CN113067708A - Charging method, charging device, electronic equipment and computer storage medium - Google Patents
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- H—ELECTRICITY
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- H04L12/00—Data switching networks
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- H04L12/14—Charging, metering or billing arrangements for data wireline or wireless communications
- H04L12/1403—Architecture for metering, charging or billing
- H04L12/1407—Policy-and-charging control [PCC] architecture
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Abstract
The embodiment of the disclosure provides a charging method, a charging device, an electronic device and a computer storage medium, wherein the method comprises the following steps: acquiring a plurality of charging data, wherein the charging data represents charging data of cloud service; performing composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service; and carrying out parallel charging on each group of data in the grouping result to obtain a charging result.
Description
Technical Field
The present disclosure relates to the field of cloud services, and in particular, to a charging method and apparatus, an electronic device, and a computer storage medium.
Background
Currently, charging (Billing) for cloud services such as Software-as-a-Service (SaaS) can be implemented by a charging engine, and charging modes of the charging engine include but are not limited to: charging according to the time of use, charging according to the number of users, charging according to the amount, charging according to the providing function, etc.; aiming at the charging of mass data, how to improve the charging capability of a charging engine is a technical problem to be solved urgently.
Disclosure of Invention
The embodiments of the present disclosure are intended to provide a charging method, device, electronic device, and computer storage medium, which can implement parallel charging of multiple sets of data, and improve charging capability to a certain extent.
The disclosed embodiment provides a charging method, which comprises the following steps:
acquiring a plurality of charging data, wherein the charging data represents charging data of cloud service;
performing composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service;
and carrying out parallel charging on each group of data in the grouping result to obtain a charging result.
In some embodiments, the performing composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result includes:
obtaining first identification information and second identification information corresponding to each piece of charging data, and storing each piece of charging data as key-value data, wherein keys (keys) in the key-value data are the first identification information and the second identification information corresponding to each piece of charging data;
and grouping the key value data corresponding to each charging data according to keys to obtain the grouping result.
In some embodiments, the performing parallel charging for each group of data in the grouping result includes:
and respectively adopting the 1 st thread to the nth thread to perform parallel charging aiming at the 1 st group data to the nth group data in the grouping result, wherein n represents the group number of the grouping result.
In some embodiments, the obtaining a plurality of charging data includes: and reading the plurality of charging data by adopting a stream processing mode.
In some embodiments, the cloud service is SaaS in a cloud environment.
The embodiment of the present disclosure further provides a charging device, where the device includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of charging data, and the charging data represents the charging data of the cloud service;
the first processing module is used for carrying out compound grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service;
and the second processing module is used for carrying out parallel charging on each group of data in the grouping result to obtain a charging result.
In some embodiments, the first processing module is configured to perform compound grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result, and the method includes:
obtaining first identification information and second identification information corresponding to each charging data, and storing each charging data as key value data, wherein keys in the key value data are the first identification information and the second identification information corresponding to each charging data;
and grouping the key value data corresponding to each charging data according to keys to obtain the grouping result.
In some embodiments, the second processing module is configured to perform parallel charging for each group of data in the grouping result, and includes:
and respectively adopting the 1 st thread to the nth thread to perform parallel charging aiming at the 1 st group data to the nth group data in the grouping result, wherein n represents the group number of the grouping result.
In some embodiments, the obtaining module is configured to obtain a plurality of charging data, including: and reading the plurality of charging data by adopting a stream processing mode.
In some embodiments, the cloud service is SaaS in a cloud environment.
The disclosed embodiments also provide an electronic device comprising a processor and a memory for storing a computer program capable of running on the processor; wherein,
the processor is configured to run the computer program to perform any one of the above charging methods.
The disclosed embodiments also provide a computer storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement any one of the above charging methods.
In the charging method, the charging device, the electronic equipment and the computer storage medium provided by the embodiment of the disclosure, a plurality of charging data are acquired, wherein the charging data represent charging data of cloud services; performing composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service; and carrying out parallel charging on each group of data in the grouping result to obtain a charging result. It can be seen that, in the embodiment of the present disclosure, by grouping multiple sets of charging data and charging each set of data in the grouping result in parallel, the charging capability for cloud services in the same time period can be improved, which is beneficial to completing real-time cost calculation of mass data.
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 accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a charging method according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a parallel processing model corresponding to charging data in an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a charging apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
The present disclosure will be described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the examples provided herein are merely illustrative of the present disclosure and are not intended to limit the present disclosure. In addition, the embodiments provided below are some embodiments for implementing the disclosure, not all embodiments for implementing the disclosure, and the technical solutions described in the embodiments of the disclosure may be implemented in any combination without conflict.
It should be noted that, in the embodiments of the present disclosure, the terms "comprises," "comprising," or any other variation thereof are intended to cover a non-exclusive inclusion, so that a method or apparatus including a series of elements includes not only the explicitly recited elements but also other elements not explicitly listed or inherent to the method or apparatus. Without further limitation, the use of the phrase "including a. -. said." does not exclude the presence of other elements (e.g., steps in a method or elements in a device, such as portions of circuitry, processors, programs, software, etc.) in the method or device in which the element is included.
For example, the charging method provided by the embodiment of the present disclosure includes a series of steps, but the charging method provided by the embodiment of the present disclosure is not limited to the described steps, and similarly, the charging device provided by the embodiment of the present disclosure includes a series of modules, but the device provided by the embodiment of the present disclosure is not limited to include the explicitly described modules, and may also include modules that need to be set when acquiring related information or performing processing based on the information.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
The disclosed embodiments may be implemented in computer systems comprising terminals and/or servers and may be operational with numerous other general purpose or special purpose computing system environments or configurations. Here, the terminal may be a thin client, a thick client, a hand-held or laptop device, a microprocessor-based system, a set-top box, a programmable consumer electronics, a network personal computer, a small computer system, etc., and the server may be a server computer system, a small computer system, a mainframe computer system, a distributed cloud computing environment including any of the above, etc.
The electronic devices of the terminal, server, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
In the related technology, charging of cloud services can be achieved through a charging engine, in a scene of charging the cloud services according to the amount, information of cloud services used by users can be reported to the charging engine by products in a cloud environment, the charging engine uniformly processes and calculates the account cost condition, and with the continuous increase of product lines in the cloud environment and the increase of the user usage amount, the real-time charging capacity of the charging engine becomes a bottleneck.
In view of the above technical problems, in some embodiments of the present disclosure, a charging method is provided, which may be applied to a charging engine of a cloud service, and can implement parallel charging of multiple groups of data, and complete real-time fee calculation under mass data.
Fig. 1 is a flowchart of a charging method according to an embodiment of the present disclosure, and as shown in fig. 1, the flowchart may include:
step 101: and acquiring a plurality of charging data, wherein the charging data represents the charging data of the cloud service.
In the embodiments of the present disclosure, the cloud service may be SaaS in a cloud environment or other types of cloud services.
In the embodiment of the present disclosure, the charging data may include information such as an identifier of a charging item to be charged and an account identifier of a user using the cloud service; each charging data may be data for charging one charging item, or may represent data for charging a plurality of charging items.
In some embodiments, the identification of the billing item may be a name of the billing item, a number of the billing item, or other identifying information; the account identification of the user using the cloud service may be an account identification number (ID), an account name, or other account identification.
In some embodiments, when the cloud service is SaaS in a cloud environment, the SaaS server may report charging data to the charging engine, so that the charging engine obtains the charging data.
Step 102: performing composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a billing item of the cloud service.
Here, the composite grouping indicates a grouping manner in which the total data is grouped by combining two or more identifiers, and in one example, the grouping may be performed according to the first identifier information, and then the grouped groups may be further divided into several groups according to the second identifier information; in another example, the groups may be grouped according to the second identification information, and then the groups may be further divided into groups according to the first identification information.
Step 103: and carrying out parallel charging on each group of data in the grouping result to obtain a charging result.
In practical applications, the steps 101 to 103 may be implemented by a Processor in an electronic Device, where the Processor may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor.
It can be seen that, in the embodiment of the present disclosure, by grouping multiple sets of charging data and charging each set of data in the grouping result in parallel, the charging capability for cloud services in the same time period can be improved, which is beneficial to completing real-time cost calculation of mass data.
Further, compared with the scheme of grouping according to a single user account identifier or charging item identifier in the related art, the embodiment of the present disclosure may perform composite grouping based on the first identifier information and the second identifier information, where the first identifier information is an identifier representing a user dimension, and the second identifier information is an identifier representing a cloud service dimension, and it can be seen that the embodiment of the present disclosure may perform data grouping through two dimensions, and may better implement uniform distribution of data through grouping; compared with the scheme of grouping according to a single user account identifier, when cloud service charging is performed on the same user using a large amount of cloud services, the grouped data can be more uniform, and the parallel charging capability can be improved; compared with the scheme of grouping according to a single charging item identifier, when the same cloud service used by a large number of users is charged, the grouped data can be more uniform, and the parallel charging capability can be improved.
In some embodiments, the plurality of charging data may be read in a stream processing manner; the stream processing is an important big data processing means, and the main characteristic is that the processed data is continuously and real-timely coming, and the continuously generated dynamic data can be read and processed based on the stream processing mode.
In some embodiments, each time the charging engine reads charging data in real time, if the group identification information corresponding to the read charging data is the same as the group identification information of an existing packet data, the read charging data may be added to the existing packet, where the group identification information includes first identification information and second identification information; when the charging engine reads the charging data in real time, if the group identification information corresponding to the read charging data is different from the group identification information of each existing grouped data, a new group can be created, and the read charging data is added into the new group to obtain new grouped data.
It can be seen that, in the embodiment of the present disclosure, the charging data is read in a stream processing manner, and the charging data read in real time can be grouped, so that the real-time processing of the charging data is realized.
In the embodiment of the present disclosure, for an implementation manner in which a plurality of charging data are grouped in a composite manner according to first identification information and second identification information to obtain a grouping result, exemplarily, first identification information and second identification information corresponding to each charging data may be obtained, each charging data is stored as key value data, and a key in the key value data is the first identification information and the second identification information corresponding to each charging data; and grouping the key value data corresponding to each charging data according to keys to obtain the grouping result.
In some embodiments, after obtaining the charging data, the first identification information and the second identification information corresponding to the charging data may be combined into a key; in some embodiments, the value (value) in the key-value data may be the content of the acquired charging data.
In some embodiments, the plurality of charging data may be compositely grouped according to the first identification information and the second identification information using Kafka message middleware or other plug-ins, where the Kafka message middleware is a key-value based data processing plug-in.
In some embodiments, a parallel processing model may be employed to process multiple billing data; fig. 2 is a schematic diagram of a parallel processing model corresponding to charging data in the embodiment of the present disclosure, and as shown in fig. 2, a plurality of obtained charging data are Input Topic (Input Topic) data, charging data 1 to charging data m represent m charging data, and m is an integer greater than 1; in the input topic data, each charging data only has a corresponding value and does not have a corresponding key; in this case, the charging engine may read, in a stream processing manner, the charging data in the input topic data in real time, analyze the read charging data, and determine a user account ID and a charging item identifier corresponding to the charging data, where the user account ID is the first identification information and the charging item identifier is the second identification information.
The charging engine can combine the user account ID corresponding to the charging data and the charging item identifier into a key, so as to reconstruct the charging data into key value data; the charging engine can write key value data corresponding to a plurality of charging data into key Topic (key Topic) data, wherein the key Topic data comprises a plurality of groups of data, keys of each group of data are the same, and keys of data in different groups of data are different; referring to fig. 2, Partition _1 to Partition _ n represent the 1 st to nth group data.
The charging engine can acquire each group of charging data from the key topic data to perform parallel charging.
It can be seen that the embodiments of the present disclosure can perform charging respectively for different packet data, so that different charging manners can be adopted for different packet data, that is, flexible charging for different packet data can be realized.
For the implementation of performing parallel charging on each group of data in the grouping result, exemplarily, the 1 st to nth threads may be respectively used for performing parallel charging on the 1 st to nth groups of data in the grouping result, where n represents the number of groups of the grouping result.
In some embodiments, each group of data may be charged in a distributed computing manner, specifically, referring to fig. 2, Thread _1 to Thread _ n represent 1 st to nth threads, an ith Thread may read and charge the ith group of data, and i takes 1 to n.
Referring to fig. 2, all data in each set of data are subjected to charging processing by one thread, that is, the same charging item of the same user is subjected to charging processing in the same thread, so that charging contents of the threads do not conflict.
It can be seen that in the embodiment of the present disclosure, parallel charging of multiple groups of data can be achieved through multiple threads, and thus, the charging engine can add threads as needed, thereby expanding the charging capability, that is, the charging engine can achieve parallel charging of charging data that is continuously added through expansion of the threads.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
On the basis of the charging method provided by the foregoing embodiment, the embodiment of the present disclosure provides a charging device.
Fig. 3 is a schematic diagram of a composition structure of a charging device according to an embodiment of the present disclosure, and as shown in fig. 3, the charging device may include:
an obtaining module 301, configured to obtain multiple charging data, where the charging data represents charging data of a cloud service;
the first processing module 302 is configured to perform composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service;
the second processing module 303 is configured to perform parallel charging on each group of data in the grouping result to obtain a charging result.
In some embodiments, the first processing module 302 is configured to compound-group the plurality of charging data according to the first identification information and the second identification information, and obtain a grouping result, where the grouping result includes:
obtaining first identification information and second identification information corresponding to each charging data, and storing each charging data as key value data, wherein keys in the key value data are the first identification information and the second identification information corresponding to each charging data;
and grouping the key value data corresponding to each charging data according to keys to obtain the grouping result.
In some embodiments, the second processing module 303 is configured to perform parallel charging for each group of data in the grouping result, including:
and respectively adopting the 1 st thread to the nth thread to perform parallel charging aiming at the 1 st group data to the nth group data in the grouping result, wherein n represents the group number of the grouping result.
In some embodiments, the obtaining module 301 is configured to obtain a plurality of charging data, including: and reading the plurality of charging data by adopting a stream processing mode.
In some embodiments, the cloud service is SaaS in a cloud environment.
In practical applications, the obtaining module 301, the first processing module 302, and the second processing module 303 may all be implemented by a processor in an electronic device, where the processor may be at least one of an ASIC, a DSP, a DSPD, a PLD, an FPGA, a CPU, a controller, a microcontroller, and a microprocessor.
In addition, each functional module in this embodiment may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware or a form of a software functional module.
Based on the understanding that the technical solution of the present embodiment essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method of the present embodiment. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Specifically, the computer program instructions corresponding to a billing method in the present embodiment may be stored on a storage medium such as an optical disc, a hard disc, a usb disk, etc., and when the computer program instructions corresponding to a billing method in the storage medium are read or executed by an electronic device, any of the billing methods of the foregoing embodiments is implemented.
Based on the same technical concept of the foregoing embodiment, referring to fig. 4, it shows an electronic device 4 provided by the embodiment of the present disclosure, which may include: a memory 401 and a processor 402; wherein,
the memory 401 is used for storing computer programs and data;
the processor 402 is configured to execute the computer program stored in the memory to implement any one of the charging methods of the foregoing embodiments.
In practical applications, the memory 401 may be a volatile memory (RAM); or a non-volatile memory (non-volatile memory) such as a ROM, a flash memory (flash memory), a Hard Disk (Hard Disk Drive, HDD) or a Solid-State Drive (SSD); or a combination of the above types of memories and provides instructions and data to the processor 402.
The processor 402 may be at least one of an ASIC, a DSP, a DSPD, a PLD, an FPGA, a CPU, a controller, a microcontroller, and a microprocessor. It is understood that the electronic devices for implementing the above-described processor functions may be other devices, and the embodiments of the present disclosure are not particularly limited.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, and the same or similar parts may be referred to each other, which are not repeated herein for brevity
The methods disclosed in the method embodiments provided by the present application can be combined arbitrarily without conflict to obtain new method embodiments.
Features disclosed in various product embodiments provided by the application can be combined arbitrarily to obtain new product embodiments without conflict.
The features disclosed in the various method or apparatus embodiments provided herein may be combined in any combination to arrive at new method or apparatus embodiments without conflict.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A charging method, characterized in that the method comprises:
acquiring a plurality of charging data, wherein the charging data represents charging data of cloud service;
performing composite grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service;
and carrying out parallel charging on each group of data in the grouping result to obtain a charging result.
2. The method of claim 1, wherein the compositely grouping the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result comprises:
obtaining first identification information and second identification information corresponding to each charging data, and storing each charging data as key value data, wherein keys in the key value data are the first identification information and the second identification information corresponding to each charging data;
and grouping the key value data corresponding to each charging data according to keys to obtain the grouping result.
3. The method of claim 1, wherein the charging in parallel for each group of data in the grouping result comprises:
and respectively adopting the 1 st thread to the nth thread to perform parallel charging aiming at the 1 st group data to the nth group data in the grouping result, wherein n represents the group number of the grouping result.
4. The method according to any one of claims 1 to 3, wherein the obtaining a plurality of charging data comprises: and reading the plurality of charging data by adopting a stream processing mode.
5. The method according to any of claims 1 to 3, wherein the cloud service is software as a service, SaaS, in a cloud environment.
6. A charging apparatus, characterized in that the charging apparatus comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of charging data, and the charging data represents the charging data of the cloud service;
the first processing module is used for carrying out compound grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result; the first identification information represents an account identification of a user using the cloud service, and the second identification information represents an identification of a charging item of the cloud service;
and the second processing module is used for carrying out parallel charging on each group of data in the grouping result to obtain a charging result.
7. The apparatus of claim 6, wherein the first processing module is configured to perform compound grouping on the plurality of charging data according to the first identification information and the second identification information to obtain a grouping result, and the compound grouping includes:
obtaining first identification information and second identification information corresponding to each charging data, and storing each charging data as key value data, wherein keys in the key value data are the first identification information and the second identification information corresponding to each charging data;
and grouping the key value data corresponding to each charging data according to keys to obtain the grouping result.
8. The apparatus of claim 6, wherein the second processing module is configured to perform parallel charging for each group of data in the grouping result, and includes:
and respectively adopting the 1 st thread to the nth thread to perform parallel charging aiming at the 1 st group data to the nth group data in the grouping result, wherein n represents the group number of the grouping result.
9. An electronic device comprising a processor and a memory for storing a computer program operable on the processor; wherein,
the processor is configured to run the computer program to perform the method of any of claims 1 to 5.
10. A computer storage medium on which a computer program is stored, characterized in that the computer program realizes the method of any of claims 1 to 5 when executed by a processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114742551A (en) * | 2022-03-30 | 2022-07-12 | 北京交通大学 | Meta-service charging data processing method and device |
| CN116192542A (en) * | 2022-12-06 | 2023-05-30 | 中国联合网络通信集团有限公司 | Charging method and device for data traffic and storage medium |
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| CN114742551A (en) * | 2022-03-30 | 2022-07-12 | 北京交通大学 | Meta-service charging data processing method and device |
| CN116192542A (en) * | 2022-12-06 | 2023-05-30 | 中国联合网络通信集团有限公司 | Charging method and device for data traffic and storage medium |
| CN116192542B (en) * | 2022-12-06 | 2024-06-04 | 中国联合网络通信集团有限公司 | Charging method and device for data traffic and storage medium |
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