CN106570030A - Calculation method and device based on big data - Google Patents
Calculation method and device based on big data Download PDFInfo
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- CN106570030A CN106570030A CN201510657338.9A CN201510657338A CN106570030A CN 106570030 A CN106570030 A CN 106570030A CN 201510657338 A CN201510657338 A CN 201510657338A CN 106570030 A CN106570030 A CN 106570030A
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2471—Distributed queries
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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
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
Abstract
The invention discloses a calculation method and device based on big data. The method comprises the following steps: S1, acquiring a service requirement of a user, and determining a target calculation data magnitude of a service according to the service requirement; S2, judging whether the target calculation data magnitude is greater than a preset threshold value or not; S3, if the target calculation data magnitude is greater than the preset threshold value, performing offline calculation on the service; S4, if the target calculation data magnitude is smaller than or equal to the preset threshold value, acquiring a current target data set, judging whether a memory required by calculation of the current target data set is larger than a preset memory or not, if the memory required by calculation of the current target data set is larger than the preset memory, performing iterative calculation on the current target data set through a cluster, otherwise, performing the iterative calculation on the current target data set through a single-chip microcomputer; S5, judging whether the iterative calculation times reach N times or not, if the iterative calculation times reach the N times, executing a step S6, otherwise, repeating the step S4; and S6, returning an iterative calculation result. Through adoption of the calculation method and device based on the big data, the calculation efficiency is increased; high real-time performance is achieved; and resources are saved.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of computational methods and device based on big data.
Background technology
Big data analysis is computer science emerging in recent years, and data analyst can be analyzed from structurized data
Go out the form of service needed.With the rapid growth of portfolio, related data are also accumulated in high speed.At present, mainly pass through
Hadoop big datas analysis system carries out offline or online analysis to the data of magnanimity.First, business personnel is by the industry of oneself
Business demand is sent to data analyst and carries out demand analyses.Secondly, data analyst is carried out from the initial data of magnanimity
Data mining, with certain dimension or specific algorithm wide table in the middle of many business is obtained.Finally, business demand is changed
For the discernible instruction of hadoop big data analysis systems, extracted with certain dimension in wide table from the middle of substantial amounts of business and met
The form of business demand, most at last result presentation to business personnel.
But, data are analyzed by said method, there are problems that following：1st, wide table is according to one in the middle of business
Determine dimension acquisition, if to obtain more fine-grained data, wide table possibly cannot be supported in the middle of current business.If with
Most fine granularity builds wide table in the middle of business, can waste substantial amounts of memory space.2nd, bordereau be based on specific business demand,
The offline result excavated.If business personnel is want according to another dimension to be analyzed, need to recalculate, and generate
New form, high cost.3rd, the incremental data of T+0 is not supported, it is necessary to which waiting second day can just see result, poor real.
4th, for each business demand, being intended to the data to magnanimity carries out the calculating of full dose, and business personnel is possible to only to use and arrives
Wherein sub-fraction, causes substantial amounts of storage resource and computing resource waste.
The content of the invention
It is contemplated that at least solving one of technical problem in correlation technique to a certain extent.For this purpose, one of the present invention
Purpose is to propose a kind of computational methods based on big data, it is possible to increase computational efficiency, real-time is high, save resources.
Second object of the present invention is to propose a kind of computing device based on big data.
To achieve these goals, first aspect present invention embodiment proposes a kind of computational methods based on big data, including：
S1, the business demand for obtaining user, and determine that the target of the business calculates data magnitude according to the business demand；S2、
Judge that whether the target calculates data magnitude more than predetermined threshold value；If S3, be more than the predetermined threshold value, to the business
Carry out calculated off line；If S4, be less than or equal to the predetermined threshold value, obtain current target data set, and judge calculate institute
Current target data set required memory is stated whether more than default internal memory, if being more than the default internal memory, by cluster to institute
State current target data set and be iterated calculating, otherwise, meter is iterated to the current target data set by unit
Calculate；S5, judge it is described iterative calculation whether reach n times, if reaching, execution step S6 otherwise, repeats step
Rapid S4；And S6, return iterative calculation result.
The computational methods based on big data of the embodiment of the present invention, determine that the target of business calculates number by the business demand for obtaining
According to magnitude, and calculated off line is carried out to business according to target calculating data magnitude determination or calculating is iterated to business, when
When being iterated calculating, determined by unit or cluster to current target data according to current target data set required memory
Set is iterated calculating, realizes and for mass data to be divided into successive ignition calculating, improves computational efficiency, and real-time is high,
Save resources.
Second aspect present invention embodiment proposes a kind of computing device based on big data, including：Determining module, for obtaining
The business demand at family is taken, and determines that the target of the business calculates data magnitude according to the business demand；Judge module,
For judging that whether the target calculates data magnitude more than predetermined threshold value；Calculated off line module, for calculating in the target
When data magnitude is more than predetermined threshold value, calculated off line is carried out to the business；Iterative calculation module, based in the target
Count when being less than or equal to the predetermined threshold value according to magnitude, obtain current target data set, and judge to calculate the current goal
Whether data acquisition system required memory is more than default internal memory, if being more than the default internal memory, by cluster to the current goal
Data acquisition system is iterated calculating, otherwise, calculating is iterated to the current target data set by unit；Return mould
Block, for when the iterative calculation reaches n times, returning iterative calculation result.
The computing device based on big data of the embodiment of the present invention, determines that the target of business calculates number by the business demand for obtaining
According to magnitude, and calculated off line is carried out to business according to target calculating data magnitude determination or calculating is iterated to business, when
When being iterated calculating, determined by unit or cluster to current target data according to current target data set required memory
Set is iterated calculating, realizes and for mass data to be divided into successive ignition calculating, improves computational efficiency, and real-time is high,
Save resources.
Description of the drawings
Fig. 1 is the flow chart of the computational methods based on big data according to an embodiment of the invention.
Fig. 2 is the flow chart of the computational methods based on big data according to a specific embodiment of the invention.
Fig. 3 is the shortest path effect diagram according to a specific embodiment of the invention.
Fig. 4 is the structural representation of the computing device based on big data according to an embodiment of the invention.
Specific embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein identical from start to finish
Or similar label represents same or similar element or the element with same or like function.Retouch below with reference to accompanying drawing
The embodiment stated is exemplary, it is intended to for explaining the present invention, and be not considered as limiting the invention.
Below with reference to the accompanying drawings the computational methods based on big data and device of the embodiment of the present invention are described.
Fig. 1 is the flow chart of the computational methods based on big data according to an embodiment of the invention.
As shown in figure 1, may include based on the computational methods of big data：
S1, the business demand for obtaining user, and determine that the target of business calculates data magnitude according to business demand.
Specifically, the business demand of user can be obtained first, then according to business demand acquisition target data set, and according to
Target data set determines the target amount of calculation of business, determines that corresponding target calculates data magnitude further according to target amount of calculation.
S2, judge that whether target calculates data magnitudes more than predetermined threshold value.
Wherein, predetermined threshold value is obtained based on cluster hardware configuration and performance.
If S3, be more than predetermined threshold value, calculated off line is carried out to business.
Specifically, if target calculates data magnitude and is more than predetermined threshold value, calculated off line can be carried out to business.
If S4, be less than or equal to predetermined threshold value, obtain current target data set, and judge calculate current target data set
Whether required memory is more than default internal memory, if more than default internal memory, being iterated to current target data set by cluster
Calculate, otherwise, calculating is iterated to current target data set by unit.
In an embodiment of the present invention, if target calculates data magnitude and is less than or equal to predetermined threshold value, business can be changed
In generation, calculates.The structure for assuming target data set is three layers, then iterative calculation can be divided into into time iteration meter of three phases, i.e., three
Calculate.At each stage, current target data set can be obtained, if calculate current target data set required memory be more than
Default internal memory, for example, calculate current target data set required memory 20G, and more than unit internal memory 16G, unit cannot be independent
Calculating task is completed, then calculating can be iterated to current target data set by cluster.Specifically, can be to current goal
Data acquisition system carries out subregion, and by cluster in multiple machine parallel computations, the result of acquisition is defined real-time results, for example
1 second.If current target data set required memory can complete calculating task, obtain less than or equal to default internal memory, unit
Result be real-time results, such as 10 milliseconds.
After the completion of iterative calculation in the first stage, the iterative calculation of second stage can be started, by that analogy.
S5, judge iterative calculation whether reach n times, if reaching, execution step S6 otherwise, repeats step S4.
S6, return iterative calculation result.
If iterative calculation reaches n times, can return to iterate to calculate result, otherwise proceed iterative calculation, until reaching
N times.Wherein, N is positive integer.
The computational methods based on big data of the embodiment of the present invention, determine that the target of business calculates number by the business demand for obtaining
According to magnitude, and calculated off line is carried out to business according to target calculating data magnitude determination or calculating is iterated to business, when
When being iterated calculating, determined by unit or cluster to current target data according to current target data set required memory
Set is iterated calculating, realizes and for mass data to be divided into successive ignition calculating, improves computational efficiency, and real-time is high,
Save resources.
Fig. 2 is the flow chart of the computational methods based on big data according to a specific embodiment of the invention, and the present embodiment is with most
It is described as a example by short path.
As shown in Fig. 2 may include based on the computational methods of big data：
S201, is divided into business multiple stages and is iterated calculating.
Shortest path is for calculating a node to the shortest path of other nodes.Be mainly characterized by centered on starting point to
Extend layer by layer outward, until expanding to terminal till, as shown in figure 3, the business of the present embodiment for calculate from source node A to mesh
The shortest path of mark node H.Traditional algorithm is needed the overall all of traversal path of node, computationally intensive, efficiency
Lowly.
In the present embodiment, shortest path can be calculated it is divided into three phases and is iterated calculatings, i.e., three time and iterate to calculate.
S202, obtains current target data set, and carries out first stage iterative calculation.
Specifically, current target data set can be the real time data bag of first stage iterative calculation, it may include source node A is arrived
Node B, source node A are to node C, source node A to node D.The iterative calculation of first stage need to only calculate above-mentioned data
, and when the internal memory needed for calculating above-mentioned data is less than certain threshold value, can directly carry out internal memory iterative calculation.
S203, obtains current target data set, and carries out second stage iterative calculation.
Specifically, current target data set can be the real time data bag of second stage iterative calculation, it may include node B is to section
Point E, node C are to node F, node D to node F, node D to node G.The iterative calculation of second stage only needs meter
Count in stating data, and when the internal memory needed for calculating above-mentioned data is less than certain threshold value, can directly carry out internal memory iteration
Calculate.
S204, obtains current target data set, and carries out phase III iterative calculation.
Specifically, current target data set can be the real time data bag of phase III iterative calculation, it may include node E is to mesh
Mark node H, node F to node G, node G to node I, node I to destination node H.The iterative calculation of phase III
Above-mentioned data need to be only calculated, and when the internal memory needed for calculating above-mentioned data is less than certain threshold value, can directly be carried out interior
Deposit iterative calculation.
S205, obtains iterative calculation result.
The result of the iterative calculation of three phases is collected, shortest path is generated, that is, obtains final iterative calculation result.
It should be appreciated that the present embodiment is only simple example, the amount of calculation of target data set is huge in practical application,
Need to be classified as multiple stages is iterated calculating, to improve computational efficiency.
The computational methods based on big data of the embodiment of the present invention, by the way that business is divided into into multiple stages calculating is iterated, and is carried
High computational efficiency, real-time is high, save resources.
For achieving the above object, the present invention also proposes a kind of computing device based on big data.
Fig. 4 is the structural representation of the computing device based on big data according to an embodiment of the invention.
As shown in figure 4, the computing device of big data should be based on may include：Determining module 110, judge module 120, offline meter
Calculate module 130, iterative calculation module 140, return module 150.
Wherein it is determined that module 110 is used to obtain the business demand of user, and determine that the target of business is calculated according to business demand
Data magnitude.
Specifically, it is determined that module 110 can obtain first the business demand of user, then target data is obtained according to business demand
Set, and the target amount of calculation of business is determined according to target data set, determine corresponding target meter further according to target amount of calculation
Count according to magnitude.
Judge module 120 is used to judge that whether target calculates data magnitude more than predetermined threshold value.
Wherein, predetermined threshold value is obtained based on cluster hardware configuration and performance.
Calculated off line module 130 is used for when target calculates data magnitude and is more than predetermined threshold value, and to business calculated off line is carried out.
Iterative calculation module 140 is used for when target calculates data magnitude less than or equal to predetermined threshold value, obtains current target data
Set, and whether judge to calculate current target data set required memory more than default internal memory, if more than default internal memory, leading to
Cross cluster and calculating is iterated to current target data set, otherwise, current target data set is iterated by unit
Calculate.
In an embodiment of the present invention, if target calculates data magnitude and is less than or equal to predetermined threshold value, module 140 is iterated to calculate
Calculating can be iterated to business.The structure for assuming target data set is three layers, then iterative calculation can be divided into into three phases,
I.e. three times iterative calculation.At each stage, current target data set can be obtained, if calculating current target data set
Required memory for example calculates current target data set required memory 20G more than default internal memory, more than unit internal memory 16G,
Unit cannot individually complete calculating task, then can be iterated calculating to current target data set by cluster.Specifically,
Can carry out subregion to current target data set, and by cluster in multiple machine parallel computations, the result of acquisition is defined reality
When result, such as 1 second.If current target data set required memory can be completed less than or equal to default internal memory, unit
Calculating task, the result of acquisition is real-time results, such as 10 milliseconds.
After the completion of iterative calculation in the first stage, iterating to calculate module 140 can start the iterative calculation of second stage, with this
Analogize.
Returning module 150 is used to, when iterative calculation reaches n times, return iterative calculation result.
The computing device based on big data of the embodiment of the present invention, determines that the target of business calculates number by the business demand for obtaining
According to magnitude, and calculated off line is carried out to business according to target calculating data magnitude determination or calculating is iterated to business, when
When being iterated calculating, determined by unit or cluster to current target data according to current target data set required memory
Set is iterated calculating, realizes and for mass data to be divided into successive ignition calculating, improves computational efficiency, and real-time is high,
Save resources.
In describing the invention, it is to be understood that term " " center ", " longitudinal direction ", " horizontal ", " length ",
" width ", " thickness ", " on ", D score, "front", "rear", "left", "right", " vertical ",
" level ", " top ", " bottom " " interior ", " outward ", " clockwise ", " counterclockwise ", " axial direction ", " footpath
To ", the orientation of the instruction such as " circumference " or position relationship be based on orientation shown in the drawings or position relationship, merely to just
In description the present invention and simplify description, rather than indicate or imply indication device or element must have specific orientation, with
Specific azimuth configuration and operation, therefore be not considered as limiting the invention.
Additionally, term " first ", " second " are only used for describing purpose, and it is not intended that indicating or implying relatively important
Property or the implicit quantity for indicating indicated technical characteristic.Thus, " first " is defined, the feature of " second " can be with
Express or implicitly include at least one this feature.In describing the invention, " multiple " are meant that at least two,
Such as two, three etc., unless otherwise expressly limited specifically.
In the present invention, unless otherwise clearly defined and limited, term " installation ", " connected ", " connection ", " Gu
It is fixed " etc. term should be interpreted broadly, it is for example, it may be fixedly connected, or be detachably connected or integral；Can
Being to be mechanically connected, or electrically connect；Can be joined directly together, it is also possible to be indirectly connected to by intermediary, can be with
It is connection or the interaction relationship of two elements of two element internals, unless otherwise clearly restriction.For this area
For those of ordinary skill, above-mentioned term concrete meaning in the present invention can be as the case may be understood.
In the present invention, unless otherwise clearly defined and limited, fisrt feature second feature " on " or D score can be with
It is the first and second feature directly contacts, or the first and second features are by intermediary mediate contact.And, fisrt feature
Second feature " on ", " top " and " above " but fisrt feature directly over second feature or oblique upper, or
Fisrt feature level height is merely representative of higher than second feature.Fisrt feature second feature " under ", " lower section " and " under
Face " can be fisrt feature immediately below second feature or obliquely downward, or be merely representative of fisrt feature level height less than second
Feature.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " tool
The description of body example " or " some examples " etc. means to combine specific features, structure, the material that the embodiment or example are described
Material or feature are contained at least one embodiment of the present invention or example.In this manual, to the signal of above-mentioned term
Property statement be necessarily directed to identical embodiment or example.And, the specific features of description, structure, material or spy
Point can in an appropriate manner be combined in any one or more embodiments or example.Additionally, in the case of not conflicting,
Those skilled in the art can be by the different embodiments or example described in this specification and the spy of different embodiments or example
Levy and be combined and combine.
Although embodiments of the invention have been shown and described above, it is to be understood that above-described embodiment be it is exemplary,
It is not considered as limiting the invention, one of ordinary skill in the art within the scope of the invention can be to above-described embodiment
It is changed, changes, replacing and modification.
Claims (8)
1. a kind of computational methods based on big data, it is characterised in that comprise the following steps：
S1, the business demand for obtaining user, and determine that the target of the business calculates data magnitude according to the business demand；
S2, judge that whether the target calculates data magnitude more than predetermined threshold value；
If S3, be more than the predetermined threshold value, calculated off line is carried out to the business；
If S4, being less than or equal to the predetermined threshold value, current target data set is obtained, and judge to calculate the current goal
Whether data acquisition system required memory is more than default internal memory, if being more than the default internal memory, by cluster to the current goal
Data acquisition system is iterated calculating, otherwise, calculating is iterated to the current target data set by unit；
S5, judge it is described iterative calculation whether reach n times, if reaching, execution step S6 otherwise, repeats step
S4；And
S6, return iterative calculation result.
2. the method for claim 1, it is characterised in that the mesh that the business is determined according to the business demand
Mark calculates data magnitude, including：
Target data set is obtained according to the business demand, and the target of the business is determined according to the target data set
Amount of calculation, and determine that corresponding target calculates data magnitude according to the target amount of calculation.
3. the method for claim 1, it is characterised in that the predetermined threshold value is obtained based on cluster hardware configuration and performance
.
4. the method for claim 1, it is characterised in that described the current target data set is entered by cluster
Row iteration is calculated, including：
Carry out subregion to the current target data set, and by the cluster in multiple machine parallel computations.
5. a kind of computing device based on big data, it is characterised in that include：
Determining module, for obtaining the business demand of user, and determines that the target of the business is calculated according to the business demand
Data magnitude；
Judge module, for judging that whether the target calculates data magnitude more than predetermined threshold value；
Calculated off line module, for when the target calculates data magnitude and is more than predetermined threshold value, carrying out offline to the business
Calculate；
Iterative calculation module, for when the target calculates data magnitude less than or equal to the predetermined threshold value, obtaining current mesh
Mark data acquisition system, and whether judge to calculate the current target data set required memory more than default internal memory, if more than described
Default internal memory, then be iterated calculating by cluster to the current target data set, otherwise, is worked as to described by unit
Front target data set is iterated calculating；
Module is returned, for when the iterative calculation reaches n times, returning iterative calculation result.
6. device as claimed in claim 5, it is characterised in that the determining module, specifically for：
Target data set is obtained according to the business demand, and the target of the business is determined according to the target data set
Amount of calculation, and determine that corresponding target calculates data magnitude according to the target amount of calculation.
7. device as claimed in claim 5, it is characterised in that the predetermined threshold value is obtained based on cluster hardware configuration and performance
.
8. device as claimed in claim 5, it is characterised in that the iterative calculation module, specifically for：
Carry out subregion to the current target data set, and by the cluster in multiple machine parallel computations.
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Application publication date: 20170419 |