CN109656575A - Big data platform quick deployment method, storage medium, electronic equipment and system - Google Patents
Big data platform quick deployment method, storage medium, electronic equipment and system Download PDFInfo
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- CN109656575A CN109656575A CN201811475906.3A CN201811475906A CN109656575A CN 109656575 A CN109656575 A CN 109656575A CN 201811475906 A CN201811475906 A CN 201811475906A CN 109656575 A CN109656575 A CN 109656575A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/60—Software deployment
- G06F8/61—Installation
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Abstract
The invention discloses a kind of big data platform quick deployment method, storage medium, electronic equipment and systems, are related to big data technical field, this method comprises: making multiple image files；Platform configuration information is added to Heat component, platform configuration information is the facility information of each host in mainframe cluster；Platform layout template is write, and is uploaded to Openstack cloud space；According to platform configuration information, Platform deployment information is obtained；Analyzing platform layout template, and Openstack Platform deployment is carried out according to Platform deployment information.The present invention utilizes OpenStack platform, can rapid deployment big data analysis platform, improve deployment efficiency, provide convenience for mapping out the work for staff.
Description
Technical field
The present invention relates to big data technical fields, and in particular to a kind of big data platform quick deployment method, storage medium,
Electronic equipment and system.
Background technique
In the case where traditional big data analysis calculates scene, the deployment of big data platform need a large amount of open source software installation and
Configuration, such as distributed system infrastructure Hadoop, Zookeeper, Hive, HBase；
Under normal conditions, building for traditional big data analysis platform needs a large amount of software installation and configuration, specially
User needs installation OS (operating system) manually and a large amount of open source software of configuration to dispose big data analysis platform, it is also necessary to volume
Outer installation management manages the working condition of a whole set of environment with monitoring software, and traditional deployment process operations are cumbersome, inefficiency
Therefore, it is badly in need of a kind of new big data platform quick deployment method, provides convenience for mapping out the work for staff.
Summary of the invention
In view of the deficiencies in the prior art, the purpose of the present invention is to provide a kind of big data platform rapid deployment sides
Method, using OpenStack platform, can rapid deployment big data analysis platform, improve deployment efficiency, be the deployment of staff
Work provides convenience.
To achieve the above objectives, the technical solution adopted by the present invention is that:
In a first aspect, the present invention provides a kind of big data platform quick deployment method comprising following steps:
Make multiple image files；
Platform configuration information is added to Heat component, the platform configuration information is the equipment letter of each host in mainframe cluster
Breath；
Platform layout template is write, and is uploaded to Openstack cloud space；
According to the platform configuration information, Platform deployment information is obtained；
The platform layout template is parsed, and Openstack Platform deployment is carried out according to the Platform deployment information.
Based on the above technical solution, the image file of this method includes at least:
Hadoop cluster host node image file, Hadoop cluster slave node image file, Hadoop company-data node
Image file, Hadoop company-data node image file, Kafka cluster host node image file, Kafka cluster are from node mirror
As file, Storm cluster host node image file and Storm cluster slave node image file.
Based on the above technical solution, this method further includes monitoring step, and the monitoring step includes:
A default monitor component, the monitor component is for monitoring the Openstack platform.
Based on the above technical solution, the monitor component of this method includes multiple monitored item, the monitored item
It includes at least: system CPU utilization rate, memory usage, memory usage amount, disk read-write operation rate, disk read-write rate, net
Network inflow and outflow rate and network inflow and outflow packet rate.
Based on the above technical solution, the monitor component of this method is gnocchi component.
Second aspect is stored with computer program, the meter the present invention also provides a kind of storage medium on the storage medium
Calculation machine program realizes the big data platform quick deployment method of above-mentioned first aspect when being executed by processor.
The third aspect stores on memory the present invention also provides a kind of electronic equipment, including memory and processor
The computer program run on processor, the processor realize the big number of above-mentioned first aspect when executing the computer program
According to platform quick deployment method.
Fourth aspect, the present invention also provides a kind of big data platform rapid deployment systems comprising:
Image file production unit is used to make multiple image files；
Platform configuration information adding unit is used to add platform configuration information, the platform configuration letter to Heat component
Breath is the facility information of each host in mainframe cluster；
Platform layout template writes unit, is used to write platform layout template, and is uploaded to Openstack cloud space；
Platform deployment information acquisition unit is used to obtain Platform deployment information according to the platform configuration information；
Platform deployment unit is used to parse the platform layout template, and is carried out according to the Platform deployment information
Openstack Platform deployment.
Based on the above technical solution, which further includes monitor component, and the monitor component is described for monitoring
Openstack platform.
Based on the above technical solution, the monitor component includes multiple monitored item, and the monitored item includes at least:
System CPU utilization rate, memory usage, memory usage amount, disk read-write operation rate, disk read-write rate, network flow become a mandarin
Rate and network inflow and outflow packet rate out.
Compared with the prior art, the advantages of the present invention are as follows:
The present invention utilize OpenStack platform, can rapid deployment big data analysis platform, improve deployment efficiency, be work
Make mapping out the work for personnel and convenience is provided.
Detailed description of the invention
Fig. 1 is a kind of step flow chart for big data platform quick deployment method that the embodiment of the present invention one provides；
Fig. 2 is a kind of structural block diagram of big data platform rapid deployment system provided by Embodiment 2 of the present invention；
Fig. 3 is a kind of structural block diagram for big data platform rapid deployment electronic equipment that the embodiment of the present invention four provides；
In figure: 1, image file production unit；2, platform configuration information adding unit；3, platform layout template writes list
Member；4, Platform deployment information acquisition unit；5, Platform deployment unit；6, monitor component.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing.
The embodiment of the present invention provides a kind of big data platform quick deployment method, storage medium, electronic equipment and system, benefit
With OpenStack platform, can rapid deployment big data analysis platform, deployment efficiency is improved, for mapping out the work for staff
Convenience is provided.
To reach above-mentioned technical effect, the general thought of the application is as follows:
A kind of big data platform quick deployment method comprising following steps:
S1, the multiple image files of production；
S2, platform configuration information is added to Heat component, platform configuration information is the equipment letter of each host in mainframe cluster
Breath；
S3, platform layout template is write, and is uploaded to Openstack cloud space；
S4, according to platform configuration information, obtain Platform deployment information；
S5, analyzing platform layout template, and Openstack Platform deployment is carried out according to Platform deployment information.
In the embodiment of the present invention, using OpenStack platform, can rapid deployment big data analysis platform, improve deployment
Efficiency provides convenience for mapping out the work for staff.
Embodiment one
Shown in Figure 1, the embodiment of the present invention provides a kind of big data platform quick deployment method, comprising the following steps:
S1, the multiple image files of production；
S2, platform configuration information is added to Heat component, platform configuration information is the equipment letter of each host in mainframe cluster
Breath；
S3, platform layout template is write, and is uploaded to Openstack cloud space；
S4, according to platform configuration information, obtain Platform deployment information；
S5, analyzing platform layout template, and Openstack Platform deployment is carried out according to Platform deployment information.
In the embodiment of the present invention, using OpenStack platform, can rapid deployment big data analysis platform, improve deployment
Efficiency provides convenience for mapping out the work for staff.
In the embodiment of the present invention, in step sl, the multiple image files of pre-production, above-mentioned image file is subsequent big number
, can be by the Glance component of Openstack platform according to image file needed for analysis platform, and when specific operation, it will be upper
State image file and be uploaded to Openstack platform, Glance component is mainly used for registering in Openstack platform, find and
Obtain image file.
Then, in step s 2, platform configuration information is added to Heat component, concrete operations can be modification
OpenStack layout component Heat code adds platform configuration information；
Wherein, platform configuration information is the facility information of each host in mainframe cluster, is used to form Platform deployment information；
And Platform deployment information is then obtained by platform configuration information, is carried out via ansible tool to host in cluster
Configuration, is finally completed the configuration of big data platform entirety.
Then, in step s3, platform layout template is write, and is uploaded to Openstack cloud space；
It needs, which is that the configured in one piece of big data platform disposes process, passes through platform layout
Template can be realized big data platform resource distribution and deployment in conjunction with platform configuration information and Platform deployment information；
In addition, when creating layout resources of virtual machine, image file is an essential ginseng in platform layout template
Number is created that the virtual machine come corresponding to the different role in big data platform by different image files；
It should be noted that platform layout template specifically can be, a character string of resource collection described, template according to
JSON YAML format describes the set of one group of ordered resource, and creation application is really to describe resource in platform layout template
Instantiation process.
After the completion of platform layout template is write, step S4 is carried out, obtains mainframe cluster parameter, platform for making mainframe cluster
Configuration information；
Wherein, mainframe cluster parameter be specially user be passed to establish big data analysis platform needed for basic parameter, such as
Network, host name, the information such as password, to customize big data platform mainframe cluster essential information；
It should be noted that platform layout template supports user to be passed to mainframe cluster parameter, and mainframe cluster parameter is flat
Parameter to be used is needed in platform layout template, is passed in specific resource type, next step Heat component can then be compiled in conjunction with platform
Row's template carries out the creation of resource with the parameter that user is passed to；
Such as network, network is different in different cloud environments, it is necessary to is defined as the parameter that user can be passed to and be carried out
The asset creation of next step.
In step s 4, according to platform configuration information, Platform deployment information is obtained；
Finally, carrying out step S5, using Heat analyzing component platform layout template, and according to Platform deployment information and put down
Platform mainframe cluster configuration information carries out Openstack Platform deployment.
It should be noted that the code of Heat component can be changed when adding platform configuration information to Heat component, and
Openstack heat component is responsible for the layout to resource in openstack, middle Heat component layout in the embodiment of the present invention
Resource are as follows: computing resource OS::Nova::Ser ver, storage resource OS::Cinder::Volume, network-related resources OS::
Neutron::Secu rityGroup, OS::Neutron::Port, resource group resource OS::Heat::ResourceGroup,
Platform configuration information OSE::BigData::ClusterConfig, platform configuration information OSE::Big Data::
ClusterDeployment。
In the embodiment of the present invention, the image file of this method is included at least:
Hadoop cluster host node image file, Hadoop cluster slave node image file, Hadoop company-data node
Image file, Hadoop company-data node image file, Kafka cluster host node image file, Kafka cluster are from node mirror
As file, Storm cluster host node image file and Storm cluster slave node image file.
In the embodiment of the present invention, this method further includes monitoring step, and monitoring step includes:
A default monitor component, monitor component is for monitoring Openstack platform.
Based on the above technical solution, the monitor component of this method includes multiple monitored item, and monitored item includes at least:
System CPU utilization rate, memory usage, memory usage amount, disk read-write operation rate, disk read-write rate, network flow become a mandarin
Rate and network inflow and outflow packet rate out.
In the embodiment of the present invention, the monitor component of this method is gnocchi component.
It should be noted that Ansible is a kind of configuration management of integrated IT system, using deployment, execution particular task
Open Source Platform, used in back-end code in the deployment of big data platform, that is, the customized big data in heat
Dispose resource.
Based on the same inventive concept, this application provides the realities of the corresponding big data platform rapid deployment system of embodiment one
Apply example, detailed in Example two
Embodiment two
As shown in Fig. 2, second embodiment of the invention provides a kind of big data platform rapid deployment system comprising:
Image file production unit 1 is used to make multiple image files；
Platform configuration information adding unit 2 is used to add platform configuration information to Heat component, and platform configuration information is
The facility information of each host in mainframe cluster；
Platform layout template writes unit 3, is used to write platform layout template, and is uploaded to Openstack cloud space；
Platform deployment information acquisition unit 4 is used to obtain Platform deployment information according to platform configuration information；
Platform deployment unit 5 is used for analyzing platform layout template, and carries out Openstack according to Platform deployment information
Platform deployment.
In the embodiment of the present invention, using OpenStack platform, can rapid deployment big data analysis platform, improve deployment
Efficiency provides convenience for mapping out the work for staff.
In the embodiment of the present invention, image file production unit 1 is advanced with, makes multiple image files, above-mentioned image text
Part is image file needed for subsequent big data analysis platform, and when specific operation, it can be by Openstack platform
Above-mentioned image file is uploaded to Openstack platform by Glance component, and Glance component is mainly used for flat in Openstack
Image file is registered, finds and obtained in platform.
Then, platform configuration information adding unit 2 adds platform configuration information to Heat component, and concrete operations can be
OpenStack layout component Heat code is modified, platform configuration information is added；
Wherein, platform configuration information is the facility information of each host in mainframe cluster, is used to form Platform deployment information；
And Platform deployment information is then obtained by platform configuration information, is carried out via ansible tool to host in cluster
Configuration, is finally completed the configuration of big data platform entirety.
Then, platform layout template writes unit 3 and then writes platform layout template, and is uploaded to Openstack cloud space；
It needs, which is that the configured in one piece of big data platform disposes process, passes through platform layout
Template can be realized big data platform resource distribution and deployment in conjunction with platform configuration information and Platform deployment information；
In addition, when creating layout resources of virtual machine, image file is an essential ginseng in platform layout template
Number is created that the virtual machine come corresponding to the different role in big data platform by different image files；
It should be noted that platform layout template specifically can be, a character string of resource collection described, template according to
JSON YAML format describes the set of one group of ordered resource, and creation application is really to describe resource in platform layout template
Instantiation process.
After the completion of platform layout template is write, step S4 is carried out, obtains mainframe cluster parameter, platform for making mainframe cluster
Configuration information；
Wherein, mainframe cluster parameter be specially user be passed to establish big data analysis platform needed for basic parameter, such as
Network, host name, the information such as password, to customize big data platform mainframe cluster essential information；
It should be noted that platform layout template supports user to be passed to mainframe cluster parameter, and mainframe cluster parameter is flat
Parameter to be used is needed in platform layout template, is passed in specific resource type, next step Heat component can then be compiled in conjunction with platform
Row's template carries out the creation of resource with the parameter that user is passed to；
Such as network, network is different in different cloud environments, it is necessary to is defined as the parameter that user can be passed to and be carried out
The asset creation of next step.
Platform deployment information acquisition unit 4 obtains Platform deployment information according to platform configuration information；
Finally, Platform deployment unit 5 utilizes Heat analyzing component platform layout template, and according to Platform deployment information and
Platform host cluster configuration information carries out Openstack Platform deployment.
It should be noted that the code of Heat component can be changed when adding platform configuration information to Heat component, and
Openstack heat component is responsible for the layout to resource in openstack, middle Heat component layout in the embodiment of the present invention
Resource are as follows: computing resource OS::Nova::Ser ver, storage resource OS::Cinder::Volume, network-related resources OS::
Neutron::Secu rityGroup, OS::Neutron::Port, resource group resource OS::Heat::ResourceGroup,
Platform configuration information OSE::BigData::ClusterConfig, platform configuration information OSE::Big Data::
ClusterDeployment。
In the embodiment of the present invention, the image file of this method is included at least:
Hadoop cluster host node image file, Hadoop cluster slave node image file, Hadoop company-data node
Image file, Hadoop company-data node image file, Kafka cluster host node image file, Kafka cluster are from node mirror
As file, Storm cluster host node image file and Storm cluster slave node image file.
It should be noted that Ansible is a kind of configuration management of integrated IT system, using deployment, execution particular task
Open Source Platform, used in back-end code in the deployment of big data platform, that is, the customized big data in heat
Dispose resource.
In the embodiment of the present invention, which further includes monitor component 6, and monitor component 6 is for monitoring Openstack platform.
Specifically, monitor component 6 can select the gnocchi component in Openstack platform.
In the embodiment of the present invention, monitor component 6 includes multiple monitored item, and monitored item includes at least: system CPU utilization rate,
Memory usage, memory usage amount, disk read-write operation rate, disk read-write rate, network inflow and outflow rate and network
Inflow and outflow packet rate.
Based on the same inventive concept, this application provides the embodiment of the corresponding storage medium of embodiment one, it is detailed in implementation
Example three
Embodiment three
Third embodiment of the invention provides a kind of computer readable storage medium, is stored thereon with computer program, meter
All method and steps or Part Methods step in first embodiment are realized when calculation machine program is executed by processor.
The present invention realizes all or part of the process in above-mentioned first embodiment, can also be instructed by computer program
Relevant hardware is completed, and computer program can be stored in a computer readable storage medium, which is being located
It manages when device executes, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, computer program includes computer program code,
Computer program code can be source code form, object identification code form, executable file or certain intermediate forms etc..Computer
Readable medium may include: any entity or device, recording medium, USB flash disk, mobile hard that can carry computer program code
Disk, magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory
(RAM, Ran dom Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs to illustrate
It is that the content that computer-readable medium includes can carry out appropriate according to the requirement made laws in jurisdiction with patent practice
Increase and decrease, such as in certain jurisdictions, according to legislation and patent practice, computer-readable medium do not include electric carrier signal and
Telecommunication signal.
Based on the same inventive concept, this application provides the embodiment of the corresponding electronic equipment of embodiment one, it is detailed in implementation
Example four
Example IV
As shown in figure 3, fourth embodiment of the invention also provides a kind of electronic equipment, including memory and processor, storage
The computer program run on a processor is stored on device, processor is realized in first embodiment when executing computer program
All method and steps or Part Methods step.
Alleged processor can be central processing unit (Central Processing Unit, CPU), can also be it
His general processor, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng processor is the control centre of computer installation, utilizes each portion of various interfaces and the entire computer installation of connection
Point.
Memory can be used for storing computer program and/or module, and processor is stored in memory by operation or execution
Interior computer program and/or module, and the data being stored in memory are called, realize the various function of computer installation
Energy.Memory can mainly include storing program area and storage data area, wherein storing program area can storage program area, at least
Application program needed for one function (such as sound-playing function, image player function etc.) etc.；Storage data area can store root
Created data (such as audio data, video data etc.) etc. are used according to mobile phone.In addition, memory may include high speed with
Machine accesses memory, can also include nonvolatile memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card
(Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (Flash Card), at least
One disk memory, flush memory device or other volatile solid-state parts.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system, server or calculating
Machine program product.Therefore, the present invention can be used complete hardware embodiment, complete software embodiment or combine software and hardware side
The form of the embodiment in face.Moreover, it wherein includes computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in computer-usable storage medium (including but not limited to magnetic disk storage and optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, equipment (system), server and computer program product
Flowchart and/or the block diagram describe.It should be understood that can be realized by computer program instructions in flowchart and/or the block diagram
The combination of process and/or box in each flow and/or block and flowchart and/or the block diagram.It can provide these calculating
Processing of the machine program instruction to general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices
Device is to generate a machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute
For realizing the function of being specified in one or more flows of the flowchart and/or one or more blocks of the block diagram
Device.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of big data platform quick deployment method, which is characterized in that itself the following steps are included:
Make multiple image files；
Platform configuration information is added to Heat component, the platform configuration information is the facility information of each host in mainframe cluster；
Platform layout template is write, and is uploaded to Openstack cloud space；
According to the platform configuration information, Platform deployment information is obtained；
The platform layout template is parsed, and Openstack Platform deployment is carried out according to the Platform deployment information.
2. the method as described in claim 1, which is characterized in that the image file includes at least:
Hadoop cluster host node image file, Hadoop cluster slave node image file, Hadoop company-data node mirror image
File, Hadoop company-data node image file, Kafka cluster host node image file, Kafka cluster are literary from node mirror image
Part, Storm cluster host node image file and Storm cluster slave node image file.
3. the method as described in claim 1, which is characterized in that further include monitoring step, the monitoring step includes:
A default monitor component, the monitor component is for monitoring the Openstack platform.
4. method as claimed in claim 3, it is characterised in that: the monitor component includes multiple monitored item, the monitored item
It includes at least: system CPU utilization rate, memory usage, memory usage amount, disk read-write operation rate, disk read-write rate, net
Network inflow and outflow rate and network inflow and outflow packet rate.
5. method as claimed in claim 3, it is characterised in that: the monitor component is gnocchi component.
6. a kind of storage medium, computer program is stored on the storage medium, it is characterised in that: the computer program is located
It manages when device executes and realizes the described in any item methods of claim 1 to 7.
7. a kind of electronic equipment, including memory and processor, the computer journey run on a processor is stored on memory
Sequence, it is characterised in that: the processor realizes the described in any item methods of claim 1 to 7 when executing the computer program.
8. a kind of big data platform rapid deployment system, characterized in that it comprises:
Image file production unit is used to make multiple image files；
Platform configuration information adding unit is used to add platform configuration information to Heat component, and the platform configuration information is
The facility information of each host in mainframe cluster；
Platform layout template writes unit, is used to write platform layout template, and is uploaded to Openstack cloud space；
Platform deployment information acquisition unit is used to obtain Platform deployment information according to the platform configuration information；
Platform deployment unit is used to parse the platform layout template, and is carried out according to the Platform deployment information
Openstack Platform deployment.
9. system as claimed in claim 8, it is characterised in that: further include monitor component, the monitor component is for monitoring institute
State Openstack platform.
10. system as claimed in claim 8, it is characterised in that: the monitor component includes multiple monitored item, the monitored item
It includes at least: system CPU utilization rate, memory usage, memory usage amount, disk read-write operation rate, disk read-write rate, net
Network inflow and outflow rate and network inflow and outflow packet rate.
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| CN201811475906.3A CN109656575A (en) | 2018-12-04 | 2018-12-04 | Big data platform quick deployment method, storage medium, electronic equipment and system |
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| CN201811475906.3A CN109656575A (en) | 2018-12-04 | 2018-12-04 | Big data platform quick deployment method, storage medium, electronic equipment and system |
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