Inserting data into an in-memory distributed nodal database

• US 8,892,558 B2
• Filed: 09/26/2007
• Issued: 11/18/2014
• Est. Priority Date: 09/26/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Barsness, Eric Lawrence, Darrington, David L., Peters, Amanda, Santosuosso, John Matthew
• Primary Examiner(s): Singh, Amresh
• Application Number:
  US11/861,343
• Publication Number:
  US 20090083276A1
• Time in Patent Office: 2,610 Days
• Field of Search: 707/100, 707/737
• US Class Current: 707/737
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/35 Clustering; Classification

Abstract

A method and apparatus loads data to an in-memory database across multiple nodes in a parallel computing system. A database loader uses SQL flags, historical information gained from monitoring prior query execution times and patterns, and node and network configuration to determine how to effectively cluster data attributes across multiple nodes. The database loader may also allow a system administrator to force placement of database structures in particular nodes.

Claims

1. A parallel computer system comprising:

an in-memory database in the memory of a plurality of fully functional compute nodes;

a database loader for pre-loading the in-memory database to optimize database efficiency by clustering database attributes into the in-memory database, wherein clustering database attributes comprises placing data corresponding to a first attribute for a plurality of records together on a first compute node of the computer system and placing data corresponding to a second attribute for the plurality of records together on a second compute node of the computer system; and

wherein the database loader clusters the database attributes based on a flag in an SQL statement that initiates placing the record in the in-memory database.

2. The parallel computer system of claim 1, wherein the parallel computer system is a massively parallel computer system.

3. The parallel computer system of claim 1 wherein the database loader determines to cluster the database attributes based on historical information for accessing the database.

4. The parallel computer system of claim 3, wherein the historical information includes information chosen from the following:

node information, network information and query historical information.

5. The parallel computer system of claim 4, wherein the node information includes node identification, timestamp, current utilization, future utilization and availability.

6. The parallel computer system of claim 4, wherein the network information includes network identification, timestamp, current utilization future utilization and availability.

7. The parallel computer system of claim 4, wherein the query information includes query identification, network used, elapsed time, node list and priority.

8. A computer implemented method for pre-loading an in-memory database into memory of a plurality of compute nodes of a parallel computer system, the method comprising the steps of:

receiving a database structure to load into the in-memory database in the plurality of compute nodes;

determining an optimized data node mapping to cluster database attributes across multiple compute nodes, wherein clustering database attributes across multiple compute nodes comprises placing data corresponding to a first attribute for a plurality of records together on a first compute node of the computer system and placing data corresponding to a second attribute for the plurality of records together on a second compute node of the computer system, wherein the database loader clusters the database attributes based on a flag in an SQL statement that initiates placing the record in the in-memory database; and

loading the database structure into the in-memory database with the determined optimized data node mapping.

9. The computer implemented method of claim 8, further comprises the step of determining the data is accessed often.

10. The computer implemented method of claim 8, further comprising the step of determining if there is a force location for the database structure in the in-memory database as indicated by a system administrator input.

11. The computer implemented method of claim 8, wherein the database attributes are clustered based on a historical information for accessing the database.

12. A computer-readable article of manufacture comprising:

a database loader for pre-loading an in-memory database in memory of a plurality of compute nodes of a parallel computer system to optimize database efficiency by clustering database attributes, wherein clustering database attributes comprises placing data corresponding to a first attribute for a plurality of records together on a first compute node of the computer system and placing data corresponding to a second attribute for the plurality of records together on a second compute node of the computer system, wherein the database loader clusters the database attributes based on a flag in an SQL statement that initiates placing the record in the in-memory database; and

non-transitory computer recordable media bearing the database loader.

13. The article of manufacture of claim 12, wherein the database loader determines to cluster the database attributes based on a historical information for accessing the database.

14. The article of manufacture of claim 12, wherein the historical information includes information chosen from the following:

node information, network information and query historical information.

15. The article of manufacture of claim 14, wherein the node information includes node identification, timestamp, current utilization future utilization and availability.

16. The article of manufacture of claim 14, wherein the network information includes network identification, timestamp, current utilization future utilization and availability.

17. The article of manufacture of claim 14, wherein the query information includes query identification, network used, elapsed time, node list and priority.