Normalizing data for fast superscalar processing

• US 8,631,039 B2
• Filed: 05/05/2010
• Issued: 01/14/2014
• Est. Priority Date: 05/05/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Surna, Aleksandras, Ismert, Erik, Rangarajan, Srikumar, Khanolkar, Nimish S.
• Primary Examiner(s): NGUYEN, KIM T
• Application Number:
  US12/773,889
• Publication Number:
  US 20110276607A1
• Time in Patent Office: 1,350 Days
• Field of Search: 707/790, 707/812
• US Class Current: 707/790
• CPC Class Codes: G06F 15/82 data or demand driven
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 9/30192 according to data descriptor, e.g. dynamic data typing
• CPC Class Codes: G06F 9/3885 using a plurality of independent parallel functional units

Abstract

A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.

Claims

1. A computer-implemented method for normalizing an in-memory representation of stored data for faster superscalar processing, the method comprising:

accessing stored data that includes multiple columns, each column having a data type;

selecting a column in the accessed data to determine an appropriate in-memory representation;

determining a data type of the selected column;

determining whether row data associated with the selected column can be normalized based at least in part on the determined data type of the selected column;

determining a fixed size value for a normalized data representation, wherein the fixed sized value is sized such that it can be processed by a superscalar processor as a single instruction; and

upon determining that the row data can be normalized, converting the row data associated with the selected column into the normalized data representation, wherein the normalized data representation is a format that allows performing parallel processing of multiple instances of the normalized data representation using the superscalar processor and that represents multiple data types as the determined fixed size value that can be processed by the superscalar processor in the single instruction,

wherein the preceding steps are performed by at least one processor.

2. The method of claim 1 wherein the stored data comprises a file-based tabular representation of a database, including tables with columns and rows of data.

3. The method of claim 1 wherein selecting the column comprises accessing a list of columns and iterating through each column in the list.

4. The method of claim 1 wherein determining the data type of the selected column comprises accessing metadata in the stored data that identifies a type of the column data declared by a database designer.

5. The method of claim 1 wherein determining whether row data can be normalized comprises receiving configuration information that determines whether the normalized data representation is used for storing data in memory, including conditions for determining whether the normalized data representation is used.

6. The method of claim 1 wherein converting the row data into the normalized data representation comprises storing the row data in a fixed size numeric value into which the system stores multiple different native data types.

7. The method of claim 1 further comprising, upon determining that the row data cannot be normalized, identifying a non-normalized data structure for storing row data associated with the selected column, and storing a pointer to an instance of the identified non-normalized data structure in a normalized data value.

8. The method of claim 7 wherein storing a pointer comprises setting the normalized data value to the pointer value and setting an unused bit of the pointer value to indicate that the normalized value points to another data structure.

9. The method of claim 1 further comprising storing row data values that fall outside of a range that the normalized data representation can contain in a non-normalized structure.

10. A computer-readable storage device comprising instructions for controlling a computer system to normalize an in-memory representation of stored data for faster superscalar processing, wherein the instructions, when executed, cause a processor to perform actions comprising:

accessing stored data that includes multiple columns, each column having a data type;

selecting a column in the accessed data to determine an appropriate in-memory representation;

determining a data type of the selected column;

determining whether row data associated with the selected column can be normalized based at least in part on the determined data type of the selected column;

determining a fixed size value for a normalized data representation, wherein the fixed sized value is sized such that it can be processed by a superscalar processor as a single instruction and

upon determining that the row data can be normalized, converting the row data associated with the selected column into the normalized data representation, wherein the normalized data representation is a format that allows performing parallel processing of multiple instances of the normalized data representation using the superscalar processor and that represents multiple data types as the determined fixed size value that can be processed by the superscalar processor in the single instruction,

wherein the preceding steps are performed by at least one processor.

11. The device of claim 10 wherein the stored data comprises a file-based tabular representation of a database, including tables with columns and rows of data.

12. The device of claim 10 wherein selecting the column comprises accessing a list of columns and iterating through each column in the list.

13. The device of claim 10 wherein determining the data type of the selected column comprises accessing metadata in the stored data that identifies a type of the column data declared by a database designer.

14. The device of claim 10 wherein determining whether row data can be normalized comprises receiving configuration information that determines whether the normalized data representation is used for storing data in memory, including conditions for determining whether the normalized data representation is used.

15. The device of claim 10 wherein converting the row data into the normalized data representation comprises storing the row data in a fixed size numeric value into which the system stores multiple different native data types.

16. The device of claim 10 further comprising, upon determining that the row data cannot be normalized, identifying a non-normalized data structure for storing row data associated with the selected column, and storing a pointer to an instance of the identified non-normalized data structure in a normalized data value.

17. The device of claim 16 wherein storing a pointer comprises setting the normalized data value to the pointer value and setting an unused bit of the pointer value to indicate that the normalized value points to another data structure.

18. The device of claim 10 further comprising storing row data values that fall outside of a range that the normalized data representation can contain in a non-normalized structure.