Data processing method, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a data processing method, electronic equipment, a storage medium and a program product. The data processing method comprises the following steps: acquiring a data volume relationship of to-be-Hash distributed data corresponding to all associated nodes; and balancing the data volume of the to-be-Hash distributed data corresponding to the associated nodes based on the data volume relationship, so that the data volume of the to-be-Hash distributed data of each associated node is kept within a preset range threshold. According to the scheme of the embodiment, it is guaranteed that the data size corresponding to each association node is kept uniform, so that uniform hash distribution is achieved on the basis of consistent hash distribution, and the situation that some association nodes in one association subsystem are overloaded and some association nodes are overloaded is avoided.

Description

Technical Field

[0001] This disclosure relates to the field of mobile communication networks, and in particular to a data processing method, electronic device, storage medium, and program product. Background Technology

[0002] DPI (Deep Packet Inspection) devices are used to analyze and manage internet protocols and data. DPIs are widely used in various business and traffic analysis systems. The underlying system primarily collects and generates XDR (Call Record) data, and then correlates and backfills the generated XDR data. The system that performs XDR data correlation and backfilling is called the correlation subsystem.

[0003] The association subsystem includes a distribution function unit, association nodes, and an XDR sending unit. The distribution function unit distributes XDR data to the association nodes in key-value format according to the UPF (User Plane Function) network element. The association nodes are used to perform association backfilling in the relationship based on the key value of each XDR data. The XDR sending unit sends the processed XDR data to the backend.

[0004] The associated nodes need to handle a huge amount of data in the live network, so multiple nodes are needed to share the load. Each associated node is independent of the others, and the front-end distribution function unit is required to perform hash distribution according to UPF network elements.

[0005] During the hash distribution process of associated nodes, there may be changes in associated nodes (e.g., the addition or removal of associated nodes). In order to ensure the correctness of backfilling when associated nodes change, consistent hash distribution needs to be guaranteed.

[0006] However, due to the uneven distribution of XDR data generated by different UPF network elements, when implementing functional hash distribution based on traditional hash algorithms, even with consistent hash distribution, some associated nodes may receive a large amount of XDR data, while others may receive a small amount. Therefore, the amount of XDR data received by associated nodes is not uniform. Thus, existing hash distribution schemes suffer from uneven hash distribution even with consistent hash distribution. Summary of the Invention

[0007] This disclosure provides a data processing method, an electronic device, a storage medium, and a program product.

[0008] In a first aspect, embodiments of this disclosure provide a data processing method, comprising:

[0009] Obtain the data size relationship of the data to be hashed and distributed for all associated nodes;

[0010] Based on the size relationship, the amount of data to be hashed and distributed corresponding to the associated nodes is balanced so that the amount of data to be hashed and distributed for each associated node remains within a preset threshold range.

[0011] Secondly, embodiments of this disclosure also provide an electronic device, including:

[0012] One or more processors;

[0013] A memory having stored one or more programs that, when executed by one or more processors, enable the one or more processors to implement the data processing method.

[0014] One or more input / output (I / O) interfaces are connected between the processor and the memory and configured to enable information exchange between the processor and the memory.

[0015] Thirdly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the data processing method described above.

[0016] Fourthly, this disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the data processing method.

[0017] This embodiment obtains the data volume relationship of the data to be hashed and distributed for all associated nodes, and performs balancing processing on the data volume of the data to be hashed and distributed for each associated node based on the data volume relationship, so that the data volume of the data to be hashed and distributed for each associated node is kept within a preset threshold range, so that the data volume of each associated node is basically the same or similar. This embodiment uses balancing technology to ensure that the data volume of each associated node is uniform when the data volume of the data to be hashed and distributed for each associated node is uneven, thereby achieving uniform hash distribution based on consistent hash distribution, avoiding the situation where some associated nodes are overburdened and some associated nodes are underburdened within an associated subsystem. Attached Figure Description

[0018] In the accompanying drawings of the embodiments disclosed herein:

[0019] Figure 1 A flowchart of a data processing method provided in this embodiment of the disclosure;

[0020] Figure 2A flowchart of a method for assigning data to be hashed from a node with a larger amount of data to a node with a smaller amount of data, as provided in an embodiment of this disclosure.

[0021] Figure 3 A flowchart of a method for assigning data to be hashed and distributed to the nith associated node among n associated nodes, as provided in this embodiment of the disclosure;

[0022] Figure 4 A block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of this disclosure, the communication-sensing data processing method and computer-readable storage medium provided in the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0024] The present disclosure will be described more fully below with reference to the accompanying drawings; however, the embodiments shown may be embodied in different forms, and the present disclosure should not be construed as limited to the embodiments set forth below. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of the disclosure.

[0025] The accompanying drawings of the embodiments disclosed herein are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the detailed embodiments to explain this disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the description of the detailed embodiments with reference to the accompanying drawings.

[0026] This disclosure may be described with reference to plan and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances.

[0027] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0028] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. The term "and / or" as used in this disclosure includes any and all combinations of one or more of the associated enumerated entries. The singular forms "a" and "the" as used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. The terms "comprising," "made of," etc., as used in this disclosure specify the presence of the stated feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0029] Unless otherwise specified, all terms used in this disclosure (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined in this disclosure.

[0030] DPI (Deep Packet Inspection) devices are used to analyze and manage internet protocols and data. DPIs are widely used in various business and traffic analysis systems. At the system's underlying level, they primarily collect and generate XDR (Call Record) data, and then perform association (multiple XDRs with the same key value can form a relationship through key-value pairs) and backfilling (associated XDRs enrich each other's fields; this enrichment process is called backfilling). The system that completes XDR association and backfilling functions is called the association subsystem.

[0031] The associated subsystem includes: a distribution function unit, associated nodes, and an XDR sending unit.

[0032] Distribution Function Unit: Based on different UPF (User Plane Function) network elements, the XDR data of each UPF network element is hashed and distributed to the associated nodes in key-value format. UPF network elements generally use the Internet Protocol IP address of the UPF, and for convenience, it can be simply referred to as UPF. UPF is a key component in 5G (Fifth-generation) networks responsible for transmitting user data between the access network and the core network.

[0033] Related nodes: The association is filled in based on the key value of each XDR data in the association relationship.

[0034] XDR sending unit: Sends the processed XDR data to the backend.

[0035] The associated nodes need to handle a huge amount of data in the live network, so multiple associated nodes are needed to share the load. Each associated node is independent of the others, requiring the front-end distribution function unit to hash and distribute the XDR data to each associated node according to the UPF network element.

[0036] During the hash distribution process of associated nodes, there may be changes in associated nodes (e.g., the addition or removal of associated nodes). In order to ensure the correctness of backfilling when associated nodes change, consistent hash distribution needs to be guaranteed.

[0037] However, UPF network elements have the following characteristics:

[0038] 1. The total number of UPF network elements is relatively small, generally between 200 and 500.

[0039] 2. The amount of XDR data generated by different UPF network elements is uneven. Note: Different UPF network elements correspond to different geographical regions, and the number of users in different geographical regions varies.

[0040] 3. The amount of XDR data generated by a single UPF network element is relatively stable, that is, the amount of data in a single UPF network element remains unchanged.

[0041] Given the characteristics of UPF network elements, when implementing functional hash distribution based on traditional hash algorithms, even with consistent hash distribution, some associated nodes may receive a large amount of XDR data, while others may receive a small amount, resulting in uneven distribution of XDR data among associated nodes. Therefore, existing hash distribution schemes suffer from uneven hash distribution even with consistent hash distribution. For example, when UPF network elements hash-distribute XDR data in key-value format, uneven hash distribution occurs, limiting the scalability of associated subsystems.

[0042] This embodiment obtains the data volume relationship of the data to be hashed and distributed for all associated nodes, and performs balancing processing on the data volume of the data to be hashed and distributed for each associated node based on the data volume relationship, so that the data volume of the data to be hashed and distributed for each associated node is kept within a preset threshold range, so that the data volume of each associated node is basically the same or similar. This embodiment uses balancing technology to ensure that the data volume of each associated node is uniform when the data volume of the data to be hashed and distributed for each associated node is uneven, thereby achieving uniform hash distribution based on consistent hash distribution, avoiding the situation where some associated nodes are overburdened and some associated nodes are underburdened within an associated subsystem.

[0043] This disclosure also provides a data processing method for uniform hash distribution based on UPF network elements. This method obtains the XDR data volume relationship among all associated nodes and performs balancing processing on the total XDR data volume of all associated nodes based on this relationship. This ensures that the total XDR data volume of each associated node remains within a preset threshold range, making the total XDR data volume of each associated node essentially the same or similar. By using balancing technology, this embodiment can maintain a uniform total XDR data volume for each associated node even when the total number of UPF network elements is limited and the XDR data volume of different UPF network elements is uneven. This solves the problem of large data volume differences between associated nodes in scenarios with large deviations in UPF network data volume in the associated subsystem, achieving uniform hash distribution and thus resolving the problem of uneven hash distribution in existing UPF network elements.

[0044] The solutions disclosed herein can be applied to any application scenario with consistent hash distribution requirements, such as any cluster, DPI collection application system, data platform, business platform, etc., with consistent hash distribution requirements. For example, they can be applied to the distribution function unit in a DPI device.

[0045] The embodiments of this disclosure will be described in detail below.

[0046] This disclosure provides a data processing method, such as... Figure 1 As shown, steps S11-S12 are included:

[0047] S11. Obtain the data size relationship of the data to be hashed and distributed for all associated nodes.

[0048] In this embodiment of the disclosure, the data to be hashed and distributed may include, but is not limited to, XDR data in the UPF network element.

[0049] In this embodiment of the disclosure, the distribution function unit can obtain statistical information of the UPF network element dimension in the associated subsystem at a fixed time every day. The statistical information may include, but is not limited to, the amount of XDR data generated by each UPF network element.

[0050] In this embodiment of the disclosure, obtaining the data size relationship of the data to be hashed and distributed corresponding to all associated nodes may include:

[0051] Sort the data volume of all associated nodes according to the size of the data to be hashed and distributed, and obtain the relationship of the data volume size.

[0052] In this embodiment of the disclosure, by sorting, the size relationship between the data volume of the data to be hashed and distributed corresponding to each associated node can be accurately obtained.

[0053] In this embodiment of the disclosure, when the data to be hashed and distributed includes XDR data in the UPF network element, the data volume of the XDR data corresponding to all associated nodes can be sorted by size in the first sorting.

[0054] In this embodiment of the disclosure, based on the summary of statistical information of UPF network elements, the total amount of XDR data generated by each associated node corresponding to the UPF network element can be calculated according to the data volume (e.g., number of XDRs) of the XDR data generated by the UPF network element and the corresponding associated node information, and the total amount of XDR data corresponding to each associated node can be sorted in the first order.

[0055] In this embodiment of the disclosure, the first sorting can be performed in descending order of the total XDR data volume corresponding to each associated node, or in ascending order of the total XDR data volume.

[0056] S12. Based on the data size relationship, balance the data volume of the data to be hashed and distributed corresponding to the associated nodes so that the data volume of the data to be hashed and distributed for each associated node is kept within a preset threshold range.

[0057] In this embodiment of the disclosure, the balancing process can refer to a peak-shaving and leveling process, which means keeping the data volume corresponding to each associated node in the same associated subsystem balanced, eliminating the peak value of the data volume, and leveling the trough value of the data volume, thereby eliminating the peak and trough values ​​of the data volume, so that the data volume of the data to be hashed and distributed in each associated node remains the same or similar.

[0058] In this embodiment of the disclosure, balancing the amount of data to be hashed and distributed for associated nodes based on the data size relationship may include:

[0059] The data to be hashed and distributed for the associated nodes with larger data volumes is assigned to the associated nodes with smaller data volumes.

[0060] In this embodiment of the disclosure, for example, XDR data in the UPF network element corresponding to the associated node with a larger total XDR data value can be assigned to the associated node with a smaller total XDR data value.

[0061] In this embodiment of the disclosure, by assigning the XDR data in the UPF network element corresponding to the associated node with a larger total XDR data value to the associated node with a smaller total XDR data value, it is possible to maintain a uniform total XDR data value for each associated node's corresponding UPF network element when the total number of UPF network elements is limited and the XDR data value of different UPF network elements is uneven. This solves the problem of large data volume differences between associated nodes in existing associated subsystems under scenarios with large deviations in UPF network data value, and achieves uniform hash distribution, thereby solving the problem of uneven hash distribution of existing UPF network elements.

[0062] In this embodiment of the disclosure, before balancing the data volume of the data to be hashed and distributed corresponding to the associated nodes based on the data volume relationship, the threshold of the range is determined according to the following method:

[0063] Calculate the average amount of data to be hashed and distributed across all associated nodes;

[0064] The preset data range centered on this average value is used as the threshold of this range.

[0065] In this embodiment of the disclosure, when performing load balancing, load balancing can be performed based on the difference in the amount of data to be hashed and distributed corresponding to each associated node, that is, to achieve load balancing among the amounts of data to be hashed and distributed corresponding to each associated node.

[0066] In this embodiment of the disclosure, balancing the amount of data to be hashed and distributed corresponding to each associated node can be achieved by calculating the average amount of data to be hashed and distributed corresponding to each associated node, such that the amount of data to be hashed and distributed corresponding to each associated node is the average amount, or by allowing the amount of data to be hashed and distributed corresponding to each associated node to fluctuate within a preset data range of the average amount, thereby ensuring that the amount of data to be hashed and distributed corresponding to each associated node meets the aforementioned range threshold, thus achieving the purpose of balancing.

[0067] In this embodiment of the disclosure, in order to ensure that the amount of data to be hashed and distributed for each associated node meets the above-mentioned range threshold, the data to be hashed and distributed for the associated node with a larger amount of data can be assigned to the associated node with a smaller amount of data.

[0068] In this embodiment of the disclosure, for example, in order to ensure that the total XDR data value corresponding to each associated node meets the above-mentioned range threshold, the XDR data in the UPF network element corresponding to the associated node with the larger total XDR data value can be assigned to the associated node with the smaller total XDR data value.

[0069] In this embodiment of the disclosure, assigning XDR data from the UPF network element corresponding to the associated node with a larger total XDR data value to the associated node with a smaller total XDR data value may include:

[0070] Assign all XDR data from any one or more UPF network elements corresponding to the associated node with a larger total XDR data volume to the associated node with a smaller total XDR data volume; or...

[0071] Assign a portion of the XDR data from any UPF element corresponding to the associated node with a larger total XDR data volume to the associated node with a smaller total XDR data volume.

[0072] In this embodiment of the disclosure, when assigning XDR data to associated nodes with a large total XDR data volume, XDR data from one or more UPF network elements can be assigned, or part or all of the XDR data from one UPF network element can be assigned.

[0073] In the embodiments disclosed herein, such as Figure 2 As shown, assigning the data to be hashed from the associated node with a larger data volume to the associated node with a smaller data volume may include steps S21-S22:

[0074] S21. Determine all n associated nodes in the first sorting based on the size of the data; where n is a positive integer, and the first associated node in the first sorting corresponds to the largest amount of data to be hashed and distributed.

[0075] S22. Assign the data to be hashed and distributed to any i-th associated node among n associated nodes to the ni-th associated node, where i is greater than or equal to 1 and less than or equal to n / 2.

[0076] In this embodiment of the disclosure, for example, assigning XDR data from the UPF network element corresponding to the associated node with a larger total XDR data value in the first sorting to the associated node with a smaller total XDR data value may include:

[0077] Determine all n associated nodes for the first sorting; where n is a positive integer, and the first associated node in the sorting corresponds to the UPF network element with the largest XDR data volume;

[0078] Assign the XDR data (which can be part or all) in the UPF network element corresponding to any i-th associated node among n associated nodes to the ni-th associated node, where i is greater than or equal to 1 and less than or equal to n / 2.

[0079] In this embodiment, XDR data from the associated node with the largest total XDR data value can be moved to the associated node with the smallest total XDR data value, until the total XDR data value of the associated node with the largest total XDR data value approaches the average of the total XDR data values ​​of all associated nodes. This process continues, moving XDR data from the associated node with the second largest total XDR data value to the associated node with the second smallest total XDR data value, and so on, until all remaining associated nodes are processed.

[0080] In the embodiments disclosed herein, such as Figure 3 As shown, assigning the data to be hashed and distributed to any i-th associated node among n associated nodes to the ni-th associated node includes steps S31-S32:

[0081] S31. Sort the data volume of the multiple sets of data to be hashed and distributed corresponding to each associated node in a second sorting;

[0082] S32. Based on the second sorting, obtain all or part of the data of the group with the largest data volume among the multiple groups of data to be hashed and distributed corresponding to the i-th associated node, and assign it to the ni-th associated node.

[0083] In this embodiment of the disclosure, for example, assigning XDR data from the UPF network element corresponding to any i-th associated node among n associated nodes to the ni-th associated node may include:

[0084] A second sort is performed on the data volume of XDR data in multiple UPF network elements corresponding to each associated node;

[0085] Get some or all of the XDR data from the UPF network element with the largest XDR data volume in the UPF network element corresponding to the i-th associated node, and assign it to the ni-th associated node.

[0086] In this embodiment of the disclosure, in order to improve the effectiveness and accuracy of XDR data movement, the amount of XDR data in the UPF network element corresponding to each associated node can be sorted in a second way to determine the size relationship of the XDR data of each UPF network element corresponding to each associated node. Based on the size relationship of the XDR data of each UPF network element, it can be determined which UPF network element's XDR data is specifically assigned to each associated node.

[0087] In this embodiment of the disclosure, when assigning data, the XDR data of the UPF network element with the largest XDR data volume in each associated node can be assigned to the associated node with the smaller total XDR data volume. Alternatively, the data can be assigned from largest to smallest according to the size of the XDR data volume of each UPF network element in each associated node, until the total XDR data volume of each associated node meets the above-mentioned range threshold.

[0088] In this disclosure, a detailed embodiment is given below.

[0089] Assume there are 7 associated nodes A, B, C, D, E, F, and G. Using consistent hashing, assume the XDR data volume of A is greater than that of B, and so on, A>B>C>D>E>F>G. Each associated subsystem processes approximately 3 UPF network element XDR data, with significant differences in the XDR data volume of each UPF network element. Assume IP1, IP2, and IP3 represent the XDR data volumes of the 3 UPF network elements in associated node A, with IP1>IP2>IP3. After sorting based on the XDR data volume of the UPF network elements and the associated node information, during XDR data assignment, the XDR data corresponding to associated node A can be assigned to associated node G, or the XDR data in the IP1 portion of associated node A can be assigned to associated node G, or both the IP1 and IP2 portions of associated node A can be assigned to associated node G. This process smooths out the peak data in associated node A and fills in the gaps in associated node G. Repeating the above steps, XDR data from associated node B can be assigned to associated node F, and XDR data from associated node C can be assigned to associated node E. All non-flowing data, such as the IP3 portion of data in associated node A, will still be distributed normally using the consistent hashing algorithm.

[0090] The above-described embodiments are explained in detail below for certain specific related nodes.

[0091] For ease of description, only the four associated nodes A1, A2, A3, and A4 mentioned above will be explained. The UPF network element information corresponding to these four associated nodes can be seen in Table 1 below:

[0092] Table 1

[0093]

[0094]

[0095] Based on the above information, the detailed processing steps of the embodiments of this disclosure can be as follows:

[0096] Step 1: The distribution function unit collects detailed XDR data volume information of all associated nodes in the UPF dimension.

[0097] Step 2: The distribution function unit obtains the total XDR data volume and node information of all associated nodes collected by the UPF network elements, and sorts them. For example, the sorting of the total XDR data volume of each UPF network element within the associated nodes is shown in Table 2 below:

[0098] Table 2

[0099] Related nodes Total XDR data volume A2 190(U4+U5+U6) A1 160(U1+U2+U3) A3 60(U7+U8+U9) A4 30(U10+U11+U12)

[0100] Step 3: The distribution processing unit performs load balancing based on the results of Step 2, assigning XDR data from the UPF network elements of the associated nodes with larger total XDR data values ​​to the associated nodes with smaller total XDR data values. The process ends after all associated nodes have been processed.

[0101] First, process the XDR data in associated node A2 and assign it to associated node A4. The results are shown in Table 3 below.

[0102] Table 3

[0103] UPF Assigned XDR data volume Target associated nodes U4 80 A4

[0104] The XDR data in associated node A1 is then reassigned to associated node A3. The results are shown in Table 4 below.

[0105] Table 4

[0106] UPF Assigned XDR data volume Target associated nodes U1 50 A3

[0107] After load balancing, the XDR data volume of each associated node is shown in Table 5 below:

[0108] Table 5

[0109] Related nodes Total XDR data volume of each associated node after assignment A2 110 A1 110 A3 110 A4 110

[0110] The destination associated nodes corresponding to the UPF after assignment are shown in Table 6 below:

[0111] Table 6

[0112]

[0113]

[0114] This disclosure also provides an electronic device 100, such as... Figure 4 As shown, it includes:

[0115] One or more processors 101;

[0116] The memory 102 stores one or more programs, which, when executed by one or more processors 101, enable the one or more processors to implement the data processing method described above.

[0117] One or more input / output I / O interfaces 103 are connected between the processor and the memory and configured to enable information exchange between the processor and the memory.

[0118] In embodiments of this disclosure, the electronic device may include, but is not limited to, a distribution function unit.

[0119] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data processing method described above.

[0120] This disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the data processing method described above.

[0121] Those skilled in the art will understand that all or some of the functional modules / units disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0122] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be executed by several physical components working together.

[0123] Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit (CPU), digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH) or other disk storage; read-only optical disc (CD-ROM), digital versatile disc (DVD) or other optical disc storage; magnetic cartridges, magnetic tapes, disk storage or other magnetic storage; and any other media that can be used to store desired information and can be accessed by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0124] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A data processing method comprising: obtaining a data size relationship of to-be-hashed distributed data corresponding to all associated nodes; based on the data size relationship, balancing the data size of the to-be-hashed distributed data corresponding to the associated nodes, so that the data size of the to-be-hashed distributed data of each associated node remains within a preset range threshold.

2. The data processing method of claim 1, wherein, The balancing of the data size of the to-be-hashed distributed data corresponding to the associated nodes based on the data size relationship comprises: assigning the to-be-hashed distributed data corresponding to the associated node with a larger data size of the to-be-hashed distributed data to the associated node with a smaller data size.

3. The data processing method of claim 2, wherein, The assigning of the to-be-hashed distributed data corresponding to the associated node with a larger data size of the to-be-hashed distributed data to the associated node with a smaller data size comprises: determining all n associated nodes according to a first sorting of the data size relationship; wherein n is a positive integer, and the first associated node in the first sorting corresponds to the largest data size of the to-be-hashed distributed data; assigning the to-be-hashed distributed data corresponding to any ith associated node of the n associated nodes to the n-i associated node, wherein i is greater than or equal to 1 and less than or equal to n / 2.

4. The data processing method of claim 3, wherein, The assigning of the to-be-hashed distributed data corresponding to any ith associated node of the n associated nodes to the n-i associated node comprises: second sorting the data size of multiple groups of to-be-hashed distributed data corresponding to each associated node; based on the second sorting, assigning all or part of the to-be-hashed distributed data with the largest data size in the multiple groups of to-be-hashed distributed data corresponding to the ith associated node to the n-i associated node.

5. The data processing method of claim 2, wherein, The to-be-hashed distributed data comprises call record detailed list (XDR) data in a user plane function (UPF) network element; The assigning of the to-be-hashed distributed data corresponding to the associated node with a larger data size of the to-be-hashed distributed data to the associated node with a smaller data size comprises: assigning all XDR data in any one or more UPF network elements corresponding to the associated node with a larger data size of the XDR data to the associated node with a smaller data size of the XDR data; or assigning part of the XDR data in any one UPF network element corresponding to the associated node with a larger data size of the XDR data to the associated node with a smaller data size of the XDR data.

6. The data processing method of claim 4, wherein, The to-be-hashed distributed data comprises call record detailed list (XDR) data in a user plane function (UPF) network element; The second sorting of the data size of multiple groups of to-be-hashed distributed data corresponding to each associated node comprises: second sorting the data size of XDR data in multiple UPF network elements corresponding to each associated node.

7. The data processing method of claim 6, wherein, The assigning of all or part of the to-be-hashed distributed data with the largest data size in the multiple groups of to-be-hashed distributed data corresponding to the ith associated node to the n-i associated node based on the second sorting comprises: acquire part or all of the XDR data in the UPF network element with the largest data volume in the plurality of UPF network elements corresponding to the ith associated node, and assign the part or all of the XDR data to the n-i th associated node.

8. The data processing method of claim 1, wherein, Before balancing the data volumes of the to-be-hashed distributed data corresponding to the associated nodes based on the data volume size relationship, the range threshold is determined according to the following method: calculate the average value of the data volumes of the to-be-hashed distributed data of all the associated nodes; take a preset data range with the average value as a center value as the range threshold. 9.An electronic device, comprising: one or more processors; a memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method of any one of claims 1-8; one or more input / output (I / O) interfaces connected between the processor and the memory and configured to implement information interaction between the processor and the memory.

10. The electronic device of claim 9, wherein, The electronic device comprises a distribution function unit. 11.A computer readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the data processing method of any one of claims 1-8 is implemented. 12.A computer program product comprising a computer program, wherein when the computer program is executed by a processor, the data processing method of any one of claims 1-8 is implemented.

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