CN105630919A - Storage method and system - Google Patents
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- CN105630919A CN105630919A CN201510971664.7A CN201510971664A CN105630919A CN 105630919 A CN105630919 A CN 105630919A CN 201510971664 A CN201510971664 A CN 201510971664A CN 105630919 A CN105630919 A CN 105630919A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/13—File access structures, e.g. distributed indices
Abstract
The invention provides a storage method and system. The storage method is used for storing mass remote-sensing images and comprises the following steps: receiving a mass remote-sensing image file; according to a quadtree index, segmenting the mass remote-sensing image file into a plurality of sub remote-sensing image files, and recording first metadata information corresponding to the sub remote-sensing image files; and storing the plurality of sub remote-sensing image files and the first metadata information. On the basis of the quadtree index, the mass remote-sensing image file is divided into a plurality of small files, the small files are stored so as to realize the storage and the management of the mass remote-sensing image file, and meanwhile, the query efficiency of the mass remote-sensing image file and the positioning capability of local images can be greatly improved.
Description
Technical field
The present invention relates to technical field of data processing, it particularly relates to a kind of storage method and system.
Background technology
In recent years, China's aerospace industry quickly grows, earth observation rapid technological improvement, various remote sensing instrument spatial resolution, spectral resolution, temporal resolution improves constantly, the data volume sharp increase of various different application remote sensing images, have accumulated substantial amounts of image data. For ZY3 satellite data, 300 days in orbit, the data volume of generation was up to 17TB. Along with the accumulation of the increase of China's number of satellite and data, how efficient storage and management mass remote sensing image data have become as a great problem that strategic point need to solve.
Traditional centralized management system is difficult to the Large Copacity of satisfying magnanimity remote sensing image data, and high concurrent, autgmentability is strong, the requirement that safety is high, has been difficult to be used for efficient storage and management mass remote sensing image data. Large-scale distributed storage system was rapidly developed in recent years, used the problem that large-scale distributed file system solves mass remote sensing data storage to progress into the visual field of people. Google have employed distributed memory system GFS to solve storage and the management of huge image data in Google Maps and GoogleEarth, adopts the BigTable problem solving small documents simultaneously. In order to realize remote sensing image fast browsing and retrieval, Google carries out the image pyramid model stored for remote sensing image picture, substantially a bulk of image picture is carried out piecemeal process, then to data according to needs one different resolution of user carry out storage with reality, formed one from top under file pyramid structure. Data set is carried out the dissecting tissue of various dimensions by the tile pyramid adopting " hierarchical block " strategy, not only highly shortened the access time of data, what can also realize between different resolution data seamless browses, and is the ideal structure of mass remote sensing data tissue.
The storage and management of traditional remote sensing image data is based on centralised storage and management, and remote sensing image file and metadata information store jointly with file, and file is managed with the form of catalogue. This method stores on redundancy backup, horizontal point spread, data load balance and hardware cost in data, it is impossible to meet above-mentioned growing data storage and calculating demand.
Development along with large-scale distributed storage system, some researchs and practice has been had to utilize distributed memory system storage and management mass remote sensing image data, applying more storage method at present is utilize distributed system (such as HDFS) to store remote sensing image data, and data are carried out quick indexing by the metadata storing remote sensing image with relevant database (Oracle/MySQL etc.). This method to some extent solves storage and the problem of management of mass remote sensing image data, but there is also problems. First, utilizing the metadata of relational data library storage mass remote sensing image to equally exist performance bottleneck, along with the increase of data volume, the table handling performance of metadata can be gradually lowered. Secondly, in relevant database, the backup of metadata needs regular manual operation, and this adds the probability of workload and loss of data undoubtedly. Finally, under normal circumstances, the size of remote sensing image is at hundreds of million to several G, therefore, read and write so big file under unified path and inevitably result in the problem that read-write efficiency is not high, if being cut by file, the small documents problem of distributed memory system and the increase of relation relational database burden can be caused when mass remote sensing image.
On the boundary that increases income, some scholar utilizes HDFS and HBase to store mass remote sensing image data, but HDFS also exists the problem that space availability ratio is low, and this makes hardware device not fully to be utilized. Meanwhile, HDFS utilizes Java language to develop, and its execution efficiency is not as high.
For the problem in correlation technique, effective solution is not yet proposed at present.
Summary of the invention
For the problem in correlation technique, the present invention proposes a kind of storage method and system, disclosure satisfy that mass remote sensing image data Large Copacity, high concurrent, the requirement of high safety etc., realize the storage and management to mass remote sensing image file, substantially increase the search efficiency to mass remote sensing image file and the stationkeeping ability to local image.
The technical scheme is that and be achieved in that:
According to an aspect of the invention, it is provided a kind of storage method, this storage method is used for storing mass remote sensing image file.
This storage method includes:
Receive mass remote sensing image file;
Index according to quaternary tree, be many sub-remote sensing image files by mass remote sensing image file division and record the first metadata information that sub-remote sensing image file is corresponding;
Store many sub-remote sensing image files and the first metadata information.
In a preferred embodiment, the first metadata information includes:
The data message of sub-remote sensing image file and storage position.
In a preferred embodiment, the data message of sub-remote sensing image file is the parameter of the geographical coverage area corresponding to sub-remote sensing image file.
In a preferred embodiment, farther include before storing many sub-remote sensing image files and the first metadata information:
The second metadata information that record mass remote sensing image file is corresponding, wherein, the second metadata information is the parameter of the geographical coverage area corresponding to mass remote sensing image file.
In a preferred embodiment, storage method farther includes:
First metadata information is backed-up.
According to a further aspect in the invention, it is provided that a kind of storage system, this storage system is used for storing mass remote sensing image file.
This storage system includes:
Receive device, be used for receiving mass remote sensing image file;
Programming model MapReduce, for indexing according to quaternary tree, is many sub-remote sensing image files by mass remote sensing image file division and records the first metadata information that sub-remote sensing image file is corresponding;
Parallel memory device, is used for storing many sub-remote sensing image files;
Distributed data base HBase, is used for storing the first metadata information.
In a preferred embodiment, the first metadata information includes:
The data message of sub-remote sensing image file and storage position.
In a preferred embodiment, the data message of sub-remote sensing image file is the parameter of the geographical coverage area corresponding to sub-remote sensing image file.
In a preferred embodiment, MapReduce is further used for the second metadata information that record mass remote sensing image file is corresponding, and wherein, the second metadata information is the parameter of the geographical coverage area corresponding to mass remote sensing image file.
In a preferred embodiment, HBase is further used for the first metadata information is backed-up.
Mass remote sensing image file division is become multiple small documents by utilizing quaternary tree index by the present invention, and multiple small documents are stored, it is achieved thereby that the storage and management to mass remote sensing image file, and substantially increase the search efficiency to mass remote sensing image file and the stationkeeping ability to local image.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, the accompanying drawing used required in embodiment will be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the flow chart of storage method according to embodiments of the present invention;
Fig. 2 is the schematic diagram of storage system according to embodiments of the present invention;
Fig. 3 is the schematic diagram of storage system according to a particular embodiment of the invention.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments. Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain, broadly fall into the scope of protection of the invention.
According to embodiments of the invention, it is provided that a kind of storage method, this storage method is used for storing mass remote sensing image file.
As it is shown in figure 1, storage method according to embodiments of the present invention includes:
Step S101, receives mass remote sensing image file.
Step S103, indexes according to quaternary tree, is many sub-remote sensing image files by mass remote sensing image file division and records the first metadata information that sub-remote sensing image file is corresponding. Concrete, in a preferred embodiment, the first metadata information includes data message and the storage position of sub-remote sensing image file. Wherein, the data message of sub-remote sensing image file is the parameter of the geographical coverage area corresponding to sub-remote sensing image file. In one embodiment, while record the first metadata information, also including the second metadata information that record mass remote sensing image file is corresponding, wherein, the second metadata information is the parameter of the geographical coverage area corresponding to mass remote sensing image file.
Step S105, stores many sub-remote sensing image files and the first metadata information. In a preferred embodiment, this step also includes the first metadata information is backed-up.
By the such scheme of the present invention, disclosure satisfy that mass remote sensing image data Large Copacity, high concurrent, the requirement of high safety etc., realize the storage and management to mass remote sensing image file, substantially increase the search efficiency to mass remote sensing image file and the stationkeeping ability to local image.
According to embodiments of the invention, additionally providing a kind of storage system, this storage system is used for storing mass remote sensing image file.
As in figure 2 it is shown, storage system according to embodiments of the present invention includes:
Receive device 21, be used for receiving mass remote sensing image file;
Programming model MapReduce22, for indexing according to quaternary tree, is many sub-remote sensing image files by mass remote sensing image file division and records the first metadata information that sub-remote sensing image file is corresponding;
Parallel memory device 23, is used for storing many sub-remote sensing image files;
Distributed data base HBase24, is used for storing the first metadata information.
In a preferred embodiment, the first metadata information includes:
The data message of sub-remote sensing image file and storage position.
In a preferred embodiment, the data message of sub-remote sensing image file is the parameter of the geographical coverage area corresponding to sub-remote sensing image file.
In a preferred embodiment, MapReduce22 is further used for the second metadata information that record mass remote sensing image file is corresponding, and wherein, the second metadata information is the parameter of the geographical coverage area corresponding to mass remote sensing image file.
In a preferred embodiment, HBase24 is further used for the first metadata information is backed-up.
In order to be better understood from the present invention, it is described in detail with specific embodiment below. The schematic diagram of storage system according to a particular embodiment of the invention as shown in Figure 3, in figure 3:
Parallel memory device is ParaStor200 parallel memory system. ParaStor200 cloud storage system have employed the parallel architecture framework representing memory technology, the network communications technology and data management technique developing direction, is a high-end storage systems nowadays processed towards magnanimity unstructured data. It can provide the massive storage space of the high speed bandwidth of TB/s level and EB level. The framework of ParaStor200 cloud storage system advanced person makes it possess superpower ability extending transversely, have only to simply increase recording controller, bigger memory capacity and more data channel can be obtained, thus obtaining higher systematic polymerization bandwidth and I/O performance. Along with the increase of recording controller, all physical resources (CPU, buffer memory, the network bandwidth and disk read-write bandwidth) are automatically obtained load balancing, meet the Data Concurrent access requirement of thousands of clients. In addition, ParaStor200 High Availabitity, full redundancy architecture design also make it have system early warning, accurately fault location and superior fault-tolerant recovery capability timely, the continuously available of operation system 7 �� 24 hours can be ensured, it is achieved the reliability of mass storage system (MSS) highest level. HBase is the realization of increasing income of BigTable, it is based on the distributed data base towards row of HDFS exploitation, the transactional of it doesn't matter type data base, for the inquiry of non-relational mass data, particularly random read/write ultra-large data set efficiency is higher in real time.
Whole framework can be divided into ParaStor200 accumulation layer, for process and retrieve the intermediate layer of massive image data, the interface layer calling these services and concrete application layer composition. Physical layer is to provide the physical store of massive image data, is provided out same access interface by ParaStor200, realizes load balancing, redundancy backup, node exception etc. function simultaneously. Intermediate layer realizes process and the storage of data by accessing the ParaStor200 interface provided. Interface layer provides unified standard access interface on the basis in intermediate layer. Application layer utilizes the interface that interface layer provides to write distributed parallel processing application program. By this framework, it is possible to achieve the efficient storage of mass remote sensing image and management.
Utilize this framework storage remote sensing image to compare conventional architectures and original Hadoop has the advantage that. First Parastor200 provides the data backup policy of copy and N+M redundancy, it is possible to meeting different application demand, wherein the space availability ratio of N+M redundancy backup reaches as high as 94%, and HDFS maximum 50%. Secondly, Parastor manages node and adopts the pattern of dual-active, two management nodes provide service simultaneously, having higher efficiency, simultaneously a management one malfunctions, another can normal operation, and the HA only one of which management node of HDFS provides service, and being existed delayed by binode synchronization, when host node breaks down, data degradation is unavoidable. Utilizing HBase to store metadata and solve traditional relational scaling concern, HBase acquiescence sorts according to key, and the meeting actively cutting when table is excessive simultaneously this ensure that the search efficiency of HBase. And the snapshot service of HBase can realize the backup to metadata in non-service affecting situation, XData-Hadoop can realize the function of an one-key backup and automated back-up simultaneously.
In the present embodiment, utilizing said frame, the concrete Stored Procedure for mass remote sensing image file is as follows:
1. user terminal submits the mass remote sensing image file needing storage to.
2. utilize MapReduce to realize the Quadtree Partition to bigger remote sensing image (such as hundreds of million or the image file more than 1G), by mass remote sensing image file according to specifying size to be divided into multiple less subfile, and record the parameter of the geographical imagery zone that quaternary tree numbering corresponding to each subfile, geospatial area etc. show for representing subfile. Simultaneously, MapReduce also can record the metadata information of former mass remote sensing image file, wherein, metadata information is the parameter of the geographical imagery zone that quaternary tree numbering, the geospatial area etc. that mass remote sensing image file is corresponding show for representing mass remote sensing image file. Especially, in the present embodiment, the piecemeal to big file is that the block size according to storage system is split, for instance the ParaStor200 in the present embodiment stores system, and its block size is 64M, therefore, when file is less than 64M, will no longer file be split.
3. the multiple subfiles after segmentation are stored in ParaStor200.
4. by each parameter of the subfile recorded in the 2nd step and subfile, the storage position in ParaStore200 generates metadata information, and it is stored in HBase, wherein, in HBase, it is numbered Key with quaternary tree, metadata information is ranked up by other information for row, and then realizes the quick search of file.
In sum, technique scheme by means of the present invention, by setting up quaternary tree index, based on Parastor200 namely HBase storage remote sensing metadata achieves, the method for parallel memory device and XData-Hadoop and the storage of big data processing platform (DPP) and management mass remote sensing image data. First, parallel memory device solves the storage problem of mass remote sensing image. Utilize quaternary tree index to realize segmentation and the index of big file simultaneously, be used for ensureing the quick location of the quick search of big file and local image. Index and segmentation procedure adopt MapReduce to realize, it is ensured that the efficiency of extensive file process. Utilize HBase to store remote sensing image metadata and index data realizes the ultra-large data set of read/write real-time, random.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (10)
1. a storage method, it is characterised in that described storage method is used for storing mass remote sensing image file, including:
Receive described mass remote sensing image file;
Index according to quaternary tree, be many sub-remote sensing image files by described mass remote sensing image file division and record the first metadata information that described sub-remote sensing image file is corresponding;
Store the plurality of sub-remote sensing image file and described first metadata information.
2. storage method according to claim 1, it is characterised in that described first metadata information includes:
The data message of sub-remote sensing image file and storage position.
3. storage method according to claim 2, it is characterised in that
The data message of described sub-remote sensing image file is the parameter of the geographical coverage area corresponding to described sub-remote sensing image file.
4. storage method according to claim 1, it is characterised in that farther include before storing the plurality of sub-remote sensing image file and described first metadata information:
Recording the second metadata information that described mass remote sensing image file is corresponding, wherein, described second metadata information is the parameter of the geographical coverage area corresponding to described mass remote sensing image file.
5. storage method according to claim 1, it is characterised in that farther include:
Described first metadata information is backed-up.
6. a storage system, it is characterised in that described storage system is used for storing mass remote sensing image file, including:
Receive device, be used for receiving described mass remote sensing image file;
Programming model MapReduce, for indexing according to quaternary tree, is many sub-remote sensing image files by described mass remote sensing image file division and records the first metadata information that described sub-remote sensing image file is corresponding;
Parallel memory device, is used for storing the plurality of sub-remote sensing image file;
Distributed data base HBase, is used for storing described first metadata information.
7. storage system according to claim 6, it is characterised in that described first metadata information includes:
The data message of sub-remote sensing image file and storage position.
8. storage system according to claim 7, it is characterised in that
The data message of described sub-remote sensing image file is the parameter of the geographical coverage area corresponding to described sub-remote sensing image file.
9. storage system according to claim 6, it is characterised in that
Described MapReduce is further used for recording the second metadata information that described mass remote sensing image file is corresponding, and wherein, described second metadata information is the parameter of the geographical coverage area corresponding to described mass remote sensing image file.
10. storage system according to claim 6, it is characterised in that
Described HBase is further used for described first metadata information is backed-up.
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| CN106407355A (en) * | 2016-09-07 | 2017-02-15 | 中国农业银行股份有限公司 | Data storage method and device |
| CN107644086A (en) * | 2017-09-25 | 2018-01-30 | 咪咕文化科技有限公司 | The location mode of spatial data |
| CN108897859A (en) * | 2018-06-29 | 2018-11-27 | 郑州云海信息技术有限公司 | A kind of metadata retrieval method, apparatus, equipment and computer readable storage medium |
| CN110070590A (en) * | 2018-01-23 | 2019-07-30 | 北京云游九州空间科技有限公司 | A kind of remote sensing image storage method and device |
| CN110782973A (en) * | 2019-10-29 | 2020-02-11 | 京东方科技集团股份有限公司 | Medical image information grading storage method and device, computer equipment and medium |
| CN110795407A (en) * | 2019-10-14 | 2020-02-14 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | File random writing method and system suitable for distributed file system |
| WO2020192225A1 (en) * | 2019-03-22 | 2020-10-01 | 深圳先进技术研究院 | Remote sensing data indexing method for spark, system and electronic device |
| CN112199528A (en) * | 2020-10-12 | 2021-01-08 | 中国科学院空天信息创新研究院 | Online acquisition method for large-scale remote sensing data |
| CN112632303A (en) * | 2020-12-30 | 2021-04-09 | 北京超图软件股份有限公司 | Distributed storage method, analysis method and device for image data |
| CN113157214A (en) * | 2021-05-11 | 2021-07-23 | 中煤航测遥感集团有限公司 | Remote sensing image display method, device, equipment and storage medium |
| CN113778341A (en) * | 2021-09-17 | 2021-12-10 | 北京航天泰坦科技股份有限公司 | Distributed storage method and device for remote sensing data and remote sensing data reading method |
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| CN106407355A (en) * | 2016-09-07 | 2017-02-15 | 中国农业银行股份有限公司 | Data storage method and device |
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| CN110782973A (en) * | 2019-10-29 | 2020-02-11 | 京东方科技集团股份有限公司 | Medical image information grading storage method and device, computer equipment and medium |
| CN110782973B (en) * | 2019-10-29 | 2022-11-11 | 京东方科技集团股份有限公司 | Medical image information grading storage method and device, computer equipment and medium |
| CN112199528A (en) * | 2020-10-12 | 2021-01-08 | 中国科学院空天信息创新研究院 | Online acquisition method for large-scale remote sensing data |
| CN112632303A (en) * | 2020-12-30 | 2021-04-09 | 北京超图软件股份有限公司 | Distributed storage method, analysis method and device for image data |
| CN113157214A (en) * | 2021-05-11 | 2021-07-23 | 中煤航测遥感集团有限公司 | Remote sensing image display method, device, equipment and storage medium |
| CN113157214B (en) * | 2021-05-11 | 2024-01-26 | 中煤航测遥感集团有限公司 | Remote sensing image display method, device, equipment and storage medium |
| CN113778341A (en) * | 2021-09-17 | 2021-12-10 | 北京航天泰坦科技股份有限公司 | Distributed storage method and device for remote sensing data and remote sensing data reading method |
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