pts db12c 01 overview v1
DESCRIPTION
OverviewTRANSCRIPT
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 1
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 2
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.
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 3
Oracle Database 12c
Technical Training
Platform Technology Solutions
Oracle Server Technologies
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Oracle Database 12c
Over 5 years in development
Over 500 new features
Over 2,500 person years of development
Over 3,000 systems used in testing
Over 1 million feature tests run daily
Over 1.2 million hours of stress tests
Development Effort
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Major Database Focus Areas
ENGINEERED
SYSTEMS
BIG
DATA
SOCIAL
BLOG
SMART
METER
101100101001
001001101010
101011100101
010100100101
CLOUD
COMPUTING
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Gartner: Enterprise Private Cloud Survey
78%
17%
5%
Yes
Maybe
No
Will you be pursuing a private cloud computing strategy by 2014?
Gartner Data Center Conference Poll
December 2011
Source: Gartner Top Five Trends for Private Cloud Computing, February 2012, Thomas J Bittman
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Oracle Database 12c c is for the Consolidation and Cloud!
ISVs: Move applications to the cloud transparently and without disruption Manage multi-tenancy (many) as one Ease of upgrade and therefore obvious cost benefits
SIs: Consolidate, standardize and enable cloud deployments with better manageability Reduced OpEx
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Private Database Cloud Architectures Using Oracle Database 11g
Dedicated Databases
share servers and OS
Virtual Machines
share servers
Schema Consolidation
share servers, OS and database
Increasing Consolidation
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Private Database Cloud Architectures Using Oracle Database 12c
Dedicated Databases
share servers and OS
Virtual Machines
share servers
Pluggable Databases
share servers, OS and database
Increasing Consolidation
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Consolidating Databases on Clouds Key Requirements
Isolation and multitenancy
Fast provisioning and cloning
Secure and highly available
No application changes
Manage many as one
Greater resource utilization
Performant and scalable
Lower IT costs
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Oracle Database Architecture Requires memory, processes and database files
System Resources
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New Multitenant Architecture Memory and processes required at container level only
System Resources
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New Multitenant Architecture Memory and processes required at container level only
System Resources
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New Multitenant Architecture Memory and processes required at container level only
System Resources
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OLTP benchmark comparison
Only 3GB of memory vs. 20GB memory used for 50 databases
Pluggable databases scaled to over 250 while separate database instances maxed at 50
Pluggable vs Separate Databases Highly Efficient: 6x Less H/W Resource, 5x more Scalable
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Consolidating Databases Step1: Upgrade databases in-place
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Consolidating Databases Step1: Upgrade databases in-place
Upgrade in Place
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Consolidating Databases Step2: Plug-in upgraded databases
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Consolidating Databases Step2: Plug-in upgraded databases
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Consolidating Databases Step2: Plug-in upgraded databases
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Managing Shared Resources Resource Management for consolidated databases
High Priority
Medium Priority
Low Priority
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Expand Cluster to Support Flexible Consolidation Model
Services
Single SGA per
CDB Instance
Improved Agility With Changing Workloads
Node1
CDB Instance 1
Node2
CDB Instance 2
Multitenant Container Database (CDB)
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Services
Single SGA per
CDB Instance
Expand Cluster to Support Flexible Consolidation Model
Node1
CDB Instance 1
Node2
CDB Instance 2
Node3
CDB Instance 3
Improved Agility With Changing Workloads
Multitenant Container Database (CDB)
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Services
Single SGA per
CDB Instance
Expand Cluster to Support Flexible Consolidation Model
Node1
CDB Instance 1
Node2
CDB Instance 2
Node3
CDB Instance 3
Improved Agility With Changing Workloads
Multitenant Container Database (CDB)
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Pluggable Databases Database Consolidation Using Oracle Database 12c
Managed Globally
by CDBA and
Locally by PDBAs
Container Database
Pluggable DBs
Data Dictionary
Tablespaces
Schemas
Users, Roles &
Services
PDBAs
ERP HR
DB
Seed CRM
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Manage Many Databases as One Backup databases as one; recover at pluggable database Level
One Backup
Point-in-time recovery
at pluggable database level
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Manage Many Databases as One One standby database covers all pluggable databases
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GOLD
SILVER
BRONZE
RAC, Data Guard, Daily Incrementals
Data Guard, Daily Incrementals
Weekly Full Backups
Managing Database Service Level Tiers Change tiers as databases become more mission critical
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Fast Provisioning Pluggable databases can be quickly provisioned from seed
0
5
10
15
20
25
Non CDB PDB Clone PDB using Copy-on-Write File
System
Time Taken to Provision New Database
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Creating Databases for Test and Development Fast, flexible copy and snapshot of pluggable databases
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Multitenant Architecture for SaaS Multitenancy Implemented by the Database, not the Application
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Perfect for ISVs. Packaged apps and reference data are easily distributed
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Perfect for ISVs. Packaged apps and reference data are easily distributed
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Perfect for ISVs. Packaged apps and reference data are easily distributed
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New Multitenant Architecture Ideal for consolidating databases on Clouds
Isolation and multitenancy
Fast provisioning and cloning
Secure and highly available
No application changes
Manage many as one
Greater resource utilization
Performant and scalable
Lower IT costs
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Automatic Data Optimization Optimize data storage based on usage
Heat Map
Smart Compression
Automated Tiering
In Database Archiving
Network Compression
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Automatic Data Optimization
An in memory heat map tracks access to segments and
blocks
Data is periodically written to disk
Information is accessible by views or stored procedures
Uses can attach policies to tables to compress or tier data
based on access to data
Tables or Partitions can be moved between compression levels
whilst data is still being accessed
Simplifying the life cycle of data
Polic
y 1
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Automatic Data Optimization Add compression and tiering policies to tables
Copyright 2012, Oracle and/or its affiliates. All rights reserved. 38
Oldest Data Most Recent Data
Polic
y 1
Polic
y 2
POLICY 1:
Compress Partitions with
row compression if they havent been modified in 30 days
POLICY 2:
Compress Partitions with
columnar compression if they
havent been modified in 180 days
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Automatic Data Optimization A heat map tracks the activity of segments and blocks
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Oldest Data Most Recent Data
Polic
y 1
Polic
y 2
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Automatic Data Optimization Policies are automatically applied to tables
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Oldest Data Most Recent Data
Polic
y 1
Polic
y 2
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Automatic Data Optimization Reduce storage footprint, read compressed data faster
Copyright 2012, Oracle and/or its affiliates. All rights reserved. Confidential Oracle Restricted 41
Oldest Data Most Recent Data
Polic
y 1
Polic
y 2
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Automatic Data Optimization Automatically tier data to lower cost storage
Copyright 2012, Oracle and/or its affiliates. All rights reserved. 42
Oldest Data Most Recent Data
Polic
y 1
Polic
y 2
Polic
y 3
POLICY 3:
If the tablespace is nearly full
compress the oldest partition
with archive compression and
move it to Tier 2 Storage
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Automatic Data Optimization Automatically tier data to lower cost storage
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Oldest Data Most Recent Data
Polic
y 1
Polic
y 2
Polic
y 3
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Extreme Availability Active Replicas
Data Guard Far Sync
Data Guard Fast Sync
Global Data Services
Active Data Guard Reporting Enhancements
Flex ASM
Rolling Upgrade using Data Guard
Application Continuity
GoldenGate Integrated Capture
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Maximum Availability Architecture
Active Data Guard Data Protection, DR
Query Offload
GoldenGate Active-active
Heterogeneous
RMAN, Oracle Secure Backup Backup to tape / cloud
Active Replica
Edition-based Redefinition,
Online Redefinition, Data Guard, GoldenGate Minimal downtime maintenance, upgrades, migrations
RAC Scalability
Server HA
Flashback Human error
correction
Production Site Application Continuity Application HA
Global Data Services Service Failover / Load Balancing
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Zero Data Loss Challenge
The longer the distance, the larger the performance impact
Trade-off between Zero Data Loss and Performance
Primary Standby
Commit
Commit Ack
Network Send
Network Ack
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Data Guard Far Sync No Compromise Between Availability and Performance
Primary Standby
Far Sync
Instance
sync
async
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Global Data Services
Global Data Services
Extends RAC-style service failover, load
balancing (within and across data
centers), and management capabilities to
a set of replicated databases
Takes into account network latency,
replication lag, and service placement
policies
Achieve higher availability, improved
manageability and maximize
performance
Load Balancing and Service Failover for Replicated Databases
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Global Data Services
Reporting client routed to best database Based on location, response time, data, acceptable
data lag
Reports will automatically run on least loaded server
Reporting client failover
If preferred database not available, will route to
another database in same region or a remote
database
Global service migration
Automatically migrates services based on
failover/switchover - if primary database is down, start
Call Center service on the new primary
Active Data Guard Example
Active Data Guard
Reporting Service
Call Center Service
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Global Data Services
Call Center Client connections and
requests transparently routed to the
closest / best database
Runtime load balancing metrics give client real-time
information on which database to issue next request
If a database fails, its global services
restarted on another replica
GoldenGate Example
GoldenGate
Call Center Service
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Flex ASM (Automatic Storage Management)
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ASM Overview Oracle Database 11.2 or earlier
ASM Cluster Pool of Storage
Node2
Disk Group B Disk Group A
Node3 Node5 Node4
Shared Disk
Groups
Wide File Striping
One to One
Mapping of ASM
Instances to
Servers
Node1
ASM Instance
Database Instance
ASM Disk
RAC Cluster
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ASM Overview Oracle Database 11.2 or earlier
ASM Cluster Pool of Storage
Node2
Disk Group B Disk Group A
Node3 Node5 Node4
Shared Disk
Groups
Wide File Striping
One to One
Mapping of ASM
Instances to
Servers
Node1
ASM Instance
Database Instance
ASM Disk
RAC Cluster
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ASM Overview
ASM Cluster Pool of Storage
Disk Group B Disk Group A Shared Disk
Groups
Wide File Striping
One to One
Mapping of ASM
Instances to
Servers
ASM Instance
Database Instance
ASM Disk
RAC Cluster
Node4 Node3 Node2 Node1 Node5
Oracle Database 12.1
Node1
runs as
ASM
Client to
Node4
Node5
runs as
ASM
Client to
Node4
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ASM Overview ASM in12.1, Business as usual
ASM Cluster Pool of Storage
Disk Group B Disk Group A Shared Disk
Groups
Wide File Striping
One to One
Mapping of ASM
Instances to
Servers
ASM Instance
Database Instance
ASM Disk
RAC Cluster
Node4 Node3 Node2 Node1 Node5
Node1 runs as ASM Client to Node 2
Node5 runs as ASM Client to Node4
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Introducing Flex ASM Oracle 12.1: Removal of One to One Mapping
ASM Cluster Pool of Storage
Disk Group B Disk Group A Shared Disk
Groups
Wide File Striping
Databases share
ASM instances ASM Instance
Database Instance
ASM Disk
RAC Cluster
Node5 Node4 Node3 Node2 Node1 Node5
Node2 runs as ASM Client to Node3
Node1 runs as ASM Client to Node 3
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Application Continuity
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Unknown Transaction State
Database and infrastructure outages can cause in-flight work to be
lost, leaving users and applications in-doubt
Often leads to
User pains
Duplicate submissions
Rebooting mid-tiers
Developer pains
Leads to poor customer experiences
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Solving Application Development Pains
Transaction Guard
A reliable protocol and API
that returns the outcome of
the last transaction
New in Oracle Database 12c
Application Continuity
Safely attempts to replay in-
flight work following outages
and planned operations
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Application Continuity
Application Servers
Database Servers
End User
A reliable replay of in flight work
Network Switches
User selects product from
application and purchases it
from the web checkout
User transaction arrives at
application infrastructure. It
makes its way through the application tiers and results in a
database transaction being
created
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Application Continuity
Application Servers
Database Servers
End User
A reliable replay of in flight work
Network Switches The infrastructure hosting the
database fails just before the
transaction is committed to the
database.
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Application Continuity
Application Servers
Database Servers
End User
A reliable replay of in flight work
Network Switches
The jdbc driver detects the
failure and checks with an
available node in the cluster,
using Transaction Guard, whether the transaction
committed or needs to be
replayed
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Application Continuity
Application Servers
Database Servers
End User
A reliable replay of in flight work
Network Switches
If the transaction needs to be
replayed, Application Continuity will submit all of the inflight work to a surviving node
in the cluster and perform a
commit. This all happens
transparently to the application
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Application Continuity
Application Servers
Database Servers
End User
A reliable replay of in flight work
Network Switches
The user receives confirmation
that his order has been
successfully completed.
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Security Defense in Depth
Data Masking at Source
Privilege Analysis
Integrated Audit Framework
Data Redaction
Database Vault Mandatory Realms
Sensitive Data Discovery
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Complete Data Security Protecting data at the source
Applications
SQL Monitoring & Blocking
Masking
Privileged user access
Encryption
Data Discovery Compliance Scan Vulnerability Scan
Activity auditing
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Soc. Sec. # 115-69-3428
DOB 11/06/71
PIN 5623
Policy enforced redaction of sensitive data
Redacting Sensitive Data Mask Application Data Dynamically
Call Center
Operator
Payroll
Processing
Call Centers
Decision Support Systems
Systems with PII, PHI, PCI data
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Privilege Analysis Reduce Attack Surface
Report on actual privileges and roles used in the database
Revoke unnecessary privileges and roles as needed
Help enforce least privilege and reduce risks
Privilege
Analysis
Create Select Update DBA role
APPADMIN role
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Data Masking
Mask At-Source
Minimize sensitive data exposure
Securely Provisioning Test Systems
01001011001010100100100100100100100100100100100010
01010100100100100111001001001001001001000010010010
11100100101010010010101010011010100101010010
Subsetted & Masked
Data Pump File
12.1
Prod Test
At-Source Masking
Production data subsetted first
Sensitive data masked separately
0100101100101010010010010010010010010010010010001
0010101001001001001110010010010010010010000100100
1011100100101010010010101010011010100101010010
Prod
Data Subset Clone &Mask
11.2
Test
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Automatic Data Optimization
Data Redaction
Data Masking
Sandboxes
Adaptive Query Plans
Temporal
Pattern Matching
Partition Maintenance Enhancements
Data Warehousing & Big Data Enabling Data Scientists
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Adaptive Execution Plans Good SQL execution without intervention
HJ
Table scan
T2
Table scan
T1
NL
Index
Scan
T2
Threshold exceeded, plan
switches
Table scan
T1
HJ
Table scan
T2
Plan decision deferred until runtime
Final decision is based on statistics collected during
execution
If statistics prove to be out of range, sub-plans can be
swapped
Bad effects of skew eliminated
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Partitioning Enhancements
Enhanced Partition Maintenance operations
Online partition move
Partition maintenance operations on multiple partitions
Asynchronous global index maintenance for DROP and TRUNCATE
Partial global and local indexes
More Efficient Data Management
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Temporal Support
Transaction Time Temporal
(Flashback Data Archive)
Tracks transactional changes to a
table over its lifetime
Typically used for compliance and
auditing
Enables the users to see the data
as it was at a point in time in the
past
Valid Time Temporal
Enables user to model & query data
for real world validity
Typically used for insurance policies,
financial markets, trade data & future
changes
Users can model concepts such as
the Life time of an insurance policy
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 74
EXEC DBMS_FLASHBACK_ARCHIVE.ENABLE_AT_VALID_TIME('CURRENT');
SELECT custid, start_time, end_time
FROM customers WHERE custid=1;
custid custname custaddr1 custaddr2
custcity custstate custzip start_time end_time
1 Acme Inc 123 Any
Street
Suite 17 Anytown CA 99999 01-JAN-13 31-July-13
1 Acme Inc 456
Another
Street
Anytown CA 99998 01-AUG-13
Valid Time Temporal Example
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Simplified Analysis of Big Data Pattern Matching
Select * from
Ticker MATCH_RECOGNIZE (
PATTERN(X+ Y+ W+ Z+)
DEFINE X AS (price < PREV(price)),
Y AS (price > PREV(price)),
W AS (price < PREV(price)),
Z AS (price > PREV(price)
)
Scalable discovery of business event
sequences
Clickstream logs: sessionization, search
behaviour
Financial transactions: fraud detection, double
bottom (W) stock analysis
Telco: dropped calls
Medical sensors: automated medical
observations and detections
Ascendin
g O
rder
W event defined as regular expression
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 76
if (lineNext == null) {
next = "";
} else {
next = lineNext.getQuantity();
}
if (!q.isEmpty() && (prev.isEmpty() || (eq(q, prev) && gt(q, next)))) {
state = "S";
return state;
}
if (gt(q, prev) && gt(q, next)) {
state = "T";
return state;
}
if (lt(q, prev) && lt(q, next)) {
state = "B";
return state;
}
if (!q.isEmpty() && (next.isEmpty() || (gt(q, prev) && eq(q, next)))) {
state = "E";
return state;
}
if (q.isEmpty() || eq(q, prev)) {
state = "F";
return state;
}
return state;
}
private boolean eq(String a, String b) {
if (a.isEmpty() || b.isEmpty()) {
return false;
}
return a.equals(b);
}
private boolean gt(String a, String b) {
if (a.isEmpty() || b.isEmpty()) {
return false;
}
return Double.parseDouble(a) > Double.parseDouble(b);
}
private boolean lt(String a, String b) {
if (a.isEmpty() || b.isEmpty()) {
return false;
}
return Double.parseDouble(a) < Double.parseDouble(b);
}
public String getState() {
return this.state;
}
}
BagFactory bagFactory = BagFactory.getInstance();
@Override
public Tuple exec(Tuple input) throws IOException {
long c = 0;
String line = "";
String pbkey = "";
V0Line nextLine;
V0Line thisLine;
V0Line processLine;
V0Line evalLine = null;
V0Line prevLine;
boolean noMoreValues = false;
String matchList = "";
ArrayList lineFifo = new ArrayList();
boolean finished = false;
DataBag output = bagFactory.newDefaultBag();
if (input == null) {
return null;
}
if (input.size() == 0) {
return null;
}
Pattern Matching Finding Double Bottom (W)
SELECT first_x, last_z
FROM ticker MATCH_RECOGNIZE (
PARTITION BY name ORDER BY time
MEASURES FIRST(x.time) AS first_x,
LAST(z.time) AS last_z
ONE ROW PER MATCH
PATTERN (X+ Y+ W+ Z+)
DEFINE X AS (price < PREV(price)),
Y AS (price > PREV(price)),
W AS (price < PREV(price)),
Z AS (price > PREV(price) AND
z.time - FIRST(x.time)
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 77
Application Development and Migrations Making Migrations to Oracle easy
Streaming Result Sets
32K Varchar
Identity Data Type
On The Fly Translations
Callout Framework
Fetch First (Top N) Queries
TSQL:
select top 10 * from table_one
PL/SQL:
OPEN v_cursor FOR
SELECT * FROM table_one
FETCH FIRST 10 ROWS ONLY;
DBMS_SQL.RETURN_RESULT(v_cursor);
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Copyright 2013, Oracle and/or its affiliates. All rights reserved. 78
Oracle Migration : Thirdparty to Oracle
Application
Thirdparty (Non-Oracle)
Database to be migrated
SQL Developer
Step 1: Provision new Oracle Database
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Oracle Migration : Thirdparty to Oracle
Application
SQL Developer
Step 2: Use SQL Developer to extract DDL/Stored Procedures
DDL/Stored Procedures
Thirdparty (Non-Oracle)
Database to be migrated
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Table data imported into Oracle
Oracle Migration : Thirdparty to Oracle
Application
SQL Developer
Step 3: Migrate data using SQL Developer or other method
Thirdparty (Non-Oracle)
Database to be migrated
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Oracle Migration : Thirdparty to Oracle
Application
SQL Developer
Step 4: Run application against migrated Oracle Database
Thirdparty (Non-Oracle)
Database to be migrated
-
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Oracle Migration : Thirdparty to Oracle Step 5: Use SQL Translation Framework to intercept SQL and translate it
SQL Developer
Application
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Oracle Migration : Thirdparty to Oracle Step 6: Run migrated application
Application
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Plug Into the Cloud
Security & Compliance
Big Data
Consolidation
Data Optimization
Application Development
High Availability
In-Memory
Performance & Scalability
Data Warehousing
-
Copyright 2013, Oracle and/or its affiliates. All rights reserved. 85
The preceding 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.
-
Copyright 2013, Oracle and/or its affiliates. All rights reserved. 86