CN111401934A - Distributed advertisement statistical method and device - Google Patents
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- CN111401934A CN111401934A CN202010105710.6A CN202010105710A CN111401934A CN 111401934 A CN111401934 A CN 111401934A CN 202010105710 A CN202010105710 A CN 202010105710A CN 111401934 A CN111401934 A CN 111401934A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0242—Determining effectiveness of advertisements
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
Abstract
The disclosure relates to a distributed advertisement statistical method, a distributed advertisement statistical device, an electronic device and a storage medium. Wherein, the method comprises the following steps: a step of confirming the statistical dimensions, which is to divide the advertisement into a plurality of statistical dimensions according to the attribute information of the advertisement, and take the statistical dimensions as the basic classification of advertisement information statistics; building a Redis distributed cluster to collect multidimensional data of the advertisement; developing a corresponding statistic collection interface for collecting the advertisement data according to the attribute information of the advertisement; an ElasticSearch database service is set up, and advertisement data collected through a statistic collection interface is stored through the ElasticSearch; and setting a timing program to finish timing synchronization and storage of the advertisement data. According to the method and the device, distributed advertisement statistics is realized based on the ElasticSearch, the system compatibility is strong, and the statistical efficiency and the retrieval rate are greatly improved.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a distributed advertisement statistics method, apparatus, electronic device, and computer-readable storage medium.
Background
Due to the particularity of the advertisement system, more and more dimensions need to be counted, and the traditional counting system is single in form and unclear in data classification, and cannot meet the large-scale distributed counting requirement of a modern social network.
The existing advertisement statistics technical scheme is that links of two statistical indexes are designed based on a technical scheme of Redis cache, exposure rate and click rate are recorded respectively, 1 is added to the collected data count, and the count data in the Redis cache is synchronized to a MySQ L database for persistent storage every hour.
The defects of the existing PHP monitoring technology are as follows: the dimension of statistics is single, the index of statistics is limited, if the concurrency is too large, the statistics content is too much, a single statistics server is easy to be dragged down, and the statistics is inaccurate.
Accordingly, there is a need for one or more methods to address the above-mentioned problems.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a distributed advertisement statistics method, apparatus, electronic device, and computer-readable storage medium, thereby overcoming, at least to some extent, one or more problems due to limitations and disadvantages of the related art.
According to an aspect of the present disclosure, there is provided a distributed advertisement statistics method, including:
a step of confirming the statistical dimensions, which is to divide the advertisement into a plurality of statistical dimensions according to the attribute information of the advertisement, and take the statistical dimensions as the basic classification of advertisement information statistics;
a Redis cluster building step, namely building a Redis distributed cluster to collect multi-dimensional data of the advertisement;
a step of developing a statistic collection interface, in which a corresponding statistic collection interface is developed according to the attribute information of the advertisement for collecting the advertisement data;
an ElasticSearch service building step, namely building an ElasticSearch database service and storing advertisement data collected through a statistic collection interface through the ElasticSearch;
and a data timing synchronization step, namely setting a timing program to finish timing synchronization and storage of the advertisement data.
In an exemplary embodiment of the present disclosure, the statistical dimension confirming step further includes:
the attribute information of the advertisement includes: click rate, display amount, user terminal equipment information and the position of the advertisement.
In an exemplary embodiment of the present disclosure, the Redis cluster building step further includes:
the Redis is a distributed memory database supported, and can improve the data reading rate by storing data into a memory.
In an exemplary embodiment of the present disclosure, the statistics collecting interface developing step further includes:
and after the development of the statistics collection interface is completed, the data collected by the advertisement display end is received and submitted by the statistics collection interface, and is classified and written into Redis according to the dimension information.
In an exemplary embodiment of the present disclosure, the ElasticSearch service building step further includes:
the key field of the ElasticSearch service is set to be capable of performing word segmentation search, and the search function of the advertisement data can be completed based on the ElasticSearch service.
In an exemplary embodiment of the present disclosure, the data timing synchronization step further includes:
the timing procedure is executed once for no hour, and the advertisement data is added to the ElasticSearch service in an incremental manner.
In one aspect of the present disclosure, a distributed advertisement statistics method apparatus is provided, including:
the statistical dimension confirmation module is used for dividing the advertisement into a plurality of statistical dimensions according to the attribute information of the advertisement, and the statistical dimensions are used as basic classification of advertisement information statistics;
the Redis cluster building module is used for building a Redis distributed cluster to collect multi-dimensional data of the advertisement;
the statistic collection interface development module is used for developing a corresponding statistic collection interface according to the attribute information of the advertisement, and is used for collecting the advertisement data;
the ElasticSearch service building module is used for building an ElasticSearch database service and storing the advertisement data collected through the statistic collection interface through the ElasticSearch;
and the data timing synchronization module is used for setting a timing program to finish timing synchronization and storage of the advertisement data.
In one aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory having computer readable instructions stored thereon which, when executed by the processor, implement a method according to any of the above.
In an aspect of the disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the method according to any one of the above.
In the distributed advertisement statistical method in the exemplary embodiment of the present disclosure, the statistical dimension confirmation step divides the advertisement into a plurality of statistical dimensions according to attribute information of the advertisement, and uses the statistical dimensions as a basic classification of advertisement information statistics; building a Redis distributed cluster to collect multidimensional data of the advertisement; developing a corresponding statistic collection interface for collecting the advertisement data according to the attribute information of the advertisement; an ElasticSearch database service is set up, and advertisement data collected through a statistic collection interface is stored through the ElasticSearch; and setting a timing program to finish timing synchronization and storage of the advertisement data. According to the method and the device, distributed advertisement statistics is realized based on the ElasticSearch, the system compatibility is strong, and the statistical efficiency and the retrieval rate are greatly improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 illustrates a flow diagram of a distributed advertisement statistics method according to an exemplary embodiment of the present disclosure;
FIG. 2 shows a schematic block diagram of a distributed advertisement statistics method apparatus according to an example embodiment of the present disclosure;
FIG. 3 schematically illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure; and
fig. 4 schematically illustrates a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other methods, components, materials, devices, steps, and so forth. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in the form of software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.
In the present exemplary embodiment, a distributed advertisement statistics method is first provided; referring to fig. 1, the distributed advertisement statistics method may include the steps of:
a statistical dimension confirming step S110, dividing the advertisement into a plurality of statistical dimensions according to the attribute information of the advertisement, and using the statistical dimensions as the basic classification of advertisement information statistics;
a Redis cluster building step S120, building a Redis distributed cluster to collect multi-dimensional data of the advertisement;
a step S130 of developing a statistics collection interface, in which a corresponding statistics collection interface is developed according to the attribute information of the advertisement, so as to collect the advertisement data;
an ElasticSearch service building step S140, building an ElasticSearch database service, and storing the advertisement data collected through the statistic collection interface through the ElasticSearch;
and a data timing synchronization step S150, setting a timing program to complete timing synchronization and storage of the advertisement data.
In the distributed advertisement statistical method in the exemplary embodiment of the present disclosure, the statistical dimension confirmation step divides the advertisement into a plurality of statistical dimensions according to attribute information of the advertisement, and uses the statistical dimensions as a basic classification of advertisement information statistics; building a Redis distributed cluster to collect multidimensional data of the advertisement; developing a corresponding statistic collection interface for collecting the advertisement data according to the attribute information of the advertisement; an ElasticSearch database service is set up, and advertisement data collected through a statistic collection interface is stored through the ElasticSearch; and setting a timing program to finish timing synchronization and storage of the advertisement data. According to the method and the device, distributed advertisement statistics is realized based on the ElasticSearch, the system compatibility is strong, and the statistical efficiency and the retrieval rate are greatly improved.
Next, the distributed advertisement counting method in the present exemplary embodiment will be further explained.
In the statistical dimension confirmation step S110, the advertisement may be divided into a plurality of statistical dimensions according to the attribute information of the advertisement, as a basic classification of advertisement information statistics.
In this exemplary embodiment, the step of confirming the statistical dimension further includes:
the attribute information of the advertisement includes: click rate, display amount, user terminal equipment information and the position of the advertisement.
In the embodiment of the present example, the statistical dimension of confirmation, in addition to the simple click amount and the display amount, is also statistically confirmed for multiple dimensions such as the device of the user terminal, the position of the advertisement, and the like.
In Redis cluster building step S120, a Redis distributed cluster may be built to collect multidimensional data of the advertisement.
In this exemplary embodiment, the Redis cluster building step further includes:
the Redis is a distributed memory database supported, and can improve the data reading rate by storing data into a memory.
In the embodiment of the example, a Redis cluster is constructed, and a Redis distributed cluster is constructed to collect multi-dimensional data of the advertisement. Redis is a memory database which can support distribution, and the speed of data storage and reading can be greatly improved by storing data into a memory instead of a hard disk.
In the step S130 of developing statistics collecting interfaces, a corresponding statistics collecting interface may be developed according to the attribute information of the advertisement, for collecting the advertisement data.
In an embodiment of this example, the step of developing the statistics collection interface further includes:
and after the development of the statistics collection interface is completed, the data collected by the advertisement display end is received and submitted by the statistics collection interface, and is classified and written into Redis according to the dimension information.
In the embodiment of the example, a statistics collection interface is developed, data collected by an advertisement display end is submitted to an advertisement distributed statistics interface, and the interface receives all submitted information and writes the information into Redis according to the dimensions of classification, time and the like.
In the ElasticSearch service construction step S140, an ElasticSearch database service may be constructed and advertisement data collected through the statistics collection interface may be stored through ElasticSearch.
In this exemplary embodiment, the step of building the ElasticSearch service further includes:
the key field of the ElasticSearch service is set to be capable of performing word segmentation search, and the search function of the advertisement data can be completed based on the ElasticSearch service.
In the embodiment of the example, an ES service is constructed, an ElasticSearch database service is constructed, data of persistent advertisement statistics are stored through ElasticSearch, and key fields are set to be capable of word segmentation searching. The elastic search is used as a search engine for many times, and also supports distributed storage, various word segmenters and massive PB-level big data search.
In the data timing synchronization step S150, a timing program may be set to complete the timing synchronization and storage of the advertisement data.
In this exemplary embodiment, the data timing synchronization step further includes:
the timing procedure is executed once for no hour, and the advertisement data is added to the ElasticSearch service in an incremental manner.
In the embodiment of the present example, a timing task is developed, a data synchronization timing task is developed, data in a cluster is stored into an ElasticSearch, the synchronization timing task is executed once per hour, and the data is added to the ES in an incremental manner.
In the embodiment of the example, the method can meet the condition of large concurrency and the multi-dimensional statistics of the advertisement data; the collection of large-flow condition data can be met by using a distributed Redis cluster; the persistent storage uses an elastic search and can support word segmentation fuzzy retrieval; the method is suitable for data collection of various types of advertisements.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
In addition, in the present exemplary embodiment, a distributed advertisement statistics method apparatus is also provided. Referring to fig. 2, the distributed advertisement statistics method apparatus 200 may include: a statistic dimension confirming module 210, a Redis cluster building module 220, a statistic collection interface developing module 230, an ElasticSearch service building module 240 and a data timing synchronization module 250. Wherein:
a statistic dimension confirming module 210, configured to divide the advertisement into a plurality of statistic dimensions according to attribute information of the advertisement, where the statistic dimensions are used as a basic classification for advertisement information statistics;
a Redis cluster building module 220, configured to build a Redis distributed cluster to collect multidimensional data of the advertisement;
a statistics collecting interface developing module 230, configured to develop a corresponding statistics collecting interface according to the attribute information of the advertisement, so as to collect the advertisement data;
an ElasticSearch service construction module 240, configured to construct an ElasticSearch database service, and store advertisement data collected through the statistics collection interface through the ElasticSearch;
and a data timing synchronization module 250, configured to set a timing program to complete timing synchronization and storage of the advertisement data.
The specific details of each distributed advertisement statistical method device module are already described in detail in the corresponding distributed advertisement statistical method, and therefore are not described herein again.
It should be noted that although several modules or units of the distributed advertisement statistics method apparatus 200 are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 300 according to such an embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: the at least one processing unit 310, the at least one memory unit 320, a bus 330 connecting different system components (including the memory unit 320 and the processing unit 310), and a display unit 340.
Wherein the storage unit stores program code that is executable by the processing unit 310 to cause the processing unit 310 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 310 may perform steps S110 to S150 as shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache memory unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 4, a program product 400 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (9)
1. A distributed advertisement statistics method, the method comprising:
a step of confirming the statistical dimensions, which is to divide the advertisement into a plurality of statistical dimensions according to the attribute information of the advertisement, and take the statistical dimensions as the basic classification of advertisement information statistics;
a Redis cluster building step, namely building a Redis distributed cluster to collect multi-dimensional data of the advertisement;
a step of developing a statistic collection interface, in which a corresponding statistic collection interface is developed according to the attribute information of the advertisement for collecting the advertisement data;
an ElasticSearch service building step, namely building an ElasticSearch database service and storing advertisement data collected through a statistic collection interface through the ElasticSearch;
and a data timing synchronization step, namely setting a timing program to finish timing synchronization and storage of the advertisement data.
2. The method of claim 1, wherein the statistical dimension validation step further comprises:
the attribute information of the advertisement includes: click rate, display amount, user terminal equipment information and the position of the advertisement.
3. The method of claim 1, wherein the Redis cluster building step further comprises:
the Redis is a distributed memory database supported, and can improve the data reading rate by storing data into a memory.
4. The method of claim 1, wherein the statistics collection interface developing step further comprises:
and after the development of the statistics collection interface is completed, the data collected by the advertisement display end is received and submitted by the statistics collection interface, and is classified and written into Redis according to the dimension information.
5. The method of claim 1, wherein said ElasticSearch service construction step further comprises:
the key field of the ElasticSearch service is set to be capable of performing word segmentation search, and the search function of the advertisement data can be completed based on the ElasticSearch service.
6. The method of claim 1, wherein the data timing synchronization step further comprises:
the timing procedure is executed once for no hour, and the advertisement data is added to the ElasticSearch service in an incremental manner.
7. A distributed advertisement statistics apparatus, the apparatus comprising:
the statistical dimension confirmation module is used for dividing the advertisement into a plurality of statistical dimensions according to the attribute information of the advertisement, and the statistical dimensions are used as basic classification of advertisement information statistics;
the Redis cluster building module is used for building a Redis distributed cluster to collect multi-dimensional data of the advertisement;
the statistic collection interface development module is used for developing a corresponding statistic collection interface according to the attribute information of the advertisement, and is used for collecting the advertisement data;
the ElasticSearch service building module is used for building an ElasticSearch database service and storing the advertisement data collected through the statistic collection interface through the ElasticSearch;
and the data timing synchronization module is used for setting a timing program to finish timing synchronization and storage of the advertisement data.
8. An electronic device, comprising
A processor; and
a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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