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

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: http://kyuoracleblog.wordpress.com/ 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 1995. 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

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

Changes in enterprise storage

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

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

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

Oracle Information Lifecycle Management (ILM)

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

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

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.

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.

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;

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

Automatic Data Optimization Examples

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:

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

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

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

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

Automatic Data Optimization Examples Check ADO parameters for ADO data migration threshold T1DATA: PCT_USED: 91.62% > 85% threshold Start migration when pct_used > 85% ,stop migration when pct_free < 30% Create data movement policy: Check the policy: policy name: P122

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

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:

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

Automatic Data Optimization Examples Check policy Check execution Recreate Policy

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

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

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:

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

Links

Sources Historical External Disk Storage Data - http://stevej-lab- notes.blogspot.com/2014/06/historical-external-disk-storage-data.html Heat Map, Automatic Data Optimization and ILM with Oracle Database - http://www.oracle.com/technetwork/database/enterprise-edition/index-090321.html DMBS_ILM_ADMIN Documentation - http://docs.oracle.com/database/121/ARPLS/d_ilm_admin.htm

Questions?

Contact us at kai_yu@dell Contact us at kai_yu@dell.com or visit Kai Oracle Blog at http://kyuoracleblog.wordpress.com/ =