CN117076543A - Performance measurement method and device, cloud native platform and computer equipment - Google Patents

Abstract

The application relates to a performance measurement method, a performance measurement device, a cloud native platform and computer equipment. The method is applied to a cloud native platform, the cloud native platform is used for measuring the efficiency of at least one platform to be measured, and platform data of the platform to be measured are obtained by obtaining configuration files corresponding to the platform to be measured; carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data; establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data; and transporting the target measurement data according to the target performance measurement model to obtain performance measurement data. The performance measurement method provided by the application can enable the acquired platform data to be more complete, thereby obtaining more accurate performance measurement data.

Description

Performance measurement method and device, cloud native platform and computer equipment

Technical Field

The present application relates to the field of computer technologies, and in particular, to a performance measurement method and apparatus, a cloud native platform, and a computer device.

Background

Performance is a more efficient, high quality, reliable, sustainable capability to interact with better business value, a process abstraction for research and development operations, and research and development efficiency metrics and evaluations. Metrics are the data products of efficacy practices. In the research and development process of each enterprise, performance index analysis by acquiring performance metric data is very important for the enterprise.

In the conventional technology, performance measurement data is determined by performing performance measurement data analysis through micro-server of platform as a service (Platform as a Service, paas) platform performance measurement and construction of a performance measurement index system.

However, the data obtained when the conventional technology performs the performance metric data analysis through the Paas platform is scattered, resulting in inaccurate performance metric data obtained finally.

Disclosure of Invention

Based on this, it is necessary to provide a performance measurement method, device, cloud native platform and computer equipment, which can improve the integration and integrity of the acquired data, so that the obtained performance measurement data is more accurate.

In a first aspect, the present application provides a performance measurement method applied to a cloud native platform, where the cloud native platform is used for measuring performance of at least one platform to be measured, the method includes:

acquiring a configuration file corresponding to a platform to be measured, and acquiring platform data of the platform to be measured according to the configuration file;

carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data;

And calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data.

In one embodiment, obtaining platform data of a platform to be measured according to a configuration file includes:

generating a data interface corresponding to the platform to be measured according to the configuration file;

platform data of the platform to be measured are obtained through the data interface.

In one embodiment, the method further comprises:

monitoring whether a new configuration file exists;

and if the new configuration file exists, acquiring new platform data of the measurement platform corresponding to the new configuration file according to the new configuration file.

In one embodiment, the formatted metrology data includes metrology data of multiple dimensions; establishing association relation on a plurality of dimensions for the formatted measurement data to obtain target measurement data, wherein the method comprises the following steps:

establishing an initial association relation between the measurement data of each dimension in the formatted measurement data according to the formatted measurement data;

and correcting the initial association relationship to obtain target measurement data.

In one embodiment, correcting the initial association relationship to obtain target metric data includes:

obtaining correction metric data; the correction measurement data is used for determining the association degree of the initial association relation;

If the association degree does not reach the preset association threshold, correcting the initial association relation according to the correction measurement data to obtain a target association relation;

and determining target measurement data according to the target association relationship and measurement data corresponding to the target association relationship.

In one embodiment, the target performance metric model includes a plurality of stream execution operators, and the computing the target performance metric data according to the target performance metric model to obtain the performance metric data includes:

and based on the container cluster management system, performing batch processing on the target measurement data by using each flow execution operator to obtain performance measurement data.

In one embodiment, the method further comprises:

acquiring identity information of a user;

and acquiring target performance measurement data corresponding to the identity information according to the identity information, and displaying the target performance measurement data.

In a second aspect, an embodiment of the present application provides a cloud native platform, including: a data collector, a protocol engine and a model engine;

the data collector is used for acquiring platform data of the platform to be measured according to a configuration file corresponding to the platform to be measured, which is connected with the cloud native platform, and sending the platform data to the protocol engine;

The protocol engine is used for carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement quantity, and establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data; and sending the target metric data to the model engine;

and the model engine is used for calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data.

In a third aspect, an embodiment of the present application provides a performance measurement apparatus applied to a cloud native platform, where the cloud native platform is used to measure performance of at least one platform to be measured, the apparatus includes:

the acquisition module is used for acquiring a configuration file corresponding to the platform to be measured and acquiring platform data of the platform to be measured according to the configuration file;

the processing module is used for carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

the dimension building module is used for building association relations on a plurality of dimensions for the formatted measurement data to obtain target measurement data;

and the determining module is used for calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data.

In a fourth aspect, an embodiment of the application provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method as provided in the first aspect above when the computer program is executed by the processor.

The performance measurement method, the device, the cloud native platform and the computer equipment are applied to the cloud native platform, and the performance of at least one platform to be measured can be measured through the cloud native platform in the computer equipment. The method comprises the steps of obtaining a configuration file corresponding to a platform to be measured, and obtaining platform data of the platform to be measured according to the configuration file; carrying out formatting unified processing on the platform data according to a preset standard protocol to obtain formatting measurement data; establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data; and calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data. In this embodiment, the target measurement data is obtained by establishing an association relationship between multi-dimensional measurement data in the formatted measurement data, so that the obtained target measurement data can be prevented from being scattered, the accuracy of performance measurement data obtained by using the target measurement data can be improved, and the performance of the platform to be measured can be more objectively represented.

Drawings

FIG. 1 is a schematic diagram of a computer device in one embodiment;

FIG. 2 is a flow chart illustrating steps of a performance metric method according to one embodiment;

FIG. 3 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 4 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 5 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 6 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 7 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 8 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 9 is a flowchart illustrating a performance measurement method according to another embodiment;

FIG. 10 is a schematic diagram of a cloud native platform in an embodiment;

FIG. 11 is a schematic diagram of a performance metric service platform based on a cloud native platform according to an embodiment;

FIG. 12 is a diagram illustrating multiple dimensions corresponding to platform data in one embodiment;

FIG. 13 is a schematic diagram of a performance metric service architecture based on a cloud native platform in one embodiment;

FIG. 14 is a schematic diagram of a performance measurement apparatus according to one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

Before the technical scheme of the embodiment of the application is specifically introduced, the technical background and technical evolution context based on the embodiment of the application are introduced. Performance is a more efficient, high quality, reliable, sustainable capability to interact with better business value, a process abstraction for research and development operations, and research and development efficiency metrics and evaluations. Metrics are the data products of efficacy practices. In the research and development process of each enterprise, performance index analysis by acquiring performance metric data is very important for the enterprise. The efficiency analysis and the efficiency practice are combined to complement each other, so that an effective efficiency data operation system is realized, and the improvement and improvement of the research and development efficiency of enterprises are facilitated.

In the conventional art, several stages of development of performance metrics include: the first stage is manual self-made self-drawing, i.e. by manually recording and storing basic performance metric data, and using a computing tool or self-made application program to form a simple data report and data analysis. The second stage is the intelligent digital application, namely the intelligent digital application is built through a DevOps platform, the basic setting of the measurement index is realized, the data source and the acquisition tool to be acquired are required to be arranged in a binding mode, and the statistics is performed by applying a simple data model. However, in the conventional technology, the obtained data are scattered and incomplete when the performance measurement data are analyzed, so that the finally obtained performance measurement data are inaccurate, and the performance of the enterprise cannot be objectively represented. In this regard, the present application provides a performance metric method.

The technical scheme related to the embodiment of the application is described below in connection with the scene to which the embodiment of the application is applied.

The performance measurement method provided by the application can be applied to computer equipment, and the internal structure of the computer equipment can be shown in figure 1. The computer equipment comprises a processor, a memory, a communication interface, a display screen and an input device which are connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a performance metric method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

In one embodiment, as shown in fig. 2, a performance measurement method is provided, where the method is applied to a computer device for illustration, it is understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server.

The cloud native platform is built in the computer equipment, the performance measurement method is applied to the cloud native platform, and the performance of at least one platform to be measured can be measured through the cloud native platform in the computer equipment. The cloud native platform in the computer equipment can be connected with one platform to be measured or a plurality of platforms to be measured to realize the measurement of the efficiency of the platform to be measured. Different metrology platforms may correspond to different data sources, which may include Jenkins, azure and Extra. That is, the cloud native platform can acquire data in different data sources, so as to implement measurement of the performance of the platform to be measured corresponding to the data sources.

In this embodiment, the method includes the steps of:

step 200, acquiring a configuration file corresponding to the platform to be measured, and acquiring platform data of the platform to be measured according to the configuration file.

The configuration file corresponding to the platform to be measured refers to the information corresponding to the data source in the platform to be measured, namely the setting and the file of the environment required when the computer equipment needs to acquire the platform data in the platform to be measured. The configuration file corresponding to the platform to be measured can be input by a user and stored in the computer equipment, or can be generated through CICD and sent to the computer equipment. Wherein CICD is a short for continuous integration (Continuous Integration) and continuous deployment (Continuous Deployment). The specific method for obtaining the configuration file corresponding to the platform to be measured is not limited in this embodiment, as long as the function can be realized.

In an alternative embodiment, assuming that the data source of the platform to be measured is Azure, the configuration file may include the name of the platform to be measured: azure; path of platform to be measured: http:// azure.com, the name of the data source interface of the platform to be measured, and at least one piece of platform data information which is required to be acquired.

After the computer equipment obtains the configuration file of the platform to be measured, the platform data of the platform to be measured, which refers to the data of the platform to be measured, which needs to be measured in performance, can be obtained according to the information in the configuration file.

And 210, carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data.

The preset standard protocol is a standard protocol which is stored in the computer equipment in advance and is required when platform data are processed. The standard protocol may include a standard format after the platform data is required to be formatted and uniformly processed. The specific content of the preset standard protocol is not limited in this embodiment as long as the function thereof can be realized. Optionally, the preset standard protocol is a protocol in Metrics (standard protocol library).

After the computer equipment acquires the platform data of the platform to be measured, the platform data is formatted and uniformly processed by using a preset standard protocol. That is, the computer device extracts the key information from the platform data acquired from the platform to be measured, and sets the extracted key information to a standard format prescribed in a preset standard protocol, so that the formatted measurement data can be obtained.

In an alternative embodiment, the preset standard protocol includes definition, collection mode, classification, priority, health degree and the like of the platform data, and the computer equipment marks and processes the platform data according to the definition, collection mode, classification, priority and health degree to obtain formatted measurement data.

And 220, establishing association relations among the formatted measurement data in multiple dimensions to obtain target measurement data.

After obtaining the formatted measurement data, the computer equipment establishes association relations on the formatted measurement data in multiple dimensions to obtain target measurement data. The platform data acquired by the computer equipment from the platform to be measured comprise multi-dimensional data, and the characteristics or attributes of the data in different dimensions are different. And the computer equipment establishes association relations among the formatted measurement data of different characteristics or different attributes to obtain target measurement data.

Step 230, calculating the target performance metric data according to the target performance metric model to obtain the performance metric data.

The target performance metric model may be pre-stored in the computer device. After obtaining the target measurement data, the computer equipment uses the target performance measurement model to calculate the target measurement data, so that the performance measurement data can be obtained. The present embodiment is not limited to the kind and structure of the target performance metric model, as long as the calculation can be performed on the target metric data.

In an alternative embodiment, different performance metrics may be associated with different performance metrics, different types of metrics may be associated with different performance metrics, or different functional metrics may be associated with different performance metrics. Assuming that the metric data is used to predict risk, the corresponding performance metric model is a prediction model; the metric data is used to determine product quality, and the corresponding performance metric model is a quality assessment model. The corresponding relation between the measurement data and the performance measurement model is prestored in the computer equipment. After obtaining the target measurement data, the computer equipment determines a target performance measurement model according to the target measurement data and the corresponding relation between the measurement data and the performance measurement model.

In an alternative embodiment, the performance metric model database is stored in the computer device, and after obtaining the target metric data, the computer device may search the performance metric model database for a performance metric model capable of operating on the target metric data, that is, a target performance metric model, according to the target metric data.

In an alternative embodiment, the target performance metric model may be any one of a deep learning network model, a machine learning model, a neural network, and an AI model.

The performance measurement method provided by the embodiment of the application is applied to the cloud native platform, and the cloud native platform is used for measuring the performance of at least one platform to be measured. The method comprises the steps of obtaining a configuration file corresponding to a platform to be measured, and obtaining platform data of the platform to be measured according to the configuration file; carrying out formatting unified processing on the platform data according to a preset standard protocol to obtain formatting measurement data; establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data; and calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data. In this embodiment, the target measurement data is obtained by establishing an association relationship between multi-dimensional measurement data in the formatted measurement data, so that the obtained target measurement data can be prevented from being scattered, the accuracy of performance measurement data obtained by using the target measurement data can be improved, and the performance of the platform to be measured can be more objectively represented.

In addition, in this embodiment, the platform data of the platform to be measured can be obtained through the configuration file corresponding to the platform to be measured, so that the cloud native platform and the platform to be measured do not need to be bound, and only the configuration file of the platform to be measured is needed in the cloud native platform. Thus, the efficiency of acquiring the platform data of the platform to be measured can be improved. In addition, in the embodiment, the platform data can be converted into the formatted measurement data with the same format by carrying out the formatting unified processing on the platform data according to the preset standard protocol, so that the subsequent processing on the formatted measurement data is convenient, and the efficiency of determining the performance measurement data can be improved.

In an alternative embodiment, the performance metric data obtained by the computer device is visual data, that is, the association relationship between the metric data of each dimension in the target metric data is shown by various schematic diagrams, for example, a histogram, and the like.

In another alternative embodiment, after obtaining the performance metric data, the computer device may perform trend analysis, comparison file, correlation analysis, detail analysis, etc. through the performance metric data to form an analysis conclusion; the risk prediction and anomaly identification can be performed through the performance measurement data, so that the problems in the platform to be measured can be found in time.

In one embodiment, after obtaining a configuration file corresponding to a platform to be measured, as shown in fig. 3, a computer device is related to an implementation manner of obtaining platform data of the platform to be measured according to the configuration file, and the steps of the implementation manner include:

and 300, generating a data interface corresponding to the platform to be measured according to the configuration file.

After the computer equipment obtains the configuration file of the platform to be measured, a data interface corresponding to the platform to be measured is generated according to the information in the configuration file, namely an application program (Application Program Interface, API) interface capable of carrying out data transmission between the platform to be measured and the cloud native platform of the computer equipment.

In an alternative embodiment, if the configuration file includes a data interface corresponding to the platform to be measured, that is, the platform to be measured has an API interface for developing external access, the data interface in the configuration file is directly obtained, that is, the data interface corresponding to the platform to be measured.

In an alternative embodiment, the platform to be measured is provided with a file server or a database, and the computer device may obtain the platform data from the file server or the database through an edge (Sidecar) service. The marginalization service may be integrated in a cloud native platform of the computer device, or deployed independently, and further integrated in the same K8S (container cluster management system) cluster with the cloud native platform.

Step 310, obtaining platform data of the platform to be measured through a data interface.

After the computer equipment obtains the data interface, the data can be obtained from the platform to be measured through the data interface, namely the platform data of the platform to be measured.

In this embodiment, a data interface generated according to a configuration file of a platform to be measured can establish communication between the platform to be measured and a cloud native platform in computer equipment, and platform data of the platform to be measured can be obtained through the data interface, so that efficiency of obtaining the platform to be measured can be improved.

In one embodiment, as shown in fig. 4, the steps of the performance metric method further include:

step 400, monitoring whether a new configuration file exists.

During operation, the computer device monitors whether a new configuration file exists. The new configuration file corresponds to the new metrology platform.

If the computer device does not monitor the new configuration file, continuing to monitor.

In an alternative embodiment, the computer device may periodically monitor whether a new profile exists or may monitor whether a new profile exists in real time.

Step 410, if a new configuration file exists, acquiring new platform data of the measurement platform corresponding to the new configuration file according to the new configuration file.

If the computer equipment monitors that the new configuration file exists, and the fact that the performance measurement is needed for the measurement platform corresponding to the new configuration file is indicated, the computer equipment acquires platform data of the new measurement platform corresponding to the new configuration file according to the acquired new configuration file.

In an alternative embodiment, the computer device generates a new data interface corresponding to the new metrology platform according to the new configuration file; platform data of the new measurement platform are acquired through the new data interface.

In another alternative embodiment, the computer device may obtain a new configuration file corresponding to the new metrology platform in response to a triggering operation of the user, and obtain new platform data according to the new configuration file. That is, the user inputs a new platform access request to the computer device, which responds to the access request to obtain new platform data. The name of the new measurement platform can be input by the user, and the new configuration file corresponding to the new measurement platform can also be input by the user. If the name of the new measurement platform is input by the user, the computer equipment acquires a corresponding new configuration file according to the name of the new measurement platform.

In this embodiment, when it is determined that a new configuration file is acquired through real-time monitoring, platform data of a new measurement platform corresponding to the new configuration file can be acquired according to the new configuration file. That is, the cloud native platform in the embodiment can simultaneously acquire platform data of a plurality of measurement platforms, so as to evaluate the performances of the plurality of measurement platforms, thereby improving the practicability and reliability of the performance measurement method.

In one embodiment, as shown in fig. 5, an implementation manner of performing format unification processing on platform data according to a preset standard protocol to obtain formatted metric data is related to the steps of the implementation manner include:

step 500, obtaining a standard format corresponding to a preset standard protocol; the standard format includes a time sequence, a metric field, and a tag field.

When the computer equipment needs to carry out formatting processing on the platform data, the corresponding standard format is determined according to a preset standard protocol. The standard format corresponding to the preset standard protocol comprises a time sequence, a measurement field and a label field. The time sequence represents an event corresponding to the platform data, the measurement field represents key information of the platform data, and the label field represents a label corresponding to the key information of the platform data.

And 510, carrying out formatting unified processing on the platform data according to the standard format to obtain the formatting measurement data.

After determining the standard format, the computer equipment performs formatting unified processing on the platform data according to the standard format, that is, converts the acquired format of the platform data into a format of the standard format, and obtains formatting measurement data.

In an alternative embodiment, the number of defects is 5 when the obtained platform data is used for testing the application program in a certain time period; the platform data is formatted according to a standard format corresponding to a preset standard protocol, and then the formatted measurement data is formatted as follows: for a certain period of time, the defect times: 5 times; wherein the time sequence is a certain time period, the tag field is the defect number, and the measurement field is 5 times.

In this embodiment, the platform data is formatted and uniformly processed according to a standard format corresponding to a preset standard protocol, so that the platform data of the platform to be measured are stored in the same format, and subsequent processing of the formatted measurement data is facilitated, so that efficiency of determining the performance measurement data can be improved.

In one embodiment, the formatted metric data includes metric data of multiple dimensions. The data interfaces corresponding to the platform to be measured generated according to the configuration file can be multiple, and measurement data of one dimension of the platform to be measured can be obtained through each data interface, so that platform data of multiple dimensions of the platform to be measured can be obtained, and the obtained formatted measurement data also comprises the measurement data of the multiple dimensions through carrying out format unified processing on the platform data.

In this case, as shown in fig. 6, an implementation manner related to establishing association relation on a plurality of dimensions for formatted metric data to obtain target metric data includes the following steps:

step 600, establishing an initial association relation between the measurement data of each dimension in the formatted measurement data according to the formatted measurement data.

If there may be an association between the plurality of dimensions of the formatted metrology data, the computer device establishes an initial association between the plurality of dimensions of the formatted metrology data after obtaining the formatted metrology data. The specific method for establishing the initial association relationship is not limited in this embodiment, as long as the function thereof can be realized.

In an alternative embodiment, the computer device may choose to format any two-dimensional metrology data in the metrology data assuming that there is an association between the two-dimensional metrology data, i.e., an association between the two-dimensional metrology data is established.

In an alternative embodiment, assuming that the data source in the platform to be measured is related data when the developer develops the application program, the obtained platform data of the platform to be measured may include the developer of the application program, the development time of the application program, the tester of the application program, the test times of the application program, the defect rate of the application program, the online time of the application program, and the like. The development process of the application program and the application program test process are measurement data of two dimensions. The initial association relationship established by the computer device may be a relationship between a developer of the application program and the number of tests of the application program, a development time of the application program, a tester of the application program, and the like.

And 610, correcting the initial association relationship to obtain target measurement data.

After obtaining the initial association relation between the measurement data of each dimension in the formatted measurement data, the computer equipment corrects the initial association relation, so that the target measurement data can be obtained. That is, whether the established initial association is accurate is detected according to the formatting metric data; if the initial association relation is detected to be accurate, determining the initial association relation and the measurement data corresponding to the initial association relation as target measurement data; if the initial association relation is detected to be inaccurate, the initial association relation is corrected, the corrected initial association relation is determined, and the measurement data corresponding to the corrected initial association relation is determined to be the target measurement data. The correcting the initial association relationship may be correcting the initial association relationship by using measurement data of other dimensions except the measurement data corresponding to the initial association relationship in the formatted measurement data; the measurement data obtained from other measurement platforms after unified processing through the format can also be used. The specific method of correcting the initial association relation is not limited in this embodiment, as long as the function thereof can be realized.

In this embodiment, by correcting the initial association relationship between the measurement data of each dimension in the formatted measurement data, the target measurement data can be obtained, so that more accurate target measurement data can be obtained, and more accurate performance measurement data can be obtained.

In one embodiment, as shown in fig. 7, an implementation manner related to correcting an initial association relation according to formatted metric data to obtain target metric data includes the following steps:

and 700, acquiring correction metric data, wherein the correction metric data is used for determining the association degree of the initial association relation.

The computer device obtains correction metric data. The correction metric data may be metric data of other dimensions except the metric data corresponding to the initial association relationship in the formatted metric data, or metric data in new formatted metric data obtained from the platform data obtained from the new metric platform after the platform data is subjected to format unified processing. The present embodiment is not limited to a specific method of acquiring correction metric data as long as the functions thereof can be realized.

After obtaining the correction measurement data, the computer equipment calculates the association degree between the dimensions in the initial association relation according to the correction measurement data. The association degree of the initial association relationship is used for representing the probability of the association relationship between the measurement data of each dimension in the initial association relationship.

Storing a preset association threshold in computer equipment, and comparing the association degree with the preset association threshold after the computer equipment obtains the association degree of the initial association relation; if the association degree exceeds the preset association threshold, the initial association relationship is accurate, namely the established initial association relationship among the measurement data of each dimension is accurate.

And 710, if the association degree does not reach the preset association threshold, correcting the initial association relationship according to the correction measurement data to obtain the target association relationship.

If the computer equipment determines that the association degree of the initial association relation does not reach the preset association threshold value, and the initial association relation is inaccurate, the initial association relation is corrected by using correction measurement data, and the target association relation is obtained.

And correcting the initial association relation according to the corrected measurement data, namely establishing the association relation between the corrected measurement data and measurement data with higher association degree in the initial association relation to obtain target measurement data.

Step 720, determining target measurement data according to the target association relationship and the measurement data corresponding to the target association relationship.

After the computer equipment obtains the target association relationship, the target association relationship and the measurement data corresponding to the target association relationship are determined to be target measurement data.

In this embodiment, the association degree of the initial association relationship is determined according to the correction metric data, and the initial association relationship is corrected according to the correction metric data to obtain the target association relationship when the association degree does not reach the preset association threshold, so that the target metric data can be obtained. Thus, the accuracy of the finally obtained target measurement data can be ensured, and the accuracy of the finally determined efficiency measurement data can be further improved.

In an alternative embodiment, the platform to be measured comprises a plurality of data interfaces, the platform data acquired from the first data interface is item data, and the platform data acquired from the second data interface is product data; carrying out formatting unified processing on the acquired platform data according to a preset standard protocol to obtain formatted project measurement data and formatted product measurement data; establishing an initial association between the formatted project measurement data and the formatted product measurement data; the platform data acquired from the third data interface is personnel data, and the platform data is subjected to formatting unified processing to obtain formatted personnel measurement data; and correcting the initial association relation by taking the formatted personnel measurement data as correction measurement data. Assuming that the established initial association relationship is a relationship between the formatted project metric data and the product A in the formatted product metric data; a relationship between the formatted project metric data and person C; and determining that a person C is responsible for the product B corresponding to the formatted product measurement data according to the formatted person measurement data, and considering that the product B corresponding to the formatted item measurement data is the product B in the formatted product measurement data, namely, the corrected association relationship (target association relationship) is the relationship between the formatted item measurement data and the product B in the formatted product measurement data.

In one embodiment, the target performance metric model includes a plurality of stream execution operators, and involves an implementation of computing target performance metric data according to the target performance metric model to obtain performance metric data, the implementation including:

and based on the container cluster management system, performing batch processing on the target measurement data by using each flow execution operator to obtain performance measurement data.

The container cluster management system (K8S) is an application for automatically deploying, expanding and managing containerization. A container cluster management system is deployed in a computer device. After obtaining the target measurement data, the computer equipment inputs the target measurement data into each flow type execution operator in the target efficiency measurement model, and each flow type execution operator can process the received target measurement data simultaneously to obtain the efficiency measurement data.

In this embodiment, the target metric data is batched by using multiple stream execution operators in the target performance model, so that the efficiency and accuracy of processing the target metric data can be improved.

In one embodiment, as shown in fig. 8, the steps of the performance metric method further include:

step 800, obtaining identity information of a user.

When a user needs to check or operate the target measurement data, the user can input corresponding identity information and log in the cloud native platform. The computer device receives identity information entered by a user. The identity information of the user may include a user name, a user password, a user department, a user position, and the like. The specific content included in the identity information of the user is not limited in this embodiment, as long as the function thereof can be realized.

Step 810, obtaining target performance measurement data corresponding to the identity information according to the identity information, and displaying the target performance measurement data.

Different identity information corresponds to different target performance metrics, that is, different users can view different target performance metrics, and different operations can be performed on the target performance metrics.

After the computer equipment acquires the identity information of the user, determining target efficiency measurement data corresponding to the identity information according to the identity information, and displaying the target efficiency measurement data on a display screen of the computer equipment so that the user can view and operate.

In this embodiment, the target performance measurement data corresponding to the identity information is obtained and displayed according to the identity information input by the user, so that different users have different rights when viewing or operating the target performance measurement data, and the user can be ensured to only find and operate the target performance measurement data in the rights, thereby ensuring the security of the target performance measurement data.

In one embodiment, the performance metric method further comprises the steps of:

and respectively storing the platform data of the platform to be measured and the platform data of the new measurement platform into different partitions in the column-type storage database.

The computer device comprises a column-type storage database, which is different from the traditional relational database, and the data in the relational database are stored in rows in a table. The columnar store database may include HBase, clickHouse, druid and HP vertical.

After the computer equipment acquires the platform data of the platform to be measured, the platform data is stored in the column type storage database. And if the computer equipment detects that the new configuration file exists, storing new platform data acquired from the measurement platform corresponding to the new configuration file in the columnar storage database. This enables a large amount of data to be stored, and the storage efficiency can be improved. In addition, the platform data of different measurement platforms are stored in the column-type storage data in an isolated mode, namely in different partitions, so that data mixing of different measurement platforms can be avoided, and misoperation can be avoided.

In an alternative embodiment, when the computer device stores each platform data in the column database, encryption and backup processing are further performed on the platform data, so that the security, the integrity and the usability of the stored data can be ensured.

In an alternative embodiment, the columnar storage databases may be integrated in the cloud native platform or may be deployed separately as clusters. If the columnar storage database is integrated in the cloud native platform, unified management and maintenance are facilitated, and consumption of network access transmission among services can be reduced.

Referring to fig. 9, an embodiment of the present application provides a performance measurement method, which includes:

step 900, obtaining a configuration file corresponding to a platform to be measured;

step 910, generating a data interface corresponding to the platform to be measured according to the configuration file;

step 920, obtaining platform data corresponding to the platform to be measured through a data interface;

step 930, obtaining a standard format corresponding to a preset standard protocol; the standard format comprises a time sequence, a measurement field and a label field;

step 940, carrying out formatting unified processing on the platform data according to the standard format to obtain formatting measurement data;

step 950, establishing an initial association relationship between the measurement data of each dimension in the formatted measurement data according to the formatted measurement data;

step 960, determining the association degree of the initial association relation according to the acquired correction measurement data;

step 970, correcting the initial association relationship according to the correction metric data to obtain a target association relationship when the association degree does not reach a preset association threshold;

Step 980, determining target measurement data according to the target association relationship and measurement data corresponding to the target association relationship;

step 990, based on the container cluster management system, performing batch processing on the target metric data by using each flow execution operator in the target performance metric model to obtain performance metric data.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

One embodiment of the present application provides a cloud native platform, as shown in fig. 10, the cloud native platform 10 includes a data collector 11, a protocol engine 12, and a model engine 13.

The data collector 11 is configured to obtain platform data of a platform to be measured according to a configuration file corresponding to the platform to be measured connected to the cloud native platform 10, and send the platform data to the protocol engine 12.

The data collectors can be integrated in the cloud native platform or can be independently deployed into clusters. If the data collector is integrated in the cloud native platform, unified management and maintenance are facilitated, and consumption of network access transmission between services can be reduced. The data collector and the columnar storage database can be integrated under the same name space of the same cluster and the same node, so that the consumption of network access transmission between services is reduced.

The protocol engine 12 is configured to perform format unified processing on the platform data according to a preset standard protocol to obtain a formatted measurement number, and establish association relationships on the formatted measurement data in multiple dimensions to obtain target measurement data; and sends the target metrology data to the model engine 13.

The model engine 13 is configured to calculate the target performance metric data according to the target performance metric model, so as to obtain the performance metric data.

For a specific description of determining performance metric data based on the data collector 11, the protocol engine 12 and the model engine 13 in the cloud native platform 10, reference may be made to the description in the embodiments of the performance metric method described above. And will not be described in detail herein.

In an alternative embodiment, collector management modules are included in the cloud native platform 10. The collector management model is used for managing the data collectors 11 in the cloud native platform 10. For each metrology platform, there is a corresponding data collector, that is, a metrology platform that needs to be measured is added to the cloud native platform 10, and the collector management module generates a new data collector, and uses the new data collector to collect the platform data of the added metrology platform. Therefore, the expansibility and maintainability of the cloud native platform can be improved.

In an alternative embodiment, a cloud native platform based performance metric service platform is shown in fig. 11. The performance metric data obtained by the performance metric model may include process performance metric data and structural performance metric data, among others. Process performance metric data refers to performance metric data obtained in the process of obtaining result performance metric data from target metric data. The performance metric sign includes a data visualization dashboard to display the resulting performance metric data.

In an alternative embodiment, data of different dimensions may be presented in the performance metric sign. The multi-dimensions are shown in fig. 12. The metric data in the dimension of the user scenario may include domain experts, department authorities, development managers, first-line development personnel, and the like; the metric data may include delivery efficiency, delivery quality, delivery capacity, etc. in the dimension of the service delivery; the metrology data in the dimension of the development campaign may include data for each of a demand phase, an analysis phase, a design phase, a development phase, a testing phase, an online phase, and a maintenance phase; metric data includes trend contrast, abnormal world, and risk prediction in the intelligent prediction dimension.

In an alternative embodiment, a cloud native platform based performance metric service architecture is shown in fig. 13. The protocol engine stores the formatted metric data in the Kafka middleware after performing format unification processing on the platform data. The formatted metric may be obtained from Kafka middleware as the model engine processes the formatted metric. The model engine batches the formatted metric data based on a plurality of Job pods (Job Pod) in the K8S. The final performance metric data is stored in a columnar storage database, and the model engine can also acquire the performance metric data from the columnar storage database for operation.

In an alternative embodiment, the cloud native platform comprises a performance measurement platform and a data acquisition platform, and the use authorities of the performance measurement platform and the data acquisition platform are managed by adopting different authority systems. That is, a user accessing the performance metric platform has no access to the data acquisition platform; a user accessing the data acquisition platform has no access to the performance metrics platform. Therefore, the safety of the data corresponding to the efficiency measurement platform and the data corresponding to the data acquisition platform can be improved.

In an alternative embodiment, the construction and deployment of the cloud native platform based performance metric platform may include: installing a K8S environment; installing a tool (Helm) for managing K8S; and installing a data collector, a protocol engine and a model engine in the performance measurement platform based on the cloud native platform through Helm. When the cloud native platform is installed, the cloud native platform can be installed offline or online. The column storage database (clickhouse) can be independently deployed into clusters, can be integrated into a data collector, and is automatically identified by using Helm according to graph structure data by taking clickhouse chart as a sub-picture of a graph of the data collector.

In deploying a cloud-native performance metrics platform, balanced load access is achieved using haproxy or nginx when the data collector communicates with a columnar store database (clickhouse). When using kafka and clickhouse for storage, remote storage is performed by NFS (Network File System ) or minio.

Based on the same inventive concept, the embodiment of the application also provides a performance measuring device for realizing the performance measuring method. The implementation of the solution provided by the apparatus is similar to that described in the above method, so the specific limitations of one or more embodiments of the performance measurement apparatus provided below may be referred to above for the limitations of the performance measurement method, and are not repeated here.

In one embodiment, as shown in fig. 14, a performance measuring apparatus 20 is provided, which is applied to a cloud native platform, wherein the cloud native platform is used for measuring performance of at least one platform to be measured, and the performance measuring apparatus 20 includes: an acquisition module 21, a processing module 22, a dimension building module 23 and a determination module 24, wherein:

the obtaining module 21 is configured to obtain a configuration file corresponding to the platform to be measured, and obtain platform data of the platform to be measured according to the configuration file.

The processing module 22 is configured to perform format unified processing on the platform data according to a preset standard protocol, so as to obtain formatted measurement data.

The dimension establishing module 23 is configured to establish association relationships between the formatted metric data in multiple dimensions, so as to obtain target metric data.

The determining module 24 is configured to calculate the target performance metric data according to the target performance metric model, so as to obtain performance metric data.

In one embodiment, the obtaining module 21 is specifically configured to generate a data interface corresponding to the platform to be measured according to a configuration file; platform data of the platform to be measured are obtained through the data interface.

In one embodiment, the performance metric apparatus 20 further includes a monitoring module. The monitoring module is used for monitoring whether a new configuration file exists; and if the new configuration file exists, acquiring new platform data of the measurement platform corresponding to the new configuration file according to the new configuration file.

In one embodiment, the processing module 22 is specifically configured to obtain a standard format corresponding to a preset standard protocol; the standard format comprises a time sequence, a measurement field and a label field; and carrying out formatting unified processing on the platform data according to the standard format to obtain the formatting measurement data.

In one embodiment, the dimension module 23 includes a build unit and a correction unit. The establishing unit is used for establishing initial association relations among the measurement data of each dimension in the formatted measurement data according to the formatted measurement data; the correcting unit is used for correcting the initial association relation to obtain target measurement data.

In one embodiment, the correction unit is specifically configured to obtain correction metric data; the correction measurement data is used for determining the association degree of the initial association relation; if the association degree does not reach the preset association threshold, correcting the initial association relation according to the correction measurement data to obtain a target association relation; and determining target measurement data according to the target association relationship and measurement data corresponding to the target association relationship.

In one embodiment, the determining module 24 is specifically configured to batch the target metric data using each of the streaming execution operators based on the container cluster management system to obtain the performance metric data.

In one embodiment, the performance metric apparatus 20 further includes a display module. The display module is used for acquiring identity information of a user; and acquiring target performance measurement data corresponding to the identity information according to the identity information, and displaying the target performance measurement data.

In one embodiment, the performance metric apparatus 20 further includes a memory module. The storage module is used for respectively storing the platform data of the platform to be measured and the platform data of the new measurement platform into different partitions in the column-type storage database.

The various modules in the performance measurement apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 1. It will be appreciated by those skilled in the art that the architecture shown in fig. 1 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements may be implemented, as a particular computer device may include more or less components than those shown, or may be combined with some components, or may have a different arrangement of components.

In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:

Acquiring a configuration file corresponding to a platform to be measured, and acquiring platform data of the platform to be measured according to the configuration file;

carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data;

and calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring a configuration file corresponding to a platform to be measured, and acquiring platform data of the platform to be measured according to the configuration file;

carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data;

and calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data.

In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:

Acquiring a configuration file corresponding to a platform to be measured, and acquiring platform data of the platform to be measured according to the configuration file;

carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data;

and calculating the target measurement data according to the target performance measurement model to obtain the performance measurement data.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims (10)

1. A performance measurement method, applied to a cloud native platform, the cloud native platform being configured to measure performance of at least one platform to be measured, the method comprising:

acquiring a configuration file corresponding to the platform to be measured, and acquiring platform data of the platform to be measured according to the configuration file;

carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

Establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data;

and calculating the target measurement data according to the target performance measurement model to obtain performance measurement data.

2. The method according to claim 1, wherein the obtaining the platform data of the platform to be measured according to the configuration file includes:

generating a data interface corresponding to the platform to be measured according to the configuration file;

and acquiring the platform data of the platform to be measured through the data interface.

3. The method according to claim 1, wherein the method further comprises:

monitoring whether a new configuration file exists;

and if the new configuration file exists, acquiring new platform data of the measurement platform corresponding to the new configuration file according to the new configuration file.

4. A method according to any one of claims 1-3, wherein the formatted metric data comprises a plurality of dimensions of metric data; establishing association relations among the formatted measurement data in multiple dimensions to obtain target measurement data, wherein the method comprises the following steps:

establishing an initial association relation between measurement data of each dimension in the formatted measurement data according to the formatted measurement data;

And correcting the initial association relation to obtain the target measurement data.

5. The method of claim 4, wherein the modifying the initial association relationship to obtain the target metric data comprises:

obtaining correction metric data; the correction measurement data is used for determining the association degree of the initial association relation;

if the association degree does not reach the preset association threshold, correcting the initial association relation according to the correction measurement data to obtain a target association relation;

and determining the target measurement data according to the target association relationship and the measurement data corresponding to the target association relationship.

6. A method according to any one of claims 1-3, wherein the target performance metric model comprises a plurality of stream execution operators, the computing the target performance metric data according to the target performance metric model to obtain performance metric data comprising:

and based on a container cluster management system, carrying out batch processing on the target measurement data by using each flow execution operator to obtain the efficiency measurement data.

7. A method according to any one of claims 1-3, wherein the method further comprises:

Acquiring identity information of a user;

and acquiring target performance measurement data corresponding to the identity information according to the identity information, and displaying the target performance measurement data.

8. A cloud native platform, comprising: a data collector, a protocol engine and a model engine;

the data collector is used for acquiring platform data of the platform to be measured according to a configuration file corresponding to the platform to be measured, which is connected with the cloud native platform, and sending the platform data to the protocol engine;

the protocol engine is used for carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement quantity, and establishing association relations on the formatted measurement data in multiple dimensions to obtain target measurement data; and sending the target metric data to the model engine;

the model engine is used for calculating the target measurement data according to the target performance measurement model to obtain performance measurement data.

9. A performance measurement apparatus for use with a cloud native platform for measuring performance of at least one platform to be measured, the apparatus comprising:

The acquisition module is used for acquiring a configuration file corresponding to the platform to be measured and acquiring platform data of the platform to be measured according to the configuration file;

the processing module is used for carrying out format unified processing on the platform data according to a preset standard protocol to obtain formatted measurement data;

the dimension building module is used for building association relations among the formatted measurement data in multiple dimensions to obtain target measurement data;

and the determining module is used for calculating the target measurement data according to the target performance measurement model to obtain performance measurement data.

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.