CN116932361A - Micro-service change evaluation method, electronic device, and storage medium - Google Patents

Micro-service change evaluation method, electronic device, and storage medium Download PDF

Info

Publication number
CN116932361A
CN116932361A CN202210328183.4A CN202210328183A CN116932361A CN 116932361 A CN116932361 A CN 116932361A CN 202210328183 A CN202210328183 A CN 202210328183A CN 116932361 A CN116932361 A CN 116932361A
Authority
CN
China
Prior art keywords
service
change
micro
monitoring data
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210328183.4A
Other languages
Chinese (zh)
Inventor
侯光林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huawei Cloud Computing Technologies Co Ltd
Original Assignee
Huawei Cloud Computing Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huawei Cloud Computing Technologies Co Ltd filed Critical Huawei Cloud Computing Technologies Co Ltd
Priority to CN202210328183.4A priority Critical patent/CN116932361A/en
Publication of CN116932361A publication Critical patent/CN116932361A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3604—Software analysis for verifying properties of programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3604—Software analysis for verifying properties of programs
    • G06F11/3612—Software analysis for verifying properties of programs by runtime analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/362—Software debugging
    • G06F11/366—Software debugging using diagnostics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/60—Software deployment
    • G06F8/65—Updates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/70—Software maintenance or management
    • G06F8/71—Version control; Configuration management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Quality & Reliability (AREA)
  • Computer Security & Cryptography (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The application discloses a micro-service change evaluation method, electronic equipment and a storage medium, and relates to the technical field of micro-services. According to the micro-service change evaluation method provided by the application, the change value of the change process of the micro-service can be determined according to the monitoring data of multiple dimensions of the micro-service monitored in real time, and the evaluation suggestion aiming at the change process of the micro-service is displayed, so that the analysis on the condition of the micro-service change process can be automatically and accurately realized; the service data of different interfaces in the changing process are not required to be observed manually, and whether the changing process needs to be stopped or not is evaluated subjectively according to the observed service data, so that the manpower resources can be saved.

Description

Micro-service change evaluation method, electronic device, and storage medium
Technical Field
The present application relates to the field of micro-service technologies, and in particular, to a micro-service change evaluation method, an electronic device, and a storage medium.
Background
Micro-services (micro-services) are a software architecture based on small functional blocks that implement a single function, and a complex large application is combined in a modular manner, where each functional block uses a collection of language-independent application program interfaces (application programming interface, APIs) to communicate with each other, and a functional block may also be referred to as a micro-service.
The application of micro-service technology in different industries is more and more widespread, and the number of micro-services contained in the same micro-service system is more and more. As the business needs increase and change, the micro services in the micro service system need to be updated or changed continuously to meet the business needs.
At present, in the micro-service changing process, a developer is usually required to judge whether the changing process is abnormal by observing whether the service data in different interfaces are normal, if so, the developer checks the reasons of log analysis abnormality again, so that the consumption of manpower resources is high, and the influence of subjective factors of the developer is easy to cause an erroneous analysis result.
Disclosure of Invention
The embodiment of the application provides a micro-service change evaluation method, electronic equipment and a storage medium, which are used for automatically and accurately analyzing the condition of a micro-service change process and saving human resources.
In a first aspect, an embodiment of the present application provides a method for evaluating a micro service change, where the method includes: in the changing process of the micro service, acquiring N-dimensional monitoring data of the micro service, determining a changing value of the changing process of the micro service according to the N-dimensional monitoring data, searching an evaluation suggestion corresponding to the changing value interval of the determined changing value in a mapping relation between preset different changing value intervals and different evaluation suggestions, serving as an evaluation suggestion for the changing process of the micro service, and displaying the evaluation suggestion for the changing process of the micro service; the evaluation advice includes any one of the following: continuing to execute the changing process on the micro-service, and suggesting to manually determine whether the changing process is normal or is returned to the state before the micro-service is changed.
According to the micro-service change evaluation method provided by the embodiment of the application, the change value of the change process of the micro-service can be determined according to the monitoring data of multiple dimensions of the micro-service monitored in real time, and the evaluation suggestion aiming at the change process of the micro-service is displayed, so that the analysis on the condition of the micro-service change process can be automatically and accurately realized; the service data of different interfaces in the changing process are not required to be observed manually, and whether the changing process needs to be stopped or not is evaluated subjectively according to the observed service data, so that the manpower resources can be saved.
In one possible implementation, the N-dimensional monitoring data may include some or all of the following: interface index data of each designated interface of the micro service; container index data for each container of the microservice; log monitoring data during micro-service changes.
In one possible implementation manner, after the monitoring data of the N dimensions of the micro service are obtained, for the ith dimension, the difference value before and after the change of the ith dimension can be determined according to the monitoring data of the ith dimension; according to the difference values before and after the change respectively corresponding to the ith dimension, the current change value of the change process of the micro service is adjusted; wherein i takes a positive integer from pass 1 to N.
In the method, the difference values before and after the change corresponding to each dimension are determined, and when the change value of the change process of the micro service is determined, the influence of the difference value before and after the change corresponding to each dimension on the change value of the change process of the micro service is considered, and the obtained change value of the change process of the micro service can be more accurate by comprehensively referencing the influence factors of each dimension.
In one possible implementation, the micro-service change process refers to a process of gradually migrating a service request received by a micro-service from an old version to a new version of each service instance. For the ith dimension, the difference value before and after the change of the ith dimension can be determined according to the old version monitoring data of each service instance contained in the micro service in the ith dimension and the new version monitoring data of each service instance contained in the micro service in the ith dimension.
In one possible implementation manner, for the ith dimension, the difference value before and after the change of the ith dimension can be determined according to the average value of the old version monitoring data of each service instance contained in the micro service in the ith dimension and the average value of the new version monitoring data of each service instance contained in the micro service in the ith dimension.
The average value of the new version monitoring data is the average value of all the new version monitoring data contained in the monitoring data of the ith dimension; the average value of the old version monitoring data may be an average value of each old version monitoring data included in the monitoring data of the ith dimension, or an average value of the old version monitoring data of each service instance of the ith dimension in the set history period.
In the method, the average value of the old version monitoring data of the service instance contained in the micro-service in the ith dimension is compared with the average value of the new version monitoring data of the service instance contained in the micro-service in the ith dimension, so that the difference value before and after the change of the ith dimension can be more reasonably determined on the premise of ensuring smaller calculated amount.
In one possible implementation manner, after the difference value before and after the change corresponding to the ith dimension is obtained, if the difference value before and after the change of the ith dimension is greater than or equal to the maximum difference threshold value corresponding to the ith dimension, increasing the current change value of the change process of the micro-service by a set gradient value; if the difference value before and after the change of the ith dimension is smaller than or equal to the minimum difference threshold value corresponding to the ith dimension, the current change value of the change process of the micro-service is reduced by a set gradient value.
In the method, a maximum difference threshold and a minimum difference threshold are set for each dimension, if the difference value before and after the change corresponding to the ith dimension is greater than or equal to the maximum difference threshold corresponding to the ith dimension, the stability and the reliability of the new version of the service instance of the micro service A are equivalent to those of the old version, and a set gradient value is increased on the basis of the change value determined by the difference value before and after the change of the ith-1 dimension; if the difference value before and after the change corresponding to the ith dimension is smaller than or equal to the minimum difference threshold value corresponding to the ith dimension, the service instance of the micro service A has obvious problems or defects compared with the old version, and the set gradient value is reduced by the change value determined based on the difference value before and after the change of the ith-1 dimension, so that the influence of the difference value before and after the change corresponding to each dimension on the change value of the micro service in the change process is reflected.
In one possible implementation, the monitoring data of N dimensions may further include alarm data for the micro-service; and if the alarm data is received, reducing the current change value of the change process of the micro service by a set gradient value.
Since the alarm data is generated by monitoring the abnormal running parameters of the micro service, once the alarm data for the micro service is received, the new version of the service instance is indicated to have potential problems, and therefore the change value of the change process of the micro service is reduced by a set gradient value so as to reflect the abnormal change process.
In a second aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the processor is configured to execute a computer program stored in the memory, and implement a method as set forth in any one of the possible designs of the first aspect.
In a third aspect, embodiments of the present application provide a computer-readable storage medium having stored therein computer-executable instructions for causing a computer to perform the method set forth in any one of the possible designs of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer program product comprising computer executable instructions for causing a computer to perform the method as set forth in any one of the possible designs of the first aspect.
The technical effects achieved by any one of the second aspect to the fourth aspect may be referred to the description of the beneficial effects in the first aspect, and the detailed description is not repeated here.
Drawings
Fig. 1 is a schematic diagram of an application scenario according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for evaluating micro-service change according to an embodiment of the present application;
FIG. 3 is a flowchart of another method for evaluating micro-service change according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a micro-service change evaluation process according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a process for acquiring monitoring data of a micro service according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a change evaluation details display interface according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a micro-service change evaluation device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. The terminology used in the description of the embodiments of the application herein is for the purpose of describing particular embodiments of the application only and is not intended to be limiting of the application.
Before describing the embodiments of the present application, some of the words in the present application are explained in order to facilitate understanding of those skilled in the art, and the words in the present application are not limited thereto.
(1) Gray level change: a change mode used in the micro service change process such as micro service update or micro service upgrade. The micro service generally includes a plurality of service instances, and the micro service change process is to change all service instances included in one micro service from an old version to a new version, and the gray level change refers to a change mode of smooth transition between the new version and the old version of the service instance of the micro service.
In the micro service change process, the received service requests can be gradually migrated from the old version of each service instance to the new version, illustratively, before the change, the received service requests are all processed by the old version of each service instance, after the change is started, 10% of the received service requests can be migrated from the old version to the new version, namely, 90% of the received service requests are processed by the old version, 10% of the service requests are processed by the new version, then the proportion of the service requests processed by the old version is gradually reduced, and the proportion of the service requests processed by the new version is increased until all the received service requests are processed by the new version, which can be called gray level change.
In the embodiments of the present application, "a plurality" refers to two or more, and in this regard, "a plurality" may be understood as "at least two" in the embodiments of the present application. "at least one" may be understood as one or more, for example as one, two or more. For example, including at least one means including one, two or more, and not limiting what is included, e.g., including at least one of A, B and C, then A, B, C, A and B, A and C, B and C, or A and B and C, may be included. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/", unless otherwise specified, generally indicates that the associated object is an "or" relationship.
Unless stated to the contrary, the embodiments of the present application refer to ordinal terms such as "first," "second," etc., for distinguishing between multiple objects and not for defining a sequence, timing, priority, or importance of the multiple objects.
The micro-service change evaluation method provided by the embodiment of the application is applied to a micro-service system capable of providing services externally. Fig. 1 shows a schematic diagram of an application scenario in an embodiment of the present application, as shown in fig. 1, a central server 100 may be connected to a plurality of terminals 200, where the terminals 200 may be understood as service invokers, and the central server 100 is configured to provide services for the service invokers. The central server 100 provides interfaces for micro services externally, each of which may provide a service function. The terminal 200 may call an interface provided by the center server 100 through a remote network communication manner to acquire data or implement a function.
As the business needs increase and change, the micro services in the micro service system need to be updated or changed continuously to meet the business needs. At present, in the micro-service changing process, a developer is usually required to judge whether the changing process is abnormal by observing whether the service data in different interfaces are normal, if so, the developer checks the reasons of log analysis abnormality again, so that the consumption of manpower resources is high, and the influence of subjective factors of the developer is easy to cause an erroneous analysis result.
Based on the above, the embodiment of the application provides a micro-service change evaluation method, which can determine the change value of the change process of the micro-service according to the monitoring data of multiple dimensions of the micro-service monitored in real time, and display the evaluation suggestion aiming at the change process of the micro-service so as to automatically and accurately analyze the condition of the micro-service change process; the service data of different interfaces in the changing process are not required to be observed manually, and whether the changing process needs to be stopped or not is evaluated subjectively according to the observed service data, so that the manpower resources can be saved.
Fig. 2 shows a flowchart of a micro service change evaluation method according to an embodiment of the present application, where the micro service change evaluation method may be performed by an electronic device, for example, may be performed by the central server 100 in fig. 1 or another electronic device connected to the central server 100. As shown in fig. 2, the method may include the steps of:
s201, acquiring monitoring data of N dimensions of the micro service in the micro service changing process.
One micro service can comprise a plurality of service instances, the change process of the micro service can adopt a gray level change mode, and the service request received by the micro service is gradually migrated from the old version to the new version of each service instance, so that the upgrade from the old version to the new version is completed.
From the moment when the micro service starts to be changed, the data of each dimension of the micro service can be monitored in real time, and the monitoring data of N dimensions of the micro service can be obtained. The monitoring data may include some or all of the following: interface index data of each designated interface of the micro service; container index data for each container of the microservice; the log of the microservices monitors the data.
Wherein the specified interface may be an interface specified by a user. For example, the interface index data of any one interface may include: the time delay of the interface processing the service request, the success rate of the interface processing the service request, the failure rate of the interface processing the service request and other monitoring data with multiple dimensions.
The micro-service corresponds to a plurality of containers, each container having container index data in which a service instance may be launched. For example, the container index data for any one container may include: the container corresponds to the monitoring data of multiple dimensions such as CPU utilization rate, memory utilization rate, disk utilization rate, process number in the container, handle number, database connection number, garbage collection (garbage collection, GC) time and the like.
The log monitoring data of the micro-service may include the number of different types of logs during the change of the micro-service.
S202, according to the monitoring data of the N dimensions, determining a change value of a change process of the micro service.
For any one dimension of the N dimensions, for example, for the ith dimension, the difference value before and after the change of the ith dimension can be determined according to the monitoring data of the ith dimension, and the current change value can be adjusted according to the difference value before and after the change of the ith dimension. Wherein, the value of i is any positive integer from 1 to N. The current change value may be understood as a change value at the current time, and the change value at the current time may be a change value adjusted according to the difference value before and after the change of the i-1 th dimension, or a change value adjusted according to the difference value before and after the change of the N-th dimension at the previous time.
After the monitoring data of the ith dimension is obtained, the difference value before and after the change of the ith dimension can be determined according to the old version monitoring data of the service instance contained in the micro service in the ith dimension and the new version monitoring data of the service instance contained in the micro service in the ith dimension.
In some embodiments, the old version of the monitoring data of the service instance contained in the micro-service in the ith dimension and the new version of the monitoring data of the service instance contained in the micro-service in the ith dimension are both contained in the monitoring data of the ith dimension. For example, suppose that the micro-service contains K service instances, at some point in time, L new versions are included in the K service instances that handle the service request, and the remaining K-L service instances are old versions. At this time, the obtained monitoring data of the ith dimension contains new version monitoring data of L service instances, old version monitoring data of K-L service instances can respectively determine the average value of the new version monitoring data of the L service instances in the ith dimension, and the average value of the old version monitoring data of the K-L service instances in the ith dimension contained in the micro service, and the difference value before and after the change of the ith dimension is determined according to the difference between the two average values.
In other embodiments, the new version of the monitoring data for the ith dimension for the service instance contained in the micro-service uses the new version of the monitoring data contained in the monitoring data for the ith dimension. The old version monitoring data of the service instance contained in the micro-service in the ith dimension can use the old version monitoring data of the service instance contained in the micro-service in the ith dimension in a set history time period; wherein the set history period is a set period before the micro-service change. For example, when L service instances in K service instances included in the micro service have been changed to new versions, an average value of L new version monitoring data included in monitoring data of an ith dimension and an average value of K old version monitoring data of the ith dimension in a set history period may be determined, and a difference value before and after the change of the ith dimension is determined according to a difference between the two average values.
In other embodiments, the new version monitoring data of the service instance included in the micro-service in the ith dimension uses the new version monitoring data included in the monitoring data of the ith dimension, and the old version monitoring data of the service instance included in the micro-service in the ith dimension may use a preset value. The preset value may be determined by the user according to an average value of the old version monitoring data of the ith dimension of the K service instances included in the micro service in a certain historical period. For example, when L service instances in K service instances included in the micro service have been changed to new versions, an average value of L new versions of monitoring data included in the monitoring data of the ith dimension may be determined, and a difference value before and after the change of the ith dimension may be determined according to a difference between the average value and a preset value.
After the difference value before and after the change of the ith dimension is determined, if the difference value before and after the change of the ith dimension is larger than or equal to the maximum difference threshold value corresponding to the ith dimension, the current change value is increased by a set gradient value; if the difference value before and after the change of the ith dimension is smaller than or equal to the minimum difference threshold value corresponding to the ith dimension, the current change value is reduced by the set gradient value.
In some embodiments, the monitoring data may also include alert data for the micro-service; the alarm data for the micro-service refers to alarm information sent when abnormal operation parameters of the micro-service are monitored. If alarm data for the micro-service is monitored, the current change value is reduced by the set gradient value.
S203, searching an evaluation suggestion corresponding to the change value interval to which the determined change value belongs in a preset mapping relation between different change value intervals and different evaluation suggestions, and taking the evaluation suggestion as the evaluation suggestion for the change process of the micro service.
In some embodiments, the mapping relationship between different change value intervals and different assessment suggestions may be pre-saved. The setting of the different change value intervals and the different evaluation suggestions corresponding to the different change value intervals can be set by a tester according to experience. For example, three modification value sections may be set, which are a first modification value section, a second modification value section, and a third modification value section, respectively, in order of modification values from high to low. The evaluation advice corresponding to the first change value interval may be: continuing to perform the change process on the micro-service, the evaluation advice corresponding to the second change value interval may be: the manual determination of whether the change process is normal is recommended, and the evaluation recommendation corresponding to the third change value interval may be: and returning to the state before the micro-service change.
If the current change value of the change process of the micro service belongs to the first change value interval, the stability and the reliability of the new version of the service instance of the micro service are equivalent to those of the old version, and evaluation suggestions for continuously executing the change process on the micro service can be given; if the current change value of the change process of the micro service belongs to the second change value interval, the new version of the service instance of the micro service is possibly problematic, but system erroneous judgment cannot be excluded, and an evaluation suggestion for manually determining whether the change process is normal can be given; if the current change value of the change process of the micro service belongs to the third change value interval, the new version of the service instance of the micro service has obvious problems or defects compared with the old version, and evaluation suggestions for returning to the pre-change state of the micro service can be given.
S204, an evaluation suggestion for the change procedure of the micro service is displayed.
The generated evaluation advice can be displayed to the user through a display screen of the electronic device, and the user can decide whether to continue to execute the changing process or return to the pre-changing state according to the displayed evaluation advice. If the user determines to fall back to the pre-change state, the electronic device can input a fall-back command through an input component such as a button arranged on the electronic device, and the electronic device receives the fall-back command input by the user, stops the change process of the micro-service and falls back to the pre-change state.
The micro-service change evaluation method provided by the embodiment of the application can automatically and accurately analyze the condition of the micro-service change process without manually observing the service data of different interfaces in the change process, and subjectively evaluating whether the change process needs to be stopped or not according to the observed service data, thereby saving human resources; in addition, the influence of errors caused by changing the version of the service instance of the micro service can be reduced, the errors can be found as early as possible in the changing process, and the errors can be timely retracted, so that the influence caused by the errors is reduced to the minimum, and the micro service system can be guaranteed to provide services stably and externally.
In order to facilitate understanding of the technical solution provided by the embodiments of the present application, the method for evaluating a micro service change provided by the embodiments of the present application is described below by way of a detailed specific example.
Illustratively, assuming that micro service a contains 10 service instances, in some upgrade of micro service a, it is necessary to upgrade all 10 service instances from an old version to a new version. The upgrading process is carried out in a gray level changing mode, and the received service request is gradually migrated from the old version to the new version of each service instance. In the changing process of the micro service a, the micro service change evaluation method shown in fig. 3 may be adopted to evaluate the performance and stability of the micro service a in the changing process in real time, and display the evaluation advice of whether to continue to execute the changing process in real time.
As shown in fig. 3, the micro service change evaluation method may include the following steps, which may be performed by a central server or by other devices that monitor or provide the micro service operation status, which is not limited by the present application:
s301, in the changing process of the micro service A, monitoring data of multiple dimensions of the micro service A are obtained in real time.
The data of each dimension of the micro service A can be monitored in real time from the change of the micro service A, and the monitoring data of a plurality of dimensions of the micro service A can be obtained.
By way of example, as shown in fig. 4, the monitoring data may include four types as follows: interface index data of each designated interface of the micro service A; alert data for microservice a; container index data for each container of the microservice a; the log of microservice a monitors data. According to the obtained monitoring data, the change value of the change process of the micro service A and the evaluation suggestion for the change process can be determined, and the determined evaluation suggestion is displayed through a display.
Wherein the specified interface may be a user-defined interface. For example, the user may add an important interface of the micro service a as a designated interface in advance. For example, if the micro service a is a commodity settlement service, the important interfaces of the micro service a include an order interface, and the user may add the order interface as a designated interface in advance, and in the changing process of the micro service a, obtain the interface index data of the order interface in real time.
The micro service a may provide a plurality of interfaces, which may also be referred to as access interfaces, for providing services to the outside. The interface index data of each designated interface of the micro service a may include monitoring data of a plurality of different dimensions, for example, the interface index data of any one interface may include: the time delay of the interface processing the service request, the success rate of the interface processing the service request, the failure rate of the interface processing the service request and other monitoring data with multiple dimensions.
Illustratively, as shown in fig. 5, the interface index data of each designated interface of the micro service a may be acquired as follows: an open measurement software development kit (open measurement software development kit, OMSDK) is integrated in the microservice a, the OMSDK being a software package of a process-level monitoring service for monitoring traffic data in a process. In the embodiment of the application, the OMSDK may collect interface data of each designated interface, the interface data of each designated interface collected by the OMSDK is transmitted to a data processing service through a kafka data transmission interface, the data processing service is used for aggregating the received interface data, the aggregated interface data is transmitted to a dataflash data processing service, the dataflash data processing service is used for obtaining interface index data according to the aggregated interface data, for example, the dataflash data processing service may calculate and obtain the success rate of processing the service request of the interface according to the number of service requests received by a certain interface and the number of service requests successfully processed. The dataflow data processing service stores the obtained interface index data of the designated interface into an influxdb database for later use in calculating the change value.
The alarm data for the micro service a may include alarm information sent by the micro service a when the micro service a detects that the operation parameters of the micro service a are abnormal, or may include alarm information sent by the micro service monitoring device when the operation parameters of the micro service a are abnormal.
Illustratively, as shown in fig. 5, the alert data of the micro service a may be obtained by: different types of alert services may be contained in micro service a: for example, grafana alarm service, active Directory (AD) alarm service, or software development kit (software development kit, SDK) alarm service, alarm data generated by any one alarm service may be transmitted to an adapter data transmission interface through a kafka data transmission interface, where the adapter data transmission interface stores the alarm data in an infuxdb database for use in calculating a change value later.
The micro service a corresponds to a plurality of containers, each of which can be started with a service instance, each container having container index data. The container index data of each container of the micro service a may also include monitoring data of a plurality of different dimensions, for example, the container index data of any one container may include: the container corresponds to the monitoring data of multiple dimensions such as CPU utilization rate, memory utilization rate, disk utilization rate, process number in the container, handle number, database connection number, GC time and the like.
Illustratively, as shown in fig. 5, a part of the container index data of each container of the micro service a, for example, the container index data such as the number of processes, the number of handles, the number of database connections, the GC time, etc., may be acquired by: integrating an OMSDK in the micro service A, wherein the OMSDK can collect container index data of each container, the container index data of each container collected by the OMSDK is transmitted to a logstack data processing service through a kafka data transmission interface, the logstack data processing service is used for aggregating the received container index data, the aggregated container index data is stored in an elastic search database, and the elastic search database can also be called an ES database and is a non-relational database.
Another part of the container index data of each container of the micro service a, for example, container index data such as CPU usage, memory usage, disk usage, etc., may be obtained by: and integrating an AOM service software package in the micro service A, wherein the AOM service software package can acquire the container index data of each container, and the container index data of each container acquired by the AOM service software package is stored into an Influxdb database through an adapter data transmission interface for later use in calculating a change value.
The log monitoring data of micro service a may include the number of different types of logs during the change of micro service a. For example, the log for recording the user registration information may include two types of logs of user registration success and user registration failure. Other types may also be included for logs used to record other information. The number of logs of each type serves as one dimension of monitoring data.
Illustratively, as shown in fig. 5, the log monitoring data of the micro service a may be obtained by: integrating a filebean service software package in the micro service A for collecting log monitoring data, transmitting the log monitoring data collected by the filebean service software package to a logstack data processing service, and aggregating the received log monitoring data by the logstack data processing service, and storing the aggregated log monitoring data into an ES database for later use in calculating a change value.
S302, determining the difference value before and after each dimension according to the monitoring data of each dimension in the plurality of dimensions.
For example, assuming that monitoring data of N dimensions of the micro service a are acquired in addition to the alarm data, the difference value before and after the change of the i dimension can be determined according to the monitoring data of the i dimension, wherein the value of i is any positive integer from 1 to N, and the difference value before and after the change of the N dimensions is obtained altogether.
The monitoring data of the ith dimension comprises monitoring data corresponding to 10 service instances of the micro service A, the monitoring data corresponding to a new version of the service instances in the 10 service instances for processing the service request are new version monitoring data, and the monitoring data corresponding to an old version of the service instances are old version monitoring data according to the progress of the gray level change process. Assuming that at the present moment, the 10 service instances for processing the service request include 4 new versions, and the remaining 6 service instances are old versions, the monitoring data of the ith dimension includes 4 new version monitoring data and 6 old version monitoring data.
When the difference value before and after the change of the ith dimension is determined, the difference value before and after the change of the ith dimension can be calculated according to the new version monitoring data and the old version monitoring data of the ith dimension, and the difference value before and after the change of the ith dimension is determined according to the difference value before and after the change of the ith dimension.
In some embodiments, the degree of difference between the ith dimension before and after the change may be calculated according to the average value of each new version of monitoring data and the average value of each old version of monitoring data contained in the monitoring data of the ith dimension. For example, an average value of each new version of monitoring data contained in the monitoring data of the ith dimension can be determined, an average value of each old version of monitoring data contained in the monitoring data of the ith dimension is determined, and a Z bilateral verification algorithm is adopted to calculate the difference degree before and after the change of the ith dimension according to the average value of the new version of monitoring data and the average value of the old version of monitoring data. When calculating the difference value before and after the change of the ith dimension, different calculation formulas can be adopted according to different data types corresponding to the ith dimension.
Illustratively, if the ith dimension is the success rate of processing the service request by the ordering interface, the success rate data belongs to the proportional data. For the proportional data, the degree of difference before and after the change can be calculated using the following formula (1):
wherein z represents the difference degree before and after the change of the ith dimension, namely the difference degree before and after the change corresponding to the success rate of processing the service request by the issuing interface, and p represents the average value of the success rate of processing the service request by the issuing interface corresponding to each service instance of the new version, namely the average value of the success rate of processing the service request by the issuing interface corresponding to each service instance of the new version in 10 service instances of the micro service A; p is p c The average value of the success rate of processing the service request by the order interface corresponding to each service instance of the old version is represented, namely the average value of the success rate of processing the service request by the order interface corresponding to each service instance of the old version in 10 service instances of the micro service A; m represents the total request quantity of the service request received by the ordering interface corresponding to the new version service instance; m is M c The total request quantity of the service requests received by the order interface corresponding to the service instance of the old version is represented.
Assuming that 4 service instances in the 10 service instances are new versions, the remaining 6 service instances are old versions, M represents the sum of request amounts of service requests received by the issuing interfaces corresponding to the 4 service instances of the new versions, and M c The sum of the request amounts of the service requests received by the order interfaces corresponding to the 6 service instances representing the old version. The success rate of processing the service requests by the order interfaces corresponding to any one service instance in the 4 service instances of the new version can be determined according to the ratio of the number of the successfully processed service requests by the order interfaces corresponding to the service instance to the request quantity of the received service requests by the order interfaces corresponding to the service instance, so that 4 success rates can be obtained in total. Calculated to obtain 4 adultsThe average value of the power is taken as the average value p of the success rate of processing the service request by the ordering interfaces of the 4 service examples of the new version. The success rate of processing the service requests by the order interfaces corresponding to any one service instance in the 4 service instances of the old version can be determined according to the ratio of the number of the successfully processed service requests by the order interfaces corresponding to the service instance to the request quantity of the received service requests by the order interfaces corresponding to the service instance, so that 6 success rates can be obtained in total. Calculating to obtain average value of 6 success rates, and using average value p of success rates of service requests processed by the subordinate interfaces of 6 service instances of old version c 。
By adopting the formula (1), the difference degree before and after the change corresponding to the success rate of processing the service request by the ordering interface can be calculated.
Illustratively, if the ith dimension is the delay of processing the service request by the ordering interface, the delay data belongs to the numerical data. For the numerical data, the degree of difference before and after the change can be calculated using the following formula (2):
wherein z represents the difference degree before and after the change corresponding to the delay of the service request processing by the issuing interface, p represents the average value of the delay of the service request processing by the issuing interface corresponding to each service instance of the new version, namely the average value of the delay of the service request processing by the issuing interface corresponding to each service instance of the new version in 10 service instances of the micro service A; p is p c The average value of the time delay of processing the service request by the order interface corresponding to each service instance of the old version is represented, namely the average value of the time delay of processing the service request by the order interface corresponding to each service instance of the old version in 10 service instances of the micro service A; m represents the total request quantity of the service request received by the ordering interface corresponding to the new version service instance; m is M c The total request quantity of the service requests received by the order interface corresponding to the service instance of the old version is represented.
Assuming that the 10 service instances include 4 new versions, the remaining 6 are oldVersion, M represents the sum of the request amounts of service requests received by the order interfaces corresponding to the 4 service instances of the new version, M c The sum of the request amounts of the service requests received by the order interfaces corresponding to the 6 service instances representing the old version. Calculating average value p of time delays of service requests processed by the ordering interfaces corresponding to the 4 service instances of the new version, and calculating average value p of time delays of service requests processed by the ordering interfaces corresponding to the 6 service instances of the old version c 。
By adopting the formula (2), the difference degree before and after the change corresponding to the time delay of the service request processed by the ordering interface can be calculated.
In the above embodiment, the old version monitoring data is an average value of each old version monitoring data contained in the monitoring data of the ith dimension acquired in real time. In other embodiments, given that there may be a situation where an old version of a service instance is affected by the network, resulting in a relatively large fluctuation in data in some or all dimensions, the old version of the monitoring data may take the average of the actual monitoring data for the old version of the ith dimension over the set history period. Illustratively, the set history period may be 1 hour or several hours before the micro service a starts to change. For example, the actual monitoring data of the old version of the ith dimension of 10 service instances may be acquired within 1 hour before the micro service a starts to change, and the average value of the acquired actual monitoring data of all the old versions of the ith dimension may be calculated. And calculating the difference degree before and after the change of the ith dimension by adopting the formula (1) or the formula (2) according to the average value of all new version monitoring data contained in the monitoring data of the ith dimension and the average value of the actual monitoring data of the old version of all service instances of the ith dimension in the set history time period.
In other embodiments, the old version of the monitoring data may also use a set value corresponding to the ith dimension. The setting value corresponding to the ith dimension may be predicted by a worker according to actual monitoring data of an old version of each service instance of the ith dimension. According to the average value of all new version monitoring data contained in the monitoring data of the ith dimension and the set value corresponding to the ith dimension, calculating the difference degree before and after the change of the ith dimension by adopting the formula (1) or the formula (2), wherein the set value is used for representing the average value of the old version monitoring data of all service instances.
By the method, the difference degree before and after the change of the ith dimension can be calculated. In one embodiment, the difference between the i-th dimension and the i-th dimension may be used as the difference between the i-th dimension and the i-th dimension.
In another embodiment, if the ith dimension is a forward index dimension with a larger success rate and the like, the difference degree before and after the change of the ith dimension can be converted into a value between 0 and 100 by adopting the following formula (3), so as to obtain a difference value score before and after the change of the ith dimension:
if the ith dimension is a negative index dimension with smaller time delay, failure rate and the like, the difference degree before and after the change of the ith dimension can be converted into a numerical value between 0 and 100 by adopting the following formula (4), so that a difference value score before and after the change of the ith dimension is obtained:
For each of the N dimensions, the difference value before and after the change of the dimension can be obtained through the above process.
S303, determining a change value of the change process of the micro service A according to the difference values before and after the change of the plurality of dimensions.
The change value of the change procedure of the micro service a is hereinafter simply referred to as a change value. In some embodiments, when the micro service a starts changing, an initial value may be set for the change value, for example, the initial value of the change value may be set to 100. In the changing process of the micro service A, the changing value can be adjusted in real time according to the calculated difference value before and after the changing of each dimension.
Continuing the above example, assuming that the difference values before and after the change in the N dimensions are obtained, the current change value may be adjusted in real time according to the difference value before and after the change in each of the N dimensions. That is, the current change value may be adjusted in real time according to the difference value before and after the change of the ith dimension, the current change value may be understood as the change value at the current time, and the change value at the current time may be the change value adjusted according to the difference value before and after the change of the ith-1 dimension, or the change value adjusted according to the difference value before and after the change of the nth dimension at the previous time, where the value of i is a positive integer from 1 to N.
For the ith dimension, if the difference value before and after the change of the ith dimension is larger than or equal to the maximum difference threshold value corresponding to the ith dimension, adding a set gradient value corresponding to the ith dimension on the basis of the current change value; if the difference value before and after the change of the ith dimension is smaller than the maximum difference threshold value corresponding to the ith dimension and larger than the minimum difference threshold value corresponding to the ith dimension, the current change value can not be adjusted; if the difference value before and after the change of the ith dimension is smaller than or equal to the minimum difference threshold value corresponding to the ith dimension, reducing the set gradient value corresponding to the ith dimension on the basis of the current change value. The set gradient value may be any value greater than 0, for example, the set gradient value may be 0.5, 1, 2, or 3.
The maximum difference threshold and the minimum difference threshold corresponding to different dimensions in the N dimensions may be the same or different. The set gradient values corresponding to different dimensions in the N dimensions may be the same or different.
In one embodiment, the maximum variance threshold for each different dimension may be set to 80, assuming the same; the minimum difference threshold corresponding to each different dimension is the same and can be set to 60; the set gradient values corresponding to the different dimensions are the same, and can be set to 2. If the difference value before and after the change of the ith dimension is greater than or equal to 80, the stability and the reliability of the service instance of the micro service A in the ith dimension are equivalent to those of the old version, and a set gradient value 2 is added on the basis of the current change value; if the difference value before and after the change of the ith dimension is within the interval (60, 80), the new version of the service instance of the micro service A in the ith dimension is possibly problematic, but system erroneous judgment cannot be eliminated, and the current change value can not be adjusted; if the difference value before and after the change of the ith dimension is less than or equal to 60, the new version of the service instance of the micro service A in the ith dimension has obvious problems or defects compared with the old version, and the set gradient value is reduced by 2 on the basis of the current change value.
Assuming that the current change value is 88, the ith dimension is the delay of processing the service request by the order interface, if the difference value before and after the change of the ith dimension is 84, since 84 is greater than the maximum difference threshold 80, it is indicated that the stability and reliability of the new version of the service instance of the micro service a are equivalent to the old version in the dimension of the delay of processing the service request by the order interface, a set gradient value 2 is added on the basis of the current change value 88, and the adjusted change value is 90. If the difference value before and after the change of the ith dimension is 75, since 75 is smaller than the maximum difference threshold 80 and larger than the minimum difference threshold 60, in the dimension of the delay of processing the service request by the next interface (60, 80), the new version of the service instance of the micro service A may be problematic, but the misjudgment of the system cannot be excluded, and the current change value may not be adjusted; if the difference value before and after the change of the ith dimension is 55, since 55 is smaller than the minimum difference threshold 60, it is indicated that the new version of the service instance of the micro service a has obvious problems or defects compared with the old version in the dimension of the delay of processing the service request by the next interface, and the set gradient value 2 is reduced based on the current change value 88.
If alarm data for the micro service A is received, reducing a set gradient value corresponding to one alarm data dimension on the basis of the current change value. The set gradient value corresponding to the alarm data dimension may be the same as or different from the set gradient value corresponding to the other dimension. Since the alarm data is generated by monitoring the abnormal operation parameters of the micro service a, once the alarm data for the micro service a is received, the new version of the service instance is described to have potential problems, and the set gradient value is reduced based on the current change value.
S304, according to the change value interval to which the change value belongs, an evaluation suggestion for the change process of the micro service A is generated.
S305, an evaluation suggestion for the change procedure of the micro service a is displayed.
In some embodiments, the mapping relationship between different change value intervals and different assessment suggestions may be pre-saved. For example, three change value intervals may be set, the first change value interval may not set an upper limit, and only the lower limit of the change value of the interval is set to 75, that is, when the change value is equal to or greater than 75, the change value belongs to the first change value interval; the upper limit of the change value of the second change value interval is 75, and the lower limit is 50, namely, when the change value is less than 75 and greater than 50, the change value belongs to the second change value interval; the third change value section may be set to have only the upper limit of the change value of 50, that is, 50 or less, without setting the lower limit. The evaluation advice corresponding to the first change value interval may be: continuing to perform the change process on the micro-service, the evaluation advice corresponding to the second change value interval may be: the manual determination of whether the change process is normal is recommended, and the evaluation recommendation corresponding to the third change value interval may be: and returning to the state before the micro-service change.
In any time in the changing process of the micro service A, according to the current changing value, in the mapping relation between the preset different changing value intervals and different evaluating suggestions, the evaluating suggestion corresponding to the changing value interval to which the current changing value belongs is searched and used as the evaluating suggestion for the changing process of the micro service A, or in the changing process of the micro service A, each time the changing value is adjusted, in the mapping relation between the preset different changing value intervals and the different evaluating suggestions, the evaluating suggestion corresponding to the changing value interval to which the adjusted changing value belongs is searched and used as the evaluating suggestion for the changing process of the micro service A.
For example, if the current change value is 82, 82 > 75, belonging to the first change value interval, indicating that the stability and reliability of the new version of the service instance of micro service a is equivalent to the old version, an evaluation suggestion may be given to continue the execution of the change procedure on micro service a. If the current change value is 70 and is between 50 and 75, and belongs to a second change value interval, the new version of the service instance of the micro service A is possibly problematic, but system erroneous judgment cannot be excluded, and an evaluation suggestion for suggesting whether the manual determination of the change process is normal can be given; if the current change value is 48, 48 < 50, and belongs to the third change value interval, the new version of the service instance of the micro service A has obvious problems or defects compared with the old version, and evaluation advice for returning to the pre-change state of the micro service A can be given.
In the changing process of the micro service A, the changing value of the changing process of the micro service A can be adjusted once every set time length according to the acquired monitoring data of a plurality of dimensions. The set time period may be 5 minutes or 10 minutes.
The generated assessment advice may be presented to the user via a display screen of the electronic device. For example, as shown in FIG. 6, the assessment suggestions may be displayed in a change assessment details page. The user may decide whether to continue the change process or to fall back to the pre-change state based on the presented evaluation advice.
In some embodiments, details of the current change value and the monitoring data for each dimension may also be displayed in the change evaluation details page. For example, as shown in fig. 6, the current change value, i.e., the total score of the evaluation is 82 points, belongs to the first change value interval, and thus the displayed evaluation advice is: it is recommended to continue the change procedure. The change evaluation detail page shown in fig. 6 also displays the case of the difference value before and after the change of each dimension, wherein "pass" represents that the difference value before and after the change of the corresponding dimension is greater than or equal to the maximum difference threshold value, and "to be confirmed" represents that the difference value before and after the change of the corresponding dimension is less than the maximum difference threshold value but greater than the minimum difference threshold value, and "fail" represents that the difference value before and after the change of the corresponding dimension is less than or equal to the minimum difference threshold value. The change evaluation detail page also displays whether the log monitoring data are normal or not and the number of the unclean alarms. According to the dimension selected by the user, the change curves of the new version monitoring data and the old version monitoring data of the dimension can be displayed in the change evaluation detail page, for example, when the user selects and inquires about the dimension of the time delay of the international mobile subscriber identity module (Subscriber Identity Module, SIM) card list interface, the change curves of the new version monitoring data and the old version monitoring data of the dimension of the time delay of the SIM card list interface can be displayed at the lower right part of the change evaluation detail page, so that the user can more intuitively know the difference condition of the new version monitoring data and the old version monitoring data.
The embodiment of the application can determine the change value by automatically detecting the data in the micro-service change process in real time, so that advice is made on whether the change process should be continued or returned, but certain change is not forcedly stopped. Because each change can possibly cause fluctuation of index values in all aspects, the embodiment of the application displays the multidimensional result, so that an issuer can more timely find out the change of the index values in all aspects according to the change value of the change process and the real-time data monitoring view of all dimensions, thereby timely making correct judgment and determining to continue changing or backing.
The monitoring data of each dimension obtained by the embodiment of the application is automatically pulled, and various monitoring requirements are not required to be specially configured before the change; in addition, the embodiment of the application uniformly displays the monitoring data of each dimension, rather than monitoring the monitoring of each dimension in a scattered way, so that the complicated operation that the monitoring data of each dimension can be seen only by switching and checking the multiple systems of staff is omitted; by automatically evaluating and giving evaluation suggestions, the discontinuity and the contingency of manually evaluating are avoided, and a large amount of manpower for changing and guaranteeing the change of a complex system can be saved.
Based on the same technical concept as the method embodiment, the embodiment of the application also provides a micro-service change evaluation device which can be applied to electronic equipment for evaluating a micro-service change process. The micro-service change evaluation device can be used for realizing the functions of the micro-service change evaluation method embodiment, so that the beneficial effects of the micro-service change evaluation method embodiment can be realized.
In some embodiments, as shown in fig. 7, a micro service change evaluation apparatus 700 provided in an embodiment of the present application may include a data acquisition unit 701, a data processing unit 702, and a result display unit 703. The micro-service change assessment device 700 is used to implement the functionality described above in the method embodiment shown in fig. 2. When the micro-service change evaluation device 700 is used to implement the functionality of the method embodiment shown in fig. 2: the data acquisition unit 701 may be used to perform S201, the data processing unit 702 may be used to perform S202, and the result presentation unit 703 may be used to perform S203 and S204. Such as: a data acquisition unit 701, configured to acquire monitoring data of N dimensions of a micro service; a data processing unit 702, configured to determine a change value of a change process of the micro service according to the monitoring data of the N dimensions; a result display unit 703, configured to find, in a mapping relationship between preset different change value intervals and different evaluation suggestions, an evaluation suggestion corresponding to a change value interval to which the determined change value belongs, as an evaluation suggestion for a change process of the micro service, and display the evaluation suggestion for the change process of the micro service; the evaluation advice includes any one of the following: continuing to execute the changing process on the micro-service, and suggesting to manually determine whether the changing process is normal or is returned to the state before the micro-service is changed.
In one possible implementation, the data processing unit 702 is specifically configured to: according to the monitoring data of the ith dimension, determining the difference values before and after the change of the ith dimension, and sequentially taking positive integers from 1 to N through i to obtain the difference values before and after the change respectively corresponding to the N dimensions; and determining a change value of the change process of the micro service according to the difference values before and after the change corresponding to the N dimensions.
In one possible implementation manner, the micro-service changing process refers to a process of gradually migrating a service request received by the micro-service from an old version to a new version of each service instance; the data processing unit 702 is specifically configured to: and determining the difference value before and after the change of the ith dimension according to the old version monitoring data of the service instance contained in the micro service in the ith dimension and the new version monitoring data of the service instance contained in the micro service in the ith dimension.
In one possible implementation, the data processing unit 702 is specifically configured to: and determining the difference value before and after the change of the ith dimension according to the average value of the old version monitoring data of the service instance contained in the micro service in the ith dimension and the average value of the new version monitoring data of the service instance contained in the micro service in the ith dimension.
In one possible implementation, the N-dimensional monitoring data includes some or all of the following: interface index data of each designated interface of the micro service; container index data for each container of the microservice; log monitoring data during micro-service changes.
In one possible implementation, the monitoring data further includes alert data for the micro-service; the data processing unit 702 is further configured to: if the alarm data is monitored, the change value of the change process of the micro service is reduced by a set gradient value.
Based on the same inventive concept as the above-described method embodiment, an electronic device is also provided in the embodiment of the present application, where the electronic device may be the central server in fig. 1 or an electronic device 100 connected to the central server for evaluating the micro-service change procedure. The electronic device may be used to implement the functions of the method embodiment shown in fig. 2, so that the beneficial effects of the method embodiment described above may be implemented.
In some embodiments, the electronic device 800 may be configured as shown in fig. 8, including a processor 801 and a memory 802 coupled to the processor 801. The processor 801 and the memory 802 may be connected to each other via a bus. The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, or the like. The buses may be divided into address buses, data buses, control buses, etc.
The memory 802 is used for storing instructions or programs executed by the processor 801, or input data required for the processor 801 to execute the instructions or programs, or data generated after the processor 801 executes the instructions or programs. The processor 801 may be a general purpose processor such as a microprocessor, or other conventional processor. The processor 801 may include one or more processing units, and the different processing units may be separate devices or may be integrated into one or more processors. The processor 801 may further include a controller, which may generate operation control signals according to the instruction operation code and the timing signals, to complete instruction fetching and instruction execution control.
In one embodiment, the processor 801 in the electronic device 800 is configured to execute computer instructions or programs stored in the memory 802 to perform the functions of the method embodiment shown in FIG. 2. When the electronic device 800 is used to implement the method shown in fig. 2, the processor 801 is configured to: in the changing process of the micro service, acquiring N-dimensional monitoring data of the micro service, determining a changing value of the changing process of the micro service according to the N-dimensional monitoring data, searching an evaluation suggestion corresponding to the changing value interval of the determined changing value in a mapping relation between preset different changing value intervals and different evaluation suggestions, serving as an evaluation suggestion for the changing process of the micro service, and displaying the evaluation suggestion for the changing process of the micro service; the evaluation advice includes any one of the following: continuing to execute the changing process on the micro-service, and suggesting to manually determine whether the changing process is normal or is returned to the state before the micro-service is changed.
In one embodiment, the electronic device 800 may further comprise a communication unit for obtaining monitoring data from other devices under the control of the processor 801. A communication unit may be understood as a transceiver or a data transceiver interface, and a plurality of transceivers may be included in a communication unit of the electronic device 800, and different transceivers may be connected to different devices.
It should be understood that the architecture illustrated in the embodiments of the present application does not constitute a specific limitation on the switch. In other embodiments of the application, the switch may include more or less components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The steps of the method in the embodiments of the present application may be implemented by means of hardware, or may be implemented by means of a processor executing a computer program or instructions. The computer program or instructions may constitute a computer program product. Embodiments of the present application also provide a computer program product comprising computer-executable instructions. In one embodiment, the computer-executable instructions are for causing a computer to perform the functions of the method embodiment shown in FIG. 2.
The computer executable instructions may be stored in a computer readable storage medium, and embodiments of the present application also provide a computer readable storage medium having the executable instructions stored therein. In one embodiment, the computer-executable instructions are for causing a computer to perform the functions of the method embodiment shown in FIG. 2.
The computer readable storage medium provided by embodiments of the present application may be a random access memory (random access memory, RAM), a flash memory, a read-only memory (ROM), a programmable read-only memory (programmableROM, PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a register, a hard disk, a removable hard disk, a CD-ROM, or any other form of computer readable storage medium known in the art.
The computer-executable instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; optical media, such as digital video discs (digital video disc, DVD); but also semiconductor media such as solid state disks.
In various embodiments of the application, where no special description or logic conflict exists, terms and/or descriptions between the various embodiments are consistent and may reference each other, and features of the various embodiments may be combined to form new embodiments based on their inherent logic. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such as a series of steps or elements. The method, system, article, or apparatus is not necessarily limited to those explicitly listed but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
Although the application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary of the arrangements defined in the appended claims and are to be construed as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is also intended to include such modifications and variations.

Claims (11)

1. A method for evaluating a micro-service change, comprising:
acquiring monitoring data of N dimensions of the micro service in the micro service changing process;
determining a change value of the change process of the micro service according to the monitoring data of the N dimensions;
searching an evaluation suggestion corresponding to the determined change value interval to which the change value belongs in a preset mapping relation between different change value intervals and different evaluation suggestions, serving as an evaluation suggestion for the change process of the micro service, and displaying the searched evaluation suggestion;
the evaluation advice includes any one of the following: and continuing to execute the changing process on the micro-service, and suggesting to manually determine whether the changing process is normal or is returned to the state before the micro-service is changed.
2. The method of claim 1, wherein the N-dimensional monitoring data comprises some or all of:
interface index data of each designated interface of the micro service;
container index data for each container of the microservice;
and the log monitoring data in the micro-service change process.
3. The method according to claim 1 or 2, wherein determining a change value of the change procedure of the micro service according to the monitoring data of the N dimensions comprises:
For the ith dimension, determining a difference value before and after changing of the ith dimension according to the monitoring data of the ith dimension; the i takes positive integers from 1 to N;
and adjusting the current change value of the change process of the micro service according to the difference values before and after the change corresponding to the ith dimension.
4. A method according to claim 3, wherein the change procedure of the micro service refers to a procedure of gradually migrating a service request received by the micro service from an old version to a new version of each service instance;
the determining the difference value before and after the change of the ith dimension according to the monitoring data of the ith dimension comprises the following steps:
and determining the difference value before and after the change of the ith dimension according to the old version monitoring data of the service instance contained in the micro service in the ith dimension and the new version monitoring data of the service instance contained in the micro service in the ith dimension.
5. The method of claim 4, wherein determining the i-th dimension pre-change and post-change difference values based on old version monitoring data of the service instance included in the micro-service in the i-th dimension and new version monitoring data of the service instance included in the micro-service in the i-th dimension comprises:
And determining the difference value before and after the change of the ith dimension according to the average value of the old version monitoring data of the service instance contained in the micro service in the ith dimension and the average value of the new version monitoring data of the service instance contained in the micro service in the ith dimension.
6. The method of claim 5, wherein the average value of the new version of the monitoring data is an average value of each new version of the monitoring data contained in the monitoring data of the ith dimension;
the average value of the old version monitoring data is the average value of all the old version monitoring data contained in the monitoring data of the ith dimension, or the average value of the old version monitoring data of all the service instances of the ith dimension in a set historical time period.
7. The method according to any one of claims 3 to 5, wherein adjusting the current change value of the micro-service change process according to the difference values before and after the change corresponding to the ith dimension, includes:
if the difference value before and after the change of the ith dimension is greater than or equal to the maximum difference threshold value corresponding to the ith dimension, increasing the current change value of the change process of the micro service by a set gradient value;
And if the difference value before and after the change of the ith dimension is smaller than or equal to the minimum difference threshold value corresponding to the ith dimension, reducing the current change value of the change process of the micro service by the set gradient value.
8. The method according to any one of claims 2 to 7, wherein the N-dimensional monitoring data further comprises alert data for a micro-service; the determining the change value of the change process of the micro service according to the monitoring data of the N dimensions further comprises:
and if the alarm data is monitored, reducing the current change value of the change process of the micro service by a set gradient value.
9. An electronic device comprising a memory and a processor;
the memory stores computer program instructions;
the processor being configured to execute the computer program instructions to implement the method of any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that computer-executable instructions for causing a computer to perform the method according to any one of claims 1-8 are stored.
11. A computer program product comprising computer executable instructions for causing a computer to perform the method of any one of claims 1 to 8.
CN202210328183.4A 2022-03-30 2022-03-30 Micro-service change evaluation method, electronic device, and storage medium Pending CN116932361A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210328183.4A CN116932361A (en) 2022-03-30 2022-03-30 Micro-service change evaluation method, electronic device, and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210328183.4A CN116932361A (en) 2022-03-30 2022-03-30 Micro-service change evaluation method, electronic device, and storage medium

Publications (1)

Publication Number Publication Date
CN116932361A true CN116932361A (en) 2023-10-24

Family

ID=88375919

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210328183.4A Pending CN116932361A (en) 2022-03-30 2022-03-30 Micro-service change evaluation method, electronic device, and storage medium

Country Status (1)

Country Link
CN (1) CN116932361A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117495173A (en) * 2023-11-03 2024-02-02 睿智合创(北京)科技有限公司 Foreground data monitoring method and system for grading upgrading switching data information

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117495173A (en) * 2023-11-03 2024-02-02 睿智合创(北京)科技有限公司 Foreground data monitoring method and system for grading upgrading switching data information
CN117495173B (en) * 2023-11-03 2024-09-24 睿智合创(北京)科技有限公司 Foreground data monitoring method and system for grading upgrading switching data information

Similar Documents

Publication Publication Date Title
US8024613B2 (en) Method and system for managing apparatus performance
CN105830037A (en) Process for displaying test coverage data during code reviews
US11032627B2 (en) Maintenance device, presentation system, and program
CN111400189A (en) Code coverage rate monitoring method and device, electronic equipment and storage medium
CN109871368B (en) Database detection method, database detection device, computer device and storage medium
CN111754123A (en) Data monitoring method and device, computer equipment and storage medium
CN107480703B (en) Transaction fault detection method and device
CN110018932B (en) Method and device for monitoring container magnetic disk
CN116932361A (en) Micro-service change evaluation method, electronic device, and storage medium
CN111897706A (en) Server performance prediction method, device, computer system and medium
CN112135287A (en) Bluetooth data transmission rate test method, system, electronic device and storage medium
CN112032031B (en) Cloud data analysis method, device and system for air compressor
CN115499302A (en) Monitoring method and device of business system, readable storage medium and electronic equipment
CN115147236A (en) Processing method, processing device and electronic equipment
CN111506455B (en) Checking method and device for service release result
CN113760672A (en) Performance test method, equipment and storage medium for multi-cloud platform resources
CN112053009A (en) Fault prediction method, device, system and storage medium
CN115858291A (en) System index detection method and device, electronic equipment and storage medium thereof
CN115509853A (en) Cluster data anomaly detection method and electronic equipment
CN115344495A (en) Data analysis method and device for batch task test, computer equipment and medium
CN114063881A (en) Disk management method and device of distributed system
CN110647533A (en) Data monitoring method, system and storage medium for structure monitoring system
CN118277085A (en) Information acquisition method and computing device
CN115168095A (en) Method, device and medium for collecting storage management software task information
CN116755996A (en) Performance test method, device, computer readable storage medium and electronic equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication