A dependency graph generation method and device based on host call logs

Abstract

This invention discloses a method and apparatus for generating dependency graphs based on host call logs. The method includes: after obtaining the dependency graph display request text, extracting key information; identifying each target service corresponding to a target time interval and a target host, and a call statistics table corresponding to the target host and each target service; for each target service, aggregating host call logs in each host call log group to determine if there are any anomalies in the dependency relationships, obtaining anomaly detection information; and generating a dependency graph corresponding to the target time interval and target host on the dependency graph display page based on the identification information of the target host, the identification information of each target service, and the anomaly detection information. This invention can automatically determine whether there are any anomalies in the dependency relationships between hosts and services based on host call logs related to the host, and generate a dependency graph corresponding to a specified time interval and host.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and apparatus for generating a dependency graph based on host call logs. Background Technology

[0002] A company's business systems typically consist of multiple services and hosts. Each service can be a software module that implements different business functions. During the operation of the business system, each service can invoke hosts within the system to perform specified operations. After each host is invoked by a service, the business system can record the invocation process by generating a host invocation log. During host performance testing, to determine whether a host can be invoked normally within a specified time interval, a dependency graph corresponding to the specified time interval and the host is usually generated based on the host invocation logs generated within that time interval. This dependency graph can be a structural diagram showing the host and the various services that depend on it within that time interval. The services that depend on the host within that time interval refer to the services that invoke the host within that time interval.

[0003] In related technologies, a common dependency graph generation scheme involves technicians manually analyzing and statistically processing host call logs generated within a specified time interval in the business system to generate a dependency graph corresponding to the specified time interval and host. This dependency graph generation scheme relies on manual operation, resulting in high time and labor costs, low efficiency, and difficulty in guaranteeing accuracy. Summary of the Invention

[0004] This invention provides a method and apparatus for generating dependency graphs based on host call logs, in order to solve the problems in related technologies where dependency graph generation schemes rely on manual operation, resulting in high time and labor costs, low efficiency, and difficulty in guaranteeing accuracy.

[0005] According to one aspect of the present invention, a method for generating a dependency graph based on host call logs is provided, comprising:

[0006] After obtaining the dependency graph display request text input by the user corresponding to the target host, the key information in the dependency graph display request text is extracted; wherein, the key information includes: the target time interval and the identification information of the target host;

[0007] Based on the target time interval and the identification information of the target host, determine each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service;

[0008] For each target service, the host call logs corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service. The aggregation result of each host call log group includes the target latency time and error rate of the host call log group.

[0009] For each target service, the historical baseline data of each aggregation result in the aggregation result statistics table of the target service is obtained from the call statistics table. Each aggregation result is compared with the historical baseline data of each aggregation result to determine whether there is an anomaly in the dependency relationship between the target host and the target service, and anomaly detection information of the target service is obtained.

[0010] Based on the identification information of the target host, the identification information of each target service, and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page.

[0011] According to another aspect of the present invention, a dependency graph generation apparatus based on host call logs is provided, comprising:

[0012] The text processing unit is used to extract key information from the dependency graph display request text corresponding to the target host after obtaining the user-input dependency graph display request text; wherein, the key information includes: the target time interval and the identification information of the target host;

[0013] The service determination unit is used to determine, based on the target time interval and the identification information of the target host, each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service;

[0014] The log aggregation unit is used to group the host call logs corresponding to the target time interval, the target host, and the target service according to a preset window length for each target service, aggregate the host call logs in each host call log group, obtain the aggregation result of each host call log group, and store the aggregation result of each host call log group in the aggregation result statistics table of the target service; wherein, the aggregation result of each host call log group includes the target latency time value and error rate of the host call log group;

[0015] An anomaly detection unit is used to obtain historical baseline data of each aggregate result in the aggregate result statistics table of the target service from the call statistics table for each target service, compare each aggregate result with the historical baseline data of each aggregate result, determine whether there is an anomaly in the dependency relationship between the target host and the target service, and obtain anomaly detection information of the target service.

[0016] The dependency graph generation unit is used to generate a dependency graph corresponding to the target time interval and the target host on the dependency graph display page based on the identification information of the target host, the identification information of each target service, and the anomaly detection information.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor;

[0019] and a memory communicatively connected to the at least one processor;

[0020] The memory stores a computer program that is executed by the at least one processor, which enables the at least one processor to execute the dependency graph generation method based on host call logs according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the dependency graph generation method based on host call logs as described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the dependency graph generation method based on host call logs as described in any embodiment of the present invention.

[0023] The technical solution of this invention involves extracting key information from the dependency graph display request text corresponding to the target host after obtaining the user-inputted text. This key information includes the target time interval and the identifier information of the target host. Then, based on the target time interval and the identifier information of the target host, each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service are determined. For each target service, the call logs of each host corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service. The aggregation result of each host call log group includes the target latency value and error rate of the host call log group. For each target service, the aggregation of the target service is obtained from the call statistics table. The historical baseline data of each aggregation result in the results statistics table is compared with the historical baseline data of each aggregation result to determine whether there are any anomalies in the dependency relationship between the target host and the target service, and to obtain the anomaly detection information of the target service. Based on the identification information of the target host, the identification information of each target service and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page. This solves the problems of dependency graph generation schemes in related technologies relying on manual operation, which has high time and labor costs, low efficiency and difficulty in guaranteeing accuracy. After obtaining the user's input of the dependency graph display request text corresponding to the host, it can automatically determine whether there are any anomalies in the dependency relationship between the host and each service that has a dependency relationship with the host within the specified time interval based on the host call log related to the host, and generate a dependency graph corresponding to the specified time interval and the host. This reduces the time and labor costs of the dependency graph generation process and improves the efficiency and accuracy of the dependency graph generation process.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1This is a flowchart of a dependency graph generation method based on host call logs provided in Embodiment 1 of the present invention.

[0027] Figure 2 This is a flowchart of a dependency graph generation method based on host call logs provided in Embodiment 2 of the present invention.

[0028] Figure 3 This is a schematic diagram of a dependency graph generation device based on host call logs provided in Embodiment 3 of the present invention.

[0029] Figure 4 A schematic diagram of the structure of an electronic device for implementing the dependency graph generation method based on host call logs according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "target," "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising," "including," and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1This is a flowchart illustrating a dependency graph generation method based on host call logs, provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where a dependency graph corresponding to a specified time interval and a host is generated based on host call logs related to hosts generated within a specified time interval in a business system. This method can be executed by a dependency graph generation device based on host call logs. This device can be implemented in hardware and / or software and can be configured in an electronic device installed within an enterprise. The electronic device can be an electronic device installed within the enterprise used to generate a dependency graph corresponding to a specified time interval and a host based on host call logs related to hosts generated within a specified time interval in a business system. Figure 1 As shown, the method includes:

[0034] Step 101: After obtaining the dependency graph display request text input by the user corresponding to the target host, extract the key information from the dependency graph display request text.

[0035] Optionally, a company's business system can be a server used to manage the company's business. The business system has multiple services and multiple hosts. Each service can be a software module used to implement different business functions of the company. Business functions include, but are not limited to, production process management functions and payment functions. Production process management functions can refer to the function of managing the company's production process. The production process can refer to the process of producing software or hardware products with specified functions. The payment function can refer to the function of obtaining payment from other companies or individuals for purchasing products. During the operation of the business system, each service can call the hosts in the business system to perform specified operations. Each service is equipped with identification information. The service identification information can be a string used to uniquely identify the service. Each host is equipped with identification information. The host identification information can be a string used to uniquely identify the host.

[0036] Optionally, after each service call to a host, the business system generates a host call log to describe the call process. The host call log can be a text file describing the call process. The host call log includes a timestamp, service identification information, host identification information, latency, and call result indication information. The timestamp can be the date, hour, minute, and second value recorded by the business system as the start time of the call process. The service identification information can be the identification information of the service that initiated the call process. The host identification information can be the identification information of the called host. The latency can be the time elapsed from the service requesting the host to the host responding to the service request, as calculated by the business system during the call process. The unit of latency is milliseconds. The call result indication information can be information used to indicate whether the call process was successful. The call result indication information is either success or failure. A success indication indicates that the call process was successful. A failure indication indicates that the call process failed. The business system maintains a host call log file. The host call log file can be a file used to store host call logs. The business system can store the generated host call logs in the host call log file.

[0037] Optionally, the dependency graph display request text corresponding to the host can be text used to instruct the electronic device to generate a dependency graph corresponding to the target time interval and the host based on host call logs generated in the business system within the target time interval. The target host can be any host in the business system performing performance testing. The dependency graph display request text corresponding to the target host can be text used to instruct the electronic device to generate a dependency graph corresponding to the target time interval and the target host based on host call logs generated in the business system within the target time interval. The target time interval is a specified time interval within a specified date. The dependency graph corresponding to the target time interval and the target host can be a structural diagram showing the target host and the various services that depend on the target host within the target time interval. The various services that depend on the target host within the target time interval refer to the services that call the target host within the target time interval. The user can be a technical personnel responsible for managing the target host. The user can send the dependency graph display request text corresponding to the target host to the electronic device via a terminal device, or input the dependency graph display request text corresponding to the target host into the electronic device.

[0038] Optionally, it can detect whether the electronic device has received a dependency graph display request text corresponding to the host. After detecting that the electronic device has received the dependency graph display request text corresponding to the target host, it acquires the received dependency graph display request text corresponding to the target host, thereby obtaining the user-input dependency graph display request text corresponding to the target host, and then extracts key information from the dependency graph display request text corresponding to the target host. The key information in the dependency graph display request text corresponding to the target host may refer to intent description information, relationship direction description information, anomaly indication information, target time interval, and the identification information of the target host contained in the dependency graph display request text. Intent description information may be text used to describe the intent of instructing the electronic device to generate a dependency graph. Relationship direction description information may be text used to describe the behavior of the host being called by the service. Anomaly indication information may be text used to instruct the electronic device to add information to the dependency graph to identify anomalies when an anomaly is detected.

[0039] Optionally, extracting key information from the dependency graph display request text includes: extracting key information from the dependency graph display request text through a text parsing module; wherein, the key information includes intent description information, relationship direction description information, anomaly indication information, target time interval, and the identification information of the target host.

[0040] Optionally, the text parsing module can be a software or hardware module installed in an electronic device to extract key information from the dependency graph display request text corresponding to the host using natural language processing technology. The input to the text parsing module is the dependency graph display request text corresponding to the host. The output of the text parsing module is the key information from the dependency graph display request text corresponding to the host. After the dependency graph display request text corresponding to the host is input into the text parsing module, the module parses the input text, extracts the key information, and then outputs the key information from the dependency graph display request text.

[0041] Optionally, the dependency graph display request text corresponding to the target host can be input into the text parsing module. The text parsing module will parse the input dependency graph display request text corresponding to the target host, extract key information from the text, and then output the key information from the dependency graph display request text corresponding to the target host. The key information from the dependency graph display request text corresponding to the target host output by the text parsing module can be obtained.

[0042] Step 102: Based on the target time interval and the identification information of the target host, determine each target service corresponding to the target time interval and the target host, and the call statistics table corresponding to the target host and each target service.

[0043] Optionally, the target services corresponding to the target time interval and target host refer to the various services that call the target host within the target time interval, i.e., the various services that have a dependency relationship with the target host within the target time interval. For each target service, the call statistics table corresponding to the target host and target service can be a data table set in the electronic device to store statistical information related to the process of the target service calling the target host. The call statistics table is set with identification information. The identification information of the call statistics table can be information used to uniquely identify the call statistics table. A day can be divided into 24 time periods on average. The duration of each time period is 1 hour. The statistical information related to the process of the target service calling the target host includes: the baseline value of latency time and the baseline value of error rate corresponding to the target host and target service for each time period. For each time period, the baseline value of latency time corresponding to the target host and target service for that time period can be a target value of latency time that needs to be less than a certain value, which is based on the host call log used to record the normal call process of the target service calling the target host that occurs in that time period. For each time period, the baseline error rate for the target host and target service for that time period can be a value that is less than a certain threshold, calculated based on the host call logs used to record normal calls from the target service to the target host that occurred during that time period.

[0044] Optionally, based on the identification information of the target time interval and the target host, determining each target service corresponding to the target time interval and the target host, and the call statistics table corresponding to the target host and each target service, includes: using a service query module, obtaining the identification information of each target service corresponding to the target time interval and the target host, and the identification information of the call statistics table corresponding to the target host and each target service from a preset database based on the identification information of the target time interval and the target host.

[0045] Optionally, the service query module can be a software or hardware module installed in the electronic device, used to retrieve the identification information of each target service corresponding to the target time interval and host, and the identification information of the call statistics table corresponding to the host and each target service, from a preset database based on the target time interval and host identification information. The preset database can be a database installed in the electronic device for storing host-related information. The preset database stores the identification information of each host and the service call information corresponding to the identification information of each host. The service call information corresponding to the host identification information includes call record information for each service that has called the host. For each service that has called the host, the service call record information includes call time information, service identification information, and the identification information of the call statistics table corresponding to the host and service. The call time information includes the timestamp of each service call to the host. The timestamp of the service call to the host is the date, hour, minute, and second value of the start time of the service call to the host process.

[0046] Optionally, the service query module retrieves the identification information of each target service corresponding to the target time interval and the target host, and the identification information of the call statistics table corresponding to the target host and each target service, from a preset database based on the identification information of the target time interval and the target host. This includes: performing the following operations through the service query module: querying the identification information of each host stored in the preset database for target identification information that is the same as the identification information of the target host, obtaining the service call information corresponding to the target identification information, and determining the service call information corresponding to the target identification information as the service call information corresponding to the identification information of the target host; for each service call record information that has called the host in the service call information corresponding to the identification information of the target host, detecting whether there is a timestamp representing a time that is within the target time interval in each timestamp of the call time information in the call record information, determining the service to which the call record information with a timestamp representing a time that is within the target time interval belongs as a target service corresponding to the target time interval and the target host, and obtaining the identification information of the service in the call record information and the identification information of the call statistics table corresponding to the target host and the service.

[0047] Thus, the identification information of each target service corresponding to the target time interval and the target host, and the identification information of the call statistics table corresponding to the target host and each target service are obtained, thereby determining each target service corresponding to the target time interval and the target host, and the call statistics table corresponding to the target host and each target service.

[0048] Step 103: For each target service, group the host call logs corresponding to the target time interval, the target host, and the target service according to the preset window length, aggregate the host call logs in each host call log group to obtain the aggregation result of each host call log group, and store the aggregation result of each host call log group in the aggregation result statistics table of the target service.

[0049] The aggregated result for each host call log group includes the target latency and error rate for the host call log group.

[0050] Optionally, for each target service, the host call logs corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service. This includes: performing the following operations for each target service through the log aggregation module: obtaining the host call logs corresponding to the target time interval, the target host, and the target service; grouping the host call logs according to a preset window length to obtain multiple host call log groups; for each host call log group, extracting the latency time and call result indication information from each host call log in the host call log group; determining the latency target value and error rate of the host call log group based on the extracted latency time and call result indication information; determining the target host identification information, the target service identification information, the time window of the host call log group, the latency target value, and the error rate as the aggregation result of the host call log group; and storing the aggregation result of the host call log group in the aggregation result statistics table of the target service.

[0051] Optionally, the log aggregation module can be a software or hardware module in an electronic device that is used to group the host call logs corresponding to the target time interval, host, and target service according to a preset window length, aggregate the host call logs in each host call log group, obtain the aggregation result of each host call log group, and store the aggregation result of each host call log group in the aggregation result statistics table of the target service.

[0052] Optionally, the host call logs corresponding to the target time interval, target host, and target service refer to the host call logs used to record the call process of the target service calling the target host that occurs within the target time interval. The host call logs stored in the host call log file that contain timestamps indicating times within the target time interval, service identifiers indicating the target service's identifier, and host identifiers indicating the target host's identifier are the host call logs corresponding to the target time interval, target host, and target service. The host call logs containing timestamps indicating times within the target time interval, service identifiers indicating the target service's identifier, and host identifiers indicating the target host's identifier can be obtained from the host call logs stored in the host call log file, thereby obtaining the host call logs corresponding to the target time interval, target host, and target service.

[0053] Optionally, the host call logs can be grouped according to a preset window length to obtain multiple host call log groups. This includes: dividing the target time interval into multiple sub-time intervals based on the preset window length, and grouping host call logs belonging to the same sub-time interval into one host call log group, thus obtaining multiple host call log groups. The preset window length can be a pre-set duration. The target time interval can be divided into multiple sub-time intervals based on the preset window length. The duration of each sub-time interval is the preset window length. Each sub-time interval is a time window. For example, the preset window length is 5 seconds.

[0054] Optionally, the latency statistics for a host call log group may include an average latency and a target latency. The average latency can be the average latency across all host call logs in the host call log group. The target latency can be a value where 99% of the latency in all host call logs in the host call log group is less than or equal to a certain value.

[0055] Optionally, the error rate of a host call log group can be the ratio of the total number of host call logs in the host call log group whose call result indication information is failure to the total number of host call logs in the host call log group.

[0056] Optionally, for each host call log group, latency and call result indication information can be extracted from each host call log in the group. The average of the extracted latency can be calculated to obtain the mean latency of the host call log group. The target latency value for the host call log group can be obtained by calculating that 99% of the extracted latency is less than or equal to this value. The error rate of the host call log group can be obtained by calculating the ratio of the total number of host call logs with failed call result indications to the total number of host call logs in the group. The time window of the host call log group can refer to the sub-time interval to which the host call logs in the group belong. The target host's identification information, the target service's identification information, the time window of the host call log group, the target latency value, and the error rate can be defined as the aggregated result of the host call log group, and the aggregated result of the host call log group can be stored in the aggregated result statistics table of the target service.

[0057] Optionally, the service aggregation result statistics table can be a data table used to store aggregation results related to the service. After obtaining the dependency graph display request text corresponding to the target host input by the user, the aggregation result statistics tables of each service can be cleared in a timely manner to facilitate the storage of the aggregation results obtained in this generation process.

[0058] Step 104: For each target service, obtain the historical baseline data of each aggregation result in the aggregation result statistics table of the target service from the call statistics table, compare each aggregation result with the historical baseline data of each aggregation result, determine whether there is an anomaly in the dependency relationship between the target host and the target service, and obtain the anomaly detection information of the target service.

[0059] Optionally, the historical baseline data for the aggregation result can refer to the baseline values ​​of latency and error rate corresponding to the target host and target service within the time period to which the aggregation result belongs. The time period to which the aggregation result belongs refers to the time window within each time period that includes the call log group of the host to which the aggregation result belongs.

[0060] Optionally, the anomaly detection information for the target service may include anomaly detection results and anomaly causes. Anomaly detection results can be information used to characterize whether there are anomalies in the dependency relationship between the target host and the target service. The anomaly detection result is either normal or abnormal. A normal result indicates that there are no anomalies in the dependency relationship between the target host and the target service. An abnormal result indicates that there are anomalies in the dependency relationship between the target host and the target service. When the anomaly detection result is normal, the anomaly cause is empty. When the anomaly detection result is abnormal, the anomaly cause can be information used to determine that there are anomalies in the dependency relationship between the target host and the target service.

[0061] Optionally, for each target service, historical baseline data of each aggregated result in the aggregated result statistics table of the target service is obtained from the call statistics table. Each aggregated result is compared with the historical baseline data of each aggregated result to determine whether there is an anomaly in the dependency relationship between the target host and the target service, and anomaly detection information of the target service is obtained. This includes: performing the following operations for each target service through the anomaly detection module: obtaining historical baseline data of each aggregated result in the aggregated result statistics table of the target service from the call statistics table corresponding to the target host and the target service; wherein, the historical baseline data of each aggregated result includes a latency baseline value corresponding to the latency target value in the aggregated result and an error rate baseline value corresponding to the error rate in the aggregated result; detecting whether the latency target value in each aggregated result is less than or equal to the corresponding latency baseline value and whether the error rate in each aggregated result is less than or equal to the corresponding error rate baseline value; and determining whether there is an anomaly in the dependency relationship between the target host and the target service based on the detection results, and obtaining anomaly detection information of the target service.

[0062] Optionally, the anomaly detection module can be a software or hardware module set in an electronic device for comparing each aggregation result in the aggregation result statistics table of the target service with the historical baseline data of each aggregation result to determine whether there is an anomaly in the dependency relationship between the target host and the target service, and to obtain the anomaly detection information of the target service.

[0063] Optionally, for each aggregation result in the aggregation result statistics table of the target service, the baseline values ​​of latency and error rate corresponding to the target host and target service for the time period to which the aggregation result belongs can be obtained from the call statistics table corresponding to the target host and target service, thereby obtaining the historical baseline data of the aggregation result. The baseline value of latency corresponding to the target host and target service for the time period to which the aggregation result belongs is the baseline value of latency corresponding to the target latency value in the aggregation result. The baseline value of error rate corresponding to the target host and target service for the time period to which the aggregation result belongs is the baseline value of error rate corresponding to the error rate in the aggregation result. It is then checked whether the target latency value in the aggregation result is less than or equal to the corresponding baseline value of latency, and whether the error rate in the aggregation result is less than or equal to the corresponding baseline value of error rate.

[0064] Optionally, based on the detection results, determine whether there is an anomaly in the dependency relationship between the target host and the target service, and obtain anomaly detection information for the target service, including: if the target latency value in all aggregated results is less than or equal to the corresponding latency baseline value and the error rate in all aggregated results is less than or equal to the corresponding error rate baseline value, then the anomaly detection result of the target service is determined to be normal, and the anomaly reason of the target service is empty; if there is a target latency value in any aggregated result that is greater than the corresponding latency baseline value and the error rate in all aggregated results is less than or equal to the corresponding error rate baseline value, then the anomaly detection result of the target service is determined to be abnormal, and the latency target value greater than the corresponding latency baseline value is determined as the target... The cause of service anomalies is determined as follows: If the target latency value in all aggregated results is less than or equal to the corresponding baseline latency value, and the error rate in any aggregated result is greater than the corresponding baseline error rate value, then the anomaly detection result of the target service is determined to be anomaly, and the error rate greater than the corresponding baseline error rate value is determined to be the cause of the anomaly of the target service; if the target latency value in any aggregated result is greater than the corresponding baseline latency value, and the error rate in any aggregated result is greater than the corresponding baseline error rate value, then the anomaly detection result of the target service is determined to be anomaly, and the target latency value greater than the corresponding baseline latency value and the error rate greater than the corresponding baseline error rate value are determined to be the causes of the anomaly of the target service.

[0065] Step 105: Based on the identification information of the target host, the identification information of each target service, and the anomaly detection information, generate a dependency graph corresponding to the target time interval and the target host on the dependency graph display page.

[0066] Optionally, the dependency graph corresponding to the target time interval and the target host can be a structural diagram consisting of nodes representing the target host, nodes representing each target service, and connecting lines between nodes with connections. This diagram displays the target host and the various target services that depend on the target host within the target time interval. The nodes representing the target host and the nodes representing each target service are nodes with connections. The dependency graph corresponding to the target time interval and the target host includes nodes representing the target host, nodes representing each target service, and connecting lines between these nodes. The dependency graph display page can be a page set up in an electronic device to display the generated dependency graph and related information. The dependency graph display page has a dependency graph display area. The dependency graph display area is a page area used to display the generated dependency graph.

[0067] Optionally, based on the identification information of the target host, the identification information of each target service, and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page. This includes: using the identification information of the target host as a node representing the target host, and using the identification information of each target service as a node representing each target service; displaying the nodes representing the target host and the nodes representing each target service in the dependency graph display area of ​​the dependency graph display page; and adding connecting lines and anomaly reasons between the nodes representing the target host and the nodes representing each target service based on the anomaly detection information of each target service, thereby generating a dependency graph corresponding to the target time interval and the target host.

[0068] Optionally, the target host's identification information can be used as a node representing the target host, and the identification information of each target service can be used as a node representing the target service. The nodes representing the target host and the nodes representing each target service are displayed in the dependency graph display area on the dependency graph display page. Then, for each target service, if the anomaly detection result is normal, a first-type connection line is added between the node representing the target host and the node representing the target service, connecting them. The anomaly detection result of the target service is displayed above the first-type connection line. If the anomaly detection result of the target service is abnormal, a second-type connection line is added between the node representing the target host and the node representing the target service, connecting them. The anomaly detection result and the cause of the anomaly are displayed above the second-type connection line. The first-type and second-type connection lines are different types of connection lines. For example, the first-type connection line is a solid green line, and the second-type connection line is a dashed red line. Thus, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page.

[0069] Optionally, it also includes: after detecting a user's click operation on the target connection line, displaying a time consumption trend chart and associated call logs corresponding to the target connection line.

[0070] Optionally, the target connector can be any connector in the dependency graph. The latency trend chart corresponding to the target connector can be a trend chart showing the latency target values ​​in each aggregation result of the aggregation result statistics table connecting the target service to the target connector. This trend chart can be generated after storing each aggregation result in the aggregation result statistics table of the target service. The associated call logs corresponding to the target connector can refer to the call logs of each host corresponding to the target time interval, target host, and target service.

[0071] Optionally, the dependency graph display page includes a trend graph display area and a log display area. The trend graph display area shows the time consumption trend graph, while the log display area shows the associated call logs. It can detect whether any connection line in the dependency graph has been clicked. Upon detecting a click on a target connection line, the trend graph display area of ​​the dependency graph display page shows the time consumption trend graph corresponding to the target connection line, and the log display area of ​​the dependency graph display page shows the associated call logs corresponding to the target connection line.

[0072] Optionally, the process also includes: after receiving the dependency graph update request text input by the user, returning to execute the following operations: for each target service, grouping the host call logs corresponding to the target time interval, target host, and target service according to a preset window length; aggregating the host call logs in each host call log group to obtain the aggregation result of each host call log group; storing the aggregation result of each host call log group in the aggregation result statistics table of the target service; redetermining the anomaly detection information of each target service; and updating the connection lines and the information above the connection lines in the dependency graph corresponding to the target time interval and target host based on the new anomaly detection information of each target service. The dependency graph update request text can be text sent by the user through the terminal device after new host call logs related to the target host are available, instructing the electronic device to redetermine the anomaly detection information of each target service and update the dependency graph corresponding to the target time interval and target host based on the new anomaly detection information of each target service.

[0073] The technical solution of this invention involves extracting key information from the dependency graph display request text corresponding to the target host after obtaining the user-inputted text. This key information includes the target time interval and the identifier information of the target host. Then, based on the target time interval and the identifier information of the target host, each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service are determined. For each target service, the call logs of each host corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service. The aggregation result of each host call log group includes the target latency value and error rate of the host call log group. For each target service, the aggregation of the target service is obtained from the call statistics table. The historical baseline data of each aggregation result in the results statistics table is compared with the historical baseline data of each aggregation result to determine whether there are any anomalies in the dependency relationship between the target host and the target service, and to obtain the anomaly detection information of the target service. Based on the identification information of the target host, the identification information of each target service and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page. This solves the problems of dependency graph generation schemes in related technologies relying on manual operation, which has high time and labor costs, low efficiency and difficulty in guaranteeing accuracy. After obtaining the user's input of the dependency graph display request text corresponding to the host, it can automatically determine whether there are any anomalies in the dependency relationship between the host and each service that has a dependency relationship with the host within the specified time interval based on the host call log related to the host, and generate a dependency graph corresponding to the specified time interval and the host. This reduces the time and labor costs of the dependency graph generation process and improves the efficiency and accuracy of the dependency graph generation process.

[0074] Example 2

[0075] Figure 2 This is a flowchart illustrating a dependency graph generation method based on host call logs, provided in Embodiment 2 of the present invention. This embodiment of the present invention can be combined with various optional solutions from one or more of the above embodiments. For example... Figure 2 As shown, the method includes:

[0076] Step 201: After obtaining the dependency graph display request text corresponding to the target host input by the user, extract the key information in the dependency graph display request text.

[0077] The key information includes the target time interval and the identification information of the target host.

[0078] Step 202: Based on the target time interval and the identification information of the target host, determine each target service corresponding to the target time interval and the target host, and the call statistics table corresponding to the target host and each target service.

[0079] Step 203: For each target service, group the host call logs corresponding to the target time interval, the target host, and the target service according to the preset window length, aggregate the host call logs in each host call log group to obtain the aggregation result of each host call log group, and store the aggregation result of each host call log group in the aggregation result statistics table of the target service.

[0080] The aggregated result for each host call log group includes the target latency and error rate for the host call log group.

[0081] Step 204: For each target service, obtain the historical baseline data of each aggregation result in the aggregation result statistics table of the target service from the call statistics table, compare each aggregation result with the historical baseline data of each aggregation result, determine whether there is an anomaly in the dependency relationship between the target host and the target service, and obtain the anomaly detection information of the target service.

[0082] Step 205: Use the identification information of the target host as a node to represent the target host, and use the identification information of each target service as a node to represent each target service.

[0083] Step 206: Display the nodes representing the target host and the nodes representing each target service in the dependency graph display area on the dependency graph display page. Based on the anomaly detection information of each target service, add connection lines and anomaly reasons between the nodes representing the target host and the nodes representing each target service, thereby generating a dependency graph corresponding to the target time interval and the target host.

[0084] Step 207: After detecting the user's click operation on the target connection line, display the time consumption trend chart and associated call log corresponding to the target connection line.

[0085] The technical solution of this invention can automatically determine whether there are any anomalies in the dependency relationship between the host and various services that depend on the host within a specified time interval based on the host call logs related to the host after obtaining the user's input request text for displaying the dependency graph corresponding to the host. It can generate a dependency graph corresponding to the specified time interval and the host. Based on the user's click operation, it can display the time consumption trend graph and associated call logs corresponding to the target connection lines in the dependency graph, thereby reducing the time and manpower costs of the dependency graph generation process and improving the efficiency and accuracy of the dependency graph generation process.

[0086] Example 3

[0087] Figure 3 This is a schematic diagram of a dependency graph generation device based on host call logs provided in Embodiment 3 of the present invention. The device can be configured in an electronic device. Figure 3 As shown, the device includes: a text processing unit 301, a service determination unit 302, a log aggregation unit 303, an anomaly detection unit 304, and a dependency graph generation unit 305.

[0088] The text processing unit 301 is used to extract key information from the dependency graph display request text input by the user corresponding to the target host after obtaining the dependency graph display request text. The key information includes the target time interval and the identification information of the target host. The service determination unit 302 is used to determine each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service, based on the identification information of the target time interval and the target host. The log aggregation unit 303 is used to group the host call logs corresponding to the target time interval, the target host, and the target service according to a preset window length for each target service, and to aggregate the host call logs in each host call log group to obtain each host call log group. The aggregation results of each host call log group are stored in the aggregation result statistics table of the target service. Each host call log group's aggregation result includes the target latency and error rate. An anomaly detection unit 304 retrieves historical baseline data of each aggregation result from the aggregation result statistics table of the target service for each target service, compares each aggregation result with its historical baseline data, and determines whether there are any anomalies in the dependency relationship between the target host and the target service, thus obtaining anomaly detection information for the target service. A dependency graph generation unit 305 generates a dependency graph corresponding to the target time interval and the target host on the dependency graph display page based on the target host's identification information, the identification information of each target service, and the anomaly detection information.

[0089] The technical solution of this invention involves extracting key information from the dependency graph display request text corresponding to the target host after obtaining the user-inputted text. This key information includes the target time interval and the identifier information of the target host. Then, based on the target time interval and the identifier information of the target host, each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service are determined. For each target service, the call logs of each host corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service. The aggregation result of each host call log group includes the target latency value and error rate of the host call log group. For each target service, the aggregation of the target service is obtained from the call statistics table. The historical baseline data of each aggregation result in the results statistics table is compared with the historical baseline data of each aggregation result to determine whether there are any anomalies in the dependency relationship between the target host and the target service, and to obtain the anomaly detection information of the target service. Based on the identification information of the target host, the identification information of each target service and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page. This solves the problems of dependency graph generation schemes in related technologies relying on manual operation, which has high time and labor costs, low efficiency and difficulty in guaranteeing accuracy. After obtaining the user's input of the dependency graph display request text corresponding to the host, it can automatically determine whether there are any anomalies in the dependency relationship between the host and each service that has a dependency relationship with the host within the specified time interval based on the host call log related to the host, and generate a dependency graph corresponding to the specified time interval and the host. This reduces the time and labor costs of the dependency graph generation process and improves the efficiency and accuracy of the dependency graph generation process.

[0090] In an optional embodiment of the present invention, when the text processing unit 301 performs the operation of extracting key information from the dependency graph display request text, it is specifically used to: extract key information from the dependency graph display request text through the text parsing module; wherein, the key information includes intent description information, relationship direction description information, anomaly indication information, target time interval, and the identification information of the target host.

[0091] In an optional embodiment of the present invention, the service determination unit 302 is specifically configured to: obtain, through the service query module, the identification information of each target service corresponding to the target time interval and the target host, and the identification information of the call statistics table corresponding to the target host and each target service from a preset database based on the identification information of the target time interval and the target host.

[0092] In an optional embodiment of the present invention, the log aggregation unit 303 is specifically configured to: perform the following operations for each target service through the log aggregation module: acquire the host call logs corresponding to the target time interval, the target host, and the target service; group the host call logs according to a preset window length to obtain multiple host call log groups; for each host call log group, extract the latency time and call result indication information from each host call log in the host call log group; determine the latency target value and error rate of the host call log group based on the extracted latency time and call result indication information; determine the identification information of the target host, the identification information of the target service, the time window of the host call log group, the latency target value, and the error rate as the aggregation result of the host call log group; and store the aggregation result of the host call log group in the aggregation result statistics table of the target service.

[0093] In an optional embodiment of the present invention, the anomaly detection unit 304 is specifically configured to: perform the following operations for each target service through the anomaly detection module: obtain historical baseline data of each aggregation result in the aggregation result statistics table of the target service from the call statistics table corresponding to the target host and the target service; wherein, the historical baseline data of each aggregation result includes a latency baseline value corresponding to the latency target value in the aggregation result and an error rate baseline value corresponding to the error rate in the aggregation result; detect whether the latency target value in each aggregation result is less than or equal to the corresponding latency baseline value and whether the error rate in each aggregation result is less than or equal to the corresponding error rate baseline value; and determine whether there is an anomaly in the dependency relationship between the target host and the target service based on the detection results, thereby obtaining anomaly detection information of the target service.

[0094] In an optional embodiment of the present invention, the dependency graph generation unit 305 is specifically configured to: use the identification information of the target host as a node to represent the target host, and use the identification information of each target service as a node to represent each target service; display the nodes to represent the target host and the nodes to represent each target service in the dependency graph display area of ​​the dependency graph display page; and add connecting lines and anomaly reasons between the nodes to represent the target host and the nodes to represent each target service according to the anomaly detection information of each target service, thereby generating a dependency graph corresponding to the target time interval and the target host.

[0095] In an optional embodiment of the present invention, the dependency graph generation unit 305 may be further configured to: after detecting a user's click operation on the target connection line, display a time consumption trend graph and associated call log corresponding to the target connection line.

[0096] The dependency graph generation apparatus based on host call logs provided in this embodiment of the invention can execute the dependency graph generation method based on host call logs provided in any embodiment of the invention, and has the corresponding functional units and beneficial effects of the execution method.

[0097] Example 4

[0098] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement the host call log-based dependency graph generation method of embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, electronic devices, blade electronic devices, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0099] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory 12 or a random access memory 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from storage unit 18 into the random access memory 13. The random access memory 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, read-only memory 12, and random access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0100] Multiple components in electronic device 10 are connected to input / output interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0101] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a dependency graph generation method based on host call logs.

[0102] In some embodiments, the dependency graph generation method based on host call logs can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on a heterogeneous hardware accelerator via read-only memory and / or a communication unit. When the computer program is loaded into random access memory and executed by a processor, one or more steps of the dependency graph generation method based on host call logs described above can be performed. Alternatively, in other embodiments, the processor can be configured to perform the dependency graph generation method based on host call logs by any other suitable means (e.g., by means of firmware).

[0103] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), payload programmable logic devices (PLCs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or electronic device.

[0105] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0106] To provide user interaction, the systems and techniques described herein can be implemented on a heterogeneous hardware accelerator, which includes: a display device (e.g., a cathode ray tube or liquid crystal display monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the heterogeneous hardware accelerator. Other types of devices can also be used to provide user interaction; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or haptic feedback); and input from the user can be received in any form (including sound input, voice input, or haptic input).

[0107] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data electronic devices), or computing systems that include middleware components (e.g., application electronic devices), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0108] A computing system can include clients and electronic devices. Clients and electronic devices are generally geographically separated and typically interact via communication networks. The client-electronic device relationship is created by computer programs running on the respective computers and establishing a client-electronic device relationship between them. Electronic devices can be cloud electronic devices, also known as cloud computing electronic devices or cloud servers, which are host products within the cloud computing service system. These address the shortcomings of traditional physical hosts and virtual private server services, such as high management difficulty and weak business scalability.

[0109] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for generating a dependency graph based on host call logs, characterized in that, include: After obtaining the dependency graph display request text input by the user corresponding to the target host, the key information in the dependency graph display request text is extracted; wherein, the key information includes: the target time interval and the identification information of the target host; Based on the target time interval and the identification information of the target host, determine each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service; For each target service, the host call logs corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service. The aggregation result of each host call log group includes the target latency time and error rate of the host call log group. For each target service, the historical baseline data of each aggregation result in the aggregation result statistics table of the target service is obtained from the call statistics table. Each aggregation result is compared with the historical baseline data of each aggregation result to determine whether there is an anomaly in the dependency relationship between the target host and the target service, and anomaly detection information of the target service is obtained. Based on the identification information of the target host, the identification information of each target service, and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page.

2. The dependency graph generation method based on host call logs according to claim 1, characterized in that, Extract key information from the dependency graph display request text, including: The text parsing module extracts key information from the dependency graph display request text; The key information includes intent description information, relationship direction description information, anomaly indication information, target time interval, and identification information of the target host. 3.The host call log based dependency graph generation method of claim 1, wherein, Based on the target time interval and the identification information of the target host, determine each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service, including: The service query module retrieves the identification information of each target service corresponding to the target time interval and the target host, as well as the identification information of the call statistics table corresponding to the target host and each target service, from a preset database based on the identification information of the target time interval and the target host. 4.The host call log based dependency graph generation method of claim 1, wherein, For each target service, the host call logs corresponding to the target time interval, the target host, and the target service are grouped according to a preset window length. The host call logs in each host call log group are aggregated to obtain the aggregation result of each host call log group. The aggregation result of each host call log group is stored in the aggregation result statistics table of the target service, including: For each target service, perform the following operations using the log aggregation module: Obtain the call logs of each host corresponding to the target time interval, the target host, and the target service; group the host call logs according to the preset window length to obtain multiple host call log groups; For each host call log group, the latency time and call result indication information of each host call log in the host call log group are extracted. Based on the extracted latency time and call result indication information, the target latency value and error rate of the host call log group are determined. The identification information of the target host, the identification information of the target service, the time window of the host call log group, the target latency value, and the error rate are determined as the aggregation result of the host call log group. The aggregation result of the host call log group is stored in the aggregation result statistics table of the target service. 5.The host call log based dependency graph generation method of claim 1, wherein, For each target service, historical baseline data of each aggregated result in the aggregated result statistics table of the target service is obtained from the call statistics table. Each aggregated result is compared with the historical baseline data of each aggregated result to determine whether there are any anomalies in the dependency relationship between the target host and the target service, thereby obtaining anomaly detection information for the target service, including: For each target service, the anomaly detection module performs the following operations: From the call statistics table corresponding to the target host and the target service, obtain the historical baseline data of each aggregation result in the aggregation result statistics table of the target service; wherein, the historical baseline data of each aggregation result includes the latency baseline value corresponding to the latency target value in the aggregation result and the error rate baseline value corresponding to the error rate in the aggregation result; Check whether the target delay time value in each aggregation result is less than or equal to the corresponding baseline delay time value, and whether the error rate in each aggregation result is less than or equal to the corresponding baseline error rate value; Based on the detection results, it is determined whether there are any anomalies in the dependency relationship between the target host and the target service, and anomaly detection information of the target service is obtained. 6.The host call log based dependency graph generation method of claim 1, wherein, Based on the identification information of the target host, the identification information of each target service, and the anomaly detection information, a dependency graph corresponding to the target time interval and the target host is generated on the dependency graph display page, including: The identification information of the target host is used as a node to represent the target host, and the identification information of each target service is used as a node to represent each target service. The dependency graph display area on the dependency graph display page displays nodes representing the target host and nodes representing each target service. Based on the anomaly detection information of each target service, connecting lines and anomaly reasons are added between the nodes representing the target host and the nodes representing each target service, thereby generating a dependency graph corresponding to the target time interval and the target host.

7. The host call log based dependency graph generation method of claim 6, wherein, Also includes: After detecting a user's click operation on the target connection line, a time consumption trend chart and associated call logs corresponding to the target connection line are displayed.

8. A host-call-log-based dependency graph generation apparatus, comprising: include: The text processing unit is used to extract key information from the dependency graph display request text corresponding to the target host after obtaining the user-input dependency graph display request text; wherein, the key information includes: the target time interval and the identification information of the target host; The service determination unit is used to determine, based on the target time interval and the identification information of the target host, each target service corresponding to the target time interval and the target host, and a call statistics table corresponding to the target host and each target service; The log aggregation unit is used to group the host call logs corresponding to the target time interval, the target host, and the target service according to a preset window length for each target service, aggregate the host call logs in each host call log group, obtain the aggregation result of each host call log group, and store the aggregation result of each host call log group in the aggregation result statistics table of the target service; wherein, the aggregation result of each host call log group includes the target latency time value and error rate of the host call log group; An anomaly detection unit is used to obtain historical baseline data of each aggregate result in the aggregate result statistics table of the target service from the call statistics table for each target service, compare each aggregate result with the historical baseline data of each aggregate result, determine whether there is an anomaly in the dependency relationship between the target host and the target service, and obtain anomaly detection information of the target service. The dependency graph generation unit is used to generate a dependency graph corresponding to the target time interval and the target host on the dependency graph display page based on the identification information of the target host, the identification information of each target service, and the anomaly detection information.

9. An electronic device, comprising: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that is executed by the at least one processor, which enables the at least one processor to perform the dependency graph generation method based on host call logs as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the dependency graph generation method based on host call logs as described in any one of claims 1-7.

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