Heat Map, Automatic Data Optimization and ILM with Oracle Database
Information Lifecycle Management (ILM) is the practice of applying policies for the effective management of information throughout its useful life. ILM includes every phase of a “row” from its beginning to its end, and consists of the policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost effective IT infrastructure from the time information is conceived through its final disposition. In Oracle Database 12c, Automatic Data Optimization (ADO) can be used to create policies, and automate actions based on those policies, to implement your ILM strategy. ADO utilizes the usage statistics collected by Heat Map, and depending on your ILM requirements, may also require the use of Partitioning, Advanced Row Compression, and Hybrid Columnar Compression. Oracle Database 12c Release 2 (12.2), the latest generation of the world’s most popular database, is now available in the Oracle Cloud.
Heat Map is an Oracle Database 12c feature that stores system-generated data usage statistics at the block and segment levels – information that can be used to automate the compression and movement of data in order to reduce storage costs, improve performance and optimize Oracle Database storage. Heat Map, used in conjunction with Automatic Data Optimization, can automate compression and storage tiering policies based on the actual usage of the data. 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). The statistics collected can be used to define compression and storage policies which will be automatically maintained throughout the lifecycle of the data. Heat Map skips internal access done for system tasks -- automatically excluding Stats Gathering, DDLs or Table Redefinitions.
Automatic Data Optimization (ADO)
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. Oracle Database evaluates ADO policies during the maintenance window, and uses the information collected by Heat Map to determine which operations to execute. All ADO operations are executed automatically and in the background, with no user intervention required. ADO policies can be specified at the segment or row level for tables and table partitions -- policies will be executed automatically in the background when policy criteria are satisfied, or they can be executed on demand. Conditions on ADO policies allow organizations to specify which criteria will initiate an ADO operation -- such as no data access, or no data modification – and when the policies take effect -- for example, after “n” days or months or years of no modification, or “n” days or months after row or partition creation, or when the tablespace containing the object meets your pre-defined tablespace fullness threshold. You aren’t limited to Heat Map data: you can also create custom conditions using PL/SQL functions, extending the flexibility of ADO to use your own data to determine when to move or compress data. Heat Map and ADO requires the Oracle Advanced Compression option.
ILM with Oracle Database
In both OLTP (Online Transaction Processing) and DW (Data Warehousing) deployments, cost-effective Information Lifecycle Management solutions can be enabled using a combination of Data Partitioning, Advanced Compression and/or Hybrid Columnar Compression. Storage tiering can be configured by creating a high performance tier with faster more expensive disks, and a high capacity tier with the slower less expensive disks. Using Data Partitioning tables can be partitioned to give the flexibility to compress or tier individual partitions based on the lifecycle of the data. IT departments can then use these features to reduce their dependency on high end storage, reduce their incremental storage costs, keep more data online for longer periods of time and improve the performance of applications that access large databases. With the data collected by Heat Map, and the automated policies available with Automatic Data Optimization, it is easy to implement ILM policies for both Smart Compression tiering and storage tiering. Smart Compression tiering allows organizations to compress different partitions of a table with different compression features, or even to implement Advanced Row Compression at the block level within a single table or partition. Storage tiering allows organizations to move tables or partitions from one tablespace to another or to free up space on a more expensive storage tier for more important data. The Oracle Advanced Compression and Oracle Partitioning options, together, provide IT department’s cost-effective information management by better optimizing storage infrastructures while also maintaining the performance and scalability that your business requires.
Heat Map and Automatic Data Optimization make Oracle Database 12c ideal for implementing ILM -- they are simple to use, there are no specialized data stores to manage, they operate independent of any hardware and they have proven performance benefits. Heat Map and Automatic Data Optimization provide total flexibility for the automated management of data compression and movement – enabling organizations to easily adapt to changes in data usage and data retention requirements, which is extremely important in order to optimally support the evolution of an organization's Information Lifecycle Management strategy.
Heat Map automatically tracks modification and query timestamps at the row and segment levels.
Automatic Data Optimization (ADO) automatically moves and compresses data according to user-defined policies based on the information collected by Heat Map.
Enables implemention of automated storage and compression tiering.
Supports OLTP and Data Warehousing compression tiering.