CN106372256A - Distributed storage method for massive Argo data - Google Patents
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- CN106372256A CN106372256A CN201610873026.6A CN201610873026A CN106372256A CN 106372256 A CN106372256 A CN 106372256A CN 201610873026 A CN201610873026 A CN 201610873026A CN 106372256 A CN106372256 A CN 106372256A
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- G06F16/24—Querying
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- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
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Abstract
The invention discloses a distributed storage method for massive Argo data. The method can be used for achieving high-efficient storage of multi-source heterogeneous, dynamic and multidimensional and massive Argo data. The method comprises the following steps: 1) a reasonable table structure is designed, 2) different data organizations and management modes are used according to different characteristics and application demands of the Argo data, 3) based on HDFS, distributed storage of the massive Argo data is realized and automatic load balancing is further realized, in data transmission, a system uses the technology of combining HDFS multiple nodes and virtual IP to solve the problem that multiple nodes cannot communicate with the outside simultaneously; 4) the massive Argo data are automatically stored in distributed cloud storage. The distributed storage method for the massive Argo data disclosed by the invention has important practical application values in Argo data management, and is further wider in application prospects.
Description
Technical field
The present invention relates to the storage method of argo data is and in particular to including metadata, information products, cut open to dissimilar
The storage of the magnanimity argo data of face data etc., plays larger practical application in argo data storage.
Background technology
International argo plan is the World Oceans observation released in 1998 by the scientist of the U.S., the state such as French and Japanese
Plan.Plan formal startup the in 2000 to implement, end in March, 2012, in World Oceans, existing more than 3500 argo section floats
It is marked on normal work.
China, since adding international argo plan, under the hosting energetically of national correlation department, has achieved great entering
Exhibition, and under the support of the Department of Science and Technology and National Bureau of Oceanography, rely on the Second Institute of Oceanograghy,SOA's satellite ocean environment
Kinetics National Key Laboratory, sets up in 2002 that " Chinese argo real time data " center " has been born China argo and floated
Target lay and its observational data reception, and the task such as collection, process and the distribution of global argo data, so that China is become
9 in the world (U.S., Britain, France, Japan, Korea S, India, Australia, Canada and China) have the ability to the whole world
Argo data center uploads one of country of argo data in real time；Up to now, center have collected -2014 years in January, 1996
During November, more than 11000 argo buoy laying on global ocean of each world argo plan members state obtained about 110
Ten thousand Inversion phenomenon of remaininging observe sectional datas.Argo data information has become as and grinds to Global Scale physical oceanography from sea basin yardstick
The key data source studied carefully, and be widely used in terms of current marine environment and weather Changeement and business.
Meanwhile, Western Pacific is the important sea area of impact China's ocean circulation and climate change, is the master of warm pool distribution again
Want the cradle of region, typhoon and Kuroshio, this sea area has the Annual variations feature of strong ocean and air and strong west
Boundary flows, and is also the crucial sea area being related to China's national security.Npoce (the northwest Pacific ocean circulation held in 2007
Test) in international Conference it has been determined that western boundary current Fu Ju area is the crucial sea area of climatic study, and western boundary current spoke is poly-
Area is also the important area directly affecting Kuroshio source ground and Our country's Climate, is to connect spice (PSW ocean circulation
With climatic test plan) and the great investigation plan such as pacswin (Indonesia's insertion flowing water source test plan) tie.Cause
This, strengthen the Real-Time Ocean investigation to this region, and obtain the firsthand information, is not only ocean and the need of atmospheric science development
Will, it is also the Important Action that China tackles climate change, even more safeguard maritime rights and interests, guarantee the necessary handss of marine safety
Section.
Argo data volume is very huge at present, and the As time goes on continuous input with new float, and quantity will not
Disconnected growth, the argo metadata of the suitable mass data library storage of needs foundation, deployment information, data acquisition, data update and dimension
The technical specification system of aspect and the sharing policies such as shield, to ensure effective storage of magnanimity argo data and efficient reading.
Content of the invention
The purpose of the present invention is the problem for overcoming prior art presence, provides a kind of distribution towards magnanimity argo data
Formula storage method.
Towards the distributed storage method of magnanimity argo data, comprise the steps:
1) it is directed to argo data form, foreground query composition requires, several table structures of big data increment Demand Design；
2) different data organization and management modes are adopted for multi-source argo data: map slice of data is with image gold word
Tower file form is organized；Argo cross-sectional data and argo metadata are in the form of table record in postgresql
Organize respectively；Argo information products gridded data is organized with document form after being visualized based on matlab；
3) based on the hdfs of hadoop, by above-mentioned multi-source argo file and folder data with different tub of tissue
Reason mode is stored beyond the clouds, stores in corresponding table record insertion postgresql data base；
4) it is directed to different types of data setting different warehouse-in step, the datamation of magnanimity argo is parsed and is stored in distribution
In formula cloud storage.
In such scheme, each step can adopt following optimal way:
Described step 1) specifically include:
2.1) argo profiling observation data is included to argo file, metadata is torn open in the data of interior different-format
Office is managed；
2.2) 5 relation tables are established in data base to store classifiedly different information, comprising: metadata table stores
The related metadata information of argo, provides and is combined inquiring about by argo type, communication mode etc.；Argo deployment information table stores
The related information of argo deployment, provides information, affiliated area information the longitude and latitude area by input marine site of argo belonging country
Domain is inquired about；Argo profile information table stores argo profile information, provides the essential information of argo, by buoy number and first number
According to table and deployment information table Connection inquiring, and it is combined inquiring about by buoy number, date and longitude and latitude range information；Argo is detailed
Observation tables of data stores the detailed observation data of argo profiling observation, provides the detailed observation data of argo cross-sectional data, single
Or the value inquiry of multiple observed parameter；The field that data query view is drawn using empty table, combination multiple queries condition, comprises to float
Mark wmo numbering, buoy type, buoy are discharged time, buoy observation date, release platform, latitude, longitude and are observed data in detail
Information.
Described step 2) in:
Map slice of data, under the same space reference, the base map of multistage different resolution is cut into m × n number of slices
According to being stored and shown, formed resolution from low to high, the ascending hierarchical data structure form of slice of data amount；
The form that argo cross-sectional data and argo metadata are passed through to record attribute data is organized, deposit into
In postgresql attribute database, after data loading, client applications is connect by unified data access to argo data
Data in mouth each tables of data to data base operates；
Argo information products gridded data utilizes matlab drawing technique to generate corresponding information products, in Put on file
Externally issued using Internet Information Service manager afterwards, and corresponding metadata information is stored, argo information is produced
The process of product gridded data warehouse-in is divided into three steps: the first step is to read the data of all kinds form, extracts target information, right
The form of database metadata table record should be formed；Second step be to extract data reorganize, eliminate one-to-many or
The relation of multi-to-multi, sets up the incidence relation between multiple tables and table；3rd step is that data is stored in data base, attribute number one by one
According to being stored in metadata table.
Described step 4) in, adopt based on hadoop hdfs basic structure, a hdfs cluster is by one
The datanodes composition of namenode and some；, as central server, the name managing file system is empty for namenode
Between and the access to file for the client；Datanode in cluster is responsible for the storage on its place node；In hdfs, literary composition
Part is divided into one or more data blocks, and is stored on one group of datanode；The name that namenode executes file system is empty
Between operate, simultaneously be responsible for determine data block to concrete datanode node mapping；Datanode is responsible for processing file system visitor
The read-write requests at family end, and carry out establishment, deletion and the duplication of data block under the United Dispatching of namenode；In data transfer
During, by the way of hdfs multinode is combined with virtual ip.
The present invention compared with prior art has the advantages that
(1) extend traditional argo storage method, be that argo storage work provides a kind of new form, and be easy to logical
Cross to be programmed on software platform and carry out model development realization.
(2) the multi-source heterogeneous, dynamic of argo marine environment synthetic data is effectively solved using distributed storage method
Complexity that the characteristic of multidimensional and magnanimity is brought is it is achieved that the efficient storage of magnanimity argo data.
Brief description
Fig. 1 is a kind of techniqueflow schematic diagram realizing the present invention；
Fig. 2 is argo initial data, database table, java class corresponding diagram.
Specific embodiment
Below in conjunction with the accompanying drawings the present invention is further elaborated.
As shown in figure 1, a kind of distributed storage method towards magnanimity argo data, comprise the steps:
The first step: for argo data form, foreground query composition requires, big data several tables of increment Demand Design are tied
Structure.Specifically, first, argo file is included argo profiling observation data, metadata interior different-format data
Carry out deconsolidation process；Then 5 relation tables are established in data base to store classifiedly different information, comprising: metadata table
The related metadata information of storage argo, provides and is combined inquiring about by argo type, communication mode etc.；Argo deployment information table
The related information of storage argo deployment, provides information, affiliated area information the longitude and latitude by input marine site of argo belonging country
Degree region is inquired about；Argo profile information table store argo profile information, the essential information of argo is provided, by buoy number with
Metadata table and deployment information table Connection inquiring, and be combined inquiring about by buoy number, date and longitude and latitude range information；argo
In detail observation tables of data stores the detailed observation data of argo profiling observation, provide argo cross-sectional data detailed observation data,
The value inquiry of single or multiple observed parameter；Data query view: this storage table is an empty table, combination multiple queries condition obtains
The field going out, is realized being separated with physical table in logic, facilitates data query and management, contains buoy wmo numbering, buoy class
Type, buoy discharge time, buoy observation date, release platform, latitude, longitude, in detail observation data message etc..In the present embodiment
Table structure is as shown in Figure 2.
Second step: adopt different data organization and management modes for multi-source argo data: map slice of data is with shadow
As pyramid file form is organized；Argo cross-sectional data and argo metadata in the form of table record
Organize respectively in postgresql；Argo information products gridded data is entered with document form after being visualized based on matlab
Row tissue.Specifically:
(1) map slice of data: the tissue of map slice of data and storage mode adopt pyramid structure.I.e. in same sky
Between with reference under, the base map of multistage different resolution is cut into m × n slice of data and is stored and show, formation resolution
From low to high, the ascending hierarchical data structure form of slice of data amount.This organizational structure is used for progressive picture and transmits,
This is also beneficial to the efficient loading of base map data for webgis application.
In pyramid structure, each layer data belongs to same resolution, and presses layer tissue formation file one by one
Clip directory form, store under every layer of file is all base map slice of datas of this resolution.Additionally, its slice of data
To represent the relative indexing relation between data slicer according to certain naming rule, to can achieve calling by width of peer's slice of data
And automatic Mosaic, and the calling by level of different stage slice of data.
(2) argo cross-sectional data and argo metadata: in view of the multiformity of argo data information and complexity, the present invention from
The practical application request of data information is set out, and data is reorganized, in order to avoid causing very big data redundancy.In the present invention
In, through the data source of pretreatment, corresponding parsing and reading manner are selected according to itself specific form, and reading is obtained
The form that can store in data base of data genaration, then data loading is realized by the data access interface of postgresql
Process.Attribute data is mainly organized in the form of recording by data organizational process, deposits into postgresql attribute number
According in storehouse.After data loading, client applications just can pass through unified data access interface to data base to argo data
Data in each tables of data is operated, and including reading, adds, deletes and updates etc..
(3) argo information products gridded data: although partial structured data has fixing data form, according to
Available data form cannot directly carry out effective network visualization, therefore needs to carry out pretreatment while data loading,
Utilize matlab drawing technique to generate corresponding information products, utilize Internet Information Service manager after Put on file
(iis) externally issue, and corresponding metadata information is effectively stored according to design of database.Argo information is produced
The process of product gridded data warehouse-in is divided into three steps: the first step is to read the data of all kinds form, extracts target information, right
The form of database metadata table record should be formed；Second step be to extract data reorganize, eliminate one-to-many or
The relation of multi-to-multi, sets up the incidence relation between multiple tables and table；3rd step is that data is stored in data base, attribute number one by one
According to being stored in metadata table.
3rd step: based on the hdfs of hadoop, by above-mentioned multi-source argo file and folder data with different
Organization and administration mode is stored beyond the clouds, stores, to realize argo sea in corresponding table record insertion postgresql data base
The distributed storage function of amount data, and it is automatically obtained load balancing.In the data transmission, system employ hdfs multinode with
The mode that virtual ip combines, solves the problems, such as that multinode cannot communication with the outside world simultaneously.
4) adopt based on hadoop hdfs basic structure, hdfs cluster is by a namenode and certain
The datanodes composition of number；Namenode, as central server, manages the name space (namespace) of file system
And the access to file for the client；Datanode in cluster is usually a node one, is responsible for its place node
On storage；Hdfs exposes the name space of file system, and user can be in the form of a file in data storage above.From interior
Portion is taken a fancy to, and a file is divided into and is divided into one or more data blocks, and these blocks are simultaneously stored on one group of datanode；
Namenode executes the namespace operation of file system, such as opens, closes, Rename file or catalogue；It also bears simultaneously
Duty determines data block to the mapping of concrete datanode node；The read-write that datanode is responsible for processing file system client please
Ask, and carry out establishment, deletion and the duplication of data block under the United Dispatching of namenode.Therefore, for different types of data
The datamation of magnanimity argo can be parsed and be stored in distributed cloud storage by setting different warehouse-in step.
Claims (4)
1. a kind of distributed storage method towards magnanimity argo data is it is characterised in that comprise the steps:
1) it is directed to argo data form, foreground query composition requires, several table structures of big data increment Demand Design；
2) different data organization and management modes are adopted for multi-source argo data: map slice of data is with image pyramid literary composition
Part folder form is organized；Argo cross-sectional data and argo metadata difference in postgresql in the form of table record
Tissue；Argo information products gridded data is organized with document form after being visualized based on matlab；
3) based on the hdfs of hadoop, by above-mentioned multi-source argo file and folder data with different organization and administration sides
Formula is stored beyond the clouds, stores in corresponding table record insertion postgresql data base；
4) it is directed to different types of data setting different warehouse-in step, the datamation of magnanimity argo is parsed and is stored in distributed cloud
In storage.
2. a kind of distributed storage method towards magnanimity argo data according to claim 1 is it is characterised in that described
Step 1) include:
2.1) argo file is included with argo profiling observation data, metadata is carried out at fractionation in the data of interior different-format
Reason；
2.2) 5 relation tables are established in data base to store classifiedly different information, comprising: metadata table stores argo phase
The metadata information closing, provides and is combined inquiring about by argo type, communication mode etc.；Argo deployment information table stores argo portion
The related information of administration, provides information, affiliated area information the longitude and latitude region by input marine site of argo belonging country to carry out
Inquiry；Argo profile information table stores argo profile information, provides the essential information of argo, by buoy number and metadata table and
Deployment information table Connection inquiring, and be combined inquiring about by buoy number, date and longitude and latitude range information；Argo observes number in detail
Store the detailed observation data of argo profiling observation according to table, the detailed observation data of argo cross-sectional data, single or multiple is provided
The value inquiry of observed parameter；The field that data query view is drawn using empty table, combination multiple queries condition, comprises buoy wmo
Numbering, buoy type, buoy are discharged time, buoy observation date, release platform, latitude, longitude and are observed data message in detail.
3. a kind of distributed storage method towards magnanimity argo data according to claim 1 is it is characterised in that described
Step 2) in:
Map slice of data, under the same space reference, the base map of multistage different resolution is cut into m × n slice of data and enters
Row storage and display, formed resolution from low to high, the ascending hierarchical data structure form of slice of data amount；
The form that argo cross-sectional data and argo metadata are passed through to record attribute data is organized, deposit into
In postgresql attribute database, after data loading, client applications is connect by unified data access to argo data
Data in mouth each tables of data to data base operates；
Argo information products gridded data utilizes matlab drawing technique to generate corresponding information products, profit after Put on file
Externally issued with Internet Information Service manager, and corresponding metadata information is stored, argo information products net
The process of data loading of formatting is divided into three steps: the first step is to read the data of all kinds form, extracts target information, corresponding shape
Become the form of database metadata table record；Second step is that the data extracting is reorganized, and eliminates one-to-many or multipair
Many relations, set up the incidence relation between multiple tables and table；3rd step is that data is stored in data base one by one, and attribute data is deposited
It is stored in metadata table.
4. a kind of distributed storage method towards magnanimity argo data according to claim 1 is it is characterised in that described
Step 4) in, adopt based on hadoop hdfs basic structure, a hdfs cluster is by a namenode and
Fixed number purpose datanodes forms；Namenode, as central server, manages name space and the client of file system
Access to file；Datanode in cluster is responsible for the storage on its place node；In hdfs, file is divided into one
Or multiple data block, and it is stored on one group of datanode；Namenode executes the namespace operation of file system, simultaneously
It is responsible for determining data block to the mapping of concrete datanode node；The read-write that datanode is responsible for processing file system client please
Ask, and carry out establishment, deletion and the duplication of data block under the United Dispatching of namenode；In data transmission procedure, adopt
The mode that hdfs multinode is combined with virtual ip.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107844522A (en) * | 2017-09-30 | 2018-03-27 | 邓娥 | A kind of destination object display methods, device and electronic equipment |
| CN108023953A (en) * | 2017-12-04 | 2018-05-11 | 北京小度信息科技有限公司 | The high availability implementation method and device of FTP service |
| CN108802854A (en) * | 2018-04-12 | 2018-11-13 | 国家海洋局第二海洋研究所 | A method of nearly surface layer flux is calculated based on Argos drifting buoys |
| CN108829918A (en) * | 2018-04-28 | 2018-11-16 | 中国海洋大学 | Intelligent buoy networking simulating method and system towards oceanographic phenomena |
| CN109344156A (en) * | 2018-09-03 | 2019-02-15 | 中国农业大学 | Magnanimity multi-source meteorological measuring distributed storage method and device |
| CN110377598A (en) * | 2018-04-11 | 2019-10-25 | 西安邮电大学 | A kind of multi-source heterogeneous date storage method based on intelligence manufacture process |
| CN112231411A (en) * | 2020-11-05 | 2021-01-15 | 中国计量大学 | Cloud storage method for multi-element heterogeneous silk legacy data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901275A (en) * | 2010-08-23 | 2010-12-01 | 华中科技大学 | Distributed storage system and method thereof |
| CN102446208A (en) * | 2011-09-02 | 2012-05-09 | 华东师范大学 | Distributed massive remote sensing image-based algorithm for quickly establishing pyramid |
| CN104572862A (en) * | 2014-12-19 | 2015-04-29 | 阳珍秀 | Mass data storage access method and system |
| CN105069703A (en) * | 2015-08-10 | 2015-11-18 | 国家电网公司 | Mass data management method of power grid |
| CN105930381A (en) * | 2016-04-13 | 2016-09-07 | 国家海洋局第二海洋研究所 | Global Argo data storage and update method based on mixed database architecture |
-
2016
- 2016-09-30 CN CN201610873026.6A patent/CN106372256A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901275A (en) * | 2010-08-23 | 2010-12-01 | 华中科技大学 | Distributed storage system and method thereof |
| CN102446208A (en) * | 2011-09-02 | 2012-05-09 | 华东师范大学 | Distributed massive remote sensing image-based algorithm for quickly establishing pyramid |
| CN104572862A (en) * | 2014-12-19 | 2015-04-29 | 阳珍秀 | Mass data storage access method and system |
| CN105069703A (en) * | 2015-08-10 | 2015-11-18 | 国家电网公司 | Mass data management method of power grid |
| CN105930381A (en) * | 2016-04-13 | 2016-09-07 | 国家海洋局第二海洋研究所 | Global Argo data storage and update method based on mixed database architecture |
Non-Patent Citations (2)
| Title |
|---|
| 胡伟忠: "海量海洋数据一体化管理研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
| 高蓟超: "Hadoop平台存储策略的研究与优化", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107844522A (en) * | 2017-09-30 | 2018-03-27 | 邓娥 | A kind of destination object display methods, device and electronic equipment |
| CN107844522B (en) * | 2017-09-30 | 2023-02-17 | 邓娥 | Target object display method and device and electronic equipment |
| CN108023953A (en) * | 2017-12-04 | 2018-05-11 | 北京小度信息科技有限公司 | The high availability implementation method and device of FTP service |
| CN110377598A (en) * | 2018-04-11 | 2019-10-25 | 西安邮电大学 | A kind of multi-source heterogeneous date storage method based on intelligence manufacture process |
| CN110377598B (en) * | 2018-04-11 | 2023-04-07 | 西安邮电大学 | Multi-source heterogeneous data storage method based on intelligent manufacturing process |
| CN108802854A (en) * | 2018-04-12 | 2018-11-13 | 国家海洋局第二海洋研究所 | A method of nearly surface layer flux is calculated based on Argos drifting buoys |
| CN108802854B (en) * | 2018-04-12 | 2020-11-06 | 自然资源部第二海洋研究所 | Method for calculating near-surface flux based on Argos drifting buoy |
| CN108829918A (en) * | 2018-04-28 | 2018-11-16 | 中国海洋大学 | Intelligent buoy networking simulating method and system towards oceanographic phenomena |
| CN108829918B (en) * | 2018-04-28 | 2020-08-11 | 中国海洋大学 | Intelligent buoy networking simulation method and system for ocean phenomenon |
| CN109344156A (en) * | 2018-09-03 | 2019-02-15 | 中国农业大学 | Magnanimity multi-source meteorological measuring distributed storage method and device |
| CN112231411A (en) * | 2020-11-05 | 2021-01-15 | 中国计量大学 | Cloud storage method for multi-element heterogeneous silk legacy data |
| CN112231411B (en) * | 2020-11-05 | 2022-04-19 | 中国计量大学 | Cloud storage method for multi-element heterogeneous silk legacy data |
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