Automated query optimization method using both global and parallel local optimizations for materialization access planning for distributed databases

• US 4,769,772 A
• Filed: 02/28/1985
• Issued: 09/06/1988
• Est. Priority Date: 02/28/1985
• Status: Expired due to Term

• Current Assignee: Honeywell Bull Incorporated (Atos SE)
• Original Assignee: Honeywell Bull Incorporated (Atos SE)
• Inventors: Dwyer, Patricia A.
• Primary Examiner(s): Williams, Jr., Archie E.
• Application Number:
  US06/706,702
• Time in Patent Office: 1,286 Days
• Field of Search: 364/200 MS File, 364/900 MS File, 364/300
• US Class Current: 707/714
• CPC Class Codes: G06F 16/24524 Access plan code generation and invalidation; Reuse of access plans
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/2471 Distributed queries
• CPC Class Codes: Y10S 707/966 Distributed
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99939 Privileged access

Abstract

In a Distributed Database System (DDS), database management and transaction management are extended to a distributed environment among a plurality of local sites which each have transaction server, file server, and data storage facilities. The Materialization and Access Planning (MAP) method of a distributed query, update, or transaction is an important part of the processing of the query, update, or transaction. Materialization and access planning results in a strategy for processing a query, update, or transaction in the distributed database management system (DSDBMS). Materialization consists of selecting data copies used to process the query, update, or transaction. This step is necessary since data may be stored at more than one site (i.e., computer) on the network. Access planing consists of choosing the execution order of operations and the actual execution site of each operation. Three access planning methods are used: General (Response), General (Total) and Initial Feasible Solution (IFS). For a distributed query, General (Response) and General (Total) decrease the communication cost and increase the local processing costs as compared to the IFS.

Claims

1. A machine implemented method for automatically determining an optimal execution strategy for a request comprising query, update or transaction operations on a distributed database system having a plurality of data site, with each site having a computer and data storage facility, and said sites are interconnected by communication lines, said machine implemented method comprising the steps of:

A. inputting an ad hoc relational query or update request from a first computer process that formulates the relational query or update request as a compacted tree having lead nodes that are select, project or join operators and non-leaf nodes that are union, intersection, difference, delete, modify or insert operators to a second computer process which thereafter performs step B;

B. for each node in the compacted tree perform the following starting at the root node;

1. if the current node is a leaf node perform the following;

a. materialization planning to choose the data sites from which data is to be accessed;

b. local process planning to determine which operations can be processed locally at each site determined by materialization planning and estimate parameters of resultant data from the local operations;

c. non-local process planning to determine strategy for remaining operations which can not be performed locally without transmission of data between sites;

d. execution strategy building to build an execution strategy for each data site in the distributed database system at which data is processed by access or manipulation;

2.

3. system. 2. The method of claim 1 wherein the step of materialization planning for a leaf node comprises the steps of:

A. choosing a site for each base relation that is an operand of the leaf node by choosing the sites that have the most number of base relations; and

B. choosing result data processor, a site where the final result of the operations will be built, by choosing the site that has the most number of base relations.

4. The method of claim 1 wherein the step of local process planning for a leaf node comprises the steps of:

A. determining the select, project and join operations that can be performed on base relations or temporary relations that reside at the same node; and

B. estimating parameters of temporary relations produced by select, project and join operators using the parameter of the base relations.

5. The method of claim 1 wherein the step of non-local process planning for a leaf node further comprises determining a strategy for performing the remaining join operators by transmission of base relations and temporary relations among the sites by an optimization criteria using the parameters of the temporary relations estimated by local process planning and the parameters of the base relations.

6. The method of claim 4 wherein the optimization criteria is to minimize the total time for communications among the sites.

7. The method of claim 5 wherein the communication time between sites is a function of the size parameter of the data to be transmitted.

8. The method of claim 6 wherein the size parameter is reduced by use of semi join operations on the base relations or temporary relations.

9. The method of claim 7 wherein the reduction of the size parameter by use of the semi join operations is estimated by use of selectivity parameters.

10. The method of claim 4 wherein the optimization criteria is to minimize the response time for communication among the sites.

11. The method of claim 9 wherein the communication time between sites is a function of the size parameter of the data to be transmitted.

12. The method of claim 10 wherein the size parameter is reduced by use of semi join operations on the base relations or temporary relations.

13. The method of claim 11 wherein the reduction of the size parameter by use of the semi join operations is estimated by use of selectivity parameters.

14. The method of claim 4 wherein the optimization criteria is to ignore the parameters and to have all base relations and temporary relations transmitted to the result data processor chosen by materialization planning and perform remaining operators at the result data processor.

15. The method of claim 1 wherein the step of building for a leaf node further comprises the step of building select, project, join and move requests resulting from the local process planning and non-local process planning steps.

16. The method of claim 1 wherein the step of materialization planning for a non-leaf node comprises the steps of:

A. determining a site for each temporary relation that is an operand of a non-leaf node based upon the result data processor chosen for each child node;

B. for a union, intersection or difference operator, choosing a result data processor, a site where the final result of the operations will be built, by choosing the site that has the most number of temporary relations; and

C. for a delete, modify or insert operator, determining all sites where the base relation to be updated resides.

17. The method of claim 1 wherein the step of local process planning for a non-leaf node comprises the steps of:

A. determining union, intersection or difference operations that can be performed on temporary relations that reside at the same node; and

B. determining delete, modify or insert operations that can be performed on base relations that reside at the same node as the temporary relations.

18. The method of claim 1 wherein the step of non-local process planning for a non-leaf node comprises the steps of:

A. for a union, intersection or difference operator, transmitting all temporary relations to the result data processor chosen by materialization planning and perform the remaining operations at the result data processor; and

B. for a delete, modify or insert operator, transmitting all temporary relations to the sites where the base relation to be updated resides as determined by materialization planning and performing the delete, modify or insert operation at those sites.

19. The method of claim 1 wherein the step of execution strategy building for a non-leaf node further comprises the step of building union, intersection, difference, delete, modify, insert and move requests resulting from the local process planning and non-local process planning steps.

2. if the current node is a non-leaf node perform the following:

a. for each child node make the child a root of a subtree and perform step 1 above for the subtree; and

b. having processed all child nodes of the current non-leaf node perform the following for the current non-leaf node;

1. materialization planning to choose the data sites from which data is to be accessed;

2. local process planning to determine which operations can be processed locally at each site determined by materialization planning and estimate parameters of resultant data from the local operations;

3. non-local process planning to determine strategy for remaining operations which can not be performed locally without transmisson of data between sites;

4. execution strategy building to build an execution strategy for each data site in the distributed database system at which data is processed by access or manipulation; and

then

C. outputting execution strategy commands from said second computer process to a third computer process that coordinates the execution of the execution strategy commands at sites within the distributed database