Oracle Database 12c Overview and New Features

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Database 12c Release 1 (12.1.0.2)
Thomas Kyte
http://asktom.oracle.com/
Oracle Server Technologies

Oracle Database 12c Overview
Oracle Database In-Memory
Oracle Database 12c for the Developer
Oracle Database 12c for Big Data
Oracle Multitenant
Other Improvements
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Oracle Database 12c (12.1.0.1)
Oracle Multitenant
• Database consolidation
• Fast Provisioning
• Manage many as one
Oracle Automatic Data Optimization
• Smart Compression
• Automate Tiering
Data Guard Far Sync
• Zero data loss over large distances
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Oracle Database 12c (12.1.0.1)
Application Continuity
• Replay of failed transaction
Data Redaction
• Masks application data dynamically
• Transparent to application
Pattern Matching
• Sophisticated inter row pattern analysis
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5
Exploit memory to
improve
performance
Continue to
improve
consolidation
Simplify access to
Big Data
Improve
application
developers
experience

Oracle Multitenant
Other Improvements
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3
4
5
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7
Flip Flops
Core
ICs on board
DIMMS
SIMMs
SSD
Flash
Small Drives
Floppy
Big Drives
1993 ~$25/mb; $26,214,400/tb
2014 ~$0.007/mb; $7,645/tb

Accelerate OLTPReal Time
Analytics
No Changes to
Applications
Exploit latest
generation
hardware
CPU
Oracle Database In-Memory Goals

Until Now Must Choose One Format and Suffer Tradeoffs
Row Format Databases vs. Column Format Databases
Row
 Transactions run faster on row format
– Example: Insert or query a sales order
– Fast processing few rows, many columns
Column
 Analytics run faster on column format
– Example : Report on sales totals by region
– Fast accessing few columns, many rows
SALES
SALES
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Oracle In-Memory Columnar Technology
• Pure in-memory column format
• Not persistent, and no logging
• Quick to change data: fast
OLTP
• 2x to 20x compression
• Enabled at table or partition
level
• Available on all hardware
platforms
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SALES
Pure In-Memory Columnar

Scans Billions of Rows per Second per CPU Core
• Each CPU core scans local
in-memory columns
• Scans use fast SIMD vector
instructions
• Originally designed for
graphics & science
• Billions of rows/sec scan
rate per CPU core
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VectorRegister
Load
multiple
region
values
Vector
Compare
all values
an 1 cycle
CPU
Memory
REGION
CA
CA
CA
CA
Example:
Find all sales
in region of CA
> 100x Faster

Oracle Multitenant
Other Improvements
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Flexible Schema development
JSON
JSON
Via RESTful service
Via Native APIs
Data persisted in database
In JSON
SQL
Data analyzed via SQL
Oracle Database 12c

Querying JSON
{
"firstName": "John",
“lastName”: "Smith",
"age": 25,
"address": {
"streetAddress": "21 2nd
Street",
"city": "New York",
"state": "NY",
"postalCode": "10021“,
"isBusiness" : false},
"phoneNumbers": [
{ "type": "home",
"number": "212 555-
1234“ },
{ "type": "fax",
"number": "646
555-4567“ } ]
}
select
c.document.firstName,
c.document.lastName,
c.document.address.city,
c.document.phoneNumbers
from customers c;
Simplified
syntax for
simple queries
Sample JSON in column customers.document

Oracle REST Data Services
Enabling RESTful access to Oracle Database
• Provides data access consistent with modern App Dev frameworks
• Can map standard http(s) URI RESTful gets and posts to SQL
• Can declaratively returns results in JSON format
• JavaScript framework friendly
• Can support high numbers of end users
• Services
• HTTP(s) relational data access
• Oracle JSON collection based schemaless access
• Oracle NoSQL access over HTTP
• Oracle APEX mid-tier, web toolkit applications, mod_plsql replacement
• Formally known as Oracle APEX Listener
• Supported feature of the Oracle Database since 2010
• Ships with Oracle Database 12c Release 1 (12.1.0.2)

Oracle Multitenant
Other Improvements
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3
4
5
6

SQL is Critical
“….the complexity of dealing with a
non-ACID data store in every part of
our business logic would be too
great, and there was simply no way
our business could function without
SQL queries.”
Google, VLDB 2013
“[Facebook] started in the Hadoop
world. We are now bringing in
relational to enhance that. ... [we]
realized that using the wrong
technology for certain kinds of
problems can be difficult.”
Ken Rudin, Facebook, TDWI 2013
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http://tdwi.org/articles/2013/05/06/facebooks-relational-platform.aspxhttps://www.linkedin.com/groups/Find-out-why-Google-decided-4434815.S.273792742

Oracle Big Data SQL
Oracle Confidential – Internal/Restricted/Highly Restricted 18
One fast SQL query, on all your data.
Oracle SQL on Hadoop and beyond
• With a Smart Scan service as in Exadata
• With native SQL operators
• With the security and certainty of Oracle Database

Accessing Big Data
Without SQL Push Down
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Hadoop Cluster
All columns and rows from the
table are returned
100’s of Terabytes of DataWEB_LOGS
Request for Data
Low utilization of
available resources
High load on
database server
Select w.sess_id,
w.cust_id,
w.page_id
From web_logs w
Where w.source_country = ‘Brazil’
And w.category = ‘TV’
And w.channel = ‘Mobile’

Big Data SQL Push Down
With SQL Push Down
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Select w.sess_id,
w.cust_id,
w.page_id
From web_logs w
Where w.source_country = ‘Brazil’
And w.category = ‘TV’
And w.channel = ‘Mobile’
WEB_LOGS
SQL shipped to BDA
10’s of Gigabytes of Data
Only columns and rows needed to
answer query are returned
Good utilization of
available resources.
SQL executed on
Hadoop cluster
Lower load on Server,
Faster response
Big Data SQL Server
Hadoop Cluster

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 21
New Data Sources for Oracle External Tables
CREATE TABLE web_logs
(click VARCHAR2(4000))
ORGANIZATION EXTERNAL
( TYPE ORACLE_HIVE
DEFAULT DIRECTORY Dir1
ACCESS PARAMETERS
(
com.oracle.bigdata.tablename logs
com.oracle.bigdata.cluster mycluster)
)
REJECT LIMIT UNLIMITED
• New set of properties
– ORACLE_HIVE and ORACLE_HDFS access drivers
– Identify a Hadoop cluster, data source, column
mapping, error handling, overflow handling,
logging
• New table metadata passed from Oracle DDL
to Hadoop readers at query execution
• Architected for extensibility
– StorageHandler capability enables future support
for other data sources
– Examples: MongoDB, HBase, Oracle NoSQL DB

Oracle Multitenant
Other Improvements
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1
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3
4
5
6

Oracle Multitenant
New architecture for consolidating databases and simplifying operations
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ERP CRM
DW Self-contained PDB for each application
• Applications run unchanged
• Rapid provisioning (via clones)
• Portability (via pluggability)
Shared memory and background
processes
• More applications per server
Common operations performed at CDB
level
• Manage many as one (upgrade, HA, backup)
• Granular control when appropriate

Multitenant New Features in 12.1.0.2
• Subset by tablespace
• Metadata-only clone
• Remote clone (including
snapshots)
• File system-agnostic cloning via
dNFS (clonedb = true)
• New SQL clause to aggregate
data across PDBs
select ENAME from
containers(scott.EMP)
where CON_ID in (45, 49);
• New “standbys” clause
• (all | none)
• Nologging clause at PDB level
• Flashback data archive,
transaction query & backout
• Temporal SQL Support
• Compatible with DB In-Memory
• Maintains state of PDBs
between CDB restarts
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Cloning
SQL
Cross PDB Queries
Standby & Logging
PRIMARY STANDBY
Additional
Features

Oracle Multitenant
Other Improvements
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Performance Improvements
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Advanced Index Compression
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• Compresses indexes to reduce their overall storage requirement
– Less space required on disk
– Better use of the database cache
• Indexes are compressed by between 1 – 3 times
• Little or no discernable overhead
• Compression Advisor extended to describe the possible benefits of this
feature

Attribute Clustering
Ordering of data so that rows are stored near one another based on column values
• Improved query performance and concurrency
– Reduced physical data access trough smart IO
– Significant IO reduction for highly selective operations
• Optimized space utilization
– Less need for indexes
– Improved compression ratios through data clustering
• Full application transparency
– Any application will benefit
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Benefits :

Zone Maps
Persisted storage index
• Stores minimum and maximum of specified columns
• Analogous to a coarse index structure
– Much more compact than an index
– Zone maps filter out what you don’t need, indexes find what you do need
• Significant performance benefits with complete application transparency
– IO reduction for table scans with predicates on the table itself or even a joined table
using join zone maps (a.k.a. “hierarchical zone map”)
• Benefits are most significant with ordered data
– Used in combination with attribute clustering or data that is naturally ordered
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X

Approximate Count Distinct
• Not every query requires a completely accurate result
– “How many distinct individuals visited our website last week?”
• New SQL function for approximate results for COUNT DISTINCT aggregates
– APPROX_COUNT_DISTINCT()
• Approximate results can be significantly faster and use less resources than
exact calculations
– 5x to 50x ++ times faster (depending upon number of distinct values and complexity
of SQL)
– Accuracy > 97% (with 95% confidence)
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1,2,3...

Rapid Home Provisioning

Oracle Rapid Home Provisioning
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Oracle Enterprise Manager
Database Cloud
Cluster
Cluster
Cluster
Cluster
Cluster
Cluster
Differential Copy
NFS Mount
Local Gold Image
S/W Distribution
Service
Catalog
Grid Home
Server
Provisioning
Monitoring and Configuration
Capacity and Resource
Patching
Performance and Tuning
Service Level
Application
Database
Grid

Database Backup Logging
Recovery Appliance
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B

Database Backup Logging Recovery Appliance
A New Approach to Data protection in the Enterprise
Backup Windows
Eliminated. No more Full
Backups
Sub second transaction
protection of critical data
Improved System
Availability by offloading
backup processing
End to end data protection
by block validation
throughout its lifecycle
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Architected for Protection of Critical Data
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CLOUD SCALE
• Scales to 1000s of Clients
• Petabytes of Data
• No expensive backup agents
DELTA PUSH
• DBs access and send only changed data
• Minimal impact on production servers
• Real-time redo ship for near-zero data loss
DELTA STORE
• Validated, compressed database change data
• Fast restores to any point-in-time using deltas
• Built on Exadata scaling & resilience
CLOUD SCALE
• Copy to tape: no production server load
• Tapes utilized all day
• Restore directly from tape
BB

Oracle Key Vault

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 37
Key Management Challenges Heard from Customers
DatabasesServers Middleware
Management Challenges
• Proliferation of encryption wallets and keys
• Authorized sharing of keys
• Key availability, retention, and recovery
• Custody of keys and key storage files
Regulatory Challenges
• Physical separation of keys from encrypted data
• Periodic key rotations
• Monitoring and auditing of keys
• Long-term retention of keys and encrypted data

Key Management with Oracle Key Vault
• Centralized management of keys, secrets, Oracle Wallets, Java Keystores and more
• Optimized solution for Oracle Stack (Database, Middleware, Systems)
• Supports industry standard OASIS KMIP protocol
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KEY VAULT

Oracle Advanced Security Transparent Data Encryption (TDE)
Oracle Wallet Scenarios
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Single
Instance
KEY VAULT
GoldenGate
Multiple DBs
Same Machine
RAC
Data Guard

Oracle Key Vault Software Appliance Platform
• Turnkey solution based on hardened stack
• Includes Oracle Database and security options
• Open x86-64 hardware to choose from
• Easy to install, configure, deploy, and patch
• Separation of duties for administrative users
• Full auditing, preconfigured reports, and alerts
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Oracle Database 12c Overview and New Features

Oracle Database 12c Overview and New Features

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