CN103617162A - Method of constructing Hilbert R-tree index on equivalent cloud platform - Google Patents
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Abstract
The invention discloses a method of constructing a Hilbert R-tree index on an equivalent structure cloud platform, wherein a Chord network of an equivalent structure is organized at a main node in the P2P cloud platform. Firstly, a data object is read through a mapping method, and a Hilbert curve code of the space where the data object is located is obtained according to the geographical location of the data object; secondly, the data object is partitioned based on the code, and the code is transmitted to a corresponding reduction method; thirdly, the reduction method is used for constructing Hilbert R-subtrees for the data object in all partitions; lastly, a safe hashing function is used for obtaining a hashed value of root node numbers of the Hilbert R-subtrees, and the hashed value is published to the main node located in a Chord loop to form the complete distributed Hilbert R-tree index. The method can be used for establishing a Hilbert R-tree concurrently, thereby shortening tree establishing time; meanwhile, the established Hilbert R-tree is distributed, so that the stability and search efficiency of the index are improved.
Description
Technical field
The present invention relates to build on a kind of reciprocity cloud platform the method for Hilbert R tree index, belong to the integration technology field of spatial data index and reciprocity cloud platform.
Background technology
Cloud computing is that a kind of business computation model is distributed in calculation task on the resource pool of a large amount of computing machines formations, makes various application systems can obtain as required computing power, storage space and information service.Google company and the cloud computing platform Hadoop that increases income etc. are used Map-Reduce parallel computing model now, the processing that this model is mass data provides general, an efficient technological frame, thereby in fields such as geographical spatial data query processing, data minings, has obtained application more and more widely.
P2P (Peer-to-Peer, peer-to-peer network) calculating refers between different system and exchanges by direct, realizes the process that computer resource and service are shared, carried out information processing.Here, resource can be processor, buffer memory and disk space etc.; Service comprises message exchange, data calculating etc.The key difference of P2P pattern and traditional client/server mode is Peer(peer-to-peer) with Peer in communication process, can abandon the role of server completely, by direct communication, obtain shared resource or service.
In the face of Spatial Data Mining, spatial decision support, Spatial Multi-Dimensional dynamic and visual analyses and simulations etc. are many, belong to the space application of computation-intensive and I/O intensity, the calculating of traditional GIS and data-handling capacity can not meet application demand well.Along with the application of grid computing technology in GIS becomes study hotspot gradually, its distributed parallel computation schema and system architecture will contribute to improve overall performance and the operational efficiency of GIS.Therefore, distributed parallel calculates the important method that solves the not enough problem of traditional GIS computing power by becoming.Organization And Management's technology of massive spatial data is the basis of all kinds of complex space application, is also the key problem of GIS technology.Wherein, Spatial Data Index Technology is the important research content of data organization and management.At present, at scientific research fields such as data base management system (DBMS) and GIS, the achievement in research of Spatial Data Index Technology is very abundant, applies comparatively extensive.Yet, less for the distributed parallel Spatial Data Index Technology achievement in research of tissue, management, access, processing and the application of massive spatial data.
ArielCary etc. have proposed to use under cloud platform that MapReduce is parallel builds sub-R tree, sub-R tree is merged, the R tree index of formation be one centralized, this centralized R tree becomes its bottleneck.
AnirbanMondal etc. have proposed to set up R under peer-to-peer network again and have set index, it is equal piece by spatial division, each peer node is safeguarded the piece of a decile, because the peer node of storage information is not necessarily continuous, may destroy the continuity of geographical space.
Summary of the invention
Technical matters to be solved by this invention is the deficiency for above-mentioned background technology, and a kind of method of geographical spatial data being set up distributed R tree index on reciprocity cloud platform is provided.
The present invention adopts following technical scheme for achieving the above object: a kind of method that builds Hilbert R tree index on peering structure cloud platform, it is characterized in that: the host node (Master) in P2P cloud platform is organized into the Chord network of peering structure, first, by mapping method (Map) reading out data object, based on its geographic position, obtain the hibert curve coding in its space of living in; Secondly, based on this coding, data object is carried out to subregion, and passed to corresponding reduction method (Reduce); Then, reduction method carries out the structure of Hilbert R subtree to the data object of each subregion; Finally, by secure hash function (SHA-1), obtain the hashed value of Hilbert R subtree root node numbering, and the host node (Master) publishing in Chord ring is upper, the distributed Hilbert R tree index of complete; Comprise the steps:
Step 1, tentation data integrates as D, establishes arbitrary data object that o ∈ D is data centralization, and o.id is the identifier of data object o, and o.p is the geographical position coordinates of data object o;
Step 2, reads in the data object in data set D with mapping method (Map), and the key word of mapping method (Map) input is o.id, is worth for o.p, and the data object o for input, according to its geographical position coordinates o.p, arrives this object map
on the Hilbert space fitting a curve on rank (exponent number of hibert curve is determined by the size of data set, data set size | D|,
and produce corresponding Hilbert coding o.hc;
Step 3, based on Hilbert coding o.hc, calls partition method f data object o is mapped to corresponding subregion, and the Hilbert coding that is input as data object of partition method f, is output as partition number, is defined as follows:
The key word of mapping method (Map) output is partition number f (o.hc), is worth for o, and according to partition method f, data object will be mapped to
in individual subregion, number of partitions is by the number decision of the host node in Chord encircles (Master), and establishing host node (Master) is that number is N,
Step 4, uses
individual reduction method (Reduce) receives the output of mapping method (Map) as input, its key word is partition number f (o.hc), value is o, each reduction method (Reduce) carries out respectively the structure of Hilbert R subtree for the data object of a certain subregion of input, and the numbering using this partition number as the Hilbert R subtree root node building;
Step 5, pass through Secure Hash Algorithm, calculate the hashed value of Hilbert R subtree root node numbering, and the corresponding host node (Master) that the root node of Hilbert R subtree corresponding to this hashed value is published in Chord ring is upper, the distributed Hilbert R tree index of complete;
So far, under reciprocity cloud platform, setting up distributed Hilbert R tree index completes, an independent host node (Master) in reciprocity cloud platform plays the effect of a partial indexes, and all host nodes (Master) based on Chord play the effect of Yi Ge global index.
The present invention has the following advantages and beneficial effect:
1) the inventive method has solved the shortcoming of prior art, has formed distributed R tree index, preferably resolves the bottleneck of centralized R tree, utilizes hibert curve to carry out subregion, preferably resolves the successional problem of geographical space.
2) for spatial data is parallel, setting up Hilbert R tree, it has significant lifting with respect to its speed of setting up Hilbert R tree index of traditional indexing means of setting up Hilbert R tree.
3) the present invention has become Chord ring by the Mater node organization of buffer memory root node, with respect to single Master node, has not only reduced the burden of Master node, and has improved the parallel processing capability of Master node, and its maintenance costs will be lower than traditional R tree; Can effectively accelerate inquiry, its maintenance costs will, lower than traditional R tree, reduce the loss that Master node failure causes.And what set up is the distributed index of Hilbert R tree, has effectively solved the bottleneck of centralized index, as: the ability of parallel query, process the maximum load of inquiry etc.
Accompanying drawing explanation
Fig. 1 is the general flow chart that builds Hilbert R tree index on peering structure cloud platform;
Fig. 2 is the Hilbert R tree index structure schematic diagram having built on peering structure cloud platform.
Embodiment
Below in conjunction with accompanying drawing, the technical scheme of invention is elaborated:
Host node (Master) in P2P cloud platform is organized into the Chord network of peering structure.On peering structure cloud platform, build the general flow chart of Hilbert R tree index, as shown in Figure 1:
Step 1, tentation data integrates as D, establishes arbitrary data object that o ∈ D is data centralization, and o.id is the identifier of data object o, and o.p is the geographical position coordinates of data object o.
Step 2, reads in the data object in data set D with mapping method (Map).The key word of mapping method (Map) input is o.id, is worth for o.p.Data object o for input, according to its geographical position coordinates o.p, arrives this object map
on the Hilbert space fitting a curve on rank (exponent number of hibert curve determines by the size of data set, herein data set size | D|,
and produce corresponding Hilbert coding o.hc.
Step 3, based on Hilbert coding o.hc, calls partition method f data object o is mapped to corresponding subregion.The Hilbert coding that is input as data object of partition method f, is output as partition number, is defined as follows:
The key word of mapping method (Map) output is partition number f (o.hc), is worth for o.According to partition method f, data object will be mapped to
in individual subregion, number of partitions determines by the number of the host node in Chord encircles, and establishing host node is that number is N,
Step 4, uses
individual reduction method (Reduce) receives the output of mapping method as input, its key word is partition number f (o.hc), value is o, each reduction method (Reduce) carries out respectively the structure of Hilbert R subtree for the data object of a certain subregion of input, and the numbering using this partition number as the Hilbert R subtree root node building.
Step 5, pass through Secure Hash Algorithm, calculate the hashed value of Hilbert R subtree root node numbering, and the root node of Hilbert R subtree corresponding to this hashed value is published on the corresponding host node in Chord ring, the distributed Hilbert R tree index of complete, as shown in Figure 2, host node (Master) in P2P cloud platform forms a Chord ring, according to Secure Hash Algorithm, Hilbert R subtree can be distributed in Chord network equably, thereby realizes the object of load balancing.
In sum, first, by mapping method (Map) reading out data object, based on its geographic position, obtain the hibert curve coding in its space of living in; Secondly, based on this coding, data object is carried out to subregion, and passed to corresponding reduction method (Reduce); Then, reduction method carries out the structure of Hilbert R subtree to the data object of each subregion; Finally, by secure hash function, obtain the hashed value of Hilbert R subtree root node numbering, and the host node (Master) publishing in Chord ring is upper, the distributed Hilbert R tree index of complete.This method can effectively be set up Hilbert R tree index, accelerates the inquiry of Hilbert R tree, and can reduce burden and the load balancing of Master node, is applicable to, on reciprocity cloud platform, spatial data is set up to Hilbert R tree index.
Claims (1)
1. on a peering structure cloud platform, build the method for Hilbert R tree index, it is characterized in that: the host node in P2P cloud platform is organized into the Chord network of peering structure, first, by mapping method reading out data object, based on its geographic position, obtain the hibert curve coding in its space of living in; Secondly, based on this coding, data object is carried out to subregion, and passed to corresponding reduction method; Then, reduction method carries out the structure of Hilbert R subtree to the data object of each subregion; Finally, by secure hash function, obtain the hashed value of Hilbert R subtree root node numbering, and publish on the host node in Chord ring, the distributed Hilbert R tree index of complete; Comprise the steps:
Step 1, tentation data integrates as D, establishes arbitrary data object that o ∈ D is data centralization, and o.id is the identifier of data object o, and o.p is the geographical position coordinates of data object o;
Step 2, reads in the data object in data set D with mapping method, and the key word of mapping method input is o.id, is worth for o.p, and the data object o for input, according to its geographical position coordinates o.p, arrives this object map
on the Hilbert space fitting a curve on rank, the exponent number of hibert curve is determined by the size of data set, data set size | D|,
and produce corresponding Hilbert coding o.hc;
Step 3, based on Hilbert coding o.hc, calls partition method f data object o is mapped to corresponding subregion, and the Hilbert coding that is input as data object of partition method f, is output as partition number, is defined as follows:
The key word of mapping method output is partition number f (o.hc), is worth for o, and according to partition method f, data object will be mapped to
in individual subregion, number of partitions determines by the number of the host node in Chord encircles, and establishing host node is that number is N,
Step 4, uses
individual reduction method receives the output of mapping method as input, its key word is partition number f (o.hc), value is o, each reduction method carries out respectively the structure of Hilbert R subtree for the data object of a certain subregion of input, and the numbering using this partition number as the Hilbert R subtree root node building;
Step 5, pass through Secure Hash Algorithm, calculate the hashed value of Hilbert R subtree root node numbering, and the root node of Hilbert R subtree corresponding to this hashed value is published on the corresponding host node in Chord ring to the distributed Hilbert R tree index of complete;
So far, set up distributed Hilbert R tree index and complete under reciprocity cloud platform, the independent host node in reciprocity cloud platform plays the effect of a partial indexes, plays the effect of Yi Ge global index based on all host nodes of Chord.
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Application publication date: 20140305 Assignee: Jiangsu Nanyou IOT Technology Park Ltd. Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS Contract record no.: 2016320000214 Denomination of invention: Method of constructing Hilbert R-tree index on equivalent cloud platform Granted publication date: 20160907 License type: Common License Record date: 20161117 |
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