Hybrid Database Table Stored as Both Row and Column Store

• US 20130166553A1
• Filed: 12/22/2011
• Published: 06/27/2013
• Est. Priority Date: 12/22/2011
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Yoon, Yongsik, Jeong, Chanho, Lee, Juchang, Bin Song, Chang, Sik Kwon, Yong, Kyun Cha, Sang
• Granted Patent: US 8,768,927 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/278 Data partitioning, e.g. horizontal or vertical partitioning

Abstract

A hybrid database table is stored as both a row and a column store. One or more techniques may be employed alone or in combination to enhance performance of the hybrid table by regulating access to, and/or the size of, the processing-intensive column store data. For example during an insert operation, the column store data may be searched for a uniqueness violation only after certain filtering and/or boundary conditions have been considered. In another technique, a hybrid table manager may control movement of data to the column store based upon considerations such as frequency of access, or underlying business logic. In still another technique, querying of the hybrid table may result in a search of the column store data only after an initial search of row store data fails to return a result.

Claims

1. A computer-implemented method comprising:

providing in a non-transitory computer readable storage medium, a database created in an application level language and comprising row partitioned store and column partitioned store;

causing a row engine to insert records into the row partitioned store;

performing an optimization based upon additional information to check for a uniqueness violation only if there are uniqueness constraints; and

selectively moving data from the row partitioned store to the column partitioned store when one or more criteria according to a data movement policy are met.

2. The computer-implemented method of claim 1 wherein the optimization comprises maintaining min/max values of unique columns in the column partitioned store with a latch-free structure to minimize contention.

3. The computer-implemented method of claim 1 wherein the optimization comprises maintaining a filter for unique columns.

4. The computer-implemented method of claim 3 wherein the filter comprises a Bloom filter.

5. The computer-implemented method of claim 3 wherein the filter is scalable.

6. The computer-implemented method of claim 3 further comprising updating or rehashing when data is moved from the row partitioned store to the column partitioned store.

19. The computer-implemented method of claim 1 wherein the criterion is based on exceeding a predetermined number of records in the row partitioned store.

20. The computer-implemented method of claim 1 wherein the criterion is based on frequency of access to memory pages comprising the row partitioned store.

21. The computer-implemented method of claim 1 wherein the criterion is based on business rules.

7. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising:

receiving records to be inserted into a database created in an application level language in a non-transitory storage medium and comprising row partitioned store and column partitioned store;

causing a row engine to insert the records into the row partitioned store;

performing an optimization based upon additional information to check for a uniqueness violation only if there are uniqueness constraints; and

selectively moving data from the row partitioned store to the column partitioned store when one or more criteria according to a data movement policy are met. receiving a query on the database;

accessing the row partitioned store and the column partitioned store; and

producing a result of processing the query using data accessed from the row partitioned store and data accessed from the column partitioned store.

8. The non-transitory computer readable storage medium of claim 7 wherein the optimization comprises maintaining min/max values of unique columns in the column partitioned store with a latch-free structure to minimize contention.

9. The non-transitory computer readable storage medium of claim 7 wherein the optimization comprises maintaining a filter for unique columns.

10. The non-transitory computer readable storage medium of claim 9 wherein the filter comprises a Bloom filter.

11. The non-transitory computer readable storage medium of claim 9 wherein the filter is scalable.

12. The non-transitory computer readable storage medium of claim 9 wherein the method further comprises updating or rehashing when data is moved from the row partitioned store to the column partitioned store.

13. A computer system comprising:

one or more processors;

a software program, executable on said computer system, the software program configured to;

receive records to be inserted into a database created in an application level language in a non-transitory storage medium and comprising row partitioned store and column partitioned store;

cause a row engine to insert the records into the row partitioned store;

perform an optimization based upon additional information to check for a uniqueness violation only if there are uniqueness constraints; and

selectively move data from the row partitioned store to the column partitioned store when one or more criteria according to a data movement policy are met. receive a query on the database;

access the row partitioned store and the column partitioned store; and

produce a result of processing the query using data accessed from the row partitioned store and data accessed from the column partitioned store.

14. The computer system of claim 13 wherein the optimization comprises maintaining min/max values of unique columns in the column partitioned store with a latch-free structure to minimize contention.

15. The computer system of claim 13 wherein the optimization comprises maintaining a filter for unique columns.

16. The computer system of claim 15 wherein the filter comprises a Bloom filter.

17. The computer system of claim 15 wherein the filter is scalable.

18. (canceled)