CN103116610A - Vector space big data storage method based on HBase - Google Patents
Vector space big data storage method based on HBase Download PDFInfo
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- CN103116610A CN103116610A CN2013100230526A CN201310023052A CN103116610A CN 103116610 A CN103116610 A CN 103116610A CN 2013100230526 A CN2013100230526 A CN 2013100230526A CN 201310023052 A CN201310023052 A CN 201310023052A CN 103116610 A CN103116610 A CN 103116610A
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Abstract
The invention relates to the field of big data storage of computers and discloses a vector space big data storage method based on the HBase. The vector space big data storage method based on the HBase includes the following steps: 1) a vector data record is collected; 2) spacial scale numbers of the vector data record are computed; 3) an ID of the vector data record is taken as a row key, a data field and an attribute field except for the ID of the vector data record are taken as a row, and a data record is created; 4) the spacial scale numbers of the vector data record is taken as the row key, IDs of all vector space data in the spacial scale numbers are taken as the row, and an index is created. The vector space big data storage method based on the HBase has the advantages of being capable of supporting distributed storage and management of vector space data of a super-large scale and supporting quick query of a geographic position range. Compared with the existing vector space data storage method, the vector space big data storage method based on the HBase has a high visit concurrent amount and supports expansion of a data scale.
Description
Technical field
The present invention relates to the large field of data storage of a kind of computing machine, particularly the large date storage method of a kind of vector space based on HBase.
Background technology
Vector data is as important a kind of Spatial data types, is widely used in the spatial information field.But, because it has the characteristics such as data volume is large, data recording is elongated, make can expand a large amount of vector data of ground store and management and efficiently the addressing space data become the topic that people are concerned about.
Geodata is with spatial data and the attribute information thereof of earth surface as basic reference framework.For the ease of obtain, store, analysis and management geographical, set up Geographic Information System, be called for short generalized information system (geographic information system).Generalized information system has represented it is really each target in the world, as road, soil, height above sea level etc.In vector data structure, a zone is divided into several polygons, and each polygon is comprised of some line segments or camber line.The vector data structure memory data output is little, pattern precision is high, easily define single spatial object, and you are more time-consuming but process spatial relationship, are usually used in describing graph data.In raster data structure, in ground, entity identifies with the row and column of grid cell, and raster data simply, is easily processed spatial relation, but memory data output is large, and pattern precision is low, is usually used in Description Image and image data.
Current, adopt file system to manage raster data more, adopt database to come the management vector data.But vector data had both comprised the suitable structural data of managing with the traditional relational database, comprised that also some are not suitable for plate structure and the unstructured data of managing with the traditional relational database.
At present each large database concept manufacturer also constantly optimizes the management of vector data, and a lot of manufacturers have realized support and the expansion to vector data on the product of oneself.Most solutions are to leave in traditional relevant database in the relevant attribute data of vector data, with the solution of real GML data storage at other database or use middleware.But most of functions are perfect not, also have some drawbacks.
And, for spatial data, vector data particularly, be not only wherein real vector data part random length, various disunity, and type and the quantity of the relevant attribute data of these vector datas are also inconsistent, such as for typical vector data file shapefile file, the meaning of the field type of the attribute data in different shapefile files, field number and field is all different, if the relevant database class storage with traditional may need to set up a lot of tables.That is to say that these data are not structurized data, using traditional relational data library storage is not just very suitable.
For current generalized information system, the data volume of spatial data is all very huge.Along with the continuous increase of vector data scale, the processing power of individual node can become bottleneck gradually, and it is serious that the problem of Single Point of Faliure also becomes gradually.Traditional relevant database operates on individual node more, supports that the cost of distributed database and configuration cost also can be higher.And the performance of most relevant database clusters and extensibility are not fine.
This shows, vector data has and destructuring or feature semi-structured and that data volume is large, storage, inquiry and management to vector data should take into full account these characteristics, with traditional relevant database, vector data are carried out store and management not too suitable.
Summary of the invention
The present invention is directed to prior art and can't adapt to the geodata googol according to the shortcoming of amount, provide a kind of and can adapt to jumbo geodata, along with the novel large date storage method of the vector space based on HBase that significantly increases the data processing performance to keep stable of data volume.
For achieving the above object, the present invention can take following technical proposals:
The large date storage method of vector space based on HBase comprises following concrete steps:
1) collect the vector data record, described vector data record comprises a data field and a plurality of attribute field;
2) calculate the spatial dimension numbering of described vector data record, described spatial dimension numbering is the grid numbering that can comprise the minimum grid of described vector data record fully, described grid is for dividing the whole world formed zone take certain longitude and latitude as the border, choose a plurality of grids with different sizes, the grid of described different sizes is sorted and is numbered according to the size of grid obtains the grid rank, and described grid numbering comprises centre coordinate, the grid rank of described grid;
3) with the ID of described vector data record as line unit RowKey, set up data recording with the data field of described vector data record and the attribute field except ID as row, described data recording is added in the HBase database, and the field name of described attribute field is the row name of described row;
4) number as line unit RowKey with the spatial dimension of described vector data record, the ID of all Vector spatial datas under numbering with described spatial dimension sets up index as row, and described index is added in the HBase database.
As preferably, described vector data is recorded as from the vector data file that comprises described vector data record and resolves gained, be perhaps to make acquisition from geodata.
As preferably, described data field is preserved with the WKB form.
As preferably, described centre coordinate is latitude and longitude coordinates.
As preferably, the row of described step 3 form row bunch.
As preferably, in described step 4, the ID of all Vector spatial datas is recorded in row.
As preferably, separate with branch between described ID.
The present invention introduces the HBase database as basic data storage system, HBase be one distributed, towards the database of increasing income of row, be a high reliability, high-performance, towards row, telescopic distributed memory system.
The present invention has significant technique effect owing to having adopted above technical scheme:
The present invention is by introducing the HBase database, high-performance in conjunction with the HBase database, creatively the spatial dimension numbering with the vector data record of the per-column memory module of the uniqueness of HBase database and geodata combines, obtained a kind of higher data handling property that has, adapt to large data throughout, have the novel geodata storage mode of enhanced scalability, improved the processing power of computing machine to geodata, have higher practical value.
Description of drawings
Fig. 1 is the schematic flow sheet of embodiment 1.
Fig. 2 is the structural representation of grid.
Fig. 3 is the another kind of structural representation of grid.
Fig. 4 is the test result schematic diagram of contrast test 1.
Fig. 5 is the test result schematic diagram of contrast test 2.
Embodiment
The present invention is described in further detail below in conjunction with embodiment.
Embodiment 1
The large date storage method of vector space based on HBase comprises following concrete steps, as shown in Figure 1:
1) the vector data record is collected and obtained to computing machine, and described vector data record comprises a data field and a plurality of attribute field, and the vector data record is made to obtain or resolve from vector data file from geodata and obtained.
2) computing machine converts the above-mentioned vector data record that obtains to the WKB form of standard.
3) calculate the spatial dimension numbering of each vector data record.At first in the world according to latitude and longitude coordinates, according to different sizing grids, earth surface is divided into the grid of different stage, as shown in Fig. 2,3, Fig. 2, Fig. 3 represent respectively take 1 and 0.1 as unit, earth surf zone to be divided the grid that obtains.Under other is divided in certain level, the surface of the earth can be divided into grid, described grid is for dividing the whole world formed zone take certain longitude and latitude as the border, choose a plurality of grids with different sizes, the grid of described different sizes is sorted and is numbered according to the size of grid obtains the grid rank.The grid of these different sizes can be identified out by a unique identifier, and the naming rule of identifier is: the latitude and longitude coordinates of grid element center+grid rank.Described spatial dimension numbering is for the vector data record, and the spatial dimension numbering of a vector data record is the grid numbering that can comprise the minimum grid of this vector data record fully, and the grid numbering is exactly above-mentioned identifier.For example, 11.50_45.00_0 just represents that rank is 0 grid, and this grid element center latitude and longitude coordinates is (11.50,45.00).
4) in the HBase database newly-built one only have one row bunch table, the logical view of this table sees the following form shown in 1, with the ID of described vector data record as line unit RowKey, set up data recording with the data field of described vector data record and the attribute field except ID as row, between ID with branch separately, described data recording is added in the HBase database, the field name of described attribute field is the row name of described row, and wherein fida1, fida2 etc. are the id of vector data record.
Table 1
5) set up separately a concordance list as secondary index in the HBase database, be convenient to inquire about according to the geographic position.The logical view of this concordance list is as shown in table 2, and this table only has row bunch, and these row bunch only have row.Spatial dimension with described vector data record is numbered as line unit RowKey, and the ID of all Vector spatial datas under numbering with described spatial dimension sets up index as row, and described index is added in the concordance list of HBase database.
Table 2
The following describes the result that the application described method of above-described embodiment 1 and existing spatial database (PostgreSQL) relatively more commonly used compare test.
Contrast test 1
Test environment is 4 Dell PowerEdge R710 rack-mount servers, and Intel is to strong 5600 serial CPU Xeon E5620, and frequency is 2.4GHz, 12G DDR3 internal memory, 582G hard disk.STE is RedHat AS4.4 operating system, and the kernel version is 2.6.32.The spatial data lab environment that is used for contrast is PostgreSQL9.1.3, and space plug-in unit Postgis1.5.3 has been installed, and PostgreSQL has used SLONY pattern synchronization to 4 node.The HBase database environment is HBase0.9.2, Haddop1.0.1, and HBase uses distributed environment, and 1 host node wherein, 3 from node.
Test data set comprises 3,000, and the efficient that records the id random challenge according to vector data tested here in 000 shapefile vector data record.Fig. 4 has shown in inquiry 2,000, article 000, the contrast situation per second query rate QPS(Query Per Second when vector data records), therefrom can find out, in the situation that single-threaded access, the handling capacity of the HBase data-base cluster of Application Example 1 described method is 2 times of PostgreSQL data-base cluster of prior art, when number of threads reached 50, the handling capacity of the HBase data set cluster of Application Example 1 described method was more than 3 times of PostgreSQL data-base cluster of prior art.
Contrast test 2
Test environment has been tested the search efficiency to the vector data record of one of them spatial dimension with contrast test 1.Fig. 5 has shown in the contrast of setting up the spatial dimension index and not setting up spatial dimension index time space range query efficient, therefrom can find out, in the time of large and also large to the inquiry amount of spatial dimension to space data quantity, the search efficiency of not setting up spatial dimension in spatial dimension index situation is almost insupportable.
In a word, the above is only preferred embodiment of the present invention, and all equalizations of doing according to the present patent application the scope of the claims change and modify, and all should belong to the covering scope of patent of the present invention.
Claims (7)
1. the large date storage method of the vector space based on HBase, is characterized in that, comprises following concrete steps:
1) collect the vector data record, described vector data record comprises a data field and a plurality of attribute field;
2) calculate the spatial dimension numbering of described vector data record, described spatial dimension numbering is the grid numbering that can comprise the minimum grid of described vector data record fully, described grid is for dividing the whole world formed zone take certain longitude and latitude as the border, choose a plurality of grids with different sizes, the grid of described different sizes is sorted and is numbered according to the size of grid obtains the grid rank, and described grid numbering comprises centre coordinate, the grid rank of described grid;
3) with the ID of described vector data record as line unit RowKey, set up data recording with the data field of described vector data record and the attribute field except ID as row, described data recording is added in the HBase database, and the field name of described attribute field is the row name of described row;
4) number as line unit RowKey with the spatial dimension of described vector data record, the ID of all Vector spatial datas under numbering with described spatial dimension sets up index as row, and described index is added in the HBase database.
2. the large date storage method of the vector space based on HBase according to claim 1, is characterized in that, described vector data is recorded as from the vector data file that comprises described vector data record and resolves gained, is perhaps to make to obtain from geodata.
3. the large date storage method of the vector space based on HBase according to claim 1, is characterized in that, described data field is preserved with the WKB form.
4. the large date storage method of the vector space based on HBase according to claim 1, is characterized in that, described centre coordinate is latitude and longitude coordinates.
5. the large date storage method of the vector space based on HBase according to claim 1, is characterized in that, the row of described step 3 form row bunch.
6. the large date storage method of the vector space based on HBase according to claim 1, is characterized in that, in described step 4, the ID of all Vector spatial datas is recorded in row.
7. the large date storage method of the vector space based on HBase according to claim 6, is characterized in that, separates with branch between described ID.
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