1

An Exploration of the Next Generation of the

Oracle DatabaseSession #134

Jim CzuprynskiZero Defect Computing, Inc.April 11, 2013

2

My Credentials

• 30+ years of database-centric IT experience

• Oracle DBA since 2001

• Oracle 9i, 10g, 11g OCP

• ~ 100 articles on databasejournal.com and ioug.org

• Teach core Oracle DBA courses (G/I+RAC, Exadata, Performance Tuning, Data Guard)

• 2009: Oracle Education Partner Trainer of the Year

• Speaker at Oracle OpenWorld, IOUG COLLABORATE11, and OUG Norway 2013

• Oracle-centric blog (Generally, It Depends)

3

ALL FEATURES DESCRIBED IN THIS PRESENTATION – EVEN THOSE THAT MAY EVENTUALLY COME TO FRUITION – ARE

ENTIRELY THE OPINIONS OF THE PRESENTER. THEY MAY OR MAY NOT REPRESENT ANY

ACTUAL FEATURES OF THE NEXT RELEASE OF THE ORACLE DATABASE. ANY ACCIDENTAL ASSOCIATION OF THE TWELFTH CARDINAL

NUMBER AND THE THIRD LETTER OF THE LATIN ALPHABET IS PURELY COINCIDENTAL. DUE TO

ITS CONTENT THIS PRESENTATION SHOULD NOT BE VIEWED BY ANYONE

Disclaimer

4

Our Agenda

•DBA 3.0: Consolidate or Perish!

•Multi-Tenancy Databases: CDBs and PDBs

•SQL Plan Management Upgrades

•Information Lifecycle Management

•Automatic Data Optimization

•Online Datafile and Partition Movement

•Policy-Based Automatic Redaction

•Q+A

5

DBA 3.0: Consolidate or Perish!

“Testing in production” is considered blasphemy…but DBAs must be able to guarantee excellent application performance before rollout of new

releases

You’ll just have to do more with less.

- Our CIOs, 2013

Engineered systems are the new 800-pound gorillas

Resource consolidation is the new realityEarlier releases used instance caging, DBRM I/O management, and Exadata IORM to enforce it New database release offers several excellent, intrinsic consolidation features

Less is more. – Ludwig Mies van der

Rohe, c.1950

6

Ex Uno, Multi:Multi-Tenancy Databases

7

Multi-Tenancy: CDBs and PDBs

The next database release offers a completely new multi-tenancy architecture for databases and instances:•A Container Database (CDBs) comprises one or more Pluggable Databases (PDBs)•CDBs are databases that contain common elements shared with PDBs•PDBs comparable to traditional databases in prior releases …•…but PDBs offer extreme flexibility for cloning, upgrading, and application workload localization

8

CDBs and Common Objects

• A CDB owns in common:

• Control files and SPFILE

• Online and archived redo logs

• Backup sets and image copies

• Each CDB has one SYSTEM, SYSAUX, UNDO, and TEMP tablespace

• Oracle-supplied data dictionary objects, users, and roles are shared globally between CDB and all PDBs

PDB1 PDB3

PDB2

CDB1

SPFILESPFILE

ORLsORLs

Control Files

Control Files

ARLsARLs BackupsBackups Image CopiesImage Copies

SYSTEMSYSTEM

UNDOTBS1

UNDOTBS1

SYSAUXSYSAUX

TEMPTEMP

Data Dictionary

Data Dictionary

RolesRolesUsersUsers

CDBs and PDBs share common objects

9

PDBs and Local Objects

PDBs also own local objects•PDBs have a local SYSTEM and SYSAUX tablespace•PDBs may have their own local TEMP tablespace•PDBs can own one or more application schemas:• Local tablespaces• Local users and roles

•PDBs own all application objects within their schemasBy default, PDBs can only see their own objects

PDB1 PDB3

PDB2

CDB1

SYSTEMSYSTEM

SYSAUXSYSAUX

TEMPTEMP

SYSTEMSYSTEM

SYSAUXSYSAUX

TEMPTEMP

SYSTEMSYSTEM

SYSAUXSYSAUX

TEMPTEMP

AP_DATAAP_DATAHR_DATA

HR_DATA

MFG_DATAMFG_DATA

AP_ROLEAP_ROLE

APAP

HR_ROLEHR_ROLE

HRHRMFG_ROLEMFG_ROLE

MFGMFG

10

Shared Memory and Processes

CDBs and PDBs also share common memory and background processes•All PDBs share same SGA and PGA•All PDBs share same background processes

• OLTP: Intense random reads and writes (DBWn and LGWR)

• DW/DSS: Intense sequential reads and/or logicaI I/O

• Batch and Data Loading: Intense sequential physical reads and physical writes

PDB1 PDB3

PDB2

CDB1

System Storage

DW and

DSS

OLTP BATCH

+ IDL

SGA & PGASGA & PGASGA & PGASGA & PGA

LGWR DBWn

Others

11

Sharing: It’s a Good Thing!

Sharing common resources - when it makes sense - tends to reduce contention as well as needless resource over-allocation:

•Not all PDBs demand high CPU cycles

•Not all PDBs have same memory demands

•Not all PDBs have same I/O bandwidth needs

•DSS/DW: MBPS

•OLTP: IOPS and LatencyResult: More instances with less hardware

12

PDBs: Ultra-Fast Provisioning

Four ways to provision PDBs:1.Clone from PDB$SEED2.Clone from existing PDB3.“Replugging” previously “unplugged” PDB4.Plug in non-CDB as new PDBCDB and PDBs stay alive during any of these operations!

PDB1

11gR2DB

CDB1

PDB3

PDB$SEEDPDB4PDB2PDB5

13

XStream: Extreme Replication

As of this new release, Oracle Streams is deprecated in favor of XStream

•XStream Outbound Server handles CDC (change data capture) sourced directly from PDB(s) committed transactions

•XStream Inbound Server(s) replicates CDC to any PDB(s) (but never to a CDB)

•Because it’s based on Oracle GoldenGate, XStream may require licensing of additional OGG features

14

SQL on Autopilot: Adaptive SQL Efficiency, Performance, and Tuning

15

Adaptive SQL Plan Management

Automatic Plan Evolution (APE) now available via package DBMS_SPM•By default, a new automatic task runs during regular maintenance window•Auto-evolution of all non-accepted plans (NAPs):•Most recently added plans get precedence•NAPs that still perform poorly: Wait 30 days•Any NAPs that perform better are automatically

enabled•New SPM report procedure shows results of

Automatic Plan Evolution

16

SPM Evolve Advisor

In prior releases:

•All SQL Plan evolution had to be performed manually

•Gathering SPM advice on whether evolution was even possible was a manual processIn this new release:

•Automatic SQL Plan Evolution tasks included

•Manual advice and implementation also supported via new DBMS_SPM procedures

•Warning! Tuning Pack licensing may be required

17

Cardinality Feedback

Cardinality Feedback was introduced in Oracle 11gR2 as part of Adaptive Cursor Sharing:

•Captures actual execution statistics during query execution

•Compares expected vs. actual cardinality during first execution of query

•During second execution, optimizer uses actual execution statistics to reparse statement’s plan

•Works best for non-skewed row sources with limited volatility

18

Adaptive Execution Plans (AEP)The optimizer can now adaptively recognize and capture multiple potential execution sub-plans within an existing execution plan:

•AEP constructs dynamic plans automatically

•AEP dynamic statistics collector buffers each row set

• If a new row count exceeds prior counts during statement execution, the optimizer will choose an alternative favored subplan (e.g. HASH JOIN instead of NESTED LOOP)

• Otherwise, AEP will utilize the original sub-plan

•Largest AEP benefit: Sub-plans whose row sets contain dramatically skewed data

19

Automatic Re-Optimization (ARO)

For some statements, ARO features may help to overcome intrinsic limitations of AEP dynamic plans:

•The optimizer discovers an inefficiency during a statement’s first execution that AEP cannot resolve (e.g. order in which row sets are joined)

•During the next execution, the optimizer gathers additional statistics to improve the join order

•All subsequent executions of the same statement improve as more execution statistics and optimizer statistics are gathered

20

SQL Plan DirectivesThe latest release offers the capability to capture and retain compilation and execution statistics within the data dictionary:•Before, a statement’s compilation and execution statistics were retained only within the Shared Pool•Now these statistics will be retained within the data dictionary instead as SQL Plan Directives (SPDs)•SPDs are not SQL statement specific! • They pertain to best methods to process row sets• Therefore, multiple future queries may benefit

•DBMS_XPLAN.DISPLAY … +NOTES tells if an SPD has been used against an existing SQL statement•New data dictionary views capture SPD metadata

21

Information Lifecycle Management (ILM) and

Automatic Data Optimization (ADO)

22

Automatic Data Optimization (ADO)

ADO offers capability to move and/or compress data based on observed usage patterns

•Uses heat maps to determine how often data has been accessed

•Tracks exactly how data has been utilized (DML vs. query, single-block vs. table scan)

•Data usage patterns can be tracked at tablespace, segment, and row level

23

Heat Maps: “How Hot Is It?”

ADO leverages heat maps to:

•Capture data usage frequencies

•Determine which compression level is most appropriate for how data is being used

•Determine which data could be moved from a high-performance storage tier to a lower-performance tier

•Decide when data should be moved between different tablespaces or partitions to limit possible out-of-space conditions

24

Heat Maps: An Example

After 3 days of more limited access: Enable ADVANCEDADVANCED

compressionAfter 30 days of only limited access:

Enable HCC QUERY LOW*HCC QUERY LOW* compression

Heat map shows heavy DML and queries:

Leave data uncompresseduncompressed

After 90 days of no access: Enable HCC ARCHIVE HIGH*HCC ARCHIVE HIGH*

compression * Will probably require Exadata, ZFS Appliance, or Pillar Axiom storage

25

Information Lifecycle Management

Information Lifecycle Management (ILM):

•Offers ability to track effectiveness of ADO policies

•Measures how much data has effectively been migrated to different storage levels based on ADO policies currently in place

•Avoids incrementing ILM activity when normal maintenance tasks (e.g. gathering optimizer statistics) should be safely ignored

26

In-Database Archiving (IDA)

Avoids unnecessary deletion of rows when they no longer contain valid data•Activated via new ROW ARCHIVAL attribute of data segment•During initial INSERT, each row’s state is set to default value of zero (0) in ORA_ARCHIVE_STATE hidden column•Rows can be marked as inactive by setting ORA_ARCHIVE_STATE to one (1)•Unless the ORA_ ARCHIVE_STATE column is mentioned in query, a row’s IDA status is invisible and only active rows will be returned to query•Inactive rows can be compressed!

27

Temporal Validity (TV)

TV allows specification of time periods when data stored within a table will be actually considered “valid”

•Implemented through new PERIOD FOR table attribute

•Specified TV dimension can then be populated as desired with both time-valid and non-time-valid data

•Non-time-valid data can be compressed until it’s no longer needed (or until needed as well!)

28

Perpetual Motion:Moving Datafiles and Partitions

Online

29

Advanced or HCC

Compression

Advanced or HCC

Compression

Online Move Datafile (OMD)Online Move Datafile (OMD) offers the ability to:•Move any datafile to other storage system without first offlining it• Move any datafile from non-ASM to ASM storage•Move any datafile to a different compression level (e.g. uncompressed to OLTP or HCC)Implications:•Data objects within the datafile’s tablespace are always accessible•DML and DDL against those data objects are never interrupted

EXT3GFS

NTFS

EXT3GFS

NTFSASMASM

SANSAN

Uncompressed or Advanced Compression

Uncompressed or Advanced Compression

Traditional Engineered

30

Online Move Partition (OMP)

Online Move Partition (OMP) offers the ability to:•Move, split, or merge partitioned objects without interrupting DML against data objects within those partitions•Migrate partitions from one compression level to another•Global and local indexes are still maintained.. all without interrupting DML against most data objects within those partitions

P2

P3

P1

P2

P3

P1

P2

P3

P1

P2

P3

P1

31

Hiding In Plain Sight:Oracle Data Redaction (ODR)

32

Policy-Based Data Redaction

Oracle Data Redaction (ODR) offers ability to:•Partially obscure or completely hide sensitive data•Implement recognized standards (PII, PHI, PCI) for confidential data•“Anonymize” data for development or QA purposes•Allow selected users to see all or just some of data values based on custom viewing policies•No changes to existing application code are required

33

ODR: Redaction Policies

Redaction policies:

•Are implemented via new package (think: DBMS_FGA, DBMS_RLS)

•Are only applied to outbound queried data

•Are applied to one or more columns in a table or view

•Can be joined together logically to provide a comprehensive redaction strategy

34

ODR: Redaction Types

Redaction types enable ODR to:

•Completely hide data (e.g. returned as empty string)

•Partially obscure data (e.g. show only last 4 digits of SSN)

•Completely obscure data by returning “anonymized” data values of same size / length

•Provide custom obfuscation for specific data using regular expressions that vary conditionally

35

ODR: Practical Examples

Redaction Type Stored Data Redacted DataFull Wojiechowski Single empty string (‘’)

Full 125,378.95 Single zero (0)Full 2013-04-15 2001-01-01 (January 1, 2001)

Partial Brzezinski Br-------ki

Partial 178,245.95 777,775.95Partial 1957-11-13 2050-01-03

Random Kowalskievich QwixzYloEmctpS

Random 931,403.24 512,937.01Random 2013-04-21 1987-12-03REGEXP 3728-336491-09887 ****-******-*9887REGEXP 4398-4519-3651-0971 $$$$-XXXX-####-0971

36

ODR: Best Practices

For optimal effectiveness:

•Grant access to ODR procedures sparingly

•Minimize the number of columns redacted

•Consider whitelisting instead of blacklisting

•Regular expressions take more resources to implement redaction than any other method, so use them sparingly

37

Please feel free to evaluate this session:http://www.ioug.org/eval

Session #134An Exploration of the Next Generation

of the Oracle DatabaseIf you have any questions or comments, feel free to:E-mail me at jczuprynski@zerodefectcomputingcom

Follow my blog (Generally, It Depends): http://jimczuprynski.wordpress.com

Follow me on Twitter (@jczuprynski)Connect with me on LinkedIn (Jim Czuprynski)

Thank You For Your Kind Attention