CN114003312A - Big data service component management method, computer device and storage medium - Google Patents
Big data service component management method, computer device and storage medium Download PDFInfo
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
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- 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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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
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Abstract
The invention provides a big data service component management method, a computer device and a storage medium, wherein the method comprises the following steps: when an instruction for installing a current service assembly is acquired, carrying out version configuration on the current service assembly and a corresponding dependent assembly; determining the address of each component installation node, and pulling a component program package to distribute cluster nodes; executing component installation and deployment, distributing configuration files and generating an operation instruction script; and executing the operation instruction scripts corresponding to the nodes based on the component dependence graph, and starting the service components. The big data service component management method can realize automatic installation and deployment of the big data platform cluster, and quickly and efficiently construct the big data cluster.
Description
Technical Field
The invention relates to the technical field of computer big data, in particular to a big data service component management method, a computer device applying the big data service component management method and a computer readable storage medium applying the big data service component management method.
Background
With the rapid development of big data and cloud computing, a big data platform is widely applied to various industries as a big data analysis tool, and management, operation and maintenance of the big data platform are an inevitable link. At present, more components are deployed based on a manual mode (Apache hadoop), the problem of version compatibility among the components needs to be solved in the deployment process, parameters needing to be configured for each service component are more, the configuration is complex, errors are easy to occur, a native webUI cannot execute start and stop operations of services, webUI interfaces among the components are mutually independent, most functions are limited to read-only operations and cannot execute other operations, the operation condition of the whole cluster is difficult to monitor in a unified mode, alarm notification is lacked, the service components are difficult to expand and upgrade, automatic manual operation is needed, and the like.
Some big data operation and maintenance management tools exist in the market at present, for example, Ambari management tools, based on Ambari as big data component operation and maintenance management services, although the Ambari management tools can be simply, conveniently and quickly installed, deployed, monitored and managed for each service component, because the community version is stopped to be updated and is no longer maintained, the version cannot be synchronously updated with the apahce big data ecological component version; the existing architecture metircs is old, codes are bloated, stack is maintained inside, and coupling is strong; the custom Stack and the integrated component are complex, and the integration of a secondary development service component is inconvenient; based on the installation of the RPM package, the patch is upgraded on the components, the RPM package needs to be reconstructed, errors are easy to occur, the construction difficulty is high, and the integration of the large data platform expansion, the upgrading and the secondary development components in the later period is not facilitated.
Disclosure of Invention
The first purpose of the invention is to provide a big data service component management method which can realize automatic installation and deployment of a big data platform cluster and quickly and efficiently construct a big data cluster.
The second purpose of the invention is to provide a computer device for realizing automatic installation and deployment of a big data platform cluster and quickly and efficiently constructing the big data cluster.
The third purpose of the present invention is to provide a computer readable storage medium for realizing the automatic installation and deployment of a big data platform cluster, and quickly and efficiently constructing a big data cluster.
In order to achieve the first object, the big data service component management method provided by the invention comprises the following steps: when an instruction for installing a current service assembly is acquired, carrying out version configuration on the current service assembly and a corresponding dependent assembly; determining the address of each component installation node, and pulling a component program package to distribute cluster nodes; executing component installation and deployment, distributing configuration files and generating an operation instruction script; and executing the operation instruction scripts corresponding to the nodes based on the component dependence graph, and starting the service components.
According to the scheme, when the current service component needs to be installed, the version configuration is carried out on the current service component and the corresponding dependent component, so that the automatic installation and deployment of the big data platform cluster are realized, and the big data cluster is constructed quickly and efficiently.
In a further aspect, the step of performing version configuration on the current service component and the corresponding dependent component includes: creating a component item of a current service component; carrying out configuration item definition on the current service assembly; the defined configuration items are saved.
Therefore, by defining the configuration item of the current service component, the version configuration information between big data components can be maintained conveniently.
In a further aspect, the step of defining the configuration item for the current service component includes: and defining configuration items according to the user level authority.
Therefore, when the configuration item definition is carried out on the current service assembly, the configuration item definition is carried out according to the user level authority, and the assembly configuration error caused by misoperation of a user can be avoided.
In a further aspect, the step of performing version configuration on the current service component and the corresponding dependent component further includes: and carrying out component dependence configuration on the current service component to generate a component dependence graph corresponding to the current service component.
It can be seen that the component dependency graph is generated by performing component dependency configuration on the current service component, so that when the current service component is installed, installation is performed according to the component dependency graph.
In a further aspect, the step of generating the operation instruction script includes: and carrying out custom operation on the operation instruction of the current service assembly.
Therefore, the operation instruction of the current service assembly is subjected to user-defined operation, the operation instruction of the service assembly can be flexibly defined, and the assembly is convenient to use and expand.
In a further aspect, the operation instruction data structure includes: component name, component version number, operation name, operation type, content, variable, node selection rule, and server IP.
In a further aspect, after the step of performing the start operation on the service component, the method further includes: and monitoring the index parameter abnormity of the current service assembly.
Therefore, after the service assembly is started, index parameter abnormity monitoring is carried out on the current service assembly, the problems that monitoring is not in place, and when the cluster is abnormal, an alarm notice cannot be timely received and processed, and online task operation is influenced can be avoided.
In order to achieve the second object of the present invention, the present invention provides a computer device including a processor and a memory, wherein the memory stores a computer program, and the computer program implements the steps of the big data service component management method when being executed by the processor.
In order to achieve the third object of the present invention, the present invention provides a computer readable storage medium, on which a computer program is stored, the computer program, when executed by a controller, implementing the steps of the big data service component management method described above.
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FIG. 1 is a flow chart of an embodiment of a big data service component management method of the present invention.
The invention is further explained with reference to the drawings and the embodiments.
Detailed Description
The embodiment of the big data service component management method comprises the following steps:
the big data service component management method of the embodiment is an application program applied to a computer and used for managing service components.
As shown in fig. 1, in this embodiment, when the big data service component management method works, step S1 is first executed to determine whether an instruction to install a current service component is obtained. And a user installs and deploys the service when the component is required to be installed, selects the service component required to be installed through the visual webpage operation interface, and installs the service component required to be installed. And when the installation confirmation operation instruction is obtained, confirming to obtain the instruction for installing the current service assembly.
When the instruction for installing the current service component is confirmed to be acquired, step S2 is executed to perform version configuration on the current service component and the corresponding dependent component. Before installing a service component, the service component needs to be configured so as to facilitate installation, update and maintenance of the service component. Meanwhile, there may be a dependency relationship between service components, and installation of one service component requires that another service component be installed first, so that when performing service components, it is necessary to configure the dependency components corresponding to the service components together.
In this embodiment, the step of performing version configuration on the current service component and the corresponding dependent component includes: creating a component item of a current service component; carrying out configuration item definition on the current service assembly; the defined configuration items are saved. When creating a component item for a current service component, the component item includes: component name, version number, and remark information, for example, component item content includes: the component name is "zookeeper", the version number is "3.4.5", and the remark information is "application coordination service". The component version numbers correspond to the component TAG versions of the private warehouse of the GIT big data component one by one, so that pulling is carried out during installation. After the creation is completed, the configuration item definition can be performed on the current service component, and the input is needed when the configuration item is defined: configuration name, value, description, file name, etc., such as: input when defining configuration items: dataDir,/var/lib/zookeeper, catalog, zo. The well-defined configuration items are stored in a database for persistence, and modification and deletion operations on version configuration are allowed. Preferably, the specific data structure of the component version configuration item is as follows: component name, version, configuration name, value, configuration domain, configuration file name, configuration type, remark, etc.
In this embodiment, the step of defining the configuration item for the current service component includes: and defining configuration items according to the user level authority. Different user levels, different operations may be performed in configuring the item definition. For example, user ratings include: the user level of BASIC and ADVANCE only allows modification but not deletion, and the CUSTOMER level allows addition, modification and deletion.
The service component is correspondingly provided with a version configuration item, and updating and maintenance can be carried out through component version configuration management. When the service component is started or restarted, firstly, whether the component configuration item has change or not is checked, when the change exists, the configuration difference comparison is carried out on the changed item and the configuration item before the change, after the update configuration is determined, the content of the corresponding component version configuration item is obtained, configuration files such as XML, YAML, cfg and the like of the corresponding component are generated according to the configuration item information, and the large data platform distributes the component configuration file to a server node where the component service is deployed for replacement updating.
In this embodiment, the step of performing version configuration on the current service component and the corresponding dependent component further includes: and carrying out component dependence configuration on the current service component to generate a component dependence graph corresponding to the current service component. In order to facilitate installation of the current service component according to the component dependency graph, component dependency configuration needs to be performed on the current service component to generate a component dependency graph corresponding to the current service component. The construction of the component dependency graph is based on component dependency configuration, a big data platform can provide a big data ecological component dependency knowledge base by default, and the structural design of dependency relationship configuration is as follows: (component name, dependent component name), for example, a dependent configuration: (kafka, zookeeper), (Atlas, kafka), i.e., installing the kafka component requires a dependent component zookeeper, which the Atlas component depends on. In addition, when service components in the platform are deleted or added, a new cluster component dependency graph needs to be reconstructed and generated based on the dependency relationship among the components according to the component dependency knowledge base, an execution link path is provided for start-stop control of the cluster service components according to the component dependency graph, and the problem that the component dependency relationship is messy due to too many service components and the start-stop of the service components cannot be sequentially executed according to the component dependency sequence is avoided.
After the version configuration is performed on the current service component and the corresponding dependent component, step S3 is executed, the node address of each component installation is determined, and the component package is pulled for cluster node distribution. After the version configuration is completed, the installation node addresses of the current service component and the dependent component need to be confirmed so as to facilitate component installation, and after the addresses are confirmed, the component program package is pulled from the GIT private warehouse to be distributed to the corresponding cluster nodes.
After the cluster node distribution of the component package is performed, step S4 is executed, component installation and deployment are performed, the configuration file distribution is performed, and an operation instruction script is generated. The operation instruction script is an operation control script of each service component, and comprises start-stop operation, configuration file synchronization operation and the like of the service components. After receiving the component program package, each cluster node carries out component installation and deployment, after compiling according to source codes of different service components, decompresses the installation package to a corresponding service component installation directory, meanwhile, copies a configuration file required by the service component to an etc directory of the installed service node, and generates an operation instruction script according to the configuration file of the component for controlling the service component to operate. Generating the operation instruction script according to the configuration file of the component is a technique known to those skilled in the art and will not be described herein.
In this embodiment, the step of generating the operation instruction script includes: and carrying out custom operation on the operation instruction of the current service assembly. Each type of service component has some special operation instructions, and the operation and maintenance of the big data component can be frequently used, and some operation and maintenance component tools are basically customized at present and are not beneficial to expansion and modification, so that the operation instructions of the components need to be flexibly customized, addition and configuration are allowed, and the information of the customized operation items can be stored in a database. Preferably, the operation instruction data structure of the service component includes: component name, component version number, operation name, operation type, content, variable, node selection rule, and server IP. For example, when instruction control of an Hdfs component needs to be customized, the following data structure definitions need to be filled in: component name [ hdfs ], component version number [ V3.0.0], operation name [ hdfs-rebalance ], operation type [ command ], content [ hadoop balancer-threshold ] variable [ { name: threshold } ], the NODE selects [ ONE _ NODE ], the server IP { hosts }, and after the definition is completed, the currently defined function can be used in the webpage operation interface. By customizing the component operation instruction, the function can be customized according to the characteristics of different component versions, the service component control function is dynamically expanded, and the flexibility of platform operation and maintenance is improved.
After the operation instruction script is generated, step S5 is executed to execute the operation instruction script corresponding to each node based on the component dependency graph, and perform a start operation on the service component. After the service components are installed and deployed, the service components need to be started, and because the service components have dependency relationships, the service components are started according to the component dependency graph, so that normal starting and running of the current service components are guaranteed.
After the service component is started, step S6 is executed to monitor the index parameter abnormality of the current service component. After the service components are started, in order to avoid that monitoring is not in place, when a cluster is abnormal, an alarm notification cannot be timely received and processed, the on-line task operation is affected, and index parameter abnormity monitoring needs to be carried out on the current service components. In this embodiment, carry out index parameter monitoring to each subassembly of big data through jmx _ exporter subassembly, carry out parameter monitoring to the server basis through node _ exporter subassembly, use the prometheus subassembly to carry out data storage, carry out the visual show of control through the grafana subassembly, the main content includes: the method comprises the steps of collecting the total number of cluster machines, online machines, offline machines, the total number of YarnTaskHdfs, the average utilization rate of cluster CPUs, the average utilization rate of memories, the average utilization rate of disks, the utilization rate of HDFS, the utilization rate of CPUs (central processing unit) Top10, the utilization rate of memories Top10, the number of IOshuffle connections read-write of HDFS, the number of shuffle success and failure times, the use condition of Yarn memories, the number of active APPs of Yarn, a cluster flow chart and other monitoring indexes of various service components.
Therefore, the big data service component management method can complete dynamic addition integration of the components through configuration management of component versions, component dependence management, component operation instruction self-definition and component installation and deployment, complete one-key deployment of the components, realize automatic installation and deployment of cluster components and flexible integration and expansion of the components, and perform intelligent operation and maintenance management such as configuration management, monitoring alarm, fault diagnosis and the like on the components, thereby helping a user to quickly construct a big data cluster, providing operation and maintenance efficiency and reducing operation and maintenance difficulty.
The embodiment of the computer device comprises:
the computer device of this embodiment includes a controller, and when the controller executes a computer program, the steps in the above-described embodiment of the big data service component management method are implemented.
For example, a computer program may be partitioned into one or more modules, which are stored in a memory and executed by a controller to implement the present invention. One or more of the modules may be a sequence of computer program instruction segments for describing the execution of a computer program in a computer device that is capable of performing certain functions.
The computer device may include, but is not limited to, a controller, a memory. Those skilled in the art will appreciate that the computer apparatus may include more or fewer components, or combine certain components, or different components, e.g., the computer apparatus may also include input-output devices, network access devices, buses, etc.
For example, the controller may be a Central Processing Unit (CPU), other general purpose controller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic, discrete hardware components, and so on. The general controller may be a microcontroller or the controller may be any conventional controller or the like. The controller is the control center of the computer device and connects the various parts of the entire computer device using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the controller may implement various functions of the computer apparatus by executing or otherwise executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. For example, the memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Computer-readable storage medium embodiments:
the modules integrated by the computer apparatus of the above embodiments, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on such understanding, all or part of the flow of the embodiment of the big data service component management method may also be implemented by a computer program instructing related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a controller, the computer program may implement the steps of the embodiment of the big data service component management method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The storage medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
It should be noted that the above is only a preferred embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept also fall within the protection scope of the present invention.
Claims (9)
1. A big data service component management method is characterized in that: the method comprises the following steps:
when an instruction for installing a current service assembly is acquired, carrying out version configuration on the current service assembly and a corresponding dependent assembly;
determining the address of each component installation node, and pulling a component program package to distribute cluster nodes;
executing component installation and deployment, distributing configuration files and generating an operation instruction script;
and executing the operation instruction script corresponding to each node based on the component dependence graph, and starting the service component.
2. The big data service component management method of claim 1, wherein:
the step of performing version configuration on the current service component and the corresponding dependent component comprises the following steps:
creating a component item of the current service component;
carrying out configuration item definition on the current service assembly;
saving the defined configuration item.
3. The big data service component management method of claim 2, wherein:
the step of defining the configuration item of the current service component comprises the following steps:
and defining configuration items according to the user level authority.
4. The big data service component management method of claim 3, wherein:
the step of performing version configuration on the current service component and the corresponding dependent component further includes:
and carrying out component dependence configuration on the current service component, and generating the component dependence graph corresponding to the current service component.
5. The big data service component management method according to any one of claims 1 to 4, wherein:
the step of generating the operation instruction script includes:
and carrying out custom operation on the operation instruction of the current service assembly.
6. The big data service component management method of claim 5, wherein:
the operation instruction data structure includes: component name, component version number, operation name, operation type, content, variable, node selection rule, and server IP.
7. The big data service component management method according to any one of claims 1 to 4, wherein:
after the step of starting the service component, the method further comprises the following steps:
and monitoring the index parameter abnormity of the current service assembly.
8. A computer device comprising a processor and a memory, wherein: the memory stores a computer program which, when executed by the processor, implements the steps of the big data service component management method according to any of claims 1 to 7.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a controller implements the steps of a big data service component management method according to any of claims 1 to 7.
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| CN114816393A (en) * | 2022-05-18 | 2022-07-29 | 北京百度网讯科技有限公司 | Information generation method, device, equipment and storage medium |
| CN114816393B (en) * | 2022-05-18 | 2023-12-19 | 北京百度网讯科技有限公司 | Information generation method, device, equipment and storage medium |
| CN115514666A (en) * | 2022-09-26 | 2022-12-23 | 郑州小鸟信息科技有限公司 | Visual deployment method and system |
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