View source for 18072222. ACCELERATING QUERY EXECUTION BY OPTIMIZING DATA TRANSFER BETWEEN STORAGE NODES AND DATABASE NODES simplified abstract (Oracle International Corporation)

=ACCELERATING QUERY EXECUTION BY OPTIMIZING DATA TRANSFER BETWEEN STORAGE NODES AND DATABASE NODES=


==Organization Name==

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


[[Category:Oracle International Corporation]]

==Inventor(s)==

[[:Category:Kamaljit Shergill of Maidenhead (GB)|Kamaljit Shergill of Maidenhead (GB)]][[Category:Kamaljit Shergill of Maidenhead (GB)]]

[[:Category:Ken Kumar of Bangalore (IN)|Ken Kumar of Bangalore (IN)]][[Category:Ken Kumar of Bangalore (IN)]]

[[:Category:Aurosish Mishra of Foster City CA (US)|Aurosish Mishra of Foster City CA (US)]][[Category:Aurosish Mishra of Foster City CA (US)]]

[[:Category:Shasank Kisan Chavan of Menlo Park CA (US)|Shasank Kisan Chavan of Menlo Park CA (US)]][[Category:Shasank Kisan Chavan of Menlo Park CA (US)]]

==ACCELERATING QUERY EXECUTION BY OPTIMIZING DATA TRANSFER BETWEEN STORAGE NODES AND DATABASE NODES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18072222 titled 'ACCELERATING QUERY EXECUTION BY OPTIMIZING DATA TRANSFER BETWEEN STORAGE NODES AND DATABASE NODES

==Simplified Explanation==

The abstract describes a technique for optimizing data transfer between storage nodes and database nodes to accelerate query execution. 

* A compute node receives a database statement and transmits selection criteria to a storage node.
* The storage node retrieves data blocks from storage based on the database statement.
* Each data block consists of rows from an index-organized table, with key and non-key sections.
* The storage node applies the selection criteria to a data block, resulting in a modified data block.
* The storage node generates modified header data for the modified data block and transmits it back to the compute node.

== Potential Applications ==
This technology could be applied in large-scale database systems, data warehouses, and data analytics platforms to improve query performance and reduce latency in data retrieval processes.

== Problems Solved ==
1. Accelerating query execution by optimizing data transfer between storage nodes and database nodes.
2. Improving overall system efficiency by reducing the time required for data retrieval and processing.

== Benefits ==
1. Faster query execution and improved system performance.
2. Enhanced scalability and reliability of database systems.
3. Reduced latency in data retrieval processes.

== Potential Commercial Applications ==
Optimizing data transfer techniques could be valuable for companies in the e-commerce, finance, healthcare, and telecommunications industries that rely on large databases for their operations.

== Possible Prior Art ==
One possible prior art in this field is the use of data caching mechanisms to improve data retrieval performance in distributed database systems. 

=== Unanswered Questions ===

=== How does this technique handle complex queries with multiple join operations? ===
The abstract does not provide information on how the technique handles complex queries involving multiple join operations. It would be interesting to know if the method is equally effective in optimizing data transfer for such queries.

=== What impact does this technique have on overall system resource utilization? ===
The abstract does not mention the impact of this technique on system resource utilization. It would be important to understand if the optimization of data transfer between storage and database nodes affects the overall resource consumption of the system.


==Original Abstract Submitted==

Techniques for accelerating query execution by optimizing data transfer between storage nodes and database nodes are provided. In one technique, a compute node receives a database statement and transmits a set of one or more selection criteria associated with the database statement to a storage node. Based on the database statement, the storage node retrieves a set of data blocks from storage. Each data block comprises multiple rows of an index-organized table (IOT), each row comprising a key section and a non-key section. The storage node applies the set of selection criteria to a data block, resulting in a modified data block. The storage node generates a modified header data for the modified data block and transmits the modified data block to the compute node.



[[Category:G06F16/2453]]
[[Category:G06F16/22]]