Determining user key-value storage needs from example queries

• US 9,336,217 B2
• Filed: 08/27/2014
• Issued: 05/10/2016
• 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): Hasan, Syed
• Assistant Examiner(s): ALLEN, NICHOLAS E
• Application Number:
  US14/470,733
• Publication Number:
  US 20140372377A1
• Time in Patent Office: 622 Days
• Field of Search: 707/634
• US Class Current: 1/1
• 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 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:

comparing properties of queries submitted to an origin datacenter to properties of training queries to determine the training queries'"'"' representativeness of the queries submitted to the origin datacenter;

increasing a number of the training queries based on the comparison such that the training queries are statistically representative of the queries submitted to the origin datacenter;

training new indices for a target datacenter based on the training queries without transfer of readable data from the training queries to the target datacenter; and

testing the new indices for performance at the target datacenter prior to a customer commitment to migration from the origin datacenter to the target datacenter.

2. The method of claim 1, further comprising:

testing the new indices for the target datacenter based on the training queries without transfer of readable data from the training queries to the target datacenter.

3. The method of claim 1, further comprising:

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

4. The method of claim 1, further comprising:

determining a degree to which terms of a search are represented in the training queries to determine the training queries representativeness of the queries submitted to the origin datacenter.

5. The method of claim 1, further comprising:

selecting the training queries based on a selection of the queries submitted to the origin datacenter over a predefined time period.

6. The method of claim 1, further comprising:

selecting the training queries based on a percentage of a population of the queries submitted to the origin datacenter.

7. The method of claim 1, further comprising:

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

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

an origin datacenter comprising one or more servers, at least one of the servers configured to;

compare properties of queries submitted to the origin datacenter to properties of training queries to determine the training queries representativeness of the queries submitted to the origin datacenter; and

increase a number of the training queries based on the comparison such that the training queries are statistically representative of the queries submitted to the origin datacenter; and

a target datacenter comprising one or more servers, at least one of the servers configured to;

train new indices for the target datacenter based on the training queries without transfer of readable data from the training queries to the target datacenter; and

test the new indices for performance at the target datacenter prior to a customer commitment to migration from the origin datacenter to the target datacenter.

9. The system of claim 8, wherein the comparison of the properties of the training queries to the properties of the queries submitted to the origin datacenter is limited based on a size of the training queries.

10. The system of claim 8, wherein the number of the training queries are increased until a proportionality equivalent to cardinalities in existing indices at the origin datacenter is obtained.

11. The system of claim 8, wherein the at least one server of the origin datacenter is further configured to provide the training queries to the target datacenter through deterministic one-way encryption.

12. The system of claim 11, wherein the at least one server of the target datacenter is further configured to generate hashed queries and a hashed database at the target datacenter from the training queries.

13. The system of claim 12, wherein the at least one server of the target datacenter is further configured to train the target datacenter to create hashed indices based on the hashed queries and the hashed database through auto-learning.

14. The system of claim 13, wherein the at least one server of the target datacenter is further configured to receive a hash table upon receiving the customer commitment to the migration, and un-hash the hashed indices employing the received hash table.

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

one or more servers, wherein at least one of the servers is configured to execute a training module, the training module configured to;

compare properties of queries submitted to an origin datacenter to properties of training queries to determine the training queries representativeness of the queries submitted to the origin datacenter;

increase a number of the training queries based on the comparison such that the training queries are statistically representative of the queries submitted to the origin datacenter;

train and test new indices for a target datacenter based on the training queries without transfer of readable data from the training queries to the target datacenter;

enable a customer to test-drive the target datacenter without a migration commitment; and

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

16. The datacenter of claim 15, wherein the datacenter is the origin datacenter, the target datacenter, and/or a third party service.

17. The datacenter of claim 15, wherein the datacenter is configured to determine the training queries representativeness of the queries submitted to the origin datacenter by employing a query submittal process, wherein the query submittal process comprises a querier configured to submit an encrypted query to an index server via a query router system.

18. The datacenter of claim 17, wherein the query router system is configured to:

receive the encrypted query from the querier;

verify an authorization of a user submitting the encrypted query through the querier, transform the encrypted query,

forward the encrypted query to the index server to perform a search;

receive search results from the index server; and

forward the search results to the user.

19. The datacenter of claim 17, wherein the encrypted query is a keyword query to enable determination of a degree to which terms of a search are represented in the training queries.