14

Exadata Installation and Configuration Overview 4-3

Initial Network Preparation 4-4

Configuration of New Exadata Servers 4-6

Answering Questions During the Initial Boot Sequence 4-7

Exadata Administrative User Accounts 4-11

Configuring a New Exadata Cell 4-12

Important I/O Metrics for Oracle Databases 4-13

Testing Performance Using CALIBRATE 4-14

Configuring the Exadata Cell Server Software 4-15

Creating Cell Disks 4-16

Creating Grid Disks 4-17

Creating Flash-Based Grid Disks 4-18

Configuring Hosts to Access Exadata Cells 4-19

Configuring ASM and Database Instances for Exadata 4-20

Configuring ASM Disk Groups for Exadata 4-21

Optional Configuration Tasks 4-22

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Practice 4 Overview: Configuring Exadata 4-31

5 Exadata Performance Monitoring and Maintenance

Objectives 5-2

Monitoring Overview 5-3

Exadata Metrics and Alerts Architecture 5-4

Monitoring Exadata with Metrics 5-6

Monitoring Exadata with Metrics: Example 5-8

Monitoring Exadata with Alerts 5-9

Displaying Alert Examples 5-11

Monitoring Exadata with Active Requests 5-13

Monitoring SQL Execution Plans 5-14

Smart Scan Execution Plan Example 5-15

Predicate Offloading Considerations 5-16

Monitoring Exadata from Your Database 5-17

Monitoring Exadata with Wait Events 5-18

Monitoring Exadata with Enterprise Manager 5-19

Additional Monitoring Tools and Utilities 5-20

Cell Maintenance Overview 5-21

Automated Cell Maintenance Operations 5-23

Replacing a Damaged Physical Disk 5-24

Replacing a Damaged Flash Card 5-26

Moving All Disks from One Cell to Another 5-27

Using the Exadata Software Rescue Procedure 5-28

Quiz 5-30

Summary 5-32

Practice 5 Overview: Monitoring Exadata 5-34

6 Exadata and I/O Resource Management

Objectives 6-2

I/O Resource Management Overview 6-3

I/O Resource Management Concepts 6-5

I/O Resource Management Plans 6-6

IORM Architecture 6-7

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Category Plan Example 6-16

ASM Allocation Unit Size 7-9

Minimum Extent Size 7-10

Quiz 7-11

Summary 7-13

Practice 7 Overview: Optimizing Database Performance with Exadata 7-15

8 Database Machine Overview and Architecture

Objectives 8-2

Introducing Database Machine 8-3

Database Machine X2-2 Full Rack 8-4

X2-2 Database Server Hardware Details (Sun Fire X4170 M2) 8-5

Start Small and Grow 8-6

Database Machine X2-8 Full Rack 8-7

X2-8 Database Server Hardware Details (Sun Fire X4800) 8-8

Database Machine Capacity 8-9

Database Machine Performance 8-10

Database Machine X2-2 Architecture 8-11

InfiniBand Network Architecture 8-13

X2-2 Leaf Switch Topology 8-14

Full Rack Spine and Leaf Topology 8-15

Scale Performance and Capacity 8-16

Scaling Out to Multiple Full Racks 8-17

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9 Database Machine Configuration

Objectives 9-2

Database Machine Implementation Overview 9-3

Configuration Worksheet Overview 9-5

Getting Started 9-6

Configuration Worksheet Example 9-7

Configuring ASM Disk Groups with Configuration Worksheet 9-11

Generating the Configuration Files 9-13

Other Pre-Installation Tasks 9-14

The Result After Installation and Configuration 9-15

Supported Additional Configuration Activities 9-17

Unsupported Configuration Activities 9-18

Migration Best Practices Overview 10-3

Performing Capacity Planning 10-4

Database Machine Migration Considerations 10-5

Choosing the Right Migration Path 10-6

Logical Migration Approaches 10-7

Physical Migration Approaches 10-9

Bulk Data Loading Overview 11-3

Preparing the Data Files 11-4

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Quiz 11-13

Summary 11-15

Practice 11 Overview: Bulk Data Loading with Database Machine 11-17

12 Backup and Recovery with Database Machine

Objectives 12-2

Backup and Recovery Overview 12-3

Using RMAN with Database Machine 12-4

General Recommendations for RMAN 12-5

Disk Based Backup Strategy 12-7

Disk Based Backup Configuration 12-8

Tape Based Backup Strategy 12-10

Tape Based Backup Configuration 12-11

Hybrid Backup Strategy 12-15

Restore and Recovery Recommendations 12-16

Backup and Recovery of Database Machine Software 12-17

Quiz 12-18

Summary 12-20

Practice 12 Overview: Using RMAN Optimizations for Database Machine 12-22

13 Monitoring and Maintaining Database Machine

InfiniBand Diagnostic Utilities 13-9

Database Machine Support Overview 13-11

Patching and Updating Overview 13-12

Maintaining Exadata Software 13-13

Maintaining Database Server Software 13-14

Maintaining Other Software 13-15

Quiz 13-16

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A New Features in Update Release 11.2.1.3.1

Objectives A-2

New Features Overview A-3

Auto Service Request (ASR) A-4

The ASR Process A-5

ASR Requirements A-6

Oracle Linux 5.5 A-7

Enhanced Operating System Security A-8

Pro-active Disk Quarantine A-9

Other New Features A-10

Summary A-11

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Oracle Internal & Or

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I t d ti Introduction

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Course Objectives

After completing this seminar, you should be able to:

Machine

• Identify the benefits of using Database Machine for Identify the benefits of using Database Machine for

different application classes

• Describe the architecture of Database Machine and its

integration with Oracle Database, Clusterware and ASM

• Complete the initial configuration of Database Machine

to Database Machine

performance

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Audience and Prerequisites

• This course is primarily designed for administrators who

will configure and administer Oracle Exadata Database

Machine.

• Prior knowledge and understanding of the following is g g g

assumed:

– Linux and general network, storage and system administration concepts.

– Oracle Database 11g: Administration Workshop I

– Oracle Database 11g: Administration Workshop II

– Oracle 11g: RAC and Grid Infrastructure Administration

– Oracle Linux: Linux Fundamentals

Audience and Prerequisites

This seminar is primarily designed for administrators who will configure and administer Oracle Exadata Database Machine

Exadata Database Machine

Please be mindful of the prerequisites because this course does not teach all aspects of the

technologies used inside Database Machine Rather it focuses on topics that are specific to

Exadata and Database Machine

Prior knowledge and understanding of Oracle Database 11g Release 2, including Automatic Storage Management (ASM) and Real Application Clusters (RAC), is assumed In addition, a working knowledge of Linux is assumed along with an understand of general networking, g g g g g

storage and system administration concepts

For students that do not meet these prerequisites, the recommended prior training includes

the following courses:

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Course Scope

— This section focuses on the architecture and key capabilities of Exadata along with how to configure, monitor and optimize it.

— This section introduces students to Database Machine.

— The installation and configuration process is covered so that students can make appropriate configuration decisions

— Students also learn how to maintain, monitor and optimize Database Machine after initial configuration

detailed hardware installation and maintenance is outside

the scope of this course.

Course Scope

This course covers two main subject areas:

The first section introduces students to Exadata Storage Server X2 2 (formerly known

• The first section introduces students to Exadata Storage Server X2-2 (formerly known

as Exadata Storage Server Version 2) Students learn about the architecture and key

capabilities of Exadata along with how to configure, monitor and optimize it

• The second section introduces students to Oracle Exadata Database Machine Students learn about the various Database Machine configurations The installation and

configuration process is covered so that students are equipped to make appropriate

up-front configuration decisions They also learn how to maintain, monitor and optimize

Database Machine after initial configuration Students are introduced to various options for migrating to Database Machine and learn how to select the best approach

Although the hardware components of Database Machine are introduced and described to

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5 Exadata Monitoring and Maintenance

6 Exadata and I/O Resource Management

7 Optimizing Database Performance with Exadata

8 Database Machine Overview and Architecture

9 Database Machine Configuration

10 Migrating Databases to Database Machine

11 Bulk Data Loading with Database Machine

12 Backup and Recovery with Database Machine

13 Database Machine Monitoring and Maintenance

Course Contents

The slide shows the ordering of lessons in this course

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However at times there are specific references to Exadata

However, at times there are specific references to Exadata hardware or Exadata software.

– Unless otherwise indicated, Exadata X2-2 (formerly known

as Exadata Version 2) is implied throughout the course

Exadata X2-2 is based on Sun hardware and is the only version of Exadata supported in Oracle Exadata Database Machine

Machine.

• Unless otherwise indicated, ‘Database Machine’ refers to

‘Oracle Exadata Database Machine’.

– Typically, Database Machine refers to the entire system including both hardware and software.

Terminology

The slide indicates the conventions used throughout this course to abbreviate the formal

product names for Exadata Storage Server and Oracle Exadata Database Machine

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Additional Resources

– http://www.oracle.com/technetwork/tutorials/index.html

– Enter the Oracle Learning Library and conduct a search for

content in the Database Machine functional category Look g y

out for demonstrations with Exadata and Database Machine

Version 2 Series in the title.

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Practice 1 Overview:

Introducing the Laboratory Environment

In this practice you will be introduced to the laboratory

environment used to support all the practices during this

course.

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E d t O i Exadata Overview

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After completing this lesson, you should be able to:

• Contrast the Exadata storage architecture with traditional

shared storage offerings

• Outline the capabilities of Exadata

to traditional storage servers

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Traditional Enterprise Database Storage

Deployment

Database Servers

Storage Arrays

Traditional Enterprise Database Storage Deployment

The graphic in the slide illustrates the traditional deployment approach for multiple databases Each database has an isolated allocation of storage resources and its bandwidth is limited by

Each database has an isolated allocation of storage resources and its bandwidth is limited by the hardware allocated to it The isolation and dedication of hardware resources to individual

databases can simultaneously lead to unused space and unused input/output (I/O) bandwidth for some databases, and overcommitted bandwidth with insufficient free space in others The right balance is almost never achieved because real-world workloads are very dynamic

Large storage arrays are used today for many enterprise database deployments These large storage arrays must be partitioned and have their bandwidth and space allocated across the

databases and applications sharing the storage array Because these storage arrays house

vast quantities of mission-critical data, they must be highly engineered, and consequentially

very expensive, to deliver high levels of reliability and availability Enterprise-class storage

arrays are not only costly to procure, they also require highly specialized skills to manage and

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Exadata Storage Deployment

Oracle Database 11g Servers

I/O Resource Management

Smart

storage

operations

High performance storage network

Storage consolidation

(Transparent to databases)

storage network

Data compression

Exadata Storage Deployment

The graphic in the slide illustrates the general deployment approach with Exadata

You can use Exadata to consolidate your storage environment Using Exadata multiple

p

• You can use Exadata to consolidate your storage environment Using Exadata, multiple databases can use storage from a single pool Exadata uses Oracle Automatic Storage Management (ASM) to evenly distribute the storage load for every database across

every available disk in the storage pool Every database can use all the available disks

to maximize performance Exadata requires the use of Oracle Database 11g Release 2

Exadata works equally well with single-instance or Oracle Real Application Clusters

(RAC) databases Users and database administrators use the same tools and

knowledge they are already familiar with Being based on industry-standard components and technologies, Exadata is inexpensive to deploy In addition, tight integration with the full suite of Oracle Database high-availability features, ensures that the reliability and

integrity needs of mission-critical environments are met

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Exadata Storage Deployment (continued)

In addition to Smart Scan, Exadata has other smart storage capabilities including the

ability to offload incremental backup optimizations, file creation operations, and more This approach yields substantial CPU memory and I/O bandwidth savings in the database

approach yields substantial CPU, memory, and I/O bandwidth savings in the database

server resulting in potentially massive performance improvements

• Exadata includes Exadata Hybrid Columnar Compression This feature provides very high levels of data compression implemented inside Exadata Exadata Hybrid Columnar

Compression allows the database to reduce the number of I/Os required to scan a table For example, for data with a compression ratio of 10 to 1, the I/Os required to scan the

data are reduced from 10 to 1 as well

• Exadata ensures that I/O resources are made available whenever, and to whichever,

database needs them based on priorities and policies that you can define The Database Resource Manager (DBRM) and Exadata I/O Resource Management (IORM) work

together to manage intradatabase and interdatabase I/O resource usage to ensure that

your defined service-level agreements (SLAs) are met when multiple applications and

databases share Exadata storage

• Finally, even for queries that do not use Smart Scan, Exadata has many advantages overFinally, even for queries that do not use Smart Scan, Exadata has many advantages over conventional storage Exadata is highly optimized for fast processing of large queries It has been carefully architected to ensure no bottlenecks in the controller or in other

components inside the storage server It makes intelligent use of high-performance flash memory to boost performance and also uses a state-of-the-art InfiniBand network that has much higher throughput than conventional storage networks

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Exadata Implementation Architecture Overview

Oracle Database 11g Servers

… Disk

Exadata software

Exadata Implementation Architecture Overview

Exadata is a self-contained storage platform that houses disk storage and runs the Exadata

Storage Server Software provided by Oracle A single Exadata server is also called a cell A

cell is the building block for a storage grid More cells provide greater capacity and I/O

bandwidth Databases are typically deployed across multiple cells, and multiple databases

can share a single cell The databases and cells communicate with each other via a

high-performance InfiniBand network

Each cell is a purely dedicated storage platform for Oracle Database files although you can

use Database File System (DBFS), a feature of Oracle Database, to store your business files

i id th d t b

inside the database

Like other storage arrays, each cell is a computer with CPUs, memory, a bus, disks, network

adapters, and the other components normally found in a server It also runs an operating

system (OS), which in the case of Exadata is Linux The Oracle-provided software resident in

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Introducing Exadata

Database – Up to 1.8 GB/sec raw data bandwidth – Up to 75,000 I/Os per second using flash

Exadata is highly optimized for use with Oracle Database Exadata delivers outstanding I/O

and SQL processing performance for data warehousing and online transaction processing

(OLTP) applications

Exadata is based on a 64 bit Intel-based Sun Fire server Oracle provides the storage server

software to impart database intelligence to the storage, and tight integration with Oracle

Database and its features Each cell is shipped with all the hardware and software

components preinstalled including the Exadata Storage Server Software, Oracle Linux

x86_64 operating system and InfiniBand protocol drivers

Since March 2010, Exadata is no longer offered as a standalone storage product Now

Exadata is only available for use in conjunction with Database Machine Individual Exadata

servers can still be purchased, however they must be connected to Database Machine

Custom configurations using Exadata are no longer supported for new installations

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Exadata Hardware Details

Local Disks 12 x 600 GB 15K RPM High Performance SAS

or 12 x 2 TB 7.2K RPM High Capacity SAS

Flash 4 x 96 GB Sun Flash Accelerator F20 PCIe Cards

Disk Controller Disk controller HBA with 512 MB battery backed cache

Network Two InfiniBand 4X QDR (40Gb/s) ports

(1 dual-port PCIe 2.0 HCA)Four embedded Gigabit Ethernet ports

Remote Management 1 Ethernet port (ILOM)

Power Supplies 2 redundant hot-swappable power supplies

Exadata Hardware Details (Sun Fire X4270 M2)

The slide shows a description of the Exadata Storage Server hardware

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Exadata Specifications

1 - Raw capacity calculated using 1 GB = 1000 x 1000 x 1000 bytes and 1 TB = 1000 x 1000 x 1000 x 1000 bytes.

2 - User Data: Actual space for uncompressed end-user data, computed after single mirroring (ASM normal redundancy)

and after allowing space for database structures such as temporary space, logs, undo space, and indexes Actual user data

capacity varies by application User Data capacity calculated using 1 TB = 1024 * 1024 * 1024 * 1024 bytes.

Exadata Specifications

Exadata is available in two configurations: with high performance (HP) disks or with high

capacity (HC) disks The table in the slide lists the key capacity and performance

specifications for both configuration options

Note: MBPS stands for megabytes per second, IOPS stands for I/Os per second.

Note: These metrics do not take into account compression With compressed data, you can

achieve much higher effective throughput rates In all cases, actual performance will vary by

application

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InfiniBand Network

InfiniBand:

– Provides highest performance available – 40 Gb/sec each direction

– Is widely used in high-performance computing since 2002

• Looks like normal Ethernet to host software: oo s e o a e e o os so a e

– All IP-based tools work transparently – TCP/IP, UDP, HTTP, SSH, and so on

• Has the efficiency of a SAN:

– Zero copy and buffer reservation capabilities

– Less configuration lower cost higher performance

– Less configuration, lower cost, higher performance

– Zero-copy, zero-loss Datagram protocol

– Open Source software developed by Oracle

– Very low CPU overhead

various network layers Buffer reservation is used so that the hardware knows exactly where

to place buffers ahead of time These are two important characteristics that distinguish

InfiniBand from normal Ethernet

InfiniBand is also supported as a unified network fabric for Exadata and the Oracle RAC

interconnect This facilitates easier configuration and fewer cables and switches You can

also use it for high-performance external connectivity, such as to connect backup servers or

ETL servers

On top of InfiniBand, Exadata uses the Zero Data loss UDP (ZDP) protocol ZDP is open

source software that is developed by Oracle It is like UDP but more reliable Its full technical

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Classic Database I/O and SQL Processing Model

With traditional storage, all the database intelligence resides in the software on the database server To illustrate how SQL processing is performed in this architecture an example of a

server To illustrate how SQL processing is performed in this architecture, an example of a

table scan is shown in the graphic in the slide

1 The client issues a SELECT statement with a predicate to filter a table and return only

the rows of interest to the user

2 The database kernel maps this request to the file and extents containing the table

3 The database kernel issues the I/Os to read all the table blocks

4 All the blocks for the table being queried are read into memory

5 SQL processing is conducted against the data blocks searching for the rows that satisfy the predicate

6 The required rows are returned to the client

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Exadata Smart Scan Model

Consolidated result set built from all Exadata cells

SQL processing

in Exadata 3 4 2 MB returned to server

Exadata Smart Scan Model

Using Exadata, database operations are handled differently Queries that perform table scans can be processed within Exadata and return only the required subset of data to the database

server Row filtering, column filtering, some join processing, and other functions can be

performed within Exadata Exadata uses a special direct-read mechanism for Smart Scan

processing The above graphic illustrates how a table scan operates with Exadata:

1 The client issues a SELECT statement to return some rows of interest

2 The database kernel determines that Exadata is available and constructs an iDB

command representing the SQL command and sends it to the Exadata cells iDB is a

unique Oracle data transfer protocol that is used for Exadata storage communications

3 The Exadata server software scans the data blocks to extract the relevant rows and

columns which satisfy the SQL command

4 Exadata returns to the database instance an iDB message containing the requested

rows and columns of data These results are not block images, so they are not stored in

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Exadata Smart Storage Capabilities

The following database functions are integrated within Exadata:

• Exadata enables predicate filtering for table scans Rather than returning all the rows for

• Exadata enables predicate filtering for table scans Rather than returning all the rows for the database to evaluate, Exadata returns only the rows that match the filter condition

The conditional operators that are supported include =, !=, <, >, <=, >=, IS [NOT] NULL, LIKE, [NOT] BETWEEN, [NOT] IN, EXISTS, IS OF type, NOT, AND, OR In addition, many common SQL functions are evaluated by Exadata during predicate filtering For a full list

of functions that can be offloaded to Exadata, use the following query:

SELECT * FROM v$sqlfn_metadata WHERE offloadable = 'YES';

• Exadata provides column filtering, also called column projection, for table scans OnlyExadata provides column filtering, also called column projection, for table scans Only

the requested columns are returned to the database server rather than all columns in a

table For tables with many columns, or columns containing LOBs, the I/O bandwidth

saved by column filtering can be very large

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– Simple star join processing is performed within Exadata.

– All data mining scoring functions are offloaded.

– Up to 10x performance gains.

Exadata Smart Storage Capabilities (continued)

• Exadata performs join processing for star schemas (between large tables and small

lookup tables) This is implemented using Bloom Filters which is a very efficient

lookup tables) This is implemented using Bloom Filters, which is a very efficient

probabilistic method to determine whether an element is a member of a set

• Exadata performs Smart Scans on encrypted tablespaces and encrypted columns For

encrypted tablespaces, Exadata can decrypt blocks and return the decrypted blocks to

Oracle Database, or it can perform row and column filtering on encrypted data

Significant CPU savings can be made within the database server by offloading the intensive decryption task to Exadata cells

CPU-• Smart Scan works in conjunction with Exadata Hybrid Columnar Compression so that

column projection and row filtering can be executed along with decompression at the

storage level to save CPU cycles on the database servers

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– I/O for incremental backups is much more efficient because only changed blocks are returned to the database server.

• Create/extend tablespace: p

– Exadata formats database blocks.

Exadata Smart Storage Capabilities (continued)

• The speed and efficiency of incremental database backups is enhanced with Exadata

The granularity of change tracking in the database is much finer with Exadata With

Exadata, changes are tracked at the individual Oracle block level rather than at the level

of a large group of blocks This results in less I/O bandwidth being consumed for

backups and faster running backups

• With Exadata, the create/extend tablespace operation is also executed much more

efficiently Instead of formatting blocks in database server memory and writing them to

storage, a single iDB command is sent to Exadata instructing it to format the blocks

Database server memory usage is reduced and I/O associated with the creation and

formatting of the database blocks is eliminated with Exadata

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Exadata Smart Scan Scale-Out Example

Database Server dbs1

eds c1 eds c2 … eds c13 eds c14

InfiniBand Storage Network

40 Gb/s Maximum

Exadata Cell

Disks (12/cell)

Each cell can deliver 1.8 GB/s.

Total of 14 cells that can deliver

14 x 1.8 = 25.2 GB/s

The example in the next three slides illustrates the power of Smart Scan in a quantifiable

manner using a typical case in which multiple Exadata cells scale-out to share a workload

manner using a typical case in which multiple Exadata cells scale out to share a workload

The database server, depicted in the upper portion of the slide, is connected to the InfiniBand storage network, which can deliver a maximum of 40 gigabits per second (Gb/s) To keep the example clear and simple, assume that the InfiniBand storage network can deliver data at 40 Gb/s with no messaging overhead We will also assume that a single database server has

access to the full I/O bandwidth of all the Exadata cells

In this scenario, there are 14 Exadata cells Assuming that each Exadata cell can deliver 1.8 g

gigabytes (GB) of I/O throughput per second, the potential scanning power of all the Exadata cells is 25.2 GB per second

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Database Server

select /*+ full(lineitem) */ count(*)

from lineitem where l_orderkey < 0;

db s1 Database asks to retrieve all blocks by doing a full table scan, and then

filters matching rows.

…eds c1 eds c2 eds c13 eds c14

to the database instance.

Exadata Cell

Disks (12/cell)

0.357 GB/s

Disks are throttled

by the network bandwidth!

Exadata Smart Scan Scale-Out Example (continued)

Now assume a 4800 gigabyte table is evenly spread across the 14 Exadata cells and a query

is executed which requires a full table scan As is commonly the case assume that the query

is executed which requires a full table scan As is commonly the case, assume that the query returns a small set of result records

Without Smart Scan capabilities, each Exadata server behaves like a traditional storage

server by delivering database blocks to the client database

Because the storage network is bandwidth-limited to 40 gigabits per second, it is not possible for the Exadata cells to deliver all their power In this case, each cell cannot deliver more than 0.357 gigabytes per second to the database and it would take approximately 16 minutes to g g y p pp y

scan the whole table

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Database Server

select /*+ full(lineitem) */ count(*)

from lineitem where l_orderkey < 0;

db s1 Database asks Exadata cells

to send back all matching rows.

…eds c1 eds c2 eds c13 eds c14

If the table is 4800 GB in size, the complete table scan will complete in approximately

three minutes and ten seconds !

E h ll t

If the table is evenly distributed across all disks, each cell cannot send more than 40 / 14 = 2.85 GB/s = 0.357 GB/s

to the database instance.

Exadata Cell

1 8 GB/s

Disks (12/cell)

Each cell can scan at a speed of 1.8 GB/s, and send its matching rows to the database instance This represents

a total scan at a speed

of 25.2 GB/s !

1.8 GB/s

Exadata Smart Scan Scale-Out Example (continued)

Now consider if Smart Scan is enabled for the same query The same storage network

bandwidth limit applies However this time the entire 4800 GB is not transported across the

storage network; only the matching rows are transported back to the database server So

each Exadata cell can process its part of the table at full speed; that is, 1.8 GB per second In this case, the entire table scan would be completed in approximately three minutes and ten

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Exadata Hybrid Columnar Compression

Warehouse Compression

Archival Compression

• 10x average storage savings

• 10x scan I/O reduction

• Optimized for query performance

• 15x average storage savings – Up to 50x on some data

• For cold or historical data

Reduced Warehouse Size

Better Performance

Can mix compression types by partition for ILM

Reclaim Disks Keep Data Online

In addition to the basic and OLTP compression capabilities of Oracle Database 11g, Exadata

includes Exadata Hybrid Columnar Compression

Exadata Hybrid Columnar Compression offers higher compression ratios for direct path

loaded data This compression capability is recommended for data that is not updated

frequently You can specify Exadata Hybrid Columnar Compression at the table, partition, and tablespace level You can also choose between two types of Exadata Hybrid Columnar

Compression, to achieve the proper trade-off between disk usage and CPU consumption,

depending on your requirements:

• Warehouse compression: This type of compression is optimized for query performance, and is intended for data warehouse applications

• Online archival compression: This type of compression is optimized for maximum

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• A compression unit is a logical structure spanning multiple

• Each row is self-contained within a compression unit.

Exadata Hybrid Columnar Compression Architecture Overview

Exadata Hybrid Columnar Compression is a new method for organizing data in database

blocks Tables are organized into sets of rows called compression units (CU) Within a

compression unit, data is organized by column and then compressed The column

organization of data brings similar values close together, enhancing compression ratios Each row is self-contained within a compression unit

In addition to providing excellent compression, Exadata Hybrid Columnar Compression works

in conjunction with Smart Scan so that column projection and row filtering can be executed

along with decompression at the storage level to save CPU cycles on the database servers

Note: Although the diagram in the slide shows a compression unit containing four data

blocks, it should not be assumed that a compression unit always contains fours blocks The

size of a compression unit is determined automatically by Oracle Database based on various factors in order to deliver the most effective compression result while maintaining excellent

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Exadata Smart Flash Cache

• Allows optimization by application table

Hundreds of I/Os per Sec

Tens of Thousands

of I/Os per Second

For many years, a constraining factor for storage performance has been the number of

random I/Os per second (IOPS) that a disk can deliver To compensate for the fact that even

a high performance disk can deliver only a few hundred IOPS, large storage arrays with

hundreds of disks are required to deliver in excess of 60,000 IOPS

Exadata provides Exadata Smart Flash Cache, a caching mechanism for frequently accessed data It is a write-through cache which is useful for absorbing repeated random reads, and

very beneficial to OLTP Using Exadata Smart Flash Cache, a single Exadata cell can support

up to 75,000 IOPS, two cells can support up to 150,000 IOPS, and so on

Exadata Smart Flash Cache focuses on caching frequently accessed data and index blocks,

along with performance critical information such as control files and file headers In addition,

DBAs can influence caching priorities using the CELL_FLASH_CACHE storage attribute for

specific database objects

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High performance cache that understands different types of

database I/O:

• Control file reads and writes are cached Control file reads and writes are cached.

• DBA can influence caching priorities.

• I/Os to mirror copies are not cached.

• Data file formatting is not cached.

Exadata Smart Flash Cache (continued)

In more recent times, vast and expensive storage arrays have introduced equally expensive

nonvolatile memory caches to improve performance However these caches know nothing

nonvolatile memory caches to improve performance However, these caches know nothing

about the applications using them, so their efficiency is limited when compared to their cost

With Exadata, each database I/O is tagged with metadata indicating the I/O type Exadata

Smart Flash Cache uses this information to make intelligent decisions about how to use the

cache This cooperation ensures the efficient use of Exadata Smart Flash Cache

For example, with ASM mirroring turned on, multiple copies of each data block must be

written to disk to deliver the desired level of data protection However, there is usually no p y

need to cache the secondary copies of a block because ASM will read the primary copy if it is available A traditional storage array would not know about this characteristic leading to

caching inefficiencies

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