CN116089247A - Micro-service index early warning method and device based on index threshold and big data analysis - Google Patents
Micro-service index early warning method and device based on index threshold and big data analysis Download PDFInfo
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Abstract
The invention provides a micro-service index early warning method and device based on index threshold and big data analysis, comprising the following steps: acquiring service information of micro-services to be monitored; determining calling relations among servers, middleware, databases, front-end pages and services corresponding to the micro-services to be monitored based on the service information of the micro-services to be monitored; acquiring a coarse granularity index reference value and a fine granularity index reference value corresponding to the micro-service to be monitored; acquiring an actual coarse grain index, comparing the actual coarse grain index with a coarse grain index reference value, and sending first alarm information to an alarm contact person when the actual coarse grain index is larger than the coarse grain index reference value; and acquiring an actual fine granularity index, comparing the actual fine granularity index with a fine granularity index reference value, and sending second alarm information to the alarm contact person when the actual fine granularity index is larger than the fine granularity index reference value. The method can better monitor the technical index of the micro-service and can effectively give an alarm to the index exceeding the reference value.
Description
Technical Field
The invention relates to the technical field of micro-services, in particular to a micro-service index early warning method and device based on index threshold and big data analysis.
Background
Micro-services, which are defined on a wiki as a variant of a software development technology-Service Oriented Architecture (SOA) architecture style, advocate the division of a single application into a small set of services that coordinate and interwork with each other, providing the final value to the user. Each service runs in its independent process, and the services communicate with each other using a lightweight communication mechanism (typically an HTTP-based RESTful API). Each service is built around a specific business and can be deployed independently to a production environment, class production environment, etc.
With the development of the internet age, more and more enterprises do not use the single architecture, and start to use the micro service, the micro service architecture well solves some problems existing in the single architecture, such as: the problems of project bloated, unable isolation of resources, unable flexible expansion, etc. However, the micro-service architecture also has some new problems when solving a series of problems existing in the single architecture, such as a difficulty in analyzing the micro-service technical performance index, and when the micro-service technical index has problems, early warning cannot be performed in time, that is, the reason that the problem of the micro-service cannot be positioned effectively and rapidly. Therefore, how to monitor the technical indexes of the micro service better and how to alarm the indexes exceeding the reference value effectively is a technical problem to be solved urgently.
Disclosure of Invention
In view of the above, the present invention provides a method and apparatus for early warning of micro-service indicators based on indicator threshold and big data analysis, so as to solve one or more problems in the prior art.
According to one aspect of the invention, the invention discloses a micro-service index early warning method based on index threshold and big data analysis, which comprises the following steps:
acquiring service information of micro-services to be monitored, wherein the service information comprises a service name, an ID address, a port number, an alarm contact person and a service type;
determining calling relations among servers, middleware, databases, front-end pages and services corresponding to the micro-services to be monitored based on the service information of the micro-services to be monitored;
acquiring a coarse granularity index reference value and a fine granularity index reference value corresponding to the micro-service to be monitored, wherein the coarse granularity index comprises a server performance index, a middleware performance index, a database performance index and server hardware information, and the fine granularity index comprises calling relation information between a front-end page performance index and the service;
acquiring an actual coarse-grain index corresponding to the micro-service to be monitored, comparing the actual coarse-grain index with the coarse-grain index reference value, and sending first alarm information to the alarm contact person when the actual coarse-grain index is larger than the coarse-grain index reference value;
and acquiring an actual fine granularity index corresponding to the micro-service to be monitored, comparing the actual fine granularity index with the fine granularity index reference value, and sending second warning information to the warning contact person when the actual fine granularity index is larger than the fine granularity index reference value.
In some embodiments of the present invention, obtaining an actual coarse-grain index corresponding to the micro-service to be monitored, and comparing the actual coarse-grain index with the coarse-grain index reference value includes:
deploying a Prometa plug-in on a server corresponding to the micro-service to be monitored;
and acquiring an actual coarse-grain index corresponding to the micro-service to be monitored based on the Prometa plug-in, and comparing the actual coarse-grain index with the coarse-grain index reference value.
In some embodiments of the present invention, obtaining an actual fine-grain index corresponding to the micro-service to be monitored, and comparing the actual fine-grain index with the fine-grain index reference value includes:
and acquiring an actual fine granularity index corresponding to the micro-service to be monitored based on a skywalking plug-in, and comparing the actual fine granularity index with the fine granularity index reference value.
In some embodiments of the present invention, obtaining an actual fine-grain index corresponding to the micro-service to be monitored based on a skywalking plugin, and comparing the actual fine-grain index with the fine-grain index reference value includes:
deploying a agent probe on the micro-service to be monitored,
acquiring an actual fine granularity index corresponding to the micro-service to be monitored based on the agent probe;
the obtained actual fine granularity index corresponding to the micro-service to be monitored is sent to a collector acquisition end;
the server alarm module obtains the actual fine granularity index received by the collector acquisition module and compares the actual fine granularity index with the fine granularity index reference value.
In some embodiments of the present invention, sending the second alert information to the alert contact includes:
and sending second alarm information to the alarm contact person through a server alarm module.
In some embodiments of the invention, the method further comprises: and acquiring the log information of the micro-service to be monitored through a logstack data collection engine, and storing the acquired log information of the micro-service to be monitored into an elastic search database.
In some embodiments of the present invention, the server performance metrics include total CPU utilization, memory utilization, maximum partition utilization, and swap partition utilization, the middleware performance metrics include total consumption, and the database performance metrics include cluster count, front-end state, back-end state, and cluster peak state.
In some embodiments of the present invention, the first alert information and/or the second alert information is a WeChat information, a SMS information, or a mail information.
According to another aspect of the present invention, there is also disclosed a micro-service indicator pre-warning system based on indicator threshold and big data analysis, the system comprising a processor and a memory, the memory having stored therein computer instructions for executing the computer instructions stored in the memory, the system implementing the steps of the method according to any of the embodiments above when the computer instructions are executed by the processor.
According to yet another aspect of the present invention, a computer-readable storage medium is also disclosed, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method according to any of the embodiments described above.
According to the micro-service index early warning method and device based on index threshold and big data analysis disclosed by the embodiment, firstly, the coarse-granularity index and the fine-granularity index of the micro-service to be monitored are monitored, the monitored coarse-granularity index is compared with the coarse-granularity index reference value, the monitored fine-granularity index is compared with the fine-granularity index reference value, and when the comparison result is that the index is abnormal, corresponding warning information is sent to the warning contact person. The early warning method can conveniently and rapidly monitor the problem index of the micro service, realize better monitoring of the technical index of the micro service, effectively and timely warn the index exceeding the reference value, reduce the service cost and further rapidly locate the cause of the problem of the micro service.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present invention are not limited to the above-described specific ones, and that the above and other objects that can be achieved with the present invention will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate and together with the description serve to explain the invention. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present invention, for convenience in showing and describing some parts of the present invention. In the drawings:
fig. 1 is a flow chart of a micro-service index early warning method based on index threshold and big data analysis according to an embodiment of the invention.
FIG. 2 is a flow chart of fine granularity index alerting according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
It should be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details not greatly related to the present invention are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
Fig. 1 is a flow chart of a micro-service index early warning method based on index threshold and big data analysis according to an embodiment of the invention, as shown in fig. 1, the index early warning method at least includes the following steps S10 to S50.
Step S10: and acquiring service information of the micro service to be monitored, wherein the service information comprises a service name, an ID address, a port number, an alarm contact person and a service type.
The service information may specifically be input by human, that is, basic information such as a service name, an ID address, a port number, an alert contact, a service type, etc. of the micro service to be monitored is input, and the input basic information may be further stored. For example, the input of the service information may be implemented based on the basic service module, where the basic service module may specifically include an application list sub-module, and the application list sub-module may configure middleware information in addition to the service information of the micro service to be monitored. The service type may specifically include redis database, mysql database, locktmq middleware, and the like. In other embodiments, the basic service module includes a dictionary table sub-module in addition to the application list sub-module, where the dictionary table sub-module is configured to configure some fields, define some values for the fields, and facilitate use of the fields in subsequent programs.
Step S20: and determining calling relations among the server, middleware, database, front-end page and service corresponding to the micro-service to be monitored based on the service information of the micro-service to be monitored.
In this step, based on the service information of the micro-service to be monitored obtained in step S10, the calling relationship among the server, middleware, database, front-end page and service corresponding to the micro-service to be monitored is determined, so that a specific index monitoring plug-in is configured on the corresponding server.
Step S30: and acquiring a coarse granularity index reference value and a fine granularity index reference value corresponding to the micro-service to be monitored, wherein the coarse granularity index comprises a server performance index, a middleware performance index, a database performance index and server hardware information, and the fine granularity index comprises calling relation information between a front-end page performance index and the service.
In this step, specific coarse-grain index reference values and fine-grain index reference values may be manually configured, or pre-stored coarse-grain index reference values and fine-grain index reference values may be directly obtained. Since the coarse-grain index includes a server performance index, a middleware performance index, a database performance index, and server hardware information, the coarse-grain index reference value includes a server performance index reference value, a middleware performance index reference value, a database performance index reference value, and a server hardware information reference value; similarly, since the fine-grained index includes call relationship information between the front-end page performance index and the service, the fine-grained index reference value includes call relationship information reference value between the front-end page performance index reference value and the service.
In an embodiment, the configuration of the coarse granularity index reference value and the fine granularity index reference value may be implemented by an alarm service module, where the alarm service module includes an alarm policy configuration sub-module, an alarm rule configuration sub-module, an alarm contact configuration sub-module, and an alarm channel configuration sub-module. The alarm policy configuration sub-module may configure alarm types, index references, alarm levels, alarm suppression, alarm intervals, alarm duration points, and the like. The alarm channel configuration submodule can configure an alarm mode, the alarm contact person configuration submodule can be used for configuring a contact person to be notified specifically, and the alarm rule configuration submodule is used for modifying the set alarm type, index reference value, alarm level, alarm suppression, alarm interval, alarm duration time point and the like. The alarm service module is used for configuring the response time alarm for the service endpoint, wherein the alarm type is selected as application monitoring based on the alarm strategy configuration submodule, the configured index reference value (endpoint response time threshold value) is greater than 300ms, and the configured alarm level is reminding, alarming or serious; the configured alarm suppression comprises statistical granularity, alarm intervals and alarm duration data points, wherein the statistical granularity represents how often the alarm detection is carried out, the alarm intervals represent how often the same alarm is given out, and the alarm duration data points represent that the threshold exceeds a plurality of times of alarm starting; illustratively, the statistical granularity is configured to be 5, the alarm interval is 120 minutes, and the alarm duration point is 5. Further, a user or a user group which wants to alarm is selected or input based on the alarm contact person configuration submodule, and the channel type of the alarm is selected based on the alarm channel configuration submodule, wherein the configured channel type is one or more of WeChat, short message and mail. After the configuration is completed, if one or more pieces of configuration information need to be changed, the configuration submodule based on the alarm rule can be completed.
In the actual monitoring process, if the response time of the interface (endpoint) is continuously monitored for 5 times to exceed 300ms, an alarm is carried out, namely, the alarm is carried out to the configured alarm contact person; when the configured channel type is enterprise WeChat, enterprise WeChat information is sent to the configured contact person to inform the contact person service that the interface response time exceeds 300ms. In addition, in the actual alarm process, the response time of the corresponding contact interface is informed that the response time is overtime, and the overtime interface can be specifically informed that the interface is an interface for realizing which service; that is, the alarm information sent to the contact person includes specific service information in addition to the alarm information about whether to timeout. Since the configured alarm interval is 120 minutes, after the current alarm is finished, the alarm is started again until the interval is 120 minutes later.
Step S40: and acquiring an actual coarse-grain index corresponding to the micro-service to be monitored, comparing the actual coarse-grain index with the coarse-grain index reference value, and sending first alarm information to the alarm contact person when the actual coarse-grain index is larger than the coarse-grain index reference value.
In the step, the actual coarse granularity index corresponding to the micro-service to be monitored is specifically obtained, and the coarse granularity index is an application performance index and specifically comprises a server performance index, a middleware performance index, a database performance index and server hardware information. Further, the server performance metrics may include total CPU utilization, memory utilization, maximum partition utilization, and swap partition utilization, the middleware performance metrics may include total consumption, and the database performance metrics may include cluster count, front end state, back end state, and cluster peak state, among others. The coarse grain index can be obtained through an index service module, and the index service module monitors the actual coarse grain index of the server, the middleware and the database in real time. It will be appreciated that the specific types of server performance metrics, middleware performance metrics, and database performance metrics listed above are just one preferred example, and that in other embodiments, each performance metric may contain other types of information. If the server to be monitored is a nginx server, the performance index of the server may further include server connection, service request number, and service byte number; if the server to be monitored for the micro-service application is a nacos server, the performance index of the server may further include a service number, an ip number, a configuration number, a long connection number, a configuration push total number, and the like; if the server to which the micro-service to be monitored is applied is Tomcat, the performance index of the server may also include status, update time, start time, JVM version, available CPU, system loading average, file open descriptor, etc.
In an embodiment, obtaining an actual coarse-grain index corresponding to the micro-service to be monitored, and comparing the actual coarse-grain index with the coarse-grain index reference value may include: deploying a Prometa plug-in on a server corresponding to the micro-service to be monitored; and acquiring an actual coarse-grain index corresponding to the micro-service to be monitored based on the Prometa plug-in, and comparing the actual coarse-grain index with the coarse-grain index reference value.
In the above embodiment, the actual coarse-grained index corresponding to the micro-service to be monitored is monitored based on the promethas plugin, that is, the promethas plugin is deployed on the server corresponding to the micro-service to be monitored in advance, so that the corresponding coarse-grained index is collected in real time based on the promethas plugin, and further, the corresponding coarse-grained index collected by the promethas plugin is stored in the timing database of the promethas. And the Prometa plug-in judges whether the monitored current coarse-grain index needs to be alarmed according to an alarm strategy, an alarm rule and the like configured in the alarm service module, namely the Prometa plug-in acquires a preset coarse-grain index reference value, compares an actual value of the actually acquired coarse-grain index with the coarse-grain index reference value, and sends first alarm information to an alarm contact person if the actual value of the actually acquired coarse-grain index exceeds a coarse-grain index threshold value.
Specifically, when the Prometa plug-in obtains that the actual value of the actually collected coarse granularity index exceeds the coarse granularity index threshold, alarm data can be assembled and sent to the alarm service module based on an http protocol, and because the alarm contact and the channel type of the alarm are configured based on the alarm service module in advance, the alarm service module can send the alarm information to the corresponding alarm contact based on the configured alarm channel type after receiving the alarm information.
Step S50: and acquiring an actual fine granularity index corresponding to the micro-service to be monitored, comparing the actual fine granularity index with the fine granularity index reference value, and sending second warning information to the warning contact person when the actual fine granularity index is larger than the fine granularity index reference value.
In this step, the actual fine-grained index corresponding to the micro-service to be monitored is specifically obtained, where the fine-grained index may specifically include the front-end page performance index and call relationship information between services. In other embodiments, the fine-grained metrics may include server performance metrics, database performance metrics, and hardware information of the server in addition to call relationship information between the front-end page performance metrics and the services. Similar to step S40, the fine grain index reference value and alert contacts may also be preconfigured. In this embodiment, obtaining the actual fine-grained index corresponding to the micro-service to be monitored may be implemented based on a call chain service module that is used to implement index statistics for fine-grained of the micro-service.
Further, obtaining an actual fine granularity index corresponding to the micro-service to be monitored, and comparing the actual fine granularity index with the fine granularity index reference value, which specifically includes: and acquiring an actual fine granularity index corresponding to the micro-service to be monitored based on a skywalking plug-in, and comparing the actual fine granularity index with the fine granularity index reference value. In this embodiment, a skywalking plug-in is used to obtain in real time the actual fine-grained index corresponding to the micro-service to be monitored.
Exemplary, the obtaining, based on a skywalking plug-in, an actual fine-grain index corresponding to the micro-service to be monitored, and comparing the actual fine-grain index with the fine-grain index reference value includes: deploying a agent probe on the micro-service to be monitored, and acquiring an actual fine granularity index corresponding to the micro-service to be monitored based on the agent probe; the obtained actual fine granularity index corresponding to the micro-service to be monitored is sent to a collector acquisition end; the server alarm module obtains the actual fine granularity index received by the collector acquisition module and compares the actual fine granularity index with the fine granularity index reference value.
Fig. 2 is a schematic flow chart of a fine granularity indicator alarm according to an embodiment of the present invention, referring to fig. 2, a skywalking plug-in mainly includes a collector receiving end and a agent collecting end, that is, when an actual fine granularity indicator corresponding to a micro service to be monitored is obtained based on the skywalking plug-in, a java-agent probe is deployed for the micro service to be monitored first, then the fine granularity indicator collected by the java-agent probe is remotely sent to the collector collecting end through a grpc protocol, the collector collecting end receives data collected by the java-agent probe and then processes the data, in the process of processing, various collected values are calculated through oal (observation analysis language), so as to obtain various indicators, and finally the processed index data is stored in a database for calling or querying an es service module.
The skywalking plugin judges whether the current fine grain index needs to be alarmed according to an alarm strategy, an alarm rule and the like configured in the alarm service module, namely the skywalking plugin acquires a preset fine grain index reference value, compares an actual value of the actually acquired fine grain index with the fine grain index reference value, and sends second alarm information to the alarm contact person if the actual value of the actually acquired fine grain index exceeds a fine grain index threshold value.
Specifically, the skywalking plug-in includes a server-alarm module, a server-bootstrap module, a oal-gradar module, a oal-rt module, and the like. In this embodiment, the alert information of the server-alarm-plug in module of the skywalking plug-in includes traceId, tags fields. All fine granularity indexes acquired by the skywalking plugin are differentiated according to different indexes based on types of index parameters by a forward method in a receiver method in a source receiver repl class of server-core, different logics are executed, alarm information is sent to a queue (queue of the skywalking) and the received fine granularity indexes are processed based on a server-alarm-plug module after the queue information is consumed by a consumer.
In some embodiments, the call chain service module is provided with three sub-modules of a dashboard, a topological graph and a link tracking, and the running state of the micro service can be intuitively and conveniently checked through the three sub-modules.
In an embodiment, sending the second alert information to the alert contact specifically includes: and sending second alarm information to the alarm contact person through a server alarm module. The server-alarm-plug module further judges whether an alarm is given by a run class after receiving a specific fine-grained index, wherein a fine-grained index reference value is configured by an alarm policy configuration sub-module and an alarm rule configuration sub-module in the alarm service module, the run class further compares the obtained actual fine-grained index with a fine-grained index threshold, and when the actual fine-grained index is greater than the fine-grained index threshold, the judgment result is that the corresponding index needs to be given an alarm. At this time, the Running rule class stores information to be alarmed; the AlarmCore class is provided with a timer which is used for counting the time of alarm information detection, if the alarm information detection is executed every 10 seconds, the alarm is triggered if the alarm information is detected; and meanwhile, whether the alarm meets the inhibition condition is detected, if so, the alarm information is sent to an alarm service module through an http protocol, and the alarm service module sends the alarm information based on the configured alarm channel type and the alarm contact.
In another embodiment of the present invention, the micro-service index early warning method further includes: and acquiring the log information of the micro-service to be monitored through a logstack data collection engine, and storing the acquired log information of the micro-service to be monitored into an elastic search database. In this embodiment, log information of the micro service is obtained in real time and stored, and specifically, the micro service may be implemented by a log service module, where the log service module mainly provides a log query function, that is, through keyword screening, the log information of the monitored service may be quickly checked. The core technology adopted by the log service module is ELK (elastic search/logstack/kibana), i.e. logs of each service are collected based on logstack, and the collected logs are further stored in an elastic search database.
Correspondingly, the invention also discloses a micro-service index early warning system based on index threshold value and big data analysis, which comprises a processor and a memory, wherein the memory is stored with computer instructions, the processor is used for executing the computer instructions stored in the memory, and the system realizes the steps of the method according to any embodiment when the computer instructions are executed by the processor.
The micro-service index early warning system based on index threshold and big data analysis comprises a basic service module, an alarm service module, an index service module and a call chain service module, wherein the basic service module is used for acquiring service information of micro-service to be monitored; the alarm service module is used for acquiring a coarse granularity index reference value and a fine granularity index reference value corresponding to the micro-service to be monitored; the index service module is used for acquiring an actual coarse-grain index corresponding to the micro-service to be monitored, comparing the actual coarse-grain index with the coarse-grain index reference value, and sending first alarm information to the alarm contact person when the actual coarse-grain index is larger than the coarse-grain index reference value; and the application chain service module is used for acquiring the actual fine granularity index corresponding to the micro-service to be monitored, comparing the actual fine granularity index with the fine granularity index reference value, and sending second alarm information to the alarm contact person when the actual fine granularity index is larger than the fine granularity index reference value.
According to the method and the device for early warning of the micro-service index based on the index threshold and the big data analysis, which are disclosed by the invention, the coarse-granularity index and the fine-granularity index of the micro-service to be monitored are monitored, the monitored coarse-granularity index is compared with the coarse-granularity index reference value, the monitored fine-granularity index is compared with the fine-granularity index reference value, and when the comparison result is abnormal, the corresponding warning information is sent to the warning contact person. The early warning method can conveniently and rapidly monitor the problem index of the micro service, realize better monitoring of the technical index of the micro service, effectively warn the index exceeding the reference value, reduce the service cost and further rapidly locate the cause of the problem of the micro service.
In addition to the above, the micro-service index early warning method based on the index threshold and the big data analysis can be used for monitoring various applications, databases, middleware and the like without invasion, acquiring indexes of the applications, supporting storage of various databases by the acquired indexes, and has excellent performance. In addition, the collected index data can be displayed according to a report form, namely, the whole state of the micro-service can be intuitively seen through observation and display, and the observable force of the whole micro-service architecture is improved; and the method can rapidly analyze the cause of the problem by rapidly positioning the problem of the micro-service.
In addition, the invention also discloses a computer readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements the steps of the method according to any of the embodiments above.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In this disclosure, features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The micro-service index early warning method based on the index threshold value and the big data analysis is characterized by comprising the following steps of:
acquiring service information of micro-services to be monitored, wherein the service information comprises a service name, an ID address, a port number, an alarm contact person and a service type;
determining calling relations among servers, middleware, databases, front-end pages and services corresponding to the micro-services to be monitored based on the service information of the micro-services to be monitored;
acquiring a coarse granularity index reference value and a fine granularity index reference value corresponding to the micro-service to be monitored, wherein the coarse granularity index comprises a server performance index, a middleware performance index, a database performance index and server hardware information, and the fine granularity index comprises calling relation information between a front-end page performance index and the service;
acquiring an actual coarse-grain index corresponding to the micro-service to be monitored, comparing the actual coarse-grain index with the coarse-grain index reference value, and sending first alarm information to the alarm contact person when the actual coarse-grain index is larger than the coarse-grain index reference value;
and acquiring an actual fine granularity index corresponding to the micro-service to be monitored, comparing the actual fine granularity index with the fine granularity index reference value, and sending second warning information to the warning contact person when the actual fine granularity index is larger than the fine granularity index reference value.
2. The micro-service index early warning method based on index threshold and big data analysis according to claim 1, wherein obtaining an actual coarse-grain index corresponding to the micro-service to be monitored, and comparing the actual coarse-grain index with the coarse-grain index reference value comprises:
deploying a Prometa plug-in on a server corresponding to the micro-service to be monitored;
and acquiring an actual coarse-grain index corresponding to the micro-service to be monitored based on the Prometa plug-in, and comparing the actual coarse-grain index with the coarse-grain index reference value.
3. The micro-service index early warning method based on index threshold and big data analysis according to claim 1, wherein obtaining an actual fine-grain index corresponding to the micro-service to be monitored, and comparing the actual fine-grain index with the fine-grain index reference value comprises:
and acquiring an actual fine granularity index corresponding to the micro-service to be monitored based on a skywalking plug-in, and comparing the actual fine granularity index with the fine granularity index reference value.
4. The micro-service index early warning method based on index threshold and big data analysis according to claim 3, wherein obtaining an actual fine-grain index corresponding to the micro-service to be monitored based on a skywalking plugin, comparing the actual fine-grain index with the fine-grain index reference value, comprises:
deploying a agent probe on the micro-service to be monitored,
acquiring an actual fine granularity index corresponding to the micro-service to be monitored based on the agent probe;
the obtained actual fine granularity index corresponding to the micro-service to be monitored is sent to a collector acquisition end;
the server alarm module obtains the actual fine granularity index received by the collector acquisition module and compares the actual fine granularity index with the fine granularity index reference value.
5. The micro-service indicator pre-warning method based on indicator threshold and big data analysis according to claim 4, wherein sending second warning information to the warning contact includes:
and sending second alarm information to the alarm contact person through a server alarm module.
6. The micro-service index early warning method based on index threshold and big data analysis according to claim 1, characterized in that the method further comprises: and acquiring the log information of the micro-service to be monitored through a logstack data collection engine, and storing the acquired log information of the micro-service to be monitored into an elastic search database.
7. The micro-service index early warning method based on index threshold and big data analysis according to claim 1, wherein the server performance index comprises a total CPU usage rate, a memory usage rate, a maximum partition usage rate and a swap partition usage rate, the middleware performance index comprises a total consumption number, and the database performance index comprises a cluster number, a front end state, a back end state and a cluster peak value state.
8. The micro-service index early warning method based on index threshold and big data analysis according to any one of claims 1 to 7, wherein the first alarm information and/or the second alarm information is a micro-letter information, a short message information or a mail information.
9. A micro-service indicator pre-warning system based on indicator threshold and big data analysis, the system comprising a processor and a memory, characterized in that the memory has stored therein computer instructions, the processor being adapted to execute the computer instructions stored in the memory, the system implementing the steps of the method according to any of claims 1 to 8 when the computer instructions are executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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| CN117251337A (en) * | 2023-09-07 | 2023-12-19 | 广州宇中网络科技有限公司 | Micro-service health dial testing method, device, equipment and storage medium |
| CN117251337B (en) * | 2023-09-07 | 2024-05-28 | 广州宇中网络科技有限公司 | Micro-service health dial testing method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117251337A (en) * | 2023-09-07 | 2023-12-19 | 广州宇中网络科技有限公司 | Micro-service health dial testing method, device, equipment and storage medium |
| CN117251337B (en) * | 2023-09-07 | 2024-05-28 | 广州宇中网络科技有限公司 | Micro-service health dial testing method, device, equipment and storage medium |
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