CN117632299A - Plug-in type micro-service treatment index calculation device - Google Patents
Plug-in type micro-service treatment index calculation device Download PDFInfo
- Publication number
- CN117632299A CN117632299A CN202311669022.2A CN202311669022A CN117632299A CN 117632299 A CN117632299 A CN 117632299A CN 202311669022 A CN202311669022 A CN 202311669022A CN 117632299 A CN117632299 A CN 117632299A
- Authority
- CN
- China
- Prior art keywords
- measurement
- plug
- calculation
- module
- data
- 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
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 115
- 238000005259 measurement Methods 0.000 claims abstract description 124
- 238000013500 data storage Methods 0.000 claims abstract description 16
- 238000013079 data visualisation Methods 0.000 claims abstract description 12
- 238000012800 visualization Methods 0.000 claims abstract description 6
- 238000003860 storage Methods 0.000 claims description 14
- 238000007405 data analysis Methods 0.000 abstract description 5
- 238000013480 data collection Methods 0.000 abstract description 3
- 238000006243 chemical reaction Methods 0.000 abstract 1
- 238000000034 method Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005067 remediation Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44521—Dynamic linking or loading; Link editing at or after load time, e.g. Java class loading
- G06F9/44526—Plug-ins; Add-ons
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a plug-in type micro-service treatment index calculation device which provides automation, plug-in type, one-stop type data collection, analysis and visualization, helps a team to measure micro-service better and faster, and provides data support for micro-service treatment. The technical proposal is as follows: the device comprises: the measurement setting module is used for realizing interfaces of measurement view and index selection, data source configuration and data acquisition frequency configuration of the micro-service measurement and transmitting configuration information to the measurement calculation scheduling module; the measurement calculation scheduling module stores the configuration information into a scheduling database, the scheduling calculation plug-in unit performs data acquisition and conversion, manages the state of a calculation task, comprehensively calculates the calculation task and stores the result into the measurement data storage module; the measurement calculation plug-in module receives an instruction to collect, convert and store measurement index data and returns a calculation result; a metric data storage module for storing metric index data; and the measurement data visualization module is used for realizing the visualization of measurement data.
Description
Technical Field
The invention relates to the technical field of micro-service architecture, in particular to a plug-in type micro-service treatment index computing device.
Background
With the advent of distributed and micro-service architecture, services are more and more, the service architecture tends to be complicated, micro-service management becomes an important subject, and the precondition of management is that core measurement data of each flow is acquired, and the following problems exist in the current acquisition of the measurement data of the micro-service:
1. the measurement data of the micro-service are scattered in different stages and tools of the development life cycle of the micro-service, and the standardization degree is low, so that the measurement data is difficult to be reserved, collected and converted into effective measurement indexes;
2. some tools can only perform some simple alarm judgment and summarization processing based on a predefined threshold on the original indexes, and data in different dimensions are not aggregated and associated, so that deeper information is mined;
3. the current definition and calculation method of the measurement index of the micro service is fuzzy, and team acceptance is difficult to obtain;
4. when a developer needs to add a new metric, all functions of data collection, analysis and visualization need to be realized from scratch, and some common capabilities cannot be reused, so that development and calculation cost is increased.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to solve the problems and provide a plug-in type micro-service treatment index calculation device which provides automatic, plug-in type, one-stop type data collection, analysis and visualization capabilities, helps research and development teams measure micro-services better and faster, and provides effective and comprehensive data support for micro-service treatment.
The technical scheme of the invention is as follows: the invention discloses a plug-in type micro-service treatment index calculating device, which comprises:
the measurement setting module is used for realizing the interface of measurement view and index selection, data source configuration and data acquisition frequency configuration of the micro-service measurement and transmitting configuration information to the subsequent measurement calculation scheduling module;
the measurement calculation scheduling module is used for receiving the configuration information transmitted by the measurement setting module and storing the configuration information in the scheduling database, then scheduling the calculation plugin in the measurement calculation plugin module to acquire and convert data, managing the state of calculation tasks, comprehensively calculating one or more calculation tasks and storing calculation results in the measurement data storage module;
the measurement calculation plug-in module is used for receiving the instruction of the measurement calculation scheduling module, collecting, converting and storing specific measurement index data and returning a calculation result;
the measurement data storage module is used for storing the final measurement index data calculated by the measurement calculation scheduling module;
and the measurement data visualization module is used for realizing the visualization of the measurement data.
According to an embodiment of the plug-in micro service governance indicator computing device of the present invention, the metric setting module is further configured to: the user is configured in two ways, one is to select the measurement view index first and then configure the needed data source parameters; another is to first configure the data source and then select the metric view index that the data source supports.
According to an embodiment of the plug-in micro-service governance index computing device, the metrics of the view indexes are multiple, and each view corresponds to respective index and computing method description and comprises a service importance view, a service call relationship view and a service performance view.
According to an embodiment of the plug-in micro service governance indicator computing device of the present invention, the metric computation scheduling module is further configured to: firstly, a measurement calculation scheduling module receives configuration information of a measurement setting module, stores the configuration information into a corresponding scheduling database, then establishes a calculation task, a scheduler analyzes the configuration information according to a corresponding relation between a view maintained by the module and a calculation plug-in the measurement calculation plug-in module, and the corresponding calculation plug-in the measurement calculation plug-in module is scheduled to acquire, convert and store measurement index data, and finally obtains final index data comprehensively according to calculation results of different calculation plug-ins.
According to an embodiment of the plug-in micro-service governance index computing device, the metric computing scheduling module further provides plug-in registration service for user-defined plug-in registration for unified scheduling by the scheduler.
According to an embodiment of the plug-in type micro-service treatment index calculation device, the measurement calculation plug-in module is composed of a plurality of measurement index calculation plug-ins, and each calculation plug-in completes a calculation task of a corresponding measurement index.
According to an embodiment of the plug-in micro-service treatment index calculation device, the measurement calculation plug-in module also provides a user to develop a calculation plug-in by himself, define input data source parameters and output measurement data formats, register the input data source parameters and the output measurement data formats into the measurement calculation scheduling module and realize custom measurement data calculation.
According to an embodiment of the plug-in micro-service governance indicator computing device of the present invention, the metric data storage module provides data storage and query capabilities, and the storage types include MySQL and Elastic Search.
According to an embodiment of the plug-in micro-service governance indicator computing device, the measurement data visualization module is based on Grafana to realize the visualization of measurement data.
Compared with the prior art, the invention has the following beneficial effects: the measurement setting module in the device provides various data sources and corresponding measurement view indexes for users, and makes detailed explanation and calculation methods for each measurement index, thereby solving the problem of fuzzy measurement index definition and calculation methods of micro services. The measurement calculation scheduling module in the device carries out comprehensive global evaluation on the online service according to the multidimensional basic index data of different research and development flows and the comprehensive calculation result, solves the problem that measurement fragment data are difficult to collect and convert into effective measurement indexes, further digs the depth information of measurement and promotes the comprehensiveness of the measurement of the micro-service. The measurement calculation plug-in module in the device of the invention realizes effective decoupling with the scheduling module through defining the input and output models, sharing the abstract layer by the similar tools, standardizing the data format and the statistical method, and the user can register the user-defined calculation plug-in into the scheduling module to realize the user-defined measurement data calculation, thereby further improving the expandability
Drawings
The above features and advantages of the present invention will be better understood after reading the detailed description of embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily to scale and components having similar related features or characteristics may have the same or similar reference numerals.
FIG. 1 is a block diagram of an embodiment of a plug-in micro service remediation metrics computing device according to the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and the specific embodiments. It is noted that the aspects described below in connection with the drawings and the specific embodiments are merely exemplary and should not be construed as limiting the scope of the invention in any way.
FIG. 1 illustrates a modular architecture of one embodiment of a plug-in micro service remediation metrics computing device of the present invention. Referring to fig. 1, the apparatus of this embodiment includes the following modules: the system comprises a measurement setting module, a measurement calculation scheduling module, a measurement calculation plug-in module, a measurement data storage module and a measurement data visualization module.
The measurement setting module is used for realizing the interface of the measurement view and index selection, the data source configuration and the data acquisition frequency configuration of the micro-service measurement, and transmitting configuration information to the subsequent measurement calculation scheduling module.
Because different metric view indexes require different data sources, a user is configured in two ways, one is to firstly select the metric view indexes and then configure the required data source parameters; another is to first configure the data source and then select the metric view index that the data source supports.
The metrics of the view are various, each view has detailed index and calculation method description, the detailed index and calculation method description comprises a service importance view, a service calling relation view, a service performance view and the like, wherein the service importance view is comprehensively calculated by 4 indexes of service attribute marking, service fan-in number, service calling quantity and availability, the calling relation view specifically comprises a single service calling relation view, an integral service calling topological view, a cyclic calling detection view, a centralized calling detection view and the like, the service performance view specifically comprises a calling time-consuming partition distribution statistic, a calling time-consuming time-sharing distribution statistic, a performance ordering TopN transverse chart, a calling quantity ordering TopN transverse chart and the like, and the data source type comprises SkyWalking, jira, a source code, gitlab and the like
Taking SkyWalking as an example of a configuration data source, skyWalking is an application performance monitoring tool of a distributed system, designed specifically for micro-services, cloud-native architecture, and container-based architecture. Application call link information is collected by loading probes-non-intrusive manner, and the collected call link information is stored in an Elastic Search. The data source configuration of SkyWalking comprises URL, user name and password of the Elastic Search, and after the configuration is completed, a measurement view is selected, each view has detailed indexes and calculation method description, and a user can select the view which is needed, and set the corresponding data acquisition frequency, for example, acquisition is performed once a day.
The measurement calculation scheduling module is used for receiving the configuration information transmitted by the measurement setting module and storing the configuration information in the scheduling database, then, the scheduler schedules a proper calculation plug-in the measurement calculation plug-in module to collect and convert data, manages the states of calculation tasks, comprehensively calculates one or more calculation tasks and stores calculation results in the measurement data storage module.
The metric calculation scheduling module is further configured to perform the following processing steps.
Firstly, a measurement calculation scheduling module receives configuration information of a measurement setting module, stores the configuration information into a corresponding scheduling database, then establishes a calculation task, a scheduler analyzes the configuration information according to a corresponding relation between a view maintained by the measurement calculation scheduling module and a calculation plug-in the measurement calculation plug-in module, the corresponding calculation plug-in the measurement calculation plug-in module is scheduled to acquire, convert and store measurement index data, and finally, final index data is comprehensively obtained according to calculation results of different calculation plug-ins.
Meanwhile, the measurement calculation scheduling module also provides plug-in registration service, and user-defined plug-ins can be registered in the plug-in registration service for uniform scheduling by a scheduler.
Taking a service importance view as an example, to measure the importance of a service, the 4 indexes of service attribute label (TAG), service fan-in number (fan), service call volume (CPS) and availability (SA) need to be considered comprehensively, wherein the service attribute label represents an importance label (important service, general service) given to the service in a service design stage, the service fan-in number represents the number of external services for calling the service, the service call volume represents the total number of times the external service calls the service, the availability = request success rate = successful request number/processing request number, and the corresponding influence factors are alpha, beta, gamma and phi respectively, so that the calculation formula of the importance is as follows: importance = alpha x TAG + beta x fan + gamma x CPS + phi x SA, to calculate the importance of a service, the metric calculation scheduling module invokes four calculation plugins already registered in the module to perform comprehensive calculation, wherein service attribute labels can be obtained through Jira, which is a project and transaction tracking tool produced by Atlassian company, and is widely applied to the working fields of defect tracking, customer service, demand collection, flow approval, task tracking, project tracking, agility management and the like. In Jira, a micro-service designer can configure importance attribute labels of services in component elements of the project, 3 indexes of service fan-in number, service call quantity and availability can be obtained through SkyWalking, and after calculation of four calculation plug-ins is completed, a measurement calculation scheduling module calculates importance measurement data of the micro-service according to an importance calculation formula and stores the importance measurement data in a measurement data storage module.
The measurement calculation plug-in module is used for receiving the instruction of the measurement calculation scheduling module, collecting, converting and storing specific measurement index data and returning a calculation result.
The measurement calculation plug-in module consists of a plurality of measurement index calculation plug-ins, each calculation plug-in completes the calculation task of the corresponding measurement index, meanwhile, a user can develop the calculation plug-in by himself, define input data source parameters and output measurement data formats, register the input data source parameters and the output measurement data formats into the measurement calculation scheduling module, realize the self-defined measurement data calculation and improve the expandability.
The service fan-in number will be described as an example.
Firstly, daily data acquisition is carried out on segment (thread index) indexes stored in an Elastic Search by SkyWalking, and page inquiry is carried out on segment index data by using scroll inquiry of an Elastic Search engine to obtain json data files of segments;
then processing the json data of segment, converting the json data into a basic data model capable of describing service fan-out number, wherein the following important parameters are stored in SkyWalking:
trance (call chain): refers to a complete service call link;
TraceID (call chain identification number): the system allocates a globally unique call chain ID for a call link;
segment (thread): all operations corresponding to one thread in trace are pointed out;
segment id (thread identification number): setting a segmentId for each thread call under the same call chain;
span (thread call): refers to a specific call in one thread in trace;
span id (thread call identification number): in the same call chain, a plurality of services have a call nesting hierarchical relationship, and a span ID is set for each call.
The measurement calculation plug-in module analyzes a span list corresponding to each trace based on Segment data stored in the Elastic Search engine, and the specific fields are as follows:
segment span id: a unique identifier of one call in the current call chain;
segment ParentSpanId: the parent call ID of the call;
EndpointName: the method specifically called is fully qualified;
ServiceCode: a name of the service to which the service belongs;
type: span type (Exit, entry, local);
layer: the hierarchy (Http, cache, DB);
tags: calling parameters and the like.
The measurement calculation plug-in module analyzes the measurement calculation plug-in module into a piece of calling basic data according to the span list, and the specific fields are as follows:
from srv: a source service name;
from Edp: a source interface name;
toSrv: a target service name;
toEdp: a target interface name;
startTime: calling a starting time;
finally, the metric computation plug-in module stores the called basic data into the Elastic Search for use by the metric data visualization module.
The measurement data storage module is used for storing the final measurement index data calculated by the measurement calculation scheduling module and providing data storage and query capability, and the storage types comprise MySQL and Elastic Search.
The measurement data visualization module is used for realizing the visualization of the measurement data based on Grafana.
Grafana is an open source monitoring data analysis and visualization suite. The method is most commonly used for carrying out visual analysis on time series data of infrastructure and application data analysis, and can also be used in other fields requiring data visual analysis. Grafana is divided into 3 parts, 1 is the configuration of the data source, mySQL and Elastic Search of the metric data storage module can be connected, 2 is the configuration of the data query statement, and 3 is the configuration and display of the metric view, including the line graph, the state graph, the pie graph, the instrument panel, the thermodynamic diagram and the like.
Taking the TopN view of the service fan-in number as an example, firstly, a data source Elastic Search is configured, and after connection is successful, query sentences are configured as follows:
the query statement firstly queries the call data of the production environment service in the corresponding time period, each call data comprises a source service name, a source interface name, a target service name and a target interface name, then aggregates the target service names, calculates the number of source services in each group, finally arranges the source services in an inverted order, takes the first 10 sources, and finally obtains the service fan-out number of Top 10.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood and appreciated by those skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disk) as used herein include Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disk) usually reproduce data magnetically, while discs (disk) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A plug-in micro-service governance indicator computing device, the device comprising:
the measurement setting module is used for realizing the interface of measurement view and index selection, data source configuration and data acquisition frequency configuration of the micro-service measurement and transmitting configuration information to the subsequent measurement calculation scheduling module;
the measurement calculation scheduling module is used for receiving the configuration information transmitted by the measurement setting module and storing the configuration information in the scheduling database, then scheduling the calculation plugin in the measurement calculation plugin module to acquire and convert data, managing the state of calculation tasks, comprehensively calculating one or more calculation tasks and storing calculation results in the measurement data storage module;
the measurement calculation plug-in module is used for receiving the instruction of the measurement calculation scheduling module, collecting, converting and storing specific measurement index data and returning a calculation result;
the measurement data storage module is used for storing the final measurement index data calculated by the measurement calculation scheduling module;
and the measurement data visualization module is used for realizing the visualization of the measurement data.
2. The plug-in micro service governance indicator computing device of claim 1, wherein the metric setup module is further configured to: the user is configured in two ways, one is to select the measurement view index first and then configure the needed data source parameters; another is to first configure the data source and then select the metric view index that the data source supports.
3. The plug-in micro service governance indicator computing device according to claim 2, wherein the metrics view indicators are multiple, each view corresponding to a respective indicator and computing method description, including a service importance view, a service call relationship view, and a service performance view.
4. The plug-in micro service governance indicator computing device of claim 1, wherein the metric computation scheduling module is further configured to: firstly, a measurement calculation scheduling module receives configuration information of a measurement setting module, stores the configuration information into a corresponding scheduling database, then establishes a calculation task, a scheduler analyzes the configuration information according to a corresponding relation between a view maintained by the module and a calculation plug-in the measurement calculation plug-in module, and the corresponding calculation plug-in the measurement calculation plug-in module is scheduled to acquire, convert and store measurement index data, and finally obtains final index data comprehensively according to calculation results of different calculation plug-ins.
5. The plug-in micro service governance indicator computing device according to claim 4, wherein the metric computation scheduling module further provides a plug-in registration service for user-defined plug-in registration for unified scheduling by the scheduler.
6. The plug-in micro service governance indicator computing device according to claim 1, wherein the metric computing plug-in module is comprised of a plurality of metric computing plug-ins, each computing plug-in performing a computing task for a corresponding metric.
7. The plug-in micro-service governance index computing device according to claim 6, wherein the metric computing plug-in module further provides a user to develop the computing plug-in by himself, define input data source parameters and output metric data formats, register into the metric computing scheduling module, and implement custom metric data computation.
8. The plug-in micro service governance indicator computing device of claim 1, wherein the metric data storage module provides data storage and query capabilities, the storage types including MySQL, elastic Search.
9. The plug-in micro service governance indicator computing device according to claim 1, wherein the metric data visualization module is configured to implement visualization of metric data based on Grafana.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311669022.2A CN117632299A (en) | 2023-12-06 | 2023-12-06 | Plug-in type micro-service treatment index calculation device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311669022.2A CN117632299A (en) | 2023-12-06 | 2023-12-06 | Plug-in type micro-service treatment index calculation device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117632299A true CN117632299A (en) | 2024-03-01 |
Family
ID=90025111
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311669022.2A Pending CN117632299A (en) | 2023-12-06 | 2023-12-06 | Plug-in type micro-service treatment index calculation device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117632299A (en) |
-
2023
- 2023-12-06 CN CN202311669022.2A patent/CN117632299A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11182388B2 (en) | Mechanism to chain continuous queries | |
| CN111008197A (en) | Data center design method for power marketing service system | |
| CN111159157B (en) | Index processing method and device for enterprise report data | |
| US8380654B2 (en) | General market prediction using position specification language | |
| CN107102948B (en) | UML-based software architecture complexity measurement method | |
| CN101957832A (en) | Unified window support for the flow of event data management | |
| CN111611458A (en) | Method for realizing system data architecture combing based on metadata and data analysis technology in big data management | |
| EP1631002A2 (en) | Automatic configuration of network performance models | |
| CN111611236A (en) | Data analysis method and system | |
| CN110728422A (en) | Building information model, method, device and settlement system for construction project | |
| CN112000587B (en) | Test man-hour automatic statistical method based on associated object operation statistics | |
| CN110490761A (en) | A kind of power grid distribution net equipment account data model modelling approach | |
| CN115470195A (en) | Index data automatic calculation method and device fusing dimension models | |
| Hesse et al. | Senska–towards an enterprise streaming benchmark | |
| Cakir et al. | Enabling real time big data solutions for manufacturing at scale | |
| WO2023169165A1 (en) | Access data processing method and apparatus, electronic device, and computer readable medium | |
| CN117632299A (en) | Plug-in type micro-service treatment index calculation device | |
| CN116257554A (en) | Demand statistical analysis method, device, equipment and medium based on virtual summary table | |
| CN110858341A (en) | Index monitoring method, device, equipment and medium based on distributed storage system | |
| CN118394800B (en) | Index query method and device, electronic equipment and readable storage medium | |
| Graf et al. | Frost: Benchmarking and exploring data matching results | |
| CN118656421A (en) | Quality assessment method and device based on multidimensional data fitting and electronic equipment | |
| CN118037087A (en) | Method and system for constructing index based on broad table | |
| CN114257528A (en) | Internet of things equipment selection method and device, electronic equipment and storage medium | |
| CN118071009A (en) | Data prediction method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |