Aggregation analysis method, system and device for full-link service request and medium

Abstract

The invention relates to the technical field of computers, and discloses a full-link service request aggregation analysis method, system and device and a medium. The method comprises the following steps: acquiring all target path segments corresponding to a target path; querying whether each target path segment exists in a preset segment frequency table or not; if the target path segment exists in the preset segment frequency table, obtaining a serial number of the target path segment in the preset segment frequency table, and judging whether the serial number is smaller than a preset numerical value or not; if the target path segment does not exist in the preset segment frequency table or the serial number is greater than or equal to a preset value, updating the target path segment to a first preset symbol; and generating a standard path based on the processed contents of all the target path segments, and obtaining target service data based on the standard path. Through the scheme of the invention, the classification precision and the processing performance of the service request data can be more prominent, and the system is ensured to have higher real-time performance and reliability in the aspects of service monitoring, performance analysis, anomaly detection and the like.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, system, device and medium for aggregate analysis of full-link business requests. Background Art

[0002] In modern distributed systems and microservice architectures, full-link business performance tracking is one of the key means to ensure system stability and performance optimization. By tracking and analyzing business requests, performance bottlenecks can be discovered in a timely manner, system architecture can be optimized, and user experience can be improved. However, with the expansion of business scale and the growth of user volume, the number and complexity of business requests have also increased dramatically. How to efficiently classify and aggregate these requests has become an important technical challenge. The URL (Uniform Resource Locator) of the business request is an important basis for classification and aggregation analysis. The general format of the URL is: protocol: / / hostname[:port] / path / [;parameters][?query]#fragment. In actual business scenarios, the protocol and hostname are usually relatively fixed and can be processed by special statistical methods. However, the parameters and query parts are highly variable and usually need to be processed with the help of other analytical methods. Therefore, the path part of the business request has become the main basis for rapid classification.

[0003] The path of a business request usually consists of multiple segments separated by backslashes ( / ). Each segment is either a word with a specific meaning or an ID used to identify a micro-resource. For example, in the two paths / user / 0abc7dfe885f6732 / login and / user / 0abc7dfe885f6732 / place_order, 0abc7dfe885f6732 is the user ID, while login and place_order are relatively fixed business operations. The characteristic of this path format is that each path is closely related to a single user, resulting in a sharp increase in the number of paths in a monitoring environment with massive users and massive requests.

[0004] In the related technology, there are two methods: direct classification based on paths and analysis and merging of paths after storage. The method based on path classification is simple and direct, but it cannot effectively distinguish requests with the same business logic and will lead to uncontrollable growth of the category cardinality. The method of path analysis and merging after storage can solve the problems of classification accuracy and category cardinality to a certain extent. However, this method requires additional processing of the stored data, which increases the complexity and affects the processing efficiency for databases that are not good at update operations. In addition, in some application scenarios, the provision that modified classified data is not allowed further limits the scope of application of this solution.

[0005] In summary, relevant technologies have certain limitations in the classification and aggregation analysis of business requests, and there is an urgent need for an efficient solution that can both ensure classification accuracy and effectively control the category cardinality. Summary of the invention

[0006] In view of this, the present invention proposes an aggregation analysis method, system, device and medium for full-link business requests, which can make the classification results of business requests more in line with business logic and user concerns, while avoiding classification expansion caused by the high cardinality of dynamically changing segments, thereby improving the accuracy of classification and significantly optimizing processing efficiency.

[0007] Based on the above purpose, an embodiment of the present invention provides an aggregation analysis method for full-link service requests, which specifically includes the following steps: Get all target path segments corresponding to the target path; Query whether each of the target path segments exists in the preset segment frequency table; If the target path segment exists in the preset segment frequency table, obtaining the sequence number of the target path segment in the preset segment frequency table, and determining whether the sequence number exceeds a preset value; If the target path segment does not exist in the preset segment frequency table or the sequence number exceeds a preset value, updating the target path segment to a first preset symbol; Based on the processed contents of all the target path segments, a standard path is generated, and the business data is updated based on the standard path.

[0008] In some implementations, the step of updating the service data based on the standard path includes: Based on a preset hash algorithm, convert the standard path to obtain a hash value; Based on the hash value, the business data corresponding to the target path is updated to a preset database table.

[0009] In some implementations, the step of constructing the preset segment frequency table includes: Build a key-value table; Based on a preset sampling period, a number of business requests are collected, and a request path corresponding to each of the business requests is obtained; Based on the second preset symbol, obtaining all first path segments corresponding to each of the requested paths; Taking each of the first path segments as a key field, determining whether each of the key fields exists in the key value table; If it exists, increase the value field corresponding to the key field by one; If it does not exist, store the key field into the key-value table, and set the value field corresponding to the key field to one; The preset segment frequency table is determined based on all key fields and all value fields in the key-value table.

[0010] In some implementations, the step of constructing the preset segment frequency table further includes: Sort all the key fields in the preset segment frequency table according to the order of all the value fields from large to small; A corresponding sequence number is assigned to each of the key fields according to the order.

[0011] In some implementations, the method for aggregate analysis of full-link service requests further includes: If the target path segment exists in the preset segment frequency table and the sequence number does not exceed the preset value, the target path segment is retained.

[0012] In some implementations, the step of obtaining all path segments corresponding to the target path includes: Based on the second preset symbol, the target path is divided to obtain all the path segments.

[0013] In some implementations, the step of generating a standard path based on the contents of all the segments includes: Based on the second preset symbol, all the processed target paths are connected in segments to generate the standard path.

[0014] Another aspect of the embodiment of the present invention further provides an aggregation analysis system for full-link service requests, including: A segmentation unit configured to obtain all target path segments corresponding to the target path; A query unit, configured to query whether each of the target path segments exists in a preset segment frequency table; a determination unit configured to obtain a sequence number of the target path segment in the preset segment frequency table if the target path segment exists in the preset segment frequency table, and determine whether the sequence number exceeds a preset value; A first processing unit, configured to update the target path segment to a first preset symbol if the target path segment does not exist in the preset segment frequency table or the sequence number exceeds a preset value; An updating unit is configured to generate a standard path based on the processed contents of all the target path segments, and update the business data based on the standard path.

[0015] According to another aspect of an embodiment of the present invention, a computer device is provided, comprising: at least one processor; and a memory, wherein the memory stores a computer program executable on the processor, and the computer program implements the steps of the above method when executed by the processor.

[0016] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program that implements the above method steps when executed by a processor.

[0017] The present invention has at least the following beneficial technical effects: the aggregation analysis method of the full-link business request of the present invention, by statistically analyzing the frequency of occurrence of each segment in the business request path to pre-establish a segment frequency table at the data collection and storage end, and using the segment frequency table to automatically distinguish between fixed segments and dynamically changing segments, so that the classification results are more in line with the business logic and user concerns, while avoiding the classification expansion caused by the high cardinality of the dynamically changing segments, which not only improves the accuracy of the classification, but also significantly optimizes the processing efficiency. In the business data storage stage, by wildcard replacement of the dynamically changing segments, the number of classification categories is effectively reduced, thereby reducing the consumption of storage and computing resources and improving the efficiency of query and analysis. In addition, this scheme can complete the classification when the data is stored in the warehouse, without the need for secondary processing of the stored data, avoiding additional computing overhead, in the scenario of massive users and high concurrent business, due to the large frequency difference between the fixed segments and the dynamically changing segments, this scheme can quickly converge to accurate classification rules, making the classification accuracy and processing performance more prominent, ensuring that the system has higher real-time and reliability in business monitoring, performance analysis and anomaly detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A block diagram of an embodiment of the method for aggregate analysis of full-link service requests provided by the present invention; Figure 2A schematic diagram of an embodiment of the overall processing flow of the aggregation analysis method for full-link service requests provided by the present invention; Figure 3 A schematic diagram of an embodiment of a service grouping process provided by the present invention; Figure 4 A schematic diagram of an embodiment of a sampling and statistical process of a preset segmented frequency table provided by the present invention; Figure 5 A schematic diagram of an embodiment of a system for aggregate analysis of full-link service requests provided by the present invention; Figure 6 A schematic diagram of the structure of an embodiment of a computer device provided by the present invention; Figure 7 A schematic diagram of the structure of an embodiment of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0021] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0022] In the existing technical solutions, the method of directly classifying based on the path has two significant problems: (1) The classification is weakly associated with the business: For example, the two requests " / user / 0000000 / login" and " / user / 1111111 / login" actually belong to the user's login business, but because of the different user IDs in the path, they will be classified into two different categories. This classification method cannot accurately reflect the actual type of business, resulting in the meaningless aggregation statistics based on this method; (2) The number of categories is too large: In an environment with a large number of users, directly classifying by path will cause the number of categories to expand dramatically. For example, a path like " / user / **** / login" may generate 70 million different categories in an environment with 70 million users. This will not only bring huge processing and storage pressure, but also make subsequent analysis and statistics work extremely difficult.

[0023] Another solution is to analyze and merge the paths of business requests after the data is stored in the database. In business performance monitoring, the URLs collected by the system are often dynamically generated and change with changes in user behavior and business processes, and the performance monitoring system usually does not have the authority or ability to access and collect all URL information. Therefore, the existing technical solution requires that the collected URL data be stored in full in the database first, and then analyzed and merged through queries. This solution can solve the above two problems to a certain extent, namely improving the accuracy of classification and controlling the category cardinality. However, this solution also has the following two limitations: (1) High processing pressure: Merging and updating the stored data will bring additional processing pressure, especially in database systems that are not good at processing updates, which is inefficient. (2) Limited application scenarios: In some scenarios, the system may not allow modification of classified data, which limits the scope of application of the solution.

[0024] Based on the above authority, the first aspect of the embodiment of the present invention proposes an embodiment of a method for aggregate analysis of full-link service requests. Figure 1 As shown, the method comprises the following steps: Step S100, obtaining all target path segments corresponding to the target path.

[0025] In the illustrated embodiment, the path corresponding to the business request data collected in real time and needing to be stored is used as the target path. The target path is split into multiple segments using a backslash ( / ). For example, when the target path is " / user / 010101 / login", it can be split into three path segments ["user", "010101", "login"], and "user", "login" and "010101" are all the target path segments corresponding to the target path.

[0026] Step S200, query whether each target path segment exists in the preset segment frequency table.

[0027] In the embodiment shown, Figure 2As shown, the aggregation analysis method of the full-link business request includes a real-time business grouping process and a pre-executed segment frequency table sampling and statistical process. The preset segment frequency table is constructed through the segment frequency table sampling and statistical process. The preset segment frequency table is a key-value table used to store the number of times each path segment appears. The key field of the preset segment frequency table is the path segment, such as "user", "login", and "010101". The value field of the preset segment frequency table is the number of times the path segment appears in all business requests. The preset segment frequency table can be cached in memory and persisted to disk periodically to ensure data persistence.

[0028] Step S300: If the target path segment exists in the preset segment frequency table, obtain the sequence number of the target path segment in the preset segment frequency table, and determine whether the sequence number exceeds a preset value.

[0029] In the embodiment shown, Figure 2-3 As shown, the business request data that needs to be stored in the database is classified, counted and quickly searched through the business grouping process. The sequence number refers to the order of the number of occurrences of the path segment in the preset segment frequency table. The preset value is used in combination with the sequence number in the preset segment frequency table to distinguish whether the target path segment is a normal segment (such as "login") or a dynamically changing segment (such as "010101"). It can be set according to actual working conditions. This embodiment does not make specific restrictions on this. For example, it can be set to 100.

[0030] Step S400: If the target path segment does not exist in the preset segment frequency table or the sequence number exceeds a preset value, the target path segment is updated to a first preset symbol.

[0031] In the embodiment shown, Figure 3 As shown, if the ranking of a path segment in the preset segment frequency table exceeds (i.e. is greater than) a preset value, it is considered to be a dynamically changing segment, otherwise it is considered to be an ordinary segment. For path segments judged to be dynamically changing segments, they are replaced with a wildcard (i.e., a first preset symbol), and the first preset symbol is usually set to "*". For ordinary segments judged to be fixed segments, they remain unchanged. For example, the path segment "010101" is replaced with "*", while the path segment "login" remains unchanged. In addition, in the illustrated embodiment, if a segment does not exist in the preset segment frequency table, it means that its occurrence number is too small and it is a low-frequency segment, which is irrelevant to the business logic. It can be replaced by a wildcard to avoid database category explosion caused by massive ID segments.

[0032] Step S500: Generate a standard path based on all processed target path segments, and update business data based on the standard path.

[0033] In the embodiment shown, Figure 3 As shown in the figure, the processed segment contents are connected with a backslash ( / ) to obtain a standardized path, i.e., a standard path. For example, the three path segments in the target path " / user / 010101 / login" are processed to obtain "user", "*", and "login" respectively, and all the processed path segments are connected with " / " to obtain a standard path " / user / * / login".

[0034] The aggregation analysis method of the full-link business request of the present invention establishes a segment frequency table in advance at the data collection and storage end by statistically analyzing the frequency of occurrence of each segment in the business request path, which can automatically distinguish between fixed segments and dynamically changing segments, so that the classification results are more in line with business logic and user concerns, while avoiding the classification expansion caused by the high cardinality of dynamically changing segments, which not only improves the accuracy of classification, but also significantly optimizes the processing efficiency. In the business data storage stage, by wildcard replacement of dynamically changing segments, the number of classification categories is effectively reduced, thereby reducing database storage and computing resource consumption, and improving the efficiency of query and analysis. In addition, this solution can complete the classification when the data is stored in the warehouse, without the need for secondary processing of the stored data, avoiding additional computing overhead, in the case of massive users and high concurrent business scenarios, due to the large frequency difference between fixed segments and dynamically changing segments, this solution can quickly converge to accurate classification rules, making the classification accuracy and processing performance more prominent, ensuring that the system has higher real-time and reliability in business monitoring, performance analysis and anomaly detection.

[0035] In some implementations, the step of acquiring target business data based on a standard path includes: converting the standard path to obtain a hash value based on a preset hash algorithm; and updating the business data corresponding to the target path to a preset database table based on the hash value.

[0036] In the illustrated embodiment, the preset hash algorithm can be set according to the actual working conditions. This embodiment does not make any specific limitation on this. Usually, a hash algorithm such as sha1 is used for calculation. The preset database table is used to store business request data according to business logic classification. A unique hash value is generated for the standardized standard path as an identifier of the aggregate classification. For example, the target path " / user / 010101 / login" is replaced by the standard path " / user / * / login", which is converted into a hash value group=sha1(" / user / * / login"). The group field is used as a unique classification identifier for the business request data in the preset database table, and is used to store and index the business request data for subsequent statistics and quick search.

[0037] The aggregate analysis method of the full-link business request of the present invention greatly reduces the number of classifications, avoids the classification of business request data being irrelevant to business logic, and avoids data explosion caused by excessive refinement. Classification storage that conforms to business logic is implemented based on standard paths, making statistical data more aggregated, improving analysis efficiency, and using hash values ​​for indexing to facilitate subsequent business monitoring and query.

[0038] In some embodiments, the steps of constructing a preset segmented frequency table include: constructing a key-value table; based on a preset sampling period, collecting a number of business requests, and obtaining a request path corresponding to each business request; based on a second preset symbol, obtaining all first path segments corresponding to each request path; taking each first path segment as a key field, and determining whether each key field exists in the key-value table; if so, adding one to the value field corresponding to the key field; if not, storing the key field in the key-value table, and setting the value field corresponding to the key field to one; based on all key fields and all value fields in the key-value table, determining the preset segmented frequency table.

[0039] In some implementations, the step of constructing the preset segmented frequency table further includes: sorting all key fields in the preset segmented frequency table according to the order of all value fields from large to small; and assigning a corresponding serial number to each key field according to the order.

[0040] In the illustrated embodiment, this solution is applicable to a business monitoring environment with a large number of users, and can intelligently classify business request paths during the data collection phase. Automatically identify each segment in the path and determine whether it is a fixed, common segment that can be used to distinguish business, or a dynamically changing segment that is only used for specific user or resource identification and can be replaced by a wildcard, i.e., an id segment. Taking " / user / 010101 / login" as an example, the path can be split into three segments: user, 010101, and login. From the perspective of information theory, the information entropy of different segments is Different from the information entropy, the information entropy refers to an indicator to measure uncertainty. The size of the information entropy depends on the probability (P) of the segment appearing in all requests. If the probability of a segment appearing is low, its information entropy is high, indicating that it is a more random and unpredictable value. The characteristic of the id segment is that its information entropy is much higher than that of the ordinary segment, which means that the frequency of occurrence of the id segment is much lower than that of the ordinary segment. For example, in a system with tens of millions of users, if each user visits a path like " / user / 010101 / login", the number of times the user and login segments appear in all requests is much greater than the id segment "010101", which may be tens of millions of times. According to the definition of information entropy, the information entropy is monotonically decreasing with the frequency of occurrence, that is, the higher the frequency of occurrence, the smaller the information entropy. Therefore, by sampling the business request data and counting the frequency of occurrence of each segment, the size of the information entropy can be directly inferred, and thus determine whether a segment is an ordinary segment or an id segment. In this way, the system can replace the wildcards in the ID segment before storage, thereby reducing the number of categories and improving the efficiency and accuracy of business monitoring. Therefore, the common segmentation and ID segmentation have the following characteristics: 1. Common segments are hard-coded in the business system development code. No matter how complex the business system is, the total number of common segments is relatively fixed, and common segments are highly correlated with business logic. 2. The ID segment is dynamically generated for specific requesting users or specific resources. The IDs generated by the same business usually have a very large base. 3. The frequency of occurrence of the two is an order of magnitude different, and the frequency of occurrence of ordinary segments is much higher than that of id segments.

[0041] In the illustrated embodiment, since the services are relatively fixed for a service system with a large number of users and a large number of requests, a very accurate segmentation frequency sampling can be obtained after only a very short sampling period. Therefore, the service classification statistics obtained by collecting service request data within a preset sampling period can quickly converge to an accurate service classification. The preset sampling period can be set according to actual working conditions, and this embodiment does not make specific restrictions on this. For example, it can be set to 24h. The preset segmentation frequency table combines the characteristics of the above ordinary segmentation and ID segmentation to realize automatic judgment of ID segmentation. Figure 4As an example, the process of constructing a segment frequency table is described. Specifically, a key-value table with a segment string as a key field and a number of occurrences as a value field is established, and the business request data within the preset sampling period is sampled, and all the segments corresponding to each business request (i.e., the first path segment) are split. For each first path segment obtained by splitting, the key-value table is searched to see whether there is a string corresponding to the first path segment. If it exists, the number of occurrences in the corresponding value field in the key-value table is increased by one. If it does not exist, the first path segment is added to the key-value table, and its corresponding value field is set to one. Furthermore, after counting all segments and their number of occurrences, the key-value table data is sorted in descending order according to the number of occurrences of the segments, and then the sequence number of the sort in the table is assigned to each segment to generate the final preset segment frequency table. Then, according to the monitored system, a preset value is configured. When the sequence number of a path segment in the preset segment frequency table exceeds the preset value, the segment is considered to be an id field, otherwise it is considered to be an ordinary field.

[0042] The aggregation analysis method of the full-link business request of the present invention, since the number of ordinary segments is relatively stable, only a short sampling time is required to form a stable classification rule, so the system can converge quickly, adapt to business changes, and improve the real-time and accuracy of classification. The preset segmentation frequency table is used to automatically distinguish between ordinary fields and id fields. When the business request data is entered into the warehouse, only one classification is required to ensure that the classification results are highly matched with the actual business needs, and avoid secondary processing of the data already entered into the warehouse, thereby reducing additional computing overhead and storage occupancy. By maintaining the segmentation frequency statistics table in the memory and persisting it regularly, efficient access is guaranteed, and the waste of resources caused by repeated calculations is avoided, making the classification accuracy and processing performance more advantageous in large-scale scenarios.

[0043] In some implementations, the aggregation analysis method of the full-link service request of the present invention further includes: if the target path segment exists in a preset segment frequency table and the sequence number does not exceed a preset value, retaining the target path segment.

[0044] If the ranking of a path segment in the preset segment frequency table does not exceed the preset value, it is considered to be a common segment. For the common segment judged as a fixed segment, it remains unchanged.

[0045] The aggregation analysis method of the full-link business request of the present invention replaces the ID segment with a wildcard, reduces the cardinality of the classification, improves the processing efficiency, retains the original value of the common segment, ensures that the classification of the business request data when it is entered into the database is consistent with the business logic, and improves the matching of the corresponding classification of the business request data in the database with the specific business that the user is concerned about.

[0046] In some implementations, the step of acquiring all path segments corresponding to the target path includes: based on a second preset symbol, acquiring all path segments of the target path.

[0047] In the illustrated embodiment, the second preset symbol is a backslash ( / ), which is used to split the target path into multiple segments in order from left to right, and the segments are retained in order to reflect the path hierarchy relationship.

[0048] In some implementations, the step of generating a standard path based on the contents of all segments includes: connecting all processed target path segments based on a second preset symbol to generate a standard path.

[0049] In the illustrated embodiment, backslashes are used to connect the processed target path segments in the order of their path hierarchy relationship to obtain a standard path.

[0050] The aggregation analysis method of the full-link business request of the present invention realizes the pre-positioning of the analysis logic to the data collection stage by presetting the segmented frequency table. When the business request data is collected, the path in the URL is directly analyzed in real time, and the business request data is classified and stored in the database according to the hash value corresponding to the generated standard path. That is, once the URL data is collected, the classification that meets the business needs can be immediately performed, and there is no need to store the business request data in the database and then perform secondary processing such as query and analysis. This avoids the storage of the entire amount of the collected original URL data and reduces the subsequent dependence on the database. It is more suitable for dynamic and high-concurrency business performance monitoring scenarios.

[0051] Based on the same inventive concept, according to another aspect of the present invention, Figure 5 As shown, an embodiment of the present invention further provides an aggregation analysis system for full-link service requests, including: The segmentation unit 110 is configured to obtain all target path segments corresponding to the target path; A query unit 120, configured to query whether each target path segment exists in a preset segment frequency table; The determining unit 130 is configured to obtain a sequence number of the target path segment in the preset segment frequency table if the target path segment exists in the preset segment frequency table, and determine whether the sequence number exceeds a preset value; The first processing unit 140 is configured to update the target path segment to a first preset symbol if the target path segment does not exist in the preset segment frequency table or the sequence number exceeds a preset value; The updating unit 150 is configured to generate a standard path based on the processed contents of all target path segments, and update the service data based on the standard path.

[0052] The aggregation analysis system of the full-link business request of the present invention establishes a segment frequency table in advance at the data collection and storage end by statistically analyzing the frequency of occurrence of each segment in the business request path, and can automatically distinguish between fixed segments and dynamically changing segments, so that the classification results are more in line with business logic and user concerns, while avoiding the classification expansion caused by the high cardinality of dynamically changing segments, which not only improves the accuracy of classification, but also significantly optimizes the processing efficiency. In the business data storage stage, by wildcard replacement of dynamically changing segments, the number of classification categories is effectively reduced, thereby reducing storage and computing resource consumption and improving the efficiency of query and analysis. In addition, this solution can complete the classification when the data is stored in the warehouse, without the need for secondary processing of the stored data, avoiding additional computing overhead, in the case of massive users and high concurrent business scenarios, due to the large frequency difference between fixed segments and dynamically changing segments, this solution can quickly converge to accurate classification rules, making the classification accuracy and processing performance more prominent, ensuring that the system has higher real-time and reliability in business monitoring, performance analysis and anomaly detection.

[0053] Based on the same inventive concept, according to another aspect of the present invention, Figure 6 As shown, an embodiment of the present invention further provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, the steps of the above method are performed.

[0054] Based on the same inventive concept, according to another aspect of the present invention, Figure 7 As shown, an embodiment of the present invention further provides a computer-readable storage medium 40, which stores a computer program 410 for executing the above method when executed by a processor.

[0055] Finally, it should be noted that a person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program can be a disk, an optical disk, a read-only storage memory (ROM) or a random access memory (RAM), etc. The above-mentioned computer program embodiments can achieve the same or similar effects as the corresponding above-mentioned any method embodiments.

[0056] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0057] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0058] It should be understood that, as used herein, the singular forms "a", "an" are intended to include the plural forms as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations including one or more of the associated listed items.

[0059] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for aggregate analysis of full-link service requests, characterized in that: include: Get all target path segments corresponding to the target path; Query whether each of the target path segments exists in the preset segment frequency table; If the target path segment exists in the preset segment frequency table, obtaining the sequence number of the target path segment in the preset segment frequency table, and determining whether the sequence number exceeds a preset value; If the target path segment does not exist in the preset segment frequency table or the sequence number exceeds a preset value, updating the target path segment to a first preset symbol; Based on all the processed target path segments, a standard path is generated, and business data is updated based on the standard path.

2. The method for aggregate analysis of full-link service requests according to claim 1, characterized in that: The step of updating the business data based on the standard path includes: Based on a preset hash algorithm, convert the standard path to obtain a hash value; Based on the hash value, the business data corresponding to the target path is updated to a preset database table.

3. The method for aggregate analysis of full-link service requests according to claim 1, characterized in that: The step of constructing the preset segment frequency table includes: Build a key-value table; Based on a preset sampling period, a number of business requests are collected, and a request path corresponding to each of the business requests is obtained; Based on the second preset symbol, obtaining all first path segments corresponding to each of the requested paths; Taking each of the first path segments as a key field, determining whether each of the key fields exists in the key value table; If it exists, increase the value field corresponding to the key field by one; If it does not exist, store the key field into the key-value table, and set the value field corresponding to the key field to one; The preset segment frequency table is determined based on all key fields and all value fields in the key-value table.

4. The method for aggregate analysis of full-link service requests according to claim 3, characterized in that: The step of constructing the preset segment frequency table also includes: Sort all the key fields in the preset segment frequency table according to the order of all the value fields from large to small; A corresponding sequence number is assigned to each of the key fields according to the order.

5. The method for aggregate analysis of full-link service requests according to claim 1, characterized in that: Also includes: If the target path segment exists in the preset segment frequency table and the sequence number does not exceed the preset value, the target path segment is retained.

6. The method for aggregate analysis of full-link service requests according to claim 3, characterized in that: The step of obtaining all path segments corresponding to the target path includes: Based on the second preset symbol, the target path is divided to obtain all the path segments.

7. The method for aggregate analysis of full-link service requests according to claim 6, characterized in that: The step of generating a standard path based on the contents of all the segments comprises: Based on the second preset symbol, all the processed target paths are connected in segments to generate the standard path.

8. An aggregate analysis system for full-link business requests, characterized in that: include: A segmentation unit configured to obtain all target path segments corresponding to the target path; A query unit, configured to query whether each of the target path segments exists in a preset segment frequency table; a determination unit configured to obtain a sequence number of the target path segment in the preset segment frequency table if the target path segment exists in the preset segment frequency table, and determine whether the sequence number exceeds a preset value; A first processing unit, configured to update the target path segment to a first preset symbol if the target path segment does not exist in the preset segment frequency table or the sequence number exceeds a preset value; An updating unit is configured to generate a standard path based on the processed contents of all the target path segments, and update the business data based on the standard path.

9. A computer device comprising: at least one processor; as well as A memory storing a computer program executable on the processor, wherein the processor executes the steps of the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are performed.

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