Risk monitoring method for cache service, storage medium and processor

Abstract

The invention discloses a risk monitoring method for a cache service, a storage medium and a processor, relates to the technical field of data caching, and solves the technical problem of low efficiency of identifying a high risk of the cache service in the prior art. The method comprises the following steps: determining a main-standby relation state of a server based on a service ID of a cache node, and collecting abnormal information; packaging the abnormal information processing program based on a Shell language to obtain an entry script; a timed task scheduler based on an operating system automatically calls an entry script and configures a timed scheduling strategy; and sending the abnormal information to a specified processor based on the enterprise WeChat API interface. According to the method, the entry script is automatically called through the timed task scheduler of the operating system, and the timed scheduling strategy is configured, so that the state of the cache service is automatically monitored, and the high-risk identification efficiency of the cache service in the running process is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data cache, and in particular relates to a risk monitoring method, a storage medium and a processor for a high-speed cache service. Background Art

[0002] The digital transformation of the home appliance industry is an important and inevitable trend in the development of the industry in recent years. Especially driven by technologies such as artificial intelligence, big data, and computing algorithms, digital transformation is bringing greater opportunities and business opportunities to the home appliance industry, as well as greater development opportunities and broader development space.

[0003] Manufacturing Execution System (MES) plays a key role in the digital transformation of the home appliance industry, which is reflected in the realization of digital management of the production process, optimization of production planning and scheduling, improvement of product quality and traceability, support for intelligent manufacturing and factory automation, realization of supply chain collaboration and management, data analysis and decision support. Among them, digital management of the production process is the cornerstone. Through real-time data collection, each link on the production line can be monitored, production data can be collected and analyzed, and digital management of the production process can be realized. By monitoring and analyzing production data, production abnormalities can be discovered in a timely manner, and corresponding measures can be taken to adjust and optimize.

[0004] In order to ensure the stability, accuracy and timeliness of real-time data collection, the Manufacturing Execution System (MES) uses cache services to ensure the timeliness of data collection through stations, and builds cache service clusters to ensure stability and accuracy. The cache service cluster has a certain disaster recovery capability. If the primary and backup node services in the cluster fall on the same physical machine, the cluster may go down due to a physical machine failure, causing the Manufacturing Execution System (MES) to become unavailable, leading to major malicious events such as production interruptions.

[0005] However, when the master and slave nodes of the redis cache are deployed on the same physical machine, the slave nodes are difficult to take over the cache service in time due to the loss of the master-slave relationship. The existing technology is difficult to quickly identify the nodes where the master-slave relationship is lost through manual investigation, resulting in low efficiency in identifying high-risk risks of the cache service. In addition, the existing technology is difficult to push warning messages to the corresponding handlers in a timely manner, resulting in the problem of insufficient timely processing of high-risk caches.

[0006] The present invention proposes a risk monitoring method, storage medium and processor for cache services to solve the above technical problems. Summary of the invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a risk monitoring method for cache services, which is used to solve the problem that when the master and slave nodes of the redis cache in the prior art are deployed on the same physical machine, the slave nodes are difficult to take over the cache services in time due to the loss of the master-slave relationship. The prior art is difficult to quickly identify the nodes with the lost master-slave relationship through manual investigation, resulting in low efficiency in identifying high-risks of the cache services; in addition, the prior art is difficult to push early warning messages to corresponding processors in a timely manner, resulting in the technical problem that the high-risks of the cache are not handled in a timely manner.

[0008] To achieve the above object, a first aspect of the present invention provides a risk monitoring method for a cache service, comprising:

[0009] S1: Get the service IDs of several cache nodes in the server where the specified IP is located;

[0010] S2: Determine the active / standby relationship status of the server based on the service ID of the cache node and collect abnormal information; wherein the active / standby relationship status includes normal and abnormal;

[0011] S3: Limit access based on the operating system firewall and cache database configuration files;

[0012] S4: Encapsulate the exception information processing program based on Shell language to obtain an entry script;

[0013] S5: The scheduled task scheduler based on the operating system automatically calls the entry script and configures the scheduled scheduling strategy;

[0014] S6: Send the exception information to the designated handler based on the WeChat for Business API interface.

[0015] It should be noted that the cache service in the present invention refers to the redis cache service.

[0016] Preferably, obtaining the service IDs of several cache nodes in the server where the specified IP is located includes:

[0017] Extract the specified IP, call the redis cluster service in the server where the specified IP is located, and obtain the service ID of each cache node in the redis cluster service; the service ID is a 41-bit hexadecimal unique code.

[0018] Preferably, the determining the active / standby relationship status of the server based on the service ID of the cache node and collecting abnormal information includes:

[0019] Extract the service ID of each cache node and obtain the preset cache server mapping table;

[0020] Determine whether there is a mapping relationship between the service IDs of any two cache nodes in each server; if yes, mark the master-slave relationship status of the corresponding server as abnormal, mark the corresponding server as an abnormal server, mark the current time as the alarm time, and mark the service IDs of the corresponding two cache nodes as abnormal cache service IDs; if no, mark the master-slave relationship status of the corresponding server as normal;

[0021] The IP address, alarm time and abnormal cache service ID of the abnormal server are integrated into the abnormal information.

[0022] Preferably, the cache server mapping table is constructed as follows:

[0023] Extract the service IDs of several cache nodes and the IP addresses of several servers; build several cache clusters, each of which includes a master node and several slave nodes;

[0024] Bind the service ID of the master node with the IP of the server where the master node is located to obtain a first mapping relationship; bind the service ID of the master node with the service IDs corresponding to several slave nodes respectively to obtain a second mapping relationship; save the first mapping relationship and the second mapping relationship to the database, and mark the database as a cache server mapping table.

[0025] Preferably, the configuration file based on the operating system firewall and cache database limits the access volume, including:

[0026] In the operating system's firewall, restrict non-industrial control networks from accessing the port where the cache service is located, and limit the maximum number of user connections allowed to access in the cache database configuration file.

[0027] Preferably, the encapsulation of the exception information processing program based on the Shell language includes:

[0028] The processing method of step S2 is written into code by using Python programming language, the code is marked as an exception information processing program, and the exception information processing program is encapsulated as an entry script according to Shell language.

[0029] Preferably, the scheduled task scheduler based on the operating system automatically calls the entry script, including:

[0030] Get the storage path of the entry script; set a cron expression, associate the cron expression with the storage path of the entry script, and obtain an automatic call instruction; execute the automatic call instruction through the Cron scheduled task scheduler of the operating system, and automatically call the entry script; wherein the cron expression is a string expression used to specify the execution time of the scheduled task.

[0031] Preferably, the sending of the abnormal information to a designated handler based on the enterprise WeChat API interface includes:

[0032] Extract the exception information and obtain the basic information of the designated handler; the basic information includes enterprise ID, application ID, key, organization ID and user ID;

[0033] Configure the exception information and basic information according to the requirements of the WeChat for Business API interface, and the WeChat for Business backend program will send the exception information to the designated handler.

[0034] A second aspect of the present invention provides a storage medium, wherein the storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above-mentioned risk monitoring method for a cache service.

[0035] A third aspect of the present invention provides a processor, which is used to run a program, wherein the program executes the above-mentioned risk monitoring method for cache services when running.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention encapsulates the exception information processing program based on the Shell language to obtain an entry script; the entry script is automatically called by the scheduled task scheduler of the operating system, and the scheduled scheduling strategy is configured; the state of the cache service is automatically monitored by the scheduled task and the entry script, which is conducive to improving the efficiency of identifying high-risk risks of the cache service during operation. In addition, the present invention obtains the exception information and the basic information of the handler, and configures the exception information and the basic information according to the requirements of the enterprise WeChat API interface, and then sends the exception information to the designated handler through the enterprise WeChat background program, so that the handler can handle the cache service in a timely manner according to the exception information, which is conducive to improving the efficiency of handling high-risk risks of the cache service. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is an overall flow chart of the high-risk monitoring method for cache services of the present invention;

[0040] Figure 2This is a flow chart of automatically calling an entry script according to a scheduled task scheduler in the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1-Figure 2 The first aspect of the present invention provides a method for monitoring risk of a cache service, comprising:

[0043] S1: Get the service IDs of several cache nodes in the server where the specified IP is located;

[0044] S2: Determine the master-slave relationship status of the server based on the service ID of the cache node and collect abnormal information; wherein the master-slave relationship status includes normal and abnormal;

[0045] S3: Limit access based on the operating system firewall and cache database configuration files;

[0046] S4: Encapsulate the exception information processing program based on Shell language to obtain an entry script;

[0047] S5: The scheduled task scheduler based on the operating system automatically calls the entry script and configures the scheduled scheduling strategy;

[0048] S6: Send the exception information to the designated handler based on the WeChat for Business API interface.

[0049] It should be noted that the cache service in the present invention refers to the redis cache service.

[0050] In this embodiment, obtaining the service IDs of several cache nodes in the server where the specified IP is located includes:

[0051] Extract the specified IP, call the redis cluster service in the server where the specified IP is located, and obtain the service ID of each cache node in the redis cluster service; the service ID is a 41-bit hexadecimal unique code.

[0052] In this embodiment, determining the active / standby relationship status of the server based on the service ID of the cache node and collecting abnormal information includes:

[0053] Extract the service ID of each cache node and obtain the preset cache server mapping table;

[0054] Determine whether there is a mapping relationship between the service IDs of any two cache nodes in each server; if yes, mark the master-slave relationship status of the corresponding server as abnormal, mark the corresponding server as an abnormal server, mark the current time as the alarm time, and mark the service IDs of the corresponding two cache nodes as abnormal cache service IDs; if no, mark the master-slave relationship status of the corresponding server as normal;

[0055] The IP address, alarm time and abnormal cache service ID of the abnormal server are integrated into the abnormal information.

[0056] In this embodiment, the cache server mapping table is constructed as follows:

[0057] Extract the service IDs of several cache nodes and the IP addresses of several servers; build several cache clusters, each of which includes a master node and several slave nodes;

[0058] Bind the service ID of the master node with the IP of the server where the master node is located to obtain a first mapping relationship; bind the service ID of the master node with the service IDs corresponding to several slave nodes respectively to obtain a second mapping relationship; save the first mapping relationship and the second mapping relationship to the database, and mark the database as a cache server mapping table.

[0059] In this embodiment, the access volume is restricted based on the firewall of the operating system and the configuration file of the cache database, including:

[0060] In the operating system's firewall, restrict non-industrial control networks from accessing the port where the cache service is located, and in the cache database configuration file, limit the maximum number of user connections allowed to access, setting the maximum number of user connections to 1000.

[0061] Exemplarily, the above steps can be processed using the following configuration files:

[0062] #System firewall configuration information

[0063] iptables-Z

[0064] iptables-A INPUT-i lo-j ACCEPT

[0065] iptables-A INPUT-p tcp--dport 22-j ACCEPT

[0066] iptables -A INPUT -s 10.17.0.0 / 16 -p tcp --dport 7000 -j ACCEPT iptables -A INPUT -s 10.17.0.0 / 16 -p tcp --dport 17000 -j ACCEPT iptables -A INPUT -s 10.17.2.0 / 24 -p tcp --dport 10050 -j ACCEPT iptables -A INPUT -s 10.17.2.0 / 24 -p tcp --dport 9080 -j ACCEPT iptables -A INPUT -s 10.17.2.0 / 24 -p tcp --dport 3085 -j ACCEPT iptables -A INPUT -p icmp --icmp-type 8 -j ACCEPT iptables -A INPUT -m state --state RELATED,ESTABLISHED -j ACCEPT iptables -P INPUT DROP

[0067] iptables -P OUTPUT ACCEPT

[0068] iptables -P FORWARD ACCEPT

[0069] # Configuration information of Redis database

[0070] # RDB configuration method

[0071] # General (general configuration)

[0072] daemonize yes pidfile / usr / local / redis-cluster / 7000 / 7000.pid dir / usr / local / redis-cluster / 7000

[0073] port 7000

[0074] bind 10.17.2.38

[0075] dbfilename dump_7000.rdb logfile redis_7000.log timeout 3

[0076] tcp-keepalive 300

[0077] tcp-backlog 511

[0078] loglevel notice databases 2

[0079] #Cluster (redis cache cluster configuration)

[0080] cluster-enabled yescluster-node-timeout 15000

[0081] cluster-config-file nodes.conf

[0082] #Append Only Mode

[0083] appendonly no

[0084] appendfilename"appendonly_7000.aof"appendfsync everysec no-appendfsync-on-rewrite no

[0085] auto-aof-rewrite-percentage 100auto-aof-rewrite-min-size 64mb aof-load-truncated yes

[0086] #Limits (maximum number of user connections)

[0087] maxclients 1000

[0088] maxmemory 3g

[0089] maxmemory-policy allkeys-lru

[0090] In this embodiment, the exception information processing program is encapsulated based on the Shell language, including:

[0091] The processing method of step S2 is written into code by using Python programming language, the code is marked as an exception information processing program, and the exception information processing program is encapsulated as an entry script according to Shell language.

[0092] The present invention encapsulates the exception information processing program based on Shell language to obtain an entry script; automatically calls the entry script through the scheduled task scheduler of the operating system and configures the scheduled scheduling strategy; and realizes automatic monitoring of the status of the cache service through the scheduled task and the entry script, which is beneficial to improving the efficiency of identifying high-risk risks of the cache service during operation.

[0093] Exemplarily, part of the code of the exception information processing program is as follows:

[0094] #! / bin / bash

[0095] time=`date+'%Y-%m-%d%H:%M:%S'`

[0096] function groupA()

[0097] {

[0098] local node1 = 10.17.2.38

[0099] local node2 = 10.17.2.39

[0100] local node1_1=`redis-cli-h$node1-p 7000-a"MLMes2019"cluster nodes|grep$node1|awk'{print$1}'`

[0101] local node1_2=`redis-cli-h$node1-p 7000-a"MLMes2019"cluster nodes|grep$node1|awk'{print$4}'`

[0102] local node2_1=`redis-cli-h$node2-p 7000-a"MLMes2019"cluster nodes|grep$node2|awk'{print$1}'`

[0103] local node2_2=`redis-cli-h$node2-p 7000-a"MLMes2019"cluster nodes|grep$node2|awk'{print$4}'`

[0104] if[$node1_2==$node2_2];then

[0105] echo "1">> / dev / null

[0106] elif[$node1_1==$node2_2]||[$node1_2==$node2_1]; then

[0107] python / opt / zabbix / alertscripts / webcat.py 1"Alarm time: $time""Alarm content: redis cluster nodes are located on the same physical machine, node IP is $node1$node2">> / dev / null

[0108] fi

[0109] }

[0110] function groupB()

[0111] {

[0112] local node1 = 10.17.2.40

[0113] local node2 = 10.17.2.41

[0114] local node1_1=`redis-cli-h$node1-p 7000-a"MLMes2019"cluster nodes|grep$node1|awk'{print$1}'`

[0115] local node1_2=`redis-cli-h$node1-p 7000-a"MLMes2019"cluster nodes|grep$node1|awk'{print$4}'`

[0116] local node2_1=`redis-cli-h$node2-p 7000-a"MLMes2019"cluster nodes|grep$node2|awk'{print$1}'`

[0117] local node2_2=`redis-cli-h$node2-p 7000-a"MLMes2019"cluster nodes|grep$node2|awk'{print$4}'`

[0118] if[$node1_2==$node2_2];then

[0119] echo "1">> / dev / null

[0120] elif[$node1_1==$node2_2]||[$node1_2==$node2_1]; then

[0121] python / opt / zabbix / alertscripts / webcat.py 1"Alarm time: $time""Alarm content: redis cluster nodes are located on the same physical machine, node IP is $node1$node2">> / dev / null

[0122] fi

[0123] }

[0124] function groupC()

[0125] {

[0126] local node1 = 10.17.2.42

[0127] local node2 = 10.17.2.43

[0128] local node1_1=`redis-cli-h$node1-p 7000-a"MLMes2019"cluster nodes|grep$node1|awk'{print$1}'`

[0129] local node1_2=`redis-cli-h$node1-p 7000-a"MLMes2019"cluster nodes|grep$node1|awk'{print$4}'`

[0130] local node2_1=`redis-cli-h$node2-p 7000-a"MLMes2019"cluster nodes|grep$node2|awk'{print$1}'`

[0131] local node2_2=`redis-cli-h$node2-p 7000-a"MLMes2019"cluster nodes|grep$node2|awk'{print$4}'`

[0132] if[$node1_2==$node2_2];then

[0133] echo "1">> / dev / null

[0134] elif[$node1_1==$node2_2]||[$node1_2==$node2_1];then

[0135] python / opt / zabbix / alertscripts / webcat.py 1"Alarm time: $time""Alarm content: redis cluster nodes are located on the same physical machine, node IP is $node1$node2">> / dev / null

[0136] fi

[0137] }

[0138] groupA

[0139] groupB

[0140] groupC

[0141] In this embodiment, the scheduled task scheduler based on the operating system automatically calls the entry script, including:

[0142] Get the storage path of the entry script; set a cron expression, associate the cron expression with the storage path of the entry script, and obtain an automatic call instruction; execute the automatic call instruction through the Cron scheduled task scheduler of the operating system, and automatically call the entry script; wherein the cron expression is a string expression used to specify the execution time of the scheduled task.

[0143] The present invention automatically calls the entry script through the scheduled task scheduler of the operating system and configures the scheduled scheduling strategy; the scheduled tasks and entry scripts realize automatic monitoring of the status of the cache service, which is beneficial to improving the efficiency of identifying high-risk risks during the operation of the cache service.

[0144] For example, the code for automatically calling the command is as follows:

[0145] 0* / 5***sh / opt / zabbix / alertscripts / redis_switch.sh> / dev / null

[0146] In this embodiment, the abnormal information is sent to the designated handler based on the enterprise WeChat API interface, including:

[0147] Extract the exception information and obtain the basic information of the designated handler; the basic information includes enterprise ID, application ID, key, organization ID and user ID;

[0148] Configure the exception information and basic information according to the requirements of the WeChat for Business API interface, and the WeChat for Business backend program will send the exception information to the designated handler.

[0149] For example, since the WeChat for Enterprise API interface can be written in a variety of programming languages, Python is used in this embodiment to implement it;

[0150] Specifically, the configuration code of the WeChat Enterprise API interface is as follows:

[0151] #! / usr / bin / env python

[0152]

[0153]

[0154]

[0155] The present invention obtains the exception information and the basic information of the handler, configures the exception information and the basic information according to the requirements of the enterprise WeChat API interface, and then sends the exception information to the designated handler through the enterprise WeChat background program, so that the handler can handle the cache service in time according to the exception information, which is beneficial to improve the processing efficiency of high-risk cache services.

[0156] A second aspect of the present invention provides a storage medium, the storage medium including a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned risk monitoring method for a cache service.

[0157] A third aspect of the present invention provides a processor for running a program, wherein the program executes the above-mentioned risk monitoring method for cache services when the program is running.

[0158] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A risk monitoring method for a cache service, characterized in that: S1: Get the service IDs of several cache nodes in the server where the specified IP is located; S2: Determine the active / standby relationship status of the server based on the service ID of the cache node and collect abnormal information; wherein the active / standby relationship status includes normal and abnormal; S3: Limit access based on the operating system firewall and cache database configuration files; S4: Encapsulate the exception information processing program based on Shell language to obtain an entry script; S5: The scheduled task scheduler based on the operating system automatically calls the entry script and configures the scheduled scheduling strategy; S6: Send the exception information to the designated handler based on the API interface.

2. A cache service risk monitoring method according to claim 1, characterized in that: The method of obtaining the service IDs of several cache nodes in the server where the specified IP is located includes: Extract the specified IP, call the redis cluster service in the server where the specified IP is located, and obtain the service ID of each cache node in the redis cluster service; the service ID is a 41-bit hexadecimal unique code.

3. A cache service risk monitoring method according to claim 1, characterized in that: The determining the active / standby relationship status of the server based on the service ID of the cache node and collecting abnormal information includes: Extract the service ID of each cache node and obtain the preset cache server mapping table; Determine whether there is a mapping relationship between the service IDs of any two cache nodes in each server; if yes, mark the master-slave relationship status of the corresponding server as abnormal, mark the corresponding server as an abnormal server, mark the current time as the alarm time, and mark the service IDs of the corresponding two cache nodes as abnormal cache service IDs; if no, mark the master-slave relationship status of the corresponding server as normal; The IP address, alarm time and abnormal cache service ID of the abnormal server are integrated into the abnormal information.

4. A cache service risk monitoring method according to claim 3, characterized in that: The cache server mapping table is constructed as follows: Extract the service IDs of several cache nodes and the IP addresses of several servers; build several cache clusters, each of which includes a master node and several slave nodes; Bind the service ID of the master node to the IP address of the server where the master node is located to obtain a first mapping relationship; The service ID of the master node is bound to the service IDs corresponding to several slave nodes respectively to obtain a second mapping relationship; the first mapping relationship and the second mapping relationship are saved to a database, and the database is marked as a cache server mapping table.

5. A cache service risk monitoring method according to claim 1, characterized in that: The configuration files of the firewall and cache database based on the operating system restrict the access volume, including: In the operating system's firewall, restrict non-industrial control networks from accessing the port where the cache service is located, and limit the maximum number of user connections allowed to access in the cache database configuration file.

6. A cache service risk monitoring method according to claim 1, characterized in that: The Shell language-based encapsulation of the exception information processing program includes: The processing method of step S2 is written into code by using Python programming language, the code is marked as an exception information processing program, and the exception information processing program is encapsulated as an entry script according to Shell language.

7. A cache service risk monitoring method according to claim 1, characterized in that: The scheduled task scheduler based on the operating system automatically calls the entry script, including: Get the storage path of the entry script; set a cron expression, associate the cron expression with the storage path of the entry script, and obtain an automatic call instruction; execute the automatic call instruction through the Cron scheduled task scheduler of the operating system, and automatically call the entry script; wherein the cron expression is a string expression used to specify the execution time of the scheduled task.

8. The risk monitoring method for cache service according to claim 1, characterized in that: The sending of the exception information to the designated handler based on the API interface includes: Extract the exception information and obtain the basic information of the designated handler; the basic information includes enterprise ID, application ID, key, organization ID and user ID; Configure the exception information and basic information according to the requirements of the WeChat for Business API interface, and the WeChat for Business backend program will send the exception information to the designated handler.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute a risk monitoring method for a cache service as claimed in any one of claims 1 to 8.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes a risk monitoring method for cache services as described in any one of claims 1 to 8.

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