Business call chain analysis method and device and electronic equipment

Abstract

The application provides a service call chain analysis method and device and electronic equipment. The service call chain analysis method provided by the application obtains service call data in a service call process, generates a first layer tree structure according to the service call data to detect parameters in the first layer tree structure, and further determines whether a call service to which the service call data belongs is abnormal. Therefore, the service call chain analysis method can determine whether the call service is normal or abnormal in the service call process, and the call chain analysis can be completed faster without waiting for the entire service call chain to be executed.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a business call chain analysis method, apparatus, and electronic device. Background Technology

[0002] For cross-system business operations, multiple systems need to collaborate. These systems call each other, so an anomaly in any one system can have a chain reaction, even causing the entire business chain to fail.

[0003] Traditional business call chain analysis methods can only be performed after the entire business call has ended, and then the call chain results are output. If there are situations such as long service time or slow response, it is necessary to wait continuously, and the business call chain results cannot be displayed in real time.

[0004] It is evident that current business call chain analysis methods require a considerable amount of time from initiating the business call request to being able to analyze the call chain results. Summary of the Invention

[0005] To address the issue that current business call chain analysis methods require the entire business call chain to be executed before analysis can be performed, this invention proposes a business call chain analysis method, apparatus, and electronic device. This business call chain analysis method eliminates the need for analysis after the entire business call is completed, thus enabling faster determination of the business call chain results.

[0006] Firstly, a business call chain analysis method includes: During the business call process, obtain the business call data; Based on the business call data, generate the first-level tree structure; The parameters in the first-level tree structure are checked to determine whether the calling service to which the business call data belongs is abnormal.

[0007] Secondly, a business call chain analysis device includes: The acquisition module is used to retrieve business call data into the cache during the business call process; The generation module is used to generate a first-level tree structure based on the business call data; The determination module is used to detect the parameters in the first-level tree structure and determine whether the result of the calling service to which the business call data belongs is abnormal.

[0008] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the business call chain analysis method as described in any of the preceding claims.

[0009] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the business call chain analysis method as described in any of the preceding claims.

[0010] The business call chain analysis method provided by this invention acquires business call data during the business call process, generates a first-level tree structure based on the business call data, and then detects the parameters in the first-level tree structure to determine whether the calling service to which the business call data belongs is abnormal. Thus, the business call chain analysis method of this invention can analyze and determine whether the calling service is normal or abnormal during the business call process, without having to wait for the entire business call chain to be executed before analysis can be performed, thereby completing the call chain analysis more quickly. Attached Figure Description

[0011] The accompanying drawings, which are provided to further illustrate the invention and constitute a part of it, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the first business call chain analysis method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the second business call chain analysis method according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the third business call chain analysis method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of data merging in the third business call chain analysis method of this invention; Figure 5 This is a flowchart illustrating a specific implementation method for a business call chain analysis method according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the modules of the service call chain analysis device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0012] Figure label: 61-Acquisition module; 62-Generation module; 63-Confirmation module. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only one part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0014] like Figure 1 As shown, the first business call chain analysis method provided by the embodiment of the present invention includes: S102: During the business call process, retrieve business call data. Therefore, the retrieved business call data is not obtained only after the entire business call has finished, allowing for timely retrieval without waiting for the entire business call to complete. Furthermore, business call data can be retrieved in chronological order for a specific moment or time period. Additionally, the business call data can be retrieved in real-time from a message queue.

[0015] S104: Generate a first-level tree structure based on the business call data. This can be done using an LSM-tree (Log-Structured Merge-Tree) algorithm to generate the first-level tree structure C0 based on the business call data; other tree structure algorithms can also be used. Details about other tree structure algorithms are not provided here. Furthermore, the first-level tree structure can be queried in real-time when it is necessary to query the business call status.

[0016] S106: Detect the parameters in the first-level tree structure to determine whether the calling service to which the business call data belongs is abnormal.

[0017] Therefore, the business call chain analysis method provided in this embodiment of the invention can analyze and determine whether the called service is normal or abnormal during the business call process, without having to wait for the entire business call chain to be completed before analysis can be performed, thus enabling faster completion of call chain analysis.

[0018] In this embodiment of the invention, business call data can be obtained and stored in a cache, and a first-level tree structure can be generated in the cache. Parameters in the first-level tree structure can also be detected. Thus, business call analysis can be completed in the cache without having to retrieve business call data from the disk or database for detection, further improving the efficiency of business call analysis.

[0019] In this embodiment of the invention, the service call data in S102 can be data at a certain point in time or within a certain time period. Correspondingly, determining whether the service call is abnormal in S106 is also based on the service call data at that point in time or within that time period.

[0020] Between S102 and S104, the process may further include: formatting the parameters in the first-level tree structure, for example, adding formatted column names to the relevant data of the parameters, and formatting the relevant data collection time of the parameters.

[0021] In S106, the parameters in the first-level tree structure may include at least one of the following: interface name, time consumption, collection time, and service return code.

[0022] In this embodiment of the invention, after S106, at least one of the following may be included: if it is determined that the calling service to which the service call data belongs is normal, mark the result of the calling service as normal; if it is determined that the calling service to which the service call data belongs is abnormal, mark the result of the calling service as abnormal and issue an alarm signal. Thus, it is possible to determine whether the calling service is abnormal based on the marking information of the calling service result.

[0023] like Figure 2 As shown, this is a second business call chain analysis method provided by an embodiment of the present invention, including: S202: Obtain business call data during the business call process.

[0024] S204: Generate the first-level tree structure based on the business call data.

[0025] S206: Input the parameters in the first-level tree structure into the time series detection model.

[0026] The parameters of the first-level tree structure can include at least one of the following: interface name, time consumption, collection time, and service return code. The time series detection model described above can be the Prophet (prediction algorithm) interface model, or other time series detection models, which will not be elaborated upon here.

[0027] S208: Output the detection result of whether the parameter is within the corresponding confidence interval. After the calculation by the time series detection model, the latest parameter obtained in S206 can be output as to whether it is within the corresponding confidence interval. If the value of the parameter is within the corresponding confidence interval, the parameter can be considered normal; otherwise, the parameter is considered abnormal.

[0028] S210: Based on the detection results, determine whether the calling service to which the service call data belongs is abnormal. For example, if the values ​​of all parameters in the first-level tree structure are within the corresponding confidence interval, the calling service to which the service call data belongs is considered normal; otherwise, the calling service to which the service call data belongs is considered abnormal.

[0029] Clearly, before S206, the process should also include: determining the time series detection model and the confidence intervals of the parameters in the time series detection model. Specifically, after each execution of S202-S210, the time series detection model and the confidence intervals of each parameter are updated based on the most recently obtained true values ​​of the parameters in the first-level tree structure.

[0030] The formula for the time series detection model can be expressed as: (1) In the formula, set The first model is a trend model used for non-periodic changes; the second model is a seasonal model used to describe periodic changes. This represents noise. The periodic variations can include seasonal factors with various periodic lengths. By default, seasonal factors with periods of 1 minute, 1 day, and 1 week are included. Since monthly seasonal factors may exist in the data, a custom seasonal factor function can also be called.

[0031] Since the above formula for the time series detection model is existing technology, the embodiments of the present invention will not elaborate on it.

[0032] Furthermore, in the embodiments of this application, the specific content of S202 can be referred to the specific content of S102, and the specific content of S204 can be referred to the specific content of S104, which will not be repeated here.

[0033] like Figure 3 As shown, this is a third business call chain analysis method provided by an embodiment of the present invention, including: S302: Obtain business call data during the business call process.

[0034] S304: Generate the first-level tree structure based on the business call data.

[0035] S306: Detect the parameters in the first-level tree structure to determine whether the calling service to which the business call data belongs is abnormal.

[0036] S308: Determine the first-level cumulative tree structure based on the result of the calling service to which the business call data belongs and the first-level tree structure; wherein, the first-level cumulative tree structure includes the first-level tree structure and the result of the calling service to which the business call data to which the first-level tree structure belongs. For example, in S304, based on the LSM-tree structure algorithm, the first-level tree structure C0 is generated according to the business call data. Then, the first-level cumulative tree structure C01 includes the first-level tree structure C0 and the result of the calling service corresponding to the first-level tree structure C0. The result of the calling service can be marked as normal or abnormal. Thus, the result of the calling service corresponding to the first-level tree structure C0 can be determined from the first-level cumulative tree structure C01.

[0037] S310: Merge the first-level cumulative tree structure in the cache with the second-level tree structure in the disk space to obtain an updated second-level tree structure. For example, when the memory block area storing the first-level cumulative tree structure C01 reaches a preset size, according to the LSM-tree tree merging mechanism, merge the memory block storing the first-level cumulative tree structure C01 with the second-level tree structure C1 in the disk space to form a new second-level tree structure new-C1 tree, replacing the original second-level tree structure C1.

[0038] S312: Merge the upper-level tree structure into the lower-level tree structure sequentially to update the lower-level tree structure until the tree structures of adjacent levels are identical. Then, determine one of the two identical adjacent tree structures as the call chain tree for the business call data and save it to disk or database. For example, this process continues until the second-level tree structure C1 on disk reaches a certain size, at which point it continues to merge downwards to form a new third-level tree structure new-C2, and so on, until... ,like Figure 4 As shown. n represents when and If the trees are the same, no further requests will be made. The results are merged to form the final call chain analysis result tree, which is then stored on disk or in a database for persistence and historical data analysis. Therefore, after the entire business call is completed, the result of the business call can be determined and analyzed by querying the final call chain analysis result tree.

[0039] Furthermore, in the embodiments of this application, the specific content of S302 can be referred to the specific content of S102, the specific content of S304 can be referred to the specific content of S104, and the specific content of S306 can be referred to the specific content of S106, and will not be repeated here.

[0040] like Figure 5 The image shows a specific implementation of the business call chain analysis method provided in this embodiment of the invention. Figure 5In this implementation, based on the Log Structure Tree (LSM-tree) data structure algorithm and supplemented by an intelligent interface evaluation algorithm, rapid judgment and aggregated storage of call chain anomalies are achieved. Real-time data is cached, while historical and aggregated data are directly saved to disk and the database, respectively, achieving separation of hot and cold data. This specific implementation includes the following steps: S1: Obtain real-time business call data transmitted from the message queue, preprocess it using a log structure tree (LSM-tree), and combine it with the interface detection model (i.e., the time series detection model mentioned above) to generate a new C0-SD tree (i.e., the first-level cumulative tree structure C01 tree mentioned above). In this process, S1 includes the following steps: S11: Receives real-time business call data transmitted from the message queue and stores the real-time business call data in the cache.

[0041] S12: Generate a real-time tree of layer C0 in the cache according to the log structure tree (LSM-tree) algorithm.

[0042] S13: When the real-time tree of layer C0 in the cache reaches a certain size, the interface detection model is triggered to process the data and generate a new cache tree C0-SD.

[0043] This specific implementation also includes the following step S2: using a log structure tree (LSM-tree) to merge data, continuously writing cached data to disk to form historical data. In this process, step S2 specifically includes: S21: When the memory block area where C0-SD is located reaches a certain size, according to the tree merging mechanism of LSM-tree, C0-SD and C1 of the disk space are merged to form a new new-C1 tree, replacing the original C1 tree.

[0044] S22: Following this pattern, once C1 on the disk reaches a certain size, it continues to be merged downwards to form a new new-C2, until... ,like Figure 4 As shown. n represents when and If the trees are the same, no further requests will be made. The results are merged to form the final call chain analysis result tree, which is then stored on disk or in a database for historical data analysis and querying. Specifically, S13 may include: S131: Construct a time-series-based interface detection model y(t). , In this formula, we set The first model is a trend model used for non-periodic changes; the second model is a seasonal model used to describe periodic changes. Indicates noise.

[0045] S132: Input the real-time data (such as interface name, time consumption, collection time, service return code) of a single first-level tree structure C0 in the input cache into the interface detection model. Specifically, this includes: adding formatted column names to the input real-time data; converting the real-time data collection time format; converting the pre-processed data according to the model input format; loading the interface detection model to predict the real-time data and obtaining a confidence interval for judging whether the real-time data is abnormal.

[0046] The input data consists of the data of the first-level tree structure C0 input during each loop execution. Each execution requires the new value of the first-level tree structure C0 and the previous result to continuously correct and obtain a new confidence interval.

[0047] S133: Determine whether the real-time data is within the confidence interval to obtain the call chain result status. If the real-time data is within the confidence interval, mark the call chain result status as normal; otherwise, mark the call chain result status as abnormal and issue an alarm.

[0048] S134: Rewrite the obtained call chain result state into the memory block area where the cached C0-SD is located, as a new cache tree, while providing fast real-time data query.

[0049] The above describes the service call chain analysis method provided in the embodiments of this specification. Based on the same inventive concept, the embodiments of this specification also provide a corresponding service call chain analysis device. Figure 6 As shown, the device specifically includes: The acquisition module 61 is used to acquire business call data into the cache during the business call process; Generation module 62 is used to generate a first-level tree structure based on the business call data; The determination module 63 is used to detect the parameters in the first-level tree structure and determine whether the result of the calling service to which the business call data belongs is abnormal.

[0050] In one implementation, the determining module 63 is specifically used for: Input the parameters from the first-level tree structure into the time series detection model; Output the detection result indicating whether the parameter is within the corresponding confidence interval; Based on the detection results, it is determined whether the calling service to which the business call data belongs is abnormal.

[0051] In one implementation, the parameters include at least one of the following: interface name, time consumption, collection time, and service return code.

[0052] In one embodiment, the device is further configured to: After detecting the parameters in the first-layer tree structure to determine whether the calling service to which the business call data belongs is abnormal, the first-layer cumulative tree structure is determined based on the result of the calling service to which the business call data belongs and the first-layer tree structure; wherein, the first-layer cumulative tree structure includes the first-layer tree structure and the result of the calling service to which the business call data to which the first-layer tree structure belongs.

[0053] In one embodiment, the device is further configured to: After determining the first-level cumulative tree structure, the first-level cumulative tree structure in the cache is merged with the second-level tree structure in the disk space to obtain the updated second-level tree structure. The upper-level tree structure is sequentially merged into the lower-level tree structure to update the lower-level tree structure until the tree structures of two adjacent levels are the same. Then, one of the two adjacent tree structures with the same structure is determined as the call chain tree of the business call data and saved to disk or database.

[0054] In one embodiment, the device is further configured to: After determining whether the service to which the business call data belongs is abnormal, the service is used to mark the call service result as normal if it is determined that the service to which the business call data belongs is normal; or If it is determined that the service to which the business call data belongs is abnormal, the result of the call service is marked as abnormal and an alarm signal is issued.

[0055] In one implementation, the generation module 62 is specifically used to: generate a first-level tree structure based on the LSM-tree structure algorithm and the business call data in the cache.

[0056] In one implementation, the time series detection model is the Prophet interface model.

[0057] In the business call chain analysis device of this invention embodiment, it is possible to analyze and determine whether the called service is normal or abnormal during the business call process, without having to wait for the entire business call chain to be completed before analysis can be performed, thus enabling faster completion of call chain analysis.

[0058] This specification also provides an electronic device, please refer to the schematic diagram. Figure 7At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0059] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0060] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0061] The processor reads the corresponding computer program from non-volatile memory into memory and then runs it, forming an application service call chain analysis device at the logical level. The processor executes the program stored in memory and is at least used to perform the following operations: during a service call, acquiring service call data; generating a first-level tree structure based on the service call data; and detecting the parameters in the first-level tree structure to determine whether the calling service to which the service call data belongs is abnormal.

[0062] The above is as stated in this application. Figure 1The service call chain analysis method disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (FP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0063] The electronic device can also perform Figure 1 A method for analyzing business call chains was developed, and a business call chain analysis device was implemented in [the context of this]. Figure 1 The functions of the embodiments shown are not described in detail here.

[0064] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described business call chain analysis method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0065] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0070] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0071] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0073] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A business call chain analysis method, characterized in that, include: During the business call process, the business call data is retrieved and stored in the cache; Based on the LSM-tree structure algorithm, a first-level tree structure is generated in the cache according to the business call data; The parameters in the first-level tree structure are checked to determine whether the calling service to which the business call data belongs is abnormal; Based on the result of the calling service to which the business call data belongs and the first-level tree structure, determine the first-level cumulative tree structure; The first-level cumulative tree structure in the cache is merged with the second-level tree structure in the disk space to obtain the updated second-level tree structure. Once the second-level tree structure in the disk space reaches a certain size, it continues to merge downwards to form a new third-level tree structure. This process continues, merging the previous level tree structure into the next level tree structure to update the next level tree structure, until the tree structures of two adjacent levels are identical. At this point, the tree structures will no longer be merged, and the final call chain analysis result tree will be formed. One of the two adjacent tree structures with the same structure will be identified as the call chain tree of the business call data and saved on the disk or in the database.

2. The business call chain analysis method according to claim 1, The step of detecting parameters in the first-level tree structure to determine whether the calling service to which the business call data belongs is abnormal includes: Input the parameters from the first-level tree structure into the time series detection model; Output the detection result indicating whether the parameter is within the corresponding confidence interval; Based on the detection results, it is determined whether the calling service to which the business call data belongs is abnormal.

3. The business call chain analysis method according to claim 1, characterized in that, The parameters include at least one of the following: interface name, time consumption, collection time, and service return code.

4. The business call chain analysis method according to claim 1, characterized in that, The first-level cumulative tree structure includes the first-level tree structure and the result of the call service to which the business call data corresponding to the first-level tree structure belongs.

5. The business call chain analysis method according to claim 1, characterized in that, After determining whether the service to which the business call data belongs is abnormal, at least one of the following is also included: If it is determined that the service to which the business call data belongs is normal, the result of the call service is marked as normal; If it is determined that the service to which the business call data belongs is abnormal, the result of the call service is marked as abnormal and an alarm signal is issued.

6. The business call chain analysis method according to claim 2, characterized in that, The time series detection model is the Prophet interface model.

7. A business call chain analysis device, characterized in that, include: The acquisition module is used to retrieve business call data into the cache during the business call process; The generation module is used to generate a first-level tree structure in the cache based on the business call data using the LSM-tree structure algorithm. The determination module is used to detect the parameters in the first-level tree structure and determine whether the result of the calling service to which the business call data belongs is abnormal. The service call chain analysis device is also used for: Based on the result of the calling service to which the business call data belongs and the first-level tree structure, determine the first-level cumulative tree structure; The first-level cumulative tree structure in the cache is merged with the second-level tree structure in the disk space to obtain the updated second-level tree structure. Once the second-level tree structure in the disk space reaches a certain size, it continues to merge downwards to form a new third-level tree structure. This process continues, merging the previous level tree structure into the next level tree structure to update the next level tree structure, until the tree structures of two adjacent levels are identical. At this point, the tree structures will no longer be merged, and the final call chain analysis result tree will be formed. One of the two adjacent tree structures with the same structure will be identified as the call chain tree of the business call data and saved on the disk or in the database.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the service call chain analysis method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the business call chain analysis method as described in any one of claims 1 to 7.

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