CN107305554A - Data query processing method and processing device - Google Patents
Data query processing method and processing device Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
This application discloses a kind of data query processing method and processing device.This method includes：According to query demand, distribution key is set up in pending data table；According to the distribution key, the pending data table is split, to set up multiple distributed data tables；A treatment progress is distributed for each distributed data table；And multithreading operation is utilized, according to the query demand, based on each distributed data table, carry out data query operation.This method splits out multiple distributed sublists by the way that the tables of data of big data quantity is split according to distribution key, and handles each sublist in a distributed manner using multithreading operation, greatly accelerates the processing time of data query, improves efficiency data query.
Description
Technical field
The present invention relates to database technical field, in particular to a kind of data query processing side
Method and device.
Background technology
At present, big data statistics has been applied in social industry-by-industry field, data analysis technique
Develop therewith.In realm of sale, when carrying out sales data statistics, it will usually utilize database
Auto correlation inquiring technology, to analyze the historical data of individual sale or consumer behavior, so that draw can
It is expected that future sales or the tendentiousness of consumer behavior.But sales data amount be all often it is hundreds of millions of,
Any of which data field is subjected to auto correlation, the computing of several hundred million square orders of magnitude is can mean that,
Normal operation system is difficult to bear so big computational load.For insurance company,
Protect operation to carry out core, be required for daily to each sale agent per capita counting statistics to go out its passing
Sales histories data, to verify that its same day sells the value-at-risk of declaration form.
The content of the invention
The present invention provides a kind of data query processing method and processing device, can accelerate data query processing
Time, lift treatment effeciency.
Other characteristics and advantage of the present invention will be apparent from by following detailed description, or part
Ground acquistion by the practice of the present invention.
According to an aspect of the present invention there is provided a kind of data query processing method, including：According to
Query demand, distribution key is set up in pending data table；According to the distribution key, split described
Pending data table, to set up multiple distributed data tables；For each distributed data table point
With a treatment progress；And multithreading operation is utilized, according to the query demand, based on each
Distributed data table, carries out data query operation.
According to an embodiment of the present invention, according to the query demand, based on each distributed number
According to table, carrying out data query operation includes：The inquiry is carried out in each distributed data table to be needed
Seek required auto correlation data query.
According to an embodiment of the present invention, using multithreading operation, according to the query demand,
Based on each distributed data table, before data query operation also include：For each described point
Cloth tables of data carries out homogeneous data duplicate checking and deletion action.
According to an embodiment of the present invention, it is that each distributed data table carries out homogeneous data
Duplicate checking and deletion action include：For each distributed data table, judgement wherein whether there is
The homogeneous data repeated；If wherein there is the homogeneous data of repetition, the homogeneous data is deleted,
And save as the distributed data table deleted after the homogeneous data in the middle of distributed data
Table；And according to the query demand, based on each distributed data table, carry out data query behaviour
Work includes：In the middle of each distributed data table and its corresponding distributed data
Table, carries out the mutual correlation data query needed for the query demand.
According to an embodiment of the present invention, the homogeneous data is determined according to the query demand.
According to another aspect of the present invention there is provided a kind of data query processing unit, including：Point
Cloth key sets up module, for according to query demand, distribution key to be set up in pending data table；Number
Module is split according to table, for according to the distribution key, splitting the pending data table, to set up
Multiple distributed data tables；Course allocation module, for being distributed for each distributed data table
One treatment progress；And data inquiry module, for utilizing multithreading operation, looked into according to described
Inquiry demand, based on each distributed data table, carries out data query operation.
According to an embodiment of the present invention, the data inquiry module includes：Auto correlation inquiry
Module, for carrying out the auto correlation data needed for the query demand in each distributed data table
Inquiry.
According to an embodiment of the present invention, the device also includes：Homogeneous data removing module, is used
It is each distribution in before the data inquiry module carries out the data query operation
Tables of data carries out homogeneous data duplicate checking and deletion action.
According to an embodiment of the present invention, the homogeneous data removing module includes：Homogeneous data
Judging submodule, for for each distributed data table, judging wherein with the presence or absence of repetition
Homogeneous data；And homogeneous data deletes submodule, for when the homogeneous data judging submodule
When judging to have the homogeneous data of repetition in the distributed data table, the homogeneous data is deleted, and
The distributed data table deleted after the homogeneous data is saved as into distributed data middle table；
And the data inquiry module includes：Mutual correlation inquires about submodule, for according to each described point
Needed for cloth tables of data and its corresponding distributed data middle table, the progress query demand
Mutual correlation data query.
According to an embodiment of the present invention, the homogeneous data is determined according to the query demand.
According to the present invention data query processing method, by by the tables of data of big data quantity according to divide
Cloth key is split, and splits out multiple distributed sublists, and using multithreading operation in a distributed manner
Each sublist is handled, the processing time of data query is greatly accelerated, improves efficiency data query.
Through measuring and calculating, by taking the tables of data of 10,000,000 data amounts as an example, the activity duration can be by original 20
Several hours foreshorten to or so half an hour, and data query processing time substantially reduces, and effect is very
Significantly.
In addition, according to some embodiments, data query method of the invention is further divided fractionation
Homogeneous data in cloth tables of data carries out duplicate checking and deletion action, using further subtracting after the operation
Having lacked needs the scale of statistics amount, so as to further reduce the time of data query processing,
Further improve treatment effeciency.
It should be appreciated that the general description of the above and detailed description hereinafter are only exemplary,
The present invention can not be limited.
Brief description of the drawings
Its example embodiment is described in detail by referring to accompanying drawing, above and other target of the invention,
Feature and advantage will become apparent.
Fig. 1 is a kind of flow of data query processing method according to an illustrative embodiments
Figure.
Fig. 2 is the stream of another data query processing method according to an illustrative embodiments
Cheng Tu.
Fig. 3 is a kind of frame of data query processing unit according to an illustrative embodiments
Figure.
Fig. 4 is the frame of another data query processing unit according to an illustrative embodiments
Figure.
Embodiment
Example embodiment is described more fully with referring now to accompanying drawing.However, example embodiment
It can in a variety of forms implement, and be not understood as limited to example set forth herein；On the contrary, carrying
Cause the present invention will more fully and completely, and by the structure of example embodiment for these embodiments
Think of comprehensively conveys to those skilled in the art.Accompanying drawing is only the schematic illustrations of the present invention, and
Not necessarily is drawn to scale.Identical reference represents same or similar part in figure, because
And repetition thereof will be omitted.
In addition, described feature, structure or characteristic can be combined one in any suitable manner
In individual or more embodiment.In the following description there is provided many details so as to providing pair
Embodiments of the present invention are fully understood.It will be appreciated, however, by one skilled in the art that can be with
Put into practice technical scheme and omit one or more in the specific detail, or can be with
Using other methods, constituent element, device, step etc..In other cases, be not shown in detail or
A presumptuous guest usurps the role of the host and causes this hair to avoid for description known features, method, device, realization or operation
Bright each side thickens.
Fig. 1 is a kind of flow of data query processing method according to an illustrative embodiments
Figure.As shown in figure 1, data query processing method 10 includes：
In step s 102, according to query demand, distribution key is set up in pending data table.
Illustrated by taking a transaction journal tables of data (TABLE_TEST) as an example.Assuming that the transaction
Pipelined data table includes 4 fields：Transaction Identification Number TRAN_ID, customer ID CUSTOMER_ID,
Dealing money MONEY, trade date TRAN_DATE.The transaction journal tables of data such as table 1
It is shown：
Table 1
Assuming that query demand is, when inquiring about easy per single cross in transaction journal tables of data, the single cross is easy
Turnover of the client before the single cross is easy.
If distribution key is not built on CUSTOMER_ID, for example with sql like language, then
Query statement is：
select a.tran_id,sum(b.money)from table_test a,table_test b
Where a.customer_id=b.customer_id and b.tran_date<a.tran_date
group by a.tran_id；
Because distribution key is not built on CUSTOMER_ID, data can be broadcasted, can be by
Redistributed according to CUSTOMER_ID, execution efficiency is very low.When data volume reach it is up to ten million
During bar, it is likely that also perform within several days endless.
And embodiment of the present invention, then distribution key can be built according to the query demand of above-mentioned example
Stand on CUSTOMER_ID.
In step S104, according to the distribution key of foundation, pending data table is split, sets up multiple
Distributed data table.
Subregion based on database, according to distribution key, splits to pending data table.Still with
Exemplified by above-mentioned transaction journal tables of data and query demand.Assuming that the subregion of database is 4, then will
Above-mentioned transaction journal tables of data is split as 4 distributed data tables according to CUSTOMER_ID, and
It is uniformly distributed CUSTOMER_ID.Disassembly principle using CUSTOMER_ID as distribution key is,
Ensure identical CUSTOMER_ID data point in same distributed data table.Assuming that after splitting
4 distributed data tables respectively as shown in table 2-5：
Distributed data table in the subregion A of table 2
Distributed data table in the subregion B of table 3
| TRAN_ID | CUSTOMER_ID | MONEY | TRAN_DATE |
| 2 | 0002 | 10.00 | 2016-01-01 |
| 10 | 0002 | 5.00 | 2016-01-07 |
| 19 | 0002 | 10.00 | 2016-01-15 |
| 20 | 0002 | 20.00 | 2016-01-15 |
| 9 | 0005 | 10.00 | 2016-01-07 |
Distributed data table in the subregion C of table 4
| TRAN_ID | CUSTOMER_ID | MONEY | TRAN_DATE |
| 3 | 0003 | 20.00 | 2016-01-02 |
| 6 | 0003 | 15.00 | 2016-01-04 |
| 17 | 0003 | 10.00 | 2016-01-13 |
| 11 | 0006 | 10.00 | 2016-01-09 |
| 12 | 0007 | 30.00 | 2016-01-11 |
Distributed data table in the subregion D of table 5
| TRAN_ID | CUSTOMER_ID | MONEY | TRAN_DATE |
| 5 | 0004 | 10.00 | 2016-01-04 |
| 7 | 0004 | 20.00 | 2016-01-04 |
| 15 | 0009 | 10.00 | 2016-01-12 |
| 16 | 0010 | 5.00 | 2016-01-13 |
| 18 | 0011 | 10.00 | 2016-01-15 |
In step s 106, it is that each distributed data table distributes a treatment progress.
Still illustrated with above-mentioned, be 4 distributed data tables respectively one treatment progress of distribution, i.e.,
4 treatment progress are distributed altogether.
In step S108, using multithreading operation, according to the query demand, based on each distribution
Formula tables of data, carries out data query operation.
For example, still being illustrated with above-mentioned, using multithreading operation, make each distributed data table
The parallel processing in respective subregion, each distributed data table and oneself progress auto correlation.For example
The distributed data table TABLE_TEST of subregion A shown in table 2, according to distribution key
CUSTOMER_ID carries out auto correlation, still so that using exemplified by sql like language, then query statement is：
select a.tran_id,sum(b.money)from table_test a,table_test b
Where a.customer_id=b.customer_id and b.tran_date<a.tran_date
group by a.tran_id；
Distributed data table in other each subregions also independently carries out above-mentioned auto correlation inquiry operation.
It should be noted that above-mentioned example is only for the purposes of understanding the method for the present invention, and it is unrestricted
The present invention.
The data query processing method 10 that embodiment of the present invention is provided, by by the number of big data quantity
Split according to table according to distribution key, split out multiple distributed sublists, and utilize multithreading behaviour
Each sublist is handled in a distributed manner, is greatly accelerated the processing time of data query, is improved number
According to search efficiency.Through measuring and calculating, by taking the tables of data of 10,000,000 data amounts as an example, the activity duration can be with
Or so half an hour is foreshortened to by twenties original hours, data query processing time substantially drops
Low, effect is very notable.
It will be clearly understood that the present disclosure describe how form and use particular example, but the present invention
Principle be not limited to any details of these examples.On the contrary, the religion based on present disclosure
Lead, these principles can be applied to numerous other embodiments.
Fig. 2 is the stream of another data query processing method according to an illustrative embodiments
Cheng Tu.As shown in Fig. 2 data query processing method 20 includes：
In step S202, according to query demand, distribution key is set up in pending data table.
In step S204, according to the distribution key of foundation, pending data table is split, sets up multiple
Distributed data table.
It is that each distributed data table distributes a treatment progress in step S206.
Above-mentioned steps are identical with step S102~S106 in data query processing method 10, herein not
Repeat again.
It is each distributed number according to query demand using multithreading operation in step S208
Homogeneous data duplicate checking and deletion action are carried out according to table.
Specifically, for each distributed data table, if wherein there is the situation that homogeneous data is repeated,
It can then carry out homogeneous data duplicate checking and deletion action, and by the distribution after duplicate checking and deletion action
Tables of data saves as distributed data middle table.The homogeneous data determines according to query demand, still with
Illustrated exemplified by above-mentioned example, in the distributed data table in above-mentioned table 2 and table 3
The client that CUSTOMER_Id is 0001 and 0002 is respectively in 2016-01-04 and 2016-01-15
There are two transactions, therefore duplicate removal can be merged according to trade date, carry out duplicate checking and delete behaviour
The distributed data middle table generated after work is as shown in table 6 and table 7：
Distributed data middle table in the subregion A of table 6
| CUSTOMER_ID | MONEY | TRAN_DATE |
| 0001 | 10.00 | 2016-01-01 |
| 0001 | 40.00 | 2016-01-04 |
| 0001 | 20.00 | 2016-01-12 |
| 0008 | 15.00 | 2016-01-12 |
Distributed data middle table in the subregion B of table 7
| CUSTOMER_ID | MONEY | TRAN_DATE |
| 0002 | 10.00 | 2016-01-01 |
| 0002 | 5.00 | 2016-01-07 |
| 0002 | 30.00 | 2016-01-15 |
| 0005 | 10.00 | 2016-01-07 |
The operation of above-mentioned generation distributed data middle table for example can be with SQL statement：
insert into table_test_1select distinct customer_id,tran_date from
table_test；
Wherein table_test is distributed data table, and table_test_1 is distributed data middle table.
In step S210, using multithreading operation, according to each distributed data table and its distribution
Formula data middle table, performs the mutual correlation data query needed for the query demand.
For example, still illustrating by above-mentioned example and using exemplified by sql like language, query statement can be：
select a.customer_id,a.tran_date,sum(b.money)as total_money from
table_test_1a,table_test b
Where a.customer_id=b.customer_id and b.tran_date<a.tran_date
group by a.customer_id,a.tran_date；
And by above-mentioned Query Result write table TABLE_TEST_2.
Afterwards, then with the left association TABLE_TEST_2 of TABLE, specific sentence can be as
Under：
slect a.tran_id,b.total_money from table_test a
Left join table_test_2on a.customer_id=b.customer_id and a.tran_date
=b.tran_date；
The data query method 20 that embodiment of the present invention is provided, further to the distributed number of fractionation
Duplicate checking and deletion action are carried out according to the homogeneous data in table, is needed using being further reduced after the operation
The scale of statistics amount, so as to further reduce the time of data query processing, further
Improve treatment effeciency.
It will be appreciated by those skilled in the art that realizing all or part of step of above-mentioned embodiment by reality
It is now the computer program performed by CPU.When the computer program is performed by CPU, perform
The above-mentioned functions that the above method that the present invention is provided is limited.Described program can be stored in one kind
In computer-readable recording medium, the storage medium can be read-only storage, disk or CD
Deng.
Further, it should be noted that above-mentioned accompanying drawing is only according to exemplary embodiment of the invention
Processing included by method is schematically illustrated, rather than limitation purpose.It can be readily appreciated that above-mentioned attached
Processing shown in figure is not intended that or limited the time sequencing of these processing.In addition, being also easy to reason
Solution, these processing for example can be performed either synchronously or asynchronously in multiple modules.
Following is apparatus of the present invention embodiment, can be used for performing the inventive method embodiment.For
The details not disclosed in apparatus of the present invention embodiment, refer to the inventive method embodiment.
Fig. 3 is a kind of frame of data query processing unit according to an illustrative embodiments
Figure.As shown in figure 3, data query processing unit 30 includes：Distribution key sets up module 302, number
Module 304, course allocation module 306 and data inquiry module 308 are split according to table.
Wherein, distribution key is set up module 302 and is used for according to query demand, in pending data table
Set up distribution key.
Tables of data, which splits module 304, is used for the distribution key according to foundation, splits pending data table,
To set up multiple distributed data tables.
Course allocation module 306 is used to distribute a treatment progress for each distributed data table.
Data inquiry module 308 is used to utilize multithreading operation, according to query demand, based on each
Distributed data table, carries out data query operation.
In certain embodiments, data inquiry module 308 includes：Auto correlation inquires about submodule 3082,
For the auto correlation data query needed for the progress query demand in each distributed data table.
The data query processing unit 30 that embodiment of the present invention is provided, by by the number of big data quantity
Split according to table according to distribution key, split out multiple distributed sublists, and utilize multithreading behaviour
Each sublist is handled in a distributed manner, is greatly accelerated the processing time of data query, is improved number
According to search efficiency.Through measuring and calculating, by taking the tables of data of 10,000,000 data amounts as an example, the activity duration can be with
Or so half an hour is foreshortened to by twenties original hours, data query processing time substantially drops
Low, effect is very notable.
Fig. 4 is the frame of another data query processing unit according to an illustrative embodiments
Figure.As shown in figure 4, data query processing unit 40 includes：Distribution key sets up module 402, number
Module 404, course allocation module 406, data inquiry module 408 and homogeneous data are split according to table
Removing module 410.
Wherein, distribution key is set up module 402 and is used for according to query demand, in pending data table
Set up distribution key.
Tables of data, which splits module 404, to be used to, according to distribution key, split pending data table, to set up
Multiple distributed data tables.
Course allocation module 406 is used to distribute a treatment progress for each distributed data table.
Homogeneous data removing module 410 is used to carry out data query operation in data inquiry module 408
Before, it is that each distributed data table carries out homogeneous data duplicate checking and deletion action.
Data inquiry module 408 is used to utilize multithreading operation, according to query demand, based on each
Distributed data table, carries out data query operation.
In certain embodiments, homogeneous data removing module 410 includes：Homogeneous data judges submodule
Block 4102 and homogeneous data delete submodule 4104, and wherein homogeneous data judging submodule 4102 is used
In for each distributed data table, judge wherein with the presence or absence of the homogeneous data repeated, like numbers
It is used for according to submodule 4104 is deleted when homogeneous data judging submodule judges there is weight in distributed data table
During multiple homogeneous data, homogeneous data is deleted, and the distributed data table after homogeneous data will be deleted
Save as distributed data middle table.Data inquiry module 408 includes：Mutual correlation inquires about submodule
4082, for according to each distributed data table and its corresponding distributed data middle table, carrying out
Mutual correlation data query needed for query demand.
The data query arrangement 40 that embodiment of the present invention is provided, further to the distributed number of fractionation
Duplicate checking and deletion action are carried out according to the homogeneous data in table, is needed using being further reduced after the operation
The scale of statistics amount, so as to further reduce the time of data query processing, further
Improve treatment effeciency.
, not necessarily must be with it should be noted that the block diagram shown in above-mentioned accompanying drawing is functional entity
Physically or logically independent entity is corresponding.It can realize that these functions are real using software form
Body, or these functional entitys are realized in one or more hardware modules or integrated circuit, or not
With realizing these functional entitys in network and/or processor device and/or microcontroller device.
Through the above description of the embodiments, those skilled in the art it can be readily appreciated that retouch here
The example embodiment stated can be realized by software, can also combine necessary hardware by software
Mode realize.Therefore, can be with software product according to the technical scheme of embodiment of the present invention
Form embody, the software product can be stored in a non-volatile memory medium (can be with
Be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are to cause one
Platform computing device (can be personal computer, server, mobile terminal or network equipment etc.)
Perform the method according to embodiment of the present invention.
The illustrative embodiments of the present invention are particularly shown and described above.It is understood that
It is that the invention is not restricted to detailed construction described herein, set-up mode or implementation method；On the contrary,
The invention is intended to cover to include various modifications in the spirit and scope of the appended claims and equivalent
Set.
Claims (10)
1. a kind of data query processing method, it is characterised in that including：
According to query demand, distribution key is set up in pending data table；
According to the distribution key, the pending data table is split, to set up multiple distributed datas
Table；
A treatment progress is distributed for each distributed data table；And
Using multithreading operation, according to the query demand, based on each distributed data table, enter
Row data query operation.
2. according to the method described in claim 1, it is characterised in that according to the query demand,
Based on each distributed data table, carrying out data query operation includes：In each distributed data table
The middle auto correlation data query carried out needed for the query demand.
3. according to the method described in claim 1, it is characterised in that utilize multithreading operation, root
According to the query demand, based on each distributed data table, also wrap before data query operation
Include：Homogeneous data duplicate checking and deletion action are carried out for each distributed data table.
4. method according to claim 3, it is characterised in that for each distributed number
Carrying out homogeneous data duplicate checking and deletion action according to table includes：For each distributed data table,
Judge wherein with the presence or absence of the homogeneous data repeated；If wherein there is the homogeneous data of repetition, delete
Saved as except the homogeneous data, and by the distributed data table deleted after the homogeneous data
Distributed data middle table；And
According to the query demand, based on each distributed data table, data query operation bag is carried out
Include：According to each distributed data table and its corresponding distributed data middle table, enter
Mutual correlation data query needed for the row query demand.
5. the method according to claim 3 or 4, it is characterised in that the homogeneous data root
Determined according to the query demand.
6. a kind of data query processing unit, it is characterised in that including：
Distribution key sets up module, for according to query demand, distribution to be set up in pending data table
Key；
Tables of data splits module, for according to the distribution key, splitting the pending data table,
To set up multiple distributed data tables；
Course allocation module, for distributing a treatment progress for each distributed data table；
And
Data inquiry module, for utilizing multithreading operation, according to the query demand, based on every
Individual distributed data table, carries out data query operation.
7. device according to claim 6, it is characterised in that the data inquiry module bag
Include：Auto correlation inquires about submodule, for carrying out the query demand in each distributed data table
Required auto correlation data query.
8. device according to claim 6, it is characterised in that also include：Homogeneous data is deleted
Except module, for being each before the data inquiry module carries out the data query operation
The distributed data table carries out homogeneous data duplicate checking and deletion action.
9. device according to claim 8, it is characterised in that the homogeneous data deletes mould
Block includes：Homogeneous data judging submodule, for for each distributed data table, judging
Wherein with the presence or absence of the homogeneous data repeated；And homogeneous data deletes submodule, for when described same
When class data judging submodule judges to have the homogeneous data of repetition in the distributed data table, delete
The homogeneous data, and the distributed data table deleted after the homogeneous data is saved as point
Cloth data middle table；And
The data inquiry module includes：Mutual correlation inquires about submodule, for according to each described point
Needed for cloth tables of data and its corresponding distributed data middle table, the progress query demand
Mutual correlation data query.
10. device according to claim 8 or claim 9, it is characterised in that the homogeneous data root
Determined according to the query demand.
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Cited By (9)
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| CN108470045A (en) * | 2018-03-06 | 2018-08-31 | 平安科技（深圳）有限公司 | The method and storage medium that electronic device, data chain type are filed |
| CN109299148A (en) * | 2018-09-29 | 2019-02-01 | 网宿科技股份有限公司 | Data query method and server |
| CN109491973A (en) * | 2018-09-25 | 2019-03-19 | 中国平安人寿保险股份有限公司 | Electronic device, declaration form delta data distribution analysis method and storage medium |
| CN109656968A (en) * | 2018-11-15 | 2019-04-19 | 中国建设银行股份有限公司 | Data query method, apparatus and storage medium under distributed environment |
| CN110019231A (en) * | 2017-12-26 | 2019-07-16 | 中国移动通信集团山东有限公司 | A kind of method that parallel database dynamically associates and node |
| CN111291112A (en) * | 2018-12-07 | 2020-06-16 | 阿里巴巴集团控股有限公司 | Read-write control method and device for distributed database and electronic equipment |
| CN112100186A (en) * | 2020-08-26 | 2020-12-18 | 金蝶软件（中国）有限公司 | Data processing method and device based on distributed system and computer equipment |
| CN112134766A (en) * | 2020-10-27 | 2020-12-25 | 拉卡拉支付股份有限公司 | Method and device for detecting high concurrency service |
| CN112307184A (en) * | 2020-10-30 | 2021-02-02 | 山东浪潮通软信息科技有限公司 | Data query method, device and computer readable medium |
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