WO2022068392A1 - Database cluster capacity expansion and reduction method, service system and storage medium - Google Patents

Abstract

A database cluster capacity expansion and reduction method, a service system and a storage medium, relating to the technical field of information. The method comprises: acquiring a detection value of an operation index of each database in a database cluster (201); obtaining a resource utilization rate of the database cluster according to the detection value of the operation index of each database (202); and if the resource utilization rate of the database cluster meets a preset capacity expansion and reduction condition, carrying out capacity expansion and reduction on the database cluster (203).

Description

Database cluster expansion and contraction method, service system, storage medium

cross reference

This application is based on the Chinese patent application with the application number "202011049631.4" and the application date is September 29, 2020, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated by reference. Apply.

technical field

The embodiments of the present application relate to the field of information technology, and in particular, to a method, a service system, and a storage medium for expanding and shrinking a database cluster.

Background technique

A database cluster is a technology that encapsulates a series of database servers in a cluster to provide external data services in a unified manner. By combining sub-database and sub-table, read-write separation, data balance, master-slave synchronization and other technologies, the bottleneck of read and write access of a single database node is solved. When the disk space of the server where the database is located in the database cluster is insufficient, the concurrent read and write requests exceed the limit that the database cluster can carry, etc., the database cluster will be expanded or reduced in capacity, and most databases are implemented by receiving external requests. Cluster scaling.

However, the expansion and contraction of database clusters by receiving external requests requires manual participation and wastes human resources.

SUMMARY OF THE INVENTION

An embodiment of the present application provides a method for expanding and shrinking a database cluster, including: acquiring detection values of operating indicators of each database in a database cluster; and obtaining resource usage of the database cluster according to the detected values of the operating indicators of each database If the resource utilization rate of the database cluster satisfies the preset capacity expansion and contraction conditions, the database cluster is expanded and contracted.

Embodiments of the present application further provide a service system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores data that can be executed by the at least one processor instructions, where the instructions are executed by the at least one processor, so that the at least one processor can execute the foregoing method for expanding or shrinking a database cluster.

Embodiments of the present application further provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the foregoing method for expanding or shrinking a database cluster is provided.

Description of drawings

1 is a schematic diagram of a system according to a first embodiment of the present application;

2 is a flowchart of a method for expanding and shrinking a database cluster according to the first embodiment of the present application;

3 is a flowchart of sub-steps of a method for expanding and shrinking a database cluster according to the first embodiment of the present application;

4 is a flowchart of a method for expanding and shrinking a database cluster according to a second embodiment of the present application;

5 is a flowchart of a method for expanding and shrinking a database cluster according to a third embodiment of the present application;

FIG. 6 is a schematic structural diagram of a service system according to a fourth embodiment of the present application.

Detailed ways

The main purpose of the embodiments of the present application is to propose a database cluster expansion and contraction method, service system, and storage medium, which can automatically realize the dynamic database cluster expansion and contraction, and use the operation indicators for analysis, so as to obtain the timing of database expansion and contraction more accurately. .

In order to make the objectives, technical solutions and advantages of the embodiments of the present application more clear, each embodiment of the present application will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that, in each embodiment of the present application, many technical details are provided for the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can be realized. The following divisions of the various embodiments are for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the various embodiments may be combined with each other and referred to each other on the premise of not contradicting each other.

To simplify the description, the comparison between Chinese and abbreviations of some parameters used in the embodiments of the present application is shown in Table 1.

Table 1: Chinese and abbreviation comparison table of some parameters

The first embodiment of the present application relates to a method for expanding and shrinking the capacity of a database cluster. The method includes: acquiring the detection value of the operation index of each database in the database cluster; obtaining the resource utilization rate of the database cluster according to the detection value of the operation index of each database; if the resource utilization rate of the database cluster satisfies a predetermined According to the set expansion and contraction conditions, the database cluster is expanded and contracted. This embodiment can automatically realize the dynamic cluster shrinking and expansion of the database, and the detection value of the running index of the database can be used for analysis, and the running status of each database in the database cluster can be obtained, so that the analysis result is more accurate.

This embodiment can be applied to a system including a plurality of servers, but is not limited thereto.

For example, as shown in FIG. 1, the system can realize the expansion and contraction of the database, and the system can have different database applications, as well as a database resource monitoring module and a database resource pool. The database resource monitoring module includes a resource cycle acquisition module, a database resource reduction module, a database resource expansion module, and a cluster analysis module. The database resource monitoring module monitors the database resource pool to determine whether expansion or contraction is required.

When the resource cycle acquisition module starts, it parses the expansion and contraction parameters configured by the user, loads the expansion and contraction parameters into the memory, obtains the parameter values defined by the cycle according to the database indicators, periodically obtains the operation indicators of each database, and detects each The data submitted to the cluster statistical analysis module; the cluster statistical analysis module periodically analyzes and counts the running indicators of all databases in the current cycle of the cluster according to the running indicators obtained by the configured cluster indicator statistical cycle; The information is normalized, the rate is normalized, and the cluster resource usage is normalized, and it is determined whether expansion or contraction is required. If expansion and contraction are required, the database resource expansion module is notified to expand the database resources. The capacity module is used to shrink the resources and return the idle resources after the shrinkage to the resource pool.

In an example, the scaling parameters include database thresholds, cluster thresholds, index impact factors, detection period parameters, database initialization methods, and database cleaning methods listed in Table 1.

The implementation details of the database cluster expansion/contraction conversion method according to the first embodiment of the present application will be specifically described below. The following content is only provided for the convenience of understanding, and is not necessary for implementing this solution.

The flowchart of the first embodiment of the present application is shown in FIG. 2 .

Step 201: Acquire detection values of operating indicators of each database in the database cluster.

In one example, according to a preset detection period, the detection values of the operation indicators of each database in the database cluster are periodically acquired.

In an example, the operation indicator is one of the following indicators: CPU utilization DCU, memory utilization DMU, disk utilization DDU, structure table quantity overrun rate DSNU, and structure table table record quantity overrun rate DTRU.

In an example, when the operation indicator is CPU utilization, memory utilization, or disk utilization, acquiring the detection value of the operation indicator of each database in the database cluster includes: collecting and obtaining each of the databases The detected value of the operation index, DCU, or DMU, or DDU; in the case that the operation index is the over-limit rate of the number of structure tables, the number of structure tables of each of the databases is collected, and the number of structure tables is obtained according to the preset number of structure tables. The threshold value calculates the structure table quantity over-limit rate of each of the databases; in the case that the operating index is the structure table table record number over-limit rate, collect and obtain the structure table record numbers of each of the databases, and according to the forecast The set threshold value of the number of records in the structure form table is used to calculate the excess rate of the number of records in the structure form table of each of the databases.

In an example, taking N times of running indicators, the database cluster manages K databases as an example.

In an example, the operation indicator is the number of structure tables exceeding the limit rate, and the number of structure tables exceeding the threshold of the number of database structure tables is calculated according to the number of structure tables in a single database obtained each time. The calculation method is: if the number of current table structures exceeds The database table structure number threshold value DSNT, then subtract the database table structure number threshold DSNT from the current number to obtain the current over-limit table structure number, and divide the over-limit by the database table structure number threshold to obtain the current database structure table number Overrun rate DSNU.

In an example, the operation indicator is the record number exceeding rate of the structure form table. First, the number of table structures exceeding the table record threshold value is calculated according to the number of records of each table in a single database obtained each time. The calculation method is as follows: if the number of the current table has exceeded the threshold value of the number of records of a single table in the database, the number of tables in the database library exceeding the threshold value is obtained by counting and accumulating. Second, divide the number of overrun tables obtained in the first step by the total number of all tables in the database to obtain the overrun rate DTRU of the number of records in the database structure form table.

Step 202: Obtain the resource usage rate of the database cluster according to the detected value of the operation index of each database.

In one example, the resource usage rate of the database cluster is obtained according to the detection values of the operation indicators of each of the databases obtained in several detection cycles.

Sub-step 301: Calculate the statistical value of the operation index of the database according to the detection value of the same operation index of the same database obtained in several detection cycles.

Continuing from the above example, taking the statistical value as the mean value, N times of running indicators are obtained, and the cluster manages K databases as an example.

When the running indicator is CPU utilization, calculate the average CPU utilization of a single database within the preset detection period SCLB=(DCU1+DCU2+...+DCUN), and obtain the average CPU utilization SCLB1 of K databases in this way , SCLB2...SCLBK.

When the running indicator is memory utilization, the average memory utilization of a single database in the calculation cycle SCMB=(DMU1+DMU2+...+DMUN), and the average memory utilization of K databases SCMB1, SCMB2... SCMBK.

When the operating indicator is the disk utilization, the average disk utilization SDLB=(DDU1+DDU2+...+DDUN) of a single database during the calculation period, so as to obtain the average disk utilization SDLB1, SDLB2...SDLBK of K databases.

The operation index is the overrun rate of the number of structured tables. Assuming that there are M overruns in N collections, the average overrun rate of the database is SSNU=(DSNU1+DSNU2+...+DSNUM)/N.

The running indicator is the over-limit rate of the number of records in the structured form table, and the average over-limit rate of the number of records in the structured sheet table of a single database in the calculation period STRU=(DTRU1+DTRU2+...+DTRUN)/N.

Sub-step 302: Calculate the statistical value of the operational indicator of the database cluster according to the statistical value of the operational indicator of each database.

Continuing from the above example, when the running index is CPU utilization, calculate the average CPU utilization of the cluster CCLB=(SCLB1+SCLB2+...+SCLBK)/K; when the running index is memory utilization, calculate the memory of the cluster Average utilization CMLB=(SCMB1+SCMB2+...+SCMBK)/K; when the operating indicator is disk usage, calculate the average disk usage of the cluster CDLB=(SDLB1+SDLB2+...+SDLBK)/K; When the operating index is the excess rate of the number of structured tables, calculate the average excess rate of the cluster database structure tables CSNU=(SSNU1+SSNU2+...+SSNUK)/K; the operating index is the excess rate of the number of records in the structured table table Next, calculate the average overrun rate of database table records in the cluster CTRU=(STRU1+STRU1+...+STRUK)/K.

Sub-step 303, normalize the statistical values of each of the operating indicators of the database cluster, and use the sum of the normalized statistical values of each of the operating indicators of the database cluster as the resource utilization rate of the database cluster.

In an example, the normalized indicator cluster resource usage rate CRR in the current period is calculated, and the calculation method is: CRR=CCLB*CCIF+CMLB*CMIF+CDLB*CDIF+CSNU*CSNIF+CTRU*CTRIF.

Step 203 , if the resource utilization rate of the database cluster satisfies the preset capacity expansion and contraction conditions, expand and contract the database cluster.

In one example, if the CRR is greater than the user-configured normalized upper limit of cluster shrinking and expansion CBNU, it is considered that capacity expansion is required; if the CRR is less than the user-configured lower normalized lower limit of cluster shrinking and expansion CBNU, it is deemed that the capacity needs to be reduced.

In this embodiment, the detection value of the operation index of each database in the database cluster is obtained; according to the detection value of the operation index of each database, the resource utilization rate of the database cluster is obtained; if the resource utilization rate of the database cluster satisfies the predetermined According to the set expansion and contraction conditions, the database cluster can be expanded and contracted, which can automatically realize the dynamic cluster expansion and contraction of the database, and the operation status of each database in the database cluster can be obtained by using the detection value of the operation index of the database for analysis. make the analysis results more accurate.

The second embodiment of the present application relates to a method for expanding and shrinking the capacity of a database cluster. The flowchart of the second embodiment of the present application is shown in FIG. 2 .

Step 401: Acquire the detection value of the operation index of each database in the database cluster.

Step 402: Obtain the resource utilization rate of the database cluster according to the detected value of the operation index of each database.

Steps 401 to 402 are substantially the same as steps 201 to 202 of the first embodiment, and to avoid repetition of expressions, they will not be repeated here.

Step 403 , if the resource utilization rate of the database cluster satisfies the capacity expansion condition in the capacity expansion/contraction condition, obtain database configuration information according to the source mode of the pre-obtained database configuration information.

In an example, the source manner of the database configuration information includes at least one of the following manners: acquiring from at least one database in the database cluster, acquiring from a local storage module, and acquiring from a remote device.

Exemplarily, the source mode of the database configuration information is to obtain from at least one database in the database cluster, that is, copy the existing tenant database information, obtain the source database addressing rules configured by the user, and find any source database information that meets the rules. , and load configuration information such as memory, CPU, disk configuration, active/standby strategy, backup strategy, and disaster recovery strategy into the configuration list, and then obtain database information, such as index information, database schema information, database table structure information, etc., and generate them separately. Related scripts, put them in the script list.

Exemplarily, the database configuration information is sourced from a local storage module, that is, a local configuration file is obtained, the configuration information in the local file is directly loaded into the configuration list, and the script list that initializes the database is loaded.

Exemplarily, the source method of the database configuration information is to obtain from the remote device, that is, to obtain the remote configuration file, download the remote configuration file to the local, and then load the configuration information downloaded to the local file into the configuration list, and set the List of scripts loaded by scripts that initialize the database.

In one example, the streaming mode can be selected when downloading files, and the content of the remote file can be loaded directly into the memory without downloading the local file, which reduces the IO of the local disk.

In step 404, a newly added database of the database cluster is obtained according to the configuration of the database configuration information.

In one example, the database configuration information includes first information representing the hardware performance of the database, second information representing the data processing strategy, and third information representing the database architecture; apply for a new database from the database resource pool according to the first information database; use the third information to initialize the new database; use the second information to set the new database after successful initialization, and use the new database after successful setting as the A new database for the database cluster.

Exemplarily, apply for a new database from the database resource pool according to the first information, that is, apply for database resources, such as memory resources, CPU resources, disk resources, etc., from the cluster resource pool according to the first information in the obtained configuration list Hardware resources, if the resources cannot be obtained, then give up this expansion; use the third information to initialize the new database, that is, initialize the newly applied database according to the obtained database initialization script, if the initialization of the database fails, then Consider that the expansion fails, clean up the applied resources, and return them to the resource pool; use the second information to set the new database after the initialization is successful, and use the new database after the setting is successful as the To add a new database to a database cluster, set database policies related to the database, such as active/standby policies, backup policies, disaster recovery policies, etc. If the database policy fails to be set, the expansion will be considered a failure, and the applied resources will be cleaned up and returned to the resources pool.

The following is an example to illustrate the expansion of the database cluster.

During initialization, the resource cycle collection module parses the configuration data of the current user, and the obtained parameter configuration is shown in Table 2.

Table 2: Initialization parameter configuration table

The way that the database expansion module obtains the current database expansion during initialization is to obtain the local configuration file from the local storage module, and load the information obtained by parsing the obtained local configuration file into the memory.

There are two databases in the current database cluster, and the operation indicators of the two databases collected by the resource period collection module within a preset detection period are shown in Table 3.

Table 3: Statistics of running indicators of the two databases

According to the detection value of the same operation index of the same database obtained in 10 detection cycles, the statistical value of the operation index of the database is calculated, and the data is shown in Table 4:

Table 4: Statistical value table of the calculated operating indicators of the database

According to the statistical value of the operation index of each database, the statistical value of the operation index of the database cluster is calculated. In this embodiment, the statistical value is an average value, as shown in Table 5.

Table 5: Statistical value table of the calculated operating indicators of the database cluster

index average value Calculation CCLB 0.753 (0.753+0.753)/2 CMLB 0.755 (0.748+0.762)/2 CDLB 0.741 (0.739+0.743)/2 CSNU 0.8477 (0.8418+0.8535)/2 CTRU 0.7164 (0.7149+0.7179)/2

Normalize the statistical value of each of the operating indicators of the database cluster, and use the normalized sum of the statistical values of each of the operating indicators of the database cluster as the resource utilization rate of the database cluster , the database cluster is normalized as follows: CRR=0.753*0.25+0.755*0.25+0.741*0.25+0.8477*0.15+0.7164*0.15=0.797, since 0.797 is already greater than the cluster expansion threshold of 0.7, the cluster statistical analysis module will send a notification to The database resource expansion module handles expansion.

After the database resource expansion module receives the notification sent by the cluster statistics analysis module, it first applies for resources from the database resource pool according to the configuration parameters of CPU, memory and disk, and then creates a database after applying for the resources, that is, according to the first information from the database resource pool. Apply for a new database.

Then, the database is initialized using the database initialization script specified in the local configuration file, that is, the new database is initialized by using the third information.

Next, configure an active/standby strategy, a backup strategy, and a disaster recovery strategy of the new database, and use the second information to set the new database after successful initialization.

Finally, the database resource information is notified to the resource period collection module and the cluster statistics analysis module, and the newly created database is added to the statistics list later, and the database expansion is completed.

In the second embodiment of the present application, dynamic factors such as CPU utilization and memory occupancy are considered when implementing the expansion and contraction of the database cluster to analyze the expansion of the database, so as to determine whether the database meets the expansion conditions of the expansion and contraction conditions, so as to realize the expansion. , which makes the timing of expansion more accurate. In addition, it can automatically expand the database without manual participation, saving human resources.

The third embodiment of the present application relates to a method for expanding and shrinking the capacity of a database cluster. The third embodiment is roughly the same as the first embodiment, except that: if the resource utilization rate of the database cluster satisfies the shrinking conditions in the expansion and shrinkage conditions, the operation indicators of the same database The statistical value is normalized, and the sum of the normalized operation indicators of the database is used as the resource utilization rate of the database; according to the resource utilization rate of each database and the resources of the database cluster According to the utilization rate, candidate databases are selected from each of the databases; and the database cluster is scaled down based on the candidate databases.

The flowchart of the third embodiment of the present application is shown in FIG. 5 .

Step 501: Acquire the detection value of the running index of each database in the database cluster.

Step 502: Obtain the resource usage rate of the database cluster according to the detected values of the operating indicators of each database.

Steps 501 to 502 are substantially the same as steps 201 to 202 of the embodiment, and are not repeated here.

Step 503: If the resource utilization rate of the database cluster satisfies the capacity reduction condition in the capacity expansion/contraction condition, normalize the statistical value of each of the operation indicators of the same database, and normalize all the normalized data. The sum of each of the operational indicators of the database is used as the resource utilization rate of the database.

In an example, the normalized index (SRR) of each database resource usage rate in the database cluster in the current period is obtained from the cluster statistical analysis module = SCLB*CCIF+SCMB*CMIF+SDLB*CDIF+SSNU*CSNIF+STRU*CTRIF .

Step 504: Select candidate databases from each of the databases according to the resource usage rate of each of the databases and the resource usage rate of the database cluster.

In an example, a database whose resource usage rate in each of the databases is lower than the resource usage rate of the database cluster is used as the candidate database.

Exemplarily, from the cluster to obtain the cluster resource utilization rate normalization index (CRR) in the current cycle from the cluster statistics analysis module; compare the SSR of each database with the obtained cluster CRR, and add the information of the database lower than the CRR to the data. The pending list L is the candidate database.

Step 505 , scale down the database cluster based on the candidate database.

In an example, if the candidate database is one, the candidate database is used as the database to be reduced, and the database to be reduced is merged into other databases except the database to be reduced in each of the databases.

If the number of candidate databases is greater than or equal to 2, at least one candidate database among the multiple candidate databases is used as the database to be reduced, and the databases to be reduced are merged into the multiple candidate databases except the to-be-reduced database. In other candidate databases outside the database; clean up the database to be reduced.

In one example, the plurality of candidate databases are sorted in descending order of resource usage to obtain a first sequence, and the plurality of candidate databases are sorted in descending order of resource usage, and the first sequence is obtained. Obtain the second sequence; take two candidate databases with the same order number in the first sequence and the second sequence as a group, and filter out multiple different groups; One of the candidate databases in the group is merged into another candidate database in the group; the one of the candidate databases serves as the database to be reduced.

In one example, to clean up the database to be reduced, the database to be reduced can be cleaned based on a preset cleaning rule, and the cleaning rules can be as follows: delete the data in the database to be reduced, and remove the database to be reduced from all delete from the database cluster; or, delete only the data in the database to be reduced.

Exemplarily, the list L to be processed is sorted according to SRR from small to large to obtain the first sequence Ls, and the second sequence Lb is obtained by sorting from large to small; the two lists Ls and Lb are traversed at the same time, when a certain traversal is performed. The database indicators of the two lists are Lsc and Lbc. If the mean value of Lsc and Lbc is less than or equal to CRR, it is considered that the two databases can be merged. Otherwise, it is not allowed to merge. A list of mergeable databases is obtained. It is worth noting that the merged databases are two different candidate databases.

Then, traverse the mergeable list H in turn. When merging, take the source database with the low resource occupancy index as the source database, and take the resource occupancy index as the target database, first determine whether the target database contains the table structure, index and other information of the source database, if not Create it first, and copy the data directly if it exists. In particular, to ensure data security, it is necessary to perform a database backup operation before performing data copying. If an exception occurs during the copying process, use the backup data to restore the database to the state before merging.

Finally, after the merge is successful, if the cleanup rules configured by the user are: clean up all, return the resources to the resource pool, delete all the information of the database, release the database resources, return them to the resource pool, and remove them from the cluster statistical analysis module Cleaned database; if the cleanup rule configured by the user is: only data is cleaned, and resources are not returned to the resource pool, the resources of the current database are retained and not returned to the resource pool.

The following uses an example to illustrate the scaling of the database cluster.

During initialization, the resource period collection module parses the configuration data of the current user. The configuration data of this embodiment is the same as that of the second embodiment, as shown in Table 2.

The database shrinking module parses the shrinking method loaded into the current database during initialization. Assuming that the current resource pool has used two databases for a tenant, the operating indicators of the two databases obtained by the resource cycle collection module are shown in Table 6. .

Table 6: Statistics of the operation indicators of the two databases obtained by the resource period collection module

According to the detection values of the same operation index of the same database obtained in several detection cycles, the statistical value of the operation index of the database is calculated, and the data is shown in Table 7:

Table 7: Statistical value table of the calculated operating indicators of the database

According to the statistical value of the operation index of each database, the statistical value of the operation index of the database cluster is calculated, as shown in Table 8.

Table 8: Statistical value table of the calculated operating indicators of the database cluster

index Index value Calculation CCLB 0.1365 (0.158+0.115)/2 CMLB 0.116 (0.118+0.114)/2 CDLB 0.121 (0.122+0.12)/2 CSNU 0 ((0)+(0))/2 CTRU 0.0426 ((0.0629)+(0.0224))/2

The average index of each resource is multiplied by the corresponding normalization factor to obtain:

The cluster normalization index CRR=0.1365*0.25+0.116*0.25+0.121*0.25+0*0.15+0.0426*0.15=0.0998, since 0.0998 is less than the cluster shrinkage threshold of 0.3, the cluster statistical analysis module will send a notification to the database resource The shrink module handles shrinking.

After the database shrinking module receives the notification sent by the cluster statistical analysis module, it first normalizes the resource indicators of a single database.

Resource usage of database 1:

SRR1=0.158*0.25+0.118*0.25+0.122*0.25+0*0.15+0.0698*0.15=0.02199.

Resource usage of database 2:

SRR2=0.1365*0.25+0.116*0.25+0.121*0.25+0*0.15+0.0461*0.15=0.01812.

Optionally, the mean of the two databases is (0.02199+0.01812)/2=0.02005<=CRR, so the data of the two databases can be merged.

Since the resource utilization of database 2 is smaller, database 2 is used as the source database and database 1 is used as the target database for consolidation.

Before merging, first back up the data of database 1 and database 2, and then determine whether database 1 contains all the complete table structures of database 2, and if not, supplement relevant content in database 1.

Next, copy the data of database 2 to database 1 after the table structure is supplemented.

Thirdly, after the data copying is completed, the database resources are released, and the information of the database 2 is notified to the resource period collection module and the cluster statistical analysis module for release. At this point, the database expansion is completed.

In the third embodiment of the present application, dynamic factors such as CPU utilization and memory occupancy are taken into account when implementing the expansion and contraction of the database cluster to analyze the expansion of the database, so as to determine whether the database satisfies the expansion and contraction conditions. Expansion makes the timing of expansion more accurate. In addition, the expansion of the database can be performed automatically without manual participation, saving human resources, and periodically obtaining the detection value of the operation index to realize the capacity reduction. Compared with the related technologies, only considering a certain The value of the time, the embodiment of the present application is more accurate, and the error is small.

The fourth embodiment of the present application relates to a service system, as shown in FIG. 6 , comprising at least one processor 601; and a memory 602 communicatively connected to the at least one processor; wherein the memory stores data that can be instructions executed by the at least one processor, where the instructions are executed by the at least one processor, so that the at least one processor can execute the foregoing method for expanding or shrinking a database cluster.

The memory and the processor are connected by a bus, and the bus may include any number of interconnected buses and bridges, and the bus connects one or more processors and various circuits of the memory. The bus may also connect together various other circuits, such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface between the bus and the transceiver. A transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other devices over a transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor.

The processor manages the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Instead, memory may be used to store data used by the processor in performing operations.

The fifth embodiment of the present application relates to a computer-readable storage medium storing a computer program. The above method embodiments are implemented when the computer program is executed by the processor.

That is, those skilled in the art can understand that all or part of the steps in the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program is stored in a storage medium and includes several instructions to make a device ( It may be a single chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

Those of ordinary skill in the art can understand that the above-mentioned embodiments are specific examples for realizing the present application, and in practical applications, various changes can be made in form and details without departing from the spirit and the spirit of the present application. scope.

Claims (15)

A method for expanding and shrinking a database cluster, comprising: Obtain the detection value of the running indicators of each database in the database cluster; Obtaining the resource usage rate of the database cluster according to the detected value of the operation index of each of the databases; If the resource utilization rate of the database cluster satisfies the preset capacity expansion and contraction conditions, the database cluster is expanded and contracted. The method according to claim 1, wherein the acquiring the detection value of the operation indicator of each database in the database cluster comprises: According to the preset detection period, periodically obtain the detection value of the operation indicators of each database in the database cluster; The obtaining of the resource utilization rate of the database cluster according to the detection value of the operation indicators of each of the databases includes: The resource usage rate of the database cluster is obtained according to the detected values of the operation indicators of each of the databases obtained in several of the detection cycles. The method according to claim 2, wherein the number of the operation indicators is greater than or equal to 2; the database is obtained according to the detection values of the operation indicators of each of the databases obtained in several of the detection cycles The resource usage of the cluster, including: Calculate the statistic value of the operation index of the database according to the detection value of the same operation index of the same database obtained in several detection cycles; Calculate the statistical value of the operational indicator of the database cluster according to the statistical value of the operational indicator of each of the databases; Normalize the statistical value of each of the operating indicators of the database cluster, and use the normalized sum of the statistical values of each of the operating indicators of the database cluster as the resource utilization rate of the database cluster . The method according to claim 1 or 2, wherein the operation indicator is one of the following indicators: CPU utilization, memory utilization, disk utilization, the rate of exceeding the limit of the number of structure tables, and the number of records in the structure table table exceeding the limit. rate limit. The method according to claim 4, wherein, in the case that the operation index is CPU utilization, memory utilization, or disk utilization, the acquiring the detection value of the operation index of each database in the database cluster comprises: Collect and obtain the detection value of the operation index of each described database; In the case that the operation indicator is the over-limit rate of the number of structured tables, the acquiring the detection value of the operation indicator of each database in the database cluster includes: collecting and obtaining the number of structured tables of each database, and according to a preset structure The table quantity threshold value calculates the over-limit rate of the structure table quantity of each described database; In the case that the operation indicator is the over-limit rate of the number of records in the structure form table, the acquiring the detection value of the operation index of each database in the database cluster includes: collecting and obtaining the number of records in the structure table table of each database, and according to The preset structural form table record quantity threshold value is used to calculate the over-limit rate of the structural form table records of each database. The method according to claim 1, wherein, if the resource utilization rate of the database cluster satisfies a preset capacity expansion and contraction condition, expanding or shrinking the database cluster comprises: If the resource utilization rate of the database cluster satisfies the expansion conditions in the expansion and contraction conditions, obtain database configuration information according to the source mode of the pre-obtained database configuration information; The newly added database of the database cluster is obtained according to the configuration of the database configuration information. The method of claim 6, wherein the database configuration information includes first information representing database hardware performance, second information representing data processing strategies, and third information representing database architecture; The configuring and obtaining the newly added database of the database cluster according to the database configuration information includes: Apply for a new database from the database resource pool according to the first information; using the third information to initialize the new database; The new database after successful initialization is set by using the second information, and the new database after the successful setting is used as the newly added database of the database cluster. The method according to claim 6, wherein the source manner of the database configuration information comprises at least one of the following manners: acquiring from at least one database in the database cluster, acquiring from a local storage module, acquiring from a remote device . The method according to claim 3, wherein, if the resource utilization rate of the database cluster satisfies a preset capacity expansion and contraction condition, expanding or shrinking the database cluster comprises: If the resource usage rate of the database cluster satisfies the scaling conditions in the scaling conditions, normalize the statistical values of the operating indicators of the same database, and normalize the normalized database The sum of each of the operating indicators is used as the resource utilization rate of the database; According to the resource usage rate of each of the databases and the resource usage rate of the database cluster, a candidate database is selected from each of the databases; The database cluster is scaled down based on the candidate database. The method according to claim 9, wherein the selecting a candidate database from each of the databases according to the resource usage rate of each of the databases and the resource usage rate of the database cluster comprises: Using a database whose resource usage rate in each of the databases is less than the resource usage rate of the database cluster as the candidate database; The shrinking of the database cluster based on the candidate database includes: If the candidate database is one, the candidate database is used as the database to be reduced, and the database to be reduced is merged into other databases except the database to be reduced in each of the databases; If the number of candidate databases is greater than or equal to 2, at least one candidate database among the multiple candidate databases is used as the database to be reduced, and the databases to be reduced are merged into the multiple candidate databases except the to-be-reduced database. In other candidate databases other than the database; Clean up the database to be reduced. The method according to claim 10, wherein the at least one candidate database among the plurality of candidate databases is used as the database to be reduced, and the database to be reduced is merged into the plurality of candidate databases except the database to be reduced In addition to other candidate databases, including: Sorting the multiple candidate databases in descending order of resource utilization to obtain a first sequence, and sorting the multiple candidate databases in descending order of resource utilization to obtain a second sequence; Taking two candidate databases with the same order number in the first sequence and the second sequence as one group, and filtering out a plurality of different groups; For each group that is screened out, one of the candidate databases in the group is merged into another candidate database in the group; the one of the candidate databases is used as the database to be reduced. The method according to claim 11, wherein the one of the candidate databases is a candidate database with a smaller resource usage rate among the two candidate databases in the group. The method according to any one of claims 10-12, wherein the cleaning the to-be-reduced database comprises: Clean up the database to be reduced based on a preset cleaning rule; the cleaning rule includes: deleting the data in the database to be reduced, and removing the database to be reduced from the database cluster; or, deleting only the database to be reduced Describe the data in the database to be reduced. A service system including: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the execution of any one of claims 1 to 13 database cluster scaling method. A computer-readable storage medium storing a computer program, when the computer program is executed by a processor, the method for expanding or shrinking a database cluster according to any one of claims 1 to 13 is implemented.

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