CN111258589A - Multi-platform adaptation method for big data operation and maintenance monitoring - Google Patents
Multi-platform adaptation method for big data operation and maintenance monitoring Download PDFInfo
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Abstract
The invention discloses a multi-platform adaptation method for big data operation and maintenance monitoring, which sequentially performs operation system check, host registration and component installation on a big data operation and maintenance monitoring tool, and enables the big data operation and maintenance monitoring tool which cannot be installed on a domestic operation system to be adapted with the domestic operation system by judging and modifying the source code of the big data operation and maintenance monitoring tool, thereby solving the problem that the big data operation and maintenance monitoring is used as a big data technology from abroad, most functional components cannot be directly operated on a domestic chip and an operation system platform, and the big data operation and maintenance monitoring is used as an important function in big data application and also needs to be subjected to domestic compatibility.
Description
Technical Field
The invention relates to the field of big data operation and maintenance monitoring, in particular to a multi-platform adaptation method for big data operation and maintenance monitoring.
Background
Like Hadoop and other open source Software, Ambari is also an item in Apache Software Foundation and is the top level item. In terms of Ambari's role, it is to create, manage, and monitor clusters of Hadoop, where Hadoop is a broad term referring to the entire ecosphere of Hadoop (e.g., Hive, Hbase, Sqoop, Zookeeper, etc.), and not just to Hadoop specifically. In one sentence, Ambari is a tool to make Hadoop and related big data software easier to use. Ambari is widely applied to building and operation and maintenance of a big data platform as a top-level open source project for monitoring big data operation and maintenance. However, by default, the official ambari only supports the cpu architecture of x86, as well as the centros and ubuntu operating systems, and cannot run on the homemade operating systems.
With the localization development of the computer industry becoming a new development trend, various kinds of software based on a localization chip and an operating system also need to realize localization compatibility. Big data is a new technology widely applied in the computer industry at present. The big data operation and maintenance monitoring is taken as a big data technology from abroad, and most functional components cannot be directly operated on a domestic chip and an operating system platform. The big data operation and maintenance monitoring is an important function in big data application, and the localization compatibility is also needed.
Disclosure of Invention
The invention aims to: the multi-platform adaptation method for the big data operation and maintenance monitoring is provided, and the problems that the big data operation and maintenance monitoring is used as a big data technology from abroad, most functional components cannot directly run on a domestic chip and an operating system platform, and the big data operation and maintenance monitoring is used as an important function in big data application and needs to be made domestic compatibility are solved.
The technical scheme adopted by the invention is as follows:
a multi-platform adaptation method for big data operation and maintenance monitoring is based on an open-source big data operation and maintenance monitoring platform and comprises the following steps:
s1, acquiring a source code of the open-source big data operation and maintenance monitoring platform;
s2, analyzing the source code obtained in the step S1, and obtaining an operating system and a version definition file in the source code;
s3, analyzing the operating system and the version definition file in the source code acquired in the step S2, judging whether the operating system of the target host is in the support range of the source code, if so, turning to the step S5, otherwise, turning to the step S4;
s4, adding the operating system of the target host into the operating system and the version definition file in the source code, and then turning to the step S5;
s5, registering the host by using the modified source code, judging whether the registration is successful, if so, completing the multi-platform adaptation of the operation and maintenance monitoring, and if not, turning to the step S6;
s6, modifying the host registration information in the source code, adding the operating system information corresponding to the host into the host registration information, and going to step S5 after the host registration information is modified.
Further, the method for installing the components of the big data operation and maintenance monitoring platform comprises the following steps:
s01, reading the component installation script of the big data operation and maintenance monitoring platform, analyzing the component installation script, judging whether the operating system of the target host belongs to the default operating system range of the component installation script, if the operating system of the target host belongs to the default operating system range of the component installation script, turning to the step S03, otherwise, turning to the step S02;
s02, adding the operating system of the target host into the operating system with the default component installation script, and then turning to the step S03;
s03, reading a software warehouse of the big data operation and maintenance monitoring platform, analyzing the software warehouse, judging whether the operating system of the target host belongs to the default operating system range of the component warehouse, if the operating system of the target host belongs to the default operating system range of the component warehouse, completing the installation of the components of the big data operation and maintenance monitoring platform, otherwise, modifying the default operating system of the component warehouse into the operating system of the target host.
Further, the open-source big data operation and maintenance monitoring platform is realized by adopting an ambari project. Like Hadoop and other open source Software, Ambari is also an item in Apache Software Foundation and is the top level item. The function of Ambari is to create, manage and monitor clusters of Hadoop, wherein Hadoop is a broad sense and refers to the entire ecosphere of Hadoop, not just Hadoop. In one sentence, Ambari is a tool to make Hadoop and related big data software easier to use.
Further, the target host adopts a kylin operating system. The Galaxy kylin is an open source server operating system developed by the university of defense science and technology. The operating system is an 863 major attacking and closing scientific research project, and aims to break monopoly of foreign operating systems and research and develop a set of server operating systems with Chinese proprietary intellectual property rights. It has the following characteristics: high safety, high reliability, high availability, cross-platform and Chinese culture.
Further, the method for determining whether the operating system of the target host is in the support range of the source code in step S3 includes the following steps:
s301, analyzing the source code, retrieving an operating system and a version definition file in the source code, and determining a path of the file;
s302, reading an operating system and a version definition file, judging whether the operating system of the target host is recorded in the operating system and the version definition file, if so, turning to the step S303, otherwise, the operating system of the host is not in the support range of the source code;
s303, verifying the operating system of the target host by using the source code, wherein if the verification is passed, the operating system of the host is in the support range of the source code; otherwise the operating system of the host is not within the support of the source code.
Further, the method for adding the operating system of the target host to the operating system and version definition file in the source code in step S4 includes the following steps:
s401, reading an operating system and a version of a target host, and defining the operating system and the version of the target host in an operating system file of a source code;
s402, reading the default parameters of the operating system check file of the source code, and modifying the default parameters into the operating system and the version of the target host.
Further, step S4 includes defining the os resource directory of the target host in the source code after adding the os of the target host to the os and version definition file in the source code.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the invention relates to a multi-platform adaptation method for big data operation and maintenance monitoring, which solves the problems that big data operation and maintenance monitoring is taken as a big data technology from abroad, most functional components cannot directly run on a domestic chip and an operating system platform, and the big data operation and maintenance monitoring is taken as an important function in big data application and needs to be subjected to domestic compatibility;
2. the multi-platform adaptation method for the big data operation and maintenance monitoring can adapt to various domestic operating systems, has good adaptation, does not need too much repeated development work, saves the cost of domestic adaptation of a big data operation and maintenance monitoring tool, and makes up the problem of domestic compatibility of the big data operation and maintenance tool.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts, wherein:
FIG. 1 is a schematic diagram of an adaptation flow of the present invention;
figure 2 is an ambari adaptation modification comparison table.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to fig. 1 and 2, the described embodiments should not be construed as limiting the present invention, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
Example 1
A multi-platform adaptation method for big data operation and maintenance monitoring is based on an open-source big data operation and maintenance monitoring platform and comprises the following steps:
s1, acquiring a source code of the open-source big data operation and maintenance monitoring platform;
s2, analyzing the source code obtained in the step S1, and obtaining an operating system and a version definition file in the source code;
s3, analyzing the operating system and the version definition file in the source code acquired in the step S2, judging whether the operating system of the target host is in the support range of the source code, if so, turning to the step S5, otherwise, turning to the step S4;
s4, adding the operating system of the target host into the operating system and the version definition file in the source code, and then turning to the step S5;
s5, registering the host by using the modified source code, judging whether the registration is successful, if so, completing the multi-platform adaptation of the operation and maintenance monitoring, and if not, turning to the step S6;
s6, modifying the host registration information in the source code, adding the operating system information corresponding to the host into the host registration information, and going to step S5 after the host registration information is modified.
Example 2
The embodiment is further based on embodiment 1, and further includes an installation method of a component of the big data operation and maintenance monitoring platform, where the installation method includes the following steps:
s01, reading the component installation script of the big data operation and maintenance monitoring platform, analyzing the component installation script, judging whether the operating system of the target host belongs to the default operating system range of the component installation script, if the operating system of the target host belongs to the default operating system range of the component installation script, turning to the step S03, otherwise, turning to the step S02;
s02, adding the operating system of the target host into the operating system with the default component installation script, and then turning to the step S03;
s03, reading a software warehouse of the big data operation and maintenance monitoring platform, analyzing the software warehouse, judging whether the operating system of the target host belongs to the default operating system range of the component warehouse, if the operating system of the target host belongs to the default operating system range of the component warehouse, completing the installation of the components of the big data operation and maintenance monitoring platform, otherwise, modifying the default operating system of the component warehouse into the operating system of the target host.
Example 3
In this embodiment, on the basis of embodiment 1, the open-source big-data operation and maintenance monitoring platform is implemented by using an ambari project. Like Hadoop and other open source Software, Ambari is also an item in Apache Software Foundation and is the top level item. The function of Ambari is to create, manage and monitor clusters of Hadoop, wherein Hadoop is a broad sense and refers to the entire ecosphere of Hadoop, not just Hadoop. In one sentence, Ambari is a tool to make Hadoop and related big data software easier to use.
Further, the target host adopts a kylin operating system. The Galaxy kylin is an open source server operating system developed by the university of defense science and technology. The operating system is an 863 major attacking and closing scientific research project, and aims to break monopoly of foreign operating systems and research and develop a set of server operating systems with Chinese proprietary intellectual property rights. It has the following characteristics: high safety, high reliability, high availability, cross-platform and Chinese culture.
Example 4
Based on embodiment 1, the method for determining whether the operating system of the target host is within the support range of the source code in step S3 includes the following steps:
s301, analyzing the source code, retrieving an operating system and a version definition file in the source code, and determining a path of the file;
s302, reading an operating system and a version definition file, judging whether the operating system of the target host is recorded in the operating system and the version definition file, if so, turning to the step S303, otherwise, the operating system of the host is not in the support range of the source code;
s303, verifying the operating system of the target host by using the source code, wherein if the verification is passed, the operating system of the host is in the support range of the source code; otherwise the operating system of the host is not within the support of the source code.
Further, the method for adding the operating system of the target host to the operating system and version definition file in the source code in step S4 includes the following steps:
s401, reading an operating system and a version of a target host, and defining the operating system and the version of the target host in an operating system file of a source code;
s402, reading the default parameters of the operating system check file of the source code, and modifying the default parameters into the operating system and the version of the target host.
Further, step S4 includes defining the os resource directory of the target host in the source code after adding the os of the target host to the os and version definition file in the source code.
Example 5
In the embodiment, as the server belongs to a domestic platform, native ambari cannot be directly installed or installed and operated by compiling source codes, and the problem can be solved only by adopting the method in the case that ambari is installed on the server by adopting a domestic CPU Feiteng and a domestic operating system Galaxy kylin;
as shown in fig. 2, ambari is adapted and modified according to the present solution, and the step of adapting includes:
first, operating system verification
1. Operating system and version definition: since ambari only supports centros and ubuntu operating systems by default, and kylin is not in the support range, kylin and its version need to be defined in the operating system file, i.e. the modification of sequence number 1 in fig. 2;
2. checking the operating system: the operating system checks that the comparison name and version are correct. The default parameter and the current operating system cannot be matched, and the parameter to be modified is kylin4, namely the modification of sequence number 2 in fig. 2;
3. defining a kylin system resource directory: ambari initialization requires a legal resource directory and the definition of the corresponding operating system, i.e. the modification of the serial numbers 3 and 4 in fig. 2.
Second, host registration
Host registration: the host registration is used to complete initialization of the ambari cluster. In the initialization process, the ambari-server service establishes connection with ambari-agent service of each host through a security protocol and receives host related information pushed by the agent. The default agent pushed machine information cannot be registered, and an operating system mismatch error can be thrown. The returned operating system information, i.e. the modification of sequence number 5 in fig. 2, needs to be modified.
Thirdly, assembly installation
Hadoop assembly installation, using zookeeper as an example: since the installation scripts and software repository default to the operating system scope when ambari installs components, the operating system type needs to be added, otherwise the installation is not possible, i.e. the modification of the serial numbers 6 and 7 in fig. 2.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A multi-platform adaptation method for big data operation and maintenance monitoring is based on an open-source big data operation and maintenance monitoring platform and is characterized in that: the method comprises the following steps:
s1, acquiring a source code of the open-source big data operation and maintenance monitoring platform;
s2, analyzing the source code obtained in the step S1, and obtaining an operating system and a version definition file in the source code;
s3, analyzing the operating system and the version definition file in the source code acquired in the step S2, judging whether the operating system of the target host is in the support range of the source code, if so, turning to the step S5, otherwise, turning to the step S4;
s4, adding the operating system of the target host into the operating system and the version definition file in the source code, and then turning to the step S5;
s5, registering the host by using the modified source code, judging whether the registration is successful, if so, completing the multi-platform adaptation of the operation and maintenance monitoring, and if not, turning to the step S6;
s6, modifying the host registration information in the source code, adding the operating system information corresponding to the host into the host registration information, and going to step S5 after the host registration information is modified.
2. The big data operation and maintenance monitoring multi-platform adaptation method according to claim 1, wherein: the method also comprises an installation method of the components of the big data operation and maintenance monitoring platform, and the installation method comprises the following steps:
s01, reading the component installation script of the big data operation and maintenance monitoring platform, analyzing the component installation script, judging whether the operating system of the target host belongs to the default operating system range of the component installation script, if the operating system of the target host belongs to the default operating system range of the component installation script, turning to the step S03, otherwise, turning to the step S02;
s02, adding the operating system of the target host into the operating system with the default component installation script, and then turning to the step S03;
s03, reading a software warehouse of the big data operation and maintenance monitoring platform, analyzing the software warehouse, judging whether the operating system of the target host belongs to the default operating system range of the component warehouse, if the operating system of the target host belongs to the default operating system range of the component warehouse, completing the installation of the components of the big data operation and maintenance monitoring platform, otherwise, modifying the default operating system of the component warehouse into the operating system of the target host.
3. The big data operation and maintenance monitoring multi-platform adaptation method according to claim 1, wherein: the open source big data operation and maintenance monitoring platform is realized by adopting an ambari project.
4. The big data operation and maintenance monitoring multi-platform adaptation method according to claim 1, wherein: the target host adopts a kylin operating system.
5. The big data operation and maintenance monitoring multi-platform adaptation method according to claim 1, wherein: the method for determining whether the operating system of the target host is in the support range of the source code in the step S3 includes the following steps:
s301, analyzing the source code, retrieving an operating system and a version definition file in the source code, and determining a path of the file;
s302, reading an operating system and a version definition file, judging whether the operating system of the target host is recorded in the operating system and the version definition file, if so, turning to the step S303, otherwise, the operating system of the host is not in the support range of the source code;
s303, verifying the operating system of the target host by using the source code, wherein if the verification is passed, the operating system of the host is in the support range of the source code; otherwise the operating system of the host is not within the support of the source code.
6. The big data operation and maintenance monitoring multi-platform adaptation method according to claim 5, wherein: the method for adding the operating system of the target host into the operating system and version definition file in the source code in the step S4 comprises the following steps:
s401, reading an operating system and a version of a target host, and defining the operating system and the version of the target host in an operating system file of a source code;
s402, reading the default parameters of the operating system check file of the source code, and modifying the default parameters into the operating system and the version of the target host.
7. The big data operation and maintenance monitoring multi-platform adaptation method according to claim 1, wherein: step S4 includes defining the os resource directory of the target host in the source code after adding the os of the target host to the os and version definition file in the source code.
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