RESOURCE ALLOCATION METHOD, RESOURCE ALLOCATION PROGRAM AND RESOURCE ALLOCATION APPARATUS

CLAIM OF PRIORITY

The present application claims priority from Japanese application JP2007-175604 filed on Jul. 3, 2007 the content of which is hereby incorporated by reference into this application.

BACKGROUND OF THE INVENTION

The present invention relates to a resource allocation method and a resource allocation program which can be appropriately applied to a relational database management system and a technique associated with a managing server which manages the resource allocation.

DBMS (DataBase Management System) is a system for responding to an query about data in a database. Especially widely used is RDBMS (Relational DataBase Management System) of which database is described in a table format. As a language describing the query about data, the SQL (Structured Query Language) is widely used.

In order to respond to a plenty of queries, it is necessary to reduce the time required for processing each of the queries. For this, conventionally, processes of the queries are distributed to a plurality of SQL and each of the SQL processes is distributed to a plurality of computers so as to be executed in a parallel process such as a pipeline process.

It should be noted that the distribution algorithm which decides which SQL is to be distributed to which computer is very important. For example, according to the technique disclosed in Joel L. Wolf, John Turek, Ming-Syan Chen and Philip S. Yu, “A Hierarchical Approach to Parallel Multiquery Scheduling”, IEEE Transactions on Parallel and Distributed Systems, 6 (6): 578-590, June 1995, each of the SQL processes is distributed according to various statistic information associated with the queries to be processed and stored in a DBMS dictionary.

Moreover, David DeWitt and Jim Gray, “Parallel Database Systems: The Future of High Performance Database Systems”, COMMUNICATIONS OF THE ACM, vol. 35, No. 6, June 1992, P. 85-P. 98 discloses an architecture for distributing a database processing load to a plurality of computers for parallel execution. In the Shared everything, Shared disk (shared use type) architecture disclosed in this document, all the computers executing the DB (database) processes can access all the data.

In the Shared nothing (non-shared use type) architecture, only the data stored in a disk connected to a local computer can be accessed. As compared to the Shared everything type architecture, in the Shared nothing type architecture, the amount of the shared resource between the configuration unit executing the DB process is small and the scalability is excellent. However, when data deviation is present in each of the computers, a load is concentrated in a particular computer and a processing overhead is generated as compared to a case when effective parallel processing is executed without deviation.

To cope with this, JP-A-2005-56391 builds a plurality of virtual computers existing in the same computer and performs computer resource allocation according to the computer CPU resource information, thereby balancing the load.

SUMMARY OF THE INVENTION

The distribution algorithm significantly affects the use efficiency of the resources (CPU and memory) of the DB access server (hereinafter, referred to as BES: Back End Server) accessing a disk. For example, when the process distribution is unsuccessful and processes are concentrated to a particular BES, the other BES should wait in an idle state.

That is, a resource imbalance occurs and it is impossible to effectively use BES resources. Especially in the Shared nothing type architecture, a BES waiting for a process occupies the disk to be accessed and the processing overhead is increased by the resource imbalance.

It is therefore an object of the present invention to solve the aforementioned problems and improve the load imbalance in execution of each SQL process on each BES so as to increase the speed of SQL execution.

In order to achieve the aforementioned object, the present invention provides a resource allocation method for a physical computer used when a database connected to a BES (Back End Server) executed in a virtual computer operating on a physical computer executes an SQL process. The method is characterized by the steps of: subjecting an inputted SQL to a syntax analysis and extracting at least one SQL process from the SQL, calculating a resource cost of the database required by the BES to perform the SQL process for each of process types contained in the SQL process, deciding an allocation ratio for allocating the resource of the physical computer to the virtual computer in accordance with a resource cost ratio of each of the BES required by each of the BES to execute the SQL process, and requesting for execution of the respective BES in the virtual computer to which the resource has been allocated so as to execute the SQL process. The method will be shown later.

The present invention improves the load imbalance in execution of each SQL process to BES, which in return increases the speed of the SQL execution speed.

Other objects, features and advantages of the invention will become apparent from the following description of the embodiments of the invention taken in conjunction with accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C show examples of SQL execution in the DBMS according to an embodiment of the present invention.

FIG. 2 is a block diagram showing an outline of the configuration of an SQL request reception process according to an embodiment of the present invention.

FIG. 3 is a block diagram showing the entire DBMS according to an embodiment of the present invention.

FIG. 4 shows configuration of a request execution server in the entire DBMS according to an embodiment of the present invention.

FIGS. 5A and 5B show configurations of statistic information and a process cost managing table according to an embodiment of the present invention.

FIGS. 6A and 6B show configurations of a resource allocation managing table and a cost managing master table according to an embodiment of the present invention.

FIG. 7 is a flowchart showing a procedure of an SQL reception unit according to an embodiment of the present invention.

FIG. 8 is a flowchart showing a procedure of a cost estimation process according to an embodiment of the present invention.

FIG. 9 is a flowchart showing a procedure of a resource estimation process according to an embodiment of the present invention.

FIGS. 10A and 10B are flowcharts showing procedures for setting the number of processing cases and the processing amount in the processing cost managing table according to an embodiment of the present invention.

FIGS. 11A and 11B are flowcharts showing procedures for setting the CPU cost and the memory cost in the processing cost managing table according to an embodiment of the present invention.

FIG. 12 is a flowchart showing a procedure for deciding a server for processing of which an execution server is undecided.

DETAILED DESCRIPTION OF THE INVENTION

Description will now be directed to DBMS according to an embodiment of the present invention with reference to drawings.

FIGS. 1A-1C show execution examples of the SQL process in the DBMS. The SQL in FIG. 1A is divided to SQL processes which are distributed to respective BES for execution as shown in FIG. 1B or FIG. 1C.

The SQL in FIG. 1A is subjected to the SQL process as follows. It should be noted that the DBMS executes the SQL process in the order shown below.

T1, T2 scanning: (Acquire data existing in T1, T2) from “SELECT T1.C1 FROM T1, T2”

T1 condition evaluation: (Judge whether the acquired data corresponds to the condition and narrows the data to the lines which corresponds to the condition) from “T1.C1>1”

T2 condition evaluation: (Judge whether the acquired data corresponds to the condition and narrow the data to the lines which corresponds to the condition) from “T2.C1>2”

T1 and T2 join: (For the narrowed T1 and T2 data, perform the join process for the lines corresponding to T1.C2=T2.C2) from “T1.C2=T2.C2”

It should be noted that the T1 condition evaluation is executed after the T1 scan. The T2 condition evaluation is executed after the T2 scan. The T1 and T2 join is executed after the T1 condition evaluation and the T2 condition evaluation. Accordingly, if only the T1 condition evaluation is completed and the T2 condition evaluation is not completed, an idle time is generated for synchronization during a period until the T2 condition evaluation is completed.

Firstly, FIG. 1B explains the CPU allocation and the SQL execution plan according to the present embodiment. The SQL execution plan defines which BES is to be executed by which algorithm for each of the divided SQL processes.

BES “1” and B ES “2” are different from each other. Three SQL processes (scan, condition evaluation, join) are allocated in time series to each BES. It should be noted that “T1 scan (33%)” indicates that the CPU allocation ratio for executing the SQL process of “T1 scan” is 33% of all the CPU resources which can be allocated. How to decide the CPU allocation ratio will be detailed later with reference to FIG. 4.

Before the SQL is executed, 33% of the CPU resource is allocated to BES “1” and 67% of the CPU resource is allocated to BES “2”. If (1) scan and (2) condition evaluation are executed by using the allocated resource, BES “1” and BES “2” almost simultaneously complete (1) the scan and (2) the condition evaluation processes. This is realized by a balanced CPU resource allocation executed in accordance with the CPU allocation ratio calculated according to a highly accurate estimation so that the respective BES calculations are simultaneously completed.

After this, as will be detailed with reference to FIG. 4, 67% of the CPU resource is allocated to BES “1” and 33% of the CPU resource is allocated to BES “2” and then (3) the join is executed. By using the highly accurate CPU allocation ratio, the parallel executions of the processes are completed almost simultaneously. Thus, even when the data amounts of the data of T1 and T2 have large unevenness, it is possible to effectively perform the SQL processes in parallel, thereby reducing the total processing time.

On the other hand, FIG. 1C explains the CPU allocation and SQL execution plan based on a comparison example. An identical amount of CPU resource is allocated to each of the BES and the allocation ratio 50% is not changed as the time elapses.

When executing SQL process (1) scan, the CPU resources of the respective BES are equal and the SQL process type “scan” is also identical. However, the data amounts of the T1 and T2 data as the objects data of the SQL process are deferent and the data amount of T2 is greater than the data amount of T1. Accordingly, the moments when the respective BES complete scan are also uneven.

Since the data amount of T1 is smaller, the BES “1” completes (1) scan and (2) condition evaluation earlier. On the other hand, the data amount of T2 is larger and BES “2” completes (1) scan and (2) condition evaluation later. Accordingly, in BES “1”, an empty time (idle in the figure) when no SQL process is executed is generated from the moment when BES “1” has completed the (2) condition evaluation until BES “2” completes (2) condition evaluation. This empty time reduces the resource use ratio of BES “1”.

Furthermore, when executing the SQL process (3), the process is distributed to BES “1” and BES “2”. Here also, if the data amounts processed by the respective BES are uneven, the moments when the respective BES complete the join are also uneven. As a result, the BES “2” which has completed (3) join should wait for completion of (3) join by the BES “1”. Accordingly, an empty time (idle in the figure) when no SQL process is executed is generated. This reduces the resource use ratio of the BES “2”.

It should be noted that according to the aforementioned document, i.e., Joel L. Wolf, John Turek, Ming-Syan Chen and Philip S. Yu, “A Hierarchical Approach to Parallel Multiquery Scheduling”, in order to increase the speed of SQL execution, it is preferable that the processes executed in parallel be completed simultaneously. However, in the method of FIG. 1C, the load imbalance is not improved sufficiently and the speed of the SQL query process cannot be increased.

FIG. 2 is a block diagram showing the entire configuration of the DBMS. The DBMS includes: a terminal device 41 for inputting an SQL request as the instruction information to use the database and displaying the content of the database; a request reception server 40 (resource allocation apparatus) for receiving the SQL request from the terminal device 41; and a request execution server 42 for executing the SQL process according to the instruction from the request reception server 40. The terminal device 41 transmits the SQL request to the DBMS (FES) 70 via a network by the communication control device 43.

The request reception server 40 includes: a CPU 44 for executing a process; a communication control device 43 for performing communication via the network; and a memory 45 for storing a program for realizing various processing units. The memory 45 contains OS 46 and DBMS (FES: Front End Server) 70 operating on the OS 46 and operates by using the CPU 44.

Here, the FES (Front End Server) is a part of the DBMS (FES) 70 in charge of the functions to interpret the inputted SQL request by a part of the DBMS (FES) 70, create an SQL execution plan 13 (see FIG. 4), and transmit an execution request of the SQL execution plan 13 to the BES 56 (see FIG. 3). The DBMS (FES) 70 includes an

SQL reception unit 47 for receiving a SQL request inputted from the terminal device 41; and a statistic information calculating unit 80 for calculating the statistic information 16 on data as a processing object of the SQL request received by the SQL reception unit 47.

The SQL reception unit 47 includes an SQL syntax analysis unit 48, a statistic information acquisition unit 49, a cost estimation unit 50, a resource estimation unit 51, a resource allocation request unit 65, an SQL execution plan creation unit 52, and an SQL execution request unit 64. Moreover, the memory contains a resource allocation table 15, a table of statistic information 16, a processing cost managing table 17, a resource allocation managing table 18, and a cost managing master table 21. The respective processing units and the tables will be detailed in FIG. 4 and FIGS. 6A, 6B.

FIG. 3 shows a detailed configuration of the request execution server 42 of FIG. 2. The request execution server 42 includes a communication control device 43, a CPU 44, a memory 45, and a memory 53. The memory 53 includes a server virtualization unit 54 having a resource allocation processing unit 55, and a plurality (two in the figure) of virtualized servers 66 (virtual computers).

The server virtualization unit 54 builds the plurality of virtual computers existing in the same computer as virtual servers 66 and allocates the CPU 44 and the memory 45 which are resources of the computer allocated in the resource pool to the respective virtual servers 66 so as to equalize the loads of the respective virtual servers 66. Thus, the respective virtual servers 66 operate by using the resources allocated by the server virtualization unit 54. It should be noted that the memory 45 and the memory 53 are realized, for example, by a flash memory and a silicon memory.

The virtualized servers 66 is realized by the LPER (Logical Partition) technique which divides one physical computer into a plurality of logical computers. Software for realizing the LPER may be, for example, VMware Server produced by the VMware company. Moreover, the virtualized servers 66 share the CPU 44 and the memory 45 of the physical computer. As is disclosed in the aforementioned document JP-A-2005-56391, the CPU 44 and the memory 45 are allocated with an arbitrary ratio to the respective virtualized servers 66 by the resource ratio process of the server virtualization unit 54.

Each of the virtualized servers 66 has an OS 46 and a BES 56 and is connected to an external storage device 60 by a magnetic storage device or the like. The external storage device 60 manages DB storage regions 62, 63. The BES 56 “1” is connected directly to the DB storage region 62 and the BES 56 “2” is connected directly to the DB storage region 63. It is possible to use the Shared nothing type architecture in which only the BES 56 having the direct connection relation has the access right to the respective storage regions 62, 63 of the connection destination or the Shared everything type architecture in which a plurality of BES share the access right to the respective DB storage regions 62, 63.

On the other hand, it is also possible to employ the Shared everything type architecture in which all the BES 56 have the access right to the DB storage regions 62, 63. Each of the BES 56 has a statistic information acquisition unit 57 and an SQL execution unit 58.

It should be noted that the DBMS (FES) 70 of FIG. 2 is arranged in the request reception server 40 which is a separate computer from the BES 56. However, the DBMS (FES) 70 and the BES 56 may be arranged in the same computer, i.e., the request execution server 42. Furthermore, the DBMS (FES) 70 may be arranged in the virtualized servers 66 of the request execution server 42.

FIG. 4 explains the outline of the SQL request reception process executed by the request reception server 40. Explanation will be given with reference to FIG. 2 when required. It should be noted that in order to simplify the explanation, attention is paid on the CPU as an example of the resource. Accordingly, the columns associated with memories of respective tables (the statistic information 16, the processing cost managing table 17, and the resource allocation managing table 18) are partially omitted. The omitted part will be explained in FIGS. 5A, 5B, 6A and 6B.

When the SQL sentence 11 is subjected to a syntax analysis, syntax analysis information (SQL process) and the column information on the table to be subjected to the SQL process are extracted. A statistic information storage region 61 is used to store the statistic information 16 such as table column information.

The processing cost managing table 17 is used to store statistic information such as the number of cases acquired from the statistic information 16 and the number of the respective BES processing cases calculated according to the syntax analysis information (SQL process) extracted from the SQL sentence 11. Furthermore, the processing cost managing table 17 stores the CPU cost of each BES calculated from the number of processing cases of each BES.

The resource allocation managing table 18 stores the CPU cost of each BES stored in the processing cost managing table 17 while correlating it with the CPU allocation ratio set according to the ratio of the CPU cost. The resource allocation table 15 stores the CPU allocation ratio of the resource allocation managing table 18 for each of the SQL processes extracted from the SQL sentence 11.

The CPU allocation process based on the CPU allocation ratio of the resource allocation table 15 is executed on the request execution server 42 and then the SQL execution plan based on the SQL process of the resource allocation table 15 is transmitted as an SQL process execution request to the request execution server 42.

The SQL reception unit 47 receives the SQL sentence 11, performs an execution request to the BES 56, and finally returns a result to the terminal device 41.

The SQL syntax analysis unit 48 subjects the received SQL sentence 11 to a syntax analysis such as an SQL phrase analysis. The SQL syntax analysis unit 48 divides the SQL process to a plurality of SQL processes so that the SQL processes are executed in parallel by a plurality of computers. For example, the SQL syntax analysis unit 48 divides the SQL request of FIG. 1A into three SQL processes: (1) scan, (2) condition evaluation, and (3) join. Furthermore, the SQL syntax analysis unit 48 not only analyzes the SQL syntax but also analyzes information on the columns required for processing the SQL sentence 11.

The statistic information acquisition unit 49 acquires statistic information 16 on the column required for processing the SQL sentence 11 according to the column information required for processing the SQL sentence 11 from the statistic information storage region 61 stored in the external storage device 60 in FIG. 3. The statistic information 16 shown in FIG. 4 executes management for each BES while correlating the table name, column name, the number of cases, and Index. For example, for the record of “#1”, the table name “T1” contains “200000” cases of records and the table name “T1” has the column name “C1” to which Index for high-speed access is not attached.

The cost estimation unit 50 estimates the number of processing cases for each of BES 56 according to the statistic information 16 acquired by the statistic information acquisition unit 49 for each of the SQL processes divided by the SQL syntax analysis unit 48. Here, the number of processing cases is a predicted value of the data cases accessed in each of the SQL processes.

The cost estimation unit 50 estimates the CPU cost according to the number of process cases of each of BES 56. Here, what is called cost is a CPU and a memory which are required for the DBMS to execute a process and which are shown in a numerical value. As the cost value increases, more CPU 44 and more memory resources are required.

Here, some joins and some sort processes need not be executed in a predetermined BES. For this, the cost estimation unit 50 makes no BES 56 to perform execution to be decided at this moment for the SQL process which need not be executed in the predetermined BES. By these processes, the cost estimation unit 50 creates the processing cost managing table 17.

The resource estimation unit 51 references the CPU cost of each SQL process described in the processing cost managing table 17 and decides the CPU allocation ratio. After the CPU allocation ratio is decided, the resource estimation unit 51 decides the CPU allocation ratio and the BES 56 to perform execution according to the CPU cost for the SQL process for which execution of the BES 56 has been made undecided. As a result, the resource estimation unit 51 creates the resource allocation managing table 18.

The resource allocation request unit 65 references the resource allocation managing table 18 and extracts the CPU allocation ratio of each BES 56 so as to create the resource allocation table 15. The resource allocation request unit 65 transmits the resource allocation table 15 to the resource allocation processing unit 55 so as to perform a resource allocation request.

The SQL execution plan creation unit 52 references the resource allocation table 15 and creates the SQL execution plan 13. The SQL execution request unit 64 transmits the SQL execution plan 13 to the SQL execution unit 58 of each BES 56 so as to perform an SQL execution request. The resource allocation processing unit 55 references the resource allocation table 15 and allocates a CPU resource.

The SQL execution unit 58 of the request execution server 42 uses the resource allocated by the resource allocation processing unit 55 so as to accesses the DB storage regions 62, 63 according to the SQL execution plan 13 and executes it and returns the results to terminal device 41.

FIG. 5A shows an example of the statistic information 16. In addition to the columns already explained in FIG. 4, the statistic information 16 includes a table capacity indicated by the table name for each BES. When the Index is “present”, instead of directly accessing the stored value, its Index is pursued to know the detailed distribution state of the values actually stored. According to the distribution information, it is possible to improve the accuracy of the estimation about the number of cases after the condition evaluation.

The estimated number of scan cases for the table having Index may be reduced to less than 100% of cases for the cases of the data stored in the table. This is because the storage position of the data required for scan can be known according to the Index and there is no need of using the 100%-method for accessing all the data in the table. However, the Index cannot be applied to particular cases (wild card search, backward matching search), which reduces the general-purpose characteristic.

FIG. 5B shows an example of the processing cost managing table 17. The processing cost managing table 17 manages the CPU cost, the memory cost, the number of processing cases, and the processing amount for each of the SQL processes. For example, for BES “1”, 200000 cases of the SQL processes of the type (1) scan are allocated. In order to execute the allocated processing amount of 20 GB, the CPU cost of 20 and the memory cost of 40 are required. Moreover, information on the SQL process for which the server used for execution is not yet decided is also simultaneously stored.

FIG. 6A shows an example of the resource allocation managing table 18. The resource allocation managing table 18 manages the CPU cost of each BES 56, the CPU allocation ratio, the memory cast, and the memory allocation ratio for each SQL process. Each of the CPU costs and the memory costs are data extracted from the processing cost managing table 17.

FIG. 6B shows an example of the cost managing master table 21. The table 21 stores information used for calculating the CPU cost and the memory cost for each SQL process. As the information used for calculating the CPU cost, a value serving as the key, its unit, and a value per unit (a predetermined coefficient) are stored. Similarly, as the information used for producing the memory cost, a value serving as the key, its unit, and a value per unit (a predetermined coefficient) are stored. For example, the CPU cost required for executing the SQL process of (1) scan is 0.0001 per unit of one processing case as the value serving as the key.

FIG. 7 is a flowchart showing a procedure of the SQL reception unit 47.

Upon reception of an SQL processing request from the terminal device 41, the SQL syntax analysis unit 48 performs a syntax analysis of the SQL sentence 11 inputted as a processing request (S101) and divides it into a plurality of SQL processes so that the requested process is processed in parallel by a plurality of BES. Moreover, column information required for processing the SQL sentence 11 is created.

The statistic information acquisition unit 49 acquires statistic information 16 on the columns required for processing the SQL sentence 11 from the statistic information storage region 61 according to the information on the columns required for processing the SQL sentence 11 created in S101 (S102).

The cost estimation unit (50) references the statistic information 16 on the columns required for processing the SQL sentence 11 and estimates a processing cost of each SQL process in each BES 56 (S103) so as to create the processing cost managing table 17. The resource estimation unit 51 references the processing cost managing table 17 and estimates the resource of each SQL process in each BES 56 (S104) so as to create the resource allocation managing table 18.

The resource allocation request unit 65 references the processing cost managing table 17, creates the resource allocation table 15 for deciding the resource allocation ratio of each BES 66, and transmits it to the resource allocation process 55 of the server virtualization unit 54 so as to request for a resource allocation (S105).

The SQL execution plan creation unit 52 references the resource allocation table 15 and creates the SQL execution plan 13 (see FIG. 4) (S106). The SQL execution request unit 64 transmits the SQL execution plan 13 created in S106 to BES 56 (S107).

FIG. 8 is a flowchart showing a procedure of the cost estimation process (S103) executed by the cost estimation unit 50.

Firstly, a loop (S201 to S206) is executed for selecting one by one non-selected BES in the processing cost table 17. Next, a loop (S202 to S205) is executed for selecting one by one non-selected SQL processes among the SQL processes to be executed by BES selected in S201.

The number of cases of the statistic information 16 is referenced to calculate the number of processing cases of each SQL process so as to set the number of processing cases in the processing cost managing table 17 (S203). Furthermore, the capacity of the statistic information 16 is referenced to calculate the processing amount of each SQL so as to set the processing amount in the processing cost managing table 17 (S204).

Firstly, a loop (S207 to S212) is executed for selecting one by one non-selected BES. Next, a loop (S208 to S211) is executed to select one by one non-selected SQL processes among the SQL processes to be executed by the BES selected in S207.

The number of the processing cases of the processing cost managing table 17 is referenced to calculate the CPU cost, which is set as the CPU cost in the processing cost managing table 17 (S209). Furthermore, the processing amount in the processing cost managing table 17 is referenced to calculate the memory cost, which is set for the memory cost in the processing cost managing table (S210).

FIG. 9 is a flowchart showing a procedure of the resource estimation process executed by the resource estimation unit 51.

Firstly, a loop (S301 to S308) is executed to select one by one non-selected BES in the resource allocation managing table 18. Next, a loop (S302 to S307) is executed to select one by one non-selected SQL processes among the SQL processes to be executed by the BES selected in S301.

The CPU cost set in the processing cost managing table 17 is referenced to calculate the CPU allocation ratio according to an expression given below (S303). According to the calculation result, the CPU allocation ratio is decided and set in the processing cost managing table 17 (S304).

CPU allocation ratio=CPU cost/(total of CPU costs in all the BES)

Furthermore, the memory cost set in the processing cost managing table 17 is set to calculate the memory allocation ration according to an expression given below (S305). According to the calculation result, the memory allocation ratio is decided and set in the processing cost managing table 17 (S306).

Memory allocation ratio=memory cost/(total of memory costs in all the BES)

After completion of the two loops, according to the CPU allocation ratio set in S304 and the memory allocation ratio, the BES 56 which executes the process of the undecided SQL process is decided (S309).

FIGS. 10A and 10B are flowcharts showing procedures for setting the number of processing cases and the processing amount in the processing cost managing table.

FIG. 10A is a flowchart for setting the number of processing cases of the SQL process in S203. According to the type of the SQL process, the process is branched to the scan, the condition evaluation, or the join (S401). The processing terminates if the process is other than these three types.

Firstly, when the SQL process is the scan (scan in S401), the number of cases of the corresponding column is acquired from the statistic information table 16 (S411).

Next, when the SQL process is the condition evaluation (condition evaluation in S401), the number of cases of the corresponding column is acquired from the statistic information table 16 (S421) so as to acquire a hit rate “0.2” corresponding to the predicate of the condition evaluation (“>” in the SQL sentence of FIG. 1A) (S422). The number of cases acquired in S421 is multiplied by the hit rate acquired in S422 so as to calculate the number of processing cases (S423).

It should be noted that the correspondence information between the predicate of the condition evaluation and the hit rate is acquired from the setting information given in advance. When creating the setting information, it is preferable that the hit rate be smaller as the condition of the condition evaluation becomes more strict. For example, since the predicate (= (equal)) requiring the same hit value as a predetermined number is more strict than the predicate (> (greater)) requiring the hit value greater than the predetermined number, the hit rate is set smaller (such as 0.1).

When the SQL process is the join (join in S401), the number of condition evaluation processing cases in each BES 56 is acquired from the statistic information table 16 (S431) so as to set the number of condition evaluation processing cases in each of the acquired BES 56 for the number of join processing cases of each BES 56 (S432).

Furthermore, the number of the processing cases decided in S411, S423, or S432 is set for the corresponding number of processing cases in the cost managing table (S441), thereby terminating the processing.

FIG. 10B is a flowchart for setting the processing amount of the SQL process in S204. According to the type of the SQL process, the process is branched to the scan, the condition evaluation, or the join (S501). The processing is terminated if the type is other than these three types.

Firstly, when the SQL process is the scan (scan in S501), the corresponding capacity is acquired from the statistic information table 16 (S511).

Next, when the SQL process is the condition evaluation (condition evaluation in S401), the corresponding capacity is acquired from the statistic information table 16 (S521) so as to acquire a hit rate “0.2” corresponding to the predicate of the condition evaluation (“>” in the SQL sentence of FIG. 1A) (S522) like in S422. The capacity acquired in S521 is multiplied by the capacity acquired in S522 so as to calculate the processing amount (S523).

When the SQL process is the join (join in S501), the processing amount of the condition evaluation in each BES 56 is acquired from the statistic information table 16 (S531) so as to set the processing amount of the condition evaluation in each BES 56 as the join processing amount each BES 56 (S532).

Furthermore, the processing amounts decided in S511, S523, or S532 is set for the corresponding processing amount in the cost managing table (S541), thereby terminating the processing.

It should be noted that for the estimation process of the processing cost (CPU cost, memory cost) of S432, S532, it is also possible to perform calculation according to the data contents of the tables stored in the DB storage regions 62, 62 (see FIG. 3) instead of performing calculation according to the data described in the statistic information 16 (see FIG. 4).

For example, when performing the join by distributing a plurality of table data to N units of BES, the record allocated to the i-th (i=1 to N) unit is a record selected from all the table data as such a record that when the value of the common columns in the plurality of tables is divided by N, the remainder is i−1. By calculating the number of records allocated as the i-th unit, the number of processing cases and the processing amount are decided. As compared to the method for performing the estimation only by using the statistic information indicating the outline of the table, the method performing estimation considering the content of the table can perform a highly accurate estimation although the calculation amount is increased.

FIGS. 11A, 11B are flowcharts showing a CPU cost and a memory cost in the processing cost managing table.

FIG. 11A is a flowchart for setting a CPU cost in S209. Firstly, the number of processing cases of the corresponding SQL process is acquired from the processing cost managing table 17 (S601). Next, a value of the CPU cost per unit in the corresponding SQL process is acquired from the cost managing master table 21 (S602).

The number of processing cases acquired in S601 is multiplied by the value of the CPU cost per unit obtained in S602 so as to calculate the CPU cost (S603). The value obtained in S603 is set for the CPU cost of the SQL process in the corresponding BES 56 in the processing cost managing table 17 (S604).

FIG. 11B is a flowchart for setting a memory cost in S210. Firstly, a processing amount of the corresponding SQL process is acquired from the processing cost managing table 17 (S701). Next, a value of the memory cost per unit in the corresponding SQL process is acquired from the cost managing master table 21 (S702).

The number of processing amount acquired in S701 is multiplied by the value of the CPU cost per unit obtained in S702 so as to calculate the memory cost (S703). The value obtained in S703 is set for the memory cost of the SQL process in the corresponding BES 56 in the processing cost managing table 17 (S704).

FIG. 12 is a flowchart showing a procedure for deciding a server of the process for which no execution server is decided.

Firstly, a loop (S801 to S806) is executed to select one by one non-selected SQL processes in the resource allocation managing table 18 (i.e., it is not judged whether the execution server is non-decided).

Next, it is judged whether the SQL process currently selected in S801 is non-decided (S802). If the SQL process is not non-decided (No in S802), the loop for the SQL currently selected in S801 is terminated (S806) and control is returned to S801 to select the next SQL process.

If Yes in S802, the resource allocation managing table 18 is referenced and the BES 56 having the highest CPU allocation ratio is acquired (S803). The BES 56 acquired in S803 is decided to be an execution server (S804).

Furthermore, in order to indicate that the BES 56 decided in S804 is a server for executing a non-decided process, the processing cost of the corresponding SQL process in the resource allocation managing table 18 is set for the BES 56 decided in S804 (S805). Here, the loop for the currently selected SQL process is terminated (S806) and control is returned to S801 to select the next SQL process.

According to the embodiment thus far described, by modifying the resource allocation ratio before executing the SQL, it is possible to improve the load imbalance upon execution of each SQL process in each BES 56. This increases the resource use efficiency and the process is completed rapidly. Accordingly, it is possible to increase the speed of the SQL process.

It should be further understood by those skilled in the art that although the foregoing description has been on embodiments of the invention, the inventions is not limited thereto and various changes and modifications may be made without departing from the spirit of the invention and the scope of the appended claims.

RESOURCE ALLOCATION METHOD, RESOURCE ALLOCATION PROGRAM AND RESOURCE ALLOCATION APPARATUS

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