more than 12 more things about oracle database 12c

Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 131

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.

More than 12 More things about Oracle Database 12cThomas Kytehttp://asktom.oracle.com


#1 Data RedactionClick icon to add picture


Oracle Data Redaction

On-the-fly redaction based upon user name, IP address, application context, and other factors

Transparent, consistent enforcement in the database Minimal impact on production work loads

Redacting Sensitive Data for Applications

Credit Card #4451-2172-9841-43685106-6342-4881-52114891-3311-0090-5055

Policy

Call Centers

Decision Support Systems

Systems with PII, PHI, PCI data


#2 Temporal Validity Click icon to add picture


Temporal Validityops$tkyte%ORA12CR1> create table addresses 2 ( empno number, 3 addr_data varchar2(30), 4 start_date date, 5 end_date date, 6 period for valid(start_date,end_date) 7 ) 8 /

Table created.


Temporal Validityops$tkyte%ORA12CR1> insert into addresses (empno, addr_data, start_date, end_date ) 2 values ( 1234, '123 Main Street', trunc(sysdate-5), trunc(sysdate-2) );

1 row created.

ops$tkyte%ORA12CR1> insert into addresses (empno, addr_data, start_date, end_date ) 2 values ( 1234, '456 Fleet Street', trunc(sysdate-1), trunc(sysdate+1) );

1 row created.

ops$tkyte%ORA12CR1> insert into addresses (empno, addr_data, start_date, end_date ) 2 values ( 1234, '789 1st Ave', trunc(sysdate+2), null );

1 row created.


Temporal Validityops$tkyte%ORA12CR1> select * from addresses as of period for valid sysdate-3;

EMPNO ADDR_DATA START_DAT END_DATE---------- ------------------------------ --------- --------- 1234 123 Main Street 12-MAY-13 15-MAY-13

ops$tkyte%ORA12CR1> select * from addresses as of period for valid sysdate;

EMPNO ADDR_DATA START_DAT END_DATE---------- ------------------------------ --------- --------- 1234 456 Fleet Street 16-MAY-13 18-MAY-13

ops$tkyte%ORA12CR1> select * from addresses as of period for valid sysdate+3;

EMPNO ADDR_DATA START_DAT END_DATE---------- ------------------------------ --------- --------- 1234 789 1st Ave 19-MAY-13


#3 SQL Text Expansion Click icon to add picture


SQL Text Expansionops$tkyte%ORA12CR1> variable x clob

ops$tkyte%ORA12CR1> begin 2 dbms_utility.expand_sql_text 3 ( input_sql_text => 'select * from all_users', 4 output_sql_text => :x ); 5 end; 6 /

PL/SQL procedure successfully completed.


SQL Text Expansionops$tkyte%ORA12CR1> print x

X--------------------------------------------------------------------------------SELECT "A1"."USERNAME" "USERNAME","A1"."USER_ID" "USER_ID","A1"."CREATED" "CREATED","A1"."COMMON" "COMMON" FROM (SELECT "A4"."NAME" "USERNAME","A4"."USER#" "USER_ID","A4"."CTIME" "CREATED",DECODE(BITAND("A4"."SPARE1",128),128,'YES','NO') "COMMON" FROM "SYS"."USER$" "A4","SYS"."TS$" "A3","SYS"."TS$" "A2" WHERE "A4"."DATATS#"="A3"."TS#" AND "A4"."TEMPTS#"="A2"."TS#" AND "A4"."TYPE#"=1) "A1"


#4 Increased Size Limit for VARCHAR2, NVARCHAR2, and RAW Data Types

Click icon to add picture


32k Strings

Varchar2, NVarchar2 and Raw datatypes may be upto 32k in size, like in PL/SQL

Compatible = 12.0.0.0 or higher Max_String_Size init.ora set to EXTENDED (default is not this) Not supported in clustered and index organized tables Will be stored out of line (LOB) but work just like long strings to your

program


#5 Easy Top-N and pagination queries Click icon to add picture


Row Limiting Clause


Row Limiting Clause

c##tkyte%CDB1> create table t 2 as 3 select * from stage;Table created.

c##tkyte%CDB1> create index t_idx on t(owner,object_name);Index created.

c##tkyte%CDB1> set autotrace on explain


Row Limiting Clause

c##tkyte%CDB1> select /*+ first_rows(5) */ owner, object_name, object_id 2 from t 3 order by owner, object_name 4 FETCH FIRST 5 ROWS ONLY;…---------------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |---------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 5 | 1450 | 7 (0)| 00:00:01 ||* 1 | VIEW | | 5 | 1450 | 7 (0)| 00:00:01 ||* 2 | WINDOW NOSORT STOPKEY | | 5 | 180 | 7 (0)| 00:00:01 || 3 | TABLE ACCESS BY INDEX ROWID| T | 87310 | 3069K| 7 (0)| 00:00:01 || 4 | INDEX FULL SCAN | T_IDX | 5 | | 3 (0)| 00:00:01 |---------------------------------------------------------------------------------------

Predicate Information (identified by operation id):---------------------------------------------------

1 - filter("from$_subquery$_003"."rowlimit_$$_rownumber"<=5) 2 - filter(ROW_NUMBER() OVER ( ORDER BY "OWNER","OBJECT_NAME")<=5)


Row Limiting Clause

c##tkyte%CDB1> select /*+ first_rows(5) */ owner, object_name, object_id 2 from t 3 order by owner, object_name 4 OFFSET 5 ROWS FETCH NEXT 5 ROWS ONLY;…---------------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |---------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 5 | 1450 | 7 (0)| 00:00:01 ||* 1 | VIEW | | 5 | 1450 | 7 (0)| 00:00:01 ||* 2 | WINDOW NOSORT STOPKEY | | 5 | 180 | 7 (0)| 00:00:01 || 3 | TABLE ACCESS BY INDEX ROWID| T | 87310 | 3069K| 7 (0)| 00:00:01 || 4 | INDEX FULL SCAN | T_IDX | 5 | | 3 (0)| 00:00:01 |---------------------------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------

1 - filter("from$_subquery$_003"."rowlimit_$$_rownumber"<=CASE WHEN (5>=0) THEN 5 ELSE 0 END +5 AND "from$_subquery$_003"."rowlimit_$$_rownumber">5) 2 - filter(ROW_NUMBER() OVER ( ORDER BY "OWNER","OBJECT_NAME")<=CASE WHEN (5>=0) THEN 5 ELSE 0 END +5)


Row Limiting Clause

c##tkyte%CDB1> select owner, object_name, object_id 2 from t 3 order by owner, object_name 4 FETCH NEXT 0.01 PERCENT ROWS ONLY;… 9 rows selected.------------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time |------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 87310 | 25M| | 1230 (1)| 00:00:01 ||* 1 | VIEW | | 87310 | 25M| | 1230 (1)| 00:00:01 || 2 | WINDOW SORT | | 87310 | 3069K| 4120K| 1230 (1)| 00:00:01 || 3 | TABLE ACCESS FULL| T | 87310 | 3069K| | 401 (1)| 00:00:01 |------------------------------------------------------------------------------------

Predicate Information (identified by operation id):---------------------------------------------------

1 - filter("from$_subquery$_003"."rowlimit_$$_rownumber"<=CEIL("from$_sub query$_003"."rowlimit_$$_total"*0.01/100))


#6 Temporary UNDO Click icon to add picture


Temporary UNDO

UNDO for temporary tables can now be managed in TEMP Reduce the amount of UNDO in the UNDO tablespace

– Better for retention periods for “real” data Reduce the size of the redo generated Allows for DML on temporary tables in Active Data Guard

ALTER SYSTEM/SESSION SET TEMP_UNDO_ENABLED=true|false


#7 Online Operations Click icon to add picture


More Online Operations

DROP INDEX ONLINE DROP CONSTRAINT ONLINE SET UNUSED COLUMN ONLINE ALTER INDEX UNUSABLE ONLINE ALTER INDEX [VISIBLE | INVISIBLE]


#8 Data Optimization Click icon to add picture


ILM: Hot/Cold Data ClassificationEnhanced Insight into Data Usage: “heat map”

Recently inserted, actively updated Infrequently updated,

Frequently Queried

Retained for long term analytics and compliance with corporate policies and regulations

ACTIVEFREQUENTACCESS

DORMANT

• Block and Segment level statistics on last Read and last Update


ILM: Automatic Compression & TieringUsage based and custom compression and tiering

ALTER TABLE ordersILM ADD CompressionPolicy COMPRESS Partitions for QueryAFTER 90 days from creation;

ALTER TABLE sales ILM ADD MovePolicyTIER Partitions TO ‘Archive_TBS’ ON OrdersClosedPolicy;


#9 Implicit Result Sets Click icon to add picture


Implicit Result Sets

Cursor variables have been around since 7.2 Used to return result sets explicitly Some other databases return them implicitly Causes migration headaches



Two new API calls:– PROCEDURE RETURN_RESULT

(rc IN OUT SYS_REFCURSOR, to_client IN BOOLEAN DEFAULT TRUE); – PROCEDURE RETURN_RESULT

(rc IN OUT INTEGER, to_client IN BOOLEAN DEFAULT TRUE);

– TO_CLIENT => true, return to client layer– TO_CLIENT=> false, return to invoker, immediate caller


Implicit Result Setsops$tkyte%ORA12CR1> declare 2 c sys_refcursor; 3 begin 4 open c for 5 select * 6 from dept; 7 8 dbms_sql.return_result(c); 9 end; 10 /PL/SQL procedure successfully completed.

ResultSet #1

DEPTNO DNAME LOC---------- -------------- ------------- 10 ACCOUNTING NEW YORK 20 RESEARCH DALLAS 30 SALES CHICAGO 40 OPERATIONS BOSTON



Two new API calls:– PROCEDURE GET_NEXT_RESULT

(c IN INTEGER, rc OUT SYS_REFCURSOR); – PROCEDURE GET_NEXT_RESULT

(c IN INTEGER, rc OUT INTEGER);

– For PL/SQL processing


Implicit Result SetsConnection conn = DriverManager.getConnection(jdbcURL, user, password);try { Statement stmt = conn.createStatement (); stmt.executeQuery ( “begin foo; end;” ); while (stmt.getMoreResults()) { ResultSet rs = stmt.getResultSet(); System.out.println("ResultSet"); while (rs.next()) { /* get results */ } }}


#10 Pluggable Databases Click icon to add picture


Oracle Database ArchitectureRequires memory, processes and database files

ERP

Database Files

BackgroundProcesses

MemoryUtilized

RAM


Oracle Database ArchitectureSeparate memory and processes required for each database

ERP

Database Files

MemoryUtilized

BackgroundProcesses

RAM

CRM

Database Files

BackgroundProcesses

MemoryUtilized

RAM

DW

Database Files

BackgroundProcesses

MemoryUtilized

RAM


New Pluggable Database Architecture

ERP

Database Files

MemoryUtilized

BackgroundProcesses

CRM

Database Files

MemoryUtilized

BackgroundProcesses

RAM

DW

Database Files

MemoryUtilized

BackgroundProcesses

RAMRAM

Memory and processes required at container level only


New Pluggable Database ArchitectureMemory and processes required at container level only

RAM ERP CRM DW

BackgroundProcesses

MemoryUtilized Container Database


#11 Database In-Memory Click icon to add picture

40Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle InternalCopyright © 2013, Oracle and/or its affiliates. All rights reserved.40

Flip FlopsCoreICs 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

41Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle InternalCopyright © 2013, Oracle and/or its affiliates. All rights reserved.

Until Now You Choose One Format and Suffer Tradeoffs

Optimizing Transaction and Query Performance Row Format Databases versus Column Format Databases

Row

Transactions run faster on row format– Fast for processing few rows, many

columns– Example: Insert or query a sales order

Column

Analytics run faster on column format– Fast for processing few columns, many

rows– Example: Report on sales totals by state

ORDER

SALES

SALES

STATE

41


OLTP is Slowed Down by Analytic Indexes

Table1 to 3OLTP

Indexes

5 to 15 AnalyticsIndexes

Most Indexes in mixed-use OLTP (e.g. ERP) databases are only used for analytics

Indexes work well for known access patterns both in-memory and on-disk

But every change to the table requires changing all analytic indexes – Slow!

42


Column Store Replaces Analytic Indexes

Table1 to 3OLTP

Indexes

In-Memory Column Store replaces analytic indexes for tables that fit in memory

Removes analytic index overhead on changes

Both predefined and ad-hoc analytic queries run fast Less tuning & admin needed

OLTP & batch often run 2x or more faster

In-Memory Column Store

43


Oracle In-Memory is Trivial to Deploy

1. Configure Memory Capacity inmemory_size = XXXX GB

2. Configure tables or partitions to be in memory alter table | partition … inmemory;

3. Later Drop analytic indexes to speed up OLTP

44


Oracle In-Memory is Transparent to ApplicationsExisting Applications Just Run Faster

Full Functionality - No restrictions on SQLTrivial to Implement - No migration of data or change of productFully Compatible - All existing applications run unchangedDB as a Service Ready - Oracle Multitenant in-memory

Uniquely Achieves All In-Memory Benefits With No Application Changes

And All Other Apps that Support Oracle Database

45

Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 12 of the corporate presentation template46

#12 A few more 12.1.0.2 bits and pieces

Insert Picture Here


Advanced Index Compression

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

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


50

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)


Graphic Section Divider

Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

Confidential – Oracle Highly Restricted

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.

more than 12 more things about oracle database 12c

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