CN112115420A - Data statistical method, system, equipment and storage medium based on discrete grouping - Google Patents
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Abstract
The invention provides a data statistical method, a system, equipment and a storage medium based on discrete grouping, wherein the method comprises the following steps: the method comprises the steps of obtaining real-time data with a plurality of attribute values, and carrying out data discretization by taking a combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups; circularly grouping the data groups to corresponding data processing equipment according to the number of the data processing equipment; acquiring data statistical conditions sent by a statistical requester, and sending the data statistical conditions to each data processing device; and combining the data information fed back by the data processing equipment and outputting the data information as a data statistical result. The invention can improve the robustness of the program, avoid the data inclination in the flow peak value, has complete state management and can ensure the accuracy and the consistency of the data.
Description
Technical Field
The present invention relates to the field of data statistics, and in particular, to a data statistics method, system, device, and storage medium based on discrete packets.
Background
Real-time data statistics under a big data scene is an important component for building a real-time data warehouse system, and real-time summary statistics is an important technical scene no matter whether applied business system display or applied analysis systems perform real-time label analysis, so that robustness and flexibility of a big data real-time summary statistical program must be guaranteed.
For a cluster system, generally, the cache is distributed, that is, different nodes are responsible for caching data in a certain range. The buffer data is usually not distributed enough, so that a large amount of buffer data is concentrated on one or several service nodes, which is called data skew. Data skew is generally caused by the inefficient implementation of load balancing.
Accordingly, the present invention provides a method, system, device and storage medium for data statistics based on discrete packets.
Disclosure of Invention
The present invention is directed to a method, a system, a device, and a storage medium for data statistics based on discrete packets, which overcome the difficulties of the prior art, improve the robustness of the program, avoid data skew at the time of a flow peak, have complete state management, and ensure the accuracy and consistency of the data.
The embodiment of the invention provides a data statistical method based on discrete grouping, which comprises the following steps:
s110, obtaining real-time data with a plurality of attribute values, and performing data discretization by taking a combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups;
s120, circularly grouping the data groups to the corresponding data processing equipment according to the number of the data processing equipment;
s130, obtaining data statistical conditions sent by a statistical requester, and sending the data statistical conditions to each data processing device;
and S140, combining the data information fed back by the data processing equipment and outputting the data information as a data statistical result.
Preferably, the real-time data having the same group tag is grouped into the same data group in step S110.
Preferably, in step S120, the data groups are uniformly distributed to the data processing devices.
Preferably, the data processing device is any one of a server, a data storage unit, and a cloud storage unit.
Preferably, in step S140, the data statistic condition is one attribute value or a combination of attribute values in the real-time data.
Preferably, the step S140 includes:
each data processing device searches according to the data statistical conditions to obtain a subset serving as data statistical information and feeds back the subset;
combining all the data statistical information subsets fed back by the data processing equipment to serve as a data statistical information set;
and feeding back the data statistical information set to the statistical requester.
Preferably, the real-time data is hotel operation data, and the group tag includes a hotel name and a user name.
The embodiment of the present invention further provides a data statistics system based on discrete packets, which is used for implementing the above data statistics method based on discrete packets, and the data statistics system based on discrete packets includes:
the data discretization module is used for acquiring real-time data with a plurality of attribute values, and performing data discretization by taking the combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups;
the cyclic grouping module is used for circularly grouping the data groups to the corresponding data processing equipment according to the number of the data processing equipment;
the data statistics module is used for acquiring data statistics conditions sent by the statistics requester and sending the data statistics conditions to each data processing device;
and the merging output module is used for combining the data information fed back by the data processing equipment and outputting the data information as a data statistical result.
The embodiment of the invention also provides a data statistical device based on discrete packets, which comprises:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the discrete packet-based data statistics method described above via execution of the executable instructions.
Embodiments of the present invention also provide a computer-readable storage medium for storing a program that, when executed, implements the steps of the above-described discrete packet-based data statistics method.
The invention aims to provide a data statistical method, a system, equipment and a storage medium based on discrete grouping, which can improve the robustness of a program, avoid data inclination in the flow peak value, have complete state management and can ensure the accuracy and consistency of data.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a flow chart of a discrete packet-based data statistics method of the present invention.
Fig. 2 and 3 are process diagrams of the discrete packet-based data statistics method of the present invention.
FIG. 4 is a block diagram of a discrete packet-based data statistics system of the present invention.
Fig. 5 is a schematic structural diagram of a discrete packet-based data statistics apparatus according to the present invention. And
fig. 6 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
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 in the drawings denote the same or similar structures, and thus their repetitive description will be omitted.
FIG. 1 is a flow chart of a discrete packet-based data statistics method of the present invention. As shown in fig. 1, an embodiment of the present invention provides a data statistics method based on discrete packets, including the following steps:
s110, obtaining real-time data with a plurality of attribute values, and performing data discretization by taking a combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups;
s120, circularly grouping the data groups to corresponding data processing equipment according to the number of the data processing equipment;
s130, obtaining data statistical conditions sent by a statistical requester, and sending the data statistical conditions to each data processing device;
and S140, combining the data information fed back by the data processing equipment and outputting the data information as a data statistical result.
In a preferred embodiment, the real-time data having the same group tag are grouped into the same data group in step S110.
In a preferred embodiment, the data sets are evenly distributed into the data processing devices in step S120.
In a preferred embodiment, the data processing device is any one of a server, a data storage unit, and a cloud storage unit.
In a preferred embodiment, in step S140, the data statistic is one attribute value or a combination of attribute values in the real-time data.
In a preferred embodiment, step S140 includes:
each data processing device searches according to the data statistical conditions to obtain a subset serving as data statistical information and feeds back the subset;
combining all data statistical information subsets fed back by the data processing equipment to serve as a data statistical information set;
and feeding back a data statistical information set to the statistical requester.
In a preferred embodiment, the real-time data is hotel operations data and the group label includes a hotel name and a user name.
The specific implementation of the method depends on a Flink window and a state storage mechanism, the window limits the time period needing real-time statistics and the output frequency of the summarized result, and the state storage mechanism ensures the accurate consistency of data. (Apache Flink is an open source stream processing framework developed by the Apache software Foundation, with the core being a distributed stream data stream engine written in Java and Scala. Flink executes arbitrary stream data programs in a data parallel and pipelined manner, and Flink's pipelined runtime system can execute batch and stream processing programs
For the OTA industry, accurate real-time data statistics can help companies provide more time-efficient traffic placement displays for millions of hotel partners. The invention solves the problem of data inclination caused by hot key in big data real-time statistics, improves the robustness and the execution efficiency of a program, and increases the flexibility of business logic through two-step processing. And the method is realized only based on the self characteristics of the Flink, so that the external dependence is reduced, and the easy maintainability of the program is improved.
Fig. 2 and 3 are process diagrams of the discrete packet-based data statistics method of the present invention. The following describes the implementation of the present invention in detail by means of fig. 2 and 3.
As shown in fig. 2, hotel operation data 1 having a plurality of attribute values is obtained, and data discretization is performed based on a combination of a hotel name and a user name in the hotel operation data as a grouping tag, so as to obtain a plurality of data sets, that is, hotel operation data having the same "hotel name and user name" in the hotel operation data is assigned to one data set, for example: will also contain "hotel name: beijing Hotel "and" user name: the hotel operation data of Zhang Xiaoming' is divided into a data group, if Mr. Zhang Xiaoming only checks in the Beijing hotel once, the data group again only contains one hotel operation data, if Mr. Zhang Xiaoming only checks in the Beijing hotel three times, the data group again only contains three hotel operation data. The data sets 11, 12, 13, 14 … …, etc. are obtained so that a sufficient discretization effect is obtained. As in other scenarios, the discrete processing may be performed by random values in order to bring the data volume of the hotel operation data in each data group as close as possible. The data groups 11, 12, 13, 14 … … and the like are circularly grouped to the corresponding data processing equipment according to the number of the plurality of distributed data processing equipment 21, 22, 23, 24 … … and the like, so that the data groups of the hotel operation data distributed to each data processing equipment are as close as possible, and the plurality of distributed data processing equipment can uniformly store the data groups 11, 12, 13 and 14 of the hotel operation data. The input data stream is divided into time windows of one day according to the service time, and the specific time window trigger frequency can be customized, but not limited thereto. Receiving the data statistic conditions 31 transmitted from the statistic requester 3, the data statistic conditions 31 are transmitted to each of the data processing apparatuses 11, 12, 13, 14 … …, and the like, respectively.
As shown in fig. 3, the data processing apparatus 21 obtains a data statistical information subset 41 from the data statistical condition 31 in the data group stored by itself, the data processing apparatus 22 obtains a data statistical information subset 42 from the data statistical condition 31 in the data group stored by itself, the data processing apparatus 23 obtains a data statistical information subset 43 from the data statistical condition 31 in the data group stored by itself, the data processing apparatus 24 obtains a data statistical information subset 44 … … from the data statistical condition 31 in the data group stored by itself, and so on. The data statistics subsets (including the data statistics subsets 41, 42, 43, 44 … …, etc.) are combined (for example, but not limited to, forming a collection) to form a data statistics collection, and finally, the data statistics collection is fed back to the statistics requester 3.
Under the structure, even if a plurality of statistical requesters initiate a plurality of requests at the same time, the calculated amount is more easily and uniformly borne by a plurality of distributed data processing devices, the speed of overall data processing is accelerated, data inclination at the flow peak is avoided, complete state management is realized, and the accuracy and consistency of data can be ensured. The improved big data real-time statistical method provided by the scheme has the advantages of high precision, high efficiency, no external storage dependence and easiness in maintenance, and can improve the robustness of a program.
FIG. 4 is a block diagram of a discrete packet-based data statistics system of the present invention. As shown in fig. 4, an embodiment of the present invention further provides a discrete packet-based data statistics system, for implementing the above discrete packet-based data statistics method, where the discrete packet-based data statistics system 9 includes:
the data discretization module 91 is used for acquiring real-time data with a plurality of attribute values, and performing data discretization by taking the combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups;
the cyclic grouping module 92 is used for circularly grouping the data groups to the corresponding data processing equipment according to the number of the data processing equipment;
the data statistics module 93 obtains the data statistics conditions sent by the statistics requester, and sends the data statistics conditions to each data processing device;
and a merging output module 94 for combining the data information fed back by the data processing device and outputting the combined data information as a data statistical result.
The data statistical system based on discrete grouping can improve the robustness of a program, avoid data inclination in the flow peak value, has complete state management and can ensure the accuracy and consistency of data.
The embodiment of the invention also provides data statistical equipment based on the discrete grouping, which comprises a processor. A memory having stored therein executable instructions of the processor. Wherein the processor is configured to perform the steps of the discrete packet-based data statistics method via execution of the executable instructions.
As shown above, the embodiment can improve robustness of the program, avoid data skew at the time of a flow peak, and have complete state management, which can ensure accurate and consistent data.
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" platform.
Fig. 5 is a schematic structural diagram of a discrete packet-based data statistics apparatus according to the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 600 shown in fig. 5 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. 5, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 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.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
Embodiments of the present invention further provide a computer-readable storage medium for storing a program, where the program implements the steps of the discrete packet-based data statistics method when executed. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of this specification, when the program product is run on the terminal device.
As shown above, the embodiment can improve robustness of the program, avoid data skew at the time of a flow peak, and have complete state management, which can ensure accurate and consistent data.
Fig. 6 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 6, a program product 800 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 storage 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 storage 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 storage 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 for aspects 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 and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the present invention is directed to a method, a system, a device and a storage medium for data statistics based on discrete packets, which can improve robustness of a program, avoid data skew at a peak flow, have complete state management, and ensure accurate and consistent data.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A method for data statistics based on discrete packets, comprising the steps of:
s110, obtaining real-time data with a plurality of attribute values, and performing data discretization by taking a combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups;
s120, circularly grouping the data groups to the corresponding data processing equipment according to the number of the data processing equipment;
s130, obtaining data statistical conditions sent by a statistical requester, and sending the data statistical conditions to each data processing device;
and S140, combining the data information fed back by the data processing equipment and outputting the data information as a data statistical result.
2. The discrete grouping-based data statistical method according to claim 1, wherein the real-time data having the same grouping label are grouped into the same data group in step S110.
3. The discrete grouping-based data statistical method according to claim 1, wherein in the step S120, the data groups are uniformly distributed to the data processing devices.
4. The discrete-packet-based data statistics method according to claim 1, wherein the data processing device is any one of a server, a data storage unit, and a cloud storage unit.
5. The discrete packet-based data statistics method as claimed in claim 1, wherein in step S140, the data statistics condition is one attribute value or a combination of attribute values in the real-time data.
6. The discrete packet based data statistics method as claimed in claim 1, wherein the step S140 comprises:
each data processing device searches according to the data statistical conditions to obtain a subset serving as data statistical information and feeds back the subset;
combining all the data statistical information subsets fed back by the data processing equipment to serve as a data statistical information set;
and feeding back the data statistical information set to the statistical requester.
7. The discrete packet-based data statistics method of claim 1, wherein the real-time data is hotel operations data and the packet label comprises a hotel name and a user name.
8. A discrete packet-based data statistics system for implementing the discrete packet-based data statistics method of claim 1, comprising:
the data discretization module is used for acquiring real-time data with a plurality of attribute values, and performing data discretization by taking the combination of at least any two attribute values in the real-time data as a grouping label to obtain a plurality of data groups;
the cyclic grouping module is used for circularly grouping the data groups to the corresponding data processing equipment according to the number of the data processing equipment;
the data statistics module is used for acquiring data statistics conditions sent by the statistics requester and sending the data statistics conditions to each data processing device;
and the merging output module is used for combining the data information fed back by the data processing equipment and outputting the data information as a data statistical result.
9. A discrete packet-based data statistics device, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the discrete packet-based data statistics method of any of claims 1 to 7 via execution of the executable instructions.
10. A computer-readable storage medium storing a program, wherein the program when executed implements the steps of the discrete packet-based data statistics method of any one of claims 1 to 7.
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