Exadata Overview … - Greater Cincinnati Oracle User …€¦ · · 2011-10-20Exadata Overview...

Rich Niemiec, Rolta TUSC(Thanks: Zhong Yang, Steven Lu, Jim Viscusi, Milton Wan, Damon Grube, Mike Messina,

Sri Avantsa, & Shyam Varan Nath (+ Oracle Learning Library for Examples)

Exadata Overview …Performance Advantages Revealed

Ohio 2011

Overview

• Terminology & the Basics about Exadata (X2-2, X2-8)

• Flash Cache

• Storage Index

• Smart Scans

• Hybrid Columnar Compression (HCC)

• Enterprise Manager & Grid Control

• Enterprise Manager Exadata Simulation

• I/O Resource Manager

• Security, Utilities, Best Practices

• Exalogic Elastic Cloud

• Exadata Storage Expansion Rack (July 2011)

• Oracle Exalytics Business Intelligence Machine (October 2, 2011)

• SPARC SuperCluster & Oracle Database Appliance (Sept. 2011)

• Other Hardware…. Summary

Goals …

• Goals

– Overview of Exadata – Easy

– Exadata Fit in Oracle Strategy

– Some Tech & Grid Examples

– Exalogic, Exadata Expansion Rack

• Non-Goals

– Making you the Expert

Computing has Shifted from

Monolithic to Decentralized

Source: Forrester Research, Inc. - 2002

Acceleration! Next Generation Hardware

• Exadata/Exalogic from Oracle

• EMC V-Max

• IBM pureScale (for DB2)

• Flash Arrays (Violin Memory)

• Fusion-IO

I saw this in a Jeff Henley Talk in

MSFT ORCL SAP BEAS SEBL PSFT IBM

Trailing 12 Months

Last updated December 18, 2002

36.79%: Oracle’s Operating Margin – August 31, 2011

Future Goal is to do this for Others:

Applications Acquisitions

Not to be confused with… Fusion Middleware/BI Acquisitions:

Exadata (X2-2)(Oracle’s picture)

14 Storage Servers

- 14x12=168 Disks

- 100T SAS or

- 336T SATA

- 5TB+ flash storage!

8 Compute Servers

• 8 x 2 sockets x 6 cores = 96 cores

• 768 GB DRAM

InfiniBand Network

• 40 Gb/sec each direction

• Fault Tolerant

EMC V-MaxNext Gen Symmetrix/DMX

• A Cluster of servers and

storage linked together by

a really fast network.

VirtualMatrix

Fusion IO

Violin Memory (Speed Now!)

Exadata is MORE than Hardware*

with compression

10 TB of user data

Requires 10 TB of IO

100 GB

with partition pruning

with Storage Indexes

with Smart Scans

Sub second

On Database

Machine

Data is 10x Smaller, Scans are 2000x faster

*Oracle Slide – Thanks!

Audience Experience?

Exadata V1? Exadata V2-2? X2-8?

10x faster than any Oracle DW 5x faster than V1

Big Difference… Much Improved!

Exadata Version 1? Exadata Version 2?

Audience Knowledge

Full RACK? Half or Quarter RACK?

Terminology & The Basics

Some Terms

• SATA Disk (337T)–

Big & Slow – Like a 33 1/3 <7200 RPM>

• SATA=Serial Advanced Technology

Attachment

• SAS Disk (100T) –

Small & Fast – Like a 45 <15K RPM>

• SAS=Serial Attached SCSI (Small

Computer System Interface)

WHAT is it (X2-2)?

• A prebuilt 8-Node RAC cluster with Super SAN

• All the CPU power you need (96 cores)

• Mega DRAM Server Memory (768G)

• Super-Mega Flash Memory (5.3T)

• Super fast interconnect (40Gb/s)

• 100T of SAS disk (28T useable)

• Database could be MUCH larger with compression!

• If you need it & can afford it – You want it!!

Introduction to RAC - Shared Data Model

Exadata puts it back into One Machine

Shared Disk Database

Shared Memory/Global Area

shared

buffer

. . .. . .Shared Memory/Global Area

shared

buffer

shared

buffer

shared

buffer

Instance 1 Instance 2 Instance N-1 Instance N

How BIG is it?

• 96 Cores (16 six core CPUs) on compute server +168 cores on storage servers (+28 six core) = 264 cores total-full rack

• 768G server DRAM (100G/sec)

• 5.3T of flash cache (75G/sec)

• 100T SAS disk (28T useable) –15K RPM (21G/s; 50K IO/s)

• 336T disk space (100T useable) – SATA 7.2K RPM

• SATA=Serial Advanced Technology Attachment

• SAS=Serial Attached SCSI (Small Computer System Interface)

• DRAM – Dynamic Random Access Memory

How FAST can it be?

• ALL Disks Combined:

– SAS – 21G/s (50,000 IOPS = 300 IOPS x 12 disk x 14)

– SATA – 12 G/s (20,000 IOPS)

• ALL Flash Cache Combined (5.4G/s per cell):

– 75G/s (1,500,000 IOPS); <20x more random I/O; 2x seq)

• Max Data Bandwidth with Disk + Cache + Compress:

– 500G/s (10x compression)

• Data Load Rate:

– 12T/hour (Full Rack)

How FAST is it?

Compared to the competition:

• 5 – 100x for Data Warehousing

• 20x faster for OLTP

• Also - Miscellaneous:

– Hot Swappable Redundant Power

– Each Database Server - Dual Port InfiniBand 40Gb/s card

– Database Servers have Disk Controller HBA (Host Bus Adapter) has 512M battery backed up cache

– Each DB Server has 4 x 1GbE interfaces & ILOM (Integrated Lights Out Management – Remote power on)

What’s Making it FAST?

• Fast Hardware!

• Many CPUs

• Flash Cache

• Lot‟s of DRAM (Parallel Query in DRAM in 11.2)

• Compression (save 10x-70x)

• Partition Pruning (save 10x-100x)

• Storage Indexes (save 5x-10x)

• Smart Scan (save 4x-10x)

• Turn a 1T search into a 500M search or even 50M

How they got these NUMBERS?

• 8 compute servers (x4170 M2‟s)– 8 servers x 2 CPU sockets x 6 cores = 96 cores (Intel L5670)

• 8 servers x 96G DRAM = 768G DRAM

• 14 Storage Servers total 336G DRAM = (1.1T Total DRAM)

• 3 InfiniBand Switches x 36 ports = 108 ports

• 14 Storage Servers (100-336T) with Flash Cache (5T+)– 96G x 4 banks = 394G flash cache per storage server

– 14 storage servers x 394G = 5.376T Flash Cache

– 12 disks per storage server x 14 servers = 168 disks

– 168 disks x 600G SAS = 101T SAS

– 168 disks x 2T SATA = 336T SATA

– Additional total storage of 9.6T on Database Servers (146G drives)

• 14 storage servers x 2 six core L5640 = 168 additional cores

Compute Servers – Like 8 Node RAC!

• 8 compute servers (x4170 M2‟s)– 8 servers x 2 CPU (L5670‟s) sockets x 6 cores = 96 cores

• 8 compute servers x 96G (12x8G) DRAM = 768G DRAM

• 4 x 300G drives x 8 = 9.6T (in addition to storage servers)

(8G each)

Storage Servers – Full Rack

• 14 Storage Servers X2-2 (x4275‟s) with Flash Cache

– 24Gx14= 336G of DRAM (in addition to database servers)

– 96G x 4 cards = 394G per storage server of flash cache

– Additional 168 CPU cores on the storage!

– 168 disks x 2T SATA = 336T SAS

12 Disks

Hot Swappable

(96G each)

InfiniBand - 40G/s Each way

• Leaf and spine switches wired at factory depending on

needs and how many Racks you‟ll have – careful!

36 Ports

Put it all together – Oracle’s picture

of the Sun Oracle Database Machine

14 Storage Servers

- 14x12=168 Disks

- 100T SAS or

- 336T SATA

- 5TB+ flash storage!

8 Compute Servers

• 8 x 2 sockets x 4 cores = 96 cores

• 768 GB DRAM

InfiniBand Network

• 40 Gb/sec each direction

• Fault Tolerant

X2-2 - One more time…

• 8 compute servers (x4170 M2‟s)

– 8 servers x 2 CPU sockets x 6 cores = 96 cores

• 8 compute servers x 96G DRAM = 768G DRAM

• 14 Storage Servers with 168 CPU cores & Flash Cache

– 96G x 4 banks = 394G DRAM per storage server

How will the X2-8 change these …

• 2 compute servers (7560 CPU at 2.26 GHz & 5T SAS)

– 2 servers (x4800‟s) x 8 CPU sockets x 8 cores = 128 cores

• 2 compute servers x 1T DRAM = 2T DRAM

• 14 Storage Servers with 168 CPU cores & Flash Cache

– 96G x 4 banks = 394G DRAM per storage server

-The Past is History

- The Future is a Mystery

- Today is a Gift

- That’s why they call it the present!

Where are YOU in History…

Where did all my disk space go?

• Lost Space:

– 100T SAS = 28T usable

– 336T SATA = 100T usable

• Apply some compression & get it back:

– 28T usable x 10 = 280T SAS

– 100T usable x 10 = 1P SATA

Full Rack or start with ½ or ¼

Full Half Quarter

Compute Servers/cores 8/96 4/48 2/24

Storage Servers/disks** 14/168 7/84 3/36

Storage SAS 100T 50T 21.6T

Storage SATA 336T 168T 72T

Flash IOPs (max) 1,500,000 750,000 375,000

InfiniBand Switches 3 3 2

Data Load Rates 12T/hr 6T/hr* 2.4T/hr*

* Estimate** 600G SAS or 2 T SATA;

Benefits Multiply*

with compression

10 TB of user data

Requires 10 TB of IO

100 GB

with partition pruning

with Storage Indexes

with Smart Scans

Sub second

On Database

Machine

Data is 10x Smaller, Scans are 2000x faster

*Oracle Slide – Thanks!

Some Case Studies

• Australian Finance Group – Process broker commission from 37 hours to 9 hours; Improved Siebel CRM 8X

• Turkey Mobile operator (97% of country) – Compression 40T to 10T

• Banca Transilvania– 30x faster queries; 30% energy savings

• Bokwang Family mart – Process 900,000 orders from 50 minutes to 7 minutes; Order filtering from 15 minutes to 30 seconds

• Enkitec – Consolidate 20 servers (two racks) into one Exadatasaving an 100k power/cooling retrofits

• Finansbank – Used advanced indexing/compression to reduce 18T to 9.5T; DWHSE load from 341 to 250 minutes. Daily backups instead of a limited weekend only backup.

Some Case Studies

• Hiscom – Cut data processing speed (data loaded from 6,000+ Coca-Cola machines) by 3,000% by consolidating Oracle and Teradata into Exadata. Also eliminated Teradata license costs.

• LinkShare – Reduced data center floor space and power by 400%; 8x increase in query speed while reducing servers and storage by eight-fold; Use Enterprise Manager for tuning/monitoring

• Polk – 10x faster queries; Reduced storage with 10-15x compression; Partitioning for performance improvements.

• SoftBank Mobile – 8x faster queries; The system is processing one trillion items in a few seconds; Customer logs analysis went from 25 hours to 7 hours.

Some Case Studies

• Sogeti USA – Apps migrated to Exadata; Reduced weekly tape backup from 4 hours to a disk backup in 5 minutes with replication to the cloud. Reduced the number of servers and consolidated databases.

• Turkcell – 1.5 billion call records a day; Used HCC to go from 250T to 25T; Reduced report time from 27 minutes to 3 minutes; Reduced 10 storage cabinets to 1 Exadata rack.

• Yamazaki Baking – 3.5 M transactions per day. Moved from 9i to 11g Exadata (32 bit to 64 bit) and improved performance 30x. Some reports used to never finish at month end – they now finish – smart scans were a huge boost.

• Thomson Reuters – 2 large UNIX servers to 1 Exadata – 11x faster

• BNP Paribas - 4 large UNIX servers to 1 Exadata – 17x faster

How Oracle saved $1B:

CONSOLIDATION! & Process

Process

Technology

People

Smart Scans

Smart Scans – 10x savings common

• HARDWARE Scans with NO Code Change:

– Filters based on WHERE clause (predicates)

– Filters on row / column / join condition

– Incremental Backup Filtering

• Works with:

– Uncommitted data

– Locked rows

– Chained rows

– Compressed Data

– Encrypted Data (11.2)

• You can SEE the benefit with Grid Control (OEM)

Smart Scans

• Bloom Filters used for Join Filtering

– A way to quickly search for matches (simplistic meaning)

– Saves space & is transparent to the user

– Makes things faster – hardware level filtering

– Tests if elements to search for are in a set

– Many types out there including Bloomier filters

– False positives are possible (rechecks to be perfect –addl. disk)

– Google BigTable uses Bloom filters to reduce disk lookups

– Join Filtering is a perfect application for this!!

Oracle performance test…

• Without Smart Scan (Push whole table via network)

– 5T Table Scan

– Network bandwidth (40Gb/s) slows things

• 40Gb/s = 5GB/s; with 14 storage cells = 0.357GB/s each

– 16 minutes, 40 seconds (5T/5GB/s)

• With Smart Scan (Limit first at hardware level)

– 5T Table Scan

– Limit result BEFORE it hits the network

• Effectively scan 21GB/s (1.5G/storage cell * 14 cells)

– 3 minutes, 58 seconds (5T/21G/s)

The SMART Flash Cache

ALL Flash Cache Combined (5.4G/s per cell): 75G/s (1,500,000 IOPS)

20x more random I/O; 2x more sequential I/O (vs. disk)

Flash Cache – 20x-50x faster than disk

• Caches HOT Data – Does as LAST step!

• PCIe based Flash cards (PCI = Peripheral Component Interconnect express)

• Knows which objects NOT to cache (FTS)

• Can specify WHAT you want to cache

– STORAGE (CELL_FLASH_CACHE KEEP)

– Table/Partition level with CREATE or ALTER

• Write through caches is used to accelerate reads –Data written to disk also written to cache for future reads.

Flash Cache

• Caches

– Hot Data/Index Blocks

– Control File reads/writes

– File header reads/writes

• Does NOT cache

– Mirror copies / Backups / Data Pump

– Tablespace Formatting

– Table Scans (rare)

24G x 4 doms = 96G (dom = disk on module – “solid state”)

96G x 4 flash cards = 394G per storage server of flash cache

14 storage servers x 394G = 5.376T Flash Cache

Flash Cache LRU

• CELL_FLASH_CACHE storage clause

– DEFAULT (normal – large I/O‟s not cached)

– KEEP (use flash cache more aggressive / May not occupy > 80%)

– NONE (flash cache not used)

• CACHE (NOCACHE) Hint

– I/O cached/not-cached in the flash cache

– SELECT /*+ CACHE */ …

• EVICT Hint – Data removed from the flash cache

• ASM rebalance data is evicted from cache when done

• Large I/O (Full Table Scans) on objects with CELL_FLASH_CACHE set to DEFAULT are not cached

Using the KEEP cache

ALTER TABLE CUSTOMER

STORAGE (CELL_FLASH_CACHE KEEP);

Table Altered.

SELECT TABLE_NAME, TABLESPACE_NAME,

CELL_FLASH_CACHE

FROM USER_TABLES

WHERE TABLE_NAME = ‘CUSTOMER’;

TABLE_NAME TABLESPACE_NAME CELL_FL

------------ ------------------- -------

CUSTOMER R_TEST KEEP

How it works…

• DB Request comes to CELLSRV (Cell storage server)

• CELLSRV (first time) gets data from disk– Data cached based on settings, hints … etc.

– Data to WRITE may also be cached after written if it is deemed that it may be needed again.

• CELLSRV (next time) checks:– In Memory Hash Table that lists what is cached

– If cached – goes to flash cache

– In not cached …may cache based on settings…etc.

• CELLCLI> list flashcache detail (allows you to monitor)

• CELLCLI> list flachcachecontent where ObjectNumber=62340 detail

(Select DATAOBJ# =from obj$ where name = „CUSTOMER‟;)

Is it working for me…

SELECT NAME, VALUE

FROM V$SYSSTAT

WHERE NAME IN (

‘physical read total IO requests’,

‘physical read requests optimized’);

Name Value

---------------------------------------------- --------

physical read total IO requests 36240

physical read requests optimized 23954

(this second line (*8192) is flash cache used)

It IS working … 4G query

SELECT NAME, VALUE, VALUE*8192 VALUE2

FROM V$SYSSTAT

WHERE NAME IN (

‘physical read total IO requests’,

‘physical read requests optimized’);

NAME VALUE VALUE2

--------------------------------- -------- --------

physical read total IO requests 10,862,844 88,988,418,048

physical read requests optimized 2,805,003 22,978,584,576

run2.....

run4 .....

It IS working … V$SQL

Select sql_text, optimized_phy_read_requests, physical_read_requests, io_cell_offload_eligible_bytes

from v$sql

where sql_text like '%FIND YOUR SQL%'

SQL_TEXT OPTIMIZED_PHY_READ_REQUESTS PHYSICAL_READ_REQUESTS

------------ --------------------------- ----------------------

IO_CELL_OFFLOAD_ELIGIBLE_BYTES

------------------------------

SELECT.... 567790 688309

4.2501E+10

Run 2.....

SELECT… 762747 906729

4.9069E+10

run 4 ....

SELECT... 1352166 1566537

6.8772E+10

FYI… NOT DB Flash Cache

• Note: Exadata PCIe card Smart Flash Cache (Exadata hardware PCIe Card Cache stored) is NOT the same as 11gR2 Database Flash Cache (file stored) that can be used with Oracle Enterprise Linux (OEL) and Solaris. In Database Flash Cache, a file can be used as data is aged out of SGA.

• To learn more about the 11gR2 Database Flash Cache, see the initialization parameters db_flash_cache_size & db_flash_cache_size.

Storage Indexes (11.2)

** Thanks Oracle for this image

Storage Index- 10x is common (11.2)

• Storage Indexes maintain summary information about the data– (like Meta Data in a way)

• A CELL LEVEL (storage) Memory Structure

• Groups things into Min/Max for various columns

• Eliminates I/Os where there is no match

• Transparent to the user

• Done at the hardware level

• Typically one index for every 1M of disk

• NOT like a B-Tree Index…more like partition elimination to skip data NOT meeting conditions

• 100% done by Oracle – NO COMMANDS NEEDED!!

Is it working for me…

SELECT NAME, VALUE

FROM V$SYSSTAT

WHERE NAME LIKE (‘%storage%’);

NAME VALUE

--------------------------------------------- -------

cell physical IO bytes saved by storage index 25604736

(actual savings from Exadata built storage index)

Check BOTH servers…

SELECT NAME, VALUE

FROM GV$SYSSTAT

WHERE NAME LIKE (‘%storage%’);

NAME VALUE

--------------------------------------------- -----------

(actual savings from Exadata built storage index)

Case two – More advanced comparing

ORDERED data to NON-ORDERED

SQL> desc table_no_order

Name Null? Type

----------------------------------------- -------- ----------------------------

OBJECT_ID NUMBER

OBJECT_NAME VARCHAR2(128)

SQL> desc table_ordered

Name Null? Type

----------------------------------------- -------- ----------------------------

OBJECT_ID NUMBER

OBJECT_NAME VARCHAR2(128)

Case two – More advanced comparing

ORDERED data to NON-ORDERED

select count(*)

from table_no_order;

COUNT(*)

--------------

482246656

select count(*)

from table_ordered;

COUNT(*)

--------------

482246656

alter system flush buffer_cache;

System altered

Non-Ordered does not use Storage Index

select count(*)

from table_no_order

where object_id=20;

COUNT(*)

--------------

Elapsed: 00:00:01.64

select name ,value from v$mystat m,v$statname n

where m.statistic#=n.statistic#

and n.name like '%storage%‘

and m.value>0;

no rows selected

alter system flush buffer_cache;

System altered

Over 10x faster… uses Storage Index

select count(*)

from table_ordered

where object_id=20;

COUNT(*)

--------------

Elapsed: 00:00:00.11

select name ,value from v$mystat m,v$statname n

where m.statistic#=n.statistic#

and n.name like '%storage%‘

and m.value>0;

NAME VALUE

---------------------------------------------------------------- --------------

cell physical IO bytes saved by storage index 1.5211E+10

Hybrid Columnar Compression (11.2)

Exadata Hybrid Columnar Compression

(EHCC) – 4-10x & 30x is common

• What is it (a HYBRID of column & row storage)?

– Data organized by column and compressed vs. row

– Tables organized in Compression Units (CU)-1000 rows?

– CU‟s span many blocks (32K)

– Good for data bulk loaded (not for OLTP – single block)

• What‟s it for?

– Query Data / DWHS (NOT frequently Updated)

• How much does it compress (old OLTP was 2-3x)?

– 10x in a typical data warehouse compression; (we got 4-11)

– 15x to 70x in archive compression (cold data); (we got 32)

Hybrid Columnar Compression

2. Stored in Compression Units

(Better compression when column data stored together)

1. Column Data Compressed

(Archive)

(Warehouse)

** Thanks Oracle for these images

• Faster Operations: Query runs without decompression

– Compressed/Processed in FLASH CACHE; lower I/O!

– Compressed when sent over InfiniBand!

– Cloned compressed!

– Backed Up compressed!

– Scans MUCH less (compressed) data

• Worth Noting:

– Use standard table compression for OLTP

– Single block lookup FASTER than other columnar storage

– Updated rows migrate to normal / lower level compression

• Fully supported:

– B-Tree Indexes

– Bitmap Indexes

– Text Index

– Materialized Views

– Partitioning

– Parallel Query

– Data Guard Physical Standby

– Logical Standby and Streams (FUTURE release)

– Smart Scans of HCC tables!

Performance Tip:

Loading a Partitioned HCC Table

Problem: Loading a HCC table is CPU bound process.

However, we usually notice "enq: HW - contention" during

loading a partitioned HCC table, which was created to address

the high Watermark contention within the parallel slaves. It

really slows down the whole loading process.

Solution: Load the table partition by partition, then

exchange partitions.

Achieved loading 1T of data in 10 min. on a full rack X2-2.

Other Oracle Compression

• Data Pump Compression

– Compression = {ALL | DATA_ONLY | NONE}

• RMAN Backup Compression

– Compression Level LOW/HIGH (New in 11.2)

• Secure File Compression

– LOW/MEDIUM/HIGH (2-3x compression)

– Deduplication & Encryption

• Normal OLTP Table Compression (since 9.2)

– 11g now supports INSERT/UPDATE

– FASTER Algorithm

• Data Guard Redo Transport Compression

Also remember … bad queries still bad!

Insert row by row…

declarev_t_object_id number;v_t2_object_id number;v_t_object_name varchar2(100);cursor mycur isselect object_id from t2;

beginopen mycur;loopfetch mycur into v_t2_object_id;exit when mycur%notfound;begin

select distinct t.object_id, t.object_name into v_t_object_id, v_t_object_name from t where t.object_id=v_t2_object_id;

if SQL%ROWCOUNT =1 theninsert into t3 values ( v_t_object_id,v_t_object_name );

end if;exception

when no_data_foundthennull;

end;end loop;close mycur;commit;

Performance Improved by 60x

In Exadata… write smart queries!

alter session enable parallel dml;

Session altered.

SQL> @row-by-row

PL/SQL procedure successfully completed.

Elapsed: 00:03:44.94

Insert all at once:

insert into t2

select distinct t.object_id, t.object_name

from t,t2 where t.object_id=t2.object_id;

98 rows created.

Elapsed: 00:00:04.95

Enterprise Manager &

Grid Control for Exadata

11gR2 Exadata

Cluster Home – Shows Alerts… etc.

(Next slides – Some are coming soon)

Cluster Charts – Shows Performance

(showing 2 DB Servers on same graph)

Targets – CLUSTER Components

Storage Servers 1-3 (Exadata Plug-In)

Enterprise Manager

Exadata Simulation

“11g R1/R2 Best Features” (more on this)

SQL Performance Analyzer

11gR2 - Options

Upgrade

Options

11gR2 – Exadata Simulation

Test a

Tuning

Set that

I’ve used

in the past

Job is

running

PL/SQL

Click on

Job after

complete

Report

Simple

Job so

benefit

Resource Management (IORM)

(FYI Only)

IORM - I/O Resource Management

• Set I/O resources for different instance

– Instance A = 50%

– Instance B = 30%

– Instance C = 20%

• Further set I/O based on users and tasks

– Instance A Interactive = 50%

– Instance A Reporting = 25%

– Instance A Batch = 15%

– Instance A ETL = 15%

• Best Solution for MIXED workloads & many instances

DBRM – Database Resource Manager

• Enhanced for Exadata

• Allows management of inter and intra DB I/O

• Inter-DB – Managed via IORM & Exadata storage software

• Intra-DB - Managed via Consumer Group

• CPU

• Undo

• DOP (Degree of Parallelism)

• Active Sessions

Grid Control - Resource Manager

Security – FYI Only

Oracle Audit Vault

Oracle Database Vault

DB Security Evaluation #19

Transparent Data Encryption

EM Configuration Scanning

Fine Grained Auditing (9i)

Secure application roles

Client Identifier / Identity propagation

Oracle Label Security (2000)

Proxy authentication

Enterprise User Security

Global roles

Virtual Private Database (8i)

Database Encryption API

Strong authentication (PKI, Kerberos, RADIUS)

Native Network Encryption (Oracle7)

Database Auditing

Government customer

Oracle Database Security\*Built over MANY years...

1977 *Oracle Slide – Thanks!

Security

• Audit Vault

• Total Recall / Flashback

• Database Vault

• Label Security

• Advanced Security

• Secure encrypted backup (also available: incremental

backup with Change Tracking File – much faster)

• Data Masking

• Data Guard

• Failure Groups – (automatic-for storage cell failure)

Utilities - FYI Only…

(Screen Shots - Oracle Learning Library)

Utilities You’ll need to Use - FYI

• CELLCLI – Cell Command Line Interface (CLI)

• DCLI – Run the same command on multiple cells at the same time

From Oracle:

Oracle Exadata Storage Server includes the DCLI utility on each cell. You can use the DCLI utility to execute commands or scripts in parallel across a defined set of cells. The DCLI tool simplifies any operations that must be run across a subset or all cells. Configuration of SSH user equivalency across all cells is an important prerequisite for optimizing the use of DCLI commands. DCLI provides the -k option to automate the distribution of SSH private keys into the AUTHORIZED_KEYS file.

• ADRCI – Automatic Diagnostic Repository Command line Interface; Quickly get diagnostics reports to send to Oracle Support when needed.

Turn Exadata on…

Checks continue…

CELLCLI – Commands

• CellCLI> list cell detail

• CellCLI> list lun

• CellCLI> list physicaldisk

• CellCLI> list flashcache detail

• CellCLI> list celldisk

• CellCLI> calibrate force

• CellCLI> create celldisk all

• CellCLI> create griddisk all harddisk prefix=„data‟ ,size=100g

• CellCLI> list griddisk

• CellCLI> list griddisk attributes name,size

• CellCLI> drop griddisk all prefix=„data‟

Run CELLCLI – Cell Detail

One Storage Server – 12 Disks + Flash

12 Physical Disks

16 Flash Modules

4 – flash cards

4 – flash modules ea. card

CALIBRATE – check performance

Performance

Create Celldisks

We have Celldisks

Flash Disks now

Create Griddisks

Create griddisk

(first 100G – fast part)

Create griddisk

(the rest of the disk)

Check griddisk size

First Part

(first 100G)

Second 458G

(the rest of the disk)

Quick Disk Basics Overview

• Start with a Physical Disk or LUN (Logical Unit Number)

• Create a Cell Disk on a single LUN

• Create two Grid Disk slices on the Cell Disk – One Hot (first – outer ring) & One Cold

• Create 2 ASM Disk Groups (Hot/Cold – Data/Reco) across many Grid Disks to distribute the I/O across Grid Disks

• Add mirroring, DG, Flashback; Failure Groups (auto) ensure mirrored ASM extents are placed on different exadata cells.

• First 2 cells need 29G SYSTEM Area x 12 disks (coldest part) for the O/S, Swap, Trace, Alerts and Exadata software & files.

• Other ways of doing this exist… see manual for more…

Best Practices

MUST haves & DON’T do!

• Must have Latest Bundle Patch (See note: 888828.1 for latest)

• Must have the correct data center COOLING!– 3 tiles with holes for full rack (400 CFM/tile) – don‟t melt it!

• Must have the correct power needs

• Must use Oracle Linux 5.5 (x86_64) or Solaris 11 Express(Selectable at install time) & Oracle11.2.02+

• Must have ASM & use RMAN for backups

• Consider StorageTek SL500 Tape backup

• Use an ASM allocation unit (AU) size of 4M

• Don‟t add any foreign hardware or – No Support!

• Don‟t change BIOS/Firmware or – No Support!

Best Practices

• Create ALL celldisk and griddisks

• Use DCLI to run on ALL Storage Servers at once

• Use IORM

• Decide Fast Recovery Area (FRA) & MAA Needs

• Database 11.2.0.1+ (11.2.1.3.1) and ASM 11.2.0.1+

• COMPATIBLE 11.2.0.1+

• Logfile size at 32G (Whoa!)

• LMT (Locally Managed Tablespaces) with at 4M uniform extents

• Move Data with Data Pump (or use INSERT /*+ APPEND */)

It’s the Real Deal!!

• Fast Hardware!

• Many CPUs!

• Fast Flash Cache!

• Lot‟s of DRAM on Database Servers and Storage

• Compression (save 10x-70x)

• Partition Pruning (save 10-100x)

• Storage Indexes (save 5-10x)

• Smart Scan (save 4-10x)

• Turn a 1T search into a 500M search or even 50M

Exadata = Paradigm Shift!

March 4, 1986 – Sun

(Stanford University Network)

March 12, 1986 – Oracle

ORCL IPO:

Open:15

Close:20.75

Up 38%

March 13, 1986 – Microsoft Next?

Oracle Taking over Hardware…

What’s Next – Exadata X2-8

(announced shortly after the X2-2)

• 2 compute servers (7560 CPU at 2.26 GHz & 5T SAS)– 2 servers x 8 CPU sockets x 8 cores = 128 cores

• 2 compute servers x 1T DRAM = 2T DRAM

• Same storage numbers…

(FUTURE?? 8 servers = 512 CPUs & 8T of DRAM)

What’s Next – Exalogic Elastic Cloud!

Built for Applications Tier

• Some points here – Oracle is leveraging those acquisitions!– Coherence is a great product / NEW Linux – Unbreakable

Enterprise Kernel!

– X2-2:- 360 CPU cores, 2.9T DRAM, 4T FlashFire SSD Read Cache, 40T SAS – Will help Fusion Apps Smoke!

– 1M HTTP/sec (X2-2) – could fit Facebook on 2 of these even thought there are 500M people on Facebook

What’s Next –

Exadata Storage Expansion Rack X2-2!

• Some points here – Oracle is leveraging those acquisitions!

– 18 storage servers (216 2T drives): 432T raw disk (194T useable), 6.75T Flash Cache, 216 CPU cores, InfiniBand connected.

– 1.9M Flash IOPS, 32K disk IOPS

– On-disk backup – 27T/hour(Great for older data / images)

8 Full Racks = 3.5 Petabytes of raw disk!

(with compression store even more!)

What’s Next –

Exadata Storage Expansion Rack X2-2!

• Exadata Storage Server Software:

– Smart Scan Technology

– Smart Flash Cache

– Storage Indexes

– Hybrid Columnar Compression

– IORM / DBRM

– Smart Scans of Data Mining Model Scoring

– ASM (Automatic Storage Management)

– Backup with RMAN

– Restores using Flashback Technologies

– Redundant power & InfiniBand Switches

117** Thanks Oracle images

Oracle Database ApplianceSmall Business (SMB) & Departmental Focus

• 2 cores to 24 cores (Xeon) – 2 core minimum/4 core for RAC

• Cluster in a box (Oracle Clusterware comes installed)

• 96G RAM x 2 servers = 192 G DRAM

• 12 T of raw storage (up to a 4T data warehouse / 3x mirror)

• Software on this is 11g / ASM / RAC / Oracle Linux

• Oracle Appliance Manager (Patching / Managing… - NEW)

• High Availability Fault Tolerant! 2 nodes / dual server

• Ready to go in a couple of hours (software & RAC preloaded)

• Auto Memory Management, Auto Tuning, Auto Disk Backup

• Phone home … calls to get service; One button patching

What’s up Next …

Exalytics Business Intelligence Machine!

Oracle Exalytics BI Machine!

Leveraging the Times Ten Acquisition

• 1T DRAM

• 3.6T storage uncompressed

• 40 Gb/s InfiniBand

• 4 x 10 core = 40 cores

• 200G/sec scan x 5x compression = 1T/sec scan

• Runs Times Ten & Essbase databases

• In memory data access & columnar compression

• Smart Storage Manager to keep the right things in memory

It’s so fast the response time is … well there is no response time, it’s just done.

What’s Next … SPARC SuperCluster!

SPARC SuperClusterThe Future is coming faster…

• New SPARC T4 microprocessor

• Runs 5x faster than T3 microprocessor

• Exadata Flash Disk Storage System

• Solaris 11 & 10 8/11 Operating System

• Oracle VM for SPARC

• Targeting SPARC Solaris install base to migrate to SPARC SuperCluster

• Run Oracle Database Applications faster and less expensive than anything available from IBM. (from Oracle Earnings announcement call Sept. 2011)

The new T4 – Why it’s SOOOOO fast!

• 5x faster than T3

• L1/L2 cache specific to a core – 16K each cache

• L3 shared by all 8 cores – 4M (new on T4)

• Prefetching of instructions & data (new on T4)

• Out of order execution (new on T4)

• Dual instruction use (new on T4)

• Memory Management Unit page size = 2G

• Dynamically threaded (see next slide)

• 14 on chip crypto functions & 10GbE networking

• Up to 8 Racks w/o adding any InfiniBand changes

8 core T4 processor has 64 threads

Up to 2 threads run simultaneously

** Thanks Oracle for this image 8 cores x 8 threads each = 64 threads x 4 T4’s x 4 servers

SuperCluster … 1.2M IO’s/sec

• 4 compute servers (SPARC T4 CPU at 3 GHz & 4.8T SAS)– 4 servers x 4 CPU sockets (T4) x 8 cores = 128 cores

– 8 threads per core 128 cores x 1024 threads (1200 threads per box)

• 4 compute servers x 1T DRAM= 4T DRAM (19.2T disk)

• 8 Storage Servers with additional cores & Flash Cache– ZFS 7320 controllers with 2 servers (two 4-core CPU & 8 disks)

– 8.66T Flash Cache

– 8x2 = 16 disks x 300G (in ZFS) + 8x4 = 32 disks x 300G (in Servers) = 14.4G SAS

– 12 disks per storage server x 8 servers = 96 disks (4 disks are 18G)

– 72 disks x 600G SAS + 20 disks x 2T = 83.2T Mix (97T total including the 14.4G)

– 92 disks x 2T SATA = 184T SATA (198T total including the 14.4G)

• 42 GB/s storage bandwidth

2K – A typewritten page5M – The complete works of Shakespeare10M – One minute of high fidelity sound2T – Information generated on YouTube in one day10T – 530,000,000 miles of bookshelves at the Library of Congress730T – Information generated in YouTube in a year20P – All hard-disk drives in 1995 (or your database in 2010)700P –Data of 700,000 companies with Revenues less than $200M1E – Combined Fortune 1000 company databases (average 1P each)1E –Next 9000 world company databases (average 100T each) 8E – Capacity of ONE Oracle11g Database (CURRENT)12E to 16E – Info generated before 1999 (memory resident in 64-bit)16E – Addressable memory with 64-bit (CURRENT)161E – New information in 2006 (mostly images not stored in DB)1Z – 1000E (Zettabyte - Grains of sand on beaches -125 Oracle DBs)100TY - 100T-Yottabytes – Addressable memory 128-bit (FUTURE)

The Future: 8 Exabytes

Look what fits in one 11g Database!

What’s Next – World On-Line in Oracle Cloud

Super Oracle System!

• Oracle‟s email system - 9 Exadata Racks

• What‟s possible:– 32 x X2-8‟s (64 node RAC cluster)

• 4096 CPUs on the compute servers

• 5376 CPUs on the storage servers

– 1024 x X2-2 Storage Expansion Racks• Roughly 450 PB of raw storage

• That‟s 4.5 Exabytes compressed (at 10x)

• To Exceed the Oracle Maximums (mirrored)– 32 X2-8‟s (10.7 PB)

– 4,123 Storage Expansion Racks (8 Exabytes at 10x)

– 100,000+ years of YouTube storage (730T/year)

– 70x Compression will give you 50+ Exabytes?

– 64 bit will allow 16E to be in memory

Exadata Overview … - Greater Cincinnati Oracle User …€¦ · · 2011-10-20Exadata Overview...

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