CN110618915A - Method and equipment for cluster deployment decision power evaluation tool and storage medium - Google Patents
Method and equipment for cluster deployment decision power evaluation tool and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3089—Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents
- G06F11/3093—Configuration details thereof, e.g. installation, enabling, spatial arrangement of the probes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/80—Database-specific techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/875—Monitoring of systems including the internet
Abstract
The invention discloses a method for cluster deployment of a decision power evaluation tool, which comprises the following steps: carrying out environment detection on each node in the cluster, and generating a configuration file according to the environment detection result of each node; acquiring an initial dependency item configuration file of each node and classifying all dependency items recorded by the initial dependency item configuration file of each node; reading a mode of deploying a decision power evaluation tool for each node in the configuration file; and acquiring the corresponding classified dependent item according to the deployment mode of each node, and deploying a deployment decision power evaluation tool on each node by using the corresponding classified dependent item. The invention also discloses a computer device and a readable storage medium. The method disclosed by the invention can self-adaptively configure the mode of deploying the decision power evaluation tool by detecting different environments of each node, simplifies the optimization dependence item and saves the time for deploying the decision power evaluation tool.
Description
Technical Field
The invention relates to the field of test tool deployment, in particular to a method, equipment and a storage medium for cluster deployment of a decision power evaluation tool.
Background
TPC-H is a set of test standard aiming at data decision support capability and published by TPC transaction performance committee, and the comprehensive processing capability of the database is inspected by simulating complex query related to business and parallel data modification operation in the database, so that the response time of the database operation and the index of query number executed in each hour are obtained.
A traditional decision power evaluation tool TPC-H is deployed in a manual deployment and installation mode, a pom.xml dependent package is downloaded through network connection in a curl mode by executing TPC-build.sh, and a maven dependent environment required by TPC-H installation is built. The deployment method lacks detection of cluster environment to be tested, and the condition of repeatedly downloading the dependent package is easy to occur, so that the test cost is increased. Especially, in the situation that the cluster environment to be tested is offline, the curl method cannot be implemented.
Disclosure of Invention
In view of the above, in order to overcome at least one aspect of the above problems, an embodiment of the present invention provides a method for deploying a decision power evaluation tool in a cluster, including:
carrying out environment detection on each node in the cluster, and generating a configuration file according to the environment detection result of each node;
acquiring an initial dependency item configuration file of each node and classifying all dependency items recorded by the initial dependency item configuration file of each node;
reading the manner of deploying a decision power evaluation tool for each node contained in the configuration file;
and acquiring the corresponding classified dependent item according to the deployment mode of each node, and deploying the deployment decision power evaluation tool on each node by using the corresponding classified dependent item.
In some embodiments, performing the environment detection on each node in the cluster further comprises:
detecting whether each node is networked or not by using the detection instruction;
responding to the condition that a node is in a networking state, and judging whether the node is provided with a Maven or not;
and in response to the node not installing the Maven, installing the Maven for the node.
In some embodiments, generating the configuration file according to a result of the environment detection of each node further comprises:
determining the deployment mode to be online deployment or offline deployment according to the networking state of each node;
and generating the configuration file by using the address of each node, the deployment mode and the networking state.
In some embodiments, obtaining the classified dependency item according to the deployment mode of each node further includes:
in response to the deployment mode being the online deployment, acquiring the classified dependent items through a network;
and in response to the deployment mode being the offline deployment, acquiring the classified dependent items through the cluster.
In some embodiments, obtaining the classified dependency item according to the deployment mode of each node further includes:
judging whether the classified dependent items of each node are the same;
determining a deployment mode of a management node according to the configuration file in response to the classified dependency items of each node being the same;
and responding to the offline deployment mode of the management node, acquiring the classified dependent items through the interior of the cluster, and issuing the classified dependent items to other nodes.
In some embodiments, further comprising:
and responding to the online deployment mode of the management node, acquiring the classified dependency items through a network, and issuing the classified dependency items to other nodes.
In some embodiments, further comprising:
and testing and verifying the decision power evaluation tool of one node.
In some embodiments, the performing test validation on the decision-making power evaluation tool of one of the nodes further comprises:
a service table is created for test validation.
Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer apparatus, including:
at least one processor; and
memory storing a computer program operable on the processor, wherein the processor when executing the program performs the steps of any of the methods of cluster deployment decision force evaluation tools described above.
Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of any one of the methods of cluster deployment decision force evaluation tools described above.
The invention has one of the following beneficial technical effects: the method disclosed by the invention has the advantages that the mode of deploying the decision power evaluation tool is configured in a self-adaptive manner by detecting different environments of each node, and all dependent items recorded in the dependent item configuration file of each node are classified, so that the dependent items are simplified and optimized, and the time for deploying the decision power evaluation tool is saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for cluster deployment of a decision-making power assessment tool according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a computer device provided in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following embodiments of the present invention are described in further detail with reference to the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
It should be noted that, in the embodiment of the present invention, the Maven value is Java compiled framework, and TCP-H is a decision power evaluation tool.
According to an aspect of the present invention, an embodiment of the present invention provides a method for deploying a decision-making power evaluation tool in a cluster, as shown in fig. 1, which may include the steps of: s1, carrying out environment detection on each node in the cluster and generating a configuration file according to the environment detection result of each node; s2, acquiring an initial dependency item configuration file of each node and classifying all dependency items recorded by the initial dependency item configuration file of each node; s3, reading the mode of deploying the decision power evaluation tool for each node contained in the configuration file; s4, acquiring the corresponding classified dependency item according to the deployment mode of each node, and deploying the deployment decision evaluation tool on each node by using the corresponding classified dependency item.
The method disclosed by the invention has the advantages that the mode of deploying the decision power evaluation tool is configured in a self-adaptive manner by detecting different environments of each node, and all dependent items recorded in the dependent item configuration file of each node are classified, so that the dependent items are simplified and optimized, and the time for deploying the decision power evaluation tool is saved.
In some embodiments, the step S1, performing environment detection on each node in the cluster may further include: detecting whether each node is networked or not by using the detection instruction; responding to the condition that a node is in a networking state, and judging whether the node is provided with a Maven or not; and in response to the node not installing the Maven, installing the Maven for the node.
Specifically, the detection instruction may be ping-c 2-f-w 100www.baidu.com-q & mvn-v.
It should be noted that ping-c 2-f-w 100www.baidu.com-q is a command for detecting network connectivity, and it adopts a limit detection method, and if it is impossible to log in www.baidu.com website 2 times within 100ms, it is determined as an offline state. mvn-v is a command to detect if Maven is installed. In addition, the detection of whether the Maven is installed or not is only carried out when the nodes are detected to be in an online state, namely, the nodes can be connected with an external network, so that when one of the nodes is detected to be in a networking state but the Maven is not installed, the Maven can be installed before the decision power evaluation tool is deployed.
In some embodiments, in step S1, generating a configuration file according to the result of the environment detection of each node may further include: determining the deployment mode to be online deployment or offline deployment according to the networking state of each node; and generating the configuration file by using the address of each node, the deployment mode and the networking state.
Specifically, after detecting each node in step S1, the networking state of each node may be obtained, and if the node is in an online state, the TCP-H tool may be deployed in an online deployment manner, and if the node is in an offline state, the TCP-H tool may be deployed in an offline deployment manner. And then generates a configuration file by using the name or IP address of each node, the deployment mode and the networking state.
For example, in some embodiments, the following instructions echo-e ' network may be included in the configuration file, wherein the instructions include: offline ' > Custom _ deployment. context ' > Custom _ deployment.configuration ' \ mvnstat ' > instant _ deployment.configuration ' \ nodal ' > Custom _ deployment.configuration ' \ metadata _ deployment.com, datamode 1. metadata.com ' > Custom _ deployment.configuration.
The network indicates a network connection condition, online and offline can be set, mvnstat indicates a maven installation condition, installed and uninstant can be set, Adaptive _ deployment indicates that an Adaptive deployment mode can set online deployment and offline deployment, Hosts indicates a node to be deployed with TPC-H, and IP or hostname can be set.
In some embodiments, the configuration file may be generated in multiple numbers, that is, each node generates one configuration file, or may be generated as an overall configuration file, that is, an overall configuration file including configuration information of all nodes is generated and stored in the management node.
In some embodiments, to make the installation process more secure, all nodes may be set to an offline deployment by a user modifying the configuration file.
Therefore, self-defined self-adaptive deployment configuration is achieved through the heterogeneity of the multiple nodes, and the requirement that a user carries out deployment and installation according to personalized selection can be met, so that the nodes can be subjected to batch differentiated deployment in the next step.
In some embodiments, in step S2, obtaining an initial dependency configuration file of each node and classifying all dependencies described in the initial dependency configuration file of each node may specifically include:
acquiring a pom.xml (initial dependency item configuration file) file recording a dependency item required by installing a TCP-H tool in a preset path of each node, and then analyzing the pom.xml file in a polling mode to simplify and stabilize the dependency item, namely classifying optional dependency, transitive dependency, same version dependency, repeated dependency and a dependency tree.
The optional dependency refers to selectable dependency, the transitive dependency refers to dependency A which needs to depend on D, the dependency D needs to depend on F, the dependency with the same version refers to a plurality of sub-dependencies belonging to the large class of the same version, the repeated dependency refers to the fact that the dependency is needed in a plurality of steps in the deployment process, the dependency tree refers to the fact that the dependency A needs to depend on B and D, and the dependency D needs to depend on F.
Specifically, for transitive dependency, the current item can be inspected, replaced or updated to a stable dependency item by using a method for removing the dependency, for example, an exclusions method is used in the code; for optional dependence, a simplified zero mode is adopted; the dependency of the same version is managed by adopting a classification dependency method and declaring the global constant variable, so that upgrading and packaging processing are facilitated; and compressing the repeated dependency, compiling the same batch, and performing self-adaptive matching.
S4, acquiring the corresponding classified dependency item according to the deployment mode of each node, and deploying the deployment decision evaluation tool on each node by using the corresponding classified dependency item.
In some embodiments, obtaining the classified dependency item according to the deployment mode of each node may further include: in response to the deployment mode being the online deployment, acquiring the classified dependent items through a network; and in response to the deployment mode being the offline deployment, acquiring the classified dependent items through the cluster.
Specifically, when a plurality of configuration files are generated, that is, each node generates one configuration file, the classified dependency items may be obtained according to the deployment manner of the node recorded in the configuration file of each node, for example, the offline deployment obtains the required dependency items through the inside of the cluster, and the online deployment obtains the classified dependency items through the network.
In some embodiments, obtaining the classified dependency item according to the deployment mode of each node may further include: judging whether the classified dependent items of each node are the same; determining a deployment mode of a management node according to the configuration file in response to the classified dependency items of each node being the same; in response to the fact that the deployment mode of the management node is offline deployment, obtaining the classified dependency items through the interior of the cluster, and sending the classified dependency items to other nodes; and responding to the online deployment mode of the management node, acquiring the classified dependency items through a network, and issuing the classified dependency items to other nodes.
Specifically, when the generated configuration file is one, that is, when a total configuration file including configuration information of all nodes is generated, it may be determined whether the required dependency items of each node are the same, and if the required dependency items are the same, all the dependency items may be directly obtained by the management node and then issued to other nodes.
It should be noted that, in the cluster, at most, there is only one networked node, so if it is determined that the deployment mode of the management node is online deployment and other nodes are offline deployment according to the configuration file, all the dependent items can be directly acquired from the network through the management node and then sent to other nodes. The source of the dependency item of the other node is obtained from the network by the management node, but is also obtained from the inside of the cluster (management node). If the deployment mode of the management node is determined to be offline deployment according to the configuration file, and other nodes are also offline deployment, all the dependent items can be directly obtained from the interior of the cluster through the management node by adopting a proximity principle, and then issued to other nodes.
In some embodiments, if the dependency items of some of the nodes are different from the dependency items required by the management node, and the dependency items of some of the nodes are the same as the dependency items required by the management node, the dependency items may be obtained by some of the nodes in a manner issued by the management node, and the dependency items may be obtained by some of the nodes in a deployment manner recorded in the configuration file, that is, the offline deployment is obtained by the cluster interior, and the online deployment is obtained by the network.
Specifically, in the process of deploying the TCP-H tool, a first step needs to rely on a and B, a second step needs to rely on a and C, and a third step needs to rely on C and F, wherein the dependence a needs to rely on D, and the dependence C needs to rely on E, after classification processing, all required dependencies for deploying the TCP-H tool, namely A, B, C, D, E and F, can be obtained, and then all dependencies (A, B, C, D, E and F) are directly obtained at one time according to a corresponding deployment mode, without obtaining the dependence a in the first step, the dependence a is obtained again in the second step, or the dependence a is obtained in the first step, and when a subsequent process is performed, the dependence D needs to be obtained again. Therefore, the dependent items are classified before deployment, so that the deployment time of the TPC-H tool can be saved, and the problem of long time consumption in the compiling process caused by repeated downloading of the dependent items is solved.
In some embodiments, the method of clustered deployment of decision-force assessment tools may further comprise: and testing and verifying the decision power evaluation tool of one node.
Specifically, the test verification can be performed by creating a service table.
In some embodiments, sh 3 may be executed to create tables Customer, Lineitem, nature, Orders, Part, Partsupp, Region, super, simulate business tables, execute Query1 to simulate price statistics report Query validation.
The method disclosed by the invention has the advantages that the mode of deploying the decision power evaluation tool is configured in a self-adaptive manner by detecting different environments of each node, and all dependent items recorded in the dependent item configuration file of each node are classified, so that the dependent items are simplified and optimized, and the time for deploying the decision power evaluation tool is saved.
Based on the same inventive concept, according to another aspect of the present invention, as shown in fig. 2, an embodiment of the present invention further provides a computer apparatus 501, comprising:
at least one processor 520; and
memory 510, memory 510 storing a computer program 511 executable on a processor, the processor 520 when executing the program performing the steps of any of the methods of cluster deployment decision power assessment tool as described above.
Based on the same inventive concept, according to another aspect of the present invention, as shown in fig. 3, an embodiment of the present invention further provides a computer-readable storage medium 601, where the computer-readable storage medium 601 stores computer program instructions 610, and the computer program instructions 610, when executed by a processor, perform the steps of any one of the above methods for cluster deployment of a decision power assessment tool.
Finally, it should be noted that, as will be understood by those skilled in the art, all or part of the processes of the methods of the above embodiments may be implemented by a computer program to instruct related hardware to implement the methods. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like. The embodiments of the computer program may achieve the same or similar effects as any of the above-described method embodiments.
In addition, the apparatuses, devices, and the like disclosed in the embodiments of the present invention may be various electronic terminal devices, such as a mobile phone, a Personal Digital Assistant (PDA), a tablet computer (PAD), a smart television, and the like, or may be a large terminal device, such as a server, and the like, and therefore the scope of protection disclosed in the embodiments of the present invention should not be limited to a specific type of apparatus, device. The client disclosed by the embodiment of the invention can be applied to any one of the electronic terminal devices in the form of electronic hardware, computer software or a combination of the electronic hardware and the computer software.
Furthermore, the method disclosed according to an embodiment of the present invention may also be implemented as a computer program executed by a CPU, and the computer program may be stored in a computer-readable storage medium. The computer program, when executed by the CPU, performs the above-described functions defined in the method disclosed in the embodiments of the present invention.
Further, the above method steps and system elements may also be implemented using a controller and a computer readable storage medium for storing a computer program for causing the controller to implement the functions of the above steps or elements.
Further, it should be appreciated that the computer-readable storage media (e.g., memory) herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example, and not limitation, nonvolatile memory can include Read Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which can act as external cache memory. By way of example and not limitation, RAM is available in a variety of forms such as synchronous RAM (DRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to comprise, without being limited to, these and other suitable types of memory.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as software or hardware depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein may be implemented or performed with the following components designed to perform the functions herein: a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and/or any other such configuration.
The steps of a method or algorithm described in connection with the disclosure herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary designs, the functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk, blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The foregoing is an exemplary embodiment of the present disclosure, but it should be noted that various changes and modifications could be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the disclosed embodiments described herein need not be performed in any particular order. Furthermore, although elements of the disclosed embodiments of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.
It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly supports the exception. It should also be understood that "and/or" as used herein is meant to include any and all possible combinations of one or more of the associated listed items.
The numbers of the embodiments disclosed in the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, of embodiments of the invention is limited to these examples; within the idea of an embodiment of the invention, also technical features in the above embodiment or in different embodiments may be combined and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present invention are intended to be included within the scope of the embodiments of the present invention.
Claims (10)
1. A method of clustering deployment decision-force assessment tools, comprising the steps of:
carrying out environment detection on each node in the cluster, and generating a configuration file according to the environment detection result of each node;
acquiring an initial dependency item configuration file of each node and classifying all dependency items recorded by the initial dependency item configuration file of each node;
reading the manner of deploying a decision power evaluation tool for each node contained in the configuration file;
and acquiring the corresponding classified dependent item according to the deployment mode of each node, and deploying the deployment decision power evaluation tool on each node by using the corresponding classified dependent item.
2. The method of claim 1, wherein performing environment detection for each node in the cluster, further comprises:
detecting whether each node is networked or not by using the detection instruction;
responding to the condition that a node is in a networking state, and judging whether the node is provided with a Maven or not;
and in response to the node not installing the Maven, installing the Maven for the node.
3. The method of claim 2, wherein generating a configuration file based on the results of the environment detection of each node, further comprises:
determining the deployment mode to be online deployment or offline deployment according to the networking state of each node;
and generating the configuration file by using the address of each node, the deployment mode and the networking state.
4. The method of claim 3, wherein obtaining the corresponding categorized dependency according to the deployment of each node further comprises:
in response to the deployment mode being the online deployment, acquiring the classified dependent items through a network;
and in response to the deployment mode being the offline deployment, acquiring the classified dependent items through the cluster.
5. The method of claim 3, wherein obtaining the corresponding categorized dependency according to the deployment of each node further comprises:
judging whether the classified dependent items of each node are the same;
determining a deployment mode of a management node according to the configuration file in response to the classified dependency items of each node being the same;
and responding to the offline deployment mode of the management node, acquiring the classified dependent items through the interior of the cluster, and issuing the classified dependent items to other nodes.
6. The method of claim 5, further comprising:
and responding to the online deployment mode of the management node, acquiring the classified dependency items through a network, and issuing the classified dependency items to other nodes.
7. The method of claim 1, further comprising:
and testing and verifying the decision power evaluation tool of one node.
8. The method of claim 7, wherein performing test validation on the decision-force evaluation tool of one of the nodes further comprises:
a service table is created for test validation.
9. A computer device, comprising:
at least one processor; and
memory storing a computer program operable on the processor, wherein the processor executes the program to perform the steps of the method according to any of claims 1-8.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 8.
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| CN113434246A (en) * | 2021-06-15 | 2021-09-24 | 竹间智能科技（上海）有限公司 | Multilayer dependency deployment method, tool, equipment and medium |
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