CN104991939A - Transaction data monitoring method and system - Google Patents
Transaction data monitoring method and system Download PDFInfo
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- CN104991939A CN104991939A CN201510398175.7A CN201510398175A CN104991939A CN 104991939 A CN104991939 A CN 104991939A CN 201510398175 A CN201510398175 A CN 201510398175A CN 104991939 A CN104991939 A CN 104991939A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
Abstract
The invention discloses a transaction data monitoring method and system. The method comprises the steps that: business data prediction values per M minutes are generated and stored in advance; the business data and the prediction values are collected and stored to the tail of a data queue library; the data queue library sends queue head data to an alarm worker and a storage worker at the same time, and the storage worker stores the received data to a monitoring database; the alarm worker judges the type of the received data, obtains a corresponding prediction value in the monitoring database according to business data ID, compares the business data with the corresponding prediction value, and judges whether to trigger an alarm according to alarm rules; if not, the alarm worker waits to receive next group of data; and if so, the alarm is triggered. According to the monitoring method and system, the business data of e-commerce websites can be monitored effectively in real time; and in addition, various alarm ways are realized and the availability of the e-commerce websites is improved.
Description
Technical field
The present invention relates to e-commerce field, particularly a kind of business datum method for supervising and system.
Background technology
Usually for e-commerce company, the ups and downs of web site traffic data volume determine the business revenue of this company, wherein business datum amount is divided into order data and non-order data, non-order data comprises CPU (the Central Processing Unit of web site requests number, response time, position success rate, cluster, CPU (central processing unit)) and linking number, network traffics, SOA (Service-Oriented Architecture, Services Oriented Achitecture) number of request and error number etc. of serving.How by finding whether website has problem to the monitoring alarm of business datum amount, it is a difficult problem for an industry.The general situation adopting report display mode to show website order data amount in prior art, for example DASHBOARD (business intelligencedashboard, the abbreviation of BI dashboard, business intelligence panel board) storage that adopts is OPENTSDB (Open-source Distributed, Scalable, Time Series Database, to increase income time series database), but this is the displaying of a form, can not do alarm, and often have delay.
Summary of the invention
The technical problem to be solved in the present invention is that e-commerce website business datum monitor mode is single, weak effect, real-time are not high and the defect of alarm disappearance in order to overcome in prior art, provide a kind of can the business datum method for supervising of Real-time Alarm and system.
The present invention solves above-mentioned technical matters by following technical proposals:
The invention provides a kind of business datum method for supervising, be characterized in, comprise:
S
1, generate every M minute business datum predicted value in advance, be stored into data source data storehouse;
S
2, from described data source data storehouse capturing service data and predicted value, be stored in data queue's storehouse tail of the queue;
S
3, described data queue storehouse its team's head data are issued simultaneously alarm worker and store worker, the data received are stored into monitor database by described storage worker;
S
4, described alarm worker judges the type of the data received, if predicted value, do not deal with and next group data of wait-receiving mode; If business datum, in described monitor database, obtain corresponding predicted value according to business datum ID, and compare business datum and corresponding predicted value, trigger alerts is judged whether according to alarm regulation, if not, then next group data of described alarm worker wait-receiving mode, if so, then trigger alerts.
Wherein, data source data storehouse can be one, also can be multiple database, and it is used for depositing business datum and predicted value, predicted value can deposit in a data source database jointly with business datum, also can deposit in different multiple data source data storehouses respectively.Wherein business datum also can come from the data source data storehouse API (Application Programming Interface, application programming interface) of broad sense.
Wherein, data queue storehouse is used for storing according to sequencing the business datum and predicted value that gather from data source data storehouse, there are team's head and tail of the queue, the tail of the queue of the data data inserting queue of new collection, each data queue all fetches data from team's head in storehouse, often take one group of data away, team's head automatically updating data is next group data.
Wherein, worker is the consumer thread in program, is divided into two types, is respectively alarm worker consumer and stores worker consumer, and often kind of worker consumer is only responsible for data or the behavior of consumption formulation, is specially and is only responsible for storing or alarm.The processing speed of data in data queue storehouse can be improved by increasing woker customer count.
Wherein, monitor database be used for store storage worker consumer obtain data, its data volume stored is different according to the difference of data acquisition granularity, if the collection per minute of data acquisition granularity once, namely often organize the business datum amount that data comprise a minute, the data time amount that so can store in monitor database is 30 days, if data acquisition granularity is gather once for one day, the data time amount that so can store in monitor database is 2 years.The data stored in monitor database compare use for during follow-up alarm operation step, also may be used for web (internet general name) end and show.
Wherein, S
4described in predicted value can be S
1in every M minute business datum predicted value generating in advance, also can be historical data values same period last week stored in monitor database.
Preferably, S
1comprise:
S
11, according to history service data volume every day of surrounding on year-on-year basis, prediction whole day business datum amount same period next week;
S
12, according to every M minute history service data volume of surrounding on year-on-year basis, according to seasonal rhythm forecast model prediction every M minute seasonal index number same period next week;
S
13, use S
11the whole day business datum amount of middle prediction and S
12every M minute seasonal index number of middle prediction is multiplied, and obtains every M minute business datum predicted value.
Wherein, seasonal rhythm forecast model originates from by the obvious demand for commodity order forecasting of seasonal effect, as beer is less in the demand in summer demand that is comparatively large, winter.According to the order total amount in each season over the years, the order volume in following 1 year each season can be doped.Circular is:
(1) according to order total amount over the years and rising tendency, calculate following 1 year order volume F, computing method have a variety of, such as least square method or moving average method;
(2) average of each year with season order is calculated, Ai;
(3) average of each annual order total amount is calculated, B;
(4) seasonal index number in each season is calculated, Ai/B;
(5) order of a following annual seasons i is: F × Ai/B
Be converted in this programme and predict every M minute business datum predicted value, order data over the years in the corresponding model of history service data volume every day of the surrounding on year-on-year basis then in this programme, 1 year in 1 day corresponding model in this programme, each season in every M minute corresponding model in this programme.
This programme S
12seasonal index number prediction every M minute same period next week adopt as previously described in computing method (2) to (4) step principle calculate; S
13in every M minute business datum predicted value calculate according to step (5) principle in aforementioned calculation method.
In this programme, for special holidays, the production method of every M minute business datum predicted value can adjust automatically, is basis for forecasting according to the business datum rising tendency of festivals or holidays over the years.
Preferably, described business datum method for supervising also comprises configuration alarm regulation and is stored into alarm regulation database.
In this programme, alarm regulation database is an independent database, only be used for storing all alarm regulation data, compare in data source data storehouse with alarm regulation deposit data, this programme can ensure that alarm worker can obtain the required alarm regulation data used fast, improves the real-time of alarm.
Preferably, S
4also comprise and from described alarm regulation database, obtain corresponding alarm regulation according to business datum ID.
Preferably, described alarm regulation comprise threshold values, chain rate, on year-on-year basis, alarm notification object or alarm notification mode.
Wherein threshold values refers to a fixing numerical value, can be decimal or negative etc.; Chain rate refers to that current period statistics is compared with last statistics, and such as in July, 2015, statistics was compared with in June, 2015 statistics; Refer to that current period statistics is compared with history same time statistics on year-on-year basis, such as in July, 2014, statistics was compared with in July, 2013 statistics.
Preferably, described alarm notification mode is mail, note propelling movement, voice or bullet window.
In this programme, alarm mode can be send mail or eject dialog box to alarm notification object, also can send SMS (short message service) or micro-letter, or directly sends alarm voice by loudspeaker.If transmission mail, can increase alarm sectional drawing in mail, such alarm notification object can according to the seriousness of sectional drawing decision problem while getting the mail simultaneously.
Preferably, S
11middle prediction whole day business datum same period next week amount adopts least square method or moving average method.
In this programme, have a variety of in prediction method prior art that whole day business datum amount adopts same period next week, can be allowed a choice according to real needs difference.
Preferably, described data queue storehouse is Zeromq, and described monitor database is Graphite.
Wherein, Zeromq is a series of interfaces being similar to Socket, is a Message Processing bank of queues.Graphite be one for gathering website real-time information and the open source projects carrying out adding up, can be used for gathering multiple website service running state information.Graphite unit can have 17000 renewal rewards theory average p.s..Put into practice and verifiedly will monitor website what occurs is very useful, its plain text agreement and drawing function can easily in a plug-and-play manner for systems that any needs are monitored.Graphite supports the polymerization of multiple latitude, it adopts RRD (read receive data register, read receive data register) annular data structure better store and display data, its data store store in the mode of file, after establishment, file size just secures.It provide very abundant API and raw data and aggregated data can be provided.
Preferably, described alarm regulation database is Redis.
Wherein, Redis is that a use ANSI C language of increasing income is write, network enabled, can also can log type, Key-Value (a corresponding value of the key) database of persistence based on internal memory, and provide multilingual API.
Preferably, the acquisition method of described history service data volume is: gather business datum amount every M minute the previous day every day; Reject section business data volume fault-time; Store residue every M minute business datum amount after rejecting.
In this programme, history service deposit data, in special history service database, can gather every M minute the previous day business datum amount every day and be stored in history service database, can reject the traffic data in the failure process time period before depositing.Can cause the abnormal decline of business datum amount due to fault or raise, the net result of the existence meeting impact prediction of this kind of dirty data, therefore can advanced row data scrubbing.
Preferably, within described M minute, be 1 minute, 5 minutes, 60 minutes or 1440 minutes, corresponding described monitor data library storage data time amount is respectively 30 days, 120 days, 180 days or 2 years.
Wherein, within every 1 minute, a business datum amount can be gathered according to data acquisition Grained Requirements difference, also can gather once for 5 minutes, 1 hour or 1 day, the acquisition granularity is selected different, the length of the time quantum can deposited in corresponding monitor database is also different, and this is determined by the memory capacity of monitor database.
Preferably, described business datum comprises order data and non-order data.
The present invention also provides a kind of business datum supervisory system, is characterized in, comprises:
Predicted value generation module, for generating every M minute business datum predicted value in advance, is stored into data source data storehouse;
Data acquisition memory module, for from described data source data storehouse capturing service data and predicted value, is stored in data queue's storehouse tail of the queue;
Data processing module, for calling described data queue storehouse, its team's head data are issued alarm worker and storage worker simultaneously, the data received are stored into monitor database by described storage worker;
Alarm judge module, judges the type of the data received for calling described alarm worker, if predicted value, do not deal with and next group data of wait-receiving mode; If business datum, in described monitor database, obtain corresponding predicted value according to business datum ID, and compare business datum and corresponding predicted value, trigger alerts is judged whether according to alarm regulation, if not, then next group data of described alarm worker wait-receiving mode, if so, then trigger alerts.
Preferably, described predicted value generation module comprises:
Whole day data volume prediction module, for history service data volume every day according to surrounding on year-on-year basis, prediction whole day business datum amount same period next week;
Seasonal index number prediction module, for the every M minute history service data volume according to surrounding on year-on-year basis, according to seasonal rhythm forecast model prediction every M minute seasonal index number same period next week;
Predictor calculation module, is multiplied with the every M minute seasonal index number that described seasonal index number prediction module is predicted by the whole day business datum amount of described whole day data volume prediction module prediction, obtains every M minute business datum predicted value.
Preferably, described business datum supervisory system also comprises alarm regulation configuration module, for configuring alarm regulation and being stored into alarm regulation database.
Preferably, described alarm judge module also for obtaining corresponding alarm regulation according to business datum ID from described alarm regulation database.
Preferably, whole day business datum amount adopts least square method or moving average method to predict same period next week in described whole day data volume prediction module.
Preferably, described predicted value generation module also comprises history service data volume acquisition module, for gathering business datum amount every M minute the previous day every day; Reject section business data volume fault-time; Store residue every M minute business datum amount after rejecting.
Positive progressive effect of the present invention is: the present invention by history service data in conjunction with seasonal rhythm forecast model, generate business datum predicted value in advance, compared with predicted value by message queue storehouse Real-time Collection business datum, and send Real-time Alarm according to alarm regulation, thus the problem of Timeliness coverage website, do corresponding process in time.The present invention can not only monitor e-commerce website business datum during efficient real, and achieves multiple alarm mode, improves the availability of e-commerce website.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the business datum method for supervising of the embodiment of the present invention.
Fig. 2 is the business datum predicted value product process figure per minute of the business datum method for supervising of the embodiment of the present invention.
Fig. 3 is the business datum supervisory system schematic diagram of the embodiment of the present invention.
Fig. 4 is the predicted value generation module schematic diagram of the business datum supervisory system of the embodiment of the present invention.
Embodiment
Mode below by embodiment further illustrates the present invention, but does not therefore limit the present invention among described scope of embodiments.
Embodiment
As shown in Figure 1, a kind of business datum method for supervising, comprising:
Step 101, generate business datum predicted value per minute in advance, be stored into data source data storehouse;
Step 102, configuration alarm regulation be stored into alarm regulation database Redis;
Step 103, from data source data storehouse capturing service data and predicted value, be stored in data queue's storehouse Zeromq tail of the queue;
Its team's head data are issued alarm worker simultaneously and are stored worker by step 104, data queue storehouse Zeromq, store worker and the data received are stored into monitor database Graphite;
Step 105, alarm worker judge the type of the data received, if predicted value, do not deal with and next group data of wait-receiving mode; If business datum, in monitor database Graphite, obtain corresponding predicted value according to business datum ID and from alarm regulation database Redis, obtain corresponding alarm regulation according to business datum ID, and compare business datum and corresponding predicted value, trigger alerts is judged whether according to alarm regulation, if do not needed, then next group data of alarm worker wait-receiving mode, if needed, then trigger alerts.For example, if drop range per minute is more than 20%, continuous 2 times, then send alarm notification to corresponding personnel, alarm notification mode can be one or more in mail, note propelling movement, voice or bullet window.Wherein, alarm regulation comprise threshold values, chain rate, on year-on-year basis, alarm notification object or alarm notification mode.
As shown in Figure 2, wherein step 101 comprises:
Step 1010, collection history service data volume, be specially, gather business datum amount per minute the previous day every day; Then section business data volume fault-time is rejected; Finally store residue business datum amount per minute after rejecting.
Step 1011, history service data volume every day according to surrounding on year-on-year basis, prediction whole day business datum amount same period next week, concrete prediction algorithm can adopt least square method or moving average method.
Step 1012, history service data volume per minute according to surrounding on year-on-year basis, according to seasonal rhythm forecast model seasonal index number per minute prediction same period next week.
Step 1013, with in step 1011 prediction whole day business datum amount be multiplied with the seasonal index number per minute predicted in step 1012, obtain business datum predicted value per minute.
In the present embodiment, business datum comprises order data and non-order data.Per minutely also can become every 5 minutes, per hour or every day, corresponding described monitor data library storage data time amount is respectively 120 days, 180 days or 2 years.For order data, when the sampling Trend Stationary of order and quantity is more time, sample frequency is per minute, otherwise is generally 5 minutes, and the order as International website is few especially, is sampled as one hour.For non-order data, general sample frequency acquiescence is all one minute, such as web site requests number etc.
As shown in Figure 3, the business datum supervisory system of the present embodiment comprises:
Predicted value generation module 201, for generating business datum predicted value per minute in advance, is stored into data source data storehouse;
Alarm regulation configuration module 202, for configuring alarm regulation and being stored into alarm regulation database Redis;
Data acquisition memory module 203, for from data source data storehouse capturing service data and predicted value, is stored in data queue's storehouse Zeromq tail of the queue;
Its team's head data are issued alarm worker and storage worker for calling data bank of queues Zeromq simultaneously, are stored worker and the data received are stored into monitor database Graphite by data processing module 204;
Alarm judge module 205, judges the type of the data received for calling alarm worker, if predicted value, do not deal with and next group data of wait-receiving mode; If business datum, in monitor database Graphite, obtain corresponding predicted value according to business datum ID and from described alarm regulation database Redis, obtain corresponding alarm regulation according to business datum ID, and compare business datum and corresponding predicted value, trigger alerts is judged whether according to alarm regulation, if not, then next group data of alarm worker wait-receiving mode, if so, then trigger alerts.
As shown in Figure 4, in the present embodiment, predicted value generation module 201 comprises:
History service data volume acquisition module 2010, for gathering business datum amount per minute the previous day every day; Reject section business data volume fault-time; Store residue business datum amount per minute after rejecting.
Whole day data volume prediction module 2011, for history service data volume every day according to surrounding on year-on-year basis, prediction whole day business datum amount same period next week;
Seasonal index number prediction module 2012, for the history service data volume per minute according to surrounding on year-on-year basis, according to seasonal rhythm forecast model seasonal index number per minute prediction same period next week;
Predictor calculation module 2013, is multiplied with the seasonal index number per minute that seasonal index number prediction module is predicted by the whole day business datum amount of whole day data volume prediction module prediction, obtains business datum predicted value per minute.
Although the foregoing describe the specific embodiment of the present invention, it will be understood by those of skill in the art that these only illustrate, protection scope of the present invention is defined by the appended claims.Those skilled in the art, under the prerequisite not deviating from principle of the present invention and essence, can make various changes or modifications to these embodiments, but these change and amendment all falls into protection scope of the present invention.
Claims (18)
1. a business datum method for supervising, is characterized in that, comprising:
S
1, generate every M minute business datum predicted value in advance, be stored into data source data storehouse;
S
2, from described data source data storehouse capturing service data and predicted value, be stored in data queue's storehouse tail of the queue;
S
3, described data queue storehouse its team's head data are issued simultaneously alarm worker and store worker, the data received are stored into monitor database by described storage worker;
S
4, described alarm worker judges the type of the data received, if predicted value, do not deal with and next group data of wait-receiving mode; If business datum, in described monitor database, obtain corresponding predicted value according to business datum ID, and compare business datum and corresponding predicted value, trigger alerts is judged whether according to alarm regulation, if not, then next group data of described alarm worker wait-receiving mode, if so, then trigger alerts.
2. business datum method for supervising as claimed in claim 1, is characterized in that, S
1comprise:
S
11, according to history service data volume every day of surrounding on year-on-year basis, prediction whole day business datum amount same period next week;
S
12, according to every M minute history service data volume of surrounding on year-on-year basis, according to seasonal rhythm forecast model prediction every M minute seasonal index number same period next week;
S
13, use S
11the whole day business datum amount of middle prediction and S
12every M minute seasonal index number of middle prediction is multiplied, and obtains every M minute business datum predicted value.
3. business datum method for supervising as claimed in claim 1, is characterized in that, described business datum method for supervising also comprises configuration alarm regulation and is stored into alarm regulation database.
4. business datum method for supervising as claimed in claim 3, is characterized in that, S
4also comprise and from described alarm regulation database, obtain corresponding alarm regulation according to business datum ID.
5. business datum method for supervising as claimed in claim 4, is characterized in that, described alarm regulation comprise threshold values, chain rate, on year-on-year basis, alarm notification object or alarm notification mode.
6. business datum method for supervising as claimed in claim 5, is characterized in that, described alarm notification mode is mail, note propelling movement, voice or bullet window.
7. business datum method for supervising as claimed in claim 2, is characterized in that, S
11middle prediction whole day business datum same period next week amount adopts least square method or moving average method.
8. business datum method for supervising as claimed in claim 1, it is characterized in that, described data queue storehouse is Zeromq, and described monitor database is Graphite.
9. business datum method for supervising as claimed in claim 3, it is characterized in that, described alarm regulation database is Redis.
10. business datum method for supervising as claimed in claim 2, it is characterized in that, the acquisition method of described history service data volume is: gather business datum amount every M minute the previous day every day; Reject section business data volume fault-time; Store residue every M minute business datum amount after rejecting.
11. business datum method for supervising as claimed in claim 1, is characterized in that, within described M minute, are 1 minute, 5 minutes, 60 minutes or 1440 minutes, and corresponding described monitor data library storage data time amount is respectively 30 days, 120 days, 180 days or 2 years.
12., as the business datum method for supervising in claim 1 to 11 as described in any one, is characterized in that, described business datum comprises order data and non-order data.
13. 1 kinds of business datum supervisory systems, is characterized in that, comprising:
Predicted value generation module, for generating every M minute business datum predicted value in advance, is stored into data source data storehouse;
Data acquisition memory module, for from described data source data storehouse capturing service data and predicted value, is stored in data queue's storehouse tail of the queue;
Data processing module, for calling described data queue storehouse, its team's head data are issued alarm worker and storage worker simultaneously, the data received are stored into monitor database by described storage worker;
Alarm judge module, judges the type of the data received for calling described alarm worker, if predicted value, do not deal with and next group data of wait-receiving mode; If business datum, in described monitor database, obtain corresponding predicted value according to business datum ID, and compare business datum and corresponding predicted value, trigger alerts is judged whether according to alarm regulation, if not, then next group data of described alarm worker wait-receiving mode, if so, then trigger alerts.
14. business datum supervisory systems as claimed in claim 13, it is characterized in that, described predicted value generation module comprises:
Whole day data volume prediction module, for history service data volume every day according to surrounding on year-on-year basis, prediction whole day business datum amount same period next week;
Seasonal index number prediction module, for the every M minute history service data volume according to surrounding on year-on-year basis, according to seasonal rhythm forecast model prediction every M minute seasonal index number same period next week;
Predictor calculation module, is multiplied with the every M minute seasonal index number that described seasonal index number prediction module is predicted by the whole day business datum amount of described whole day data volume prediction module prediction, obtains every M minute business datum predicted value.
15. business datum supervisory systems as claimed in claim 13, it is characterized in that, described business datum supervisory system also comprises alarm regulation configuration module, for configuring alarm regulation and being stored into alarm regulation database.
16. business datum supervisory systems as claimed in claim 15, is characterized in that, described alarm judge module also for obtaining corresponding alarm regulation according to business datum ID from described alarm regulation database.
17. business datum supervisory systems as claimed in claim 14, is characterized in that, whole day business datum amount adopts least square method or moving average method to predict same period next week in described whole day data volume prediction module.
18. business datum supervisory systems as claimed in claim 14, it is characterized in that, described predicted value generation module also comprises history service data volume acquisition module, for gathering business datum amount every M minute the previous day every day; Reject section business data volume fault-time; Store residue every M minute business datum amount after rejecting.
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| CN111523084A (en) * | 2020-04-09 | 2020-08-11 | 京东方科技集团股份有限公司 | Service data prediction method and device, electronic equipment and computer readable storage medium |
| CN116091202A (en) * | 2022-12-29 | 2023-05-09 | 北京君航微金信息科技有限公司 | Financial business monitoring and early warning method and system |
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