CN117763157A - Knowledge graph construction method, knowledge graph construction device, computer equipment and storage medium - Google Patents
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Abstract
The present application relates to the field of microservices, and in particular, to a knowledge graph construction method, a knowledge graph construction device, a computer device, and a storage medium. The method comprises the following steps: acquiring software data and hardware data in a micro-service system; extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system; extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system; determining the association relation among the entities according to the attribute information of the entities; and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities. The method and the device improve the comprehensiveness and accuracy of knowledge graph construction.
Description
Technical Field
The present application relates to the field of microservices, and in particular, to a knowledge graph construction method, a knowledge graph construction device, a computer device, and a storage medium.
Background
With the flourishing development of cloud technology, micro-service architecture is widely applied to automated deployment, scheduling and management of services. The micro service architecture is an architecture mode for splitting a single application into a plurality of small services and cooperatively completing the system functions.
Observability is one of the important features of micro-service architecture, which provides rich observation data to describe the current running state of the micro-service system, such as metrics, logs, call chain data, etc. Because the same micro-service may be deployed on different physical machines, and the different micro-services are mutually called through network communication, the micro-service system often forms a complex topological relationship. In order to make effective use of this topology, existing methods mostly use the operation and maintenance knowledge graph method to model the micro-service system. At present, an operation and maintenance knowledge graph is often generated by determining entities and relations according to an acquired operation and maintenance corpus.
However, the accuracy is not high only according to the generation method of the operation and maintenance corpus, which may result in information loss, so improvement is needed.
Disclosure of Invention
Based on this, it is necessary to provide a knowledge graph construction method, apparatus, computer device and storage medium capable of improving the accuracy of knowledge graph construction in view of the above-described technical problems.
In a first aspect, the present application provides a knowledge graph construction method, which includes:
acquiring software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
In one embodiment, the construction of the knowledge graph of the micro service system according to each entity, attribute information of each entity and association relation among each entity includes:
determining repeated entities in each entity according to the attribute information of each entity;
performing de-duplication treatment on repeated entities in each entity;
and constructing a knowledge graph construction of the micro-service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
In one embodiment, performing deduplication processing on duplicate entities in each entity includes: for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity;
for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity;
and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
In one embodiment, the software data in the micro-service system includes service data of each service in the link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by the index monitoring tool.
In one embodiment, the naming convention includes at least one of a naming convention of a business center, a naming convention of a deployment unit, a naming convention of a cluster, a naming convention of a domain name, a naming convention of a node, and a naming convention of a virtual machine, wherein the node is used for deploying an application service.
In one embodiment, the attribute information includes at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
In one embodiment, the association relationship between the entities includes at least one of a calling relationship, a subordinate relationship, an inclusion relationship, a deployment relationship, and a provisioning relationship.
In a second aspect, the present application further provides a knowledge graph construction apparatus, where the apparatus includes:
the acquisition module is used for acquiring software data and hardware data in the micro-service system;
the entity extraction module is used for extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
the attribute extraction module is used for extracting attribute information of each entity from software data and hardware data by adopting a regular expression of the micro-service system;
the determining module is used for determining the association relation among the entities according to the attribute information of the entities;
the construction module is used for constructing the knowledge graph construction of the micro-service system according to each entity, the attribute information of each entity and the association relation among the entities.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
acquiring software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
According to the knowledge graph construction method, the knowledge graph construction device, the computer equipment and the storage medium, the software data and the hardware data in the micro-service system are collected from the operation and maintenance monitoring tool, the naming specification and regular expression-based method is used for extracting the association relationship among the entities and the attribute information of each entity, the knowledge graph construction mode of the micro-service system is simple, the data sources are rich, and the hierarchical relationship and the calling relationship of the micro-service system can be comprehensively displayed.
Drawings
FIG. 1 is a flow chart of a knowledge graph construction method in one embodiment;
FIG. 2 is a schematic diagram of a knowledge graph construction model in one embodiment;
FIG. 3 is a block diagram of a knowledge graph construction method device in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The research and development of the cloud platform business relates to a plurality of business centers and hundreds of micro services, and a complex calling relationship exists between micro service application and equipment, so that the fault cause is difficult to locate. The complex system architecture and the large number of business applications bring great challenges to the development of cloud operation and maintenance. To solve the deficiencies of the conventional operation and maintenance, gartner first proposed the concept of AIOps (Artificial Intelligence for IT Operations, intelligent operation and maintenance). AIOps introduces an artificial intelligence method into operation and maintenance work, relies on a large amount of data provided by a monitoring component, and improves the quality and reliability of IT service.
The knowledge graph is a structured semantic knowledge base and consists of entities, relations and semantic descriptions, and has unique advantages in the fields of intelligent analysis and knowledge reasoning, so that in recent years, a plurality of researches are conducted to introduce the knowledge graph into AIOps. The existing operation and maintenance knowledge graph construction method often uses methods such as data cleaning, clustering and the like to determine entities and relations according to an obtained operation and maintenance corpus, and further generates a knowledge graph. On one hand, the existing method is low in accuracy, information can be lost, and the operation efficiency is low; on the other hand, the data comes from the operation and maintenance document, and the calling relation between the software and hardware devices in the micro-service system is ignored.
The embodiment provides a knowledge graph construction method, which adopts a bottom-up method, namely, firstly, data sources are collected, then, preprocessing and entity identification are carried out on the data, and finally, entities and relations are organized into a graph structure.
Specifically, as shown in fig. 1, a knowledge graph construction method is provided, and the method is applied to computer equipment for illustration, and includes the following steps:
s101, acquiring software data and hardware data in a micro service system.
The software data and the hardware data in the micro-Service system comprise equipment data collected by an index monitoring tool Prometaus, K8S Service data, service data in a link monitoring tool Skywalk and call chain data thereof. These semi-structured data are acquired through Prometaus API, K8S script and Skywalk API, respectively, and converted to structured form for storage.
S102, extracting each entity from the software data and the hardware data according to the naming standards of the micro service system.
It will be appreciated that entity identification can be accomplished using a rule-based approach, as the naming of each service and device within a microservice system follows a naming convention.
Specifically, the concept, i.e., the collection of each entity class of the software and hardware data, mainly includes an entity Center (service Center) at the Center level, an entity Unit (application Unit) at the Unit level, an entity K8S-Pod at the Device level, and a Device (Device).
S103, extracting attribute information of each entity from the software data and the hardware data by adopting a regular expression of the micro-service system.
The conceptual attribute is an attribute set of each entity, and includes a name, a center, an IP address, and the like.
Specifically, the call chain, the application deployment unit, the Pod and the device data are read respectively, and the regular expression is used for identifying the cluster, the name, the device ip, the port and the naming space.
S104, according to the attribute information of each entity, determining the association relation among the entities.
It will be appreciated that the association relationship, i.e., the set of relationships between entities, includes call, belong, has, deployment, and provider.
S105, constructing a knowledge graph construction of the micro service system according to each entity, attribute information of each entity and association relation among the entities.
Exemplary, as shown in table 1, are the ontology elements of the operation and maintenance knowledge graph.
TABLE 1
Specifically, as shown in fig. 2, an operation and maintenance knowledge graph ontology model is constructed based on the above ontology element analysis. The units belong to a certain Center, call relations exist among the units and between the units and the devices, and the Unit can be deployed on a single Device or can be used as a cluster Unit to contain a plurality of devices. The K8S-Pod is deployed on the Device to provide a running environment and resource management for the Unit.
In the knowledge graph construction method, the software data and the hardware data in the micro-service system are collected from the operation and maintenance monitoring tool, the naming specification and regular expression-based method is used for extracting the association relationship among the entities and the attribute information of each entity, the knowledge graph construction mode of the micro-service system is simple, the data sources are rich, and the hierarchical relationship and the calling relationship of the micro-service system can be comprehensively displayed.
In one embodiment, the present embodiment provides an optional way to construct a knowledge graph of the micro service system according to each entity, attribute information of each entity, and association relationships between each entity, that is, provides a way to refine S105. The specific implementation process can comprise the following steps: determining repeated entities in each entity according to the attribute information of each entity; performing de-duplication treatment on repeated entities in each entity; and constructing a knowledge graph construction of the micro-service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
Optionally, performing deduplication processing on duplicate entities in each entity includes: for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity; for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity; and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
It is understood that knowledge fusion refers to integrating data and knowledge from different data sources into a more complete, accurate, consistent knowledge. Because the data sources of the operation and maintenance knowledge graph comprise Prometheus data, K8S data and Skywalk data, and the entity and the relationship extracted from the data sources have repeated problems, knowledge fusion technology is required to be applied, and the data with different sources are subjected to de-duplication through entity alignment and relationship alignment methods.
Optionally, the embodiment mainly applies a rule-based entity alignment policy, and designs an entity alignment scheme according to a naming rule regular expression of the software and hardware data: (1) For the Center entity, according to the data naming rule, a regular expression is used for extracting a naming space, namely mapping and deduplication are achieved. (2) For the Service entity, it is necessary to deduplicate Service data acquired from Skywalking topology data and Service acquired from promethas. The deployment unit names of both may be identified using regular expressions, which are merged into the same entity. (3) For K8S-Pod and Device entities, deduplication of the endpoint attributes in the K8S service data and the Pod, host data obtained from Prometaus is required. The regular expression is used to identify ip addresses from the endings, and then mapped to K8S-Pod and Device entities according to ip.
In one embodiment, the software data in the micro-service system includes service data for each service in the link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by the index monitoring tool.
Further, the naming convention includes at least one of a naming rule of the service center, a naming rule of the deployment unit, a naming rule of the cluster, a naming rule of the domain name, a naming rule of the node, and a naming rule of the virtual machine, wherein the node is used for deploying the application service.
Further, the attribute information includes at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
Further, the association relationship between the entities includes at least one of a calling relationship, a subordinate relationship, a containing relationship, a deployment relationship and a providing relationship.
Specifically, the semi-structured and structured data needs to complete entity identification, attribute extraction and relationship extraction before knowledge can be formed. The invention adopts an entity relationship joint extraction method, and extracts the entity and the relationship at the same time. In the data acquisition flow, the semi-structured software and hardware data are already converted into structured data and stored in a relational database, so that the relational data can be directly converted into an entity-relation-entity triplet based on the field relation among tables, and the attribute of the entity can be directly acquired according to the fields of the tables.
Illustratively, in one embodiment, the knowledge graph construction method described above is implemented within a single docker container, the operating environment and the required key components of which are as follows: java environment: openjdk11.0.8neo4j version: neo4j-community 4.3.21; mysql version: 5.6.51.
optionally, after the software and hardware data are collected, the data are in a structured form according to the field Mysql. Because naming of each service and device in the micro-service system follows naming standards, entity identification can be accomplished using rule-based approaches. The call chain, the application deployment unit, the Pod and the device data are read respectively, and the regular expression is used for identifying the cluster, the name, the device ip, the port and the naming space. Establishing a belong relation between the nodes and the corresponding centers according to the corresponding relation between the namespaces and the service centers; extracting devices contained in the clusters by using the regular expressions, and respectively establishing a has relation; the end or device of the current service agent may be determined by the end field of the K8S service data, thereby establishing a provided or reduced_in relationship.
Different data may have different naming schemes and designations for the same entity, e.g. IP addresses are used in the cluster to indicate the virtual machine devices involved, and device names are used in the device table to indicate a certain virtual machine. Therefore, entity alignment is also required in the construction process, and is also completed by using regular expressions, organization structure relations among tables and among fields in a rule-based manner. In the micro-service system, software and hardware data are frequently changed along with the updating of the service, and the data of the knowledge graph are also updated at regular time.
The embodiment provides an operation and maintenance knowledge graph construction method based on a micro-service system call chain. Aiming at the problems of low construction accuracy and imperfect knowledge in the prior art, the method provided by the embodiment constructs the operation and maintenance knowledge graph through the steps of data acquisition, ontology construction, knowledge extraction, knowledge fusion and knowledge storage. Specifically, firstly, semi-structured and structured knowledge such as software and hardware data, call chain data and the like of a micro-service system is obtained through a plurality of operation tools; then, referring to experience of field experts, designing ontology data modeling according to the operation and maintenance data, and constructing entities, attributes and relations based on the modeling; because the data sources of the knowledge are different, knowledge fusion is also required; and finally, storing the knowledge graph by using Neo4 j. The method introduces the collected data of a plurality of operation and maintenance tools, more comprehensively reflects the relation among the devices, improves the accuracy and usability of the knowledge graph, and is used as a basic component of intelligent operation and maintenance.
It should be understood that, although the steps in the flowcharts related to the above embodiments 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.
Based on the same inventive concept, the embodiment of the application also provides a knowledge graph construction method device for realizing the knowledge graph construction method. The implementation scheme of the solution provided by the device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiment of the device of the method for constructing a knowledge graph provided below may refer to the limitation of the method for constructing a knowledge graph hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 3, there is provided a knowledge graph construction method apparatus 1, including: an acquisition module 11, an entity extraction module 12, an attribute extraction module 13, a determination module 14 and a construction module 15, wherein:
an acquisition module 11, configured to acquire software data and hardware data in the micro service system;
an entity extraction module 12, configured to extract each entity from the software data and the hardware data according to the naming convention of the micro service system;
the attribute extraction module 13 is used for extracting attribute information of each entity from the software data and the hardware data by adopting a regular expression of the micro-service system;
a determining module 14, configured to determine an association relationship between entities according to attribute information of each entity;
and the construction module 15 is used for constructing the knowledge graph construction of the micro-service system according to each entity, the attribute information of each entity and the association relation among the entities.
In one embodiment, the construction module 15 is further configured to: determining repeated entities in each entity according to the attribute information of each entity;
performing de-duplication treatment on repeated entities in each entity;
and constructing a knowledge graph construction of the micro-service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
In one embodiment, the construction module 15 is further configured to:
for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity;
for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity;
and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
In one embodiment, the software data in the micro-service system includes service data for each service in the link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by the index monitoring tool.
In one embodiment, the naming convention includes at least one of a naming convention for a business center, a naming convention for a deployment unit, a naming convention for a cluster, a naming convention for a domain name, a naming convention for a node, and a naming convention for a virtual machine, wherein the node is used for deploying an application service.
In one embodiment, the attribute information includes at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
In one embodiment, the association relationship between the entities includes at least one of a calling relationship, a subordinate relationship, an inclusion relationship, a deployment relationship, and a provisioning relationship.
All or part of the modules in the knowledge graph construction method device can be realized by software, hardware and a combination 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, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data of the knowledge graph construction method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a knowledge graph construction method.
Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or 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 software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
In one embodiment, when the processor executes the computer program to construct the logic of knowledge graph construction of the micro service system according to each entity, attribute information of each entity and association relation among each entity, the following steps are specifically implemented: determining repeated entities in each entity according to the attribute information of each entity; performing de-duplication treatment on repeated entities in each entity; and constructing a knowledge graph construction of the micro-service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
In one embodiment, when the processor executes logic for performing deduplication processing on duplicate ones of the entities, the following steps are specifically implemented: for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity; for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity; and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
In one embodiment, the software data in the micro-service system includes service data for each service in the link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by the index monitoring tool.
In one embodiment, the naming convention includes at least one of a naming convention for a business center, a naming convention for a deployment unit, a naming convention for a cluster, a naming convention for a domain name, a naming convention for a node, and a naming convention for a virtual machine, wherein the node is used for deploying an application service.
In one embodiment, the attribute information includes at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
In one embodiment, the association relationship between the entities includes at least one of a calling relationship, a subordinate relationship, an inclusion relationship, a deployment relationship, and a provisioning relationship.
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 software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
In one embodiment, the computer program, according to each entity, attribute information of each entity and association relation between each entity, specifically implements the following steps when the logic for constructing the knowledge graph of the micro service system is executed by the processor: determining repeated entities in each entity according to the attribute information of each entity; performing de-duplication treatment on repeated entities in each entity; and constructing a knowledge graph construction of the micro-service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
In one embodiment, the logic of the computer program for performing deduplication processing on duplicate entities in each entity is executed by the processor, and specifically implements the following steps: for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity; for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity; and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
In one embodiment, the software data in the micro-service system includes service data for each service in the link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by the index monitoring tool.
In one embodiment, the naming convention includes at least one of a naming convention for a business center, a naming convention for a deployment unit, a naming convention for a cluster, a naming convention for a domain name, a naming convention for a node, and a naming convention for a virtual machine, wherein the node is used for deploying an application service.
In one embodiment, the attribute information includes at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
In one embodiment, the association relationship between the entities includes at least one of a calling relationship, a subordinate relationship, an inclusion relationship, a deployment relationship, and a provisioning relationship.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming standards of the micro-service system;
extracting attribute information of each entity from software data and hardware data by adopting a regular expression of a micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro-service system according to each entity, attribute information of each entity and association relation among the entities.
In one embodiment, the computer program, according to each entity, attribute information of each entity and association relation between each entity, specifically implements the following steps when the logic for constructing the knowledge graph of the micro service system is executed by the processor: determining repeated entities in each entity according to the attribute information of each entity; performing de-duplication treatment on repeated entities in each entity; and constructing a knowledge graph construction of the micro-service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
In one embodiment, the logic of the computer program for performing deduplication processing on duplicate entities in each entity is executed by the processor, and specifically implements the following steps: for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity; for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity; and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
In one embodiment, the software data in the micro-service system includes service data for each service in the link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by the index monitoring tool.
In one embodiment, the naming convention includes at least one of a naming convention for a business center, a naming convention for a deployment unit, a naming convention for a cluster, a naming convention for a domain name, a naming convention for a node, and a naming convention for a virtual machine, wherein the node is used for deploying an application service.
In one embodiment, the attribute information includes at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
In one embodiment, the association relationship between the entities includes at least one of a calling relationship, a subordinate relationship, an inclusion relationship, a deployment relationship, and a provisioning relationship.
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 the various 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 various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being 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 above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (11)
1. The knowledge graph construction method is characterized by comprising the following steps of:
acquiring software data and hardware data in a micro-service system;
extracting each entity from the software data and the hardware data according to the naming specification of the micro service system;
extracting attribute information of each entity from the software data and the hardware data by adopting a regular expression of the micro-service system;
determining the association relation among the entities according to the attribute information of the entities;
and constructing a knowledge graph construction of the micro service system according to each entity, attribute information of each entity and association relation among the entities.
2. The method according to claim 1, wherein the constructing the knowledge graph construction of the micro service system according to each entity, attribute information of each entity, and association relation between each entity comprises:
determining repeated entities in each entity according to the attribute information of each entity;
performing de-duplication treatment on repeated entities in each entity;
and constructing a knowledge graph construction of the micro service system according to the de-duplicated entities, the attribute information of the entities and the association relation among the entities.
3. The method of claim 2, wherein performing deduplication processing on duplicate ones of the entities comprises:
for the repeated entity of the center type, carrying out deduplication processing on the repeated entity according to the naming space of the repeated entity;
for the repeated entity of the service type, performing deduplication processing on the repeated entity according to the deployment unit name of the repeated entity;
and for the repeated entity of the container type, performing deduplication processing on the repeated entity according to the IP address of the container.
4. The method of claim 1, wherein the software data in the micro-service system includes service data for each service in a link monitoring tool and call chain data between each service; the hardware data in the micro-service system comprises equipment data and cluster service data collected by an index monitoring tool.
5. The method of claim 1, wherein the naming convention comprises at least one of a naming convention for a business center, a naming convention for a deployment unit, a naming convention for a cluster, a naming convention for a domain name, a naming convention for a node, and a naming convention for a virtual machine, wherein a node is used to deploy an application service.
6. The method of claim 1, wherein the attribute information comprises at least one of a namespace name, a unit name, a home center, a center name, a call chain identification, a node name, a node address, a device name, and a device address.
7. The method of claim 1, wherein the association between entities comprises at least one of a calling relationship, a subordinate relationship, a containment relationship, a deployment relationship, and a provisioning relationship.
8. A knowledge graph construction apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring software data and hardware data in the micro-service system;
the entity extraction module is used for extracting each entity from the software data and the hardware data according to the naming standards of the micro service system;
the attribute extraction module is used for extracting attribute information of each entity from the software data and the hardware data by adopting a regular expression of the micro-service system;
the determining module is used for determining the association relation among the entities according to the attribute information of the entities;
the construction module is used for constructing the knowledge graph construction of the micro service system according to each entity, the attribute information of each entity and the association relation among the entities.
9. 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.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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