CN110275885A - Multi-level track data storage device based on Hadoop - Google Patents
Multi-level track data storage device based on Hadoop Download PDFInfo
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- CN110275885A CN110275885A CN201910503990.3A CN201910503990A CN110275885A CN 110275885 A CN110275885 A CN 110275885A CN 201910503990 A CN201910503990 A CN 201910503990A CN 110275885 A CN110275885 A CN 110275885A
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Abstract
A kind of multi-level track data storage device based on Hadoop, comprising: reading unit, suitable for reading the initial track data with default time-space attribute；Zoning unit is suitable for carrying out multi-layer division according to time-space attribute to read initial track data, obtains corresponding block number evidence；Index construct unit is suitable for obtained block number and indexes according to corresponding data in block is constructed；Storage unit, suitable for storing the block number evidence and corresponding data in block index.The search efficiency of stored track data can be improved in above-mentioned scheme.
Description
Technical field
The invention belongs to technical field of data processing, deposit more particularly to a kind of multi-level track data based on Hadoop
Storage device.
Background technique
With the progress of location technology, for example, support GPS positioning mobile phone it is universal, a large amount of track data is by people
Class, vehicle generate.Manage and handle the base that these a large amount of track datas are many cities calculating, intelligent transportation application program
Plinth, including traffic modeling, user behavior analysis, Resource Distribution and Schedule etc..System for managing and analyzing track data
It is not only critically important in scientific research, it is also critically important in practical applications.By taking the share-car company of Largest In China drop drop as an example, utilize
Track data provides the services such as travel time prediction, requirement forecasting, share-car scheduling.Therefore, for track data management and analysis
Propose a variety of methods.
In practical applications, what a large amount of mobile phone and the vehicle equipped with position positioning device generated magnanimity daily has one
Determine the track data of feature.For management system " the PIST:An Efficient and Practical of track data
Indexing Technique for Historical Spatio-Temporal Point Data " in Geoln-
It is suggested in formatica periodical, which, which puts track data, constructs spatial index for basic unit, it counts rail first
Then mark data construct spatial index and divide data point in the distribution of Spatial Dimension.Finally to the data root inside each division
It constructs and indexes according to time dimension.Chakka proposes the management system using sub-trajectory as basic unit in CIDR meeting
" Indexing large trajectory data sets with SETI ", the system is equally first according to statistical data structure
Spatial index is built, track data is then divided according to spatial index, and the sub-trajectory of same mobile object in each division is made
Temporal index is constructed for basic processing unit.Similar traditional integrated system manage and handle real trace data in terms of by
It in index and storage organization is handled based on single node, inefficiency seems helpless to location-based service is based in real time,
Be not suitable for distributed application scenarios, the track data in face of magnanimity can not accomplish efficient storage and inquiry.
In recent years, the various systems based on MapReduce are also proposed for spatial data analysis, these systems are in cloud ring
There is scalability, and more efficient than the system of centralization in border, " A MapReduce framework for
Spatial data " system that proposes in ICDE meeting is based on cloud computing technology, a task is assigned to different calculating
On node, concurrently processing request.
But the storage method of existing track data, it there is a problem that search efficiency is low.
Summary of the invention
Present invention solves the technical problem that being how to improve the search efficiency of stored track data.
In order to achieve the above object, the present invention provides a kind of multi-level track data storage device based on Hadoop,
Described device includes:
Reading unit, suitable for reading the initial track data with default time-space attribute；
Zoning unit is suitable for carrying out multi-layer division according to time-space attribute to read initial track data, obtain pair
The block number evidence answered；
Index construct unit is suitable for obtained block number and indexes according to corresponding data in block is constructed；
Storage unit, suitable for storing the block number evidence and corresponding data in block index.
Optionally, the initial track data with default time-space attribute have (Oid, Loc, Time, A1 ..., An)
Structure；Wherein, Oid indicates that object identity attribute, Loc indicate the space attribute of the initial track data, described in Time expression
The timestamp attribute of initial track data, A1 to An indicate the public attribute of the initial track data.
Optionally, the zoning unit, suitable for divide by the initial track data according to object identity attribute
To corresponding level-one partition data；It is divided obtained level-one partition data to obtain corresponding second level according to space attribute
Partition data；Obtained secondary partition data are subjected to further subdivision according to timestamp attribute and obtain the block number evidence.
Optionally, the zoning unit, the cryptographic Hash of the object identity attribute suitable for calculating the initial track data；It presses
The initial track data are divided to obtain according to the cryptographic Hash and preset first time granularity being calculated corresponding more
A level-one partition data.
Optionally, the zoning unit, suitable for inciting somebody to action by constructing Quadtree Spatial Index on the space attribute space
Obtained level-one partition data divides to obtain corresponding multiple secondary partition data.
Optionally, described device further include:
Expanding element, suitable for when needing to store the incremental data of the block number evidence, to the incremental data according to space-time
Attribute carries out multi-layer division, obtains corresponding increment block number evidence；By the increment block number according to the corresponding block number according into
Row merges.
Optionally, the storage unit, suitable for data in block index to be stored in the beginning of block file, and it is suitable according to the time
The data in block is continuously stored in after the data in block index by sequence.
Compared with prior art, the invention has the benefit that
Above-mentioned scheme has the initial track data of default time-space attribute by reading, to read initial track
Data carry out multi-layer division according to time-space attribute, obtain corresponding block number evidence, are obtained block number according to corresponding piece of building
Interior data directory, and the block number evidence and corresponding data in block index are stored, it can be examined to track data
Suo Shi is positioned using data of the space-time data to inquiry, without being traversed to all track datas, therefore can be with
The search efficiency for improving track data, promotes the usage experience of user.
Detailed description of the invention
In order to more clearly explain the technical solutions in the embodiments of the present application, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, the drawings in the following description are only some examples of the present application, for
For those of ordinary skill in the art, without any creative labor, it can also be obtained according to these attached drawings
His attached drawing.
Fig. 1 is a kind of process signal of multi-level trajectory data storage method based on Hadoop of the embodiment of the present invention
Figure；
Fig. 2 is that a kind of pair of track data of the embodiment of the present invention carries out the schematic diagram of multi-layer division；
Fig. 3 is the track data storage organization schematic diagram in the embodiment of the present invention；
Fig. 4 is a kind of structural representation of multi-level track data storage device based on Hadoop of the embodiment of the present invention
Figure.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.Related directionality instruction in the embodiment of the present invention (such as upper and lower, left and right,
It is forward and backward etc.) it is only used for the relative positional relationship explained under a certain particular pose (as shown in the picture) between each component, movement feelings
Condition etc., if the particular pose changes, directionality instruction is also correspondingly changed correspondingly.
As stated in the background art, trajectory data storage method in the prior art asking there is inefficiency in inquiry
Topic.
To solve the above problems, technical solution of the present invention has the initial track number of default time-space attribute by reading
According to, to read initial track data according to time-space attribute carry out multi-layer division, obtain corresponding block number evidence, be acquired
Block number indexed according to corresponding data in block is constructed, and the block number evidence and corresponding data in block index are stored, can
To be positioned using data of the space-time data to inquiry, without to all tracks when being retrieved to track data
Data are traversed, therefore the search efficiency of track data can be improved, and promote the usage experience of user.
It is understandable to enable above-mentioned purpose of the invention, feature and beneficial effect to become apparent, with reference to the accompanying drawing to this
The specific embodiment of invention is described in detail.
Fig. 1 is a kind of process signal of multi-level trajectory data storage method based on Hadoop of the embodiment of the present invention
Figure.Referring to Fig. 1, a kind of multi-level trajectory data storage method based on Hadoop be can specifically include:
Step S101: the initial track data with default time-space attribute are read.
In an embodiment of the present invention, the initial track data with default time-space attribute have (Oid, Loc,
Time, A1 ..., An) structure；Wherein, Oid indicates that object identity attribute, Loc indicate that the space of the initial track data belongs to
Property, Time indicate that the timestamp attribute of the initial track data, A1 to An indicate the public attribute of the initial track data,
Such as acceleration.
Step S102: multi-layer division is carried out according to time-space attribute to read initial track data, is obtained corresponding
Block number evidence.
It in specific implementation, can when carrying out multi-layer division according to time-space attribute to read initial track data
To be divided to obtain corresponding level-one partition data for the initial track data first, in accordance with object identity attribute, then by institute
Obtained level-one partition data is divided to obtain corresponding secondary partition data according to space attribute, finally by obtained two
Grade partition data carries out further subdivision according to timestamp attribute and obtains the block number evidence.In an embodiment of the present invention, it is pressing
When being divided to obtain corresponding level-one partition data by the initial track data according to object identity attribute, it can calculate first
The cryptographic Hash of the object identity attribute of the initial track data, and according to the cryptographic Hash and preset first time being calculated
Granularity divides the initial track data to obtain corresponding multiple level-one partition datas.In another embodiment of the present invention
In, when obtained level-one partition data is divided to obtain corresponding multiple secondary partition data according to space attribute,
It can be carried out by way of constructing Quadtree Spatial Index on the space attribute space.
Referring to fig. 2, in an embodiment of the present invention, using MapReduce job execution to read initial track
Data carry out multi-layer division according to time-space attribute.Specifically:
MapReduce operation carries out level-one subregion according to object identity attribute Oid to initial track data first, obtains pair
N level-one subregion bucket, i.e. bucket-000, bucket-001 ... the bucket-00n answered, and to each fraction
Area bucket-00i (1≤i≤n) generates corresponding index.Specifically, map function reads initial data, output <bucket_
Id, record >, wherein bucket_id, i.e. bucket-000, bucket-001 ... bucket-00n, for according to table schema
In the level-one partition identification that is calculated of level-one partitioning strategies specified, record is corresponding track data content.?
The reduce stage is grouped by the level-one partition identification bucket_id of level-one subregion bucket, and reduce function is by the institute of group
There is record that individual output file is written.Meanwhile it safeguards the sample of a group in memory, creation level-one subregion bucket's
Index.
Then, level-one subregion bucket file further division is obtained into corresponding multiple second levels according to space attribute Loc
Subregion region.In an embodiment of the present invention, the division of Quadtree Spatial Index is constructed on space attribute by map function
Method divides each bucket, obtains corresponding multiple secondary partition region, R0~R9 as shown in Figure 2.
When division obtains secondary partition, according still further to the time granularity finer than level-one subregion to secondary partition
Region is divided, and obtains multiple three-level subregion block, and export<block_id,record>.Wherein, block_id
For the mark of three-level subregion block, record is corresponding track data content.
After key is grouped, reduce function receives all records for belonging to the same three-level subregion block.
Step S103: it is indexed for obtained block number according to corresponding data in block is constructed.
In specific implementation, when finishing initial track data according to time-space attribute progress multi-layer division, next
Data in block index, i.e. metadata can be generated to divide obtained data in block block.Wherein, the data of the metadata
Content includes the definition of the common properties in track data, from level-one subregion bucket to secondary partition region and from two fractions
Mapping relations etc. of the area region to three-level subregion block.
Step S104: the block number evidence and corresponding data in block index are stored.
In specific implementation, the block number evidence and corresponding data in block index are stored, i.e., it will be after division
Block number is written in distributed file system (HDFS) in a particular format according to (blockfile).It, can be with specifically, referring to Fig. 3
Index is stored in the beginning of file first in block, and p is the offset address of the storage location of real data；For actual track number
According to part, sort sequentially in time, then according to the sequential storage of column, the same attribute Coutinuous store, then by level-one subregion
It is stored under the same catalogue of table to secondary partition and secondary partition to the mapping relations of three-level subregion write-in meta data file.
In specific implementation, the multi-level trajectory data storage method based on Hadoop can also include:
Step S105: when needing to store the incremental data of the block number evidence, to the incremental data according to time-space attribute
Multi-layer division is carried out, corresponding increment block number evidence is obtained.
Wherein, the incremental data of the block number evidence is i.e. with stored block number according to space-time unique attribute having the same
New track data to be stored.When there are the incremental data of the block number evidence, this can be obtained according to step S102 wait deposit
The corresponding block number of the incremental data of storage is according to get the corresponding increment block number evidence of incremental data is arrived, and details are not described herein.
Step S106: the increment block number is merged according to the corresponding block number evidence.
In specific implementation, when obtain the corresponding increment block number of corresponding incremental data according to when, then it is fixed by index in block
The physical location of incremental data is arrived in position, successively the attribute data of definition is written the position navigated to, updates index in block and completes
Merge, then by meta data manager merge block data file replacement current block data file, and directly add those not with
Existing block number according to file mergences increment block data file.
It, can when inquiring the track data of storage after being stored track data using above-mentioned step
With input inquiry range first, the combination of object identity attribute Oid, space attribute Loc, time attribute Time in this way, the present invention
By metadata and block number according to index in the block of blockfile come location data.
For example, for inquiring Q (I, S, T), wherein I is the Oid of object identity, and S is spatial dimension, and T is time range.
Firstly, the level-one subregion about I and T is found according to level-one subregion, it is then fixed according to the Quadtree Spatial Index in secondary partition
The secondary partition intersected in the above-mentioned level-one subregion in position with S.In that region, time range is further found out according to three-level subregion
The block number evidence handed over T-phase.
It for each block number evidence being related to, is indexed by reading in block, obtains the actual data bits corresponded to as identifying I
It sets, reads in memory, then use space range S and time range T records these and carries out final filtration, then returns and meets item
The data of part.
The present invention is the multi-level trajectory data storage method based on Hadoop, uses a kind of specific data lattice
Formula, the system that can optimize time-space attribute are realized, it is sufficient to be applied to very big data set, have any possible range to realize
All Object Queries efficient parallel processing, be the actual storage data definition section space-efficient file format on HDFS.
Meanwhile parallel algorithm being described using MapReduce, added with executing all Object Queries, primary data load and incremented data
It carries.These algorithms have scalability and high efficiency when handling very big data set.
The above-mentioned method in the embodiment of the present invention is described in detail, below will be to the above-mentioned corresponding dress of method
It sets and is introduced.
Fig. 4 shows the structure of multi-level track data storage device of one of the embodiment of the present invention based on Hadoop
Schematic diagram.Referring to fig. 4, a kind of multi-level track data storage device 40 based on Hadoop, may include reading unit
401, zoning unit 402, index construct unit 403 and storage unit 404, in which:
Reading unit 401, suitable for reading the initial track data with default time-space attribute.In one embodiment of the invention
In, the initial track data with default time-space attribute have (Oid, Loc, Time, A1 ..., An) structure；Wherein, Oid
Indicate that object identity attribute, Loc indicate that the space attribute of the initial track data, Time indicate the initial track data
Timestamp attribute, A1 to An indicate the public attribute of the initial track data.
Zoning unit 402 is suitable for carrying out multi-layer division according to time-space attribute to read initial track data, obtain
Corresponding block number evidence.In an embodiment of the present invention, the zoning unit 402, being suitable for will be described first according to object identity attribute
Beginning track data is divided to obtain corresponding level-one partition data；By obtained level-one partition data according to space attribute into
Row divides and obtains corresponding secondary partition data；Obtained secondary partition data are further thin according to the progress of timestamp attribute
Get the block number evidence；In an alternative embodiment of the invention, the zoning unit 402 is suitable for calculating the initial track number
According to object identity attribute cryptographic Hash；According to the cryptographic Hash and preset first time granularity being calculated to the track primary
Mark data are divided to obtain corresponding multiple level-one partition datas；In still another embodiment of the process, the zoning unit is fitted
In by constructing Quadtree Spatial Index on the space attribute space, obtained level-one partition data is divided to obtain pair
The multiple secondary partition data answered.
Index construct unit 403 is suitable for obtained block number and indexes according to corresponding data in block is constructed.
Storage unit 404, suitable for storing the block number evidence and corresponding data in block index.It is real in the present invention one
It applies in example, the storage unit 404, suitable for data in block index to be stored in the beginning of block file, and sequentially in time will
The data in block is continuously stored in after the data in block index.
In specific implementation, the multi-level track data storage device 40 of the dress based on Hadoop can also include expanding
Open up unit 405, in which:
Expanding element 405, suitable for when needing to store the incremental data of the block number evidence, to the incremental data according to when
Null attribute carries out multi-layer division, obtains corresponding increment block number evidence；By the increment block number evidence and the corresponding block number evidence
It merges.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, described
The step of multi-level trajectory data storage method based on Hadoop is executed when computer instruction is run.Wherein, described
Multi-level trajectory data storage method based on Hadoop refers to the introduction of preceding sections, repeats no more.
The embodiment of the invention also provides a kind of terminal, including memory and processor, energy is stored on the memory
Enough computer instructions run on the processor, the processor executed when running the computer instruction it is described based on
The step of multi-level trajectory data storage method of Hadoop.Wherein, the multi-level track data storage based on Hadoop
Method refers to the introduction of preceding sections, repeats no more.
Using the above scheme in the embodiment of the present invention, there are the initial track data of default time-space attribute by reading,
Multi-layer division is carried out according to time-space attribute to read initial track data, obtains corresponding block number evidence, is obtained
Block number is indexed according to corresponding data in block is constructed, and the block number evidence and corresponding data in block index are stored, can be with
It when being retrieved to track data, is positioned using data of the space-time data to inquiry, without to all track numbers
According to being traversed, therefore the search efficiency of track data can be improved, promotes the usage experience of user.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, the present invention
Claimed range is delineated by the appended claims, the specification and equivalents thereof from the appended claims.
Claims (7)
1. a kind of multi-level track data storage device based on Hadoop characterized by comprising
Reading unit, suitable for reading the initial track data with default time-space attribute；
Zoning unit is suitable for carrying out multi-layer division according to time-space attribute to read initial track data, obtain corresponding
Block number evidence；
Index construct unit is suitable for obtained block number and indexes according to corresponding data in block is constructed；
Storage unit, suitable for storing the block number evidence and corresponding data in block index.
2. the multi-level track data storage device according to claim 1 based on Hadoop, which is characterized in that the tool
Having the initial track data of default time-space attribute has (Oid, Loc, Time, A1 ..., An) structure；Wherein, Oid indicates object
Identity property, Loc indicate that the space attribute of the initial track data, Time indicate the timestamp category of the initial track data
Property, A1 to An indicates the public attribute of the initial track data.
3. the multi-level track data storage device according to claim 2 based on Hadoop, which is characterized in that described point
Area's unit obtains corresponding level-one partition data suitable for being divided the initial track data according to object identity attribute；
Obtained level-one partition data is divided to obtain corresponding secondary partition data according to space attribute；By obtained two
Grade partition data carries out further subdivision according to timestamp attribute and obtains the block number evidence.
4. the multi-level track data storage device according to claim 3 based on Hadoop, which is characterized in that described point
Area's unit, the cryptographic Hash of the object identity attribute suitable for calculating the initial track data；According to the cryptographic Hash that is calculated and
Preset first time granularity divides the initial track data to obtain corresponding multiple level-one partition datas.
5. the multi-level track data storage device according to claim 3 based on Hadoop, which is characterized in that described point
Area's unit, suitable for by constructing Quadtree Spatial Index on the space attribute space, by obtained level-one partition data
Division obtains corresponding multiple secondary partition data.
6. the multi-level track data storage device according to any one of claims 1 to 5 based on Hadoop, feature exist
In, further includes:
Expanding element, suitable for when needing to store the incremental data of the block number evidence, to the incremental data according to time-space attribute
Multi-layer division is carried out, corresponding increment block number evidence is obtained；By the increment block number according to the corresponding block number according to closing
And.
7. the multi-level track data storage device according to claim 1 based on Hadoop, which is characterized in that described to deposit
Storage unit, suitable for data in block index to be stored in the beginning of block file, and it is sequentially in time that the data in block is continuous
It is stored in after the data in block index.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110737786A (en) * | 2019-10-09 | 2020-01-31 | 北京明略软件系统有限公司 | data comparison collision method and device |
| CN111177195A (en) * | 2019-12-18 | 2020-05-19 | 北京明略软件系统有限公司 | Data comparison collision method and device |
-
2019
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110737786A (en) * | 2019-10-09 | 2020-01-31 | 北京明略软件系统有限公司 | data comparison collision method and device |
| CN111177195A (en) * | 2019-12-18 | 2020-05-19 | 北京明略软件系统有限公司 | Data comparison collision method and device |
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