CN109656981A - A kind of data statistical approach and system - Google Patents
A kind of data statistical approach and system Download PDFInfo
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- CN109656981A CN109656981A CN201811376444.XA CN201811376444A CN109656981A CN 109656981 A CN109656981 A CN 109656981A CN 201811376444 A CN201811376444 A CN 201811376444A CN 109656981 A CN109656981 A CN 109656981A
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Abstract
The present invention relates to a kind of data statistical approach and systems, which comprises step 1, obtains business datum；Step 2, the business datum is handled, is obtained for storing the persistant data into permanent storage media, by persistant data storage into Kafka cluster；Step 3, the persistant data in the Kafka cluster is read according to objects of statistics preset in Spark technology and database, statistical rules and statistical dimension are also preset in the database, real-time streaming statistics is carried out to the persistant data according to the statistical rules and the statistical dimension, obtains statistical result；Step 4, the statistical result is exported according to preset exhibition method.Technical solution of the present invention can greatly improve the versatility and real-time of statistical method and system.
Description
Technical field
The present invention relates to big data technical field more particularly to a kind of data statistical approach and systems.
Background technique
With the arriving of big data era, data are rapidly increased with mysterious speed, in order to carry out to mass data
Effectively management, needs to count data.Statistics is the collection to a certain phenomenon related data, arrangement, calculating, analysis, solution
The activities such as release, state not only can carry out quantitative and qualitative analysis to things itself, find the inherent law of things using statistics,
Associated comprehensive analysis can also be carried out to different things, find the inner link between things.Currently, common statistics
Method customizes corresponding statistic logic generally directed to different users, corresponding statistical rules is arranged, according to the statistical rules pair
Business datum is counted.On the one hand, statistical method is to design and develop out according to the specific requirements of different user, Bu Nengfu
With；On the other hand, after statistical work is often just carried out after business datum generation, such as business datum generates, system is every mark
Business datum of statistics of fixing time, real-time are poor.
Summary of the invention
In order to improve the versatility and real-time of big data statistical method and system, the present invention provides a kind of data statistics side
Method and system.
The technical scheme to solve the above technical problems is that
In a first aspect, the present invention provides a kind of data statistical approach, which comprises
Step 1, business datum is obtained.
Step 2, the business datum is handled, is obtained for storing the lasting number into permanent storage media
According to by persistant data storage into Kafka cluster.
Step 3, described in being read in the Kafka cluster according to objects of statistics preset in Spark technology and database
Persistant data is also preset with statistical rules and statistical dimension in the database, is tieed up according to the statistical rules and the statistics
Degree carries out real-time streaming statistics to the persistant data, obtains statistical result.
Step 4: the statistical result is exported according to preset exhibition method.
Second aspect, the present invention provides a kind of data statistics system, the system comprises:
Module is obtained, business datum is obtained.
Processing module handles the business datum, and it is lasting into permanent storage media for storing to obtain
Data, by persistant data storage into Kafka cluster.
Statistical module reads the institute in the Kafka cluster according to objects of statistics preset in Spark technology and database
Persistant data is stated, is also preset with statistical rules and statistical dimension in the database, according to the statistical rules and the statistics
Dimension carries out real-time streaming statistics to the persistant data, obtains statistical result.
Output module exports the statistical result according to preset exhibition method.
The beneficial effect of a kind of data statistical approach of the invention and system is: obtaining business datum, business datum is to use
In source data of statistics, such as total marks of the examination, the wage of company personnel of student etc., business datum is persisted as can be permanent
The persistant data of preservation, persistant data be for storing the data into permanent storage media, by persistant data store to
In Kafka cluster, persistant data can also be saved into database, so as to the use of other systems work, according to Spark technology
Targetedly read the persistant data in Kafka cluster with objects of statistics preset in database, objects of statistics may include people,
Tissue and other objects, can filter out specified persistant data, be carried out according to statistical dimension and statistical rules to persistant data
Interative computation obtains statistical result and is saved in database by statistical result persistently at the statistical result for capableing of persistence,
And statistical result is exported with preset exhibition method, statistical result can be known in real time convenient for user, and figure can be used in exhibition method
The mode of shape or table, such as histogram, cake chart.And statistical result can be saved and be used as next iteration operation in memory
Base value, can be avoided from outside obtain data needed for time loss, improve statistic processes in arithmetic speed.Of the invention
Multiple and different objects of statistics, multiple and different statistical dimension and multiple and different can be preset in technical solution in database
Statistical rules need to only select suitable objects of statistics, statistical dimension and statistical rules for different users according to demand,
Carry out statistical data with statistical method through the invention, do not have to individually designed statistical method, the logical of statistical method greatly improved
The property used；And since Kafka cluster processing data speed is fast, handling capacity is high, Spark arithmetic speed is fast, delay is low, therefore, energy
Enough real-time statistics business datums, and statistical result is subjected to real-time exhibition, convenient and efficient.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of data statistical approach of the embodiment of the present invention；
Fig. 2 is a kind of structural schematic diagram of data statistics system of the embodiment of the present invention.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
As shown in Figure 1, a kind of data statistical approach provided in an embodiment of the present invention, which comprises
Step 1, business datum is obtained.
Step 2, the business datum is handled, is obtained for storing the lasting number into permanent storage media
According to by persistant data storage into Kafka cluster.
Step 3, described in being read in the Kafka cluster according to objects of statistics preset in Spark technology and database
Persistant data is also preset with statistical rules and statistical dimension in the database, is tieed up according to the statistical rules and the statistics
Degree carries out real-time streaming statistics to the persistant data, obtains statistical result.
Step 4, the statistical result is exported according to preset exhibition method.
In the present embodiment, obtain business datum, business datum is source data for statistics, such as student examination at
Business datum is persisted as capableing of the persistant data of persistence by achievement, wage of company personnel etc., and persistant data is for depositing
The data in permanent storage media are stored up, persistant data is stored into Kafka cluster, persistant data can also be saved to number
It is targeted according to objects of statistics preset in Spark technology and database so as to the use of other systems work according in library
The persistant data in Kafka cluster is read, objects of statistics may include people, tissue and other objects, can filter out specified hold
Long data are iterated operation to persistant data according to statistical dimension and statistical rules, obtain statistical result, statistical result is held
It long at the statistical result for capableing of persistence, is saved in database, and statistical result is exported with preset exhibition method, be convenient for
User can know statistical result in real time, and the mode of figure or table, such as histogram, cake chart can be used in exhibition method.And
Statistical result can be saved the base value as next iteration operation in memory, can be avoided and obtained needed for data from outside
Time loss improves the arithmetic speed in statistic processes.Can be preset in database in technical solution of the present invention it is multiple not
Same objects of statistics, multiple and different statistical dimensions and multiple and different statistical rules, only need to be according to need for different users
Ask the suitable objects of statistics of selection, statistical dimension and statistical rules, can statistical method through the invention carry out statistical data,
Without individually designed statistical method, the versatility of statistical method greatly improved；And since Kafka cluster handles data speed
Fastly, handling capacity is high, and Spark arithmetic speed is fast, delay is low, therefore, can real-time statistics business datum, and statistical result is carried out
Real-time exhibition, convenient and efficient.
Specifically, Kafka cluster is that a kind of distributed post of high-throughput subscribes to message system, can handle consumer
Everything flow data in the website of scale has high-performance, persistence, the backup of more copies, lateral expansion capability.With with
Lower advantage: high-throughput, low latency, Kafka cluster is per second to handle hundreds of thousands message, postpones minimum only several milliseconds；It can
Expansion, Kafka cluster support heat to expand；Persistence, reliability, message is persisted to local disk, and supports data
Backup prevents loss of data；Fault-tolerance allows node failure in Kafka cluster；High concurrent can support that thousands of clients is same
When read and write.
Spark is the cluster distributed computing system of a low latency for super large data acquisition system, is to aim on a large scale
Data processing and the computing engines of Universal-purpose quick designed, have the advantage that speed is fast, can be substantially improved and apply in memory
In the speed of service, or even the speed of service that can be applied on disk promotes 10 times；Easy-to-use, Spark itself is one included
More than the operator set of 80 high-orders, developer can be allowed quickly to write program using Java, Scala or Python etc.,
And Spark can be applied and interactively inquire data in shel l；It is general, other than Map and Reduce is operated,
Spark also supports SQL query, flow data, machine learning and chart data processing, and developer can be in a data pipe use-case
It is middle that a certain function is used alone or is used in conjunction with these functions.
Preferably, the database include multiple and different objects of statistics, multiple and different statistical rules and
Multiple and different statistical dimensions, the step 1 further include before following steps:
The objects of statistics, the statistical rules and the statistical dimension, the objects of statistics are pre-selected according to demand
To need the target that counts, the statistical rules is specific statistical calculation method, the statistical dimension be statistics direction with
Granularity.
It should be noted that objects of statistics may include the object of people, tissue or other attributes.Statistical dimension may include the time
Year, month, day may be selected in dimension and organization dimensionality, such as time dimension.The statistical calculation method that statistical rules defines may include asking
Quantity maximizing, is summed, averages and asked to minimum value etc..
Specifically, different users need to only select corresponding template according to their needs, not need to customize statistics side respectively
Method writes statistic logic, can widespread adoption, realize the general of statistical method between different user.
Preferably, the database further includes multiple and different data sources and multiple and different exhibition methods, the step 1
Before further include following steps:
The data source and the exhibition method are pre-selected as needed.
Specifically, according to the demand of user, business datum is obtained in specified data source, data area can be reduced,
Statistical Speed is improved, such as: for the total marks of the examination for counting one class of student of Senior, so that it may select each section of high three all students
Achievement is data source, is further high 31 classes of students according to objects of statistics, carries out operation to the achievement of high 31 classes of students,
Obtain statistical result.
Preferably, the specific implementation of the step 2 are as follows:
Business datum described in persistence, and corresponding theme and identification code are arranged to the business datum, it is held described in acquisition
Long data, by persistant data storage into the Kafka cluster, wherein the theme is topic, with the statistics pair
As correspondence, for reading corresponding persistant data, the identification code is key value, with the statistical dimension and the statistical rules
It is corresponding, for loading corresponding statistical dimension and statistical rules.
Specifically, persistence is that data are transformed into the mechanism of permanent state from instantaneous state, and the data of instantaneous state are
It is unable to persistence, and the data of permanent state can be with persistence, therefore data are saved permanently to the storage such as database
When equipment, the data of instantaneous state should be first converted to the data of permanent state, persistence business datum, the wink that exactly will acquire
When business datum be persisted as capableing of the persistant data of persistence, each objects of statistics includes a topic, each statistics
Dimension includes a key value, and each statistical rules also includes a key value, by the topic of persistant data and objects of statistics
Topic is matched, and just can determine that persistant data according to preset objects of statistics, and according to the key value of persistant data respectively with
The key value of statistical dimension and the key value of statistical rules are matched, and determine corresponding statistical dimension and statistical rules, convenient for into
Row subsequent arithmetic.Persistant data can also be saved to database, be used convenient for other operations, such as: the same persistant data pair
When answering multiple objects of statistics, persistant data is saved to database, when being counted to different objects of statistics, can be obtained at any time
Get the persistant data.
Preferably, the step 3 specifically comprises the following steps:
Step 3.1, the topic is determined according to the preset objects of statistics, utilizes Spark technology reading and institute
The corresponding persistant data of topic is stated, the key value corresponding with the persistant data is obtained.
Step 3.2, the statistical rules and the statistical dimension are determined according to the key value.
Step 3.3, operation is carried out to the persistant data according to the statistical rules and the statistical dimension, described in acquisition
Statistical result.
Specifically, topic is determined according to objects of statistics, extracts the persistant data of identical topic, using Spark technology energy
It is enough quickly to read out corresponding persistant data, Spark can be used the mode based on Receiver obtain it is lasting in Kafka cluster
Data, the mode based on Direct that can also be used obtains the persistant data in Kafka cluster, according to preset statistical rules and system
It counts dimension and operation is carried out to persistant data, such as minimize to persistant data, maximizing, sum and average,
The persistant data in Kafka cluster can be monitored by Spark technology, as the topic of the persistant data of Kafka cluster deposit and pre-
Operation is carried out to the persistant data is read when corresponding at once if counting corresponding topic, realization real-time streaming statistics can obtain
Real-time statistics result.Since statistical calculation is the process of a continuous iteration, statistical result is subjected to storage as next
The base value of secondary iteration when statistical calculation starts next time, carries out operation on the base value of storage, does not have to obtain number from outside again
According to can reduce the time loss in calculating process, improve arithmetic speed, realize real-time statistics.
Preferably, the specific implementation of the step 4 are as follows:
Statistical result described in persistence, by statistical result storage into the database, and by the statistical result
It is shown with the preset exhibition method.
Specifically, preset exhibition method may include figure and table etc., such as: histogram, cake chart and line chart etc..
By forms such as charts, the connection that can be clearly seen that between data can preferably be analyzed data.
As shown in Fig. 2, a kind of data statistics system provided in an embodiment of the present invention, the system comprises:
Module is obtained, business datum is obtained.
Processing module handles the business datum, and it is lasting into permanent storage media for storing to obtain
Data, by persistant data storage into Kafka cluster.
Statistical module reads the institute in the Kafka cluster according to objects of statistics preset in Spark technology and database
Persistant data is stated, is also preset with statistical rules and statistical dimension in the database, according to the statistical rules and the statistics
Dimension carries out real-time streaming statistics to the persistant data, obtains statistical result.
Output module exports the statistical result according to preset exhibition method.
Preferably, the database include multiple and different objects of statistics, multiple and different statistical rules and
Multiple and different statistical dimensions, the system also includes setup module, the setup module is specifically used for:
The objects of statistics, the statistical rules and the statistical dimension, the objects of statistics are pre-selected according to demand
To need the target that counts, the statistical rules is specific statistical calculation method, the statistical dimension be statistics direction with
Granularity.
Preferably, the processing module is specifically used for:
Business datum described in persistence, and corresponding topic and key value is arranged to the business datum, it is held described in acquisition
Long data, by persistant data storage into the Kafka cluster, wherein the topic is corresponding with the objects of statistics,
The key value is corresponding with the statistical dimension and the statistical rules.
Preferably, the statistical module is specifically used for:
The topic is determined according to the preset objects of statistics, is read and the topic using the Spark technology
The corresponding persistant data obtains the key value corresponding with the persistant data.
The statistical rules and the statistical dimension are determined according to the key value.
Operation is carried out to the persistant data according to the statistical rules and the statistical dimension, obtains the statistics knot
Fruit.
Preferably, the output module is specifically used for:
Statistical result described in persistence, by statistical result storage into the database, and by the statistical result
It is shown with the preset exhibition method.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of data statistical approach, which is characterized in that the described method includes:
Step 1, business datum is obtained；
Step 2, the business datum is handled, is obtained for storing the persistant data into permanent storage media, it will
The persistant data storage is into Kafka cluster；
Step 3, it is described lasting in the Kafka cluster to be read according to objects of statistics preset in Spark technology and database
Data are also preset with statistical rules and statistical dimension in the database, according to the statistical rules and the statistical dimension pair
The persistant data carries out real-time streaming statistics, obtains statistical result；
Step 4, the statistical result is exported according to preset exhibition method.
2. data statistical approach according to claim 1, which is characterized in that the database includes multiple and different described
Objects of statistics, multiple and different statistical rules and multiple and different statistical dimensions, further include before the step 1 as
Lower step:
The objects of statistics, the statistical rules and the statistical dimension are pre-selected according to demand, and the objects of statistics is to need
The target to be counted, the statistical rules are specific statistical calculation method, and the statistical dimension is direction and the granularity of statistics.
3. data statistical approach according to claim 2, which is characterized in that the specific implementation of the step 2 are as follows:
Business datum described in persistence, and corresponding theme and identification code are arranged to the business datum, obtain the lasting number
According to by persistant data storage into the Kafka cluster, wherein the theme is corresponding with the objects of statistics, the mark
It is corresponding with the statistical dimension and the statistical rules to know code.
4. data statistical approach according to claim 3, which is characterized in that the step 3 specifically comprises the following steps:
Step 3.1, the theme is determined according to the preset objects of statistics, is read and the theme pair using Spark technology
The persistant data answered obtains the identification code corresponding with the persistant data；
Step 3.2, the statistical rules and the statistical dimension are determined according to the identification code；
Step 3.3, operation is carried out to the persistant data according to the statistical rules and the statistical dimension, obtains the statistics
As a result.
5. data statistical approach according to any one of claims 1 to 4, which is characterized in that the specific implementation of the step 4
Are as follows:
Statistical result described in persistence, by statistical result storage into the database, and by the statistical result with institute
Preset exhibition method is stated to be shown.
6. a kind of data statistics system, which is characterized in that the system comprises:
Module is obtained, business datum is obtained；
Processing module handles the business datum, obtains for storing the persistant data into permanent storage media,
By persistant data storage into Kafka cluster；
Statistical module, according to objects of statistics preset in Spark technology and database read in the Kafka cluster described in hold
Long data are also preset with statistical rules and statistical dimension in the database, according to the statistical rules and the statistical dimension
Real-time streaming statistics is carried out to the persistant data, obtains statistical result；
Output module exports the statistical result according to preset exhibition method.
7. data statistics system according to claim 6, which is characterized in that the database includes multiple and different described
Objects of statistics, multiple and different statistical rules and multiple and different statistical dimensions, the system also includes setting moulds
Block, the setup module are specifically used for:
The objects of statistics, the statistical rules and the statistical dimension are pre-selected according to demand, and the objects of statistics is to need
The target to be counted, the statistical rules are specific statistical calculation method, and the statistical dimension is direction and the granularity of statistics.
8. data statistics system according to claim 7, which is characterized in that the processing module is specifically used for:
Business datum described in persistence, and corresponding theme and identification code are arranged to the business datum, obtain the lasting number
According to by persistant data storage into the Kafka cluster, wherein the theme is corresponding with the objects of statistics, the mark
It is corresponding with the statistical dimension and the statistical rules to know code.
9. data statistics system according to claim 8, which is characterized in that the statistical module is specifically used for:
The theme is determined according to the preset objects of statistics, is read using the Spark technology corresponding with the theme
The persistant data obtains the identification code corresponding with the persistant data；
The statistical rules and the statistical dimension are determined according to the identification code；
Operation is carried out to the persistant data according to the statistical rules and the statistical dimension, obtains the statistical result.
10. according to the described in any item data statistics systems of claim 6 to 9, which is characterized in that the output module is specifically used
In:
Statistical result described in persistence, by statistical result storage into the database, and by the statistical result with institute
Preset exhibition method is stated to be shown.
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