CN101982820A - Curve display and inquiry method for large data quantity - Google Patents
Curve display and inquiry method for large data quantity Download PDFInfo
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Abstract
The invention provides a curve display and inquiry method for large data quantity, belonging to the field of computer data analysis. The method comprises the following steps: firstly, acquiring a user inquiry condition, and judging whether the total data number is more than that of drawing points; if yes, dividing time intervals into n numbered segments with equal data quantity, and calculating the number of the points to be obtained in the time intervals; drawing a curve according to the maximum value and minimum value of the time intervals and the number of the points to be obtained in the time intervals; specifically starting a thread for each time interval for processing curve inquiry and view update in the time intervals; after processing n numbered threads completely, completing curve drawing; and if a user needs to select a certain interval for checking detailed data from the drawn curve, repeating the processes. In the method, by adopting a multi-thread mechanism, part of the data can be obtained for drawing within quicker time, thus preventing all the data from being drawn and greatly saving system memory.
Description
Technical field
The invention belongs to the computer data analysis field, be specifically related to a kind of curve display querying method of big data quantity.
Background technology
Along with the development of infotech, a lot of fields all are stored in the database as the business datum of finance, telecommunications and large scale system test etc., and these data have following characteristics:
Data volume is big: data volume is very big, can reach the data volume of TB level.
The temporal correlation of data storage form: lot of data all is the major key storage with time in database.
The time density of data is big: data time density refers to greatly all can have one or more of data recording each second.
After data all store, how existing data are carried out difficult point and the emphasis that query analysis has just become work.A kind of method commonly used is a retrieve data from database, and the form of data with curve shown, and makes data analyst to analyze data retrieved trend and local data intuitively.But because the time density of data is big, calculate according to 1 record in 1 second, be exactly 86400 of one day data volumes, total according to 1 week be 604800 for data analysis cycle data volume then.So many data are once all retrieved and drawn out curve is little feasible, on the one hand data is retrieved to come out from database and need expend a large amount of time, and very long response wait time is that the user is difficult to receive; Googol can a large amount of memory headroom of consume analysis software according to amount on the other hand.
Traditional solution all is that lot of data is handled with the form of paging, promptly retrieve the data of fixed qty (as 2000) at every turn and draw out curve, when the user clicks down one page, again the data retrieval of another fixed qty is come out to draw.Some defectives have been done like this, each 2000 data have as previously described only related to about 33 minutes data, analytical cycle for 1 week seems too of short duration, the analyst can't judge the roughly development trend of 1 weekly data, also can't locate the residing time interval of abnormal data fast simultaneously.
Another solves thinking is to adopt common curve compression algorithm such as Douglas-Poiker method, line segment filtration method, hang down and apart from limit value method etc. data are compressed, thereby carries out curve plotting.The advantage of these algorithms is can both take out to embody the unique point of curvilinear characteristic, thereby makes the unique point curve of drawing out can approach primary curve preferably.But, there are two problems in these algorithm application in the environment of this very big data volume:
1. these algorithms all need to travel through all data, and the computing of going forward side by side between the line data could be taken out unique point.If according to 1 data in foregoing 1 second, from database, inquire the data in a week and the computing do not carried out between any data all needs the long period, add data operation, the time that curve plotting needs obviously is unacceptable.
2. though these algorithms can both amount of compressed data, but compression degree all depends on certain threshold values, all need specify certain to hang down apart from threshold values as data filter with hanging down apart from the limit value method as the Douglas-Poiker method, the line segment filtration method needs the threshold values of specified line segment length as data filter.Need different threshold values for different data, the analyst often can't directly provide appropriate threshold.
At present, a curve display inquiry at this big data quantity data does not also have solution preferably.
Summary of the invention
The present invention is directed to the magnanimity time series data that is stored in the database, long time interval for appointment, traditional method effectively extracted data is carried out the problem of curve display analysis to these time interval data, has proposed a kind of curve display querying method of big data quantity.
A kind of curve display querying method of big data quantity comprises the steps:
Step 1: obtaining the data base querying condition of user's input and needing the number of the data point of extraction is graphical pointv number p_count;
Step 2: the total number totle that from database, obtains data in the time period that the user will inquire about.If, changeing step 3 greater than p_count, carries out the total number totle of data; Otherwise directly according to querying condition constructing SQL (Structured Query Language, Structured Query Language (SQL)) statement is submitted query requests to database, and obtains result set and carry out curve plotting and show for the user and check analysis, preserve the curve of being drawn then, finish this curve plotting;
Step 3: data volumes such as time interval are divided into the n section, and n is according to formula: n=totle/p_count, and result of calculation 4 houses 5 are gone into to round and obtained.The data of the big more extraction of n will can embody the statistical nature of raw data more, but because the thread of the big more unlatching of n is many more, the system resource of consumption is also just many more, and therefore, the maximum occurrences of n is 50;
Step 4: the number of calculating the point that will obtain in each time period respectively;
The number p of the point that concrete i the time period will obtain
iObtain according to following process:
At first, from database, obtain the data maximal value max of i time period
iWith data minimum value min
i
Then, obtain the absolute distance d of the value of i time period
i=max
i-min
i
Then, calculate the absolute distance sum of the value of n time period
At last, determine the number p of the point that i time period will obtain
i: work as d
iEqual at 0 o'clock, p
i=2, work as d
iBe not equal at 0 o'clock, p
iFor according to formula: p
i=d
i/ sum*p_count result of calculation 4 houses 5 integers of going into to obtain; Wherein, 0＜i≤n;
Step 5: initialization curve plotting view, maximal value and minimum value with each time period of obtaining in the step 4 are drawn in the curve display view, open n thread then, the number p of the point that i thread will obtain according to i the time period that obtains in the step 4
iHandle curve inquiry and the view update of i time period;
Step 6: after n thread all disposed, curve plotting finished, and the user checks and analyzes and preserve the curve of being drawn;
Step 7: if the user will further check the data of certain section time interval, then from the curve display view of having drawn, select this section time interval, forward step 2 then to and carry out,, then finish this curve plotting if the user does not need further to check data.
The querying condition of user's input comprises in the described step 1: the parameter numbering of the zero-time of the data of expectation inquiry, concluding time, expectation inquiry; Graphical pointv number p_count be user expectation this be drawn on the number of the data point on the screen, can concrete data be set by user input, default value can be set to 3000, specifically can set according to the resolution of screen.
The number p of the point that i thread will obtain according to i the time period that obtains in the step 4 in the described step 5
iHandle curve inquiry and the view update of i time period, detailed process is: the number p of the point that i thread obtained according to i time period in the step 4
iConstructing SQL statement, and submit to database, database returns the result set of i time period, and described SQL statement will be obtained p uniformly by the result set of i time period of traversal from i time period
iIndividual, i thread just upgrades the curve display view whenever getting a data point, finishes up to current results collection traversal.
In the described step 7, by selected certain interval of mouse, horizontal ordinate time range that then should the interval is as new query time interval on the curve display view of having drawn for the user.
The advantage of the curve display querying method of a kind of big data quantity of the present invention is:
(1) high responsiveness of curve plotting: adopt multi-thread mechanism,, can in than the time faster, the acquisition unit divided data draw, response speed is preferably arranged according to the distribution segmentation retrieval process of data in each time period;
(2) greatly save Installed System Memory: owing to only draw the point of specifying number in the big data quantity, avoided drafting, saved Installed System Memory greatly to total data; And in theory,, need not data analyst and provide the point that any threshold values can extract approximate specific data number, and then draw out curve according to the data of the inventive method for the arbitrary data amount;
(3) broad perspectives of curve plotting: long-time data are being analyzed, can't once draw under the situation of total data, get a little more in zone to the data vary within wide limits, get less a little changing little zone, can be good at keeping the statistical nature of virgin curve, and comprised the maximin of every segment data in the curve, concerning data analyst, have bigger reference value;
(4) user friendly: handle simultaneously simultaneously by the time segmentation on the one hand and draw, the drawing process of entire curve is from coarse to fine and then progressively draws out the contour curve of whole time interval that the user can progressively see the drawing process of curve in this process; If the user is interested for the data in wherein certain time interval of the contour curve of having drawn on the other hand, selected again interested time interval on contour curve, then repaint the data and curves in this interval, make user's important data of positioning analysis fast.
Description of drawings
Fig. 1 is the flow chart of steps of curve display query method of the present invention;
Fig. 2 is the synoptic diagram of in the embodiment of the invention maximin of each segmentation being drawn;
Fig. 3 is the synoptic diagram after each thread peek drafting finishes in the embodiment of the invention;
The synoptic diagram of Fig. 4 for not using the inventive method that all data are shown.
Embodiment
The objective of the invention is to propose a kind of curve display querying method of big data quantity; by segmentation is carried out in long-time interval; by section constructs database query requests; adopt multi-thread mechanism that each section carried out the traversal of data result collection and obtaining of unique point simultaneously; the curve display view on the processing on backstage and foreground is asynchronous to carry out; accelerated the drawing response speed; making the curve plotting of big data quantity be embodied as one has thick to thin; the process of Hui Zhiing progressively; make the tracing analysis personnel to hold to the curvilinear trend of long-time big data quantity; locate the time interval of unusual or the interested curve of analyst fast; draw thereby carry out quafric curve, well solved problems such as efficient and storage space.
The present invention is described in further detail below in conjunction with drawings and Examples.
The application system of the test data management in the existing space industry.The major function that this application system provides comprises carries out data loading and data query analysis to the spacecraft test figure of magnanimity.The gauge outfit of general data table is time, [parameter 1], [parameter 2], [parameter 3] in the database of this application system ... between instant as major key, be accurate to millisecond, the data type of storage is the long data, and other data message defines the parameter numbering as a certain row in the table.Database Systems adopt the Oracle9i database.The data query analyzing subsystem of this application system is inquired about and curve plotting test data based on the method that the present invention proposes.
Store the data of half a year in the database, the user needs 10 days the data of query argument A01, time range be on October 17th, 06 to October 27, as shown in Figure 1, the implementation method of the curve plotting of a kind of big data quantity of the present invention is as follows:
Step 1: obtain the number p_count of data base querying condition with the data point that needs to extract of user's input, the data query analyzing subsystem adopts the Java multipad to realize, provides the querying condition inputting interface to be used to obtain user's initial conditions.The querying condition of user's input comprises: the parameter of the zero-time of the data of expectation inquiry, concluding time, expectation inquiry is AO1, and p_count gets default value 3000.
Step 2: obtain total number of data in the time period that the user will inquire about from database, be designated as totle, this time query time length is 10 days, and the totle=405300 that obtains is far longer than 3000, changes step 3.
Step 3: data volumes such as time interval are divided into the n section, and the obtaining value method of n is: n=totle/p_count, n are integer.The number p_avg=totle/n of the point after the segmentation in each time period; Inquire the time point information on data sementation border according to the number p_avg of the point in each time period from database, the time range that obtains each section is respectively (t
1, t
2) ... (t
i, t
I+1) ... (t
n, t
N+1).
In the embodiment of the invention because of n=totle/p_count=405300/3000＞50, so time range is divided into 50 sections, every section total data number is 8106, utilize the ROWNUM constructing SQL statement in the Oracle inquiry, ROWNUM is the numbering that the oracle systematic order is assigned as the row that returns from inquiry, find the beginning and ending time point of each segment data respectively, SQL statement is SELECT time FROM (SELECT time, ROWNUM from table WHERE time BETWEEN 1161043100000 AND, 1161935558000 AND (A01 IS NOT NULL)) WHERE MOD (ROWNUM-1, p_abg)=0, wherein table is the title of the table of correspondence, p_avg=totle/n, 1161043100000 represent for the lint-long integer of the inquiry zero-time 07:58:20 on the 17th October in 2006 among the embodiment, the 1161935558000th, and October 2006 poll-final time, the lint-long integer of 15:52:38 on the 27th was represented.
Step 4: the number p that calculates the point that will obtain in each time period respectively
i, wherein i represents i time period, 0＜i≤50.
p
iComputation process as follows: from database, obtain the data maximal value max in the i section time
iWith data minimum value min
i, and calculate the absolute distance d of the value of every period
i: d
i=max
i-min
i, calculate each absolute distance sum that is worth of n section time
Then can get the number of getting of i section time: work as d
iBe not equal at 0 o'clock, p
i=d
i/ sum*p_count; d
i/ sum represents absolute distance shared ratio in each section absolute distance sum of the value of i section time, total the number p_count of the point that the needs that this ratio multiply by again extract, what obtain is exactly the some number of the data that will extract the i section time.Last result adopts 4 houses, 5 modes of going into to obtain integer.
Work as d
iEqual at 0 o'clock, p
i=2.When absolute distance is 0, the data no change in this time period is described, therefore, two points of head and the tail of choosing i time interval can be represented the curvilinear characteristic of this time period.
Calculate the absolute distance sum of each value of 50 periods in the embodiment of the invention
, further according to p
iObtaining value method obtain each time period and should get a number and be respectively: 878,2,2,2,841,2,2,2,9,2,2,126,27,2,2,2,2,2,2,2,2,2,2,129,363,2,9,9,2,246,2,2,2,2,2,2,2,2,2,239,2,2,2,2,2,2,2,2,2,27.
Step 5: initialization curve plotting view, obtained maximal value and the minimum value of each time period with in the step 4 all and be drawn in the curve display view.The maximal value of 50 time slices and minimum value are altogether 100 data points in the embodiment of the invention, adopt the form of line that 100 points are shown in the curve display view, as shown in Figure 2, horizontal ordinate express time, ordinate represent that parameter is numbered the numerical value among the A01.Open 50 threads, each thread is responsible for handling curve inquiry and the view update of a time period, and processing procedure is as follows: according to the number p of the point that this time period will obtain in the step 4
iConstructing SQL statement, and submit to database, ergodic data storehouse result set is whenever got a data point and is promptly upgraded the curve display view, and this process finishes up to current results collection traversal.The data point that each thread obtains all is drawn in the same coordinate system; The structure foundation of SQL statement is the time range (t of each segmentation
1, t
2) ... (t
i, t
I+1) ... (t
50, t
51) and the time corresponding section in to get a little number p
i, be SELECT time as the SQL statement of i thread process, A01 FROM (SELECT time, A01, ROWNUM FROM table WHERE time BETWEEN t
iAND t
I+1AND A01 IS NOT NULL) (ROWNUM-1, itv)=0, wherein table claims parameter itv=p_avg/p for corresponding table name to WHERE MOD
i, itv represents the even space-number of getting at interval a little of needs, t
i, t
I+1The starting and ending time value of representing i time interval respectively.
Step 6: when 50 threads all dispose, then curve plotting finishes.The user can check the curve of being drawn, and whether the trend of analytic curve integral body is analyzed it and existed unusually, and the curve data of being drawn is preserved for follow-up check analysis.
Step 7: if user certain interval of Curve selection from having drawn, the detail data of checking this time interval then forwards step 2 to and carries out; If the user does not respond, do not need further to check data, this curve plotting process finishes.What the data query analyzing subsystem was drawn employing in the embodiment of the invention is the graph making class libraries Jfreechart kit of an opening on the JAVA platform, and the user realizes the selection of time interval in curve display view frame favored area by mouse.
The curve plotting of the embodiment of the invention finishes the demonstration of back in the curve display view as shown in Figure 3, and the data point number of drafting is 3010 altogether, owing to calculate p
iCalculating adopted and rounded up so total get a number and only can be similar to p_count.The displayed map that contrast and Fig. 4 do not use the inventive method that all data are drawn, as can be seen, Fig. 3 has well embodied the curvilinear characteristic of Fig. 4 with less data point, and the horizontal ordinate express time among Fig. 3 and Fig. 4, ordinate represent that parameter is numbered the numerical value among the A01.From present embodiment as can be seen this aspect method be effective, greatly save Installed System Memory, and response speed preferably arranged.Spacecraft field tests at the embodiment of the invention, the amplitude that the variation of most of parameter all should be followed certain Changing Pattern or variation is in the interval of certain appointment, data analyst can be easy to find these features by the curve of drawing, and the location abnormal data.Whether method of the present invention exists unusually and provides convenience analyzing this spacecraft, make things convenient for data analyst to check the curve tendency of long-time data at short notice, judge whether to exist unusual according to this curve, and can locate the position of abnormal data fast, further analyze this spacecraft according to the abnormal data that navigates to which place go wrong.
Claims (7)
1. the curve display querying method of a big data quantity is characterized in that, this method may further comprise the steps:
Step 1: the data base querying condition and the graphical pointv number p_count that obtain user's input;
Step 2: the total number totle that from database, obtains data in the time period that the user will inquire about, if the total number totle of data is greater than graphical pointv number p_count, changeing step 3 carries out, otherwise directly according to the querying condition constructing SQL statement, submit query requests to database, and obtain result set and carry out curve plotting and show for the user and check analysis, preserve the curve of being drawn then, finish this curve plotting; Described SQL represents Structured Query Language (SQL);
Step 3: data volumes such as time interval are divided into the n section, and wherein n is according to formula: n=totle/p_count, and result of calculation 4 houses 5 are gone into to round and obtained;
Step 4: the number of the point that obtains to obtain in each time period; The number p of the point that concrete i the time period will obtain
iObtain according to following process:
At first, from database, obtain the data maximal value max of i time period
iWith data minimum value min
i
Then, obtain the absolute distance d of the value of i time period
i=max
i-min
iThen, calculate the absolute distance sum of the value of n time period
At last, determine the number p of the point that i time period will obtain
i: work as d
iEqual at 0 o'clock, p
i=2, work as d
iBe not equal at 0 o'clock, p
iFor according to formula: p
i=d
i/ sum*p_count result of calculation 4 houses 5 integers of going into to obtain; Wherein, 0＜i≤n;
Step 5: the initialization curve plotting shows view, maximal value and minimum value with each time period of obtaining in the step 4, be drawn in the curve display view, open n thread then, the number p of the point that i thread should obtain according to i the time period that obtains in the step 4
iHandle curve inquiry and the view update of i time period;
Step 6: after n thread all disposed, curve plotting finished, and the user checks and analyzes and preserve shown curve;
Step 7: if the user will further inquire about the data of certain section time interval, then from the curve display view of having drawn, select this section time interval, forward step 2 then to and carry out,, then finish this curve plotting if the user does not need further to check data.
2. the curve display querying method of a kind of big data quantity according to claim 1 is characterized in that, the described data base querying condition of step 1 comprises the parameter numbering of zero-time, concluding time and the expectation inquiry of the data of expectation inquiry.
3. the curve display querying method of a kind of big data quantity according to claim 1 is characterized in that, the described graphical pointv number of step 1 p_count is 3000.
4. the curve display querying method of a kind of big data quantity according to claim 1 is characterized in that, the described n of step 3, and its maximum occurrences is 50.
5. the curve display querying method of a kind of big data quantity according to claim 1 is characterized in that, the number p of the point that described i the thread of step 5 will obtain according to i the time period that obtains in the step 4
iHandle curve inquiry and the view update of i time period, detailed process is: the number p of the point that i thread obtained according to i time period in the step 4
iConstructing SQL statement, and submit to database, database returns the result set of i time period, and described SQL statement will be obtained p uniformly by the result set of i time period of traversal from i time period
iIndividual, i thread just upgrades the curve display view whenever getting a data point, finishes up to current results collection traversal.
6. the curve display querying method of a kind of big data quantity according to claim 1 is characterized in that, described curve plotting instrument adopts the graph making class libraries Jfreechart kit of an opening on the JAVA platform.
7. the curve display querying method of a kind of big data quantity according to claim 1, it is characterized in that, Curve selection this section time interval of user described in the step 7 from having drawn, specifically be on the curve display view of having drawn by selected certain time interval of mouse, with the horizontal ordinate time range of this time interval as new query time interval.
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