CN109871394A - A kind of full dose distribution high concurrent calculation method and device - Google Patents
A kind of full dose distribution high concurrent calculation method and device Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of full dose distribution high concurrent calculation method and devices, are related to big data technical field, and the resistance to compression mechanism that can be avoided hotspot caching in the prior art is not high, and database brings very big pressure, thus the problem of reducing stability.The present invention includes: the incremental computations program stopped in distributed system, deploys public library, price library, caching and business valence library in the distributed system；Start full dose scheduler task, and generates the full dose table in business valence library；After full dose scheduler task execution, the full dose table is updated；Start the incremental computations program.High concurrent full dose of the present invention suitable for distributed system calculates.
Description
Technical field
The present invention relates to big data technical field more particularly to a kind of full dose distribution high concurrent calculation method and devices.
Background technique
Currently, all kinds of online transaction platforms, ticket sale system, game server etc., the access pressure of required carrying is increasingly
Height, concurrency all the time have become astronomical figure.For example, the price system of many large size electric business, per second to need to receive thousand
Ten thousand grades even more than one hundred million grades of inquiry price access.
In order to cope with the pressure of high concurrent access, it usually needs calculate the result of data in advance.And much it is at present
System is the queried access that magnanimity is coped with using the method for hotspot caching.This can extenuate problem to a certain extent, but hot spot
The resistance to compression mechanism of caching is not high, very big pressure can be brought with database, to reduce stability.
Summary of the invention
The embodiment of the present invention provides a kind of full dose distribution high concurrent calculation method and device, can be avoided the prior art
The resistance to compression mechanism of middle hotspot caching is not high, and database brings very big pressure, thus the problem of reducing stability.
In order to achieve the above objectives, the embodiment of the present invention adopts the following technical scheme that
Stop the incremental computations program in distributed system, public library is deployed in the distributed system, price library, is delayed
It deposits and business valence library；
Start full dose scheduler task, and generates the full dose table in business valence library；
After full dose scheduler task execution, the full dose table is updated；
Start the incremental computations program.
It is described to update the full dose table, comprising:
Initial data is obtained in batches from the public library, price library and caching；
Price data is calculated using the initial data；
The price data being calculated batch is inserted into the full dose table.
The full dose scheduler task includes:
Distributed task dispatching table is generated, is had recorded in the distributed task dispatching table: x timed task, y
Jboss is applied and z tables of data, and the z tables of data are obtained from the initial data, and x, y and z are positive integers, timing
The quantity of task matches with the jboss quantity applied；
After initializing the full dose scheduler task, each timed task locks a tables of data；
After n-th of jboss is finished using the timed task of upper operation, upper operation is applied to n-th of jboss
The tables of data of timed task locking be unlocked, n-th of jboss is locked again using the timed task of upper operation later
One tables of data being not locked out, and execute again.
Also, redis machine group and mysql machine group, the target service system docking are distributed for target service system
Business valence library, business valence library are used to provide price data for the target service system；Appoint for full dose scheduling
Business distribution redis cache resources and mysql resource, wherein the redis cache resources distributed are not belonging to as the target service
The redis machine group of system distribution, the mysql resource distributed are not belonging to the mysql machine distributed for the target service system
Device group.
In the present embodiment, enable to calculation stages user that can normally access.By utilizing application server resource, adopt
With Distributed Parallel Computing method, to need the application side of this data to provide accurate data, suitable under high concurrent scene
Full dose mass data calculates.Using full dose calculation method, can calculate in advance that data side needs as a result, to improve height simultaneously
Send out response time and the calling amount of access.In the distributed calculating process of full dose, foreground price real-time query is not influenced, simultaneously
There are other increment factors to come, can guarantee the accuracy and integrality of data with the price data of the same commodity of real-time update.
The resistance to compression mechanism for also avoiding hotspot caching in the prior art is not high, and database brings very big pressure, to reduce stability
The problem of.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to needed in the embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is system architecture schematic diagram provided in an embodiment of the present invention；
Fig. 2 is method flow schematic diagram provided in an embodiment of the present invention；
Fig. 3,4 be specific example provided in an embodiment of the present invention schematic diagram；
Fig. 5 is apparatus structure schematic diagram provided in an embodiment of the present invention.
Specific embodiment
Technical solution in order to enable those skilled in the art to better understand the present invention, with reference to the accompanying drawing and specific embodiment party
Present invention is further described in detail for formula.Embodiments of the present invention are described in more detail below, the embodiment is shown
Example is shown in the accompanying drawings, and in which the same or similar labels are throughly indicated same or similar element or has identical or class
Like the element of function.It is exemplary below with reference to the embodiment of attached drawing description, for explaining only the invention, and cannot
It is construed to limitation of the present invention.Those skilled in the art of the present technique are appreciated that unless expressly stated, odd number shape used herein
Formula " one ", "one", " described " and "the" may also comprise plural form.It is to be further understood that specification of the invention
Used in wording " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that
In the presence of or add other one or more features, integer, step, operation, element, component and/or their group.It should be understood that
When we say that an element is " connected " or " coupled " to another element, it can be directly connected or coupled to other elements, or
There may also be intermediary elements.In addition, " connection " used herein or " coupling " may include being wirelessly connected or coupling.Here make
Wording "and/or" includes one or more associated any cells for listing item and all combinations.The art
Technical staff is appreciated that unless otherwise defined all terms (including technical terms and scientific terms) used herein have
Meaning identical with the general understanding of the those of ordinary skill in fields of the present invention.It should also be understood that such as general
Those terms, which should be understood that, defined in dictionary has a meaning that is consistent with the meaning in the context of the prior art, and
Unless defined as here, it will not be explained in an idealized or overly formal meaning.
Method flow in the present embodiment can specifically execute in a kind of system as shown in Figure 1, which includes:
Each end equipment of operation system, Redis clusters of machines, management server, background data base and user terminal, system is mutual
Channel can be established by internet, and data interaction is carried out by respective data transmission port.Wherein:
Operation system disclosed in the present embodiment and management server, specifically can be on hardware view work station,
The equipment such as supercomputer, or a kind of server cluster for data processing being made of multiple servers.Wherein, it manages
Server is managed, refers to the server apparatus for managing Redis clusters of machines and real-time monitoring operation system, usually
Management server be provided in the Operation and Maintenance Center for each operation system of background maintenance.
Redis clusters of machines disclosed in the present embodiment specifically can be by multiple servers on hardware view, delay
Deposit a kind of device clusters for data processing of machine composition.Cache resources in Redis clusters of machines be used to store backstage
Data in database, in order to which operation system is when receiving the access request of user terminal transmission, from Redis clusters of machines
Middle extraction data return to user terminal, and relative to acquisition data are directly accessed the database, extracting from caching can be improved number
According to feedback speed.
In background data base, the required data used when operation system operation are stored, such as: price data, logistics number
According to, inventory data etc..Background data base specifically can be using database schema common at present, type.
Operation system specifically can be on hardware view and be made of multiple servers, and one kind is for runing online industry
Inventive system, ordering system, notice system for being runed in the system of business, such as online shopping platform etc..
User terminal can specifically be made into an independent equipment in fact, or be integrated in a variety of different media datas and play system
In system, such as smart phone, tablet computer (Tablet Personal Computer), laptop computer (Laptop
Computer), personal digital assistant (personal digital assistant, abbreviation PDA) etc..Such as: the online purchase of access
The user terminal of object platform, be usually used by a user in price queries, place an order, merchandise news browsing etc. operation.
The embodiment of the present invention provides a kind of full dose distribution high concurrent calculation method, as shown in Figure 2, comprising:
Incremental computations program in S101, stopping distributed system.
Wherein, public library, price library, caching and business valence library are deployed in the distributed system.Management server is used
Incremental computations program and full dose scheduler task in control distributed system.
Wherein: public library can be understood as one kind of database, for storing common data resource, not use a point library generally
Divide the database schema of table.
Price library is exactly that full dose and incremental computations will rely on, and mainly use the database schema for dividing table point library.
After business valence library is exactly full dose and incremental computations, the database for the data landing finally calculated, and
It is main to use the database schema for dividing table point library.
In practical applications, distributed system can have many factors when the business that operation needs full dose to calculate
Thus the case where variation, can trigger incremental computations, will be updated same table after triggering incremental computations, to record the change of generation
Change.
S102, starting full dose scheduler task, and generate the full dose table in business valence library.
S103, the full dose scheduler task execution after, update the full dose table.
S104, the starting incremental computations program.
Specifically, described update the full dose table, comprising:
Initial data is obtained in batches from the public library, price library and caching.Price is calculated using the initial data
Data.The price data being calculated batch is inserted into the full dose table.Wherein, initial data refers to full dose and incremental computations
It needs to rely on basic data, includes reserve price, the data such as ratio and preferential list of raising the price or information.
Price data is calculated using the initial data, includes: the purchasing price that commodity are indicated on shopping page,
The price for finally needing customer payment on the basis of purchasing price and after the preferential list of removing.
Price data batch is inserted into the full dose table, refers to being inserted into data for multiple price datas as a batch, execute
Insert action is inserted into full dose table simultaneously, relative to the mode that each price data is sequentially inserted into full dose table, batch energy
Enough improve the efficiency of data insertion full dose table.
Wherein, price data includes the pricing information of the commodity of each category, price fluctuation range etc., price fluctuation model
The specific section enclosed, can be set by technical staff, can also be also calculated in real time according to preset computation model.Specifically
, it is as shown in Figure 3, during management server executes full dose scheduler task, using multi-process multi-threaded parallel, and can be with
Multitask is split into according to business dimension, task is dynamically assigned to idle thread execution.And it can be located in each tables of data
Reason finishes Europe, and logger task executes the stage, in order to which next time can restore to execute after stopping, in case the interruption of full dose scheduler task is laid equal stress on
Tables of data is reprocessed after opening.
In the present embodiment, enable to calculation stages user that can normally access.By utilizing application server resource, adopt
With Distributed Parallel Computing method, to need the application side of this data to provide accurate data, suitable under high concurrent scene
Full dose mass data calculates.Using full dose calculation method, can calculate in advance that data side needs as a result, to improve height simultaneously
Send out response time and the calling amount of access.In the distributed calculating process of full dose, foreground price real-time query is not influenced, simultaneously
There are other increment factors to come, can guarantee the accuracy and integrality of data with the price data of the same commodity of real-time update.
In the present embodiment, the implementation procedure of the full dose scheduler task generally comprises:
Generate distributed task dispatching table.After initializing the full dose scheduler task, each timed task locks one
Tables of data.
After n-th of jboss is finished using the timed task of upper operation, upper operation is applied to n-th of jboss
The tables of data of timed task locking be unlocked, n-th of jboss is locked again using the timed task of upper operation later
One tables of data being not locked out, and execute again.
Wherein, it is had recorded in the distributed task dispatching table: x timed task, y jboss application and z data
Table, the z tables of data are obtained from the initial data, and x, y and z are positive integers, and quantity and the jboss of timed task are answered
Quantity matches, wherein and the quantity that " matching " can be understood as timed task is identical as the quantity that jboss is applied, or
The quantity of person jboss application is the integral multiple of the quantity of timed task.Such as it is shown in Fig. 4, distributed task dispatching table is set,
12 points of morning of every night runs on time, 100 timed tasks, 100 jboss applications, 1000 tables of data.
Each timed task can lock a tables of data before running, in 100 jboss using upper, meeting when race for the first time
100 tables of data are locked, during this calculating, if there is a jboss application is run through, will continue to run 101
Tables of data, and so on, before full dose task is run through, 100 jboss applications are all being operated, and go to last 100 data
When table, jboss application can be released slowly,
Full dose calculating is carried out in the cluster of backstage, and the database of redis and mysql can be made full use of in calculating process
Resource, redis and mysql resource may be generally referred to as redis example and mysql example.
, can be with dilatation jboss quantity and mysql quantity if performance can also be improved, high concurrent parallel computation is final out
Data result
Further, further includes:
Redis machine group and mysql machine group are distributed for target service system；And it is distributed for the full dose scheduler task
Redis cache resources and mysql resource,
Wherein, business valence library described in the target service system docking, business valence library are used to be the target service
System provides price data.The redis cache resources distributed are not belonging to the redis machine distributed for the target service system
Group, the mysql resource distributed are not belonging to the mysql machine group distributed for the target service system.Such as: according to application
Machine data information distributes redis machine group and mysql machine group for target service system, so that the redis of queried access
Machine group and mysql machine group are separated with the redis machine group and mysql machine group of real-time calculating.It ensure that in calculating
In the case of, the redis caching and mysql of system can be by normal access queries prices, so that the base that price service normally provides
Under plinth, full dose calculating is carried out.Also, due to before calculating successfully, having no effect on the data inside redis machine group and mysql,
Assuming that redis machine group and mysql still can normally provide service under the premise of calculating failure.
Realize operation system it is online in the state of carry out full dose calculating when, wherein non-interrupting service system normally look into
It askes, access, improves the scalability of system.The case where fighting for redis resource and mysql especially for multiple operation systems,
Be conducive to the allocative efficiency that management server improves cache resources.
Further, due to when high concurrent is inquired, still can by caching process access request, also just reduce to
The number of database access inquiry, while guaranteeing the stability of database, also maintains the performance of query service.
The embodiment of the present invention also provides a kind of full dose distribution high concurrent computing device, as shown in Figure 5, comprising:
Task management module is disposed in the distributed system for stopping the incremental computations program in distributed system
Public library, price library, caching and business valence library；
Processing module for starting full dose scheduler task, and generates the full dose table in business valence library；
Update module, for updating the full dose table after full dose scheduler task execution；
The task management module is also used to start the incremental computations program.
Wherein, the update module, specifically for obtaining original number in batches from the public library, price library and caching
According to；Price data is calculated using the initial data；The price data being calculated batch is inserted into the full dose table.
The processing module is specifically used for generating distributed task dispatching table, record in the distributed task dispatching table
: x timed task, y jboss application and z tables of data, the z tables of data are obtained from the initial data, x, y
It is all positive integer with z, quantity and the jboss quantity applied of timed task match；
After initializing the full dose scheduler task, each timed task locks a tables of data；
After n-th of jboss is finished using the timed task of upper operation, upper operation is applied to n-th of jboss
The tables of data of timed task locking be unlocked, n-th of jboss is locked again using the timed task of upper operation later
One tables of data being not locked out, and execute again.
Further, further includes: resource module, for distributing redis machine group and mysql machine for target service system
Device group, business valence library described in the target service system docking, business valence library for the target service system for providing
Price data；Redis cache resources and mysql resource are distributed for the full dose scheduler task, wherein the redis distributed is slow
It deposits resource and is not belonging to the redis machine group distributed for the target service system, the mysql resource distributed is not belonging to as institute
State the mysql machine group of target service system distribution.
In the present embodiment, enable to calculation stages user that can normally access.By utilizing application server resource, adopt
With Distributed Parallel Computing method, to need the application side of this data to provide accurate data, suitable under high concurrent scene
Full dose mass data calculates.Using full dose calculation method, can calculate in advance that data side needs as a result, to improve height simultaneously
Send out response time and the calling amount of access.In the distributed calculating process of full dose, foreground price real-time query is not influenced, simultaneously
There are other increment factors to come, can guarantee the accuracy and integrality of data with the price data of the same commodity of real-time update.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for equipment reality
For applying example, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to embodiment of the method
Part explanation.The above description is merely a specific embodiment, but protection scope of the present invention is not limited to
This, anyone skilled in the art in the technical scope disclosed by the present invention, the variation that can readily occur in or replaces
It changes, should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the protection model of claim
Subject to enclosing.
Claims (8)
1. a kind of full dose distribution high concurrent calculation method characterized by comprising
Stop the incremental computations program in distributed system, deploy in the distributed system public library, price library, caching and
Business valence library；
Start full dose scheduler task, and generates the full dose table in business valence library；
After full dose scheduler task execution, the full dose table is updated；
Start the incremental computations program.
2. the method according to claim 1, wherein described update the full dose table, comprising:
Initial data is obtained in batches from the public library, price library and caching；
Price data is calculated using the initial data；
The price data being calculated batch is inserted into the full dose table.
3. according to the method described in claim 2, it is characterized in that, the full dose scheduler task includes:
Distributed task dispatching table is generated, have recorded in the distributed task dispatching table: x timed task, y jboss are answered
Tables of data are opened with z, the z tables of data are obtained from the initial data, and x, y and z are positive integer, the number of timed task
Amount matches with the jboss quantity applied；
After initializing the full dose scheduler task, each timed task locks a tables of data；
After n-th of jboss is finished using the timed task of upper operation, n-th of jboss is determined using upper operation
When task locking tables of data be unlocked, n-th of jboss locks one using the timed task of upper operation again later
The tables of data being not locked out, and execute again.
4. according to the method in claim 2 or 3, which is characterized in that further include:
Redis machine group and mysql machine group, business valence described in the target service system docking are distributed for target service system
Library, business valence library are used to provide price data for the target service system；
Redis cache resources and mysql resource are distributed for the full dose scheduler task, wherein the redis cache resources distributed
It is not belonging to the redis machine group distributed for the target service system, the mysql resource distributed is not belonging to as the target industry
The mysql machine group of business system distribution.
5. a kind of full dose distribution high concurrent computing device characterized by comprising
Task management module deploys public affairs in the distributed system for stopping the incremental computations program in distributed system
Library, price library, caching and business valence library altogether；
Processing module for starting full dose scheduler task, and generates the full dose table in business valence library；
Update module, for updating the full dose table after full dose scheduler task execution；
The task management module is also used to start the incremental computations program.
6. device according to claim 5, which is characterized in that the update module is specifically used for from the public library, valence
Initial data is obtained in batches in Ge Ku and caching；Price data is calculated using the initial data；The price number that will be calculated
According to being inserted into the full dose table in batches.
7. device according to claim 6, which is characterized in that the processing module is specifically used for generating distributed task scheduling
Dispatch list has recorded in the distributed task dispatching table: x timed task, y jboss application and z tables of data, the z
It opens tables of data to obtain from the initial data, x, y and z are positive integer, the quantity of quantity and the jboss application of timed task
Match；
After initializing the full dose scheduler task, each timed task locks a tables of data；
After n-th of jboss is finished using the timed task of upper operation, n-th of jboss is determined using upper operation
When task locking tables of data be unlocked, n-th of jboss locks one using the timed task of upper operation again later
The tables of data being not locked out, and execute again.
8. device according to claim 6 or 7, which is characterized in that further include:
Resource module, for distributing redis machine group and mysql machine group, the target service system for target service system
Business valence library is docked, business valence library is used to provide price data for the target service system；For the full dose tune
Degree task distributes redis cache resources and mysql resource, wherein the redis cache resources distributed are not belonging to as the target
The redis machine group of operation system distribution, the mysql resource distributed are not belonging to as target service system distribution
Mysql machine group.
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