Method and system for adaptively building a column store database from a temporal row store database based on query demands

• US 9,836,507 B2
• Filed: 09/17/2014
• Issued: 12/05/2017
• Est. Priority Date: 09/17/2014
• Status: Active Grant

• Current Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Inventors: Hu, Ron Chung, Shi, Guangyu, Mortazavi, Masood, Ku, Chi Yong, Chen, Mengmeng
• Primary Examiner(s): Fleurantin, Jean B
• Application Number:
  US14/488,775
• Publication Number:
  US 20160078089A1
• Time in Patent Office: 1,175 Days
• Field of Search: 707714
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/24544 Join order optimisation
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

A method for dynamically building a column store database from a row store database. The method includes establishing the row store database for storing data, wherein each row includes a plurality of attributes, and wherein data in row store database is current to a temporal point in time. The method includes establishing the column store database including data structured to satisfy received analytic queries. The method includes beginning from an initial state of the column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of the row store database into the column store database to satisfy the corresponding subsequently received analytic query.

Claims

1. A computer system for accessing information, comprising:

a non-transitory memory storage comprising instructions; and

one or more processors in communication with the memory, wherein the one or more processors execute the instructions to;

establish a row store database for storing data, wherein each row of said row store database comprises a plurality of attributes, wherein said data in said row store database is current to a temporal point in time;

establish a column store database comprising data structured to satisfy received analytic queries; and

begin from an initial state of said column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of said row store database into said column store database to satisfy said corresponding subsequently received analytic query,

wherein said importing a targeted amount of data causes the one or more processors to further execute the instructions to;

receive a first analytic query, wherein said first analytic query is directed to a queried range of primary key attributes for a referenced attribute vector corresponding to a referenced attribute in said plurality of attributes;

determine if a covered range of said primary key attributes in said referenced attribute vector is within said queried range of primary key attributes; and

when said covered range is not within said queried range, import said targeted amount of data from said row store database during execution of said first analytic query to enhance said covered range in order to encompass said queried range.

2. The computer system of claim 1, wherein said determining if a covered range of said computer-executable instructions causes the one or more processors to further execute the instructions to:

access metadata comprising information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.

3. The computer system of claim 1, wherein the one or more processors further execute the instructions to:

update metadata to reflect said targeted amount of data, wherein said metadata comprises information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.

4. The computer system of claim 1, wherein said importing data of said computer-executable instructions causes the one or more processors to further execute the instructions to:

perform a full table scan of said row store database to access said targeted amount of data.

5. The computer system of claim 1, wherein said importing data of said computer-executable instructions causes the one or more processors to further execute the instructions to:

perform a partial table scan of said row store database to access said targeted amount of data.

6. The computer system of claim 1, wherein the one or more processors further execute the instructions to:

persistently store said column store database updated with said targeted amount of data to disk.

7. A computer-implemented method for dynamically building a column store database from a row store database, comprising;

establishing said row store database for storing data, wherein each row of said row store database comprises a plurality of attributes, wherein said data in said row store database is current to a temporal point in time;

establishing said column store database comprising data structured to satisfy received analytic queries; and

beginning from an initial state of said column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of said row store database into said column store database to satisfy said corresponding subsequently received analytic query,

wherein said importing a targeted amount of data comprises;

receiving a first analytic query, wherein said first analytic query is directed to a queried range of primary key attributes for a referenced attribute vector corresponding to a referenced attribute in said plurality of attributes;

determining if a covered range of said primary key attributes in said referenced attribute vector is within said queried range of primary key attributes; and

when said covered range is not within said queried range, importing said targeted amount of data from said row store database during execution of said first analytic query to enhance said covered range in order to encompass said queried range.

8. The computer-implemented method of claim 7, wherein said determining if a covered range comprises:

accessing metadata comprising information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.

9. The computer-implemented method of claim 7, further comprising:

updating metadata to reflect said targeted amount of data, wherein said metadata comprises information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.

10. The computer-implemented method of claim 7, wherein said importing a targeted amount of data comprises:

performing a full table scan of said row store database to access said targeted amount of data.

11. The computer-implemented method of claim 7, wherein said importing a targeted amount of data comprises:

performing a partial table scan of said row store database to access said targeted amount of data.

12. The computer-implemented method of claim 7, further comprising:

persistently storing said column store database updated with said targeted amount of data to disk.

13. A non-transitory computer-readable storage medium storing computer instructions for dynamically building a column store database from a row store database, that when executed by one or more processors, cause the one or more processors to perform the steps of:

establishing said row store database for storing data, wherein each row of said row store database comprises a plurality of attributes, wherein said data in said row store database is current to a temporal point in time;

establishing said column store database comprising data structured to satisfy received analytic queries; and

beginning from an initial state of said column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of said row store database into said column store database to satisfy said corresponding subsequently received analytic query,

wherein said importing a targeted amount of data causes the one or more processors to further perform the steps of;

receiving a first analytic query, wherein said first analytic query is directed to a queried range of primary key attributes for a referenced attribute vector corresponding to a referenced attribute in said plurality of attributes;

determining if a covered range of said primary key attributes in said referenced attribute vector is within said queried range of primary key attributes; and

when said covered range is not within said queried range, importing said targeted amount of data from said row store database during execution of said first analytic query to enhance said covered range in order to encompass said queried range.

14. The non-transitory computer-readable storage medium of claim 13, wherein said determining if a covered range causes the one or more processors to further perform the steps of:

accessing metadata comprising information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.

15. The non-transitory computer-readable storage medium of claim 13, wherein the one or more processors further perform the steps of:

updating metadata to reflect said targeted amount of data, wherein said metadata comprises information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.

16. The non-transitory computer-readable storage medium of claim 13, wherein said importing data causes the one or more processors to further perform the steps of:

performing a full table scan of said row store database to access said targeted amount of data.

17. The non-transitory computer-readable storage medium of claim 13, wherein said importing data causes the one or more processors to further perform the steps of:

performing a partial table scan of said row store database to access said targeted amount of data.