Oracle International Corporation

Hariharan Lakshmanan

Prashant Gaharwar

Amit Ganesh

Vineet Marwah

Shasank Kisan Chavan

Ajit Mylavarapu

Tirthankar Lahiri

Dhruvil Shah

Juan R Loaiza

Andrew Witkowski

Jesse Kamp

Niloy Mukherjee

Teck Hua Lee

Sujatha Muthulingam

Viral Shah

You Jung Kim

Nirav Vyas

Kamaljit Shergill

Yu Chieh Fu

Mahendra Maiti

Sooyeon Jo

Saraswathy Narayan

Gary Smith

Vicente Hernandez Jimenez

Prashant Gaharwa

Techniques are provided for optimizing storage of database records in segments using sub-segments. A base segment is a container used for storing records that belong to a database object. A database management system receives a request to load, into the database object, a first set of records that are in a first state. In response to receiving the request, the system generates a new sub-segment, which is a container that is separate from the base segment. The system stores the first set of records, in their first state, within the sub-segment. The system then monitors one or more characteristics of the database system. In response to the one or more characteristics satisfying criteria, the system performs a migration of one or more records of the first set of records from the sub-segment to the base segment. During the migration, the system converts the one or more records from the first state to a second state and stores the one or more records, in their second state, in the base segment.

Automatic, online and space efficient clustering and compression of very large database objects

Techniques are provided for determining an optimal configuration for an in-memory store based on both benefits and overhead that would result from having database elements populated in the in-memory store. The techniques include determining an overhead-adjusted benefit score for each element based, at least in part, on (a) a scan-benefit value, (b) a scan-overhead value, and (c) a DML-overhead value. Based on the plurality of overhead-adjusted benefit scores, the database determines an optimal configuration of the in-memory store, and then evicts in-memory copies of elements and/or loads in-memory copies of elements based on the optimal configuration.

Optimize in-memory column store for mixed workloads

Techniques are provided for optimizing workload performance by automatically discovering and implementing performance optimizations for in-memory units (IMUs). A system maintains a set of IMUs for processing database operations in a database. The system obtains a database workload information for the database system and filters the database workload information to identify database operations in the database workload information that may benefit from performance optimizations. The system analyzes the database operations to identify a set of performance optimizations and ranks the performance optimizations based on their potential benefit. The system selects a subset of the performance optimizations, based on their ranking, and generates new versions of IMUs that reflect the performance optimizations. The system performs verification tests on the new versions of IMUs and analyzes the tests to determine whether the new versions of IMUs yield expected performance benefits. The system then categorizes the new set of IMUs into a first set of IMUs to be retained and a second set of IMUs to be discarded. The system then makes the first set of IMUs available to the current workload and discards the second set of IMUs.

Optimize workload performance by automatically discovering and implementing in-memory performance features

A method, apparatus, and system for policy driven data placement and information lifecycle management in a database management system are provided. A user or database application can specify declarative policies that define the movement and transformation of stored database objects. The policies are associated with a database object and may also be inherited. A policy defines, for a database object, an archiving action to be taken, a scope, and a condition before the archiving action is triggered. Archiving actions may include compression, data movement, table clustering, and other actions to place the database object into an appropriate storage tier for a lifecycle phase of the database object. Conditions based on access statistics can be specified at the row level and may use segment or block level heatmaps. Policy evaluation occurs periodically in the background, with actions queued as tasks for a task scheduler.

Policy driven data placement and information lifecycle management

Techniques are provided for a DBMS automating ILM on indexes, based on index composition, to efficiently reduce index storage footprints. According to an embodiment, a user sets an index-specific ILM (ISILM) policy, which comprises one or both of an index-test requirement and a time requirement. Based on the ISILM policy being met, or on some other way of initiating analysis, the DBMS automatically analyzes the data blocks storing the index to determine an index condition metric (e.g., percentage of free space). This analysis is performed on a sample of data blocks storing the index without blocking the index from other operations during the analysis. The condition metric for the entire index is estimated based on analysis of the sample data blocks. Using the determined condition metric for an index, the DBMS automatically selects an option for optimally managing the index (e.g., coalesce, shrink space, index rebuild, no action, etc.).

Automated information lifecycle management of indexes

Techniques related to automatic cache management are disclosed. In some embodiments, one or more non-transitory storage media store instructions which, when executed by one or more computing devices, cause performance of an automatic cache management method when a determination is made to store a first set of data in a cache. The method involves determining whether an amount of available space in the cache is less than a predetermined threshold. When the amount of available space in the cache is less than the predetermined threshold, a determination is made as to whether a second set of data has a lower ranking than the first set of data by at least a predetermined amount. When the second set of data has a lower ranking than the first set of data by at least the predetermined amount, the second set of data is evicted. Thereafter, the first set of data is cached.

On-demand cache management of derived cache

A method, apparatus, and system for automated information lifecycle management using low access patterns in a database management system are provided. A user or the database can store policy data that defines an archiving action when meeting an activity-level condition on one or more database objects. The archiving actions may include compression, data movement, and other actions to place the database object in an appropriate storage tier for a lifecycle phase of the database object. The activity-level condition may specify the database object meeting a low access pattern, optionally for a minimum time period. Various criteria including access statistics for the database object and cost characteristics of current and target compression levels or storage tiers may be considered to determine the meeting of the activity-level condition. The policies may be evaluated on an adjustable periodic basis and may utilize a task scheduler for minimal performance impact.

Automated information lifecycle management using low access patterns

A method, apparatus, and system for periodic performance optimization through heatmap based management of an in-memory area are provided. A heatmap is maintained to track database accesses, and a sliding most recent time window of the heatmap is externalized to a desired granularity level to provide access statistics regarding candidate elements to be possibly placed in the in-memory area. Initially and on a periodic basis, an appropriate knapsack algorithm is chosen based on an analysis on the computational costs versus the benefits of applying various knapsack algorithms for the candidate elements. Using the chosen algorithm in conjunction with a selected performance model, an optimized configuration of the in-memory area is determined. The optimized configuration indicates a set of elements chosen from the candidate elements, optionally specified with compression levels. A task scheduler then schedules the appropriate tasks, working in a coordinated fashion, to reconfigure the in-memory area according to the optimized configuration.

Periodic performance optimization through heatmap based management of an in-memory area

A method, apparatus, and system for custom policy driven data placement and information lifecycle management in a database management system are provided. A user or database application can specify declarative custom policies that define the movement and transformation of stored database objects. A custom policy defines, for a database object, a custom function to evaluate on an associated database object to determine whether an archiving action is triggered. Archiving actions may include compression, data movement, table clustering, and other actions to place the database object into an appropriate storage tier for a lifecycle phase of the database object. The custom function is defined by the database user, and can flexibly include any customized business logic using data sources internal and external to the database, including database access statistics such as segment level or block level heatmaps.

Custom policy driven data placement and information lifecycle management

Techniques related to efficient data storage and retrieval using a heterogeneous main memory are disclosed. A database includes a set of persistent format (PF) data that is stored on persistent storage in a persistent format. The database is maintained on the persistent storage and is accessible to a database server. The database server converts the set of PF data to sets of mirror format (MF) data and stores the MF data in a hierarchy of random-access memories (RAMs). Each RAM in the hierarchy has an associated latency that is different from a latency associated with any other RAM in the hierarchy. Storing the sets of MF data in the hierarchy of RAMs includes (1) selecting, based on one or more criteria, a respective RAM in the hierarchy to store each set of MF data and (2) storing said each set of MF data in the respective RAM.

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Belmont, CA, United States of America

Hariharan Lakshmanan