Data association method and device, computer device and storage medium

Abstract

This application relates to a data association method, apparatus, computer equipment, storage medium, and computer program product, and pertains to the field of cloud computing. The method includes: acquiring address data corresponding to each node to be associated; the node categories of the nodes to be associated include host machines and virtual machines; determining at least one pair of nodes to be associated based on the address data corresponding to each node to be associated, the pair including a first node to be associated and a second node to be associated, the first node to be associated and the second node to be associated corresponding to different node categories; for any pair of nodes to be associated, determining first data of the first node to be associated stored in a first database and second data of the second node to be associated stored in a second database, and establishing an association relationship between the first data and the second data. This method can establish association relationships between data of nodes to be associated from different node categories.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, and in particular to a data association method, apparatus, computer device, and storage medium. Background Technology

[0002] Currently, cloud computing platforms (such as OpenStack) can provide users with Infrastructure as a Service (IaaS). The OpenStack cloud platform uses the Ceilometer monitoring component to collect and process data about IaaS on the host machine. This data includes, for example, instance usage, network traffic, and storage usage.

[0003] Cloud computing platforms (such as OpenStack) can also provide users with Platform as a Service (PaaS). Users can leverage open-source container orchestration platforms (such as Kubernetes) to automate the deployment, scaling, and management of containerized applications within virtual machines. The Kubernetes container orchestration platform uses the Prometheus monitoring system to monitor and collect data about Platform as a Service within virtual machines.

[0004] Because the monitoring methods for Infrastructure as a Service (IaaS) data on the host machine and Platform as a Service (PAS) data on the virtual machine are different, users cannot intuitively know whether there is a correlation between IaaS data and PAS data. Summary of the Invention

[0005] Therefore, it is necessary to provide a data association method, apparatus, computer device, computer-readable storage medium, and computer program product that can establish relationships between data to address the aforementioned technical problems.

[0006] Firstly, this application provides a data association method. The method includes:

[0007] Obtain the address data corresponding to each node to be associated; the node categories of the nodes to be associated include host machines and virtual machines;

[0008] Based on the address data corresponding to each node to be associated, at least one pair of nodes to be associated is determined. The pair of nodes to be associated includes a first node to be associated and a second node to be associated. The node categories corresponding to the first node to be associated and the second node to be associated are different.

[0009] For any pair of nodes to be associated, determine the first data of the first node to be associated stored in the first database and the second data of the second node to be associated stored in the second database, and establish the association between the first data and the second data.

[0010] In one embodiment, the address data corresponding to the host machine includes the address data of the virtual machines deployed in the host machine, and determining at least one pair of nodes to be associated based on the address data corresponding to each node to be associated includes:

[0011] For any of the virtual machines, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, a pair of nodes to be associated is created based on the virtual machine and the host machine, and the virtual machine is designated as the first node to be associated in the pair of nodes to be associated, and the host machine is designated as the second node to be associated in the pair of nodes to be associated; the first data of the first node to be associated includes data about Platform as a Service in the virtual machine; the second data of the second node to be associated includes data about Infrastructure as a Service in the host machine.

[0012] In one embodiment, obtaining the address data corresponding to each node to be associated includes:

[0013] If the node category of the node to be associated is the host machine, then at least one virtual machine deployed in the host machine is identified;

[0014] The address data of each virtual machine deployed in the host machine is used as the address data corresponding to the host machine.

[0015] In one embodiment, the method further includes:

[0016] The data category of the first data is obtained from the first database storage, and the data category of the second data is obtained from the second database;

[0017] Establishing the association between the first data and the second data includes:

[0018] If the data category of the first data is consistent with the data category of the second data, an association relationship is established between the first data and the second data.

[0019] In one embodiment, establishing the association between the first data and the second data further includes:

[0020] If the data category of the first data is inconsistent with the data category of the second data, it is determined whether the data category of the first data and the data category of the second data are related according to a preset data category correspondence relationship; the data category correspondence relationship is used to characterize the correspondence relationship between each data category of the first node to be associated and each data category of the second node to be associated.

[0021] When the data category of the first data is associated with the data category of the second data, an association relationship is established between the first data and the second data.

[0022] In one embodiment, the method further includes:

[0023] Obtain the first collection period of the first node to be associated and the second collection period of the second node to be associated;

[0024] Acquire the first acquisition time of the first data and the second acquisition time of the second data;

[0025] Establishing the association between the first data and the second data includes:

[0026] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, determine the second acquisition time that matches each of the first acquisition times.

[0027] If the first acquisition time of the first data matches the second acquisition time of the second data, an association relationship is established between the first data and the second data.

[0028] In one embodiment, establishing the association between the first data and the second data when the first acquisition time of the first data matches the second acquisition time of the second data includes:

[0029] When the first acquisition time of the first data matches the second acquisition time of the second data, the data category of the first data is obtained from the first database storage, and the data category of the second data is obtained from the second database.

[0030] If the data category of the first data is consistent with the data category of the second data, an association relationship is established between the first data and the second data.

[0031] In one embodiment, determining the second acquisition time that matches each of the first acquisition times based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated includes:

[0032] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, a reference node and a reference node are determined among the first node to be associated and the second node to be associated.

[0033] Based on the acquisition cycle of the reference node and the acquisition time of each data point of the reference node, the matching time period corresponding to each acquisition time of the reference node is determined.

[0034] For any of the acquisition times of the reference node, if the acquisition time of the reference node belongs to the matching time period corresponding to the acquisition time of the reference node, it is determined that the acquisition time of the reference node matches the acquisition time of the reference node.

[0035] Secondly, this application also provides a data association device. The device includes:

[0036] The first acquisition module is used to acquire the address data corresponding to each node to be associated; the node categories of the nodes to be associated include host machines and virtual machines;

[0037] The first determining module is used to determine at least one pair of nodes to be associated based on the address data corresponding to each node to be associated. The pair of nodes to be associated includes a first node to be associated and a second node to be associated, and the first node to be associated and the second node to be associated correspond to different node categories.

[0038] The module is configured to, for any pair of nodes to be associated, determine the first data of the first node to be associated stored in the first database and the second data of the second node to be associated stored in the second database, and establish the association between the first data and the second data.

[0039] In one embodiment, the address data corresponding to the host machine includes the address data of the virtual machines deployed in the host machine, and determining at least one pair of nodes to be associated based on the address data corresponding to each node to be associated includes:

[0040] For any of the virtual machines, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, a pair of nodes to be associated is created based on the virtual machine and the host machine, and the virtual machine is designated as the first node to be associated in the pair of nodes to be associated, and the host machine is designated as the second node to be associated in the pair of nodes to be associated; the first data of the first node to be associated includes data about Platform as a Service in the virtual machine; the second data of the second node to be associated includes data about Infrastructure as a Service in the host machine.

[0041] In one embodiment, the first acquisition module is specifically used for:

[0042] If the node category of the node to be associated is the host machine, then at least one virtual machine deployed in the host machine is identified;

[0043] The address data of each virtual machine deployed in the host machine is used as the address data corresponding to the host machine.

[0044] In one embodiment, the data association device further includes:

[0045] The second acquisition module is used to acquire the data category of the first data from the first database storage and the data category of the second data from the second database.

[0046] The establishment module is specifically used for:

[0047] If the data category of the first data is consistent with the data category of the second data, an association relationship is established between the first data and the second data.

[0048] In one embodiment, the establishment module is further configured to:

[0049] If the data category of the first data is inconsistent with the data category of the second data, it is determined whether the data category of the first data and the data category of the second data are related according to a preset data category correspondence relationship; the data category correspondence relationship is used to characterize the correspondence relationship between each data category of the first node to be associated and each data category of the second node to be associated.

[0050] When the data category of the first data is associated with the data category of the second data, an association relationship is established between the first data and the second data.

[0051] In one embodiment, the data association device further includes:

[0052] The third acquisition module is used to acquire the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated.

[0053] The fourth acquisition module is used to acquire the first acquisition time of the first data and the second acquisition time of the second data;

[0054] The establishment module is specifically used for:

[0055] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, determine the second acquisition time that matches each of the first acquisition times.

[0056] If the first acquisition time of the first data matches the second acquisition time of the second data, an association relationship is established between the first data and the second data.

[0057] In one embodiment, the establishment module is specifically used for:

[0058] When the first acquisition time of the first data matches the second acquisition time of the second data, the data category of the first data is obtained from the first database storage, and the data category of the second data is obtained from the second database.

[0059] If the data category of the first data is consistent with the data category of the second data, an association relationship is established between the first data and the second data.

[0060] In one embodiment, the establishment module is specifically used for:

[0061] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, a reference node and a reference node are determined among the first node to be associated and the second node to be associated.

[0062] Based on the acquisition cycle of the reference node and the acquisition time of each data point of the reference node, the matching time period corresponding to each acquisition time of the reference node is determined.

[0063] For any of the acquisition times of the reference node, if the acquisition time of the reference node belongs to the matching time period corresponding to the acquisition time of the reference node, it is determined that the acquisition time of the reference node matches the acquisition time of the reference node.

[0064] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps described in the first aspect.

[0065] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps described in the first aspect.

[0066] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the steps described in the first aspect.

[0067] The aforementioned data association method, apparatus, computer equipment, storage medium, and computer program product acquire address data corresponding to each node to be associated. The node categories of the nodes to be associated include host machines and virtual machines. Based on the address data corresponding to each node to be associated, at least one pair of nodes to be associated is determined, including a first node to be associated and a second node to be associated, where the first and second nodes to be associated correspond to different node categories. For any pair of nodes to be associated, the first data of the first node to be associated, stored in a first database, and the second data of the second node to be associated, stored in a second database, are determined, and an association relationship is established between the first and second data. In this method, based on the address data corresponding to each node to be associated, a pair of nodes to be associated, including a first node to be associated and a second node to be associated, is determined, thereby establishing an association relationship between the first data of the first node to be associated and the second data of the second node to be associated. Since the first and second nodes to be associated correspond to different node categories, this method can establish association relationships between data of nodes to be associated from different node categories. It is understood that the first node to be associated can include a virtual machine, and the second node to be associated can include the host machine. This means that this method can establish a relationship between the data of the virtual machine and the data of the host machine, allowing users to intuitively understand the relationship between them. Furthermore, since the first data of the first node to be associated is stored in the first database, and the second data of the second node to be associated is stored in the second database, this method can establish a relationship between the data of the virtual machine and the data of the host machine without increasing the storage capacity of any single database (either the first or the second database). Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating a data association method in one embodiment;

[0069] Figure 2 This is a flowchart illustrating a method for establishing associations in one embodiment;

[0070] Figure 3 This is a flowchart illustrating the method for establishing associations in another embodiment;

[0071] Figure 4 This is a flowchart illustrating a method for matching acquisition times in one embodiment;

[0072] Figure 5 This is a flowchart illustrating the method for establishing associations in another embodiment;

[0073] Figure 6 This is a structural block diagram of a data association device in one embodiment;

[0074] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0076] In one embodiment, such as Figure 1 As shown, a data association method is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0077] Step 102: Obtain the address data corresponding to each node to be associated.

[0078] Among them, the node categories of the nodes to be associated include host machines and virtual machines.

[0079] In this embodiment, the terminal obtains the address data corresponding to each node to be associated. The address data includes Internet Protocol Addresses (IP addresses), and the address data corresponding to the host machine includes the address data of the virtual machines deployed on that host machine. In one embodiment, the host machine is deployed on the OpenStack cloud computing platform, and the terminal queries the Internet Protocol Addresses of each host machine and each virtual machine through the OpenStack Application Programming Interface (API). The terminal can also query the physical resource data of the nodes to be associated through the OpenStack API. The physical resource data includes the computing data, network data, and storage data of the nodes to be associated. Specifically, the physical resource data includes the identity document (id) of the node to be associated, the data category of the node to be associated, the Internet Protocol Address of the storage node corresponding to the node to be associated, the network hardware device information of the node to be associated, the region to which the node to be associated belongs, the availability zone (azone) to which the node to be associated belongs, and the cluster to which the node to be associated belongs. For example, the terminal calls the OpenStack API through a physical resource collector to query the physical resource data and Internet Protocol Addresses of each host machine. It's understandable that different OpenStack application programming interfaces (APIs) might be used to access different data.

[0080] Step 104: Determine at least one pair of nodes to be associated based on the address data corresponding to each node to be associated.

[0081] Among them, the node pairs to be associated include a first node to be associated and a second node to be associated, and the node categories corresponding to the first node to be associated and the second node to be associated are different.

[0082] In this embodiment, the terminal determines at least one pair of nodes to be associated based on the address data corresponding to each host machine and the address data corresponding to each virtual machine. It can be understood that each pair of nodes to be associated includes only one host machine and one virtual machine deployed on that host machine. If multiple virtual machines are deployed on a host machine, then that host machine belongs to multiple pairs of nodes to be associated. For example, assuming that host machine 1 has virtual machine 1 and virtual machine 2 deployed on it, then host machine 1 belongs to node pair 1 (host machine 1, virtual machine 1) and node pair 2 (host machine 1, virtual machine 2).

[0083] Step 106: For any pair of nodes to be associated, determine the first data of the first node to be associated stored in the first database and the second data of the second node to be associated stored in the second database, and establish the association between the first data and the second data.

[0084] In this embodiment, for any pair of nodes to be associated, the terminal determines the first data of the first node to be associated in the first database and the second data of the second node to be associated in the second database. For any pair of nodes to be associated, the terminal establishes an association between the first data of the first node to be associated and the second data of the second node to be associated. In one embodiment, the host machine's data is stored in the ClickHouse database, and the virtual machine's data is stored in the database corresponding to the Prometheus monitoring system. The data (including the first data and the second data) includes a data category, a data value, and a collection time. Optionally, the data can be detection data or monitoring data. When the data is monitoring data, the data category is a monitoring category, and the data value is a monitoring value. The monitoring category includes, but is not limited to, Central Processing Unit (CPU) utilization and memory utilization.

[0085] In one embodiment, the terminal configures access to the ClickHouse database and the corresponding database of the Prometheus monitoring system within the Grafana analytics platform. For any pair of nodes to be associated, the terminal establishes an association between the first data of the first node to be associated and the second data of the second node to be associated within the Grafana analytics platform. Therefore, the terminal can display the topology information of the virtual machine and the host machine, as well as the association between the various data related to Infrastructure as a Service in the host machine and the various data related to Platform as a Service in the virtual machine, within the Grafana analytics platform.

[0086] In one embodiment, the terminal further integrates the physical resource data of the node to be associated with the initial data of the node to be associated to obtain the data of the node to be associated. The initial data includes data category, data value, and collection time. For example, the terminal collects the initial data of the host machine through a resource monitoring indicator collector. Specifically, for any data category, the terminal, according to the second collection period corresponding to the data category, calls the application programming interface (API) used for data collection through the resource monitoring indicator collector to collect the initial data of the host machine. Optionally, the terminal can first store the physical resource data and initial data of each node to be associated in an intermediate database, and after integration processing, store the data of the node to be associated in a target database (including a first database or a second database). For example, the intermediate database can be Elasticsearch, and the terminal can call a REST application programming interface to retrieve the data stored in Elasticsearch. Since the data before integration processing is stored in the intermediate database, while the data after integration processing is stored in the target database, the storage pressure on a single database can be reduced, and the data retrieval efficiency can be accelerated.

[0087] For example, the data of the host machine is stored in the database in the following format: data name {data category, host machine identification number, host machine Internet Protocol address, region to which the host machine belongs, azone to which the host machine belongs, cluster to which the host machine belongs, Internet Protocol address of the virtual machine deployed on the host machine}, data value, collection time. For example, the CPU usage data of the host machine is stored in the database in the following format: cpu_usage{id="", hostId="", hostIpAddress="", azone="", region="", cluster="", vm1="", vm2="}, value, timestamp. For any pair of nodes to be associated, the terminal determines the first data of the first node to be associated in the first database storage based on the address data (or identification number) of the first node to be associated, and determines the second data of the second node to be associated in the second database storage based on the address data (or identification number) of the second node to be associated.

[0088] In the aforementioned data association method, based on the address data corresponding to each node to be associated, a pair of nodes to be associated, including a first node and a second node, is determined, thereby establishing an association between the first data of the first node and the second data of the second node. Since the first and second nodes to be associated correspond to different node categories, this method can establish associations between data of nodes of different node categories. It can be understood that the first node to be associated can include a virtual machine, and the second node to be associated can include a host machine; that is, this method can establish an association between the data of a virtual machine and the data of a host machine, allowing users to intuitively understand the association between the data of the virtual machine and the data of the host machine. Furthermore, since the first data of the first node to be associated is stored in the first database, and the second data of the second node to be associated is stored in the second database, this method can establish an association between the data of the virtual machine and the data of the host machine without increasing the storage capacity of any single database (either the first or the second database).

[0089] In one embodiment, the address data corresponding to the host machine includes the address data of the virtual machines deployed on the host machine. Based on the address data corresponding to each node to be associated, at least one pair of nodes to be associated is determined, including:

[0090] For any virtual machine, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, a pair of nodes to be associated is created based on the virtual machine and the host machine, and the virtual machine is the first node to be associated in the pair of nodes to be associated, and the host machine is the second node to be associated in the pair of nodes to be associated.

[0091] The first data of the first node to be associated includes data about Platform as a Service in the virtual machine; the second data of the second node to be associated includes data about Infrastructure as a Service in the host machine.

[0092] In this embodiment, the terminal performs pairing processing on each host machine and each virtual machine based on the address data corresponding to the host machine and the address data corresponding to the virtual machine. For any virtual machine, if the address data corresponding to the host machine includes the address data corresponding to the virtual machine, the terminal determines that the address data corresponding to the virtual machine matches the address data corresponding to the host machine. For any virtual machine, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, the terminal creates a pair of nodes to be associated based on the virtual machine and the host machine, and designates the virtual machine as the first node to be associated in the pair, and the host machine as the second node to be associated in the pair.

[0093] In this embodiment, by determining whether the virtual machine and the host machine match based on the address data corresponding to the virtual machine and the address data corresponding to the host machine, and if the virtual machine and the host machine match, a pair of nodes to be associated is created based on the virtual machine and the host machine that matches the virtual machine. Therefore, this method can create pairs of nodes to be associated that match in address data, thereby providing a prerequisite for subsequent methods to establish data association relationships based on the pairs of nodes to be associated.

[0094] In one embodiment, obtaining the address data corresponding to each node to be associated includes:

[0095] If the node category of the node to be associated is a host machine, determine at least one virtual machine deployed in the host machine; use the address data of each virtual machine deployed in the host machine as the address data corresponding to the host machine.

[0096] In this embodiment, the terminal identifies the node category of each node to be associated. For any node to be associated, if the node category is a host machine, the terminal queries at least one virtual machine deployed on that host machine and uses the address data of each virtual machine deployed on the host machine as the address data corresponding to that host machine. For any node to be associated, if the node category is a virtual machine, the terminal uses the address data of that virtual machine as the address data corresponding to that virtual machine.

[0097] In this embodiment, the address data of each virtual machine deployed on the host machine is used as the address data corresponding to the host machine, and the address data of the virtual machine itself is used as the address data corresponding to the virtual machine. Therefore, this method can determine that the address data corresponding to each node to be associated is the address data of the virtual machine, thus providing a prerequisite for subsequently determining the node pair to be associated based on the address data of the node to be associated.

[0098] In one embodiment, such as Figure 2 As shown, the method also includes:

[0099] Step 202: Obtain the data category of the first data from the first database storage and the data category of the second data from the second database;

[0100] Establish the relationship between the first data and the second data, including:

[0101] Step 204: If the data category of the first data is consistent with the data category of the second data, establish the association between the first data and the second data.

[0102] In this embodiment, for any first data, the terminal obtains the data category of the first data from the first database and queries the second database for the data category of the first data. If the data category of the first data is found in the second database (i.e., the data category of the first data matches the data category of the second data), the terminal establishes an association between the first data corresponding to that data category and the second data corresponding to that data category. For example, assuming the data category of first data 1 is memory usage rate, the terminal queries the second database to see if a second data 1 corresponding to memory usage rate exists. If so, the terminal establishes an association between the first data 1 and the second data 1 corresponding to memory usage rate.

[0103] In another embodiment, for any first data, the terminal obtains the data category of the first data from the first database, and queries the data category of the second data associated with the data category of the first data (referred to as the first associated data category for easy distinction) based on the data category of the first data and a preset data category correspondence. For any first associated data category corresponding to the first data, the terminal determines the second data corresponding to the first associated data category from the second database based on the first associated data category, and establishes an association relationship between the first data and the second data corresponding to the first associated data category.

[0104] In another embodiment, for any second data, the terminal retrieves the data category of the second data from the second database, and queries the data category of the first data associated with the data category of the second data (referred to as the second associated data category for easy distinction) based on the data category of the second data and a preset data category correspondence. For any second associated data category corresponding to the second data, the terminal determines the first data corresponding to the second associated data category from the first database based on the second associated data category, and establishes an association relationship between the first data corresponding to the second associated data category and the second data.

[0105] In this embodiment, when the data category of the first data and the data category of the second data are consistent, an association relationship is established between the first data of the first node to be associated and the second data of the second node to be associated. Since the node categories of the first node to be associated and the second node to be associated are different, this method can establish an association relationship between the data of nodes to be associated with different node categories.

[0106] In one embodiment, such as Figure 2 As shown, the method also includes:

[0107] Step 206: If the data category of the first data is inconsistent with the data category of the second data, determine whether the data category of the first data and the data category of the second data are related according to the preset data category correspondence relationship.

[0108] The data category correspondence is used to represent the correspondence between the data categories of the first node to be associated and the data categories of the second node to be associated. The data category correspondence can be one-to-many, many-to-many, or many-to-one. The data category correspondence is preset according to actual needs and human experience.

[0109] In this embodiment, when the data category of the first data and the data category of the second data are inconsistent, the terminal queries the data category of the second data associated with the data category of the first data (referred to as the first associated data category for ease of distinction) based on the data category of the first data and a preset data category correspondence, and determines whether the data category of the second data belongs to the first associated data category. For example, suppose that in the preset data category correspondence, data category n is associated with data category 1 of the first data 1, where data category n belongs to the data category of the second data. If the data category of the second data 1 is data category 2, then the terminal queries the data category n associated with data category 1 of the first data 1 based on the data category 1 of the first data 1 and the preset data category correspondence. The terminal then determines whether the data category 2 of the second data 1 belongs to data category n.

[0110] In another embodiment, when the data category of the first data is inconsistent with the data category of the second data, the terminal queries the data category of the first data associated with the data category of the second data (referred to as the second associated data category for easy distinction) based on the data category of the second data and a preset data category correspondence, and determines whether the data category of the first data belongs to the second associated data category.

[0111] Step 208: If the data category of the first data is associated with the data category of the second data, establish the association between the first data and the second data.

[0112] In this embodiment, if the data category of the second data belongs to the first associated data category, the terminal determines that the data category of the first data is associated with the data category of the second data. Alternatively, if the data category of the first data belongs to the second associated data category, the terminal determines that the data category of the first data is associated with the data category of the second data. When the data categories of the first data and the second data are associated, the terminal establishes an association between the first data and the second data.

[0113] In this embodiment, when the data category of the first data and the data category of the second data are inconsistent, an association relationship is established between the first data of the first node to be associated and the second data of the second node to be associated, based on a preset data category correspondence. Since the first and second nodes to be associated correspond to different node categories, this method can establish an association relationship between data of nodes to be associated with different node categories.

[0114] In one embodiment, the method further includes:

[0115] Obtain the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated; obtain the first acquisition time of the first data and the second acquisition time of the second data;

[0116] Establish the relationship between the first data and the second data, including:

[0117] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, determine the second acquisition time that matches each first acquisition time; if the first acquisition time of the first data matches the second acquisition time of the second data, establish the association relationship between the first data and the second data.

[0118] In this embodiment, the terminal acquires a first acquisition period of a first node to be associated and a second acquisition period of a second node to be associated. The terminal acquires a first acquisition time of first data and a second acquisition time of second data. For any first acquisition time, the terminal determines at least one second acquisition time that matches the first acquisition time based on the first acquisition period of the first node to be associated in the node pair, the second acquisition period of the second node to be associated in the node pair, and the first acquisition time. If the first acquisition time of the first data matches the second acquisition time of the second data, the terminal establishes an association between the first data and the second data.

[0119] In this embodiment, a second acquisition time matching the first acquisition time is determined based on the first acquisition period and the second acquisition period. When the first acquisition time of the first data matches the second acquisition time of the second data, an association relationship is established between the first data and the second data. Since the node categories of the nodes to be associated corresponding to the first data and the second data are different, this method can establish association relationships between data of nodes of different node categories.

[0120] In one embodiment, such as Figure 3 As shown, when the first acquisition time of the first data matches the second acquisition time of the second data, the association between the first data and the second data is established, including:

[0121] Step 302: If the first acquisition time of the first data matches the second acquisition time of the second data, retrieve the data category of the first data from the first database storage and the data category of the second data from the second database.

[0122] In this embodiment of the application, when the first acquisition time of the first data matches the second acquisition time of the second data, the terminal obtains the data category of the first data from the first database and queries the data category of the first data from the second database.

[0123] Step 304: If the data category of the first data is consistent with the data category of the second data, establish the association between the first data and the second data.

[0124] In this embodiment, when the data category of the first data is found in the second database (i.e., the data category of the first data is consistent with the data category corresponding to the second data), the terminal establishes an association between the first data corresponding to that data category and the second data corresponding to that data category. When the data category of the first data is inconsistent with the data category of the second data, the terminal determines the target data category associated with the data category based on a preset data category correspondence, and establishes an association between the first data corresponding to the data category and the second data corresponding to the target data category.

[0125] In another embodiment, when the first acquisition time of the first data matches the second acquisition time of the second data, the terminal obtains the data category of the first data from the first database, and determines the target data category associated with the data category of the first data based on a preset data category correspondence and the data category of the first data. For any first data, the terminal determines the second data corresponding to the target data category from the second database based on the target data category, and establishes an association between the first data corresponding to the data category and the second data corresponding to the target data category.

[0126] In this embodiment, an association relationship is established between the first data of the first node to be associated and the second data of the second node to be associated, based on the acquisition period, acquisition time, data category, and data category correspondence. Since the first and second nodes to be associated correspond to different node categories, this method can establish an association relationship between data of nodes to be associated with different node categories.

[0127] In one embodiment, such as Figure 4 As shown, based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, the second acquisition time matching each first acquisition time is determined, including:

[0128] Step 402: Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, determine the baseline node and the reference node among the first node to be associated and the second node to be associated.

[0129] In this embodiment, for any pair of nodes to be associated, the terminal compares the first acquisition period of the first node to be associated in the pair with the second acquisition period of the second node to be associated. For any pair of nodes to be associated, if the first acquisition period of the first node to be associated is greater than the second acquisition period of the second node to be associated, the terminal uses the first node to be associated as the base node and the second node to be associated as the reference node. For any pair of nodes to be associated, if the first acquisition period of the first node to be associated is less than the second acquisition period of the second node to be associated, the terminal uses the second node to be associated as the base node and the first node to be associated as the reference node.

[0130] When the data collection periods (referred to as alternative collection periods for ease of distinction) for each data category of the nodes to be associated are different, the collection period for the nodes to be associated can optionally be the average, maximum, median, or mode of each data category's collection period, or it can be a preset collection period for the data category. For example, node pair 1 to be associated includes a first node to be associated 1 and a second node to be associated 1, wherein the first collection period for the first node to be associated 1 is 1 second, and the second collection period for the second node to be associated 1 is 1 minute. Since 1 second is less than 1 minute, in node pair 1 to be associated, the first node to be associated 1 is the reference node, and the second node to be associated 1 is the baseline node.

[0131] Step 404: Based on the acquisition cycle of the reference node and the acquisition time of each data point of the reference node, determine the matching time period corresponding to each acquisition time of the reference node.

[0132] In this embodiment, for any data from a reference node, the terminal calculates the matching time period corresponding to the data acquisition time of the reference node based on the data acquisition cycle of the reference node and the data acquisition time of the reference node. The initial time of the matching time period is the data acquisition time of the reference node, and the duration of the matching time period is the data acquisition cycle of the reference node.

[0133] Step 406: For any acquisition time of the reference node, if the acquisition time of the reference node belongs to the matching time period corresponding to the acquisition time of the reference node, determine that the acquisition time of the reference node matches the acquisition time of the reference node.

[0134] In this embodiment, for any acquisition time of the reference node in the node pair to be associated, if the acquisition time of the reference node belongs to the matching time period corresponding to the acquisition time of the reference node, the terminal determines that the acquisition time of the reference node matches the acquisition time of the reference node. Referring to the example in step 402, assume that the node pair to be associated 1 includes a first node to be associated 1 and a second node to be associated 1, where the first node to be associated 1 is the reference node 1 and the second node to be associated 1 is the reference node 1, as shown in Table 1 below. It is understood that the data in the following table is only for illustrative purposes and does not constitute a limitation on the actual data and acquisition time.

[0135] Table 1

[0136]

[0137] In Table 1 above, all times represent the collection time. It can be understood that the matching time period for data 1 is [8:00, 8:01), and the matching time period for data 2 is [8:01, 8:02]. Since 8:00 and 8:02 belong to the matching time period [8:00, 8:01), the collection times for data 3 and data 4 are both matching the collection time of data 1. Similarly, the collection time for data 5 is matching the collection time of data 2.

[0138] In another embodiment, for ease of distinction, for any pair of nodes to be associated, the data category to which the data of the first node to be associated in the pair belongs is called the first data category; and the data category to which the data of the second node to be associated in the pair belongs is called the second data category. For any data category, the terminal compares the first acquisition period corresponding to the first data category with the second acquisition period corresponding to the second data category, and determines the benchmark node corresponding to the maximum acquisition period and the reference node corresponding to the minimum acquisition period in the pair of nodes to be associated. For any data category of the benchmark node, the terminal calculates the matching time period corresponding to each acquisition time of the data category of the benchmark node based on the acquisition period corresponding to the data category of the benchmark node and the acquisition time corresponding to each acquisition time of that data category of the benchmark node. For example, referring to the example in step 402, assume that the pair of nodes to be associated 1 includes a first node to be associated 1 and a second node to be associated 1, the first node to be associated 1 is the reference node 1, and the second node to be associated 1 is the benchmark node 1, as shown in Table 2 below. Assume that the acquisition period of data category 1 of the benchmark node 1 is 1 minute, and the acquisition period of data category 2 of the benchmark node 1 is 3 minutes.

[0139] Table 2

[0140]

[0141] For data 6, the terminal calculates the matching time period for data category 1 of baseline node 1 at 8 o'clock based on the data collection period of 1 minute for data category 1 of baseline node 1 and the data collection time corresponding to data category 1 of baseline node 1 at 8 o'clock. The matching time period for data category 2 of baseline node 1 at 8 o'clock is [8 o'clock, 8 o'clock 01 minutes]. Similarly, the matching time period for data category 2 of baseline node 1 at 8 o'clock is [8 o'clock, 8 o'clock 03 minutes].

[0142] To facilitate differentiation, when the data category corresponding to the base node's data is consistent with the data category corresponding to the operation node's data, the terminal uses the collection time of the reference node belonging to the matching time period corresponding to that data category as the collection time of the reference node that matches the collection time of the base node. Referring to Table 2, if the terminal identifies that both data 6 and data 8 belong to data category 1, and the collection time of data 8 at 8:00 belongs to the matching time period [8:00, 8:01] corresponding to data 6, then the terminal uses 8:00 for data 8 as the collection time that matches 8:00 for data 6. Similarly, if the terminal identifies that both data 7 and data 9 belong to data category 2, but the collection time of data 9 at 8:04 does not belong to the matching time period [8:00, 8:03] corresponding to data 7, then the terminal determines that 8:04 for data 9 is not the collection time that matches 8:00 for data 7.

[0143] In this embodiment, for any pair of nodes to be associated, a baseline node and a reference node are determined based on the first and second acquisition periods, thereby determining the matching time period for the acquisition times corresponding to each data point of the baseline node. For any acquisition time of the baseline node, the acquisition time of the reference node belonging to the matching time period is taken as the acquisition time that matches the acquisition time of the baseline node. Therefore, this method can determine the second acquisition time that matches the first acquisition time, thus providing a prerequisite for the subsequent method of establishing data association relationships based on the matching relationship between the first and second acquisition times.

[0144] To enable those skilled in the art to better understand this application, such as Figure 5 As shown, the present application will be described below through specific embodiments.

[0145] In the step of obtaining the identity document (id) of the host list, the terminal obtains the identity document of each host in the Infrastructure as a Service (IaaS) layer.

[0146] In the step of obtaining the list of identity documents (ids) for monitoring metrics of compute nodes, the compute node is equivalent to the host machine, and the monitoring metrics are equivalent to data categories. Specifically, the terminal obtains the identity documents for each data category from the host machine.

[0147] In the step of paginated querying of historical indicators, the terminal queries each data category of the host machine monitored by the Ceilometer monitoring component paginated according to the identification number of each data category.

[0148] In the step of querying historical indicator data using MO, MO is equivalent to the morm function, and the indicator data is equivalent to the initial data on the host machine. The terminal calls the morm function to query the data corresponding to each data category on the host machine.

[0149] In the step of parsing the metric data, the metric data is equivalent to the initial data of the host machine. The terminal integrates the physical resource data of the host machine with the initial data to obtain the host machine's data, and then stores the host machine's data in the ClickHouse database.

[0150] In the query completion step, the terminal determines whether all initial data of each host machine has been integrated. If so, the query is completed; otherwise, it returns to the step of paginating and querying historical indicators until all initial data of each host machine has been integrated.

[0151] In the step of traversing the metric data, the metric data is equivalent to the initial data of the virtual machine. The terminal determines whether an association has been established between all the data of each virtual machine and the data of each host machine. If yes, the traversal of the metric data is completed and the processing ends; otherwise, the Prometheus metric object is obtained based on the metric identity document (id) until an association has been established between all the data of each virtual machine and the data of each host machine.

[0152] In the step of obtaining a Prometheus metric object based on the metric identity document (id), the metric is equivalent to the data category of the virtual machine, the metric identity document is equivalent to the identity document of the data category of the virtual machine, and the Prometheus metric object is equivalent to the initial data of the Prometheus monitoring system.

[0153] In the step of setting indicator label data, the label is equivalent to the data category of the virtual machine, and the label data is equivalent to the physical resource data of the virtual machine. The terminal integrates the physical resource data of the virtual machine with the initial data to obtain the virtual machine data.

[0154] In the step of updating memory metric data, memory is equivalent to the database of the Prometheus monitoring system, and metric data is equivalent to virtual machine data. The terminal stores the virtual machine data into the database of the Prometheus monitoring system. The terminal establishes an association between the virtual machine data and the host machine data based on the virtual machine address data contained in the virtual machine data and the virtual machine address data contained in the host machine data. The specific method for establishing the association can be found in the aforementioned embodiments, and will not be repeated here.

[0155] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0156] Based on the same inventive concept, this application also provides a data association apparatus for implementing the data association method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data association apparatus embodiments provided below can be found in the limitations of the data association method described above, and will not be repeated here.

[0157] In one embodiment, such as Figure 6 As shown, a data association device is provided, comprising:

[0158] The first acquisition module 602 is used to acquire the address data corresponding to each node to be associated; the node categories of the nodes to be associated include host machines and virtual machines;

[0159] The first determining module 604 is used to determine at least one pair of nodes to be associated based on the address data corresponding to each node to be associated. The pair of nodes to be associated includes a first node to be associated and a second node to be associated, and the node categories corresponding to the first node to be associated and the second node to be associated are different.

[0160] The module 606 is used to determine, for any pair of nodes to be associated, the first data of the first node to be associated stored in the first database and the second data of the second node to be associated stored in the second database, and to establish the association between the first data and the second data.

[0161] In the aforementioned data association device, based on the address data corresponding to each node to be associated, a pair of nodes to be associated, including a first node to be associated and a second node to be associated, is determined, thereby establishing an association relationship between the first data of the first node to be associated and the second data of the second node to be associated. Since the first and second nodes to be associated correspond to different node categories, this method can establish association relationships between data of nodes of different node categories. It can be understood that the first node to be associated may include a virtual machine, and the second node to be associated may include a host machine; that is, this method can establish an association relationship between the data of a virtual machine and the data of a host machine, allowing users to intuitively understand the association relationship between the data of the virtual machine and the data of the host machine. Furthermore, since the first data of the first node to be associated is stored in the first database, and the second data of the second node to be associated is stored in the second database, this method can establish an association relationship between the data of the virtual machine and the data of the host machine without increasing the storage data volume of a single database (the first database or the second database).

[0162] In one embodiment, the address data corresponding to the host machine includes the address data of the virtual machines deployed on the host machine. Based on the address data corresponding to each node to be associated, at least one pair of nodes to be associated is determined, including:

[0163] For any virtual machine, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, a pair of nodes to be associated is created based on the virtual machine and the host machine. The virtual machine is designated as the first node to be associated in the pair, and the host machine is designated as the second node to be associated in the pair. The first data of the first node to be associated includes data about Platform as a Service in the virtual machine. The second data of the second node to be associated includes data about Infrastructure as a Service in the host machine.

[0164] In one embodiment, the first acquisition module 602 is specifically used for:

[0165] If the node type of the node to be associated is a host machine, determine at least one virtual machine deployed in the host machine;

[0166] The address data of each virtual machine deployed on the host machine is used as the address data of the corresponding host machine.

[0167] In one embodiment, the data association device further includes:

[0168] The second acquisition module is used to acquire the data category of the first data from the first database storage and the data category of the second data from the second database.

[0169] Module 606 is specifically used for:

[0170] If the data category of the first data is the same as that of the second data, establish a relationship between the first data and the second data.

[0171] In one embodiment, the establishment module 606 is further configured to:

[0172] If the data categories of the first data and the second data are inconsistent, the correlation between the data categories of the first data and the second data is determined according to the preset data category correspondence. The data category correspondence is used to characterize the correspondence between each data category of the first node to be associated and each data category of the second node to be associated.

[0173] When the data category of the first data is associated with the data category of the second data, establish the association between the first data and the second data.

[0174] In one embodiment, the data association device further includes:

[0175] The third acquisition module is used to acquire the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated.

[0176] The fourth acquisition module is used to acquire the first acquisition time of the first data and the second acquisition time of the second data;

[0177] Module 606 is specifically used for:

[0178] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, determine the second acquisition time that matches each of the first acquisition times.

[0179] If the first acquisition time of the first data matches the second acquisition time of the second data, establish the correlation between the first data and the second data.

[0180] In one embodiment, the establishment module 606 is specifically used for:

[0181] When the first acquisition time of the first data matches the second acquisition time of the second data, the data category of the first data is obtained from the first database storage, and the data category of the second data is obtained from the second database.

[0182] If the data category of the first data is the same as that of the second data, establish a relationship between the first data and the second data.

[0183] In one embodiment, the establishment module 606 is specifically used for:

[0184] Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, a reference node and a reference node are determined in the first node to be associated and the second node to be associated.

[0185] Based on the acquisition cycle of the benchmark node and the acquisition time of each data point of the benchmark node, determine the matching time period corresponding to each acquisition time of the benchmark node.

[0186] For any acquisition time of the reference node, if the acquisition time of the reference node belongs to the matching time period corresponding to the acquisition time of the reference node, the acquisition time of the reference node is determined to match the acquisition time of the reference node.

[0187] Each module in the aforementioned data association device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0188] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a data association method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0189] Those skilled in the art will understand that Figure 7The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0190] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0191] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0192] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0193] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0194] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0195] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0196] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data association method, characterized in that, The method includes: Obtain the address data corresponding to each node to be associated; the node categories of the nodes to be associated include host machines and virtual machines; Based on the address data corresponding to each node to be associated, at least one pair of nodes to be associated is determined. The pair of nodes to be associated includes a first node to be associated and a second node to be associated. The node categories corresponding to the first node to be associated and the second node to be associated are different. For any of the node pairs to be associated, determine the first data of the first node to be associated stored in the first database and the second data of the second node to be associated stored in the second database, and establish the association between the first data and the second data; The address data corresponding to the host machine includes the address data of the virtual machines deployed on the host machine. Determining at least one pair of nodes to be associated based on the address data corresponding to each node to be associated includes: for any virtual machine, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, creating a pair of nodes to be associated based on the virtual machine and the host machine, and designating the virtual machine as the first node to be associated in the pair, and the host machine as the second node to be associated in the pair; the first data of the first node to be associated includes platform-as-a-service data in the virtual machine; the second data of the second node to be associated includes infrastructure-as-a-service data in the host machine. The method further includes: for any virtual machine, if the address data corresponding to the host machine includes the address data corresponding to the virtual machine, determining that the address data corresponding to the virtual machine matches the address data corresponding to the host machine; The step of determining the first data of the first node to be associated in the first database storage and the second data of the second node to be associated in the second database storage includes: determining the first data of the first node to be associated in the first database storage based on the address data or identity number of the first node to be associated, and determining the second data of the second node to be associated in the second database storage based on the address data or identity number of the second node to be associated. The method further includes, before determining the first data of the first node to be associated in the first database storage based on the address data or identity number of the first node to be associated, and determining the second data of the second node to be associated in the second database storage based on the address data or identity number of the second node to be associated, the method further includes: integrating the physical resource data of the first node to be associated with the initial data of the first node to be associated to obtain the first data, and storing the first data in the first database; integrating the physical resource data of the second node to be associated with the initial data of the second node to be associated to obtain the second data, and storing the second data in the second database, wherein the physical resource data includes the identity number of the corresponding node to be associated, and the initial data includes the data category, data value, and collection time; The method further includes: obtaining the data category of the first data from the first database storage and obtaining the data category of the second data from the second database; establishing the association between the first data and the second data includes: establishing the association between the first data and the second data when the data category of the first data is consistent with the data category of the second data.

2. The method according to claim 1, characterized in that, The step of obtaining the address data corresponding to each node to be associated includes: If the node category of the node to be associated is the host machine, then at least one virtual machine deployed in the host machine is identified; The address data of each virtual machine deployed in the host machine is used as the address data corresponding to the host machine.

3. The method according to claim 1, characterized in that, The step of establishing the association between the first data and the second data further includes: If the data category of the first data is inconsistent with the data category of the second data, the association between the data category of the first data and the data category of the second data is determined according to a preset data category correspondence relationship; the data category correspondence relationship is used to characterize the correspondence between each data category of the first node to be associated and each data category of the second node to be associated. When the data category of the first data is associated with the data category of the second data, an association relationship is established between the first data and the second data.

4. The method according to claim 1, characterized in that, The method further includes: Obtain the first collection period of the first node to be associated and the second collection period of the second node to be associated; Acquire the first acquisition time of the first data and the second acquisition time of the second data; Establishing the association between the first data and the second data includes: Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, determine the second acquisition time that matches each of the first acquisition times. If the first acquisition time of the first data matches the second acquisition time of the second data, an association relationship is established between the first data and the second data.

5. The method according to claim 4, characterized in that, When the first acquisition time of the first data matches the second acquisition time of the second data, establishing the association between the first data and the second data includes: When the first acquisition time of the first data matches the second acquisition time of the second data, the data category of the first data is obtained from the first database storage, and the data category of the second data is obtained from the second database. If the data category of the first data is consistent with the data category of the second data, an association relationship is established between the first data and the second data.

6. The method according to claim 4, characterized in that, The step of determining the second acquisition time that matches each of the first acquisition times based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated includes: Based on the first acquisition period of the first node to be associated and the second acquisition period of the second node to be associated, a reference node and a reference node are determined among the first node to be associated and the second node to be associated. Based on the acquisition cycle of the reference node and the acquisition time of each data point of the reference node, the matching time period corresponding to each acquisition time of the reference node is determined. For any of the acquisition times of the reference node, if the acquisition time of the reference node belongs to the matching time period corresponding to the acquisition time of the reference node, it is determined that the acquisition time of the reference node matches the acquisition time of the reference node.

7. A data association device, characterized in that, The device includes: The first acquisition module is used to acquire the address data corresponding to each node to be associated; the node categories of the nodes to be associated include host machines and virtual machines; The first determining module is used to determine at least one pair of nodes to be associated based on the address data corresponding to each node to be associated. The pair of nodes to be associated includes a first node to be associated and a second node to be associated, and the first node to be associated and the second node to be associated correspond to different node categories. The module is configured to, for any pair of nodes to be associated, determine the first data of the first node to be associated stored in the first database and the second data of the second node to be associated stored in the second database, and establish the association between the first data and the second data. The address data corresponding to the host machine includes the address data of the virtual machines deployed in the host machine. The first determining module is configured to: for any virtual machine, if the address data corresponding to the virtual machine matches the address data corresponding to the host machine, create a pair of nodes to be associated based on the virtual machine and the host machine, and designate the virtual machine as the first node to be associated in the pair of nodes to be associated, and the host machine as the second node to be associated in the pair of nodes to be associated; the first data of the first node to be associated includes data about Platform as a Service in the virtual machine; the second data of the second node to be associated includes data about Infrastructure as a Service in the host machine. The device is further configured to: for any virtual machine, if the address data corresponding to the host machine includes the address data corresponding to the virtual machine, determine that the address data corresponding to the virtual machine matches the address data corresponding to the host machine; The establishment module is configured to: determine the first data of the first node to be associated in the first database storage based on the address data or identity number of the first node to be associated, and determine the second data of the second node to be associated in the second database storage based on the address data or identity number of the second node to be associated; The device is further configured to: before determining the first data of the first node to be associated in the first database storage based on the address data or identity number of the first node to be associated, and determining the second data of the second node to be associated in the second database storage based on the address data or identity number of the second node to be associated, integrate the physical resource data of the first node to be associated with the initial data of the first node to be associated to obtain the first data, and store the first data in the first database; integrate the physical resource data of the second node to be associated with the initial data of the second node to be associated to obtain the second data, and store the second data in the second database, wherein the physical resource data includes the identity number of the corresponding node to be associated, and the initial data includes the data category, data value, and collection time; The data association device further includes: a second acquisition module, used to acquire the data category of the first data from the first database storage and the data category of the second data from the second database; the establishment module is further used to: establish an association relationship between the first data and the second data when the data category of the first data is consistent with the data category of the second data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that... Therefore, when the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program When executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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