CN116909584A

CN116909584A - Deployment method, device, equipment and storage medium of space-time big data engine

Info

Publication number: CN116909584A
Application number: CN202310501042.2A
Authority: CN
Inventors: 张鸿辉; 冯亮; 罗伟玲; 吴�灿; 陈建桥; 吴泳兴
Original assignee: Guodi Spacetime Information Technology Beijing Co ltd; Guangdong Guodi Planning Technology Co ltd
Current assignee: Guodi Spacetime Information Technology Beijing Co ltd; Guangdong Guodi Planning Technology Co ltd
Priority date: 2023-05-06
Filing date: 2023-05-06
Publication date: 2023-10-20
Anticipated expiration: 2043-05-06
Also published as: CN116909584B

Abstract

The invention discloses a deployment method, a device, equipment and a storage medium of a space-time big data engine, wherein the method comprises the following steps: acquiring a deployment file and a configuration file of a space-time big data engine; according to the IP address of the server to be deployed and the name of the engine software to be deployed, the installation package is sent to a source path of the engine software to be deployed; a preset deployment environment setting instruction is sent to a server to be deployed so that the server to be deployed sets a corresponding deployment environment; the server to be deployed installs the engine software to be deployed and GIS component dependent items into an installation path of the engine software to be deployed; and sending an operation configuration instruction containing the target operation configuration script to the server to be deployed, so that the server to be deployed configures a corresponding operation environment in a corresponding configuration area according to the target operation configuration script. The invention can improve the deployment efficiency and accuracy of the space-time big data engine.

Description

Deployment method, device, equipment and storage medium of space-time big data engine

Technical Field

The present invention relates to the field of big data technologies, and in particular, to a method, an apparatus, a device, and a storage medium for deploying a spatio-temporal big data engine.

Background

With the rapid development of information technology, data resources become one of important basic strategic resources of the country, and the continuous innovative development of new generation information technology creates new conditions for the development of space-time big data, but also puts forward new requirements on the space-time big data technology. The space-time big data engine function is based on space-time big data technology, processes space-time big data, and achieves rapid query and analysis of space-time big data through high-efficiency indexing capability and strong analysis processing capability, and provides stable and high-efficiency functions of access, management, query analysis, service development and the like of space-time big data for users.

However, deployment and update of the space-time big data engine have higher technical requirements on operation and maintenance personnel, the space-time big data engine architecture consists of 7 areas, namely a load balancing layer, a portal layer, a hosting service layer, a model algorithm layer, a space-time analysis mining layer, a database service layer and a space-time storage layer, and the space-time big data engine is generally constructed by manually deploying the operation and maintenance personnel layer by layer in GIS basic software in the prior art. The manual deployment method requires operation and maintenance personnel to prepare engine software packages and GIS component dependent items in advance, then each server is installed or upgraded one by one, corresponding configuration is manually modified, whether the engine software and the configuration file are correct or not is verified in a manual mode, and the deployment method is quite tedious, and when a large number of servers needing to be deployed exist, the deployment efficiency is low and errors are easy to occur. Therefore, a deployment method of the space-time big data engine is needed to automatically deploy, and the deployment efficiency and accuracy of the space-time big data engine are improved.

Disclosure of Invention

The invention provides a deployment method, a device, equipment and a storage medium of a space-time big data engine, which are used for solving the technical problems of lower deployment efficiency and easiness in error in the existing deployment method of the space-time big data engine.

In order to solve the above technical problems, an embodiment of the present invention provides a deployment method of a spatio-temporal big data engine, including:

acquiring a deployment file and a configuration file of a space-time big data engine; wherein the deployment file comprises: the method comprises the steps of an IP address of a server to be deployed, a name of engine software to be deployed, a GIS component dependent item, a source path of the engine software to be deployed, an installation path of the engine software to be deployed and an installation script of the engine software to be deployed; the configuration file comprises: running configuration scripts corresponding to each configuration area;

according to the IP address of the server to be deployed and the name of the engine software to be deployed, acquiring an installation package of the engine software to be deployed and the GIS component dependent item, and then sending the installation package to a source path of the engine software to be deployed;

according to the IP address of the server to be deployed, a preset deployment environment setting instruction is sent to the server to be deployed, so that the server to be deployed sets a corresponding deployment environment according to the deployment environment setting instruction;

According to the IP address of the server to be deployed, an installation instruction containing an installation script of the engine software to be deployed and an installation path of the engine software to be deployed is sent to the server to be deployed, so that the server to be deployed extracts the installation package according to a source path of the engine software to be deployed, and according to the installation script of the engine software to be deployed and the installation package, the engine software to be deployed and GIS component dependent items are installed in the installation path of the engine software to be deployed;

extracting a target operation configuration script corresponding to the server to be deployed from the configuration file according to the configuration area corresponding to the server to be deployed;

and sending an operation configuration instruction containing the target operation configuration script to the server to be deployed according to the IP address of the server to be deployed, so that the server to be deployed configures a corresponding operation environment in a corresponding configuration area according to the target operation configuration script.

Preferably, the configuration area includes: the system comprises a load balancing layer, a portal layer, a hosting service layer, a model algorithm layer, a space-time analysis mining layer, a database service layer and a space-time storage layer.

Preferably, the deployment environment setting instruction includes: creating a user and user group instruction, a node clock synchronization instruction, a server firewall closing instruction, a firewall start-up prohibition self-starting instruction, a Selinux closing instruction, a configuration file NFS shared directory instruction, a local source warehouse instruction, a limit setting instruction and a VM.Swappness setting instruction;

Setting a corresponding deployment environment according to the deployment environment setting instruction, including:

creating a user and a user group on a local server according to the user and user group creation instruction;

setting the node clock synchronization of the server according to the node clock synchronization instruction;

according to the instruction for closing the firewall of the server, closing the firewall of the server;

according to the firewall starting-up prohibition self-starting instruction, setting the firewall starting-up prohibition self-starting of the server;

closing the Selinux of the server according to the Selinux closing instruction;

creating a configuration file NFS shared directory on a local server according to the configuration file NFS shared directory creation instruction;

according to the instruction for creating the local source warehouse, creating a local source warehouse on a local server;

setting Limits of the server according to the Limits setting instruction;

and setting the VM.Swappness of the server according to the VM.Swappness setting instruction.

Preferably, the installation instruction further includes: oracle JDK installation package;

before the server to be deployed extracts the installation package according to the source path of the engine software to be deployed, and installs the engine software to be deployed and GIS component dependent items into the installation path of the engine software to be deployed according to the installation script of the engine software to be deployed and the installation package, the method further comprises:

And the server to be deployed extracts the Oracle JDK installation package according to the installation instruction, and uninstalls the OpenJDK in the local server and installs the Oracle JDK according to the Oracle JDK installation package.

Preferably, the target running configuration script includes: a first running configuration script and a second running configuration script;

the server to be deployed configures a corresponding operation environment in a corresponding configuration area according to the target operation configuration script, and the method comprises the following steps:

the server to be deployed configures a corresponding operation environment in the local server according to the first operation configuration script;

and configuring the engine cluster relation, the GIS component dependency relation and the operation environment of the database master-slave hot standby relation between the server to be deployed and other servers to be deployed in the same configuration area in the corresponding configuration area according to the second operation configuration script by the server to be deployed.

As a preferred solution, after the server to be deployed configures a corresponding running environment in the corresponding configuration area according to the target running configuration script, the method further includes:

acquiring a test file of a space-time big data engine; wherein, the test file includes: the method comprises the steps of enabling IP addresses of servers to be tested, names of engine software to be tested, cluster port numbers in each configuration area, engine cluster relations among the servers to be tested, GIS component dependency relations and database master-slave hot standby relations;

Testing the server to be deployed according to the test file, and judging whether the engine software to be deployed and GIS component dependent items of the server to be deployed are installed correctly, and whether the corresponding operation configuration scripts in the local server of the server to be deployed, the engine cluster relationship between the server to be deployed and other servers to be deployed in the same configuration area, the GIS component dependent relationship and the operation configuration scripts of the database master-slave hot standby relationship are configured correctly.

Based on the foregoing embodiment, another embodiment of the present invention provides a deployment apparatus for a spatio-temporal big data engine, including: the system comprises a file acquisition module, an installation package sending module, a deployment environment setting module, an installation package installation module, a target operation configuration script extraction module and an operation environment configuration module;

the file acquisition module is used for acquiring deployment files and configuration files of the space-time big data engine; wherein the deployment file comprises: the method comprises the steps of an IP address of a server to be deployed, a name of engine software to be deployed, a GIS component dependent item, a source path of the engine software to be deployed, an installation path of the engine software to be deployed and an installation script of the engine software to be deployed; the configuration file comprises: running configuration scripts corresponding to each configuration area;

The installation package sending module is used for obtaining an installation package of the engine software to be deployed and the GIS component dependent item according to the IP address of the server to be deployed and the name of the engine software to be deployed, and then sending the installation package to a source path of the engine software to be deployed;

the deployment environment setting module is used for sending a preset deployment environment setting instruction to the server to be deployed according to the IP address of the server to be deployed so that the server to be deployed sets a corresponding deployment environment according to the deployment environment setting instruction;

the installation package installation module is used for sending an installation instruction containing an installation script of engine software to be deployed and an installation path of the engine software to be deployed to the server to be deployed according to the IP address of the server to be deployed, so that the server to be deployed extracts the installation package according to the source path of the engine software to be deployed, and installs the engine software to be deployed and GIS component dependent items into the installation path of the engine software to be deployed according to the installation script of the engine software to be deployed and the installation package;

the target operation configuration script extraction module is used for extracting the target operation configuration script corresponding to the server to be deployed from the configuration file according to the configuration area corresponding to the server to be deployed;

The running environment configuration module is used for sending a running configuration instruction containing the target running configuration script to the server to be deployed according to the IP address of the server to be deployed, so that the server to be deployed configures a corresponding running environment in a corresponding configuration area according to the target running configuration script.

As a preferred solution, the deployment device of the spatio-temporal big data engine further includes: a test module;

the test module is used for acquiring test files of the space-time big data engine; wherein, the test file includes: the method comprises the steps of enabling IP addresses of servers to be tested, names of engine software to be tested, cluster port numbers in each configuration area, engine cluster relations among the servers to be tested, GIS component dependency relations and database master-slave hot standby relations;

and testing the server to be deployed according to the test file, and judging whether the engine software to be deployed and GIS component dependent items of the server to be deployed are installed correctly, and whether the corresponding operation configuration scripts in the local server of the server to be deployed, the engine cluster relationship between the server to be deployed and other servers to be deployed in the same configuration area, the GIS component dependent relationship and the operation configuration scripts of the database master-slave hot standby relationship are configured correctly.

On the basis of the above embodiment, a further embodiment of the present invention provides a deployment apparatus of a spatio-temporal big data engine, where the apparatus includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the deployment method of the spatio-temporal big data engine according to the embodiment of the present invention.

On the basis of the above embodiment, a further embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, where when the computer program runs, the device where the computer readable storage medium is located is controlled to execute the deployment method of the spatio-temporal big data engine described in the above embodiment of the present invention.

Compared with the prior art, the embodiment of the invention has the following beneficial effects:

in the invention, a deployment file and a configuration file of a space-time big data engine are obtained; according to the IP address of the server to be deployed and the name of the engine software to be deployed, acquiring an installation package of the engine software to be deployed and the GIS component dependent item, and then sending the installation package to a source path of the engine software to be deployed; controlling each server to be deployed to set a corresponding deployment environment; then controlling a server to be deployed to extract an installation package according to a source path of engine software to be deployed, and installing the engine software to be deployed and GIS component dependent items into the installation path of the engine software to be deployed according to an installation script of the engine software to be deployed and the installation package; and controlling the deployment server to configure a corresponding operation environment in the corresponding configuration area according to the target operation configuration script.

According to the method and the system, operation and maintenance personnel are not required to install and configure the operation environment on each server one by one according to the prepared engine software package and GIS component dependent items, only the deployment files and the configuration files of the space-time big data engine are required to be obtained, the engine software package and the GIS component dependent items required by each server to be deployed for automatic installation are controlled according to the deployment files and the configuration files, and the operation environment is configured, so that the deployment of the space-time big data engine can be completed, the automatic deployment of the space-time big data engine is realized, and the deployment efficiency and the accuracy of the space-time big data engine are improved.

Drawings

FIG. 1 is a flow chart of a method for deploying a spatio-temporal big data engine according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a deployment area involved in a deployment method of a spatio-temporal big data engine according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of a deployment device of a spatio-temporal big data engine according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

Referring to fig. 1, a flowchart of a deployment method of a spatio-temporal big data engine according to an embodiment of the present invention includes the following specific steps:

s1, acquiring a deployment file and a configuration file of a space-time big data engine; wherein the deployment file comprises: the method comprises the steps of an IP address of a server to be deployed, a name of engine software to be deployed, a GIS component dependent item, a source path of the engine software to be deployed, an installation path of the engine software to be deployed and an installation script of the engine software to be deployed; the configuration file comprises: running configuration scripts corresponding to each configuration area;

The spatio-temporal big data engine focuses on solving two types of problems: the management problem of emerging space-time big data and the calculation performance problem of traditional space-time data. Aiming at the two problems, the space-time big data engine architecture comprises two very important parts, namely a space-time big data analysis technology and is specially aimed at processing and analysis mining of space-time big data; the other is the distributed reconstruction of classical GIS functions, which is specifically directed to the management and processing of classical spatio-temporal data. By combining the big data technology of IT from the GIS kernel level depth, such as a distributed storage technology, a distributed computing framework, a stream data processing framework and the like, the storage, indexing, management and analysis capabilities of the space-time big data are built in GIS basic software, so that more people can manage and analyze the space-time big data with less programming or even without programming, and the threshold of space-time big data analysis is greatly reduced. Meanwhile, by utilizing the distributed storage and distributed calculation framework of IT, the classical spatial-temporal data processing and spatial-temporal analysis method of the classical GIS is reconstructed, and the order of magnitude of the classical spatial-temporal data processing and analysis performance is improved.

The space-time big data engine architecture consists of 7 layers of a load balancing layer, a portal layer, a managed service layer, a model algorithm layer, a space-time analysis mining layer, a database service layer and a space-time storage layer. (1) load balancing layer: the request forwarding and GIS resource load balancing capability for space-time big data resource management system access tracks the sites of GIS servers to see which GIS servers are removed or which new GIS servers are added, and then forwards traffic to the site computers currently participating. Therefore, the system can be generally used together with other load balancing components, and can realize the load balancing of the GIS servers in the space-time big data resource management system;

(2) Portal layer: providing an enterprise-level space-time big data resource management system, which can uniformly manage GIS data resources, GIS application resources and GIS function resources of enterprises;

(3) Hosting service layer: geographic resources are provided, and GIS functions are converted into online services. The resources and functions comprise vector data, raster data, BIM data, live three-dimensional data, other 3D model data, table data, text, unstructured data and the like, and GIS functions comprise geographic drawing, geographic processing, element editing, network analysis, OGC support, data access, mobile terminal data extraction and the like;

(4) The model algorithm layer provides multi-source data access and management capability and full-flow space-time data management capability, supports drag-type online modeling, and provides a large number of fine-granularity space-time large data analysis operators, so that a user can more efficiently create a service model algorithm, interface call and process service system integration application;

(5) Space-time analysis mining layer: aiming at massive space-time data, table data and complex business processes, the rapid analysis calculation and insight mining capability is provided, tens of space-time big data analysis tools and operators are provided through a built-in distributed calculation frame and a butt joint third-party distributed calculation frame, and the analysis tools and operators comprise aggregation, regression, detection, clustering and the like, so that the patterns, trends, abnormal information and the like possibly hidden in the data are insight. By deeply combining with user service logic, a service model is built, so that complex analysis which can be completed in days and weeks originally can be completed in minutes, and the efficiency of analysis and processing of huge space-time data is greatly improved;

(6) Database service layer: with PostgreSQL technology, storing thousands of element layer data hosted in portals, including hosted element layers created from the output of spatio-temporal big data analysis, using master-slave hot standby technology to guarantee high availability of data storage, if a master becomes unavailable (e.g., a master crashes, power down or loses network connectivity), a standby promotes to master storage. Once the backup becomes the main data storage, the backup file is sent to the same position sent by the previous host, if the previous host resumes work, for example, the power supply of the machine is unplugged and the power supply is connected, and the previous host becomes the backup machine for data storage backup;

(7) Space-time storage layer: the Hadoop big data technology is used for storing massive GIS data, and the method has the characteristics of rapidness, real-time performance, high concurrency, high throughput and the like; wherein, HDFS stores hundred million-level CSV text data, HBase stores 10 hundred million-level vector and raster data, mongoDB stores 100 hundred million-level two-three-dimensional tile data, and elastic search stores 10 hundred million-level scale dynamic data.

Referring to fig. 2, a schematic diagram of a deployment area related to a deployment method of a spatio-temporal big data engine according to an embodiment of the present invention is configured for 7 levels of the spatio-temporal big data engine architecture, where contents and functions to be implemented for the 7 levels are as follows: (1) The load balancing area deploys Nginx and Tomcat clusters, and provides access request forwarding and GIS resource load balancing capacity for the space-time big data resource management system; (2) The Portal area deploys GIS Portal and CDH Manager, and provides an enterprise-level space-time big data resource management system which can uniformly manage GIS data resources, GIS application resources and GIS function resources of enterprises; (3) The managed service area is provided with a GeoServer cluster, and provides geographic resources and GIS function conversion online service functions; (4) The Geospark DME and OGIS DME are deployed in the model algorithm area, so that the multi-source data access and management capability and the full-flow space-time data management capability are provided, and the online modeling of the towed big data is supported; (5) The space-time analysis mining area deploys GIS component dependent items such as Spark clusters, geoMesa, geotrellis, geoTools, GDAL and the like, and provides rapid analysis calculation and insight mining capability aiming at massive space-time data, table data and complex business processes; (6) The database service area deploys PostgreSQL database clusters and PostGIS component dependent items, stores thousands of element layer data managed in portals, and ensures high availability of data storage by adopting a master-slave hot standby technology; (7) The space-time storage area is deployed HDFS, HBase, mongoDB, elasticSearch to store massive GIS data in a large data cluster.

When the 7-layer region is deployed, firstly acquiring a deployment file and a configuration file of a space-time big data engine; wherein the deployment file comprises: the method comprises the steps of an IP address of a server to be deployed, a name of engine software to be deployed, a GIS component dependent item, a source path of the engine software to be deployed, an installation path of the engine software to be deployed and an installation script of the engine software to be deployed;

the deployment file is defined as: installFile= { InstallObject1, installObject2, … …, installObjectN }; where N represents the number of deployment objects.

Deployment object instrallobject= { IP, engineSoftware, sourcePath, targetPath, installationAction }; wherein, IP is the IP address of the server to be deployed, engineSoftware is the name of the engine software to be deployed and the collection of the GIS component dependent item elements thereof, sourcePath is the source path of the engine software to be deployed, targetPath is the installation path of the engine software to be deployed, and InformationAction is the installation action of the engine software to be deployed.

Defining an ip=an IP address string of the server to be deployed;

defining an EngineSoftware = name string of the engine software to be deployed; engineSoftware= { Software1, software2, … …, software N }; wherein N represents the number of the engine software to be deployed and the GIS component dependent items;

Defining SourcePath = source path string |null of the engine software to be deployed;

defining a targetpath=an installation path character string |null of the engine software to be deployed;

definition of InstallionAction = InstallUninstall Update Test; wherein, installal represents performing an Install operation, uninstall represents an Uninstall operation, update represents an Update operation, and Test represents a Test operation.

Wherein, the configuration file includes: running configuration scripts corresponding to each configuration area;

the configuration file is defined as: configuration file= { configuration object1, configuration object2, &..configuration object n }; where N represents the number of configuration objects.

Configuration entry configuration object= { EngineSoftware, masterIP, slaveIP [ ] }.

MasterIP = master server IP address string of server to be deployed.

SlaveIP [ ] = A slave server IP address string array of the server to be deployed is defined.

S2, acquiring an installation package of the engine software to be deployed and the GIS component dependent item according to the IP address of the server to be deployed and the name of the engine software to be deployed, and then sending the installation package to a source path of the engine software to be deployed;

according to the IP address of the server to be deployed in the deployment object, the name of the engine Software to be deployed and the source path of the engine Software to be deployed, the installation package of the engine Software to be deployed and the GIS component dependent item is obtained, and the communication connection module sends the installation package of the engine Software to be deployed and the GIS component dependent item Software to the appointed directory of the target server host to be deployed, namely, the source path of the engine Software to be deployed by using the scp command' scp $ { Software $ { IP } $ { Softpath } ".

S3, sending a preset deployment environment setting instruction to the server to be deployed according to the IP address of the server to be deployed, so that the server to be deployed sets a corresponding deployment environment according to the deployment environment setting instruction;

preferably, the deployment environment setting instruction includes: creating a user and user group instruction, a node clock synchronization instruction, a server firewall closing instruction, a firewall start-up prohibition self-starting instruction, a Selinux closing instruction, a configuration file NFS shared directory instruction, a local source warehouse instruction, a limit setting instruction and a VM.Swappness setting instruction; setting a corresponding deployment environment according to the deployment environment setting instruction, including: creating a user and a user group on a local server according to the user and user group creation instruction; setting the node clock synchronization of the server according to the node clock synchronization instruction; according to the instruction for closing the firewall of the server, closing the firewall of the server; according to the firewall starting-up prohibition self-starting instruction, setting the firewall starting-up prohibition self-starting of the server; closing the Selinux of the server according to the Selinux closing instruction; creating a configuration file NFS shared directory on a local server according to the configuration file NFS shared directory creation instruction; according to the instruction for creating the local source warehouse, creating a local source warehouse on a local server; setting Limits of the server according to the Limits setting instruction; and setting the VM.Swappness of the server according to the VM.Swappness setting instruction.

According to the IP address of the server to be deployed, a preset deployment environment setting instruction is sent to the server to be deployed, and the method comprises the following steps: the method comprises the steps of creating a user and user group instruction, a node clock synchronization instruction, a server firewall closing instruction, a firewall start-up prohibition instruction, a Selinux closing instruction, a configuration file NFS shared directory creating instruction, a local source warehouse creating instruction, a Limits setting instruction and a VM.Swappness setting instruction.

(1) The server to be deployed creates users and user groups on the local server according to the user and user group creation instruction: creating a user group through a groupadd instruction' groupadd-g $ { GID } $ { GroupName }; GID is group number, groupName is group name; creating a user by means of the instruction "useradd-g $ { GID } -u $ { userID } -d $ { Home } -m $ { CreateUserHome }"; useradd is a command to create a user, $ { GID } is a user group number, $ { UserID } is a user number, $ { Home } is a master directory when the user is logged in, and $ { createuser Home } is a directory that is automatically created if the directory represented by $ { Home } does not exist.

(2) The server to be deployed sets the node clock synchronization of the server according to the node clock synchronization instruction: the cluster master node configures clock synchronization, a modification/etc/ntp.conf ∈ $ { IP } Mask $ { Mask } nododifynotrap/sensor $ { IP }, a fuse $ { IP } structure 10", a command of ' service ntpd restart, chkconfig ntpd on ' is executed, and other nodes of the cluster synchronize the master node ' ntpdate $ { IP }; wherein, $ { IP } is the master node IP address of the server to be deployed in the cluster, and, $ { Mask } is the master node subnet Mask of the server to be deployed in the cluster.

(3) The server to be deployed closes the firewall of the server according to the instruction for closing the firewall of the server; according to the firewall start-up prohibition self-starting instruction, setting the firewall start-up prohibition self-starting of the server: the firewall is forbidden to start up automatically by the instruction of 'systemctl stop firewall.service', which is used for closing the firewall of the server; firewalld.

(4) The server to be deployed closes the Selinux of the server according to the Selinux closing instruction: shut down Selinux, edit/etc/Selinux/config, and set SELINUX to disabled.

(5) The server to be deployed creates a configuration file NFS shared directory on the local server according to the configuration file NFS shared directory creation instruction: the cluster master node creates NFS shared directory 'mkdir $ { share_DIR }, chmod-R777 $ { share_DIR }, registers NFS shared directory vim/etc/export to join "$ { share_DIR }, (inscure, rw, sync, no_root_squar)," rpcbind, NFS power-on self-start, executes' chkconfig rpcbind on, chkconfig NFS on, chkconfig-list @ egrep 'nfs\b|rpcbind' ", other cluster nodes mount NFS shared directory vim/etc/fstab to join" $ { share_DIR }, $ { share_DIR } NFS/n default 1 2/n$ { share_DIR }, { share_DIR "$ 1 2" +; where, $ { IP } is the shared directory server IP address, $ { share_DIR } is the shared directory server file directory address.

(6) The server to be deployed creates a local source warehouse on the local server according to the instruction for creating the local source warehouse: creating a local source repository, starting an httpd service "service httpd start", installing a createpo tool "RPM-ivh createpo-xxx.noarch.rpm", mounting an image to the folder "mount-oloop/var/www/html/RedHat-7.9-x86_64-bin-DVD1.iso/var/www/html/RedHat", creating a repository information file, modifying the RedHat.repo file to join "name=RedHat/nbaseurl=http:// 127.0.0.1/RedHat// n gpgcheck=1 gpkey=http:// 127.0.1/RedHat/RPM-GPG-Y-RedHat-7" content, and executing "createpo" under directory/var/www/ml/hthat:

(7) The server to be deployed sets Limits of the server according to the Limits setting instruction: setting Limits, editing "/etc/security/Limits. Conf" add "$ { USER } soft nofile 65536$ { USER } hard nofile 65536$ { USER } soft nproc 25059$ { USER } hard nproc 25059" $ { USER } is the USER name of the engine software.

(8) The server to be deployed sets the VM.Swappness of the server according to the VM.Swappness setting instruction: VM.Swappiness is set, and the "echo 'vm.max_map_count=262626144' >/etc/sysctl.conf/n echo 'vm.swaappiness=1' >/etc/sysctl.conf" command is executed.

S4, according to the IP address of the server to be deployed, an installation instruction containing an installation script of the engine software to be deployed and an installation path of the engine software to be deployed is sent to the server to be deployed, so that the server to be deployed extracts the installation package according to a source path of the engine software to be deployed, and according to the installation script of the engine software to be deployed and the installation package, the engine software to be deployed and GIS component dependent items are installed in the installation path of the engine software to be deployed;

preferably, the installation instruction further includes: oracle JDK installation package; before the server to be deployed extracts the installation package according to the source path of the engine software to be deployed, and installs the engine software to be deployed and GIS component dependent items into the installation path of the engine software to be deployed according to the installation script of the engine software to be deployed and the installation package, the method further comprises: and the server to be deployed extracts the Oracle JDK installation package according to the installation instruction, and uninstalls the OpenJDK in the local server and installs the Oracle JDK according to the Oracle JDK installation package.

According to the IP address of the server to be deployed, an installation instruction comprising an installation script of engine software to be deployed, an installation path of the engine software to be deployed and an Oracle JDK installation package is sent to the server to be deployed, the server to be deployed extracts the Oracle JDK installation package according to the installation instruction, and according to the Oracle JDK installation package, the OpenJDK in a local server is unloaded and the Oracle JDK is installed, a corresponding running environment is configured, and the specific operation of deploying the Oracle JDK is as follows:

Each zone instrallfile= { instralobject 1, instralobject 2}.

Wherein InstallObject1= { $ { IP }, { "java-1.8.0-openjdk-1.8.0.65-3.b17.el7.x86_64", "java-1.8.0-openjdk-header-1.8.0.65-3.b17.el7.x86_64" }, null, null, uninstal }; where $ { IP } is the host IP address of the server to be deployed.

InstallObject2＝(${IP},{“jdk-8u131-linux-x64.rpm”},null,

"$ { TargetPath }", installs); where $ { IP } is the host IP address of the server to be deployed and $ { TargetPath } is the installation path of the server host to be deployed.

S5, extracting a target operation configuration script corresponding to the server to be deployed from the configuration file according to the configuration area corresponding to the server to be deployed;

judging which region of the seven layers of regions is the server to be deployed, and then extracting the operation configuration script of the corresponding region from the configuration file to serve as the target operation configuration script corresponding to the server to be deployed.

S6, according to the IP address of the server to be deployed, sending a running configuration instruction containing the target running configuration script to the server to be deployed, so that the server to be deployed configures a corresponding running environment in a corresponding configuration area according to the target running configuration script.

Preferably, the target running configuration script includes: a first running configuration script and a second running configuration script; the server to be deployed configures a corresponding operation environment in a corresponding configuration area according to the target operation configuration script, and the method comprises the following steps: the server to be deployed configures a corresponding operation environment in the local server according to the first operation configuration script; and configuring the engine cluster relation, the GIS component dependency relation and the operation environment of the database master-slave hot standby relation between the server to be deployed and other servers to be deployed in the same configuration area in the corresponding configuration area according to the second operation configuration script by the server to be deployed.

After setting the corresponding deployment environment according to the deployment environment setting instruction, the server to be deployed automatically deploys a load balancing area, a portal area, a managed service area, a model algorithm area, a space-time analysis mining area, a database service area and a space-time storage area according to an installation script, an installation package and a target operation configuration script of the engine software to be deployed, wherein the specific deployment process is as follows:

(1) Automatically deploying Nginx and Tomcat clusters on a server to be deployed in a load balancing area, and configuring an operation environment; and configuring the Nginx-Tomcat load balancing relation according to the target running configuration script.

Load balancing area instralfile= { instralobject 1, instralobject 2}.

InstalObject1= ($ { MasterIP }, { "nginx-1.12.0.Tar.gz", "apache-tomcat-9.0.16.Tar.gz" }, null, "$ { TargetPath }, installs); wherein, $ { MasterIP } is the IP address of the main node of the server to be deployed in the installation cluster, and, $ { TargetPath } is the installation path of the host of the server to be deployed.

InstalObject2= ($ { SlaveIP }, { "nginx-1.12.0.Tar.gz", "apache-tomcat-9.0.16.tar.gz" }, null, "$ { TargetPath }, installs); where $ { SlaveIP } is the slave node IP address of the server to be deployed in the installation cluster and $ { TargetPath } is the installation path of the server host to be deployed.

Load balancing zone configuration file= { configuration object }.

Configurationobject= { "nmginx-Tomcat", $ { MasterIP }, $ { SlaveIP }, script configuration keep, firewall, distribution policy, etc.; wherein, $ { MasterIP } is the server to be deployed master node IP address in the cluster, and, $ { SlaveIP } is the server to be deployed slave node IP address in the cluster.

(2) And automatically deploying GIS Portal and CDH Manager on the server to be deployed in the Portal area, and configuring the running environment.

Portal area instrallfile= { instralobject 1, instralobject 2}.

InstallObject1= ($ { IP }, { "Portal_for_ArcGIS_Linux_1091_180199.Tar. Gz", "/Setup-m sillent-l yes" }, null, "$ { TargetPath }," Installs); where $ { IP } is the host IP address of the server to be deployed and $ { TargetPath } is the installation path of the server host to be deployed.

InstallObject2= { $ { IP }, { "cloudera-manager-agent-6.2.1-1426065.el7.x86_64.Rpm", "cloudera-manager-daemons-6.2.1-1426065.el7.x86_64.Rpm", "cloudera-manager-server-db-2-6.2.1-1426065.el7.x86_64.Rpm" }, null, $ { TargetPath }, intall }; where $ { IP } is the host IP address of the server to be deployed and $ { TargetPath } is the installation path of the server host to be deployed.

Portal zone configuration file= { configuration object1, configuration object2}

Based on configuration object 1= { "GIS port", null, null }, the script configures gisport authorization, account, meat content directory, component dependency, etc.

Based on configuration object 2= { "CDH Manager", null, null }, script configuration Cloudera Express, repository settings, CDH package local source dependencies, check correctness, etc.

(3) And automatically deploying the GeoServer cluster on the server to be deployed of the managed service area, and configuring a cluster running environment.

Managed service area instralfile= { instralobject 1, instralobject 2, instralobject 3}.

InstallObject1= { $ { MasterIP }, { "gelator-2.20-SNAPSHOT-bin.zip }, {" gelator-2.20-SNAPSHOT-activeMQ-browser-plug in.zip }, "gelator-2.20-SNAPSHOT-jms-cluster-plug in.zip" }, null, "$ { TargetPath }," Intall }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the installation cluster, and, $ { TargetPath } is the installation path of the host of the server to be deployed.

InstallObject2= { $ { SlaveIP1}, { "gelator-2.20-SNAPSHOT-bin.zip", "gelator-2.20-SNAPSHOT-acteMQ-browser-plug in.zip", "gelator-2.20-SNAPSHOT-jms-cluster-plug in.zip" }, null, "$ { TargetPath }", installl }; where $ { SlaveIP1} is the slave node IP1 address of the server to be deployed in the installation cluster, and $ { TargetPath } is the installation path of the server host to be deployed.

InstallObject3= { $ { SlaveIP2}, { "gelator-2.20-SNAPSHOT-bin.zip", "gelator-2.20-SNAPSHOT-acteMQ-browser-plug in.zip", "gelator-2.20-SNAPSHOT-jms-cluster-plug in.zip" }, null, "$ { TargetPath }", installl }; where $ { SlaveIP2} is the slave node IP2 address of the server to be deployed in the installation cluster, and $ { TargetPath } is the installation path of the server host to be deployed.

Managed service area configuration file= { configuration object }

Configuration object= { "GeoServer", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration mq-browser.xml, jms-cluster.properties, jms-impregnated-browser.properties, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

(4) Automatically deploying Geospark DME and OGIS DME on a server to be deployed in the model algorithm area, and configuring an operating environment.

Model algorithm region instrallfile= { instralobject }.

InstallObject= ($ { IP }, { "geospark_dme. Tar. Gz, { ogis_dme. Tar. Gz", }, null, "$ { TargetPath }," Installs); where $ { IP } is the host IP address of the server to be deployed and $ { TargetPath } is the installation path of the server host to be deployed.

Model algorithm region configurionfile= { configurionobject }

Based on the configurationobject= { "DME", null, null }, script configuration DME-jvm runtime environment, etc.

(5) And automatically deploying Spark clusters on servers to be deployed in the space-time analysis mining area, and configuring GIS component dependent items such as cluster operation environments and GeoMesa, geotrellis, geoTools, GDAL.

The spatiotemporal analysis excavates the regions InstalFile= { InstalObject 1, instalObject 2, instalObject 3}.

InstalObject1= ($ { MasterIP }, { "spark-2.4.0+cdh6.2.1.Tar. Gz", "geometry-hbase_2.11-2.0.2. Tar. Gz", "geotrellis-ras_2.10-0.10.2. Zip", "geotrellis-21.2-bin. Zip", "gdal-3.6.1.Tar. Gz" }, null, "(TargetPath }", install); wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the installation cluster, and, $ { TargetPath } is the installation path of the host of the server to be deployed.

InstalObject2= ($ { SlaveIP1}, { "spark-2.4.0+cdh6.2.1.Tar. Gz", "geometry-hbase_2.11-2.0.2. Tar. Gz", "geotrellis-ras_2.10-0.10.2. Zip" }, null, "$ { TargetPath }, install); where $ { SlaveIP1} is the slave node IP1 address of the server to be deployed in the installation cluster, and $ { TargetPath } is the installation path of the server host to be deployed.

InstalObject3= ($ { SlaveIP2}, { "spark-2.4.0+cdh6.2.1.Tar. Gz", "geometry-hbase_2.11-2.0.2. Tar. Gz", "geotrellis-ras_2.10-0.10.2. Zip" }, null, "$ { TargetPath }, install); where $ { SlaveIP2} is the slave node IP2 address of the server to be deployed in the installation cluster, and $ { TargetPath } is the installation path of the server host to be deployed.

The spatiotemporal analysis mining area configurionfile= { configurionobject 1, configurionobject 2, configurionobject 3, configurionobject 4, configurionobject 5}.

The configuration object1 = { "Spark", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, the script configures files such as a scale environment and files, spark-env.sh, spark-defaults.conf; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

Configuration object 2= { "geomeasa", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration geometry-spark-js, geometry-spark-core, geometry-spark-sql, memory index, space partition, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

ConfigurationObject 3= { "Geotrellis", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration pycharm, geotrellis-pyspark, geotrellis-band, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

Configuring GeoTools-jvm running environments and the like based on the configuration objects 4= { "GeoTools", $ { MasterIP }, null }, script; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster.

Based on configuration object 5= { "GDAL", $ { MasterIP }, null }, script configuration GDAL-dock mirror image, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster.

(6) Automatically deploying PostgreSQL database clusters and PostGIS component dependent items on a server to be deployed in a database service area, and configuring an operation environment; and configuring the PostgreSQL master-slave hot standby relation according to the configuration file.

Database service area instralfile= { instralobject 1, instralobject 2}.

InstalObject1= ($ { MasterIP }, { "postgresql-11.2.Tar.gz", "postgis-3.1.0.Tar.gz" }, null, "$ { TargetPath }," Install); wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the installation cluster, and, $ { TargetPath } is the installation path of the host of the server to be deployed.

InstalObject2= ($ { SlaveIP }, { "postgresql-11.2.Tar. Gz", "postgis-3.1.0.Tar. Gz" }, null, "$ { TargetPath }," Installs); wherein, $ { SlaveIP } is the slave node IP address of the server to be deployed in the installation cluster, and, $ { TargetPath } is the installation path of the server host to be deployed.

Database service area configuration file= { configuration object1, configuration object2}.

Based on configuration object 1= { "PostgreMS", $ { MasterIP }, $ { SlaveIP }, script configures files such as pg_hba.conf, postgresql.conf, recovery.conf; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP } is the slave node IP address of the server to be deployed in the cluster.

Based on the configuration object 2= { "PostGIS", $ { MasterIP }, $ { SlaveIP }, script configuration proj4, geos, libxml, gdal, json-c, postGIS plug-in, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP } is the slave node IP address of the server to be deployed in the cluster.

(7) Automatically deploying HDFS, HBase, mongoDB, elasticSearch clusters on servers to be deployed of the space-time storage area, and configuring a cluster running environment.

Spatiotemporal storage area instralfile= { instralobject 1, instralobject 2, instralobject 3}.

InstalObject1= ($ { MasterIP }, { "hdfs-3.0.0+cdh6.2.1.Tar. Gz", "hbase-2.1.0+cdh6.2.1.Tar. Gz", "elastiscearch-7.8.0. Tar. Gz", "mongodb-3.4.10.Tar. Gz", "zookeeer 3.4.5+cdh6.2.1.Tar. Gz" }, null, "$ { TargetPath }, instanal); wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the installation cluster, and, $ { TargetPath } is the installation path of the host of the server to be deployed.

InstalObject2= ($ { SlaveIP1}, { "hdfs-3.0.0+cdh6.2.1.Tar. Gz", "hbase-2.1.0+cdh6.2.1.Tar. Gz", "elastiscearch-7.8.0. Tar. Gz", "mongodb-3.4.10.Tar. Gz", "zookeepin 3.4.5+cdh6.2.1.Tar. Gz" }, null, "$ { TargetPath }," Installl); where $ { SlaveIP1} is the slave node IP1 address of the server to be deployed in the installation cluster, and $ { TargetPath } is the installation path of the server host to be deployed.

InstalObject3= ($ { SlaveIP2}, { "hdfs-3.0.0+cdh6.2.1.Tar. Gz", "hbase-2.1.0+cdh6.2.1.Tar. Gz", "elastiscearch-7.8.0. Tar. Gz", "mongodb-3.4.10.Tar. Gz", "zookeepin 3.4.5+cdh6.2.1.Tar. Gz" }, null, "$ { TargetPath }," Installl); where $ { SlaveIP2} is the slave node IP2 address of the server to be deployed in the installation cluster, and $ { TargetPath } is the installation path of the server host to be deployed.

The spatiotemporal storage area configuration file= { configuration object1, configuration object2, configuration object3, configuration object4}.

Configuration object1 = { "HDFS", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration environment variables, hadoop-env.sh, core-site.xml, HDFS-site.xml, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

ConfigurationObject 2= { "HBase", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration environment variables, HBase-env.sh, HBase-site.xml, regionservers, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

ConfigurationObject 3= { "MongoDB", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration environment variables, config.conf, card.conf, mongos.conf, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

Configuration object 4= { "elastic search", $ { MasterIP }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2}, script configuration environment variables, elastic search. Yml, elastic search. In. Sh, etc.; wherein, $ { MasterIP } is the master node IP address of the server to be deployed in the cluster, and, $ { SlaveIP [ ] } is the slave node IP address array of the server to be deployed in the cluster.

Preferably, after the server to be deployed configures a corresponding running environment in the corresponding configuration area according to the target running configuration script, the method further includes: acquiring a test file of a space-time big data engine; wherein, the test file includes: the method comprises the steps of enabling IP addresses of servers to be tested, names of engine software to be tested, cluster port numbers in each configuration area, engine cluster relations among the servers to be tested, GIS component dependency relations and database master-slave hot standby relations; testing the server to be deployed according to the test file, and judging whether the engine software to be deployed and GIS component dependent items of the server to be deployed are installed correctly, and whether the corresponding operation configuration scripts in the local server of the server to be deployed, the engine cluster relationship between the server to be deployed and other servers to be deployed in the same configuration area, the GIS component dependent relationship and the operation configuration scripts of the database master-slave hot standby relationship are configured correctly.

According to the IP address of the server to be tested, the name of the engine software to be tested, the cluster port numbers in each configuration area, the engine cluster relationship among the servers to be tested, the GIS component dependency relationship and the database master-slave hot standby relationship in the test file, verifying whether the engine software to be deployed and the GIS component dependency items of the servers to be deployed in the 7 areas are installed correctly, the corresponding operation configuration scripts in the local servers of the servers to be deployed, and whether the engine cluster relationship, the GIS component dependency relationship and the operation configuration scripts of the database master-slave hot standby relationship between the servers to be deployed and other servers to be deployed in the same configuration area are configured correctly, wherein the specific test process is as follows:

the engine software test file is defined as: testlist= { TestObject1, testObject2 ". . . TestObjectN }; where N is the number of test objects.

Defining test objects as: testobject= { EngineSoftware, masterIP, port, slaveIP [ ] }; where the EngineSoftware represents the engine Software or cluster under test, enginesoftware= { Software1, software2, … …, software n }, types include:

According to the IP in the TestObject, the accessibility of a server is tested by utilizing a ping command of 'ping-c $ { IP }, whether an engine software process exists or not is tested by utilizing a display ps and a search grep command of' ps-ef|grep $ { EngineSoftware } |grep-vgep ', and whether a specified Port is successfully opened is tested by utilizing' telnet $ { IP } $ { Port }.

(1) And when verifying whether the Nginx-Tomcat load balance is properly configured, the test module closes all Tomcat on the load balance area. And by starting the Tomcat of the slave server, whether the website can be normally accessed or not is tested to judge.

Testlist= { TestObject1, testObject2}, of the nmginx-Tomcat load balancing area is verified.

TestObject 1= { "nmginx-Tomcat", $ { MasterIP1}, $ { Port1}, $ { SlaveIP1}; wherein, $ { MasterIP1} is the IP address of the master node of the server to be deployed in the Nginx cluster, $ { Port1} is the Port of the master node of the server to be deployed in the cluster, and $ { SlaveIP1} is the IP address of the slave node of the server to be deployed in the Nginx cluster.

TestObject 2= { "nmginx-Tomcat", $ { MasterIP2}, $ { Port2}, $ { SlaveIP2}; wherein, $ { MasterIP2} is the IP address of the master node of the server to be deployed in the Tomcat cluster, $ { Port2} is the Port of the master node of the server to be deployed in the cluster, and $ { SlaveIP2} is the IP address of the slave node of the server to be deployed in the Tomcat cluster.

(2) When the GIS Portal and CDH Manager are verified to be correctly configured, the state result is returned through the engine software API, and whether the website can be normally accessed is judged.

Testlist= { TestObject1, testObject2} of the verification portal area.

TestObject 1= { "GIS Port", $ { IP }, $ { Port }, null }; wherein, $ { IP } is the node IP address of the server to be deployed by GISPortal, and, $ { Port } is the node Port of the server to be deployed by GIS Portal.

TestObject 2= { "CDH Manager", $ { IP }, $ { Port }, null }; wherein, $ { IP } is the node IP address of the CDH Manager to be deployed server, and, $ { Port } is the node Port of the CDH Manager to be deployed server.

(3) When verifying that the GeoServer cluster is properly configured, the status result is returned through GeoServer Cluster API, and whether options in the website Cluster Configuaration can be configured normally is judged.

Verify testlist= { TestObject }, of GeoServer hosting service area.

Testobject= { "GeoServer", $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

(4) When verifying whether the DME model algorithm is properly configured, judging whether the SpringCloud-Swagger interface can be normally accessed through the algorithm model polling.

Testlist= { TestObject1, testObject2}, validating DME model algorithm region.

TestObject 1= { "DME", $ { IP1}, $ { Port1}, null }; where, $ { IP1} is the node IP address of the GeoSparkME node where the server is to be deployed, $ { Port1} is the Port of the GeoSparkME node where the server is to be deployed.

TestObject 2= { "DME", $ { IP2}, $ { Port2}, null }; wherein, $ { IP2} is the node IP address of the server to be deployed by the OGIS DME, and, $ { Port2} is the node Port of the server to be deployed in the OGIS DME.

(5) When verifying Spark cluster and GIS dependency item configuration, returning a result through the CDH Manager API state to judge, if the result is that Green represents the cluster configuration success, the result is that Yellow represents the cluster configuration is effective but hidden danger exists, and the result is that Red represents the cluster configuration failure; the GIS dependent item configuration is judged by relying on Jar package API running results, and if the running results are True, the configuration is successful.

Verify the testlist= { TestObject1, testObject2} of the spatiotemporal analysis mining area.

TestObject 1= { "Spark", $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

TestObject 2= { "geomeasa", "georellis", "GeoTools", "GDAL" }, $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

(6) And when verifying whether the PostgreSQL master-slave hot standby cluster and the PostGIS dependency are properly configured, randomly generating a database name TestDB according to the current system time. According to MasterIP in TestObject, a database named TestDB is created on a server serving as a host. Checking the name of the database on a server serving as a slave according to SalveIP in the TestObject, and if the database contains TestDB, proving that the master-slave mode configuration is successful; the PostGIS dependency is judged by creating a PostGIS database in the PostgreSQL, and if the PostGIS database is successfully created, the PostGIS dependency configuration is proved to be successful.

Testlist= { TestObject }, of the validation database service area.

Testobject= { "PostgreMS", $ { MasterIP }, $ { Port }, $ { SlaveIP }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the PostgreSQL cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, and $ { SlaveIP } is the IP address of the slave node of the server to be deployed in the PostgreSQL cluster.

(7) When verifying whether the HDFS, HBase, mongoDB, elasticSearch cluster is correctly configured, the test module returns a result through the CDH Manager API state to judge that if the result is that Green represents that the cluster configuration is successful, the result is that Yellow represents that the cluster configuration is effective but hidden danger exists, and the result is that Red represents that the cluster configuration fails.

Testlist= { TestObject1, testObject2, testObject3, testObject4} of the spatiotemporal storage area is verified.

TestObject 1= { "HDFS", $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

TestObject 2= { "HBase", $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

TestObject 3= { "mongdb", $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

TestObject 4= { "elastic search", $ { MasterIP }, $ { Port }, $ { SlaveIP [ ] { SlaveIP1, slaveIP2 }; wherein, $ { MasterIP } is the IP address of the master node of the server to be deployed in the cluster, $ { Port } is the Port of the master node of the server to be deployed in the cluster, $ { SlaveIP [ ] }, is the IP address array of the slave node of the server to be deployed in the cluster.

After the deployment and configuration process is finished, accuracy and availability detection is automatically performed according to the TestList. If the deployment and configuration process is successfully executed, the deployment and configuration process is successfully executed according to the deployment file and the configuration file on behalf of each server to be deployed, and the software installation and configuration condition of each server does not need to be checked manually. If errors are encountered in the execution process, the deployment module initializes the wrong server, and re-executes the deployment and configuration process until the maximum retry number is automatically detected or exceeded.

From the above, the invention provides a deployment method of space-time big data engines, which does not need operation and maintenance personnel to install and configure the operation environment on each server one by one according to the prepared engine software package and GIS component dependency items, only needs to obtain the deployment file and the configuration file of the space-time big data engine, controls each server to be deployed to automatically install the needed engine software package and GIS component dependency items according to the deployment file and the configuration file, and configures the operation environment, so that the deployment of the space-time big data engines can be completed, the automatic deployment of the space-time big data engines is realized, and the deployment efficiency and the accuracy of the space-time big data engines are improved.

Example two

Referring to fig. 3, a schematic structural diagram of a deployment device of a spatio-temporal big data engine according to an embodiment of the present invention is shown, where the device includes: the system comprises a file acquisition module, an installation package sending module, a deployment environment setting module, an installation package installation module, a target operation configuration script extraction module and an operation environment configuration module;

Preferably, the deployment device of the spatio-temporal big data engine further comprises: a test module;

Example III

Accordingly, an embodiment of the present invention provides a deployment device of a spatio-temporal big data engine, where the device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the deployment method of the spatio-temporal big data engine according to the embodiment of the present invention when the processor executes the computer program.

Example IV

Accordingly, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, and when the computer program runs, the device where the computer readable storage medium is located is controlled to execute the deployment method of the spatio-temporal big data engine described in the embodiment of the present invention.

In summary, the invention provides a deployment device, equipment and storage medium for a space-time big data engine, which do not need operation and maintenance personnel to manually install and configure an operation environment on each server one by one according to a prepared engine software package and GIS component dependency items, only acquire deployment files and configuration files of the space-time big data engine, control the engine software package and GIS component dependency items required by each server to be deployed to be automatically installed according to the deployment files and configuration files, and configure the operation environment, so that the deployment of the space-time big data engine can be completed, the automatic deployment of the space-time big data engine is realized, and the deployment efficiency and accuracy of the space-time big data engine are improved.

It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

It will be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor, a memory.

The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is a control center of the device, connecting the various parts of the overall device using various interfaces and lines.

The memory may be used to store the computer program, and the processor may implement various functions of the device by running or executing the computer program stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.

The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium, and when executed by a processor, the computer program can implement the steps of the above-mentioned method embodiments. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.

Claims

1. A method for deploying a spatio-temporal big data engine, comprising:

2. The deployment method of the spatio-temporal big data engine according to claim 1, wherein the configuration area includes: the system comprises a load balancing layer, a portal layer, a hosting service layer, a model algorithm layer, a space-time analysis mining layer, a database service layer and a space-time storage layer.

3. The deployment method of the spatio-temporal big data engine according to claim 1, wherein the deployment environment setting instruction includes: creating a user and user group instruction, a node clock synchronization instruction, a server firewall closing instruction, a firewall start-up prohibition self-starting instruction, a Selinux closing instruction, a configuration file NFS shared directory instruction, a local source warehouse instruction, a limit setting instruction and a VM.Swappness setting instruction;

closing the Selinux of the server according to the Selinux closing instruction;

setting Limits of the server according to the Limits setting instruction;

4. The deployment method of the spatio-temporal big data engine of claim 2, wherein the install instruction further comprises: oracle JDK installation package;

5. The deployment method of the spatio-temporal big data engine of claim 4, wherein the target running configuration script comprises: a first running configuration script and a second running configuration script;

6. The deployment method of the spatio-temporal big data engine according to claim 5, further comprising, after the server to be deployed configures the corresponding running environment in the corresponding configuration area according to the target running configuration script:

7. A deployment apparatus for a spatio-temporal big data engine, comprising: the system comprises a file acquisition module, an installation package sending module, a deployment environment setting module, an installation package installation module, a target operation configuration script extraction module and an operation environment configuration module;

8. The deployment apparatus of the spatiotemporal big data engine of claim 7, further comprising: a test module;

9. A deployment device of a spatiotemporal big data engine, characterized by comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the deployment method of a spatiotemporal big data engine according to any of claims 1 to 6 when executing the computer program.

10. A storage medium comprising a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium resides to perform the deployment method of the spatiotemporal big data engine of any of claims 1 to 6.