View source for 18374944. ELASTIC COLUMN STORE WITH MINIMAL IMPACT ON WORKLOAD USING SMART EVICTION AND FAST REPOPULATION simplified abstract (Oracle International Corporation)

=ELASTIC COLUMN STORE WITH MINIMAL IMPACT ON WORKLOAD USING SMART EVICTION AND FAST REPOPULATION=


==Organization Name==

[[:Category:Oracle International Corporation|Oracle International Corporation]]


[[Category:Oracle International Corporation]]

==Inventor(s)==

[[:Category:Hariharan Lakshmanan of Brisbane CA (US)|Hariharan Lakshmanan of Brisbane CA (US)]][[Category:Hariharan Lakshmanan of Brisbane CA (US)]]

[[:Category:Teck Hua Lee of Newark CA (US)|Teck Hua Lee of Newark CA (US)]][[Category:Teck Hua Lee of Newark CA (US)]]

[[:Category:Vinita Subramanian of Campbell CA (US)|Vinita Subramanian of Campbell CA (US)]][[Category:Vinita Subramanian of Campbell CA (US)]]

[[:Category:Gary Smith of Auburn CA (US)|Gary Smith of Auburn CA (US)]][[Category:Gary Smith of Auburn CA (US)]]

[[:Category:Lijian Wan of Mountain View CA (US)|Lijian Wan of Mountain View CA (US)]][[Category:Lijian Wan of Mountain View CA (US)]]

[[:Category:Shasank Kisan Chavan of Mountain View CA (US)|Shasank Kisan Chavan of Mountain View CA (US)]][[Category:Shasank Kisan Chavan of Mountain View CA (US)]]

[[:Category:Venkat Raman Senapati of Sunnyvale CA (US)|Venkat Raman Senapati of Sunnyvale CA (US)]][[Category:Venkat Raman Senapati of Sunnyvale CA (US)]]

==ELASTIC COLUMN STORE WITH MINIMAL IMPACT ON WORKLOAD USING SMART EVICTION AND FAST REPOPULATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18374944 titled 'ELASTIC COLUMN STORE WITH MINIMAL IMPACT ON WORKLOAD USING SMART EVICTION AND FAST REPOPULATION

==Simplified Explanation==

The patent application describes techniques for implementing an in-memory columnar data store that can dynamically adjust its size based on performance prediction data generated from database workload information. The system allocates memory for various components in a database system and determines optimal memory allocations for the in-memory columnar data store based on performance predictions.

* The system maintains allocations of volatile memory for multiple memory-consuming components in a database system.
* Performance prediction data is received for each memory-consuming component, containing predictions for different memory allocation sizes.
* A target memory allocation is determined for the in-memory columnar data store based on the performance predictions.
* An incrementally adjusted amount of memory is calculated for the in-memory columnar data store and allocated accordingly.

== Potential Applications ==
This technology could be applied in various database management systems to optimize memory usage and improve performance based on workload predictions.

== Problems Solved ==
1. Efficient memory management in database systems.
2. Dynamic adjustment of memory allocations based on performance predictions.

== Benefits ==
1. Improved performance in database operations.
2. Optimal memory allocation for different components.
3. Enhanced scalability and flexibility in memory usage.

== Potential Commercial Applications ==
Optimizing memory usage in large-scale database systems for enterprises.

== Possible Prior Art ==
One possible prior art could be techniques for dynamic memory allocation in computer systems based on workload predictions.

== Unanswered Questions ==

=== How does the system handle unexpected changes in workload that were not predicted? ===
The system may need to have mechanisms in place to dynamically adjust memory allocations in real-time based on actual workload changes.

=== What impact does this dynamic memory allocation have on overall system stability and reliability? ===
It would be important to assess the potential risks and trade-offs of dynamically adjusting memory allocations on system stability and reliability.


==Original Abstract Submitted==

Techniques are provided for implementing an in-memory columnar data store that is configured to either grow or shrink in response to performance prediction data generated from database workload information. A system maintains allocations of volatile memory from a given memory area for a plurality of memory-consuming components in a database system. The system receives for each memory-consuming component, performance prediction data that contains performance predictions for a plurality of memory allocation sizes for the memory-consuming components. The system determines a target memory allocation for an in-memory columnar data store based on the performance predictions. The system determines an incrementally adjusted amount of memory for the in-memory columnar data store and causes the incrementally adjusted amount to be allocated to the in-memory columnar data store.



[[Category:G06F12/02]]