CN110880990B - Configuration checking method and device for big data cluster component and computing equipment - Google Patents
Configuration checking method and device for big data cluster component and computing equipment Download PDFInfo
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- CN110880990B CN110880990B CN201911207203.7A CN201911207203A CN110880990B CN 110880990 B CN110880990 B CN 110880990B CN 201911207203 A CN201911207203 A CN 201911207203A CN 110880990 B CN110880990 B CN 110880990B
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Abstract
The invention discloses a configuration checking method, a device and computing equipment of a big data cluster component, relates to the technical field of computers, and is used for reducing the workload of configuration personnel in the configuration checking of the existing big data cluster component and improving the configuration checking efficiency and the configuration checking accuracy, wherein the method comprises the following steps: acquiring configuration information of a main node of a big data cluster, and logging in the main node according to the configuration information; acquiring starting parameters of a main node big data component corresponding to a main node and address IP information of a slave node; acquiring a configuration file of the main node big data component according to the starting parameters, and carrying out configuration check on the main node big data component based on the configuration file; after the configuration check of the master node big data assembly is completed, based on the IP information of the slave nodes, the first node is automatically jumped by utilizing a host jump technology, and after the configuration file of the slave node big data assembly corresponding to the first node is obtained, the configuration check of the slave node big data assembly is carried out.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a configuration checking method and device for a big data cluster component and computing equipment.
Background
With the rapid growth in the size and number of large data platforms, the risk of security configuration of large data components is also increasingly valued. In the conventional configuration check of the big data component, configuration personnel are required to configure a process in the big data component to be checked, an installation path of the process and the like. However, because the number of nodes of the big data platform is extremely large, the number of big data components corresponding to each node is also large, and the configuration file paths of the big data components are different from each other. Moreover, for big data components of big data platforms (also called big data clusters) such as cloudera big data platform and hotspot big data platform (HDP), the path of the configuration file may be dynamically changed, and after the components are restarted each time, the path of the configuration file may be changed, and a large amount of time is needed to reconfigure the path of each big data component, thereby bringing a lot of configuration difficulties to configuration inspectors.
Disclosure of Invention
The application provides a method, a device and a computing device for configuration checking of a big data cluster component, which are used for reducing the workload of configuration personnel in the existing configuration checking of the big data cluster component and improving the configuration checking efficiency and the accuracy of the configuration checking.
In one aspect, a configuration checking method for a big data cluster component is provided, where the method includes:
acquiring configuration information of a main node of a big data cluster, and logging in the main node according to the configuration information;
acquiring starting parameters of a main node big data component corresponding to the main node and address IP information of a slave node;
acquiring a configuration file of the main node big data component according to the starting parameters, and carrying out configuration check on the main node big data component based on the configuration file;
after the configuration check of the master node big data assembly is completed, automatically jumping to a first node by using a host jumping technology based on the IP information, and after the configuration file of the slave node big data assembly corresponding to the first node is obtained according to the step of obtaining the master node configuration file, performing configuration check on the slave node big data assembly, wherein the first node is a slave node in the big data cluster.
In one possible design, obtaining a configuration file of a master node big data component corresponding to the master node includes:
obtaining the running main node big data component and the identification ID information of the main node big data component from the big data cluster through a component viewing jps command;
and obtaining the starting parameter of the main node big data component through a component filtering ps command.
In one possible design, obtaining a configuration file of the big data component of the master node according to the startup parameter includes:
analyzing the starting parameters to obtain an installation path of the main node big data component;
determining a configuration file path of the main node big data component according to the installation path;
and obtaining the configuration file of the main node big data component according to the configuration file path.
In one possible design, analyzing the startup parameters to obtain an installation path of the main node big data component includes:
determining whether the starting parameter contains the home attribute of the main node big data component, and if so, determining the installation path of the main node big data component according to the home attribute; alternatively, the first and second liquid crystal display panels may be,
determining whether the starting parameters contain the configuration file information of the starting of the main node big data component, and if so, determining the installation path of the main node big data component according to the configuration file information; alternatively, the first and second electrodes may be,
and determining whether the starting parameters contain the directory information of the custom library corresponding to the main node big data component, and if so, determining the installation path of the main node big data component according to the directory information.
In one possible design, the configuration check of the master node big data component includes:
analyzing the configuration content of the configuration file of the main node big data component to obtain the configuration content of the main node big data component;
determining whether the configuration content matches content configured for the main node big data component on an automatic configuration checking IDR device;
and if the configuration of the main node big data component is matched with the configuration of the main node big data component, determining that the configuration of the main node big data component has risk.
In one possible design, upon determining that there is a risk in the configuration of the master node big data component, the method further comprises:
prompt information is generated indicating the at-risk big data component.
In a possible design, all the slave nodes in the big data cluster are set to a secure shell protocol SSH password-free login, and after the configuration check of the big data component of the master node is completed, the slave node automatically jumps to the first node by using a host jump technology, specifically:
and after the configuration verification of the main node big data component is completed, logging in the first node from the main node based on the SSH secret-free login and the host jump technology.
In one possible design, the big data cluster includes at least one slave node, and after the configuration check of the slave node big data component is completed, the method further includes:
returning to the master node;
and based on the SSH secret-free login, sequentially logging in the slave nodes except the first node from the master node to the at least one slave node by utilizing the host jump technology, and respectively performing configuration check on the big data components corresponding to the slave nodes.
In a second aspect, an apparatus for checking configuration of a big data cluster component is provided, the apparatus comprising:
the system comprises a first obtaining module, a second obtaining module and a third obtaining module, wherein the first obtaining module is used for obtaining configuration information of a master node of a big data cluster and address marking IP (Internet protocol) information of slave nodes and logging in the master node according to the configuration information;
the second obtaining module is used for obtaining the starting parameters of the main node big data assembly corresponding to the main node and the address IP information of the slave node;
the configuration checking module is used for acquiring a configuration file of the main node big data component according to the starting parameters and checking the configuration of the main node big data component based on the configuration file;
and the skipping module is used for automatically skipping to a first node by utilizing a host skipping technology after finishing the configuration check of the master node big data assembly based on the IP information, and performing the configuration check of the slave node big data assembly after acquiring the configuration file of the slave node big data assembly corresponding to the first node according to the step of acquiring the master node configuration file, wherein the first node is a slave node in the big data cluster.
In one possible design, the obtaining module is specifically configured to:
obtaining the running main node big data component and the identification ID information of the main node big data component from the big data cluster through a component viewing jps command;
and obtaining the starting parameter of the main node big data component through a component filtering ps command.
In one possible design, the obtaining module is further configured to:
analyzing the starting parameters to obtain an installation path of the main node big data component;
determining a configuration file path of the main node big data component according to the installation path;
and obtaining the configuration file of the main node big data component according to the configuration file path.
In one possible design, the apparatus further includes an analysis module to:
determining whether the starting parameter contains the home attribute of the main node big data component, and if so, determining the installation path of the main node big data component according to the home attribute; alternatively, the first and second electrodes may be,
determining whether the starting parameters contain configuration file information of the starting of the main node big data component, and if so, determining an installation path of the main node big data component according to the configuration file information; alternatively, the first and second electrodes may be,
and determining whether the starting parameters contain the directory information of the user-defined library corresponding to the main node big data component, and if so, determining the installation path of the main node big data component according to the directory information.
In one possible design, the configuration checking module is specifically configured to:
analyzing the configuration content of the configuration file of the main node big data component to obtain the configuration content of the main node big data component;
determining whether the configuration content matches content configured for the main node big data component on an automatic configuration checking IDR device;
and if the configuration of the main node big data component is matched with the configuration of the main node big data component, determining that the configuration of the main node big data component has risk.
In one possible design, the apparatus further includes a prompting module to:
prompt information is generated indicating the big data component at risk.
In a possible design, when all the slave nodes in the big data cluster are set to secure shell protocol SSH secure login, the skip module is specifically configured to:
and after the configuration verification of the main node big data component is completed, logging in the first node from the main node based on the SSH secret-free logging and the host computer jumping technology.
In one possible design, after the big data cluster includes at least one slave node and the configuration check of the slave node big data component is completed, the jumping module is further configured to:
returning to the master node;
and based on the SSH secret-free login, sequentially logging in the slave nodes except the first node from the master node to the at least one slave node by utilizing the host jump technology, and respectively performing configuration check on the big data components corresponding to the slave nodes.
In a third aspect, a computing device is provided, the computing device includes a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps included in the configuration checking method for the big data cluster component in the above aspects.
In a fourth aspect, a computer-readable storage medium is provided, which stores computer-executable instructions for causing a computer to perform the steps included in the configuration checking method for a big data cluster component in the above aspects.
In a fifth aspect, there is provided a computer program product which, when run on a computing device, causes the computing device to perform a method that implements the first aspect of the embodiments of the present disclosure and any of the possible aspects of the first aspect.
In the embodiment of the application, the configuration information of the master node in the big data cluster can be obtained first, and then the master node can be logged in according to the configuration information of the master node to obtain the starting parameter of the master node big data component corresponding to the master node and the IP information of the slave node; therefore, the configuration file of the main node big data component can be obtained according to the starting parameters, and the configuration check is carried out on the main node big data component corresponding to the main node based on the configuration file; and then after the configuration check of the master node is completed, automatically jumping to each slave node in the big data cluster by using a host jumping technology based on the acquired slave node IP information, and acquiring the configuration file of the big data component corresponding to each slave node from each slave node so as to perform the configuration check on the big data component of each slave node, thereby completing the configuration check on the big data components corresponding to all nodes in the whole big data cluster. That is to say, in the application embodiment, the configuration file of the big data component can be obtained through the automatically obtained starting parameter of the big data component, and then the big data component in the big data cluster is configured, so that the problem that a configuration person manually configures configuration information of the big data component corresponding to each node in the big data cluster can be avoided, and the problem that it is difficult to reconfigure the configuration file of the big data component after the big data component is restarted in a cloudera big data cluster and an HDP big data cluster can be solved, so that the configuration work of a configuration checker can be reduced, the configuration checking efficiency can be improved, configuration errors caused by insufficient professional knowledge of the configuration person can be reduced, and the configuration checking accuracy can be improved.
In addition, the host jump technology is utilized to automatically jump to other slave nodes to perform configuration check on the big data component corresponding to the slave node, namely the master node can access the big data component corresponding to other slave nodes to perform configuration check, so that the slave node does not need to log in from the IDR equipment, and the configuration workload of configuration personnel on the slave node can be reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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 will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;
FIG. 2 is a flowchart of a configuration checking method for a big data cluster component according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram illustrating a connection relationship between an IDR device and a master-slave node in a big data cluster according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a connection relationship between an IDR device and a master-slave node in a big data cluster in the related art;
FIG. 5a is a schematic diagram of a configuration checking device for big data cluster components;
FIG. 5b is a schematic diagram of another configuration checking apparatus for big data cluster components;
fig. 6 is a schematic structural diagram of a control computing device in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present invention will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the technical solutions of the present invention. All other embodiments obtained by a person skilled in the art based on the embodiments described in the present application without any creative efforts shall fall within the protection scope of the technical solution of the present invention.
The terms "first" and "second" in the description and claims of the present application and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the term "comprises" and any variations thereof, which are intended to cover non-exclusive protection. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. The "plurality" in the present application may mean at least two, for example, two, three or more, and the embodiments of the present application are not limited.
Some terms referred to herein are explained below to facilitate understanding by those skilled in the art.
An IDR device: the equipment (sensitive data Discovery and Risk) is set when the equipment leaves a factory, can check the Risk type of the big data component, and can be used for carrying out configuration check on the big data component in the big data cluster. The method can also be called as automatic configuration checking equipment in the embodiment of the application.
The idea of the present application is described below.
Because the nodes of the big data cluster are numerous, if the existing big data assembly configuration checking mode is adopted to check the nodes of the big data cluster, not only is a configuration worker spend a great deal of labor time to configure each node in the big data cluster assembly, the workload of the configuration worker is increased, and the configuration checking cost is increased, but also the configuration worker cannot know the configuration of each node in the big data cluster, so that configuration errors occur, and the configuration checking result of the big data cluster is influenced.
In view of this, the present applicant provides an automatic checking scheme for a big data cluster component, in which an automatic configuration checking device logs in a master node in a big data cluster, obtains a configuration file of the big data component by analyzing a start parameter of the big data component corresponding to the master node, thereby performing configuration checking on the master node big data component according to the obtained configuration file, and after completing configuration checking on the master node, automatically jumps to each slave node in the big data cluster by using a host jump technique based on the obtained slave node IP information, obtains the configuration file of the big data component corresponding to each slave node on each slave node, so as to perform configuration checking on the big data component of each slave node, and further complete configuration checking on the big data components corresponding to all nodes in the whole big data cluster. Therefore, the configuration information of the big data assembly corresponding to each node in the big data cluster can be prevented from being manually configured by the configuration personnel, so that the configuration workload of the configuration checking personnel can be reduced, the configuration checking efficiency is improved, configuration errors caused by insufficient professional knowledge of the configuration personnel can be reduced, and the configuration checking accuracy is improved.
After introducing the design concept of the embodiment of the present application, some simple descriptions are provided below for application scenarios to which the technical solution provided by the embodiment of the present application is applicable, and it should be noted that the application scenarios described below are only used for illustrating the embodiment of the present invention and are not limited. In specific implementation, the technical scheme provided by the embodiment of the application can be flexibly applied according to actual needs.
Referring to fig. 1, fig. 1 is an application scenario used in the technical solution of the present application. In this scenario, a big data cluster 101, an auto-configuration checking IDR device 102 of big data cluster components, and a switch 103 are included. The IDR device 102 may be a server, and may be connected to a large data cluster through a switch 103.
The big data cluster 101 may be composed of several servers, each server may be regarded as a node in the big data cluster, each node may correspond to one or more big data components, for example, may correspond to a Hadoop component, a kafka component, a hive component, and so on, and when configuring the big data cluster, one node of several nodes may be regarded as a master node, and the other nodes may be regarded as slave nodes. Each slave node in the big data cluster 101 may be configured as a Secure remote login protocol Secure Shell (SSH), that is, without using a user name and a password of the node, another node may log in to the node.
The switch 103 may be a Top of Rack (TOR) switch or a core switch, and may be specifically selected according to the number of nodes in the big data cluster, which is not limited herein.
To further illustrate the technical solutions provided by the embodiments of the present application, the following detailed description is made with reference to the accompanying drawings and the detailed description. Although the embodiments of the present application provide the method operation steps as shown in the following embodiments or figures, more or less operation steps may be included in the method based on the conventional or non-inventive labor. In steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the embodiments of the present application. The method can be executed in sequence or in parallel according to the method shown in the embodiment or the figure when the method is executed in an actual processing procedure or a device.
Please refer to the automatic configuration checking method for big data cluster components shown in fig. 2, and the following describes the technical solution of the present application with reference to fig. 2.
Step 201: and acquiring configuration information of the main node of the big data cluster, and logging in the main node according to the acquired configuration information.
In this embodiment of the present application, before performing configuration check on a big data cluster, a configuration person may configure a master node address IP of the big data cluster and telnet ssh information used for logging in to a master node in an IDR device, where the ssh information includes a user name, a password, port information, and the like. Specifically, the IP information and ssh information of the master node may be collectively referred to as configuration information of the master node, and after configuration is completed by a configuration staff, the IDR device may obtain the configuration information of the master node of the big data cluster. And, the master node can log on to the master node of the big data cluster by using the configuration information of the master node.
Step 202: and acquiring the starting parameters of the main node big data assembly corresponding to the main node and the address IP information of the slave node.
In the embodiment of the present application, as described above, a large data cluster may include a plurality of slave nodes, each having a fixed IP information. The IDR device may automatically obtain the IP information of all the slave nodes in the big data cluster through a function getDataNodeStats () of a JAVA API (Application Programming Interface), so that a configurator may be prevented from manually configuring corresponding IP information for each slave node in the big data cluster in the IDR device, thereby reducing the workload of the configurator and saving labor cost.
Specifically, the configuration file of the master node big data component corresponding to the master node can be obtained through the following steps:
the first step is as follows: and the component viewing jps command is used for acquiring the running main node big data component and the component identification ID information corresponding to the main node big data component from the big data cluster.
The running master node big data component is the master node big data component running in the big data cluster, that is, the master node big data component corresponding to the master node can be found out from all the components included in the big data cluster through jps commands, and the ID information of the master node big data component is obtained.
The second step is that: obtaining a starting parameter of a big data component of the master node through a component filtering ps command;
in a specific practical process, as described above, the master node big data component may include one or more components, and when the master node big data component is subjected to configuration check, one of the master node big data components may be subjected to configuration check, or all the components included in the master node big data component may be subjected to configuration check. Specifically, when the master node big data component comprises a plurality of components, if only one of the big data components needs to be configured and checked, the big data component can be filtered through the ps command and the starting parameters of the big data component can be obtained; if configuration checking needs to be performed on a plurality of components included in the master node big data component, each component in the master node big data component can be sequentially filtered through a ps command, and the starting parameters of each component are obtained. Therefore, the big data component needing configuration checking can be selected at will through the ps command, and the starting parameter corresponding to the big data component is obtained, so that the flexibility of configuration checking on the main node big data component corresponding to the main node is improved.
For example, the main node big data component comprises three big data components, namely a hive component, a hadoop component and a kafka component, and if the need arises, the hive component in the main node big data component can use commands: psaux | grep HiveServer2| grep-v grep | awk-F \ { print $2 }'; and acquiring the ID number HiveID of the live component from the obtained ID information of the main node big data component, and then acquiring the starting parameter of the live component through ps aux | grephuiD | grep-v grep. If configuration checks need to be performed on the hive component, the hadoop component and the kafka component, the start parameters of the hive component, the hadoop component and the kafka component can be obtained by using the ps command.
Step 203: and acquiring a configuration file of the main node big data component according to the starting parameters, and carrying out configuration check on the main node big data component based on the configuration file.
In this embodiment of the present application, as described above, each node in the big data cluster may correspond to multiple big data components, and after the IDR device logs in to a master node of the big data cluster, start parameters of one or more big data components corresponding to the master node may be obtained, and then a configuration file of the big data component corresponding to the master node is obtained according to the start parameters. The big data component corresponding to the master node may be referred to as a master node big data component, and the configuration content of the configuration nodes of the configuration files may be checked, so as to perform configuration checking on the master node big data component.
In a specific practical process, after obtaining a start parameter of a main node big data component to be configured and checked, the start parameter may be analyzed to obtain an installation path of the main node big data component, and then a configuration file path of the main node big data component may be determined according to the obtained installation path, and further a configuration file of the main node big data component may be obtained according to the configuration file path.
The types of components included in the master node big data component are various, the obtained starting parameters may also have different contents, and further, the starting parameter analysis modes of the big data components of different types are also different, specifically, the starting parameters are analyzed, and the modes of obtaining the installation path of the master node big data component include, but are not limited to, the following modes:
the first method comprises the steps of determining whether a home attribute of a main node big data component is contained in a starting parameter, and if so, determining an installation path of the main node big data component according to the home attribute.
For example, assuming that the big data component of the master node is a hadoop component, since the hadoop component is composed of a management node and a data node, the management node is responsible for managing files in the hadoop component, and plays a target role. When the configuration of the hadoop component is checked, the following starting parameters of the management node in the hadoop component can be obtained, and whether the starting parameters of the hadoop component contain the home attribute of the hadoop component or not is determined. If yes, acquiring the value of a start attribute value hadoop.home.dir as/usr/hdp/3.0.1.0-187/hadoop from the start parameter; and then the installation path of the hadoop component is/usr/hdp/3.0.1.0-187/hadoop according to the value of the starting attribute.
The starting parameters of the hadoop component are as follows:
and secondly, determining whether the starting parameters contain the configuration file information of the starting of the main node big data component, and if so, determining the installation path of the main node big data component according to the configuration file information.
For example, assuming that the master node big data component is a kafka component, the obtained start parameters of the kafka component are:
and analyzes whether the startup parameters contain the configuration file information of the kafka component. If so, determining the configuration file attribute of the kafka component according to the start information, so as to determine the value of the configuration file attribute of the kafka component from the start parameters: file:/usr/hdp/3.0.1.0-187/kafka/bin/./config/log 4j. And analyzing the value of the attribute of the configuration file, the installation directory of the kafka component can be/usr/hdp/3.0.1.0-187/kafka.
And thirdly, determining whether the starting parameters contain the directory information of the custom library corresponding to the main node big data component, and if so, determining the installation path of the main node big data component according to the directory information.
For example, assuming that the main node big data component is a hive component, the obtained startup parameters of the hive component are as follows:
and further analyzing whether the starting parameter comprises the directory information of the customized library of the hive component, if so, acquiring the directory attribute of the customized library of the hive component from the starting parameter: aux jars path, and obtains the value of the custom library directory attribute of the hive component from the startup parameters:file:///opt/cloudera/parcels/CDH- 5.15.0-1.cdh5.15.0.p0.21/lib/hive/auxlib/hive-exec-1.1.0-cdh5.15.0-core.jar(ii) a Furthermore, the value of the custom library directory attribute of the hive component can be analyzed to obtain the installation path of the hive component/opt/cloudera/parcels/CDH-5.15.0-1.cdh5.15.0.p0.21/lib/hive。
In this embodiment of the present application, a configuration checking item of a supported big data component is generally configured on an IDR device, that is, what configuration checking needs to be performed on the big data component corresponding to each node in a big data cluster is generally configured. Therefore, after the configuration file of the main node big data component is obtained, the configuration content of the configuration file of the main node big data component can be analyzed to obtain the configuration content of the main node big data component; then, whether the configuration content is matched with the content which is configured for the main node big data component on the IDR equipment for automatic configuration and checking in advance can be determined; and if the configuration of the main node big data component is not matched, determining that the configuration of the main node big data component has risk.
For example: the main node big data component corresponding to the main node can be a hadoop component, and the configured check item can be Kerberos authentication for judging whether the hadoop component is used or not; and further, the file names, such as configuration check, of the hadoop components which are configured by the configuration personnel on the IDR equipment in advance can be obtained as follows: core-site.xml; configuring as an xml file, configuring the check nodes as follows: security, authentication; the content of the configuration node is information such as kerberos. Therefore, whether the content of the node hadoop security authentication with the file name of core-site xml is kerberos or not can be determined from the obtained configuration file, if so, the configuration of the hadoop component can be determined to be qualified, and if not, the configuration of the hadoop component is indicated to be unqualified, so that the risk exists.
Further, as an optional implementation manner, when it is determined that the configuration of the master node big data component is not qualified and there is a risk, prompt information for indicating the big data component with the risk may be generated, where the prompt information may be voice information, and for example, the prompt information may be played by a voice playing device of the IDR device; the information may also be text information, for example, the text information may be displayed in a display interface of the IDR device, or may be sent to the corresponding terminal device in the form of a short message. Therefore, the generated prompt information can prompt configuration personnel which big data assemblies have risks, so that the configuration personnel can solve the big data assemblies with risks in time, and the conditions that the normal operation of the big data assemblies is influenced or operation errors occur are avoided.
Step 204: after configuration check of the master node big data assembly is completed, based on IP information of the slave nodes, the master node is automatically jumped to the first node by using a host jump technology, and after a configuration file of the slave node big data assembly corresponding to the first node is obtained, configuration check is performed on the slave node big data assembly, wherein the first node is a slave node in the big data cluster.
In the implementation of the present application, as described above, a large data cluster may include a master node and a plurality of slave nodes, and the login manner of each slave node may be set as SSH privacy-free login. Then, after the configuration verification of the big data component corresponding to the master node of the big data cluster is completed, the host jump technology and the SSH privacy-free login function can be used to jump from the master node to other slave nodes based on the IP information of the slave nodes, and then the configuration verification of the slave node big data component corresponding to the slave node can be performed in the manner of performing the configuration verification of the master node big data component in the foregoing steps. Because an SSH password-free login mode can be adopted to jump from the master node to the slave node, the slave node does not need to be logged in from the IDR equipment, and the user name and the password do not need to be configured for the slave node on the IDR equipment, the configuration work of configuration personnel on the user name and the password of the slave node can be reduced, and the labor cost is saved. Correspondingly, the configuration checking speed of the slave node big data assembly corresponding to the slave node can be increased to a certain extent by automatically logging in the slave node in an SSH (secure session authentication) secret-free logging manner, and the configuration checking speed of the big data assembly in the whole big data cluster is further increased.
Further, after configuration checking of the big data components of the slave nodes is completed, the master node may be returned, and based on SSH privacy-free login, the master node logs in to the slave nodes except the first node in the at least one slave node in sequence by using a host jump technique, and configuration checking of the big data components corresponding to other slave nodes is performed respectively until configuration checking of the big data components corresponding to all the nodes is completed. That is, a configuration person may not be required to manually coordinate the configuration information for each node in the big data cluster, but by acquiring startup parameters of the big data component corresponding to each node in the big data cluster, further analyzing the obtained starting parameters to obtain the configuration file of the big data assembly in the big data cluster, so as to analyze the configuration file to complete the configuration checking work of big data components included in the big data cluster, therefore, even after the configuration file path is changed due to the restart of the big data component, the configuration file of the big data component can be easily obtained through the method, therefore, the problem that the configuration verification is difficult due to the change of the configuration path file after the restart of some big data components can be avoided, meanwhile, the situation that the configuration checking accuracy of the big data assembly is low due to configuration errors caused by manual configuration of configuration personnel can be reduced.
Specifically, as shown in fig. 3, it is assumed that A, B, C, D nodes are included in the large data cluster, where a is the master node and B-D are the slave nodes. The IDR device may log on to a master node a in the big data cluster through the switching device shown in fig. 1, perform configuration check on a master node big data component corresponding to the master node, after completing configuration of the master node big data component, may log on to a B slave node from the a master node through an SSH privacy-free login manner by using a host jump technology, and perform configuration check on a big data component corresponding to the B slave node. And after the configuration check of the big data component corresponding to the slave node B is completed, returning to the master node A, logging in the slave node C from the master node A, and performing the configuration check of the big data component corresponding to the slave node C. Similarly, after the configuration check of the C slave node is completed, the C slave node returns to the A master node, and then the A master node logs in the D slave node so as to perform the configuration check on the big data component corresponding to the D slave node. As shown in fig. 4, compared with the prior art shown in fig. 4, when it is necessary to traverse all the master nodes and the slave nodes of the large data cluster through the IDR device to perform configuration check on the large data components corresponding to the master nodes and the slave nodes, the configuration workload of the configuration staff on the user names and the passwords of the slave nodes can be reduced, the workload of the configuration staff is reduced, and the configuration check efficiency of the large data cluster is correspondingly improved.
Further, in the configuration check corresponding to the slave node, if it is determined that a big data component which is not in accordance with the configuration exists in the big data components corresponding to the slave node, prompt information can be generated according to the manner that the big data component corresponding to the prompt master node is not in accordance with the configuration, and the prompt personnel are prompted about the big data component which is not in accordance with the configuration and has a risk, so that the configuration personnel can make configuration changes in time, thereby avoiding affecting the accuracy of the operation result of the big data cluster, or even affecting the normal operation of the big data cluster, and the details are not repeated herein.
Therefore, by the method, the configuration information of the master node in the big data cluster can be obtained first, and then the configuration information of the master node is logged into the master node according to the configuration information of the master node, the starting parameter of the master node big data component corresponding to the master node and the IP information of the slave node are obtained, then the configuration file of the master node big data component corresponding to the master node can be obtained according to the starting parameter, and then the master node big data component corresponding to the master node is configured according to the configuration file; after the configuration check of the master node is completed, the configuration check of the big data assemblies corresponding to all the nodes in the whole big data cluster is completed by automatically jumping to all the slave nodes in the big data cluster by utilizing a host jumping technology based on the acquired IP information of the slave nodes and acquiring the configuration files of the big data assemblies corresponding to all the slave nodes on all the slave nodes so as to perform the configuration check of the big data assemblies of all the slave nodes. Therefore, the configuration information of the big data assemblies corresponding to the nodes in the big data cluster can be prevented from being manually configured by the configuration personnel, and the problem that the configuration file of the big data assembly is difficult to reconfigure after the path of the configuration file is changed due to the restart of the big data assemblies of the big data cluster such as cloudera and HDP can be naturally solved, so that the configuration work of the configuration checking personnel can be reduced, the configuration checking efficiency can be improved, the configuration errors caused by insufficient professional knowledge of the configuration personnel can be reduced, and the configuration checking accuracy can be improved.
In addition, the host jump technology is utilized to automatically jump to other slave nodes to perform configuration check on the big data component corresponding to the slave node, namely the master node can access the big data component corresponding to other slave nodes to perform configuration check, so that the slave node does not need to log in from the IDR equipment, and the configuration workload of configuration personnel on the slave node can be reduced.
Based on the same inventive concept, the embodiment of the present application provides a configuration checking device for a big data cluster component, where the configuration checking device for the big data cluster component may be a hardware structure, a software module, or a hardware structure plus a software module. The configuration checking device of the big data cluster component can be realized by a chip system, and the chip system can be formed by a chip and can also comprise the chip and other discrete devices. Referring to fig. 5a, the configuration checking apparatus for a big data cluster component according to the embodiment of the present application includes a first obtaining module 501, a second obtaining module 502, a configuration checking module 503, and a skipping module 504, where:
a first obtaining module 501, configured to obtain configuration information of a master node and address-identifying IP information of a slave node of a big data cluster, and log in the master node according to the configuration information;
a second obtaining module 502, configured to obtain a start parameter of a master node big data component corresponding to the master node and address IP information of the slave node;
the configuration checking module 503 is configured to obtain a configuration file of the master node big data component according to the starting parameter, and perform configuration checking on the master node big data component based on the configuration file;
and a jump module 504, configured to automatically jump to a first node by using a host jump technology based on the IP information after completing configuration check on the master node big data component, and perform configuration check on the slave node big data component after acquiring the configuration file of the slave node big data component corresponding to the first node according to the step of acquiring the master node configuration file, where the first node is a slave node in the big data cluster.
In a possible implementation, the first obtaining module 501 is specifically configured to:
obtaining a running main node big data component and identification ID information of the main node big data component from a big data cluster through a component checking jps command;
and obtaining the starting parameters of the main node big data component through the component filtering ps command.
In a possible implementation, the second obtaining module 502 is further configured to:
analyzing the starting parameters to obtain an installation path of the main node big data component;
determining a configuration file path of the main node big data component according to the installation path;
and obtaining the configuration file of the main node big data component according to the configuration file path.
In a possible implementation manner, as shown in fig. 5b, the configuration checking apparatus for large data cluster component shown in fig. 5b further includes an analysis module 505, where the analysis module 505 is configured to:
determining whether the home attribute of the main node big data component is contained in the starting parameter, and if so, determining the installation path of the main node big data component according to the home attribute; alternatively, the first and second electrodes may be,
determining whether the starting parameters contain configuration file information for starting the main node big data component, and if so, determining an installation path of the main node big data component according to the configuration file information; alternatively, the first and second liquid crystal display panels may be,
and determining whether the starting parameters contain the directory information of the custom library corresponding to the main node big data component, and if so, determining the installation path of the main node big data component according to the directory information.
In a possible implementation, the configuration checking module 503 is specifically configured to:
analyzing the configuration content of the configuration file of the main node big data component to obtain the configuration content of the main node big data component;
determining whether the configuration content is matched with the content configured for the main node big data component on the automatic configuration checking IDR equipment;
and if the configuration of the main node big data component is not matched, determining that the configuration of the main node big data component has risk.
In one possible implementation, as shown in fig. 5b, the configuration checking apparatus for a big data cluster component shown in fig. 5b further includes a prompting module 506, where the prompting module 506 is configured to:
prompt information is generated indicating the at-risk big data component.
In a possible implementation manner, when all the slave nodes in the large data cluster are set to secure shell protocol SSH secure login, the jumping module 504 is specifically configured to:
and after the configuration verification of the main node big data assembly is completed, logging in the first node from the main node based on SSH secret-free logging and host jumping technology.
In one possible implementation, after the big data cluster includes at least one slave node and the configuration check of the big data component of the slave node is completed, the jumping module 504 is further configured to:
returning to the main node;
based on SSH secret-free login, sequentially logging in the slave nodes except the first node from the master node to at least one slave node by utilizing a host jump technology, and respectively performing configuration check on the big data components corresponding to the slave nodes.
With regard to the configuration checking apparatus for a big data cluster component in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be elaborated here.
The division of the modules in the embodiments of the present disclosure is illustrative, and is only a logical function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present disclosure may be integrated in one processor, may also exist alone physically, or may also be integrated in one module by two or more modules. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
Based on the same inventive concept, the embodiment of the present application further provides a computing device, which is, for example, the IDR device in fig. 1. As shown in fig. 6, the computing in this embodiment of the present application includes at least one processor 601, and a memory 602 and a communication interface 603 connected to the at least one processor 601, and a specific connection medium between the processor 601 and the memory 602 is not limited in this embodiment of the present application, and in fig. 6, the processor 601 and the memory 602 are connected by a bus 600 as an example, the bus 600 is represented by a thick line in fig. 6, and a connection manner between other components is merely illustrated schematically and is not limited by the present application. The bus 600 may be divided into an address bus, a data bus, a control bus, etc., and is shown with only one thick line in fig. 6 for ease of illustration, but does not represent only one bus or type of bus.
In the embodiment of the present application, the memory 602 stores instructions executable by the at least one processor 601, and the at least one processor 601 may execute the steps included in the configuration checking method for a big data cluster component described above by executing the instructions stored in the memory 602.
The processor 601 is a control center of the computing, and can connect various parts of the whole computing by using various interfaces and lines, and by executing or executing instructions stored in the memory 602 and calling data stored in the memory 602, various functions of the computing device and processing data are performed, thereby performing overall monitoring on the computing device. Optionally, the processor 601 may include one or more processing units, and the processor 601 may integrate an application processor and a modem processor, wherein the processor 601 mainly processes an operating system, a user interface, an application program, and the like, and the modem processor mainly processes wireless communication. It will be appreciated that the modem processor described above may not be integrated into the processor 601. In some embodiments, the processor 601 and the memory 602 may be implemented on the same chip, or in some embodiments, they may be implemented separately on separate chips.
The processor 601 may be a general-purpose processor, such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like, that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in a processor.
The memory 602, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The Memory 602 may include at least one type of storage medium, which may include, for example, a flash Memory, a hard disk, a multimedia card, a card-type Memory, a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Programmable Read Only Memory (PROM), a Read Only Memory (ROM), a charged Erasable Programmable Read Only Memory (EEPROM), a magnetic Memory, a magnetic disk, an optical disk, and the like. The memory 602 is 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 computer, but is not limited to such. The memory 602 in the embodiments of the present application may also be circuitry or any other device capable of performing a storage function for storing program instructions and/or data.
The communication interface 603 is a transmission interface that can be used for communication, and data can be received or transmitted through the communication interface 603. Taking a computing device as the IDR device 102 in fig. 1 as an example, when it is determined that a big data component in a big data cluster does not meet configuration requirements and is in risk, a prompt message may be sent to other devices through the communication interface 603.
Based on the same inventive concept, embodiments of the present application further provide a computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed on a computer, the computer is caused to execute the steps of the configuration checking method for a big data cluster component as described above.
In some possible embodiments, the aspects of the configuration checking method for big data cluster components provided in this application embodiment may also be implemented in the form of a program product, which includes program code for causing a computer to perform the steps of the configuration checking method for big data cluster components according to various exemplary embodiments of the present invention described in the foregoing when the program product runs on the computer.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A configuration checking method for a big data cluster component is characterized by comprising the following steps:
acquiring configuration information of a main node of a big data cluster, and logging in the main node according to the configuration information;
acquiring starting parameters of a main node big data component corresponding to the main node and address IP information of slave nodes, wherein the slave nodes in the big data cluster are all set to be a secure remote login protocol SSH (secure remote login) secret-free login;
acquiring a configuration file of the main node big data component according to the starting parameters, and carrying out configuration check on the main node big data component based on the configuration file;
after configuration verification of the master node big data assembly is completed, automatically jumping to a first node based on the SSH secret-free login and host jumping technology based on the IP information, and performing configuration verification on the slave node big data assembly after acquiring a configuration file of the slave node big data assembly corresponding to the first node according to the step of acquiring the master node configuration file, wherein the first node is a slave node in the big data cluster.
2. The method of claim 1, wherein obtaining startup parameters for a master node big data component corresponding to the master node comprises:
obtaining the running main node big data component and the identification ID information of the main node big data component from the big data cluster through a component viewing jps command;
and obtaining the starting parameters of the main node big data component through a component filtering ps command.
3. The method of claim 1, wherein obtaining a configuration file for the master node big data component according to the startup parameters comprises:
analyzing the starting parameters to obtain an installation path of the main node big data assembly;
determining a configuration file path of the main node big data component according to the installation path;
and obtaining the configuration file of the main node big data component according to the configuration file path.
4. The method of claim 3, wherein analyzing the startup parameters to obtain an installation path of the master node big data component comprises:
determining whether the starting parameter contains the home attribute of the main node big data component, and if so, determining the installation path of the main node big data component according to the home attribute; alternatively, the first and second electrodes may be,
determining whether the starting parameters contain configuration file information of the starting of the main node big data component, and if so, determining an installation path of the main node big data component according to the configuration file information; alternatively, the first and second liquid crystal display panels may be,
and determining whether the starting parameters contain the directory information of the user-defined library corresponding to the main node big data component, and if so, determining the installation path of the main node big data component according to the directory information.
5. The method of claim 1, wherein the configuration check of the master node big data component comprises:
analyzing the configuration content of the configuration file of the main node big data component to obtain the configuration content of the main node big data component;
determining whether the configuration content is matched with configuration content corresponding to the main node big data component configuration on the IDR equipment through automatic configuration checking;
and if the configuration of the main node big data component is matched with the configuration of the main node big data component, determining that the configuration of the main node big data component has risk.
6. The method of claim 5, wherein upon determining that there is a risk in the configuration of the master node big data component, the method further comprises:
prompt information is generated indicating the at-risk big data component.
7. The method of claim 1, wherein the big data cluster includes at least one slave node, and after completing the configuration check of the slave node big data component, the method further comprises:
returning to the master node;
and based on the SSH secret-free login, sequentially logging in the slave nodes except the first node from the master node to the at least one slave node by utilizing the host jump technology, and respectively performing configuration check on the big data components corresponding to the slave nodes.
8. An apparatus for checking configuration of a big data cluster component, comprising:
the system comprises a first obtaining module, a second obtaining module and a third obtaining module, wherein the first obtaining module is used for obtaining configuration information of a main node of a big data cluster and logging in the main node according to the configuration information;
a second obtaining module, configured to obtain a start parameter of a master node big data component corresponding to the master node and address IP information of a slave node, where the slave nodes in the big data cluster are all set to secure remote login protocol SSH secure login;
the configuration checking module is used for acquiring a configuration file of the main node big data component according to the starting parameter and carrying out configuration checking on the main node big data component based on the configuration file;
and the skip module is used for automatically skipping to a first node based on the SSH secret-free login and host skip technology after completing configuration check of the main node big data assembly, and performing configuration check on the slave node big data assembly after acquiring the configuration file of the slave node big data assembly corresponding to the first node according to the step of acquiring the main node configuration file, wherein the first node is the slave node in the big data cluster.
9. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the steps comprised by the method of any one of claims 1 to 7 in accordance with the obtained program.
10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the steps comprising the method of any one of claims 1-7.
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