CN114417000A - Micro-service dividing method, device, equipment and storage medium - Google Patents
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Abstract
The application provides a micro-service dividing method, a device, equipment and a storage medium, wherein the micro-service dividing method comprises the following steps: acquiring a command for the single application; converting the command of the single application into a word vector, and obtaining a vector set D comprising the word vector of the single application; unsupervised clustering is conducted on the vector set D, so that commands applied by the monomers are grouped based on clustering results; and calculating clustering evaluation values under all K values based on a Davies-Boulding index formula, and determining a partitioning result of the single application based on the K value with the minimum clustering evaluation value. The method and the device can overcome the defects of low dividing efficiency and time consumption of manual dividing.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for partitioning a micro service.
Background
At present, division of micro services depends on manual division, and the mode has the defects of high communication cost and low division efficiency.
Disclosure of Invention
An embodiment of the present application provides a method, an apparatus, a device and a storage medium for partitioning a micro service, so as to improve the partitioning efficiency of the micro service.
To this end, a first aspect of the present application discloses a micro-service partitioning method, the method comprising:
acquiring a command for the single application;
converting the command of the single application into a word vector, and obtaining a vector set D comprising the word vector of the single application;
unsupervised clustering is conducted on the vector set D, so that commands applied by the monomers are grouped based on clustering results;
and calculating clustering evaluation values under all K values based on a Davies-Boulding index formula, and determining a partitioning result of the single application based on the K value with the minimum clustering evaluation value.
In the first aspect of the present application, as an optional implementation manner, the unsupervised clustering of the vector set D to group the commands applied to the monomers based on the clustering result includes:
unsupervised clustering is performed on the vector set D based on a K-MEANS algorithm to group the commands applied by the monomers based on the clustering results.
In the first aspect of the present application, as an optional implementation manner, the unsupervised clustering of the vector set D based on the K-MEANS algorithm to group the commands applied to the singles based on the clustering result includes:
establishing K candidate classification clusters;
determining K data points based on the vector set D and respectively serving as the first of the K candidate classification clusters: a center of mass;
calculating Euclidean distance between each word vector in the vector set D and each first centroid;
determining the first centroid corresponding to the minimum Euclidean distance of the word vectors in the vector set D;
and taking the candidate classification cluster to which the first centroid corresponding to the minimum Euclidean distance belongs as the micro-service division result of the single application.
In the first aspect of the present application, as an optional implementation manner, after the taking the candidate classification cluster to which the first centroid corresponding to the minimum euclidean distance belongs as the micro service partition result of the monolithic application, the method further includes:
calculating a second centroid of the candidate classification cluster based on the micro-service partition result of the single application;
calculating a difference between the second centroid and the first centroid;
and if the difference is smaller than a preset centroid threshold, determining that the application division of the single body is completed, and if the difference is larger than or equal to the preset centroid threshold, performing iterative unsupervised clustering on the vector set D based on a K-MEANS algorithm.
In the first aspect of the present application, as an optional implementation manner, the establishing K candidate classification clusters includes:
acquiring the upper limit of the number of micro services which can be accepted by a server;
and establishing K candidate classification clusters according to the micro service quantity upper limit, wherein K is equal to the micro service quantity upper limit.
In the first aspect of the present application, as an optional implementation manner, the commands of the monolithic application include a command to place a purchase order, a command to cancel an order, a command to pay, a command to ship goods, and a command to return goods in the mall system.
In the first aspect of the present application, as an optional implementation manner, the converting the command of the monolithic application into a word vector includes:
and converting the command of the single application into a word vector based on a word2vector model.
A second aspect of the present application discloses a microservice partitioning apparatus, the apparatus comprising:
the acquisition module is used for acquiring a command aiming at the single application;
the vector conversion module is used for converting the command of the single application into a word vector and obtaining a vector set D comprising the word vector of the single application;
the clustering module is used for carrying out unsupervised clustering on the vector set D so as to group the commands applied by the monomers based on a clustering result;
and the evaluation module is used for calculating clustering evaluation values under all K values based on a Davies-Boulding index formula and determining the division result of the single application based on the K value with the minimum clustering evaluation value.
A third aspect of the present application discloses a microservice partitioning apparatus, the apparatus comprising:
a processor coupled to a memory storing executable program code;
the processor calls the executable program code stored in the memory to execute the microservice partitioning method according to the first aspect of the present application.
A fourth aspect of the present application discloses a storage medium, where the storage medium stores computer instructions, and the computer instructions are used to execute the micro-service partitioning method of the first aspect of the present application when being called.
The beneficial effect of this application does: the method and the device can acquire the command for the single application and convert the command for the single application into word vectors, obtain a vector set D comprising the word vectors for the single application, perform unsupervised clustering on the vector set D, group the commands for the single application based on clustering results, calculate clustering evaluation values under all K values based on a Davies-Boulding index formula, and determine the division result of the single application based on the K value with the smallest clustering evaluation value. Compared with the prior art, the method and the device have the advantages that the monomer application can be automatically divided, so that the defects of low dividing efficiency and time consumption in manual division can be overcome.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a micro service partitioning method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a microservice partitioning apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a microservice partitioning apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a micro-service partitioning method according to an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application includes the following steps:
101. acquiring a command for the single application;
102. converting the command of the single application into a word vector, and obtaining a vector set D comprising the word vector of the single application;
103. unsupervised clustering is carried out on the vector set D so as to group the commands applied to the monomers based on clustering results;
104. and calculating the clustering evaluation value under each K value based on a Davies-Boulding index formula, and determining the division result of the single application based on the K value with the minimum clustering evaluation value.
In the application embodiment, the monolithic application refers to a micro service, for example, the monolithic application is an electronic mall system, wherein the electronic mall system can provide a commodity purchasing service for a user.
In this embodiment of the application, the command of the monolithic application refers to a command used in a working process of the monolithic application, for example, for an electronic mall system, the command of the monolithic application may be a payment command, an order canceling command, and the like, where the monolithic application can be divided according to a function that the monolithic application can implement according to the command of the monolithic application, for example, the electronic mall system has a payment function, and then the electronic mall system can be divided into payment-type micro services.
In the embodiment of the present application, the command of the monolithic application includes a plurality of commands at the same time, for example, in some scenarios, the monolithic application command includes a cancel order command and a payment command at the same time.
In this embodiment of the present application, a specific process of acquiring a command of a monolithic application may be:
and responding to an input instruction of an operator, analyzing the input instruction and obtaining a single application command, wherein the operator further carries an incident storm based on DDD (domain driven design), so that the input instruction carrying the single application command is obtained.
In the embodiment of the present application, the vector set D includes word vectors of a plurality of single applications, for example, the vector set D includes word vectors of a single application a and word vectors of a single application B.
In the embodiments of the present application, please refer to the prior art for the Davies-Boulding index formula, which is not described in detail in the embodiments of the present application.
The method and the device for dividing the single application can convert the single application command into the word vector by obtaining the single application command, obtain the vector set D comprising the single application word vector, simultaneously perform unsupervised clustering on the vector set D, group the single application command based on the clustering result, calculate the clustering evaluation value under each K value based on the Davies-building index formula, and determine the dividing result of the single application based on the K value with the minimum clustering evaluation value. Compared with the prior art, the method and the device have the advantages that the monomer application can be automatically divided, so that the defects of low dividing efficiency and time consumption in manual division can be overcome.
In the embodiment of the application, the Davies-Boulding index formula is a clustering algorithm evaluation index algorithm, namely the Davies-Boulding index formula can calculate the ratio of the sum of the intra-class distances to the inter-class distances, and can optimize the selection of the k value. Further, the Davies-Boulding index formula is adopted to calculate the clustering evaluation value under each K value, so that the condition that local optimization is caused by only calculating the target function in the K-means algorithm can be avoided.
In the embodiment of the present application, as an optional implementation manner, step 102: unsupervised clustering of vector set D to group commands applied to the singletons based on the clustering results, comprising the sub-steps of:
unsupervised clustering is performed on the vector set D based on the K-MEANS algorithm to group commands applied to the monomers based on the clustering results.
In this alternative embodiment, unsupervised clustering of the vector set D can be performed by the K-MEANS algorithm to group commands applied to the monomers based on the clustering results. On the other hand, in the process of unsupervised clustering of the vector set D by adopting the K-MEANS algorithm, unsupervised clustering can be carried out on the vector set D according to the category of fewer known clustering samples by adopting the K-MEANS algorithm, and then the total clustering time and complexity can be reduced by adopting the K-MEANS algorithm.
In this embodiment of the present application, as an optional implementation manner, unsupervised clustering is performed on the vector set D based on the K-MEANS algorithm to group commands applied to the monomers based on a clustering result, including:
establishing K candidate classification clusters;
determining K data points based on the vector set D and respectively serving as first centroids of the K candidate classification clusters;
calculating the Euclidean distance between each word vector in the vector set D and each first centroid;
determining a first centroid corresponding to the minimum Euclidean distance of the word vectors in the vector set D;
and taking the candidate classification cluster to which the first centroid corresponding to the minimum Euclidean distance belongs as a micro-service division result of the single application.
In this optional embodiment, specifically, K vectors are randomly selected from the vector set D, and then K data points may be determined and respectively used as the first centroids of the K candidate classification clusters;
calculating the Euclidean distance between each word vector in the vector set D and each first centroid;
determining a first centroid corresponding to the minimum Euclidean distance of the word vectors in the vector set D;
and taking the candidate classification cluster to which the first centroid corresponding to the minimum Euclidean distance belongs as a micro-service division result of the single application.
In this embodiment, as an optional implementation manner, after taking the candidate classification cluster to which the first centroid corresponding to the minimum euclidean distance belongs as the micro service partition result of the monolithic application, the method of this embodiment further includes the following sub-steps:
calculating a second centroid of the candidate classification cluster based on the micro-service division result of the single application;
calculating a difference between the second centroid and the first centroid;
and if the difference is smaller than the preset centroid threshold, determining that the application division of the single body is completed, and if the difference is larger than or equal to the preset centroid threshold, performing iterative unsupervised clustering on the vector set D based on a K-MEANS algorithm.
In the embodiment of the present application, when the difference is smaller than the preset centroid threshold, iterative unsupervised clustering training is performed based on steps 101, 102, 103, and 104.
In the embodiment of the present application, as an optional implementation manner, the steps of: establishing K candidate classification clusters, comprising the following sub-steps:
acquiring the upper limit of the number of micro services which can be accepted by a server;
and establishing K candidate classification clusters according to the upper limit of the number of the micro services, wherein K is equal to the upper limit of the number of the micro services.
In the embodiment of the present application, as an optional implementation manner, the commands of the monolithic application include a command to place a order, a command to cancel an order, a command to pay, a command to ship goods, and a command to return goods in the mall system.
In the embodiment of the present application, as an optional implementation manner, the steps of: converting the command of the single application into a word vector based on the word2vector model, comprising the following sub-steps:
the commands of the monolithic application are converted into word vectors based on the word2vector model.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a micro-service partitioning apparatus according to an embodiment of the present application. As shown in fig. 2, the apparatus of the embodiment of the present application includes the following functional modules:
an obtaining module 201, configured to obtain a command for a monolithic application;
the vector conversion module 202 is configured to convert the command of the monomer application into a word vector based on the word2vector model, and obtain a vector set D including the word vector of the monomer application;
a clustering module 203, configured to perform unsupervised clustering on the vector set D to group commands applied to the monomers based on a clustering result;
and the evaluation module 204 is configured to calculate a clustering evaluation value under each K value based on a Davies-Boulding index formula, and determine a partitioning result of the single application based on the K value with the smallest clustering evaluation value.
The device of the embodiment of the application can further acquire the command for the single application and convert the command for the single application into word vectors by executing the micro-service dividing method, obtain a vector set D comprising the word vectors for the single application, simultaneously perform unsupervised clustering on the vector set D, group the commands for the single application based on clustering results, calculate clustering evaluation values under all K values based on a Davies-Boulding index formula, and determine the dividing result of the single application based on the K value with the smallest clustering evaluation value. Compared with the prior art, the method and the device have the advantages that the monomer application can be automatically divided, so that the defects of low dividing efficiency and time consumption in manual division can be overcome.
Please refer to the embodiments of the present application for further details of the embodiments of the present application, which are not described herein.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of a micro service partition apparatus according to an embodiment of the present application. As shown in fig. 3, the micro-service dividing apparatus according to the embodiment of the present application includes:
a memory 302 coupled to the processor 301 storing executable program code;
the processor 301 calls the executable program code stored in the memory 302 to execute the microservice partitioning method according to the first embodiment of the present application.
The device of the embodiment of the application can further acquire the command for the single application and convert the command for the single application into word vectors by executing the micro-service dividing method, obtain a vector set D comprising the word vectors for the single application, simultaneously perform unsupervised clustering on the vector set D, group the commands for the single application based on clustering results, calculate clustering evaluation values under all K values based on a Davies-Boulding index formula, and determine the dividing result of the single application based on the K value with the smallest clustering evaluation value. Compared with the prior art, the method and the device have the advantages that the monomer application can be automatically divided, so that the defects of low dividing efficiency and time consumption in manual division can be overcome.
Example four
The embodiment of the application discloses a storage medium, wherein a computer instruction is stored in the storage medium, and when the computer instruction is called, the storage medium is used for executing the micro-service dividing method of the embodiment of the application.
The storage medium of the embodiment of the application can further acquire the command for the monomer application and convert the command for the monomer application into word vectors by executing the micro-service partitioning method, obtain a vector set D comprising the word vectors for the monomer application, and simultaneously perform unsupervised clustering on the vector set D, so that the commands for the monomer application can be grouped based on clustering results, the clustering evaluation values under all K values are calculated based on a Davies-Boulding index formula, and the partitioning result of the monomer application is determined based on the K value with the smallest clustering evaluation value. Compared with the prior art, the method and the device have the advantages that the monomer application can be automatically divided, so that the defects of low dividing efficiency and time consumption in manual division can be overcome.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for micro-service partitioning, the method comprising:
acquiring a command for the single application;
converting the command of the single application into a word vector, and obtaining a vector set D comprising the word vector of the single application;
unsupervised clustering is conducted on the vector set D, so that commands applied by the monomers are grouped based on clustering results;
and calculating clustering evaluation values under all K values based on a Davies-Boulding index formula, and determining a partitioning result of the single application based on the K value with the minimum clustering evaluation value.
2. The method of claim 1, wherein said unsupervised clustering of the vector set D to group commands applied by the singlets based on clustering results comprises:
unsupervised clustering is performed on the vector set D based on a K-MEANS algorithm to group the commands applied by the monomers based on the clustering results.
3. The method of claim 2, wherein the unsupervised clustering of the vector set D based on the K-MEANS algorithm to group commands applied by the singlets based on clustering results comprises:
establishing K candidate classification clusters;
determining K data points based on the vector set D and respectively serving as first centroids of the K candidate classification clusters;
calculating Euclidean distance between each word vector in the vector set D and each first centroid;
determining the first centroid corresponding to the minimum Euclidean distance of the word vectors in the vector set D;
and taking the candidate classification cluster to which the first centroid corresponding to the minimum Euclidean distance belongs as the micro-service division result of the single application.
4. The method of claim 3, wherein after the candidate classification cluster to which the first centroid corresponding to the minimum Euclidean distance belongs is taken as a micro-service partitioning result of the monolithic application, the method further comprises:
calculating a second centroid of the candidate classification cluster based on the micro-service partition result of the single application;
calculating a difference between the second centroid and the first centroid;
and if the difference is smaller than a preset centroid threshold, determining that the application division of the single body is completed, and if the difference is larger than or equal to the preset centroid threshold, performing iterative unsupervised clustering on the vector set D based on a K-MEANS algorithm.
5. The method of claim 3, wherein the establishing K candidate taxonomy clusters comprises:
acquiring the upper limit of the number of micro services which can be accepted by a server;
and establishing K candidate classification clusters according to the micro service quantity upper limit, wherein K is equal to the micro service quantity upper limit.
6. The method of claim 1, wherein the commands for the monolithic application include orders placed, orders canceled, payment commands, shipping commands, and return commands in a mall system.
7. The method of claim 1, wherein said converting the commands of the monolithic application into word vectors comprises:
and converting the command of the single application into a word vector based on a word2vector model.
8. A microservice partitioning apparatus, the apparatus comprising:
the acquisition module is used for acquiring a command aiming at the single application;
the vector conversion module is used for converting the command of the single application into a word vector and obtaining a vector set D comprising the word vector of the single application;
the clustering module is used for carrying out unsupervised clustering on the vector set D so as to group the commands applied by the monomers based on a clustering result;
and the evaluation module is used for calculating clustering evaluation values under all K values based on a Davies-Boulding index formula and determining the division result of the single application based on the K value with the minimum clustering evaluation value.
9. A microservice partitioning apparatus, the apparatus comprising:
a processor coupled to a memory storing executable program code;
the processor calls the executable program code stored in the memory to execute the microservice partitioning method of any of claims 1-7.
10. A storage medium storing computer instructions for performing the microservice partitioning method of any of claims 1-7 when invoked.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115061663A (en) * | 2022-06-17 | 2022-09-16 | 中国兵器工业信息中心 | Micro-service dividing method and device based on customer demands, electronic equipment and medium |
| CN116501383A (en) * | 2023-06-26 | 2023-07-28 | 亚信科技（中国）有限公司 | Micro-service distribution method and device, electronic equipment and readable storage medium |
| CN117311801A (en) * | 2023-11-27 | 2023-12-29 | 湖南科技大学 | Micro-service splitting method based on networking structural characteristics |
-
2022
- 2022-03-01 CN CN202210193549.1A patent/CN114417000A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115061663A (en) * | 2022-06-17 | 2022-09-16 | 中国兵器工业信息中心 | Micro-service dividing method and device based on customer demands, electronic equipment and medium |
| CN116501383A (en) * | 2023-06-26 | 2023-07-28 | 亚信科技（中国）有限公司 | Micro-service distribution method and device, electronic equipment and readable storage medium |
| CN116501383B (en) * | 2023-06-26 | 2023-08-22 | 亚信科技（中国）有限公司 | Micro-service distribution method and device, electronic equipment and readable storage medium |
| CN117311801A (en) * | 2023-11-27 | 2023-12-29 | 湖南科技大学 | Micro-service splitting method based on networking structural characteristics |
| CN117311801B (en) * | 2023-11-27 | 2024-04-09 | 湖南科技大学 | Micro-service splitting method based on networking structural characteristics |
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