Determining user key-value storage needs from example queries

• US 8,849,757 B2
• Filed: 03/29/2012
• Issued: 09/30/2014
• Est. Priority Date: 03/29/2012
• Status: Active Grant

• Current Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Original Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Inventors: Kruglick, Ezekiel
• Primary Examiner(s): Beausoliel, Jr., Robert
• Assistant Examiner(s): ALLEN, NICHOLAS E
• Application Number:
  US13/640,469
• Publication Number:
  US 20130262382A1
• Time in Patent Office: 915 Days
• Field of Search: None
• US Class Current: 707/634
• CPC Class Codes: G06F 16/119 Details of migration of file systems migration mechanisms in storage systems G06F3/0647
• CPC Class Codes: G06F 16/13 File access structures, e.g. distributed indices arrangements of input from, or output to, record carriers G06F3/06
• CPC Class Codes: G06F 16/148 File search processing
• CPC Class Codes: G06F 16/217 Database tuning G06F16/2282 takes precedence; database performance monitoring G06F11/3409
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Technologies are generally provided for conveying tree and index needs of a database by collecting and forwarding a statistically representative set of data requests so that a new datacenter can use its existing auto-learning and adjustment techniques to create local indices before receiving its first real or live requests. In some examples, requests at an origin datacenter may be collected to provide a small population of queries that are representative of a larger population of queries. Filters and/or anonymizers may be employed to adhere to data sharing rules and restrictions while still allowing a new datacenter to build indices and test performance. Thus, the new datacenter may be trained asynchronously from another site and without actual data changing hands. Training and testing of destination database services may be completed before committing to customer migration.

Claims

1. A method to determine customer key-value storage needs through example queries in transfers between cloud computing environments, the method comprising:

receiving training queries from an origin datacenter;

comparing properties of queries submitted to the origin datacenter to properties of a collection of the training queries;

increasing a number of the training queries based on the comparison;

training new indices for a target datacenter based on the increased number of training queries, wherein the new indices are trained without transfer of readable data from the training queries to the target datacenter;

testing the new indices for performance at the target datacenter, wherein the new indices are tested without transfer of readable data from the training queries to the target datacenter; and

in response to receiving a request for customer migration, activating an index database at the target datacenter with the new indices.

2. The method according to claim 1, further comprising:

providing the increased number of training queries to the target datacenter through deterministic one-way encryption.

3. The method according to claim 2, further comprising:

generating a collection of hashed queries and a hashed database at the target datacenter based on the encrypted collection of the increased number of training queries received from the origin datacenter.

4. The method according to claim 3, further comprising:

training the target datacenter to create new hashed indices based on the collection of hashed queries and the hashed database through auto-learning.

5. The method according to claim 3, further comprising:

upon receiving the request for the customer migration, receiving a hash table; and

un-hashing the hashed indices employing the received hash table.

6. The method according to claim 2, wherein comparing the properties of the queries includes determining a degree to which terms of a search are represented in the collection of the training queries.

7. The method according to claim 2, wherein comparing the properties of the queries is limited based on a size of the collection of the training queries.

8. A computing device to determine customer key-value storage needs through example queries in transfers between cloud computing environments, the computing device comprising:

a memory configured to store instructions; and

a processing unit configured to execute a training module in conjunction with the instructions, wherein the training module is configured to;

receive training queries from an origin datacenter;

compare properties of queries submitted to the origin datacenter to properties of a collection of the training queries;

increase a number of the training queries based on the comparison;

provide the increased number of training queries to a target datacenter through deterministic one-way encryption;

train new indices for the target datacenter based on the increased number of training queries;

test the new indices for performance at the target datacenter, wherein the target datacenter enables a customer to test the new indices for performance without a commitment to a customer migration; and

in response to receiving a request for the customer migration, activate an index database at the target datacenter with the new indices.

9. The computing device according to claim 8, wherein the comparison of the properties of the queries is based on obtaining a proportionality equivalent to cardinalities in existing indices at the origin datacenter.

10. The computing device according to claim 8, wherein the training module is further configured to:

select the training queries based on a statistical analysis of queries submitted to the origin datacenter.

11. The computing device according to claim 8, wherein the training module is further configured to:

select the training queries by choosing queries submitted to the origin datacenter over a predefined time period.

12. The computing device according to claim 8, wherein the computing device is part of one of the origin datacenter or a third party service.

13. The computing device according to claim 8, wherein the computing device is part of the target datacenter.

14. A computer-readable storage medium having instructions stored thereon to determine customer key-value storage needs through example queries in transfers between cloud computing environments, the instructions comprising:

receiving training queries from an origin datacenter;

comparing properties of queries submitted to the origin datacenter to properties of a collection of the training queries;

increasing a number of the training queries based on the comparison;

providing the increased number of training queries to a target datacenter through deterministic one-way encryption;

training new indices for the target datacenter based on the increased number of training queries, wherein the new indices are trained without transfer of readable data from the training queries to the target datacenter;

testing the new indices for performance at the target datacenter, wherein the target datacenter enables a customer to test the new indices for performance without a commitment to a customer migration and the new indices are tested without transfer of readable data from the training queries to the target datacenter; and

in response to receiving a request for customer migration, activating an index database at the target datacenter with the new indices.

15. The computer-readable storage medium according to claim 14, wherein the instructions further comprise:

generating a collection of hashed queries and a hashed database at the target datacenter based on the encrypted collection of the increased number of training queries received from the origin datacenter; and

training the target datacenter to create new hashed indices based on the collection of hashed queries and the hashed database through auto-learning.

16. The computer-readable storage medium according to claim 15, wherein the instructions further comprise:

upon receiving the request for the customer migration, receiving a hash table; and

un-hashing the hashed indices employing the received hash table.

17. The computer-readable storage medium according to claim 14, wherein comparing the properties of the queries includes determining a degree to which terms of a search are represented in the collection of the training queries based on obtaining a proportionality equivalent to cardinalities in existing indices at the origin datacenter.

18. The computer-readable storage medium according to claim 14, wherein the instructions further comprise:

selecting the training queries based on a statistical analysis of queries submitted to the origin datacenter; and

/or

selecting the training queries by choosing queries submitted to the origin datacenter over a predefined time period.

19. The computer-readable storage medium according to claim 14, wherein the instructions further comprise:

employing one or more of a filter and/or an anonymizer to adhere to data sharing rules and restrictions.