CN107092544B - Monitoring method and device - Google Patents
Monitoring method and device Download PDFInfo
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- CN107092544B CN107092544B CN201610349960.8A CN201610349960A CN107092544B CN 107092544 B CN107092544 B CN 107092544B CN 201610349960 A CN201610349960 A CN 201610349960A CN 107092544 B CN107092544 B CN 107092544B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3017—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is implementing multitasking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
Abstract
The application discloses a monitoring method and a monitoring device, relates to the technical field of service monitoring, and can improve the precision of service monitoring. The main technical scheme of the application is as follows: when behavior data of a user is received, user identification information of the user is obtained; detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and monitoring a configuration result according to the preset rule.
Description
Technical Field
The present application relates to the field of service monitoring technologies, and in particular, to a monitoring method and apparatus.
Background
The service monitoring is to monitor a system service interface, specifically, to monitor service-related information provided by a system to a caller, for example, information such as time consumption, input parameters, return information, whether to report an error, and the number of calls. The service monitoring result can be output through a console, or written into a log file, or persisted (persistency) to a database, so that the system can perform online analysis and offline analysis on the service execution condition.
At present, existing service monitoring is based on the dimensionality of the service itself, and the specific implementation mode is as follows: the background monitoring system counts the user behaviors belonging to the same type, measures the relevant performance of the service system through the statistical information in unit time, and gives out relevant alarm, namely, the macroscopic statistical information in unit time is utilized to find out problems and give feedback. For example, the background monitoring system summarizes the requests for accessing the interface a within one minute, detects whether the number of errors occurring when the requests are processed is larger than an alarm threshold value, and if so, makes a relevant alarm.
However, for the horizontal service dimension-based monitoring, when the corresponding alarm condition is not reached, the relevant condition of the service invoked by a single user cannot be fed back in a targeted manner, which may cause a hidden trouble of a service system failure, and further may cause a low accuracy of service monitoring. For example, when a user orders a meal online, only one to two stores have many dishes, when the dishes are loaded, the corresponding query service consumes a long time or causes timeout, when a large number of users visit the stores and load the dishes at the same time, the service system is in failure, but if only a small number of users temporarily trigger the problem, and the number of the users does not exceed the alarm threshold, the existing service monitoring mode cannot monitor the problem.
Disclosure of Invention
In view of this, embodiments of the present application provide a monitoring method and apparatus, and mainly aim to solve the problem that the accuracy of service monitoring is low due to the existing horizontal service monitoring method based on the service dimension.
In order to achieve the purpose, the application provides the following technical scheme:
in one aspect, the present application provides a monitoring method, including:
when behavior data of a user is received, user identification information of the user is obtained;
detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes;
if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data;
and monitoring a configuration result according to the preset rule.
In another aspect, the present application provides a monitoring device, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring user identification information of a user when behavior data of the user is received;
the detection unit is used for detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes;
a configuration unit, configured to configure, if the detection unit detects that a monitoring model corresponding to the user identification information exists, a node corresponding to the behavior data in the monitoring model according to the behavior data;
and the monitoring unit is used for monitoring a configuration result according to the preset rule.
By means of the technical scheme, the technical scheme provided by the embodiment of the application at least has the following advantages:
according to the monitoring method and the monitoring device, when behavior data of a user is received, user identification information of the user is obtained firstly; then detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and finally, monitoring a configuration result according to the preset rule. Compared with the existing transverse service monitoring mode based on the service dimensionality, the method and the system can construct the behavior expression of the user and the system service into a monitoring model bound with preset rules, can detect the relevant condition of the user calling the service through the preset rules bound on the nodes in the monitoring model corresponding to the user identification information, and further can realize targeted feedback on the relevant condition of the single user calling the service, thereby realizing real-time monitoring based on the user dimensionality, improving the perception capability of the service system on the single user behavior, completing monitoring on the user and the corresponding service through monitoring the quality and the state of the monitoring model, and improving the precision of service monitoring.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a monitoring method according to an embodiment of the present application;
FIG. 2 is a flow chart of another monitoring method provided by the embodiments of the present application;
FIG. 3 illustrates an example diagram of a directed cyclic graph model provided by an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating an example monitoring system provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram illustrating a monitoring apparatus provided in an embodiment of the present application;
fig. 6 shows a schematic structural diagram of another monitoring device provided in an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
An embodiment of the present application provides a monitoring method, as shown in fig. 1, the method includes:
101. and when the behavior data of the user is received, acquiring the user identification information of the user.
The user identification information may be user name information, an ID (Identity) number, an IP address, and the like. The behavior data may include data related to user behavior, and may be obtained specifically according to log information corresponding to the server, or may be obtained by collecting by the client, and the like.
The execution subject of the embodiment of the present application may be a monitoring device disposed in the server, or may be a monitoring module disposed in the server. For example, when the server processes a service request sent by a client, the server records corresponding log information and transmits the log information to a monitoring device in the server, and the monitoring device acquires user identification information from the log information after receiving the log information.
102. And detecting whether a monitoring model corresponding to the user identification information exists.
The monitoring model comprises different nodes, and preset rules are bound on the nodes. One node may correspond to one user behavior type, that is, a node of a different user behavior type corresponding to the user identifier is recorded in the monitoring model corresponding to the user identifier information, and the user behavior type may be a behavior type such as invoking a service interface, for example, accessing a page of the store a by invoking a service interface of the store a. The preset rules can be written in advance by technicians according to actual requirements of the user behavior types and configured in a preset database corresponding to the server, for example, the user behavior type is a visiting shop b page, and in order to detect whether a web crawler crawling behavior for the shop b page exists, the preset rules bound on the node corresponding to the user behavior type can be configured to be that the number of times the node is visited is smaller than an alarm threshold value.
Specifically, whether a monitoring model corresponding to the user identification information exists in the server or not is detected. For the embodiment of the present application, one piece of user identification information may correspond to one monitoring model, and if there is no monitoring model corresponding to the user identification information in the server, the monitoring model may be created, for example, when a new user sends a service request through the client for the first time, and when the server receives and processes the service request, the monitoring model corresponding to the new user identification information may be created. Specifically, a node in the monitoring model may be configured according to behavior data, for example, according to a user behavior type recorded in log information, the log information is filled into a node object corresponding to the log information, and meanwhile, 1 may be added to the access times of the node in an accumulated manner; and the preset rule corresponding to the node can be obtained from the preset database and bound on the node according to the user behavior type corresponding to the node.
It should be noted that, specifically, whether a monitoring model corresponding to the user identification information exists in the server may be detected by calculating a hash value corresponding to the user identification information. For example, when the hash value corresponding to the user identification information is already recorded in the server, it may be determined that the user is an old user, and the monitoring model corresponding to the user identification information may be acquired from the preset database; when the hash value corresponding to the user identification information is not recorded in the server, it can be determined that the user is a new user, a monitoring model corresponding to the new user needs to be created, and the hash value corresponding to the new user identification information is recorded.
103. And if the monitoring model corresponding to the user identification information exists, configuring nodes corresponding to the behavior data in the monitoring model according to the behavior data.
For example, the type of the user behavior in the received log information is to access a menu list page, a node corresponding to the menu list page is determined from the monitoring model corresponding to the user identification information, and the log information is filled into the node.
104. And monitoring the configuration result according to a preset rule.
For example, when it is detected that the time consumed by the server for processing the acquisition request of the dish list page service interface in the newly added log information of the node is greater than or equal to an alarm threshold value for 200 milliseconds, it is determined that the configured node does not conform to a preset rule corresponding to the node, and the user identification information in the log information corresponding to the node is user a, and at this time, an updated monitoring graph after the node corresponding to user a is configured may be output, so that a monitoring person can judge that user a is abnormal; the display screen corresponding to the monitoring equipment can also be used for displaying character warning information which takes more than or equal to 200 milliseconds for the user a to access the loading time of the dish list page, and meanwhile, warning audio can be output through the audio output end corresponding to the monitoring equipment, so that operation and maintenance personnel can be reminded of carrying out related maintenance in time.
According to the monitoring method provided by the embodiment of the application, when behavior data of a user is received, user identification information of the user is obtained firstly; then detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and finally, monitoring a configuration result according to the preset rule. Compared with the existing transverse service monitoring mode based on the service dimensionality, the method and the system can construct the behavior expression of the user and the system service into a monitoring model bound with preset rules, can detect the relevant condition of the user calling the service through the preset rules bound on the nodes in the monitoring model corresponding to the user identification information, and further can realize targeted feedback on the relevant condition of the single user calling the service, thereby realizing real-time monitoring based on the user dimensionality, improving the perception capability of the service system on the single user behavior, completing monitoring on the user and the corresponding service through monitoring the quality and the state of the monitoring model, and improving the precision of service monitoring.
Further, another monitoring method is provided in an embodiment of the present application, and as shown in fig. 2, the method includes:
201. and when detecting that the newly added log information exists in the preset log file, acquiring the user identification information from the log information.
And the preset log file stores log information recorded when the server processes service requests sent by different clients each time. For example, the log information records longitude and latitude of a corresponding location of the client, a city where the client is located, time for receiving the service request, a tag string of the service request, an IP (Internet Protocol) address of a user mobile phone, a type of a mobile phone operating system, information of a network environment where the user is located, and the like. The user identification information may be user name information, an ID number, an IP address, and the like.
Specifically, when the server receives a service request sent by the client, the server processes the service request according to the service field information and the embedded point information carried in the service request, and obtains returned result information. The service field information may include longitude and latitude lon and lat of a location corresponding to the client, city where the location is located, and other information. The embedded point information may include information such as time current _ time for receiving the service request, a mark string rn of the service request, an IP address of the user's mobile phone, the type os of the mobile phone operating system, and the network environment where the user is located. The returned result information may include information such as time consumed for processing the service request, whether the result is returned normally, and a user behavior type.
And then the server generates log information according to the returned result information, the service field information and the embedded point information and stores the log information in a preset log file. For example, a user accesses a shop list page through a client, the client collects service field information and embedded point information of the operation, the two kinds of information are used as parameters to call a shop list page information acquisition interface provided by a server, the server receives a service request corresponding to the operation, and the result is returned to the client through various query and calculation logics. Meanwhile, the service end records the service field information, the buried point information and the return result information of the service request into a preset log file through a log, and the format can be as follows:
lon:120.11111；lat:30.11111；city:330100；current_time:2016-02-01
12:30:12；rn:46e86a2697014；ip:10.111.22.11；os:iphone5；.....
202. and detecting whether a monitoring model corresponding to the user identification information exists in the server.
The monitoring model comprises different nodes, one node corresponds to one user behavior type, preset rules are bound on the nodes, and the monitoring model further comprises associated edges corresponding to the nodes. It should be noted that the specific form of the monitoring model may be determined according to specific service requirements, and for the access behavior of the user, the associated edges corresponding to the nodes in the monitoring model are all directional, so the monitoring model may be a directed graph model. For example, a user a accesses a webpage a first, and then clicks a link in the webpage a to jump to an access webpage B, and in the monitoring model corresponding to the user a, the direction of the associated edge between a node 1 corresponding to the webpage a and a node 2 corresponding to the webpage B is from the node 1 to the node 2. The directed graph model can well show the access behavior of the user. Further, the directed graph model can be divided into a directed graph ring model and a directed acyclic graph model according to whether a loop exists in the directed graph model, for example, a user a accesses a webpage a first, then jumps to a webpage B from the webpage a, and then jumps to the webpage a from the webpage B, and the loop exists in the directed graph model corresponding to the user a, so that the monitoring model corresponding to the user a is the directed cyclic graph model.
Specifically, user behaviors, pages, interfaces and the like on an APP (Application) can be reasonably coded, and a monitoring model is constructed, wherein a node in the monitoring model corresponds to a page, attributes of the node correspond to return information of a service interface and page self information, an associated edge of the node corresponds to a behavior of a user, and the attributes of the associated edge correspond to embedded point information (equipment information, calling service interface information and the like) of the user behavior. The nodes are bound with preset rules, the preset rules can be specifically divided into general rules and specific rules, for example, the specific rules can be configured that the number of times of access to the nodes corresponding to the shop list page within one minute is less than 100, if the number of times of access within one minute is more than or equal to 100, an alarm is given, and the specific rules are suitable for service monitoring of behaviors such as data crawling of a web crawler; the general rule may be configured to take less than 200 milliseconds to load a page corresponding to a node and to alert if the load takes greater than or equal to 200 milliseconds.
For example, as shown in fig. 3, a user accesses a shop list page, a dish list page, a next order page, and an order list page through a mobile phone-installed takeout APP client, where there are corresponding nodes in a directed cyclic graph model corresponding to the user identification information, which are a shop list page node, a dish list page node, a next order page node, and an order list page node, respectively, where the shop list page node is bound with a real-time rule, and specifically, when it takes more than 200 milliseconds for a server to process a service interface request corresponding to the shop list page, an alarm is triggered; the lower single page node is bound with a real-time rule and a timing rule, the real-time rule is specifically that when a server processes a service interface request corresponding to the lower single page and returns a serious error, an alarm is triggered, and the timing rule is specifically that a red packet message is sent to a user after the server fails to place an order within 10 minutes.
It should be noted that, specifically, whether a monitoring model corresponding to the user identification information exists may be detected by calculating a hash value corresponding to the user identification information. For example, when a hash value corresponding to user identification information has been recorded, it may be determined that the user is an old user, and a monitoring model corresponding to the user identification information may be obtained from a preset database; when the hash value corresponding to the user identification information is not recorded, it may be determined that the user is a new user, a monitoring model corresponding to the new user needs to be created, and the hash value corresponding to the new user identification information is recorded.
203a, if a monitoring model corresponding to the user identification information exists in the server, determining a node corresponding to the user behavior type in the log information from the monitoring model.
For the embodiment of the present application, step 203a further includes: detecting whether a node corresponding to the user behavior type in the log information exists in the monitoring model; if not, creating a node corresponding to the user behavior type in the log information, and configuring the node according to the log information; step 205a may specifically include: and if so, determining a node corresponding to the user behavior type in the log information from the monitoring model.
For example, if there is no node corresponding to the user behavior type in the log information in the monitoring model, a corresponding node may be created in the monitoring model, the log information is filled into the node, the number of times the node is accessed is configured to be 1, and a preset rule corresponding to the user behavior type is obtained from a preset database and is bound to the node.
204a, configuring the nodes according to the log information.
For the embodiment of the present application, step 206a may specifically include: configuring an associated edge corresponding to the node according to the service field information and the buried point information in the log information; and configuring the nodes according to the returned result information in the log information.
205a, detecting whether the configured node conforms to a preset rule bound on the node.
For the embodiment of the present application, if the preset rule bound on the node is that the access times corresponding to the node are smaller than the preset time threshold within the preset time interval, where the preset time interval and the preset time threshold may be configured according to the actual needs of the service, for example, the preset time interval may be configured to be 5 seconds, 30 seconds, and the like, and the preset time threshold may be configured to be 50 times, 100 times, and the like. Step 204a may specifically include: and configuring the nodes according to the log information and accumulating the access times corresponding to the nodes. Step 205a may specifically include: detecting whether the access times of the nodes in the preset time interval are smaller than the preset time threshold value or not; and if the access times of the nodes in the preset time interval are greater than or equal to the preset time threshold, determining that the configured nodes do not conform to the preset rules bound on the nodes.
For example, the preset rule bound on the node is that the access times corresponding to the node in 30 seconds are less than 50 times, log information is filled into the node, 1 is added to the accumulation of the access times corresponding to the node, whether the access times in 30 seconds of the node are less than 50 times is detected, and when the access times in 30 seconds of the node are less than 50 times, the configured node is determined to accord with the preset rule bound on the node; and when the access times of the node in 30 seconds are more than or equal to 50 times, determining that the configured node does not conform to the preset rule bound on the node.
For the embodiment of the present application, if the preset rule bound on the node is that the time consumed for processing the service request is less than a preset time threshold, where the preset time threshold may be configured according to the actual requirement of the service, for example, the preset time threshold may be configured to 200 milliseconds, 220 milliseconds, and the like. Step 205a may specifically include: detecting whether the time consumed for processing the service request in the newly added log information of the node is less than the preset time threshold value; and if the time is greater than or equal to the preset time threshold, determining that the configured node does not conform to a preset rule bound on the node.
For example, the preset rule bound on the node is that the time consumed by the server to process the service request is less than 200 milliseconds, whether the time consumed by the server to process the service request in the newly added log information of the node is less than 200 milliseconds is detected, and when the time consumed by the server to process the service request in the newly added log information of the node is less than 200 milliseconds, the configured node is determined to accord with the preset rule bound on the node; and when the time consumed by the server for processing the service request in the newly added log information of the node is greater than or equal to 200 milliseconds, determining that the configured node does not conform to the preset rule bound on the node.
204b, configuring nodes in the monitoring model and preset rules corresponding to the nodes according to the log information.
Specifically, according to a user behavior type in the log information, the log information is filled into a node corresponding to the user behavior type, the number of access times of the node is configured to be 1, and a preset rule corresponding to the user behavior type is obtained from a preset database and is bound to the node.
For the embodiment of the present application, after step 206b, the method further includes: and detecting whether the configured node conforms to a preset rule bound on the node.
For example, when detecting that newly added log information exists in a preset log file, the log information is sent to a jstorm cluster of a real-time computing engine, the jstorm cluster acquires the log information and performs hash value verification according to a user ID, it is determined that a monitoring model corresponding to a user IP does not exist in a server, and then a machine jstorm _ node is selected to identify the log information, so that the following information can be identified:
the current time: 2016-02-0112:30:12
The type of the behavior: visiting shop front page
Whether to return normally: is that
Time consumption: 120ms
The corresponding user identification: ip:10.111.22.11
According to the information, a monitoring model is newly established, log information is filled into a monitoring model node corresponding to the user behavior type according to the user behavior type in the log information, the access frequency of the node is configured to be 1 time, a preset rule corresponding to the user behavior type is obtained from a preset database and is bound on the node, finally, a series of preset rules bound on the node are triggered, the preset rule is that whether the access frequency of the node is greater than or equal to an alarm threshold value within one continuous minute of a task or not, and if the access frequency is greater than or equal to the alarm threshold value, an alarm is given.
206. And if the configured node is detected not to conform to the preset rule bound on the node, outputting alarm information.
The alarm information may be text alarm information, picture alarm information, audio alarm information, video alarm information, and the like.
For example, if the preset rule bound on the node is that the access times corresponding to the node in the preset time interval are smaller than the preset time threshold, and when it is detected that the access times of the node in the preset time interval are greater than or equal to the preset time threshold, the alarm information is output so as to remind the operation and maintenance personnel of performing the related maintenance in time.
It should be noted that a series of subsequent behaviors of the user and information (including service-related information, such as time consumption, whether an error is reported, and the like) returned by the server can be continuously summarized, and a monitoring model corresponding to the user is continuously constructed. For example, as shown in fig. 4, a monitoring model corresponding to a user may be continuously constructed and maintained in a memory of the jstorm cluster, so as to construct a service monitoring system based on user dimensions. In the service monitoring system, the behavior of a user on the APP can correspond to a monitoring model within a certain time, and the rule bound on the monitoring model can be triggered in real time, so that real-time monitoring based on user dimensionality can be realized.
Further, until a corresponding rule is triggered or the monitoring model is no longer accessed for a certain time, the relevant information of the monitoring model is stored in a database in a serialized mode for later use.
According to another monitoring method provided by the embodiment of the application, when behavior data of a user is received, user identification information of the user is obtained firstly; then detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and finally, monitoring a configuration result according to the preset rule. Compared with the existing transverse service monitoring mode based on the service dimensionality, the method and the system can construct the behavior expression of the user and the system service into a monitoring model bound with preset rules, can detect the relevant condition of the user calling the service through the preset rules bound on the nodes in the monitoring model corresponding to the user identification information, and further can realize targeted feedback on the relevant condition of the single user calling the service, thereby realizing real-time monitoring based on the user dimensionality, improving the perception capability of the service system on the single user behavior, completing monitoring on the user and the corresponding service through monitoring the quality and the state of the monitoring model, and improving the precision of service monitoring.
Further, as a specific implementation of the method shown in fig. 1, an embodiment of the present application provides a monitoring apparatus, and as shown in fig. 5, the apparatus may include: an acquisition unit 51, a detection unit 52, a configuration unit 53, and a monitoring unit 54.
The obtaining unit 51 may be configured to obtain the user identification information of the user when the behavior data of the user is received. The acquiring unit 51 is a main functional module in the device for acquiring user identification information in user behavior data.
The detecting unit 52 may be configured to detect whether a monitoring model corresponding to the user identification information exists in the server, where the monitoring model includes different nodes, and a preset rule is bound to the nodes. The detecting unit 52 is a main functional module of the present apparatus for detecting whether a monitoring model exists in a server.
The configuring unit 53 may be configured to, if the detecting unit 52 detects that the monitoring model corresponding to the user identification information exists, configure a node corresponding to the behavior data in the monitoring model according to the behavior data. The configuration unit 53 is a main functional module in the device for configuring the monitoring model nodes according to the user behavior data.
The monitoring unit 54 may be configured to monitor a configuration result according to the preset rule. The monitoring unit 54 is a main functional module for performing service monitoring in the present apparatus.
It should be noted that the apparatus embodiment corresponds to the foregoing method embodiment, and specifically, reference may be made to the corresponding description in fig. 1, for convenience of reading, details of the foregoing method embodiment are not repeated in this apparatus embodiment again, but it should be clear that the apparatus in this embodiment can correspondingly implement all the contents in the foregoing method embodiment.
According to the monitoring device provided by the embodiment of the application, when behavior data of a user is received, user identification information of the user is obtained firstly; then detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and finally, monitoring a configuration result according to the preset rule. Compared with the existing transverse service monitoring mode based on the service dimensionality, the method and the system can construct the behavior expression of the user and the system service into a monitoring model bound with preset rules, can detect the relevant condition of the user calling the service through the preset rules bound on the nodes in the monitoring model corresponding to the user identification information, and further can realize targeted feedback on the relevant condition of the single user calling the service, thereby realizing real-time monitoring based on the user dimensionality, improving the perception capability of the service system on the single user behavior, completing monitoring on the user and the corresponding service through monitoring the quality and the state of the monitoring model, and improving the precision of service monitoring.
Further, as a specific implementation of the method shown in fig. 2, an embodiment of the present application provides another monitoring apparatus, as shown in fig. 6, the apparatus may include: an acquisition unit 61, a detection unit 62, a configuration unit 63, and a monitoring unit 64.
The obtaining unit 61 may be configured to obtain the user identification information of the user when the behavior data of the user is received. The acquiring unit 61 is a main functional module in the device for acquiring user identification information in user behavior data.
The detecting unit 62 may be configured to detect whether a monitoring model corresponding to the user identification information exists in the server, where the monitoring model includes different nodes, and a preset rule is bound to the nodes. The detecting unit 62 is a main functional module in the present apparatus for detecting whether a monitoring model exists in a server.
The configuring unit 63 may be configured to configure, if the detecting unit 62 detects that the monitoring model corresponding to the user identification information exists, a node corresponding to the behavior data in the monitoring model according to the behavior data. The configuration unit 63 is a main function module of the device that configures the monitoring model nodes according to the user behavior data.
The monitoring unit 64 may be configured to monitor a configuration result according to the preset rule. The monitoring unit 64 is a main functional module for service monitoring in the device.
Specifically, the monitoring unit 64 includes: a detection module 641 and an output module 642.
The detecting module 641 may be configured to detect whether the configured node conforms to a preset rule bound to the node.
The output module 642 may be configured to output an alarm message if the configured node detected by the detection module 641 does not comply with the preset rule bound to the node.
Optionally, the behavior data may be log information corresponding to a user, and a node in the monitoring model corresponds to a user behavior type.
Further, the configuration unit 63 includes: a determination module 631, a configuration module 632.
The determining module 631 may be configured to determine a node corresponding to a type of user behavior in the log information from the monitoring model.
The configuring module 632 may be configured to configure the node determined by the determining module according to the log information.
If the preset rule bound on the node indicates that the access times corresponding to the node are smaller than a preset time threshold within a preset time interval, the configuration module 632 may be specifically configured to configure the node according to the log information and accumulate the access times corresponding to the node.
The detecting module 641 may be specifically configured to detect whether the number of access times of the node in the preset time interval is smaller than the preset number threshold.
The detecting module 641 is further specifically configured to determine that the configured node does not conform to the preset rule bound to the node if it is detected that the number of times of access to the node in the preset time interval is greater than or equal to the preset number threshold.
If the preset rule bound on the node is that the time consumed by the server to process the service request is less than the preset time threshold, the detecting module 641 may be specifically configured to detect whether the time consumed by the server to process the service request in the log information newly added to the node is less than the preset time threshold.
The detecting module 641 is further specifically configured to determine that the configured node does not conform to the preset rule bound to the node if it is detected that the time is greater than or equal to the preset time threshold.
The obtaining unit 61 may be specifically configured to, when it is detected that there is newly added log information in the preset log file, obtain the user identification information from the log information, where the preset log file stores log information recorded when the server processes service requests sent by different clients each time.
Optionally, the monitoring model may further include an associated edge corresponding to the node.
The configuration module 632 may be further configured to configure the associated edge corresponding to the node according to the service field information and the buried point information in the log information.
The configuration module 632 may be further configured to configure the node according to the returned result information in the log information.
Further, the configuration unit 63 further includes: a detection module 633, a creation module 634.
The detecting module 633 may be further configured to detect whether a node corresponding to a user behavior type in the log information exists in the monitoring model.
The creating module 634, may be configured to create a node corresponding to a user behavior type in the log information if the detecting module 633 detects that there is no node corresponding to the user behavior type in the log information in the monitoring model.
The configuring module 632 may be further configured to configure the node created by the creating module 634 according to the log information.
The determining module 631 may be specifically configured to determine, if the detecting module 623 detects that a node corresponding to a user behavior type in the log information exists in the monitoring model, the node corresponding to the user behavior type in the log information from the monitoring model.
Further, the apparatus further comprises: a creating unit 65.
The creating unit 65 may be configured to create a monitoring model corresponding to the user identification information if the detecting unit 62 detects that there is no monitoring model corresponding to the user identification information.
The configuring unit 63 may further be configured to configure, according to the behavior data, a node in the monitoring model created by the creating unit 65 and a preset rule corresponding to the node.
Optionally, the monitoring model may be a directed graph model.
It should be noted that the apparatus embodiment corresponds to the foregoing method embodiment, and specifically, reference may be made to the corresponding description in fig. 2, so that for convenience of reading, details of the foregoing method embodiment are not repeated in this apparatus embodiment again, but it should be clear that the apparatus in this embodiment can correspondingly implement all the contents in the foregoing method embodiment.
The monitoring device comprises a processor and a memory, the acquisition unit, the detection unit, the configuration unit, the monitoring unit, the creation unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem that the service monitoring precision is low due to the fact that the service monitoring mode based on the service dimensionality in the horizontal direction at present is solved by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
According to another monitoring device provided by the embodiment of the application, when behavior data of a user is received, user identification information of the user is obtained firstly; then detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and finally, monitoring a configuration result according to the preset rule. Compared with the existing transverse service monitoring mode based on the service dimensionality, the method and the system can construct the behavior expression of the user and the system service into a monitoring model bound with preset rules, can detect the relevant condition of the user calling the service through the preset rules bound on the nodes in the monitoring model corresponding to the user identification information, and further can realize targeted feedback on the relevant condition of the single user calling the service, thereby realizing real-time monitoring based on the user dimensionality, improving the perception capability of the service system on the single user behavior, completing monitoring on the user and the corresponding service through monitoring the quality and the state of the monitoring model, and improving the precision of service monitoring.
The present application further provides a computer program product adapted to perform program code for initializing the following method steps when executed on a data processing device: when behavior data of a user is received, user identification information of the user is obtained; detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes; if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data; and monitoring a configuration result according to the preset rule.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of monitoring methods, apparatus, and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include permanent or non-permanent memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (18)
1. A method of monitoring, comprising:
when behavior data of a user is received, user identification information of the user is obtained;
detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes;
if the node exists, configuring a node corresponding to the behavior data in the monitoring model according to the behavior data;
monitoring a configuration result according to the preset rule;
the monitoring the configuration result according to the preset rule specifically includes:
detecting whether the configured node accords with a preset rule bound on the node;
if not, alarm information is output.
2. The monitoring method according to claim 1, wherein the behavior data is log information corresponding to the user, a node in the monitoring model corresponds to a user behavior type,
configuring a node corresponding to the behavior data in the monitoring model according to the behavior data specifically includes:
determining a node corresponding to the user behavior type in the log information from the monitoring model; and configuring the nodes according to the log information.
3. The monitoring method according to claim 2, wherein if the predetermined rule bound to the node is that the number of access times corresponding to the node is smaller than a predetermined number threshold within a predetermined time interval, configuring the node according to the log information specifically includes:
configuring the nodes according to the log information and accumulating the access times corresponding to the nodes;
the detecting whether the configured node conforms to a preset rule bound to the node specifically includes:
detecting whether the access times of the nodes in the preset time interval are smaller than the preset time threshold value or not;
and if the access times of the nodes in the preset time interval are greater than or equal to the preset time threshold, determining that the configured nodes do not conform to the preset rules bound on the nodes.
4. The monitoring method according to claim 2, wherein if the preset rule bound to the node is that the time consumed for processing the service request is less than a preset time threshold, the detecting whether the configured node meets the preset rule bound to the node specifically includes:
detecting whether the time consumed for processing the service request in the newly added log information of the node is less than the preset time threshold value;
and if the time is greater than or equal to the preset time threshold, determining that the configured node does not conform to a preset rule bound on the node.
5. The monitoring method according to claim 2, wherein the obtaining of the user identification information of the user when the behavior data of the user is received specifically comprises:
when detecting that newly added log information exists in a preset log file, acquiring user identification information from the log information, wherein the log information recorded when a server processes service requests sent by different clients each time is stored in the preset log file.
6. The monitoring method according to claim 2, wherein the monitoring model further comprises associated edges corresponding to the nodes,
the configuring the node according to the log information specifically includes:
configuring an associated edge corresponding to the node according to the service field information and the buried point information in the log information;
and configuring the nodes according to the returned result information in the log information.
7. The monitoring method of claim 2, wherein prior to determining the node corresponding to the type of user behavior in the log information from the monitoring model, the method further comprises:
detecting whether a node corresponding to the user behavior type in the log information exists in the monitoring model;
if not, creating a node corresponding to the user behavior type in the log information, and configuring the node according to the log information;
the determining, from the monitoring model, a node corresponding to a user behavior type in the log information specifically includes:
and if so, determining a node corresponding to the user behavior type in the log information from the monitoring model.
8. The monitoring method according to claim 1, wherein after detecting whether the monitoring model corresponding to the user identification information exists, the method further comprises:
if not, a monitoring model corresponding to the user identification information is created;
and configuring nodes in the monitoring model and preset rules corresponding to the nodes according to the behavior data.
9. The monitoring method according to any one of claims 1 to 8, wherein the monitoring model is a directed graph model.
10. A monitoring device, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring user identification information of a user when behavior data of the user is received;
the detection unit is used for detecting whether a monitoring model corresponding to the user identification information exists or not, wherein the monitoring model comprises different nodes, and preset rules are bound on the nodes;
a configuration unit, configured to configure, if the detection unit detects that a monitoring model corresponding to the user identification information exists, a node corresponding to the behavior data in the monitoring model according to the behavior data;
the monitoring unit is used for monitoring a configuration result according to the preset rule;
the monitoring unit includes:
the detection module is used for detecting whether the configured node conforms to a preset rule bound on the node;
and the output module is used for outputting alarm information if the detection module detects that the configured node does not accord with the preset rule bound on the node.
11. The monitoring device according to claim 10, wherein the behavior data is log information corresponding to a user, a node in the monitoring model corresponds to a user behavior type, and the configuration unit includes:
the determining module is used for determining a node corresponding to the user behavior type in the log information from the monitoring model;
and the configuration module is used for configuring the nodes determined by the determination module according to the log information.
12. The monitoring device according to claim 11, wherein if the predetermined rule for binding on the node is that the number of visits the node corresponds to within a predetermined time interval is less than a predetermined threshold number of visits the node corresponds to within a predetermined time interval,
the configuration module is specifically configured to configure the node according to the log information and accumulate access times corresponding to the node;
the detection module is specifically configured to detect whether the number of access times of the node within the preset time interval is smaller than the preset number threshold;
the detection module is specifically configured to determine that the configured node does not conform to the preset rule bound to the node if it is detected that the access frequency of the node is greater than or equal to the preset frequency threshold within the preset time interval.
13. The apparatus for monitoring set forth in claim 11, wherein if the predetermined rule for binding on the node is that the time consumed for processing the service request is less than a predetermined time threshold,
the detection module is specifically configured to detect whether time consumed for processing the service request in the newly added log information of the node is less than the preset time threshold;
the detection module is specifically configured to determine that the configured node does not conform to the preset rule bound to the node if it is detected that the time is greater than or equal to the preset time threshold.
14. The monitoring device of claim 11,
the obtaining unit is specifically configured to, when it is detected that newly added log information exists in the preset log file, obtain user identification information from the log information, where the preset log file stores log information recorded when a server processes a service request sent by different clients each time.
15. The monitoring device of claim 11, wherein the monitoring model further comprises associated edges corresponding to the nodes,
the configuration module is specifically further configured to configure the associated edge corresponding to the node according to the service field information and the buried point information in the log information;
the configuration module is specifically further configured to configure the node according to the returned result information in the log information.
16. The monitoring device of claim 11, wherein the configuration unit further comprises: a detection module and a creation module;
the detection module is used for detecting whether a node corresponding to the user behavior type in the log information exists in the monitoring model or not;
the creating module is used for creating a node corresponding to the user behavior type in the log information if the detecting module detects that the node corresponding to the user behavior type in the log information does not exist in the monitoring model;
the configuration module is further configured to configure the node created by the creation module according to the log information;
the determining module is specifically configured to determine, if the detecting module detects that a node corresponding to the user behavior type in the log information exists in the monitoring model, a node corresponding to the user behavior type in the log information from the monitoring model.
17. The monitoring device of claim 10, wherein the device further comprises: a creating unit;
the creating unit is used for creating the monitoring model corresponding to the user identification information if the detecting unit detects that the monitoring model corresponding to the user identification information does not exist;
and the configuration unit is further used for configuring the nodes in the monitoring model created by the creation unit and the preset rules corresponding to the nodes according to the behavior data.
18. The monitoring device of any one of claims 10 to 17, wherein the monitoring model is a directed graph model.
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| CN107562551A (en) * | 2017-09-05 | 2018-01-09 | 北京小度信息科技有限公司 | Monitoring method, device and the computer-readable recording medium of outside access system |
| WO2019109756A1 (en) | 2017-12-05 | 2019-06-13 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for cheat examination |
| CN109961200A (en) * | 2017-12-25 | 2019-07-02 | 北京嘀嘀无限科技发展有限公司 | Monitoring and reminding method, monitoring and reminding system, computer equipment and storage medium |
| CN108228431A (en) * | 2018-01-04 | 2018-06-29 | 北京中关村科金技术有限公司 | A kind of method and system of configurationization reptile quality-monitoring |
| CN110120964B (en) * | 2018-02-07 | 2022-07-08 | 北京三快在线科技有限公司 | User behavior monitoring method and device and computing equipment |
| CN108322350B (en) * | 2018-02-27 | 2021-06-01 | 创新先进技术有限公司 | Service monitoring method and device and electronic equipment |
| CN110874307A (en) * | 2018-08-29 | 2020-03-10 | 北京京东尚科信息技术有限公司 | Event buried point collecting and reporting method and device |
| CN109583758B (en) * | 2018-11-30 | 2021-07-13 | 广州净松软件科技有限公司 | Early warning rule correction method and device of monitoring system and computer equipment |
| CN109766370A (en) * | 2018-12-27 | 2019-05-17 | 口碑(上海)信息技术有限公司 | Data processing method, data service system and equipment |
| CN110489311B (en) * | 2019-03-01 | 2023-04-18 | 北京亿赛通科技发展有限责任公司 | Method and system for automatically detecting computer user behaviors and automatically updating detection model |
| CN112839008B (en) * | 2019-11-22 | 2024-02-06 | 北京沃东天骏信息技术有限公司 | Access monitoring method, device and system |
| CN111045950B (en) * | 2019-12-16 | 2023-06-30 | 上海钧正网络科技有限公司 | Performance problem point determining method, device, data analysis system and storage medium |
| CN113138905A (en) * | 2021-05-11 | 2021-07-20 | 北京京东拓先科技有限公司 | Software function monitoring method and device |
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| CN103049163B (en) * | 2012-12-28 | 2016-12-28 | 华为技术有限公司 | User interface intelligent display control method and device |
| CN104636237A (en) * | 2015-01-19 | 2015-05-20 | 上海新炬网络信息技术有限公司 | Monitoring method for Web application system service performance based on Java platform |
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