Inserting data into an in-memory distributed nodal database

• US 9,183,283 B2
• Filed: 03/06/2013
• Issued: 11/10/2015
• Est. Priority Date: 09/26/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Barsness, Eric L., Darrington, David L., Randles, Amanda, Santosuosso, John M.
• Primary Examiner(s): Singh, Amresh
• Application Number:
  US13/786,554
• Publication Number:
  US 20130185301A1
• Time in Patent Office: 979 Days
• Field of Search: 707/737
• US Class Current: 1/1
• 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 database loader loads data to an in-memory database across multiple nodes in a parallel computing system. The 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 use flag in an SQL statement that indicates an anticipated access frequency of placing a record of the plurality of records 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 further clusters the database attributes based on a record flag in the SQL statement that initiates placing a range of columns of the data that should be clustered together in the in-memory database.

4. The parallel computer system of claim 1 wherein the database loader determines to cluster the database attributes based on node information comprising node identification, timestamp, current utilization, future utilization and availability.

5. The parallel computer system of claim 1 wherein the database loader determines to cluster the database attributes based on network information comprising network identification, timestamp, current utilization future utilization and availability.

6. The parallel computer system of claim 1 wherein the database loader determines to cluster the database attributes based on query information comprising query identification, network used, elapsed time, node list and priority.

7. 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 use flag in an SQL statement that indicates an anticipated access frequency of placing a record of the plurality of records in the in-memory database; and

non-transitory computer recordable media bearing the database loader.

8. The article of manufacture of claim 7 wherein the database loader further clusters the database attributes based on a record flag in the SQL statement that initiates placing a range of columns of the data that should be clustered together in the in-memory database.

9. The article of manufacture of claim 7 wherein the database loader determines to cluster the database attributes based on node information comprising node identification, timestamp, current utilization, future utilization and availability.

10. The article of manufacture of claim 7 wherein the database loader determines to cluster the database attributes based on network information comprising network identification, timestamp, current utilization future utilization and availability.

11. The article of manufacture of claim 7 wherein the database loader determines to cluster the database attributes based on query information comprising query identification, network used, elapsed time, node list and priority.

12. A massively parallel computer system comprising:

an in-memory database in the memory of a plurality of fully functional compute nodes in the massively parallel computer system;

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, wherein the database loader determines to cluster the database attributes based on historical information for accessing the database, wherein the historical information comprises node information, network information and query historical information;

wherein the database loader clusters the database attributes based on a use flag in an SQL statement that indicates an anticipated access frequency of placing a record of the plurality of records in the in-memory database; and

wherein the database loader further clusters the database attributes based on a record flag in the SQL statement that initiates placing a range of columns of the data that should be clustered together in the in-memory database.

13. The parallel computer system of claim 12 wherein the node information includes node identification, timestamp, current utilization, future utilization and availability.

14. The parallel computer system of claim 12 wherein the network information includes network identification, timestamp, current utilization future utilization and availability.

15. The parallel computer system of claim 12 wherein the query information includes query identification, network used, elapsed time, node list and priority.