1. Leveraging new Oracle 12c features to reduce storage costs
Wesley Vaske
Kai Yu
Dell Oracle Solutions Engineering

2. About Authors Wes Vaske, System Engineer, Dell Databases Engineering
Introduction
Bio
Kai Yu, Senior Principal Engineer, Dell Database Engineering
19 years Oracle DBA/Apps DBAS and Solutions Engineering
Specializing in Oracle RAC, Oracle VM and Oracle EBS
Oracle ACE Director, Oracle papers author/presenter
2011 OAUG Innovator of Year, 2012 Oracle Excellence Award: Technologist of the Year: Cloud Architect by Oracle Magazine
My Oracle Blog:
Co-author Apress Book “Expert Oracle RAC 12c”
About Authors
I work for Dell Oracle Solutions engineering lab. My responsibility is to
develop the Oracle related solutions based on Dell infrastructure and
helped our customer to architect the infrastructure for their IT system,
such as Oracle database as well as a private cloud like solution.
I started as Oracle DBA in In last 6 years, my focus has been on
the architect work: Oracle RAC, Virtualization, cloud. I not only architect
the solutions, I also helped to implement all the components from
hardware to software. I have been sharing my experience in the Oracle
Community by presenting at major Oracle conference. This year is my
7th consecutive year of speaking at Oracle OpenWorld. I am also one of
The Oracle ACE directors and received couple of awards by community and
Oracle Cooperation. I have uploaded more than 50 of my presentations
And 20 whitepapers on my Oracle blog

3. Dell Oracle Solution Engineering Providing solutions on the whole stack from the ground up
Solutions Deliverable List
Validated integration
Best practices
Virtualization
Oracle EM12c
Oracle Applications
Performance Study

4. What we do Recent project: High Performance Oracle Workloads with the Dell Acceleration Appliances for Database (DAAD)

5. Dell Integrated Systems for Oracle Database

6. Agenda Changes in enterprise storage Oracle ILM Heat Map
Automatic Data Optimization
Examples of using Oracle ILM
Questions

7. Changes in enterprise storage

8. Decreasing cost and increasing capacity
Price / gigabyte continues to drop dramatically but is outpaced by increasing demand for storage.
This leads to increased spending on storage in spite of decreasing costs.
Source: Historical External Disk Storage Data: IDC Worldwide tracking report

9. Storage tiering – Why?
It allows small capacities of high speed flash to accelerate much larger workloads.
It allows slow, high capacity disks to store cold data for high performance applications.
Use the right tool for the job:
SLC Flash for Write Intensive workloads
MLC Flash for Read Intensive workloads
15k SAS for sequential workloads
7.2k SATA/NL-SAS for low performance / archival
Tape for offsite / backup / archival
SSD
15k SAS
NL SAS / SATA
Tape

10. Storage tiering – How? Tiering Layer Description Products
Disadvantages
Advantages
The storage admin moves or assigns storage to applications/servers based on expected performance/cost requirements.
Manual
All storage products
Requires multiple storage products
Application awareness dependent on storage admin
Complex configurations
The storage appliance moves blocks or pages of data from one type of disk to another type of disk based on rules or usage.
Storage
Dell Compellent
Dell EqualLogic
EMC FAST
NetApp
Storage snapshots can be utilized
Simplest way to implement storage tiering
Not application aware
Can’t combine different vendors in a full-featured configuration.
Application has access to different storages with varying cost and performance and moves data between these storage tiers.
Application
Oracle ADO
VMware vSAN
Microsoft Storage Spaces
Best theoretical performance
Able to define storage tiers based on better data
Vendor Agnostic
Consistent application of tiering rules
Unable to use storage snapshots over entire dataset

11. Oracle Information Lifecycle Management (ILM)

12. Information Lifecycle Management (ILM)
ILM is the practice of applying specific policies for effective information management.
With respect to databases, ILM refers to the processes and practices of aligning digital records—table “rows”—with the most appropriate and cost effective IT infrastructure at each phase of its useful life.
Examples:
Moving transaction data to a data warehouse for improved analytics performance
Compressing tables or partitions as data ages to reduce storage capacity
Moving data for completed transactions to read-only storage
Active
Less Active
Historical
Archive

13. New features in Oracle Database 12c
While ILM has been practiced for decades the explosion of new data and the proliferation of storage technologies has lead to innovations in improving management of automated ILM.
Heat Map
Stores system-generated data usage statistics at the block and segment levels
Segment level Heat Map tracks the time of last modification and access of tables and partitions
Row level Heat Map tracks modification times for individual rows (aggregated to the block level)
Heat Map skips internal access done for system tasks—automatically excluding Stats Gathering, DDLs or Table Redefinitions
Automatic Data Optimization
Allows you to create policies for data compression (Smart Compression) and data movement, to implement storage and compression tiering
Smart Compression refers to the ability to utilize Heat Map information to associate compression policies, and compression levels, with actual data usage

14. Heat Map DBA_HEAT_MAP_SEGMENT:
Views for interacting with Heat Map:
Heat Map can be enabled at the system or session level
To support ADO it must be enabled at the system level
You can use the DMBS_HEAT_MAP PL/SQL package to get more detail information from Heat Map via a set of procedures.
View
Description
V$HEAT_MAP_SEGMENT
Displays real-time access information, including object name, object number and container ID.
DBA_HEAT_MAP_SEGMENT:
Displays the latest segment access time for all segments visible to the specified user. (Support ALL or USER in place of DBA)
DBA_HEAT_MAP_SEG_HISTOGRAM
Displays access information for all segments visible to the specified user. (Support ALL or USER in place of DBA.)
DBA_HEATMAP_TOP_OBJECTS
Displays access information for the top 1,000 objects.
DBA_HEATMAP_TOP_TABLESPACES:
Displays access information for the top 100 tablespaces.

15. Automatic Data Optimization (ADO)
Compression types:
Data can be compressed at the table, partition/segment, or row level (rows are aggregated to the block level)
Data can be tiered at the table or partition level (there is no row-based tiering)
Compression algorithm
Description
COMPRESS;
Basic table compression. The same as “ROW STORE COMRESS BASIC”
ROW STORE COMPRESS BASIC:
The standard option (see above)
ROW STORE COMPRESS ADVANCED:
Oracle DB compresses the data during Data Manipulation Language (DML) operations. This is recommended for Online Transaction Processing Applications (OLTP)
COLUMNSTORE COMPRESS FOR QUERY:
Enables hybrid columnar compression in which the data is transformed into a column-oriented format then compressed. This is useful for Data Warehouses (OLAP)
COLUMNSTORE COMPRESS FOR ARCHIVE:
Same as above but compressed at a higher level. This is useful for archiving as it has a negative impact on performance.
NOCOMPRESS:
Disables table compression. The default.

16. Oracle ILM – ADD POLICY
ILM policies are created via “CREATE TABLE” or “ALTER TABLE” commands.
Policies will indicate compression or tiering, row/segment/table level, rule for execution.
Example: Segments in the orders table are compressed as columnar data if 30 days pass with no modifications.
ALTER TABLE orders ILM ADD POLICY
COLUMN STORE COMPRESS FOR QUERY HIGH SEGMENT
AFTER 30 DAYS OF NO MODIFICATION;
Example: Data in the orders table will be tiered to the tablespace “archiveTableSpace” based on the parameters set via the DBMS_ILM_ADMIN.customize_ilm procedure. With the default options, this policy would tier partitions when the source tablespace reaches 85% used. Partitions would be tiered based on the frequency and date of table access.
TIER DATA TO archiveTableSpace;

17. Oracle ILM - Administration
DBMS_ILM_ADMIN Provides an interface to customized Automatic
Data Optimization (ADO) policy execution.
customize_ilm Allows you to set parameters for ILM.
disable_ilm Procedure to turn off background ILM
enable_ilm Procedure to turn on background ILM
clear_heat_map_all Procedure to delete all rows except dummy row
set_heat_map_all Procedure to update/insert a row for all tables
set_heat_map_table Procedure to update/insert a row for this table/segment
clear_heat_map_table Procedure to clear all/some statistics for table
set_heat_map_start Procedure to set start date of heat map data

18. Automatic Data Optimization Examples

19. Automatic Data Optimization Examples
Heat Map tracks usage information at the row and segment level :
Views: DBA_HEAT_MAP or USER_HEAT_MAP
Partition
Table
Add ADO policy: three parts
Ado action: data compression or data movement
Level: segment or row level for table or partition
Condition: what condition will initiate an ADO action.
ADO policy examples:

20. Automatic Data Optimization Examples
Ado policy for data movement
Thresholds at which ADO data movement policy will be started/stopped
Tablespace percent used
Tablespace percent free
%used > 85% start
%free < 30%  stop
Tablespaces on tier storage (in this example)
T1DATA tablespace is built on 12x SLC SSDs in RAID10
T2DATA tablespace is built on 12x 1TB 7.2k rpm Hard disks in RAID10

21. Automatic Data Optimization Examples
Example 1 : Create segment-level ADO policy to automatically compress table after 10 days no modification
Create compression policy
. Check the policy
Display Heat map tracking data

22. Automatic Data Optimization Examples
Execute the ADO policy immediately
Check the task status: policy_name: P102, task_id: 2146
Verify the tables compression

23. Automatic Data Optimization Examples
Example 2 : Create ADO policy to move objects from T1DATA to T2DATA
Current TBS:
Check Tablespaces space usage: PCT_FREE, PCT_USED

24. Automatic Data Optimization Examples
Check ADO parameters for ADO data migration threshold
T1DATA: PCT_USED: % > 85% threshold
Start migration when pct_used > 85% ,stop migration when pct_free < 30%
Create data movement policy:
Check the policy: policy name: P122

25. Automatic Data Optimization Examples
Execute the ADO policy immediately
Check the task status : policy_name: P122, task_id: 2214

26. Automatic Data Optimization Examples
Check the result
Tablespace space usage: free space on T1DATA tablespace:
T1DATA 8.38% free T1DATA: 15.38% free
Before Aftersage:

27. Automatic Data Optimization Examples
Example 3 : Compress partitions
Check partitions and
their write time
Create policy

28. Automatic Data Optimization Examples
Check policy
Check execution
Recreate Policy

29. Automatic Data Optimization Examples
Check policy: policy_name: P145, task_id: 2358
Check execution of task
Check the result

30. Automatic Data Optimization Examples
Example 4: Move partition to tier 2 storage T2DATA:
Current tablespace:
Create policy:
Execute the policy:

31. Automatic Data Optimization Examples
Why ?? T1DATA’s PCT_USED (84.62%) < 85% threshold
Force the policy to get executed by changing T1DATa PCT_USED parameter
Execute the policy again:

32. Automatic Data Optimization Examples
Check the task:
Check the result
Tablespace before the data movement

33. Links

34. Sources
Historical External Disk Storage Data - notes.blogspot.com/2014/06/historical-external-disk-storage-data.html
Heat Map, Automatic Data Optimization and ILM with Oracle Database -
DMBS_ILM_ADMIN Documentation -

35. Questions?

36. Contact us at kai_yu@dell
Contact us at or visit Kai Oracle Blog at =