CN116954908A - Graph database cluster node scheduling method, system, terminal and medium - Google Patents
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Abstract
The application discloses a graph database cluster node scheduling method, a system, a terminal and a medium, wherein the method comprises the following steps: acquiring a cluster creation instruction sent by a user, wherein the cluster creation instruction comprises a cluster definition, the cluster definition comprises cluster resources, and the cluster resources comprise cluster names, storage capacity, external ports and graph nodes; automatically authorizing and establishing cluster resources; and constructing a cluster according to the cluster resources. According to the method, resources are automatically created and clusters are built according to the required cluster definition data provided by the user, so that the cluster resource is built according to the definition of the graph database clusters, the cluster resource is scheduled to build clusters, and customized business operation is realized.
Description
Technical Field
The application relates to the technical field of computers, in particular to a graph database cluster node scheduling method, a system, a terminal and a medium.
Background
Container orchestration technology is one method for managing and reconciling containerized applications. With the popularity of container technology (e.g., docker), developers and operators need an efficient way to manage a large number of container instances. Container orchestration techniques (e.g., kubernetes, docker Swarm, and Apache Mesos) have evolved.
The container arrangement technique mainly solves the following problems:
1. automated deployment: automatically deploying and expanding containerized applications reduces the need for manual operations and the possibility of errors.
2. Load balancing and service discovery: load balancing and service discovery functions are built in to ensure high availability and performance of applications.
3. Fault recovery and self-repair: the containers are detected for failure and automatically restarted, thereby improving the reliability of the application.
4. Resource management and optimization: and automatically scheduling the containers according to the resource requirements and the restrictions to realize the effective utilization of the cluster resources.
An increasing number of businesses in today's business environment are beginning to employ container orchestration management tools. Kubernetes (k 8s for short) has become the first choice for many businesses as the container orchestration tool currently in the market place. Kubernetes provides a series of powerful node controllers, such as deployment, statefulset and daemonset, etc., to meet the needs of different scenarios. The delegation strategy is suitable for deploying stateless services, and can ensure that copies of an application program are evenly distributed in a cluster, so that high availability and load balancing are realized. The statefulset strategy is aimed at stateful services, and allocates unique persistent identification for each copy, and orderly deploys the statefulset strategy according to a defined sequence, so that consistency and reliability of data are ensured. Whereas the daemonset policy applies to services that need to run on each node in the cluster, it can automatically create and manage container instances on each node. With these excellent node scheduling strategies, kubernetes provides a highly scalable and flexible container orchestration capability. Although the controllers provided by Kubernetes are excellent, they have significant limitations and cannot meet the actual manufacturing process. The main problems are as follows:
1. single control logic: the built-in controller employs fixed control logic to manage the state and deployment of the application. Such single control logic may not be able to meet the needs of complex applications, such as having a specific start-up sequence, dependency, or custom behavior. Some applications may require more flexible, higher level control logic to manage their state and deployment.
2. Lack of traffic awareness: the built-in controller lacks business awareness for the application. It focuses mainly on the number and state of copies of an application, but lacks knowledge of the business logic and performance metrics of the application. This may result in the controller not making intelligent decisions based on the actual needs of the application, such as dynamically adjusting the number of copies or optimizing resource allocation based on traffic load.
3. Restricted deployment strategy: built-in controllers provide some basic deployment strategies such as: scrolling updates, however, may be limiting in some complex deployment scenarios. For example, some applications may require custom deployment policies such as greyscale distribution, canary distribution, etc., but these policies are difficult to implement in built-in controllers.
4. Reliability problems: especially in case of node failure or network problems. This may result in the fact that the Pods cannot start or restart in time. Pod is the smallest deployable unit of Kubernetes, and typically contains one or more tightly-associated containers.
5. Resource linkage and state synchronization: after deployment, the resources are automatically associated, and when the resources do not meet expectations, the resources cannot be tuned to expected states. Such as the front-end service providing the port 32505 for external access, the service may not be used properly when the port is tampered with.
It is because the existence of the above problems results in the graph database not being directly run in the k8s environment.
The native controller only deploys nodes, and cannot be customized to perform some business operations: such as the most basic nodes building clusters, cluster startup/shutdown, gallery resource association, etc.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the application provides a graph database cluster node scheduling method, a system, a terminal and a medium, which can create cluster resources according to cluster definition, schedule the cluster resources to construct clusters and realize customized business operation.
In a first aspect, a graph database cluster node scheduling method provided by an embodiment of the present application includes:
acquiring a cluster creation instruction sent by a user, wherein the cluster creation instruction comprises a cluster definition, the cluster definition comprises cluster resources, and the cluster resources comprise cluster names, storage capacity, external ports and graph nodes;
automatically authorizing and establishing cluster resources;
and constructing a cluster according to the cluster resources.
In a second aspect, a graph database cluster node scheduling system provided by an embodiment of the present application includes: the system comprises an acquisition module, a resource creation module and a cluster construction module, wherein the acquisition module is used for acquiring a cluster creation instruction sent by a user, the cluster creation instruction comprises a cluster definition, and the cluster definition comprises cluster resources;
the resource creation module is used for automatically authorizing and creating cluster resources;
the cluster construction module is used for constructing clusters according to cluster resources.
In a third aspect, an embodiment of the present application provides an intelligent terminal, including a processor, an input device, an output device, and a memory, where the processor is connected to the input device, the output device, and the memory, respectively, and the memory is configured to store a computer program, where the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the method described in the foregoing embodiment.
In a fourth aspect, embodiments of the present application also provide a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method described in the above embodiments.
The application has the beneficial effects that:
the embodiment of the application provides a graph database cluster node scheduling method, which automatically creates resources and builds clusters only by providing required cluster definition data by a user, realizes the establishment of cluster resources according to the graph database cluster definition, schedules cluster resource to build clusters, and realizes customized business operation.
The embodiment of the application provides a graph database cluster node scheduling system, an intelligent terminal and a medium, and has the same beneficial effects as the graph database cluster node scheduling method based on the same inventive concept.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for scheduling nodes in a graph database set according to a first embodiment of the present application;
FIG. 2 is a flow chart showing a practical example of constructing clusters and modifying clusters in the first embodiment of the present application;
FIG. 3 is a flow chart showing a practical example of automatic abnormality recovery in the first embodiment of the present application;
FIG. 4 is a flow chart of a method of cluster deployment in a first embodiment of the application;
FIG. 5 is a block diagram of a second embodiment of the present application for a graph database cluster node scheduling system;
fig. 6 shows a block diagram of an intelligent terminal according to a third embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
Example 1
Referring to fig. 1, a flowchart of a graph database cluster node scheduling method according to a first embodiment of the present application is shown, where the method includes the following steps:
acquiring a cluster creation instruction sent by a user, wherein the cluster creation instruction comprises a cluster definition, the cluster definition comprises cluster resources, and the cluster resources comprise cluster names, storage capacity, external ports and graph nodes;
automatically authorizing and establishing cluster resources;
and constructing a cluster according to the cluster resources.
The steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer-executable instructions, and while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein. The method embodiment provided by the first embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar intelligent terminal.
Through the steps, only the user provides the needed cluster definition data, the resources are automatically created and the clusters are built, the cluster resources are built according to the definition of the graph database clusters, the cluster resource is scheduled to build the clusters, and the customized business operation is realized.
In addition, in order to implement parameter modification on the created cluster, the method further comprises the following steps of constructing the cluster according to the cluster resources:
receiving an instruction for modifying a cluster sent by a user;
the cluster resources are modified according to instructions to modify the clusters.
As shown in fig. 2, a flow diagram of a practical example of constructing a cluster and modifying a cluster is shown. User demand 1: want to create a cluster, the cluster definition: cluster name: T-Cluster; node number: 2; home store: 3G; the bolt is to the external port: 32767, requirement 2: modifying the created Cluster T-Cluster, and adding rest external ports on the Cluster: 32505.
the specific steps of constructing the cluster are as follows:
obtaining a cluster creation instruction sent by a user, wherein the cluster instruction comprises a cluster definition, and the cluster definition comprises: cluster name: T-Cluster; node number: 2; home store: 3G; the bolt is to the external port: 32767;
automatically authorizing resource creation and creating storage; then creating a Pod and associating the Pod with a corresponding storage; creating a service; when all Pods are started, the Pods are automatically associated into a cluster, and then the cluster is started by de-authorization.
Adding rest to external ports in the created Cluster T-Cluster: 32505.
as can be seen from this practical example, in the node scheduling method in the graph database cluster provided by the embodiment of the application, the cluster is modified by creating the cluster resource scheduling cluster resource construction cluster, so that the creation flow of the cluster is simplified, the creation and management of the cluster are facilitated, and the customized business operation is realized.
When the starting node fails, creating a new Pod and multiplexing Storage of the old Pod, adding the current Pod into the cluster after the cluster is completed, refreshing authorization and then carrying out data balancing (preventing inconsistent cluster data). Namely: when a certain node fails, a new node is automatically started to multiplex the data of the failed node and added into the cluster; when the gallery is a special node, the gallery is automatically scheduled to the original physical node, and when the nodes are not connected, a new node is automatically added to maintain the gallery stable, so that state recovery is realized.
As shown in fig. 3, a flow chart showing a practical example of automatic exception recovery is shown.
The specific method for automatically carrying out the exception recovery comprises the following steps:
monitoring a cluster definition in real time, wherein the cluster definition comprises: cluster name: T-Cluster; node number: 2; home store: 3G; the bolt is to the external port: 32767; rest external port: 32505;
matching the monitored cluster definition with the cluster definition when the cluster is established to obtain a matching result;
if the matching result is inconsistent, an abnormal condition occurs, and the rest is found to be an external port: 32505 the misdelete occurs;
and restoring the external port by the rest deleted by mistake, so that the restored cluster definition is the same as the cluster definition when the cluster is created.
Through the practical example, the method for scheduling the graph database cluster nodes provided by the embodiment of the application realizes automatic cluster exception recovery, ensures that the graph database is consistent with the expected time or is tuned towards the expected state, greatly simplifies the graph database cluster management action, and improves the cluster availability.
As shown in fig. 4, a method flow diagram of cluster deployment is shown. The step of constructing the cluster according to the cluster resource further comprises the following steps:
judging whether the cluster is deployed or not;
if not, returning to execute to judge whether the cluster is deployed or not;
if so, judging whether to start the cluster;
if yes, starting the cluster, and balancing data;
if not, the process is ended.
The specific method for deployment comprises the following steps: and determining that the graph nodes are distributed on corresponding proper physical nodes according to the calculated amount of data, the load condition of the physical nodes, the change condition of the number of required nodes, the node fault condition and the physical machine fault condition.
In order to enable the graph database to better serve users, the embodiment of the application can be based on old cluster data scheduling when the clusters are deployed, and is convenient for the recovery of the clusters. Different deployment modes may also be formulated to create new clusters. Such as: the exclusive mode can be set to occupy the physical machine resources only, which has a large amount of data and needs to execute a large amount of algorithm operations. The sharing mode can be used if only simple inquiry is made, so that a single physical machine can deploy a plurality of graph nodes, and the use cost is reduced. If a faster query is desired, the graph nodes can be distributed on the same physical machine. If a more stable insurance graph cluster is desired, the graph nodes may not be distributed on the same physical node. If a larger load is desired, the graph node may be allowed to automatically add a new node to split the load when the load is high. And the customized deployment is realized by selecting and starting the proper physical machine according to different conditions.
In the first embodiment, a graph database cluster node scheduling method is provided, and correspondingly, the application further provides a graph database cluster node scheduling system. Fig. 5 is a block diagram of a cluster node scheduling system for a database according to a second embodiment of the present application. Since the apparatus embodiments are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
Example 2
Referring to fig. 5, a block diagram of a graph database cluster node scheduling system according to a second embodiment of the present application is shown, where the system includes: the system comprises an acquisition module, a resource creation module and a cluster construction module, wherein the acquisition module is used for acquiring a cluster creation instruction sent by a user, the cluster creation instruction comprises a cluster definition, and the cluster definition comprises cluster resources; the resource creation module is used for automatically authorizing and creating cluster resources; the cluster construction module is used for constructing clusters according to the cluster resources.
The system can automatically create resources and construct clusters only by providing the needed cluster definition data by the user, thereby realizing the construction of cluster resources according to the definition of the graph database clusters, the cluster resource construction of the clusters is scheduled, and the customized business operation is realized.
The system also comprises a cluster resource modification module, wherein the cluster resource modification module is used for modifying cluster resources according to a cluster modification instruction sent by a user. The cluster resource modification module realizes modification of the clusters, simplifies the creation flow of the clusters, facilitates creation and management of the clusters, and realizes customized business operation.
The system also comprises an abnormal recovery module, wherein the abnormal recovery module is used for monitoring the cluster definition in real time, matching the monitored cluster definition with the cluster definition when the cluster is created, obtaining a matching result, and if the matching result is inconsistent, indicating that the abnormal condition exists, modifying the monitored cluster definition to be consistent with the cluster definition when the cluster is created. The abnormal recovery module realizes automatic cluster abnormal recovery, ensures that the moment of the graph library is consistent with the expected moment or in tuning towards the expected state, greatly simplifies the graph database cluster management action and improves the cluster availability.
The system also comprises a deployment module, wherein the deployment module is used for judging whether the cluster is deployed or not, and if not, the execution is returned to judge whether the cluster is deployed or not; if so, judging whether to start the cluster; if yes, the cluster is started, and data balancing is performed. The specific method for deployment comprises the following steps: and determining that the graph nodes are distributed on corresponding proper physical nodes according to the calculated amount of data, the load condition of the physical nodes, the change condition of the number of required nodes, the node fault condition and the physical machine fault condition.
In order to make the graph database better serve users, old cluster data scheduling can be based on when the clusters are deployed, so that cluster recovery is facilitated. Different deployment modes may also be formulated to create new clusters. Such as: the exclusive mode can be set to occupy the physical machine resources only, which has a large amount of data and needs to execute a large amount of algorithm operations. The sharing mode can be used if only simple inquiry is made, so that a single physical machine can deploy a plurality of graph nodes, and the use cost is reduced. If a faster query is desired, the graph nodes can be distributed on the same physical machine. If a more stable insurance graph cluster is desired, the graph nodes may not be distributed on the same physical node. If a larger load is desired, the graph node may be allowed to automatically add a new node to split the load when the load is high. And the customized deployment is realized by selecting and starting the proper physical machine according to different conditions.
Example 3
As shown in fig. 6, a block diagram of an intelligent terminal according to a third embodiment of the present application is shown, where the terminal includes a processor, an input device, an output device, and a memory, where the processor is connected to the input device, the output device, and the memory, respectively, and the memory is used to store a computer program, where the computer program includes program instructions, and where the processor is configured to invoke the program instructions to perform the method described in the foregoing embodiments.
It should be appreciated that in embodiments of the present application, the processor may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input devices may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of a fingerprint), a microphone, etc., and the output devices may include a display (LCD, etc.), a speaker, etc.
The memory may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
In a specific implementation, the processor, the input device, and the output device described in the embodiments of the present application may execute the implementation described in the method embodiment provided in the embodiments of the present application, or may execute the implementation of the system embodiment described in the embodiments of the present application, which is not described herein again.
Example 4
A fourth embodiment of the present application provides a computer-readable storage medium storing a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the method described in the above embodiments.
The computer readable storage medium may be an internal storage unit of the terminal according to the foregoing embodiment, for example, a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the terminal. The computer-readable storage medium is used to store the computer program and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working procedures of the terminal and the unit described above may refer to the corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In several embodiments provided by the present application, it should be understood that the disclosed terminal and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A graph database cluster node scheduling method, comprising:
acquiring a cluster creation instruction sent by a user, wherein the cluster creation instruction comprises a cluster definition, the cluster definition comprises cluster resources, and the cluster resources comprise cluster names, storage capacity, external ports and graph nodes;
automatically authorizing and establishing cluster resources;
and constructing a cluster according to the cluster resources.
2. The method of claim 1, further comprising, after the step of building clusters from the cluster resources:
receiving an instruction for modifying a cluster sent by a user;
the cluster resources are modified according to instructions to modify the clusters.
3. The method of claim 1, further comprising, after the step of building clusters from the cluster resources: the specific method for automatically carrying out the exception recovery comprises the following steps:
monitoring cluster definition in real time;
matching the monitored cluster definition with the cluster definition when the cluster is established to obtain a matching result;
if the matching result is inconsistent, indicating that an abnormal condition exists;
the listening cluster definition is modified to be consistent with the cluster definition at the time the cluster was created.
4. The method of claim 1, further comprising, after the step of building clusters from the cluster resources:
judging whether the cluster is deployed or not;
if not, returning to execute to judge whether the cluster is deployed or not;
if so, judging whether to start the cluster;
if yes, starting the cluster, and balancing data;
if not, the process is ended.
5. The method of claim 4, wherein the specific method of deploying comprises: and determining that the graph nodes are distributed on the corresponding physical nodes according to the calculated amount of the data, the load condition of the physical nodes, the change condition of the number of the needed nodes, the node fault condition and the physical machine fault condition.
6. A graph database cluster node scheduling system, comprising: the system comprises an acquisition module, a resource creation module and a cluster construction module, wherein the acquisition module is used for acquiring a cluster creation instruction sent by a user, the cluster creation instruction comprises a cluster definition, and the cluster definition comprises cluster resources;
the resource creation module is used for automatically authorizing and creating cluster resources;
the cluster construction module is used for constructing clusters according to cluster resources.
7. The system of claim 6, further comprising a cluster resource modification module for modifying cluster resources according to a modify cluster instruction sent by a user.
8. The system of claim 6, further comprising an anomaly recovery module, wherein the anomaly recovery module is configured to monitor the cluster definition in real time, and match the monitored cluster definition with the cluster definition when the cluster is created, to obtain a matching result, and if the matching result is inconsistent, to indicate that there is an anomaly, to modify the monitored cluster definition to be consistent with the cluster definition when the cluster is created.
9. An intelligent terminal comprising a processor, an input device, an output device and a memory, the processor being connected to the input device, the output device and the memory, respectively, the memory being for storing a computer program comprising program instructions, characterized in that the processor is configured to invoke the program instructions to perform the method of any of claims 1-5.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-5.
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| CN202310932621.2A CN116954908A (en) | 2023-07-26 | 2023-07-26 | Graph database cluster node scheduling method, system, terminal and medium |
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| CN202310932621.2A CN116954908A (en) | 2023-07-26 | 2023-07-26 | Graph database cluster node scheduling method, system, terminal and medium |
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