CN109902093B - Rapid statistical method for mobile application - Google Patents
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- CN109902093B CN109902093B CN201811534621.2A CN201811534621A CN109902093B CN 109902093 B CN109902093 B CN 109902093B CN 201811534621 A CN201811534621 A CN 201811534621A CN 109902093 B CN109902093 B CN 109902093B
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Abstract
The invention provides a quick statistical method for mobile application, which comprises the following steps: the server receives application data; the server splices the received application data and stores the application data into a redis cache; acquiring reported data, determining whether the equipment reporting the data is newly added equipment or not by combining the information recorded in the database, and landing the acquired reported information and the equipment type in a current day file according to a format; generating a one-dimensional total amount result file by combining newly-added reported data on the current day with a one-dimensional calculation intermediate file calculated on the previous day at a preset time, and generating a one-dimensional calculation result file on the current day; and then, performing two-dimensional total amount calculation, generating a two-dimensional total amount result file according to newly-added reported data of the current day and the two-dimensional calculation intermediate file calculated in the previous day, and generating a two-dimensional calculation result file of the current day. The invention can greatly reduce the time required for traversing the historical data, thereby obviously reducing the time required for daily statistics. The user can see the update of the daily data more quickly.
Description
Technical Field
The invention relates to the technical field of mobile application, in particular to a quick statistical method for mobile application.
Background
The statistics of the mobile application plays an important role in the operation work of the mobile app, and users can conveniently know the installation amount, the activity, the geographical location information of user groups, the equipment information and the like of the app. The traditional server side statistical method is to log each reported original message and perform statistics once a day according to all message information. However, when there are many historical messages, it takes a long time to perform calculation and analysis; another solution is to process such big data by distributed computation, which can increase the processing speed, but does not reduce the overall computation analysis amount, and the complexity of the overall system is increased by the distributed computation of multiple machines.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide a quick statistical method for mobile application.
In order to achieve the above object, an embodiment of the present invention provides a fast statistical method for a mobile application, including:
step S1, the server receives the application data reported when the mobile application APP is started;
step S2, the server splices the received application data and stores the application data into a re dis buffer;
step S3, acquiring reported data from redis, cleaning the data, determining whether the device reporting the data is a new device or not by combining the information recorded in the database, setting a new mark, and landing the acquired reported information and the device type in a current day file according to a format;
step S4, generating a one-dimensional total amount result file for the newly added report data of the current day in the preset time by combining the one-dimensional calculation intermediate file calculated in the previous day, and generating the one-dimensional calculation result file of the current day and landing for the next day; and then, performing two-dimensional total amount calculation, generating a two-dimensional total amount result file according to newly-added reported data of the current day and the two-dimensional calculation intermediate file calculated in the previous day, and generating the two-dimensional calculation result file of the current day and landing the two-dimensional calculation result file for the next day.
Further, in step S1, the application data reported by the mobile application includes: application version, device ID number, networking mode, mobile phone model.
Further, in the step S2, the data format of the application data stored in the re dis buffer is as follows:
and applying id | version number | equipment id | equipment detailed information | reporting time | mobile phone system type | reporting i | application use time length information.
Further, in step S3, the determining the type of the data after being cleaned includes the following steps:
performing database query judgment on the cleaned data,
if the data type is new, flagstone is marked as 1,
if the data type is updating, marking the flagstone as 3 and inserting the data into the database, otherwise marking the flagstone as 2;
and then stores the data in a re dis buffer.
Further, each piece of reported data queries the application id and the equipment id according to the query condition, judges whether a record exists in the database, and if not, adds a new record. Marking the report as a new device report; if the application id and the device id exist but the version numbers are not consistent, the data is marked as an update, and the data format is as follows:
and applying id | version number | equipment id | equipment detailed information | reporting time | system type (Ios/android) | new equipment mark | reporting i | application use time length information.
Further, the performing one-dimensional total amount statistical calculation on the data recorded in the database includes:
operating a load intermediate file, wherein the load intermediate file comprises the use duration and the starting times of each app, calculating the yesterday total, accumulating the use duration and the starting times of each app, and saving and discarding the accumulated use duration and starting times so as to facilitate the next continuous use;
traversing a yesterday result file, acquiring the total equipment amount of each app from the yesterday result file, traversing a yesterday file newly-added file, acquiring data with flagstone of 1, and increasing the equipment amount of the app;
acquiring a file list of the last month, traversing each file, removing the duplicate of the data, and calculating the weekly activity and the monthly activity of the app;
and collecting the results to generate an active quantity result file, wherein the format is as follows:
application id | total number of devices |7 daily activity |30 daily activity |7 daily usage duration |7 daily usage times |30 daily usage times | total duration | is always usage times.
Further, the performing two-dimensional total amount statistical calculation on the data recorded in the database includes:
acquiring the device quantity and the geographic position information set of the app version from the history result file;
traversing the newly added file, and increasing the device amount of the app version;
traversing the newly added files, and newly adding the geographical position information set;
running the Load intermediate file, and acquiring different networking modes of app versions, mobile phone models, resolution and the number of system versions;
traversing the newly added files, and increasing the number of networking modes, mobile phone models, resolutions and system versions based on the intermediate files;
a Dump intermediate file, which is used for outputting the calculated result Dump for the next use;
and aggregating the information to generate an increment result file, wherein the format is as follows:
and applying id | version number | equipment total | geographic position information statistical result | connection mode statistical result | equipment model statistical result | resolution statistical result | operating system statistical result.
According to the rapid statistical method for the mobile application, the calculation of the historical data is completed by loading the result data of the intermediate calculation every day, and the newly added data statistics is processed by distinguishing the incremental reporting and the historical equipment reporting. And the result data structure of the daily equipment information statistical calculation is stored in a disk while the result information is counted, so that the calculation process in the next day can continue to work on the basis of the previous statistics without restarting to traverse all data, the time for traversing historical data is greatly reduced, and the time for daily statistics is obviously reduced. The user can see the update of the daily data more quickly. In addition, the invention carries out preprocessing before storing the original reported data to distinguish the reported records of the new and old equipment, so that the daily statistical information statistics is simplified to only need to traverse the reported information of the newly added equipment, and the statistical speed is further accelerated. The invention can directly acquire data from the memory data file of the previous calculation result and perform incremental operation on the data, thereby avoiding performing operation on historical data every time and greatly accelerating the operation process. And, have fault-tolerant high characteristic, if the operation is wrong in a day, can calculate from a day before the day again. The change of various attributes of the App can be displayed more intuitively and clearly, historical results are stored in a database, and a user can also select a certain period of time more intuitively to observe the change of various attributes of the App.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a fast statistics method for mobile applications according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1, the method for fast statistics of mobile applications according to the embodiment of the present invention is characterized by comprising the following steps:
and step S1, the server receives the reported application data when the mobile application APP is started.
In this step, when the mobile app starts an application, the mobile app reports an application datum, where the application datum reported by the mobile application includes: application version, device ID number, networking mode, mobile phone model, etc. The data is then structured and calculated to ultimately generate some information about the application for viewing by the customer.
And step S2, splicing the received application data by the server and storing the application data into a re dis buffer.
In this step, the data format of the application data stored in the re dis buffer is as follows:
and applying id | version number | equipment id | equipment detailed information | reporting time | mobile phone system type | reporting i | application use time length information.
And step S3, acquiring the reported data from the redis, cleaning the data, determining whether the device reporting the data is a newly added device or not by combining the information recorded in the database, setting a newly added mark, and landing the acquired reported information and the device type in a current day file according to a format. Namely, whether the cleaned data is a newly added device or not is set by combining the device information in the database, and if the device information database does not exist, the data is recorded in the database.
Specifically, the reporting data is acquired from the redis, whether the device reporting the data is a newly added device is determined by combining the information recorded in the database, and the acquired reporting information and the device type (newly added and existing devices) are dropped into the current-day file according to the format.
Specifically, the type judgment of the cleaned data comprises the following steps:
and the statistical server acquires reported data from the re dis cache for cleaning, eliminates dirty data, and queries the database of the cleaned data to judge the type of the data. Wherein the data types include: newly increased reporting, conventional reporting and updated reporting.
When the data type is judged to be newly increased, marking the flag flagstone of the new device as 1;
and when the data type is judged to be updated, marking the flag flagstone of the new device as 3 and inserting the data into the database, otherwise, marking the flag flagstone of the new device as 2.
The data is then saved to redis-3 in the following format, where the new device flag flagstone is not 0, but 1, 2, 3.
The new device flag flagstone can be obtained by database query, each piece of reported data queries the application id and the device id according to query conditions, whether a record exists in the database is judged, and if not, a record is newly added. And marking the report as a new device report. If the application id and the device id exist but the version numbers are not consistent, the data is marked as an update, and the data format is as follows:
and applying id | version number | equipment id | equipment detailed information | reporting time | system type (Ios/android) | new equipment mark | reporting i | application use time length information.
The field is reported information including system type, model, version, resolution, networking mode, etc., and the information is a json structure.
Such as: { "appVersion": 0.0.21"," widget version ": 0.0.1", "engineVersion": 1.2.84"," channel ": apicloud", "jailbrew": true "," model ": 4G +", and "manufac")
turer":"GXI","operator":"unknown","systemVersion":"6.1","systemType":"android","resolution":"720*1280","width":720,"height":1280,"connectedType":"wifi","deviceId":"A000002CEBB587","longitude":"0.0","latitude":"0.0"}。
It should be noted that, the reporting and recording program, receiving _ work, runs once every half hour, takes out data from the re dis, and stores the files in a classified manner according to the identifiers, where the format stored in the file is as follows:
TX application id version number device id;
tx, wherein new device flag is 1 or 3;
reporting i | application use duration information by applying id | version number | equipment id | equipment detailed information | reporting time | system type (Ios/android) | new equipment mark | reporting i | application use duration information;
all data in re dis;
reporting i | application use duration information by applying id | version number | equipment id | equipment detailed information | reporting time | system type (Ios/android) | new equipment mark | reporting i | application use duration information;
tx application id version number.
And step S4, generating a one-dimensional total amount result file for the newly added report data of the current day in the preset time by combining the one-dimensional calculation intermediate file calculated in the previous day, and generating the one-dimensional calculation result file of the current day and landing for the next day. For example, after 0 o' clock on the next day, a one-dimensional total amount result file is generated by combining the newly added report data on the current day with the one-dimensional calculation intermediate file calculated on the previous day, and meanwhile, a one-dimensional calculation result file on the current day is generated and falls to the ground.
And then, performing two-dimensional total amount calculation, generating a two-dimensional total amount result file according to newly-added reported data of the current day and the two-dimensional calculation intermediate file calculated in the previous day, and generating the two-dimensional calculation result file of the current day and landing the two-dimensional calculation result file for the next day.
Specifically, the one-dimensional total amount statistical calculation (one _ dimension. oy) of the data recorded in the database includes:
operating a load intermediate file, wherein the load intermediate file comprises the use duration and the starting times of each app, calculating the yesterday total, accumulating the use duration and the starting times of each app, storing and discarding the accumulated use duration and starting times so as to facilitate the next continuous use;
traversing a yesterday result file, acquiring the total equipment amount of each app from the yesterday result file, traversing a yesterday file newly-added file, acquiring data with flagstone of 1, and increasing the equipment amount of the app;
acquiring a file list of the last month, traversing each file, removing the duplicate of the data, and calculating the weekly activity and the monthly activity of the app;
collecting the results to generate an active quantity result file, a Dump intermediate statistical result file and a data format: { aphid: [ longtime, suppetertime ], … }
The one-dimensional total amount calculation result file format per day is as follows:
application id | total number of devices |7 daily activity |30 daily activity |7 daily usage duration |7 daily usage times |30 daily usage times | total duration | is always usage times.
Then, a Two-dimensional total amount statistical calculation (Two _ dimension. oy) is performed on the data recorded in the database, including:
acquiring the device quantity and the geographic position information set of the app version from the history result file;
traversing the newly added file, and increasing the device amount of the app version;
traversing the newly added files, and newly adding the geographical position information set;
running the Load intermediate file, and acquiring different networking modes of app versions, mobile phone models, resolution and the number of system versions;
traversing the newly added files, and increasing the number of networking modes, mobile phone models, resolutions and system versions based on the intermediate files;
a Dump intermediate file, which is used for outputting the calculated result Dump for the next use;
intermediate file data format:
{deviceCountModel_resultHash:{appId|versionCoe|modelType:count,…},deviceCountResolution_resultHash:{appId|versionCode|resoulutionType:count,…},startupCountConnType_resultHash:{appId|versionCode|connType:count,…},deviceCountOS_resultHash:{appId|versionCode|osType:count,…}}
and aggregating the information to generate an increment result file, wherein the format is as follows:
and applying id | version number | equipment total | geographic position information statistical result | connection mode statistical result | equipment model statistical result | resolution statistical result | operating system statistical result.
According to the rapid statistical method for the mobile application, the calculation of the historical data is completed by loading the result data of the intermediate calculation every day, and the newly added data statistics is processed by distinguishing the incremental reporting and the historical equipment reporting. And the result data structure of the daily equipment information statistical calculation is stored in a disk while the result information is counted, so that the calculation process in the next day can continue to work on the basis of the previous statistics without restarting to traverse all data, the time for traversing historical data is greatly reduced, and the time for daily statistics is obviously reduced. The user can see the update of the daily data more quickly. In addition, the invention carries out preprocessing before storing the original reported data to distinguish the reported records of the new and old equipment, so that the daily statistical information statistics is simplified to only need to traverse the reported information of the newly added equipment, and the statistical speed is further accelerated. The invention can directly acquire data from the memory data file of the previous calculation result and perform incremental operation on the data, thereby avoiding performing operation on historical data every time and greatly accelerating the operation process. And, have fault-tolerant high characteristic, if the operation is wrong in a day, can calculate from a day before the day again. The change of various attributes of the App can be displayed more intuitively and clearly, historical results are stored in a database, and a user can also select a certain period of time more intuitively to observe the change of various attributes of the App.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A fast statistical method for mobile applications is characterized by comprising the following steps:
step S1, the server receives the application data reported when the mobile application APP is started;
step S2, the server splices the received application data and stores the application data into a redis buffer;
step S3, acquiring reported data from redis, cleaning the data, determining whether the device reporting the data is a new device or not by combining the information recorded in the database, setting a new mark, and landing the acquired reported information and the device type in a current day file according to a format;
step S4, generating a one-dimensional total amount result file for the newly added report data of the current day in the preset time by combining the one-dimensional calculation intermediate file calculated in the previous day, and generating the one-dimensional calculation result file of the current day and landing for the next day;
performing one-dimensional total amount statistical calculation on the recorded data in the database, wherein the one-dimensional total amount statistical calculation comprises the following steps:
operating a load intermediate file, wherein the load intermediate file comprises the use duration and the starting times of each app, calculating the one-dimensional total amount of yesterday, accumulating the use duration and the starting times of each app, storing the accumulated use duration and starting times and going out dump to facilitate the next continuous use;
traversing a yesterday result file, acquiring the total amount of equipment of each app from the yesterday result file, traversing a yesterday file newly added file, acquiring data with a new equipment flag flagstone of 1, and increasing the equipment amount of the app;
acquiring a file list of the last month, traversing each file, removing the duplicate of the data, and calculating the weekly activity and the monthly activity of the app;
and (3) collecting the weekly activity and the monthly activity of the app to generate an activity result file, wherein the format is as follows:
the application id | the total number of devices |7 daily activity |30 daily activity |7 daily usage duration |7 daily usage times |30 daily usage times | total usage times;
then, two-dimensional total amount calculation is carried out, a two-dimensional total amount result file is generated according to newly-added reported data of the current day and the two-dimensional calculation intermediate file calculated in the previous day, and meanwhile, the two-dimensional calculation result file of the current day is generated and falls to the ground for the next day;
performing two-dimensional total amount statistical calculation on the recorded data in the database, wherein the two-dimensional total amount statistical calculation comprises the following steps:
acquiring the device quantity and the geographic position information set of the app version from the history result file;
traversing newly-added reported data, and increasing the device amount of the app version;
traversing newly-added reported data, and newly adding a geographical position information set;
running the Load intermediate file, and acquiring different networking modes of app versions, mobile phone models, resolution and the number of operating system versions;
traversing the newly added reported data, and counting the networking mode, the mobile phone type, the resolution and the number of the operating system versions based on the intermediate file;
a Dump intermediate file, which is used for outputting the calculated result Dump for the next use;
the method comprises the following steps of collecting the device amount and the geographic position information set of the newly added app version, different networking modes of the app version, the mobile phone model, the resolution, the number of operating system versions and the intermediate file of the Dump, and generating an increment result file, wherein the format is as follows:
and applying id | version number | equipment total | geographic position information statistical result | connection mode statistical result | equipment model statistical result | resolution statistical result | operating system statistical result.
2. The method for fast statistics of mobile applications as claimed in claim 1, wherein in step S1, the application data reported by the mobile application includes: application version, device ID number, networking mode, mobile phone model.
3. The fast statistical method for mobile applications according to claim 1, wherein in said step S2, the data format of said application data stored in the redis buffer is as follows:
and applying id | version number | equipment id | equipment detailed information | reporting time | mobile phone system type | reporting id | application use time length information.
4. The fast statistical method for mobile applications as claimed in claim 1, wherein the step S3 of performing type judgment on the cleaned data comprises the following steps:
performing database query judgment on the cleaned data,
if the data type is new, flagstone is marked as 1,
if the data type is updating, marking the flagstone as 3 and inserting the data into the database, otherwise marking the flagstone as 2;
and then stores the data in a re dis buffer.
5. The fast statistical method for mobile applications as claimed in claim 1 or 4, wherein each piece of reported data queries application id and device id according to query conditions, determines whether there is a record in the database, if there is no record, adds a new record; simultaneously marking the report as a new device report; if the application id and the device id exist but the version numbers are not consistent, the data is marked as an update, and the data format is as follows:
and applying id | version number | equipment id | equipment detailed information | reporting time | system type | new equipment mark | reporting id | application use time length information.
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Effective date of registration: 20211022 Address after: 100094 No. 68 North Qing Road, Beijing, Haidian District Patentee after: YONYOU NETWORK TECHNOLOGY Co.,Ltd. Address before: 100176 508, taixiang business building, No.1, Longxiang Road, Haidian District, Beijing Patentee before: POMELO(BEIJING)MOBILE TECHNOLOGY Co.,Ltd. |