Method and system for managing data using parallel processing in a clustered network

• US 20050071842A1
• Filed: 08/04/2004
• Published: 03/31/2005
• Est. Priority Date: 08/04/2003
• Status: Abandoned Application

• Current Assignee: Totaletl Incorporated
• Original Assignee: Totaletl Incorporated
• Inventors: Shastry, Arun K.
• Application Number:
  US10/910,948
• Publication Number:
  US 20050071842A1
• Time in Patent Office: Days
• US Class Current: 718/100
• CPC Class Codes: G06F 2209/506 Constraint
• CPC Class Codes: G06F 9/5038 considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration scheduling strategies G06F9/4881 and subgroups

Abstract

An ETL/EAI data warehouse management system and method for processing data by dynamically distributing the computational load across a cluster network of distributed servers using a master node and multiple servant nodes, where each of the servant nodes owns all of its resources independently of the other nodes.

Claims

1. A method for managing data comprising:

receiving a job at a master processing node of a cluster of computer processing nodes;

separating the job into a plurality of job steps;

assigning each of the job steps to a particular servant node of the cluster of computer processing nodes;

maintaining a schedule of assigned job steps in a repository that provides information related to job step completion and availability of data at servant nodes;

sending job steps to the individual servant nodes based on the schedule of assigned job steps;

extracting data from at least one data source;

processing job steps on extracted data at servant nodes; and

storing data from the processed job steps into a target destination.

2. A method of claim 1 wherein assigning the job steps comprises:

(i) identifying jobs steps that are dependent on processed data from other job steps as dependent job steps;

(ii) assigning independent job steps to servant nodes for parallel processing; and

(iii) assigning dependent job steps to other servant nodes for processing after data is available from other job steps.

3. A method of claim 2 wherein the data source can be either an external source memory or a cached memory from a node in the cluster of computer processing nodes.

4. A method of claim 3 further comprising sending processed data from a servant node'"'"'s cached memory to other servant nodes for use in subsequent job steps.

5. A method of claim 2 wherein the target destination can be either an external target destination or a cached memory of the servant node in the cluster of computer processing nodes.

6. A method of claim 1 wherein the master node periodically polls the secondary nodes to determine the secondary nodes'"'"' availability for processing.

7. A method of claim 6 wherein the master node updates the schedule of assigned jobs based on changes in availability of servant nodes.

8. A method of claim 6 wherein a servant node acts as a master node if a predetermined period of time passes without any servant node receiving a periodic poll from the master node.

9. A method of claim 1 wherein nodes, data sources, target destination, and the repository communicate through the use of Enterprise Java Beans.

10. A method for managing data comprising:

receiving a job at a master processing node of a cluster of computer processing nodes;

separating the job into a plurality of job steps;

identifying jobs steps that are dependent on processed data from other job steps as dependent job steps;

assigning independent job steps to servant nodes for parallel processing;

assigning dependent job steps to other servant nodes for processing after data is available from other job steps;

maintaining a schedule of assigned job steps in a repository that provides information related to job step completion and availability of data at servant nodes;

sending job steps to the individual servant nodes based on the schedule of assigned job steps;

extracting data from at least one data source, wherein the data source can be either an external source memory or a cached memory from a node in the cluster of computer processing nodes;

processing job steps on extracted data at servant nodes; and

storing data from the processed job steps into a target destination.

11. A cluster of computer processing nodes for managing data comprising:

a repository that stores a schedule that provides information related to job step completion and availability of data at the processing nodes;

a master node and at least one servant node, each node in communication with the other nodes in the cluster, where;

(1) the master node (a) receives a job, (b) separates the job into a plurality of job steps, (c) assigns each of the job steps to a particular servant node, (d) stores a schedule of assigned job steps in the repository, and (e) sends the assigned job steps to servant nodes based on the schedule of assigned jobs; and

(2) a servant node (a) receives a job step from the master node, (b) communicates with the repository to determine availability of data, (c) extracts data from a data source, (d) processes the job step on the extracted data, and (e) notifies the repository when the job step has been processed.

12. A cluster of computer processing nodes of claim 11 wherein the master node further:

(i) identifies jobs steps that are dependent on processed data from other job steps as dependent job steps;

(ii) assigns independent job steps to servant nodes for parallel processing; and

(iii) assigns dependent job steps to other servant nodes for processing after data is available from other job steps.

13. A cluster of computer processing nodes of claim 12 wherein the servant node further stores data from the processed job step in either its own cached memory or an external target destination.

14. A cluster of computer processing nodes of claim 12 wherein the data source is either an external source memory or a cached memory from a node in the cluster of computer processing nodes.

15. A cluster of computer processing nodes of claim 12 wherein the servant node further sends data from its own cached memory to another node in the cluster of computer processing nodes.

16. A cluster of computer processing nodes of claim 11 wherein the master node periodically polls the secondary nodes to determine the secondary nodes'"'"' availability for processing.

17. A cluster of computer processing nodes of claim 16 wherein the master node updates the schedule of assigned jobs based on changes in availability of servant nodes.

18. A cluster of computer processing nodes of claim 16 wherein a servant node can act as a master node if a predetermined period of time passes without any servant node receiving a periodic poll from the master node.

19. A cluster of computer processing nodes of claim 11 wherein nodes, data sources, target destination, and the repository communicate through the use of Enterprise Java Beans.

20. A computer-readable medium having stored thereon sequences of

instructions, the sequences of instructions including instructions, when

executed by a processor, causes the processor to perform:

receiving a job at a master processing node of a cluster of computer processing nodes;

separating the job into a plurality of job steps;

assigning each of the job steps to a particular servant node of the cluster of computer processing nodes by;

(i) identifying job steps that are dependent on processed data from other job steps as dependent job step;

(ii) assigning independent job steps to servant nodes for parallel processing; and

(iii) assigning dependent job steps to other servant nodes for processing after data is available from other job steps;

maintaining a schedule of assigned job steps in a repository;

sending job steps to the individual servant nodes based on the schedule of assigned job steps;

extracting data from at least one data source;

processing job steps on extracted data at servant nodes; and

storing data from the processed job steps into a target destination.