18201378. OPTIMIZE IN-MEMORY COLUMN STORE FOR MIXED WORKLOADS simplified abstract (Oracle International Corporation)

OPTIMIZE IN-MEMORY COLUMN STORE FOR MIXED WORKLOADS

Organization Name

Oracle International Corporation

Inventor(s)

Mahendra Maiti of Fremont CA (US)

Hariharan Lakshmanan of Brisbane CA (US)

Shasank Kisan Chavan of Menlo Park CA (US)

OPTIMIZE IN-MEMORY COLUMN STORE FOR MIXED WORKLOADS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18201378 titled 'OPTIMIZE IN-MEMORY COLUMN STORE FOR MIXED WORKLOADS

Simplified Explanation

The patent application describes techniques for determining the optimal configuration of an in-memory store based on the benefits and overhead associated with populating database elements in the store.

• The techniques involve calculating an overhead-adjusted benefit score for each element, considering factors such as scan-benefit value, scan-overhead value, and DML-overhead value.
• The database then uses the plurality of overhead-adjusted benefit scores to determine the best configuration for the in-memory store, making decisions on evicting or loading in-memory copies of elements accordingly.

Potential Applications

This technology could be applied in various industries where optimizing in-memory storage configurations can improve performance and efficiency, such as:

• Database management systems
• E-commerce platforms
• Financial services for real-time data processing

Problems Solved

By determining the optimal configuration of an in-memory store, this technology addresses the following issues:

• Minimizing overhead associated with storing database elements in memory
• Maximizing the benefits of having data readily available for processing

Benefits

The benefits of this technology include:

• Improved performance and response times for data retrieval and processing
• Efficient utilization of memory resources by prioritizing elements based on their benefits and overhead

Potential Commercial Applications

With its ability to optimize in-memory storage configurations, this technology could be valuable in commercial applications such as:

• Cloud computing services
• Big data analytics platforms
• IoT (Internet of Things) devices for real-time data processing

Possible Prior Art

One possible prior art in this field is the use of caching algorithms to optimize data storage in memory, but the specific approach of calculating overhead-adjusted benefit scores for elements in an in-memory store may be a novel aspect of this technology.

How does this technology compare to existing methods of optimizing in-memory storage configurations?

This article does not provide a direct comparison with other techniques or algorithms commonly used for optimizing in-memory storage configurations. It would be helpful to understand the specific advantages or limitations of this approach compared to existing methods.

What are the potential challenges or limitations of implementing this technology in real-world systems?

The article does not address the practical considerations or potential obstacles that may arise when implementing this technology in actual database management systems or applications. Understanding the challenges involved in deploying this solution could provide valuable insights for developers and stakeholders.

Original Abstract Submitted

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.