CN108268489A - A kind of method and apparatus for assessing data platform - Google Patents
A kind of method and apparatus for assessing data platform Download PDFInfo
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- CN108268489A CN108268489A CN201611259558.7A CN201611259558A CN108268489A CN 108268489 A CN108268489 A CN 108268489A CN 201611259558 A CN201611259558 A CN 201611259558A CN 108268489 A CN108268489 A CN 108268489A
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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
The invention discloses it is a kind of assess data platform method and apparatus, the method includes：It parses the relevant structured query language SQL statement of data entity in data platform and obtains redundant data；Include the estimation items of redundant data according to the analysis of Epanechnikow kernel functions；The data platform is assessed according to estimation items after analysis.A kind of device for assessing data platform is also disclosed in the embodiment of the present invention, can assess data platform in real time according to the estimation items including redundant data, convenient for the related setting of the follow-up data platform of adjustment in time, ensure that the working efficiency of data platform.
Description
Technical field
The present invention relates to computer realm more particularly to a kind of method and apparatus for assessing data platform.
Background technology
With the rapid development of the applications such as mobile Internet, Internet of Things, there is explosive growth in global metadata amount.Data
Amount is skyrocketed through implying and has come into the big data epoch.Not only data scale is increasing, and data type
Mostly and the requirement of real-time height of processing data all substantially increases the complexity that big data is handled.
And the signaling data of the communications field has data volume super big, the requirement of real-time for analyzing business also gradually increases,
So the health degree assessment to signalling analysis system big data platform is particularly important.
In the prior art, when alarm and failure occur in system resource or processing, relevant processing is just carried out, it can not logarithm
The analysis of normalization is carried out according to platform.
Invention content
An embodiment of the present invention provides a kind of methods for assessing data platform, can be according to the estimation items including redundant data
Assessment data platform in real time convenient for the related setting of the follow-up data platform of adjustment in time, ensure that the working efficiency of data platform.
The embodiment of the present invention additionally provides a kind of device for assessing data platform, can be according to the estimation items reality of redundant data
When assess data platform, convenient for the related setting of the follow-up data platform of adjustment in time, ensure that the working efficiency of data platform.
A kind of method for assessing data platform, the method includes：
It parses the relevant structured query language SQL statement of data entity in data platform and obtains redundant data；
Include the estimation items of redundant data according to the analysis of Epanechnikow kernel functions；
The data platform is assessed according to estimation items after analysis.
Optionally, the relevant SQL statement of data entity obtains redundant data in the parsing data platform, including：
Redundant data is obtained using the relevant SQL statement of data entity in editing distance arithmetic analysis data platform.
Optionally, it is described superfluous using the relevant SQL statement acquisition of data entity in editing distance arithmetic analysis data platform
Remainder evidence, including：
The SQL statement is parsed, obtains the data handling path and data source of each model table；
With the combined and spliced data source corresponding data structure of the mode of character and the data handling path, institute is formed
State the processing feature character string of model table；
Using the processing feature character string of editing distance algorithm more different model tables two-by-two, redundant data is obtained.
Optionally, the estimation items that redundant data is included according to the analysis of Epanechnikow kernel functions, including：
Mean square error, which is minimized, according to history redundant data obtains bandwidth parameter；
The estimation items are analyzed according to bandwidth parameter, redundant data and Epanechnikow kernel functions.
Optionally, the estimation items, further include：
Space using data, system load data, storage specification data, standardization level data, data using data or
One or more of temperature assessment data classification；
The estimation items for including redundant data according to the analysis of Epanechnikow kernel functions, including：
For different classifications, minimize mean square error according to history categorical data and obtain the corresponding bandwidth parameter of classification；
The estimation items are analyzed according to the corresponding bandwidth parameter of classification, categorical data and Epanechnikow kernel functions；
It is described to assess the data platform according to estimation items after analysis, including：
According to estimation items after the corresponding analysis of classification and the corresponding weight of classification, the data platform is assessed.
A kind of device for assessing data platform, described device include：
Parsing module obtains redundant digit for the relevant structured query language SQL statement of data entity in data platform
According to；
Analysis module includes the estimation items of redundant data for the analysis of Epanechnikow kernel functions；
Evaluation module, for assessing the data platform according to estimation items after analysis.
Optionally, the parsing module is additionally operable to related using data entity in editing distance arithmetic analysis data platform
SQL statement obtain redundant data.
Optionally, the parsing module is additionally operable to parse the SQL statement, obtains the data processing road of each model table
Diameter and data source；With the combined and spliced data source corresponding data structure of the mode of character and the processing path, form
The processing feature character string of the model；Using the processing feature character string of editing distance algorithm more different models two-by-two, obtain
Obtain redundant data.
Optionally, the analysis module is additionally operable to obtain bandwidth parameter according to history redundant data minimum mean square error；
The estimation items are analyzed according to bandwidth parameter, redundant data and Epanechnikow kernel functions.
Optionally, the estimation items, further include：
Space using data, system load data, storage specification data, standardization level data, data using data or
One or more of temperature assessment data classification；
The analysis module is additionally operable to for different classifications, and minimizing mean square error according to history categorical data obtains
The corresponding bandwidth parameter of classification；Institute is analyzed according to the corresponding bandwidth parameter of classification, categorical data and Epanechnikow kernel functions
State estimation items；
The evaluation module is additionally operable to, according to estimation items after the corresponding analysis of classification and the corresponding weight of classification, assess institute
State data platform.
It can be seen from the above technical proposal that in embodiments of the present invention, data entity phase in data platform is parsed first
The SQL statement of pass obtains redundant data；Then include the estimation items of redundant data according to the analysis of Epanechnikow kernel functions；Most
Afterwards, the data platform is assessed according to estimation items after analysis.Since data can be assessed in real time according to the estimation items of redundant data
Platform then convenient for the related setting of the follow-up data platform of adjustment in time, ensure that the working efficiency of data platform.
Description of the drawings
From below in conjunction with the accompanying drawings to the present invention specific embodiment description in may be better understood the present invention wherein,
The same or similar reference numeral represents the same or similar feature.
From below in conjunction with the accompanying drawings to the present invention specific embodiment description in may be better understood the present invention wherein,
The same or similar reference numeral represents the same or similar feature.
Fig. 1 is the method flow schematic diagram that the embodiment of the present invention assesses data platform；
Fig. 2 parses the relevant SQL statement of data entity in data platform for the embodiment of the present invention and obtains redundant data flow
Schematic diagram；
Fig. 3 is the estimation items flow diagram that analysis of the embodiment of the present invention includes redundant data；
Fig. 4 is the apparatus structure schematic diagram that the embodiment of the present invention assesses data platform.
Specific embodiment
For the object, technical solutions and advantages of the present invention is made to express to be more clearly understood, below in conjunction with the accompanying drawings and specifically
The present invention is further described in more detail for embodiment.
In embodiments of the present invention, due to not fully taking into account various fortuitous events while data platform is established,
Therefore in data platform there are redundant datas, it is not necessary to redundant data the working efficiency of data platform can be caused low.Parsing
The relevant SQL statement of data entity obtains redundant data in data platform；Include according to the analysis of Epanechnikow kernel functions superfluous
The estimation items of remainder evidence；Finally assess the data platform.Since data can be assessed in real time according to the estimation items of redundant data
Platform, then the related setting convenient for the follow-up data platform of adjustment in time, which is reduced, generates redundant data, and then ensure that data are put down
The working efficiency of platform.
It is the method flow schematic diagram for assessing data platform referring to Fig. 1, specifically includes following steps：
101st, it parses the relevant SQL statement of data entity in data platform and obtains redundant data.
SQL is a kind of data base querying and programming language, for accessing data, inquiry, update and administrative relationships number
According to library system.By the parsing of task daily record, the relevant SQL statement of each data entity in data platform is obtained, and then obtain
Redundant data.
Redundant data is obtained for the relevant SQL statement of data entity in parsing data platform referring to Fig. 2, is specifically included：
1011st, SQL statement is parsed, obtains the data handling path and data source of each model table.
Model table is the abstract of entity table in database and summarizes, if the entity table that table structure is identical but time point is different
It can be abstracted as a model table, when concrete analysis using model table as object, will avoid repetition and the redundancy of analysis result.
The relevant SQL statement of data entity is parsed, obtains the data handling path and data source of each model table.Data handling path
Refer to the logical path in the data handling procedure.
1012nd, with the combined and spliced data source corresponding data structure of the mode of character and processing path, model table is formed
Processing feature character string.
It analyzes data source model and obtains data structure, it will be at the data of the data structure of data source model and model table
Line of reasoning diameter, it is combined and spliced in the mode of character, form the processing feature character string of each model table.
Such as：The feature string of model table TABLE1 is【Table structural information】+【Processing procedure information】(COL1|COL2|
COL3) (TIME_ID=201612), wherein data handling path are the corresponding data of TIME_ID characters.
1013rd, using the processing feature character string of editing distance algorithm more different model tables two-by-two, redundant data is obtained.
Similarity of character string algorithm is for determining the whether similar algorithm of two character strings, specifically including：Editing distance
The similarity of character string algorithms such as algorithm (Jaro-Winkler Distance), Longest Common Substring algorithm (LCS) and GST algorithms.
A kind of any of the above-described similarity of character string algorithm may be used in the present invention, but in algorithms selection, on the one hand
The data characteristics of consideration telecommunication service is needed, is on the other hand accounted for the performance of charactor comparison.First, in data characteristics
The character string of tables of data processing procedure is SQL syntax composition, it is a kind of orderly character string, therefore its string matching
What process should be also ordered into.This is all suitable for editing distance algorithm (Jaro-Winkler Distance) and GST algorithms
, and GST algorithms can also solve the comparison that two character strings change sequence.But due to the higher O of GST Algorithms T-cbmplexities
(), can not meet the system performance requirements that 10,000 tables are disposed in 3 hours substantially in actual code operation, and character
Unification sequence is good when the problem of string sequence can also pass through character pre-processing, without solving in the algorithm, therefore at this
Editing distance algorithm is employed in invention, is the illustration of editing distance algorithm below：
Two given character string S1And S2Distance be：
M is matched number of characters；T is the number of transposition.
Two respectively from S1And S2If character be apart no more thanWhen, it is considered as the two
Character string is matched.And the character that these are mutually matched then determines the number t of transposition, exactly different order in simple terms
The half for matching the number of character is the number t to replace.
For example, the character of MARTHA and MARHTA is all matched, but in these matched characters, T and H will be changed
MARTHA could be become MARHTA by position, then T and H is exactly the matching character of different sequences, t=2/2=1.
The distance of so the two character strings is：
And Jaro-Winkler then gives start-up portion with regard to the identical higher score of character string, defines a prefix
P gives two character strings, if the part that prefix part has length to be l is identical, Jaro-Winkler distances are：
dw=dj+[lp(1-dj)] (2)
djIt is the distance of two character strings；L is the identical length of prefix, but regulation is up to 4；P is then adjustment point
Not so several constants are likely to occur d, it is specified that no more than 0.25wThis constant definition is 0.1 by the situation more than 1.
In this way, the Jaro-Winkler distances of above mentioned MARTHA and MARHTA are：
dw=0.944+ [3*0.1 (1-0.944)]=0.961
According to practical experience, when the Jaro-Winkler distances of the feature string of two different model tables are more than 0.9,
Then think that the processing procedure of two model tables is similar to being characterized in, then the two models are exactly redundancy.That is at this time superfluous
Complementary degree is 1.
Count daily redundancy number as unit of day, the number of daily redundancy number divided by daily all model tables obtains
To day redundancy.
Redundancy number monthly is counted as unit of the moon, redundancy number monthly divided by monthly the number of all model tables obtains
To moon redundancy.
Data redudancy, that is,+0.3 month redundancy of redundant data=0.7 day redundancy.Two angles of comprehensive day and the moon in this way
In redundancy obtain redundant data.That is, redundant data is as unit of day and is counted as unit of the moon.So as to ensure
The coverage area of redundant data and time range.
102nd, include the estimation items of redundant data according to the analysis of Epanechnikow kernel functions.
The development trend of following related data can be analyzed using Density Estimator algorithm.That is, according to
Epanechnikow kernel functions analyze the estimation items for including redundant data, it is possible to be informed in commenting after assessment in following time
Item is estimated the result is that developing toward the good aspect or developing to bad direction.Data are reevaluated according to aforesaid way development trend to put down
Platform.
The Density Estimator algorithm that Rosenblatt and Parzen is proposed is that current most effective and most widely used one kind is non-
Non-parametric density algorithm for estimating.Data distribution characteristics only are obtained from training sample, can be used for estimating arbitrary shape
Density function.Element variable and density estimation is described below.
If x1、x2、x3..., xnFor the independent same distribution variables of codomain R, all distribution functions are f (x), x ∈
R。
(3) be referred to as density function f (x) density estimation, wherein K () be kernel function；H is bandwidth parameter.
For convenience, remember Kh(u)=K (u/h) h, then formula (3) can be expressed as：
By formula (3) it is found that the Density Estimator of distribution function f is related with given sample set, the selection also with kernel function K
It is related with the selection of bandwidth parameter h.
Wherein, the present invention selects kernel function of the Epanechnikow kernel functions as analysis distribution function f (x).
Epanechnikow kernel functions：
K (u)=0, | u | ＞ 1
It is the estimation items flow diagram that analysis includes redundant data referring to Fig. 3, specifically includes：
1021st, it minimizes mean square error according to history redundant data and obtains bandwidth parameter
Bandwidth parameter can minimize mean square error according to history redundant data and obtain.
The choosing method of bandwidth parameter h is as follows：Using integrated square error MISE (h), as judging densitometry quality
Criterion.
Wherein：
AMISE (h) is referred to as progressive Square operator error.σ is the average for the distance that each data deviate average, it is from equal
Root after poor quadratic sum is average, it can reflect the dispersion degree of a data set.Wherein
Minimize AMISE (h), it is necessary to h is located at some median, it in this way can be to avoid fh(x) there is excessive deviation (too light
It is sliding) or excessive variance (i.e. excessively smooth).AMISE (h) is minimized about h to show preferably accurately to balance in AMISE (h)
The exponent number of bias term and variance item, optimal bandwidth are：
Wherein, K (x), f (x) are history redundant datas.First mean square error is minimized according to history redundant data to obtain
Bandwidth parameter.
1022nd, the estimation items are analyzed according to bandwidth parameter, redundant data and Epanechnikow kernel functions
According to 1021 calculate band data, in 101 or redundant data and bring Epanechnikow kernel functions into
Formula 4 analyzes the estimation items for including redundant data.
103rd, the data platform is assessed according to estimation items after analysis
Data platform can be predicted and assessed according to the estimation items after analysis.For example, presently, there are redundant datas
In the case of, the development trend of data platform is developed toward the good aspect or is developed to the direction of difference.
It parses the relevant SQL statement of data entity in data platform and obtains redundant data；According to Epanechnikow core letters
Number analysis includes the estimation items of redundant data；Finally assess the data platform.Due to can be according to the estimation items of redundant data
Assessment data platform in real time, that is to say, that the development trend of data platform can be chosen using technical scheme of the present invention.So
Generation redundant data is reduced, and then ensure that the work of data platform convenient for the follow-up related setting for adjusting data platform in time
Efficiency.
In addition, on the basis of above-described embodiment, estimation items can also include space using data, system load data,
Storage specification data, standardization level data, data use one or more of data or temperature assessment data.Namely
It says, estimation items may also include said one or multiple classifications on the basis of including redundant data.
For different classifications, the corresponding band of data is obtained first, in accordance with mean square error is minimized according to history categorical data
Wide parameter.Namely different classifications corresponds to different bandwidth parameters.Such as：Space corresponds to the first bandwidth parameter using data；It deposits
It stores up authority data and corresponds to the second bandwidth parameter.
Classification is obtained according to the corresponding bandwidth parameter of classification, categorical data and Epanechnikow kernel function analysis and evaluation items
Estimation items after corresponding analysis.Different classes of occupied weighted, according to according to estimation items after the corresponding analysis of classification and
The corresponding weight of classification assesses data platform.
It is the apparatus structure schematic diagram for assessing data platform referring to Fig. 4, the device is corresponding with method in embodiment one.Tool
Body includes：Parsing module 401, analysis module 402 and evaluation module 403.
Parsing module 401 obtains redundancy for the relevant structured query language SQL statement of data entity in data platform
Data.
SQL is a kind of data base querying and programming language, for accessing data, inquiry, update and administrative relationships number
According to library system.By the parsing of task daily record, the relevant SQL statement of each data entity in data platform is obtained, and then obtain
Redundant data.
Analysis module 402 includes the estimation items of redundant data for the analysis of Epanechnikow kernel functions；
Evaluation module 403, for assessing the data platform according to estimation items after analysis.
Specifically, parsing module 401, is additionally operable to relevant using data entity in editing distance arithmetic analysis data platform
SQL statement obtains redundant data.
Specifically, parsing module 401, is additionally operable to parse the SQL statement, obtains the data handling path of each model table
And data source；With the combined and spliced data source corresponding data structure of the mode of character and the processing path, institute is formed
State the processing feature character string of model；Using the processing feature character string of editing distance algorithm more different models two-by-two, obtain
Redundant data.Detailed process can be found in step 101.
Specifically, analysis module 402, is additionally operable to obtain bandwidth parameter according to history redundant data minimum mean square error；
The estimation items are analyzed according to bandwidth parameter, redundant data and Epanechnikow kernel functions.
The development trend of following related data can be analyzed using Density Estimator algorithm.That is, according to
Epanechnikow kernel functions analyze the estimation items for including redundant data, it is possible to be informed in commenting after assessment in following time
Item is estimated the result is that developing toward the good aspect or developing to bad direction.Data are reevaluated according to aforesaid way development trend to put down
Platform.
In addition, estimation items on the basis of including redundant data, further include：Space using data, system load data, deposit
It stores up authority data, standardization level data, data and uses one or more of data or temperature assessment data classification.
Specifically, analysis module 402, is additionally operable to for different classifications, mean square error is minimized according to history categorical data
Difference obtains the corresponding bandwidth parameter of classification；According to the corresponding bandwidth parameter of classification, categorical data and Epanechnikow kernel functions
Analyze the estimation items.
Specifically, evaluation module 403, is additionally operable to according to estimation items after the corresponding analysis of classification and the corresponding weight of classification,
Assess the data platform.
The device technique effect that embodiment two assesses data platform is identical with embodiment of the method in corresponding embodiment one,
This is repeated no more.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe is described in detail the present invention with reference to foregoing embodiments, it will be understood by those of ordinary skill in the art that：Its according to
Can so modify to the technical solution recorded in foregoing embodiments either to which part or all technical features into
Row equivalent replacement；And these modifications or replacement, the essence of corresponding technical solution is not made to be detached from various embodiments of the present invention technology
The range of scheme.
Claims (10)
- A kind of 1. method for assessing data platform, which is characterized in that the method includes：It parses the relevant structured query language SQL statement of data entity in data platform and obtains redundant data；Include the estimation items of redundant data according to the analysis of Epanechnikow kernel functions；The data platform is assessed according to estimation items after analysis.
- 2. the method for data platform is assessed according to claim 1, which is characterized in that data are real in the parsing data platform The relevant SQL statement of body obtains redundant data, including：Redundant data is obtained using the relevant SQL statement of data entity in editing distance arithmetic analysis data platform.
- 3. the method for data platform is assessed according to claim 2, which is characterized in that described to utilize editing distance arithmetic analysis The relevant SQL statement of data entity obtains redundant data in data platform, including：The SQL statement is parsed, obtains the data handling path and data source of each model table；With the combined and spliced data source corresponding data structure of the mode of character and the data handling path, the mould is formed The processing feature character string of type table；Using the processing feature character string of editing distance algorithm more different model tables two-by-two, redundant data is obtained.
- 4. the method for data platform is assessed according to claim 1, which is characterized in that described according to Epanechnikow core letters Number analysis includes the estimation items of redundant data, including：Mean square error, which is minimized, according to history redundant data obtains bandwidth parameter；The estimation items are analyzed according to bandwidth parameter, redundant data and Epanechnikow kernel functions.
- 5. the method for data platform is assessed according to claim 1, which is characterized in that the estimation items further include：Space uses data or temperature using data, system load data, storage specification data, standardization level data, data Assess one or more of data classification；The estimation items for including redundant data according to the analysis of Epanechnikow kernel functions, including：For different classifications, minimize mean square error according to history categorical data and obtain the corresponding bandwidth parameter of classification；The estimation items are analyzed according to the corresponding bandwidth parameter of classification, categorical data and Epanechnikow kernel functions；It is described to assess the data platform according to estimation items after analysis, including：According to estimation items after the corresponding analysis of classification and the corresponding weight of classification, the data platform is assessed.
- 6. a kind of device for assessing data platform, which is characterized in that described device includes：Parsing module obtains redundant data for the relevant structured query language SQL statement of data entity in data platform；Analysis module includes the estimation items of redundant data for the analysis of Epanechnikow kernel functions；Evaluation module, for assessing the data platform according to estimation items after analysis.
- 7. the device of data platform is assessed according to claim 6, which is characterized in that the parsing module is additionally operable to utilize The relevant SQL statement of data entity obtains redundant data in editing distance arithmetic analysis data platform.
- 8. the device of data platform is assessed according to claim 7, which is characterized in that the parsing module is additionally operable to parse The SQL statement obtains the data handling path and data source of each model table；With the combined and spliced number of the mode of character According to source corresponding data structure and the processing path, the processing feature character string of the model is formed；It is calculated using editing distance The processing feature character string of method more different models two-by-two obtains redundant data.
- 9. the device of data platform is assessed according to claim 6, which is characterized in that the analysis module is additionally operable to foundation History redundant data minimizes mean square error and obtains bandwidth parameter；According to bandwidth parameter, redundant data and Epanechnikow cores Estimation items described in Functional Analysis.
- 10. the device of data platform is assessed according to claim 6, which is characterized in that the estimation items further include：Space uses data or temperature using data, system load data, storage specification data, standardization level data, data Assess one or more of data classification；The analysis module is additionally operable to for different classifications, and minimizing mean square error according to history categorical data obtains classification Corresponding bandwidth parameter；According to the commentary of the corresponding bandwidth parameter of classification, categorical data and Epanechnikow kernel functions analysis institute Estimate item；The evaluation module is additionally operable to, according to estimation items after the corresponding analysis of classification and the corresponding weight of classification, assess the number According to platform.
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