CN107783731A - A kind of big data real-time processing method and processing system - Google Patents
A kind of big data real-time processing method and processing system Download PDFInfo
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- CN107783731A CN107783731A CN201710664834.6A CN201710664834A CN107783731A CN 107783731 A CN107783731 A CN 107783731A CN 201710664834 A CN201710664834 A CN 201710664834A CN 107783731 A CN107783731 A CN 107783731A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/0604—Improving or facilitating administration, e.g. storage management
- G06F3/0607—Improving or facilitating administration, e.g. storage management by facilitating the process of upgrading existing storage systems, e.g. for improving compatibility between host and storage device
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
- G06F3/0638—Organizing or formatting or addressing of data
- G06F3/0643—Management of files
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
- G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
Abstract
This application discloses a kind of big data real-time processing method and processing system, the real-time processing method comprises the following steps：Receiving real-time data；According to information rate and receive the N number of main controlled node of total amount establishment；By data distribution into N number of main controlled node；Obtain the processing capability in real time data of cluster server；N number of working node is selected according to the disposal ability of cluster server；N number of main controlled node transmits data to N number of working node.The big data processing method and its device that the application proposes, Internet resources can be saved according to the provided matching data processing speed of acquisition speed, while shorten the response time, provide the user real-time data, services.
Description
Technical field
The application belongs to field of computer technology, in particular to a kind of big data real-time processing method and its processing
System.
Background technology
It is increasing for the demand of data with the rapid popularization of network, such as in ICU intensive care units, from monitoring
Device gateway continually collects patient vital sign data, from examining work station to produce assay data, and these are real-time
Data need rapid, accurately processing.Existing big data processing method is confined to the ability of network transmission, and uses single line
The processing mode of journey, single server, so that the data run cycle is grown, customer experience is reduced, even if using multiprocessor,
Due to coordination problem between processor, and cause processor cluster from playing its maximum ability to work.
The content of the invention
In view of this, the application proposes a kind of big data real-time processing method and its system, is directed to solving prior art
In for big data disposal ability deficiency, the not strong technical problem of real-time.
In order to solve the above technical problems, the application adopts the following technical scheme that：
The application protects a kind of big data real-time processing method, comprises the following steps：
Receiving real-time data；
According to information rate and receive the N number of main controlled node of total amount establishment；
By data distribution into N number of main controlled node；
Obtain the processing capability in real time data of cluster server；
N number of working node is selected according to the disposal ability of cluster server；
N number of main controlled node transmits data to N number of working node.
Wherein creating N number of main controlled node according to information rate and reception total amount includes following sub-step：
Calculate information rate；
Interval calculation receives total amount to schedule；
Assess the minimum and maximum processing capability of main controlled node；
Select optimal main controlled node number N；
Create N number of main controlled node.
Wherein data distribution is included to N number of main controlled node：
Split data into N parts；
For every part of data label allocation；
By label storage into data distribution list；
Every part of data are assigned in corresponding main controlled node, main controlled node is handled it.
Wherein obtaining the processing capability in real time data of cluster server is included according to idle server in cluster server
The memory capacity of CPU disposal abilities and memory, judge the processing capability in real time of cluster.
Wherein N number of working node is selected to include according to the disposal ability of cluster server, to each in cluster server
The disposal ability of server is ranked up, and selects top n as working node.
The application also protects a kind of task controller, including such as lower component：
Receiver, for receiving real-time data；
Processor, for creating N number of main controlled node according to information rate and reception total amount；By data distribution to N number of
In main controlled node；Obtain the processing capability in real time data of cluster server；N number of work is selected according to the disposal ability of cluster server
Make node；N number of main controlled node transmits data to N number of working node.
Wherein processor includes creation module, for creating N number of main controlled node according to information rate and reception total amount,
Creation module includes following submodule：
Speed computing unit, calculate information rate；
Total amount calculating unit is received, interval calculation receives total amount to schedule；
Disposal ability computing module, assess the minimum and maximum processing capability of main controlled node；
Selecting unit, select the disposal ability of optimal main controlled node number N and main controlled node；
Creating unit, create N number of main controlled node.
Wherein processor includes distribution module, for by data distribution, into N number of main controlled node, distribution module to include as follows
Subassembly：
Cutting unit, split data into N parts；
Tag unit, it is every part of data label allocation；
List cell, by label storage into data distribution list；
Allocation unit, every part of data are assigned in corresponding main controlled node, main controlled node is handled it.
Wherein obtaining the processing capability in real time data of cluster server includes the CPU processing according to idle server in cluster
The memory capacity of ability and memory, judge the processing capability in real time of cluster.
A kind of big data processing system is also claimed in the application, including：
Multiple client, for gathering real time data；
Task controller as described above；
Cluster server, the task data issued for handling task controller.
The beneficial effect of the application is：The big data processing method and its device that the application proposes, can be according to data
The provided matching data processing speed of picking rate, Internet resources are saved, while shorten the response time, carried for user
For real-time data, services.
Brief description of the drawings
Fig. 1 is the composition schematic diagram of the application big data real time processing system；
Fig. 2 is the flow chart of the application big data real-time processing method；
Fig. 3 is the workflow diagram that the application creates N number of main controlled node；
Fig. 4 is the structure chart of the application task controller；
Fig. 5 is the structure chart of processor in the application task controller；
Fig. 6 is the structure chart of the application creation module；
Fig. 7 is the structure chart of the application distribution module.
Embodiment
The big data real-time processing method and processing system of the application, to the transmission speed of data gathered in real time and collection
Amount is assessed, and so as to dynamic generation main controlled node, and is created according to the assessment of the disposal ability of the cluster server connected
The working node to match with the main controlled node of dynamic generation, so as to pass through the speed to big data and cluster server work energy
The comprehensive assessment of power, on the basis of network condition, disposal ability, data acquisition ability is considered, it is excellent to provide the user performance
The big data real-time processing method and processing system of change.
Wherein, the structure chart of the system is as shown in figure 1, including multiple client, client 1 arrives client N, task control
Device 101, wherein cluster server 102, cluster server 102 include M server, and wherein those skilled in the art can manage
Solution, N, M are approximate number, are represented multiple.Wherein client is responsible for gathering real time data, by taking ICU CICUs as an example, data
Source of generation mainly there is in bedside monitoring equipment and institute clinical data store.As long as it be able to can all be made with the equipment of gathered data
For client, N number of client in the system is connected in system, is responsible for providing real time data；Task controller is according to client
The speed and data total amount dynamic creation main controlled node of end transmission data, and selected according to the processing capability in real time of cluster server
Adaptable working node, to handle the data of main controlled node transmission.Realized by the big data processing system to big data
In real time, accurately and rapidly handle.
Wherein the workflow diagram of the system is as shown in Fig. 2 the big data processing method comprises the following steps：
S201, receiving real-time data；
Task controller 101 receives the real-time task data that multiple client is sent, such as the various medical datas of patient,
Including Monitoring Data in vital sign, inspection laboratory indexes, therapeutic process etc., these data have real-time, emergency, number
Measure the characteristics of big, therefore referred to as big data.Client is arranged the data of collection, with wired or be wirelessly sent to
Task controller 101.
S202, according to information rate and receive total amount and create N number of main controlled node.
After task controller 101 receives task data, following sub-step as shown in Figure 3 is performed：
S301, calculate information rate；
The known approaches measurement data such as network speed, speed of download can be used to receive speed.
S302, to schedule interval calculation receive total amount；
Preset or predetermined time interval is specified by user every time, according to speed of download and time interval, calculating connects
The data total amount of receipts.
S303, the minimum and maximum processing capability for assessing main controlled node；
According to the capacity of memory and read or write speed and the disposal ability of task controller 101, main controlled node is calculated
Minimum and maximum processing capability, further, can distribute to Thread Count and the storage of main controlled node according to task controller
The minimum and maximum processing capability of device Capacity Assessment main controlled node.
The optimal main controlled node number N of S304, selection and main controlled node disposal ability.
According to the reception speed of data, total amount and the minimum and maximum disposal ability of main controlled node are received, selects master control
Node number N and main controlled node disposal ability, ensure that the processing of data can be completed, and do not waste the line of task controller
Number of passes.Wherein the disposal ability of main controlled node can distribute to its Thread Count and memory span according to task controller 101
Weigh.
S305, create N number of main controlled node.
Task controller 101 creates N number of master control section according to the number N of main controlled node and the disposal ability of main controlled node
Point, a background program for being referred to as Nimbus is run on each main controlled node.
S203, by data distribution into N number of main controlled node；
By the data distribution of acquisition into N number of main controlled node, in distribution, it is contemplated that data include the continuity of content
And the response speed of client is returned to, the data for coming from a client are given into a main controlled node as far as possible.
Including following sub-step：
S2031, split data into N parts；
The data of reception are divided into N parts by task controller 101, in partition data, by the data from a client
Assign to as far as possible in portion.
S2032, it is every part of data label allocation；
It can be every part of data distribution label in sequence, also can be every part of data label allocation according to other rules, its
In the labels of every part of data in the whole network be unique.
S2033, by label storage into data distribution list；
Label is saved in data distribution list, follow-up transmission, response are required for using the label.
S2034, every part of data are assigned in corresponding main controlled node, main controlled node is handled it；
Every part of data are assigned to main controlled node by task controller 101, and Nimbus background programs thereon are responsible for receiving number
According to, Uniform data format, send the work such as data.The data from same client are particularly among one bag
, same main controlled node is sent to as far as possible to be handled.This is because, often deposited between the data from same client
In continuity, it is easy to the continuous processing of main controlled node.
After execution of step S203, with continued reference to Fig. 2, other steps are performed：
S204, the processing capability in real time data for obtaining cluster server；
Obtaining the processing capability in real time data of cluster server is included at the CPU according to idle server in cluster server
The memory capacity of reason ability and memory, judge the processing capability in real time of cluster.
S205, N number of working node selected according to the disposal ability of cluster server；
The disposal ability of each server in cluster server is ranked up, selects top n as working node.Often
A background program for being referred to as Supervisor is run on individual working node, is responsible for monitoring from Nimbus and distributes to what it was performed
Task, the progress of work of execution task is started or stoped accordingly.Each progress of work performs Topology subset；One
Individual operating Topology is made up of the multiple progresses of work being distributed on different operating node.
S206, N number of main controlled node transmit data to N number of working node
The Nimbus of main controlled node is established with the Supervisor of corresponding working node and communicated, and is sent the data to correspondingly
Working node Supervisor, Supervisor establishes the progress of work for performing corresponding data task, data carried out
Processing.After processing is completed, call kill orders to kill the progress of work by Supervisor, and terminate it in whole processing
Afterwards, task controller calls kill orders to kill Nimbus and Supervisor, discharges main controlled node and working node.
The workflow of the application is simply described above in association with accompanying drawing 1-3, with reference to Fig. 4-7, introduces the application's
The detailed composition of task controller 101.
As shown in figure 4, task controller 101 includes such as lower component：
Receiver 401, for receiving real-time data；
Receiver 401, receive the various data that client is sent.
Processor 402, for creating N number of main controlled node according to information rate and reception total amount；By data distribution to N
In individual main controlled node；Obtain the processing capability in real time data of cluster server；Selected according to the disposal ability of cluster server N number of
Working node；N number of main controlled node transmits data to N number of working node.
Wherein the structure of processor 402 is as shown in figure 5, including such as lower component：
Creation module 501, for creating N number of main controlled node according to information rate and reception total amount；
As shown in fig. 6, creation module 501 includes following submodule：
Speed computing unit 601：Calculate information rate；
The known approaches measurement data such as network speed, speed of download can be used to receive speed.
Receive total amount calculating unit 602：Interval calculation receives total amount to schedule；
Preset or predetermined time interval is specified by user every time, according to speed of download and time interval, calculating connects
The data total amount of receipts.
Disposal ability computing module 603：Assess the minimum and maximum processing capability of main controlled node；
According to the capacity of memory and read or write speed and the disposal ability of task controller 101, main controlled node is calculated
Minimum and maximum processing capability, further, can distribute to the Thread Count of main controlled node and deposit according to task controller 101
The minimum and maximum processing capability of reservoir Capacity Assessment main controlled node.
Selecting unit 604, select the disposal ability of optimal main controlled node number N and main controlled node.
According to the reception speed of data, total amount and the minimum and maximum disposal ability of main controlled node are received, selects master control
Node number N and main controlled node disposal ability, ensure that the processing of data can be completed, and do not waste the line of task controller
Number of passes.Wherein the disposal ability of main controlled node can distribute to its Thread Count and memory span according to task controller 101
Weigh.
Creating unit 605, create N number of main controlled node.
Creating unit 605 creates N number of main controlled node according to the number N of main controlled node and the disposal ability of main controlled node,
A background program for being referred to as Nimbus is run on each main controlled node.
With continued reference to Fig. 5, in addition to：
Distribution module 502, for by data distribution into N number of main controlled node；
As shown in fig. 7, distribution module 502 includes following subassembly：
Cutting unit 701, split data into N parts；
The data of reception are divided into N parts by cutting unit 701.
Tag unit 702, it is every part of data label allocation；
It can be every part of data distribution label in sequence, also can be every part of data label allocation according to other rules, its
In the labels of every part of data in the whole network be unique.
List cell 703, by label storage into data distribution list；
Label is saved in data distribution list, follow-up transmission, response are required for using the label.
Allocation unit 704, every part of data are assigned in corresponding main controlled node, main controlled node is handled it；
Every part of data are assigned to main controlled node by allocation unit 704, Nimbus background programs thereon be responsible for receiving data,
Uniform data format, send the work such as data.
Judge module 503, for obtaining the processing capability in real time data of cluster server；
Obtaining the processing capability in real time data of cluster server is included at the CPU according to idle server in cluster server
The memory capacity of reason ability and memory, judge the processing capability in real time of cluster.
Selecting module 504, for selecting N number of working node according to the disposal ability of cluster server；
The disposal ability of each server in cluster server is ranked up, selects top n as working node.Often
A background program for being referred to as Supervisor is run on individual working node, is responsible for monitoring from Nimbus and distributes to what it was performed
Task, the progress of work of execution task is started or stoped accordingly.Each progress of work performs Topology subset；One
Individual operating Topology is made up of the multiple progresses of work being distributed on different operating node.
Sending module 505, for causing N number of main controlled node to transmit data to N number of working node.
The Nimbus of main controlled node is established with the Supervisor of corresponding working node and communicated, and is sent the data to correspondingly
Working node Supervisor, Supervisor establishes the progress of work for performing corresponding data task, data carried out
Processing.After processing is completed, call kill orders to kill the progress of work by Supervisor, and terminate it in whole processing
Afterwards, task controller calls kill orders to kill Nimbus and Supervisor, discharges main controlled node and working node.
Here description of the invention and application are illustrative, are not wishing to limit the scope of the invention to above-described embodiment
In.The deformation and change of embodiments disclosed herein are possible, real for those skilled in the art
The replacement and equivalent various parts for applying example are known.It should be appreciated by the person skilled in the art that the present invention is not being departed from
Spirit or essential characteristics in the case of, the present invention can in other forms, structure, arrangement, ratio, and with other components,
Material and part are realized.In the case where not departing from scope and spirit of the present invention, embodiments disclosed herein can be entered
The other deformations of row and change.
Claims (10)
1. a kind of big data real-time processing method, comprises the following steps：
Receiving real-time data；
According to information rate and receive the N number of main controlled node of total amount establishment；
By data distribution into N number of main controlled node；
Obtain the processing capability in real time data of cluster server；
N number of working node is selected according to the disposal ability of cluster server；
N number of main controlled node transmits data to N number of working node.
2. processing method as claimed in claim 1, wherein according to information rate and receiving the N number of main controlled node of total amount establishment
Including following sub-step：
Calculate information rate；
Interval calculation receives total amount to schedule；
Assess the minimum and maximum processing capability of main controlled node；
Select optimal main controlled node number N；
Create N number of main controlled node.
3. processing method as claimed in claim 1, wherein data distribution is included to N number of main controlled node：
Split data into N parts；
For every part of data label allocation；
By label storage into data distribution list；
Every part of data are assigned in corresponding main controlled node, main controlled node is handled it.
4. processing method as claimed in claim 1, wherein obtaining the processing capability in real time data of cluster server includes basis
The CPU disposal abilities of idle server and the memory capacity of memory in cluster server, judge the real-time processing energy of cluster
Power.
5. processing method as claimed in claim 1, wherein selecting N number of working node bag according to the disposal ability of cluster server
Include, the disposal ability of each server in cluster server is ranked up, select top n as working node.
6. a kind of task controller, including such as lower component：
Receiver, for receiving real-time data；
Processor, for creating N number of main controlled node according to information rate and reception total amount；By data distribution to N number of master control
In node；Obtain the processing capability in real time data of cluster server；N number of work section is selected according to the disposal ability of cluster server
Point；N number of main controlled node transmits data to N number of working node.
7. task controller as claimed in claim 1, wherein processor include creation module, for according to information rate
N number of main controlled node is created with total amount is received, creation module includes following submodule：
Speed computing unit, calculate information rate；
Total amount calculating unit is received, interval calculation receives total amount to schedule；
Disposal ability computing module, assess the minimum and maximum processing capability of main controlled node；
Selecting unit, select the disposal ability of optimal main controlled node number N and main controlled node；
Creating unit, create N number of main controlled node.
8. task controller as claimed in claim 6, wherein processor include distribution module, for by data distribution to N number of
In main controlled node, distribution module includes following subassembly：
Cutting unit, split data into N parts；
Tag unit, it is every part of data label allocation；
List cell, by label storage into data distribution list；
Allocation unit, every part of data are assigned in corresponding main controlled node, main controlled node is handled it.
9. task controller as claimed in claim 6, wherein obtaining the processing capability in real time data of cluster server includes root
According to the CPU disposal abilities of idle server in cluster and the memory capacity of memory, the processing capability in real time of cluster is judged.
10. a kind of big data processing system, including：
Multiple client, for gathering real time data；
Task controller as described in one of claim 6-9；
Cluster server, the task data issued for handling task controller.
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| CN110442508A (en) * | 2018-05-03 | 2019-11-12 | 阿里巴巴集团控股有限公司 | Test assignment processing method, device, equipment and medium |
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| CN113965587A (en) * | 2021-09-18 | 2022-01-21 | 苏州浪潮智能科技有限公司 | Data acquisition method, device, equipment and medium of artificial intelligence platform |
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| CN107783731B (en) | 2020-12-01 |
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