CN110413571A - Based on the extensive remote sensing image data distributed storage method of MongoDB - Google Patents
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Abstract
The invention discloses be based on the extensive remote sensing image data distributed storage method of MongoDB, the following steps are included: using allocation methods, it constructs remotely-sensed data distributed storage frame based on MongoDB, judge whether there is remote sensing image data file of the same name, by two set of remote sensing image data file storage to GridFS file memory mechanism, carry out fragment to data, by remote sensing image data storage on the different server into cluster, search remote sensing image data；The present invention is by building MongoDB fragment aggregated structure, remote sensing image data is stored using GridFS file memory mechanism, the read or write speed for overcoming conventional store method is slow, horizontal direction extension is difficult, the problems such as especially storing and accessing inefficiency under mass data background, can be extending transversely to database progress, it is more suitable for managing extensive remote sensing image data.
Description
Technical field
The present invention relates to remotely-sensed data storing technology fields, more particularly to are based on the extensive remote sensing image data of MongoDB
Distributed storage method.
Background technique
With the rapid development of remote sensing technology, the communication technology and computer technology, the mankind enter big data era, data
Explosive growth is presented, remotely-sensed data is as a kind of spatial information carrier, the characteristics of because of its strong timeliness and coverage count,
It plays an important role in various fields such as agricultural, forestry, disaster monitoring, military and national defenses.In face of the remotely-sensed data of magnanimity, such as
What carries out efficient storage and management, becomes current problem in the urgent need to address.
The storage mode of remote sensing image mainly includes following several at present: (1) file system is in conjunction with relevant database
Storage mode the description information of data is stored in relevant database that is, by image data storage to storage equipment；
(2) based on the storage mode of relevant database, image data is with the storage of blob data type；(3) depositing based on file system
Storage mode, image data are saved in storage equipment, are organized by file mode to it.But with image number
According to being gradually increased for amount, relational database is increasingly obvious in the problems in extensive spatial data management, including read or write speed it is slow,
Horizontal direction extends difficulty etc., and it is particularly problematic especially to store and access inefficiency under mass data background, uncomfortable
The data management under big data background is closed, in addition, the storage mode based on file system can also bring to obtain the process of data
Inconvenience, data search are difficult.Therefore, the present invention proposes to be based on the extensive remote sensing image data distributed storage method of MongoDB,
To solve shortcoming in the prior art.
Summary of the invention
In view of the above-mentioned problems, the present invention proposes to be based on the extensive remote sensing image data distributed storage method of MongoDB, lead to
It crosses and builds MongoDB fragment aggregated structure, a kind of extensive remote sensing image data distributed storage method is proposed, using GridFS
File memory mechanism stores remote sensing image data, of the invention based on the extensive remote sensing image data distribution of MongoDB
Formula storage method can overcome under the slow read or write speed of conventional store method, horizontal direction extension difficulty and mass data background
The problems such as storing and accessing inefficiency, it is extending transversely to database progress, it is more suitable for managing extensive remote sensing image data.
The present invention proposes to be based on the extensive remote sensing image data distributed storage method of MongoDB, comprising the following steps:
Step 1: using allocation methods, constructs the remotely-sensed data distributed storage frame based on MongoDB, is then directed to
Remote sensing image data to be stored, retrieval whether there is remote sensing image data file of the same name in the database；
Step 2: when there are when remote sensing image data file of the same name, terminate remote sensing image data storage operation in database；
Step 3: when remote sensing image data file of the same name is not present in database, using GridFS file memory mechanism
Store remote sensing image data；
Step 4: by two set of remote sensing image data file storage to GridFS file memory mechanism, two collect
Conjunction is respectively designated as rs.files and rs.chunks；
Step 5: selecting " files_id " for piece key in rs.chunks set, carries out fragment, remote sensing image to data
Data are stored on the different server in cluster, and rs.files set completes remote sensing image data storage without fragment；
Step 6: when inquiring the remote sensing image data of storage, existed first according to given remotely-sensed data file name
Rs.files set in searched, determine " _ id " field of document, according to rs.files gather in " _ id " field with
The corresponding relationship of " files_id " field in rs.chunks set, determines the codomain of " n " in rs.chunks set, according to " n "
Value sequence takes out image data file.
Further improvement lies in that: the remotely-sensed data distributed storage frame in the step 1 based on MongoDB is by fragment
Server, routing server and configuration server are built-up, and the sliced service device is for storing actual data, the road
By server for the request that client is sent to be addressed and positioned, the configuration server is for storing routing and fragment
Configuration information.
Further improvement lies in that: routing is the entrance of client request data-base cluster in the routing server, is owned
Request will be coordinated by mongos, and corresponding request of data is forwarded on corresponding sliced service device.
Further improvement lies in that: high availability and data consistency in order to guarantee data, in the sliced service device
Using the scheme of " duplication collection+fragment " in data fragmentation, each fragment of the sliced service device is by one group of mongod example
An arbitration node is arranged in the duplication collection of composition in each fragment, for selecting new host node when host delay machine.
Further improvement lies in that: rs.files gathers the metamessage for storing remote sensing image data in the step 4,
Rs.chunks gathers the binary data for storing remote sensing image.
Further improvement lies in that: " _ id " is expressed as the unique identification of each document in the step 6.
Further improvement lies in that: the value of " files_id " is opposite with the value of " _ id " in rs.files in the step 6
It answers, wherein the key in rs.files set includes " _ id ", " length ", " chunksize ", " uploadDate " and " md5 ",
The meaning of each key in rs.files set are as follows: the unique identification of each remote sensing image data file of " _ id " expression,
Byte number, " chunksize " that " length " indicates that remote sensing image data file includes indicate composition remote sensing image data file
Each of piece size, " uploadDate " indicate that the uplink time of remote sensing image data file, " md5 " indicate remote sensing image number
According to MD5 check value.
Further improvement lies in that: in the step 6 when remote sensing image data file size is greater than the value of chunksize,
Remote sensing image data file is divided into multiple pieces according to the size of chunksize, each block number evidence is stored in rs.chunks
In multiple documents of set, finally the metamessage of remote sensing image data file is stored in rs.files set, rs.files
" _ the id " of set is corresponding with " files_id " that rs.chunks gathers, when only one remote sensing image data file,
Only one document in rs.files set.
The invention has the benefit that invention proposes a kind of extensive remote sensing by building MongoDB fragment aggregated structure
Image data distributed storage method stores remote sensing image data using GridFS file memory mechanism, of the invention
Based on the extensive remote sensing image data distributed storage method of MongoDB can overcome the read or write speed of conventional store method it is slow,
The problems such as storing and accessing inefficiency under horizontal direction extension difficulty and mass data background carries out lateral expansion to database
Exhibition, is more suitable for managing extensive remote sensing image data.
Detailed description of the invention
Fig. 1 is the method for the present invention flow diagram.
Fig. 2 is the remotely-sensed data distributed storage circuit theory schematic diagram based on MongoDB in the present invention.
Fig. 3 is clustered deploy(ment) situation schematic diagram in the embodiment of the present invention.
Fig. 4 is that disparate databases image data is inserted into time comparing result schematic diagram in the embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
According to Fig. 1,2,3,4, the present embodiment proposes to be based on the extensive remote sensing image data distributed storage of MongoDB
Method, comprising the following steps:
Collect the U.S. USGS landsat images landsat5 and landsat8, European Space Agency's ESA sentry's data
The wind and cloud data of sentinel1/2/3 and China Satecom's Meteorological Center, detailed data information are as shown in table 1:
Table 1
Step 1: using allocation methods, constructs the remotely-sensed data distributed storage frame based on MongoDB, is then directed to
Remote sensing image data to be stored, retrieval whether there is remote sensing image data file of the same name in the database, be based on MongoDB
Remotely-sensed data distributed storage frame be made of sliced service device, routing server and configuration server, the sliced service
Device is for storing actual data, in order to guarantee the high availability and data consistency of data, in the number of the sliced service device
According to, using the scheme of " duplication collection+fragment ", each fragment of the sliced service device is by one group of mongod example structure in fragment
At duplication collection, an arbitration node is set in each fragment, for selecting new host node, the road when host delay machine
By server for the request that client is sent to be addressed and positioned, routing is client request data in routing server
The entrance of library cluster, all requests will be coordinated by mongos, and corresponding request of data is forwarded to corresponding sliced service
On device, the configuration server is used to store the configuration information of routing and fragment；
Step 2: when there are when remote sensing image data file of the same name, terminate remote sensing image data storage operation in database；
Step 3: when remote sensing image data file of the same name is not present in database, using GridFS file memory mechanism
Store remote sensing image data；
Step 4: by two set of remote sensing image data file storage to GridFS file memory mechanism, two collect
Conjunction is respectively designated as rs.files and rs.chunks, and rs.files gathers the metamessage for storing remote sensing image data,
Rs.chunks gathers the binary data for storing remote sensing image；
Step 5: selecting " files_id " for piece key in rs.chunks set, carries out fragment, remote sensing image to data
Data are stored on the different server in cluster, and rs.files set completes remote sensing image data storage without fragment；
Step 6: when inquiring the remote sensing image data of storage, existed first according to given remotely-sensed data file name
Rs.files set in searched, determine " _ id " field of document, according to rs.files gather in " _ id " field with
The corresponding relationship of " files_id " field in rs.chunks set, determines the codomain of " n " in rs.chunks set, according to " n "
Value sequence takes out image data file, and " _ id " is expressed as the unique identification of each document, the value and rs.files of " files_id "
The value of " _ id " is corresponding in set, wherein rs.files set in key include " _ id ", " length ", " chunksize ",
" uploadDate " and " md5 ", the meaning of each key in rs.files set are as follows: " _ id " indicates each remote sensing image data
Byte number, " chunksize " that the unique identification of file, " length " indicate that remote sensing image data file includes indicate that composition is distant
Feel the size of each of image data file piece, uplink time, " md5 " of " uploadDate " expression remote sensing image data file
The MD5 check value for indicating remote sensing image data, when remote sensing image data file size is greater than the value of chunksize, by remote sensing
Image data file is divided into multiple pieces according to the size of chunksize, and each block number evidence is stored in rs.chunks set
In multiple documents, finally the metamessage of remote sensing image data file is stored in rs.files set, rs.files set
" _ id " is corresponding with " files_id " that rs.chunks gathers, when only one remote sensing image data file, rs.files
Only one document in set.
The performance of remotely-sensed data distributed storage method storage binary file based on MongoDB is verified, is selected
Selecting relevant database PostgreSQL and MongoDB database, group compares as a comparison, and wherein PostgreSQL is function
Open source Object-Relational Database Management System that can be most powerful, can support numerous types of data and interface, to MongoDB and
The remote sensing image data of different data amount is inserted into PostgreSQL database, the average time-consuming for calculating insertion is obtained such as Fig. 4
Experimental result, the results showed that, with being gradually increased for remote sensing image file data amount, MongoDB and PostgreSQL store number
Can all it increase according to the required time, but the storage performance of MongoDB is relatively more stable, in addition, storing same remote sensing image
When data file, the time of PostgreSQL database consumption will be more than MongoDB database, especially insertion big data quantity
When remote sensing image data file, the two storing data spent time difference is become apparent, it is possible to it obtains, it is extensive in storage
Remote sensing image data when, the performance of MongoDB is better than PostgreSQL.
The present invention proposes that a kind of extensive remote sensing image data distribution is deposited by building MongoDB fragment aggregated structure
Method for storing stores remote sensing image data using GridFS file memory mechanism, of the invention extensive based on MongoDB
Remote sensing image data distributed storage method can overcome the read or write speed of conventional store method is slow, horizontal direction extension it is difficult with
And the problems such as under mass data background storing and accessing inefficiency, database is carried out extending transversely, is more suitable for managing big rule
Mould remote sensing image data.
The basic principles, main features and advantages of the invention have been shown and described above.The technical staff of the industry should
Understand, the present invention is not limited to the above embodiments, and the above embodiments and description only describe originals of the invention
Reason, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes and improvements
It all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended claims and its equivalent circle
It is fixed.
Claims (8)
1. being based on the extensive remote sensing image data distributed storage method of MongoDB, which comprises the following steps:
Step 1: using allocation methods, constructs the remotely-sensed data distributed storage frame based on MongoDB, then for wait deposit
The remote sensing image data of storage, retrieval whether there is remote sensing image data file of the same name in the database；
Step 2: when there are when remote sensing image data file of the same name, terminate remote sensing image data storage operation in database；
Step 3: it when remote sensing image data file of the same name is not present in database, is stored using GridFS file memory mechanism
Remote sensing image data；
Step 4: by two set of remote sensing image data file storage to GridFS file memory mechanism, two set are divided
Rs.files and rs.chunks are not named as it；
Step 5: selecting " files_id " for piece key in rs.chunks set, carries out fragment, remote sensing image data to data
It is stored on the different server in cluster, rs.files set completes remote sensing image data storage without fragment；
Step 6: when inquiring the remote sensing image data of storage, first according to given remotely-sensed data file name in rs.files
Searched in set, determine " _ id " field of document, according to rs.files gather in " _ id " field and rs.chunks gather
In " files_id " field corresponding relationship, determine rs.chunks set in " n " codomain, according to " n " value sequence take out image
Data file.
2. according to claim 1 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: the remotely-sensed data distributed storage frame in the step 1 based on MongoDB by sliced service device, routing server and is matched
Server composition is set, the sliced service device is used to send client for storing actual data, the routing server
Request be addressed and position, the configuration server is used to store routing and the configuration information of fragment.
3. according to claim 2 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: routing is the entrance of client request data-base cluster in the routing server, and all requests will be assisted by mongos
It adjusts, corresponding request of data is forwarded on corresponding sliced service device.
4. according to claim 2 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: high availability and data consistency in order to guarantee data, using " duplication collection in the data fragmentation of the sliced service device
The scheme of+fragment ", each fragment of the sliced service device is the duplication collection being made of one group of mongod example, at each point
One arbitration node is set in piece, for selecting new host node when host delay machine.
5. according to claim 1 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: rs.files gathers the metamessage for storing remote sensing image data in the step 4, and rs.chunks gathers for storing
The binary data of remote sensing image.
6. according to claim 1 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: " _ id " is expressed as the unique identification of each document in the step 6.
7. according to claim 1 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: the value of " files_id " is corresponding with the value of " _ id " in rs.files set in the step 6, wherein rs.files collection
Key in conjunction includes " _ id ", " length ", " chunksize ", " uploadDate " and " md5 ", each in rs.files set
The meaning of a key are as follows: " _ id " indicates that the unique identification of each remote sensing image data file, " length " indicate remote sensing image data
Byte number that file includes, " chunksize " indicate each of composition remote sensing image data file piece size,
" uploadDate " indicates that the uplink time of remote sensing image data file, " md5 " indicate the MD5 check value of remote sensing image data.
8. according to claim 7 be based on the extensive remote sensing image data distributed storage method of MongoDB, feature exists
In: in the step 6 when remote sensing image data file size is greater than the value of chunksize, remote sensing image data file is pressed
Multiple pieces are divided into according to the size of chunksize, by each block number according to being stored in multiple documents of rs.chunks set, most
Afterwards by the metamessage of remote sensing image data file be stored in rs.files set in, rs.files set " _ id " with
" files_id " of rs.chunks set is corresponding, when only one remote sensing image data file, in rs.files set only
There is a document.
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Effective date of registration: 20210323 Address after: No. 9 Dengzhuang South Road, Haidian District, Beijing 100094 Applicant after: Aerospace Information Research Institute,Chinese Academy of Sciences Address before: No. 9 Dengzhuang South Road, Haidian District, Beijing 100094 Applicant before: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences |
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Application publication date: 20191105 |