1 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
The following is intended to outline our general product
direction. It is intended for information purposes only, and
may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality,
and should not be relied upon in making purchasing
decisions. The development, release, and timing of any
features or functionality described for Oracle's products
remains at the sole discretion of Oracle.
2 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Application Development
Big Data & Data Warehousing
Consolidation
Database as a Service
Data Optimization
High Availability
In-Memory
Performance & Scalability
Security & Compliance
Released on June 25th 2013
3 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Engineered for Clouds and Big Data
Customer Initiatives
5 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Big Data
Database as a Service
Cloud
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Database Consolidation on CloudsTraditional consolidation methods
Virtual Machines Clustered Databases
Co
nso
lida
tio
nD
en
sity
Schema Consolidation
Share Servers, OS & DatabaseShare Servers Share Servers & OS
6 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Co
nso
lida
tio
nD
en
sity
Oracle MultitenantSimplifies consolidation, enables Database as a Service
Pluggable DatabasesVirtual Machines Clustered Databases
Share Servers, OS & DatabaseShare Servers & OSShare Servers
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Oracle Database ArchitectureRequires memory, processes and database files
System Resources
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New Multitenant ArchitectureMemory and processes required at container level only
System Resources
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Oracle Multitenant for ConsolidationMore efficient utilization of system resources
System Resources
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Oracle Multitenant for Test and DevelopmentFast, flexible copy and snapshot of pluggable databases
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Oracle Multitenant for ProvisioningFast Provisioning, Snapshot Clones
25
20
15
10
5
0
Time Taken to Provision New Database
Non CDB PDB Clone PDB using Copy-on-Write File
System
13 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
RAC, Data Guard
Oracle Multitenant for Database as a ServicePick from standard sizes and service levels
GOLD
Data Guard
✔
SILVER
BRONZE Backups✔
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Demo
Self-Service
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v
Provisioning
Oracle Multitenant for Database as a ServiceTrivially migrate tiers as databases become more mission critical
GOLD RAC, Data Guard
SILVER
BRONZE✔
✔Data Guard
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Backups
Delivering Database as a ServiceOracle Enterprise Manager, Oracle Multitenant and Oracle Exadata
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Engineered for Database as a Service
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19 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
IDC Converged & Integrated Systems Survey Results
Rate how likely is it that your organization will utilize converged systems over the next three years?
2012 2013
0 = N o t a t a ll L ik e ly 1 . 0 %
1 1 . 0 %
2 2 .9 %
3 1 . 3 %
4 3 .9 %
5 4 .5 %
6 4 .5 %
1 2 .7 %
6 .2 %
1 . 6 %
6 0 .4 %
0 % 2 0 % 4 0 % 6 0 % 8 0 %
8
9
1 0 = E x t re m e ly L ik e ly
N = 308
Base = All Respondents
Source: Source: IDC Converged and Integrated Systems End-User
Survey, July, 2013
0 = N o t a t a ll L ik e ly 1 3 .7 %
1 2 .7 %
2 1 . 3 %
3 2 . 3 %
4 4 .0 %
5 1 4 .3 %
6 1 6 .3 %
7 1 3 .0 %
8 8 . 7 %
9 2 . 3 %
1 0 = E x t re m e ly L ik e ly 2 1 .3 %
0 % 2 0 % 4 0 %
N = 300
Base = All Respondents
Source: IDC Converged Systems Survey, July 2012
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7
6 0 % 8 0 %
What Customers ThinkAbout Engineered Systems for Oracle Databases.
“Standardizing our database services and configurations has yielded benefits
across many dimensions.” Andy Wottenhofer, University of Minnesota
“Oracle Database Appliance enables us to provide a single system solution that's
affordable and simple to deploy.” Luigi Giuri, Wirex
“There are a lot less people involved to support the Oracle Exadata system.” Alex
Mann, Garmin
“Oracle Exadata achieved a large reduction in administration time along with huge
storage savings costs and greatly improved performance.” Eric Zonneveld, KPN
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INTRODUCING
ORACLE DATABASE BACKUP LOGGING RECOVERY APPLIANCE
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Database Backup and RecoveryTypical customer challenges.
“We take regular backups, but have no confidence that backups will work when
needed.” Global Payments Company
“With close to 1000 databases to protect, standardization and automation of
backup/recovery is very important to us.” Pharmacy Benefit Management Company
“Trying to reduce our backup windows has traditionally been a problem for us
especially with our larger databases.” Global Research Company
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Database Protection Without CompromiseOracle Database Backup Logging Recovery Appliance
000s of
Databases
Validated and
compressed changes
Redo and
change data
Replicate changes
offsite to the Cloud
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What Customers ThinkAbout the Oracle Database Backup Logging Recovery Appliance.
“This new appliance can solve our recovery problems for us.” Global Payments
Company
“It will allow us to simplify the DBA and storage management disconnect.” Global
Oil Company
“With this appliance the database backup process is dramatically simplified, and
much shorter backup windows.” Global Research Company
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Customer Initiatives
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Database as a Service
Cloud Big Data
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Engineered for Big Data & Data Warehousing
Oracle
Exadata
Oracle
Oracle Big Data
Appliance
Acquire Analyze
Exalytics
DecideOrganize
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Big Data
The Customer ’s View
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v
“Find transactions that suggest fraudulent activity”
• Recognize event patterns in sequence
• Simplify and scale analysis of Big Data
• Find answers as quickly as possible
Big Data AnalyticsScalable discovery of business events
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Simplifying Big Data AnalyticsSQL Pattern Matching with Oracle Database 12c
• Clickstream logs:
– sessionization, search behaviour
• Business transactions:
– fraud detection, stock analysis
• Sensor data:
– Automated observations and detections
Select * from
Transactions MATCH_RECOGNIZE (
…
PATTERN(S X{2,4} Y)
DEFINE S AS (type = T),
X AS (loc =
PREV(loc)), Y AS (loc
!= PREV(loc))
…
)
Ascendin
gO
rder
Possible fraudulent
banking transactions
defined as regular
expression
31 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
return (status ? 0 : 1);}
public static void main(String[] args) throws Exception {int exitCode = ToolRunner.run(new FraudFinder(), args);//int exitCode = 0; if (exitCode == 0) {
exitCode = ToolRunner.run(new AggregateJob(), args);}System.exit(exitCode);
}}
package fraudfinder;
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool;
public class AggregateJob extends Configured implements Tool {@Overridepublic int run(String[] args) throws Exception {
boolean status = false;
Configuration conf = this.getConf();
Job job = new Job(conf, "aggregator");
job.setJarByClass(AggregateJob.class);
job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class);
job.setMapperClass(AggregateMapper.class); job.setReducerClass(AggregateReducer.class);
job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[1])); FileOutputFormat.setOutputPath(job, new Path(args[1] + "_aggregate"));
status = job.waitForCompletion(true);
select count(*) number_of_occurences, sum(fraudulant_amount) amount_defrauded,
fraud_occured_at_loc
from (select * from transactions)
MATCH_RECOGNIZE
( PARTITION BY ACCOUNT_ID ORDER BY TRANS_TIME
MEASURES
x.trans_time as last_trans_time,
y.trans_time as time_of_fraud,
y.amount as fraudulant_amount,
x.location_id as previous_location,
y.location_id as fraud_occured_at_loc,
ROUND(FRAUD_ANALYSIS.DISTANCE_RATIO(Y.LOCATION_ID, X.LOCATION_ID, Y.TRANS_TIME,
X.TRANS_TIME),2) as fraud_likleyhood
ONE ROW PER MATCH
PATTERN (S X{2,4} Y)
DEFINE S as (trans_type='T'),
X as (trans_type='T' and S.LOCATION_ID = X.LOCATION_ID ),
Y as (trans_type='T'
AND Y.LOCATION_ID != X.LOCATION_ID
)
)
where fraud_likleyhood > 1
group by fraud_occured_at_loc
order by number_of_occurences desc
18 Lines of SQL
Using SQL Pattern MatchingFewer lines of code required and runs over 50x faster
return (status ? 0 : 1);}
}
package fraudfinder;
import fraudfinder.locutil.DistanceRatioCalculator; import java.util.ArrayList;
public class PatternMatcher {private ArrayList<PatternElement> patternFifo = new ArrayList<PatternElement>(); private PatternElement xPE;private PatternElement yPE; private boolean match; private int matchXCount= 0;private int maxMatchXCount = Integer.MAX_VALUE;
public void addPatternElement(PatternElement element) {
32int patt
Cern
oLpenygrtihg
=h
pta©tter
2nF0ifo1.s3iz,eO();
racle and/or its affiliates. All rights reserved.match = false;if (!element.isTransTypeT()) {
clearPattern();
650+ Lines of Java Map Reduce
INTRODUCING
ORACLE DATABASE IN-MEMORY OPTION
34 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Oracle Database In-MemoryGoals
100X Faster Queries: Real-Time Analytics
• Get instantaneous query results
• Querying OLTP database or data warehouse
2X Increase Transaction Processing Rates
• Insert rows 3 to 4X faster
35 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Optimizing Transaction and Query PerformanceRow Format Databases versus Column Format Databases
Row
• Transactions run faster on row format
– Insert or query a sales order
– Fast processing few rows, many columns
ORDER
SALES
Column
• Analytics run faster on column format
– Report on sales totals by state
– Fast accessing few columns, many rows
SALES
S
T
A
T
E
36 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
• Simultaneously active and
transactionally consistent
• Simultaneously active and
transactionally consistent
Dual Format In-Memory DatabaseBOTH row and column in-memory formats for same data/table
Sales
Column
Memory
Sales
Row
Memory
AnalyticsOLTP
• 100X Faster Analytics &
reporting: column format
• 2X Faster OLTP: row format
• 100X Faster Analytics &
reporting: column format
• 2X Faster OLTP: row format
FormatFormat
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Demo
Oracle Database v
In-Memory
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lnd!ex
8 RunTraditionalR u n 12'c ln,Mernory
6i!Parallel
Traditional In-Memory 12c In-Memory Option
180Million Rows Scanned I Sec
12473Million Rows Scanned I Sec
170,_
0
I12eIn-Memory
Option
iDl l_
0
·uo
TraditionalIn-Memory 'Q)
l l_
0
25-AUG 13 WBB>f..S 31·AUG 13
Oracle Database In-Memory is Trivial to DeployNo changes required to existing applications
1. Configure Memory Capacity• inmemory_area = XXXX GB
2. Configure tables or partitions to be in memory• alter table | partition … inmemory;
3. Later Drop analytic indexes to speed up OLTP
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Oracle Database In-Memory OptionLeading edge In-Memory technology
• Seamlessly integrated into Oracle Database 12c
• Delivers extreme performance for
– Analytics and ad-hoc reporting on live data
– Enterprise OLTP and Data Warehousing
– Scale-up and scale-out
• Trivial to deploy for all applications and customers
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Engineered for Clouds and Big Data
42 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Big Data
Database as a Service
Cloud
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