Granted Patent B1

US 12,724,758 · App. 19/093,799 · Granted Sep 1, 2026

Inventors: Ron Kimchi (Tel Aviv, IL); Ofir Ehrlich (Tel Aviv, IL); Dmitry Kuznetsov (Tel Aviv, IL); Assaf Natanzon (Tel Aviv, IL); Yaniv Ptashnik (Tel Mond, IL); Ran Mizrachi (Mishmar, IL); Peleg Kazaz (Tel Aviv, IL); Benjamin Gruenbaum (Herzliya, IL); Orly Amir (Ramat Gan, IL); Ilya Surdin (Ramat Gan, IL)

Assignee: Eon IO, Ltd.

G06F16/221G06F16/215G06F16/2255G06F16/284G06F18/22

Abstract

Claims (71)

1 . A method for generating a quarriable backup of database applications to allow database operations, comprising:

accessing a plurality of database applications deployed in a cloud environment;

exporting data stored in the database applications to a plurality of files, each file utilizing a column-oriented storage format;

generating metadata based on the plurality of exported files, wherein the metadata includes at least a hash map of related-columns and a list of related-tables;

wherein generating the metadata further comprises:

normalizing column values of tables in the database applications;

wherein normalizing the column values of tables in the database applications further comprises:

determining a category for each column of each table in the database applications;

normalizing column values of each column;

wherein normalizing the column values of each column further comprises applying a normalization function selected based on the category of a respective column; and

saving the normalized column values in the quarriable backup;

computing similar values;

unifying columns names of tables in the database applications;

identifying related-columns based on at least the normalizing columns values;

determining related-tables based on the identified related-columns; and

generating a quarriable backup based on a plurality of exported files and the metadata.

2 . The method of claim 1 , wherein the plurality of exported files is in a column-oriented storage format.

3 . The method of claim 1 , wherein identifying the related-columns further comprises: computing hash-based signatures for at least two columns in the normalized column values; and designating columns as having similar values upon determining that columns share a threshold overlap in their computed hash-based signatures, wherein at least two columns having similar values are related-columns.

4 . The method of claim 3 , wherein determining the related-tables further comprises:

designating at least two tables having related-columns as related-tables.

5 . The method of claim 1 , wherein determining the category of each column further comprises: prompting a large language model (LLM) with at least a set of values of a column to determine the category of the column.

6 . The method of claim 1 , wherein the quarriable backup includes tables or data of different database applications.

7 . The method of claim 6 , further comprising:

performing the database operations on the quarriable backup including tables or data of different database applications.

8 . The method of claim 7 , wherein a database operation includes any one of: retrieve, insert, delete, and update.

9 . The method of claim 1 , wherein a database application of the database applications includes any one of: a database hosted on a virtual machine and a relational database service (RDS) running on an infrastructure of the cloud environment.

10 . A non-transitory computer-readable medium storing a set of instructions for generating a quarriable backup of database applications to allow database operations, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:

access a plurality of database applications deployed in a cloud environment;

export data stored in the database applications to a plurality of files, each file utilizing a column-oriented storage format;

generate metadata based on the plurality of exported files, wherein the metadata includes at least a hash map of related-columns and a list of related-tables;

wherein generating the metadata further comprises:

normalizing column values of tables in the database applications;

wherein normalizing the column values of tables in the database applications further comprises:

determining a category for each column of each table in the database applications;

normalizing column values of each column;

wherein normalizing the column values of each column further comprises applying a normalization function selected based on the category of a respective column; and

saving the normalized column values in the quarriable backup;

computing similar values;

unifying columns names of tables in the database applications;

identifying related-columns based on at least the normalizing columns values;

determining related-tables based on the identified related-columns; and

generate a quarriable backup based on a plurality of exported files and the metadata.

11 . A system for generating a quarriable backup of database applications to allow database operations comprising: one or more processors configured to:

access a plurality of database applications deployed in a cloud environment;

export data stored in the database applications to a plurality of files, each file utilizing a column-oriented storage format;

generate metadata based on the plurality of exported files, wherein the metadata includes at least a hash map of related-columns and a list of related-tables;

wherein generating the metadata further comprises:

normalizing column values of tables in the database applications;

wherein normalizing the column values of tables in the database applications further comprises:

determining a category for each column of each table in the database applications;

normalizing column values of each column;

wherein normalizing the column values of each column further comprises applying a normalization function selected based on the category of a respective column; and

saving the normalized column values in the quarriable backup;

computing similar values;

unifying columns names of tables in the database applications;

identifying related-columns based on at least the normalizing columns values;

determining related-tables based on the identified related-columns; and

generate a quarriable backup based on a plurality of exported files and the metadata.

12 . The system of claim 11 , wherein the plurality of exported files is in a column-oriented storage format.

13 . The system of claim 11 , wherein the one or more processors, when determining the category of each column, are configured to: prompt a large language model (LLM) with at least a set of values of a column to determine the category of the column.

14 . The system of claim 11 , wherein the one or more processors, when identifying the related-columns, are configured to: compute hash-based signatures for at least two columns in the normalized column values; and designate columns as having similar values upon determining that columns share a threshold overlap in their computed hash-based signatures, wherein at least two columns having similar values are related-columns.

15 . The system of claim 13 , wherein the one or more processors, when determining the related-tables, are configured to:

designate at least two tables having related-columns as related-tables.

16 . The system of claim 11 , wherein the one or more processors, when determining the category of each column, are configured to: prompt a large language model (LLM) with at least a set of values of a column to determine the category of the column.

17 . The system of claim 11 , wherein the quarriable backup includes tables or data of different database applications.

18 . The system of claim 16 , wherein the one or more processors are further configured to:

perform the database operations on the quarriable backup including tables or data of different database applications.

19 . The system of claim 17 , wherein a database operation includes any one of:

retrieve, insert, delete, and update.

20 . The system of claim 11 , wherein a database application of the database applications includes any one of:

a database hosted on a virtual machine and a relational database service (RDS) running on an infrastructure of the cloud environment.

Assignments (1)

Continuity (2)

References Cited (31)