Fuse processing method and device, computer program product and electronic device

Abstract

The present disclosure relates to the field of computer technology, and to a method and device for fusing processing, a computer program product, and an electronic device. The method for fusing processing includes: collecting response time data of the application's dependent interface in the previous statistical period; performing aggregation processing on the response time data, and determining interface performance indicator data corresponding to different time periods based on the aggregation processing result, the interface performance indicator data is used to describe the distribution of the interface response time; obtaining the target interface performance indicator data corresponding to the current time period, and determining the fusing threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fusing processing on the dependent interface in the current time period based on the fusing threshold. The present disclosure can improve the accuracy of fusing processing.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and more specifically, to a fuse processing method, a fuse processing device, a computer program product, and an electronic device. Background Art

[0002] In a distributed architecture system, there are multiple applications that are interdependent. Circuit breaking means that in a distributed system, when the response time of a service interface is too long or the failure rate is too high, requests to the service are automatically stopped to avoid affecting the entire system. For example, when multiple applications are interdependent, if the downstream system service is unavailable, the upstream application will be unavailable. In order to prevent the failure of the downstream system from affecting the operation of the system, circuit breaking measures can be introduced in the system to solve this type of problem.

[0003] Currently, the fuse threshold can be reasonably set and the fuse process can be controlled based on the fuse threshold. However, the fuse threshold setting of related technologies is inaccurate and cannot reflect the actual fuse requirements, which leads to inaccurate fuse processing results to a certain extent.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide a fuse processing method and device, a computer program product and an electronic device, thereby improving the accuracy of fuse processing at least to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a fuse processing method is provided, including: collecting response time data of an application's dependent interface in a previous statistical period; aggregating the response time data, and determining interface performance indicator data corresponding to different time periods based on the aggregation processing results, the interface performance indicator data being used to describe the distribution of interface response time; obtaining target interface performance indicator data corresponding to a current time period, and determining a fuse threshold corresponding to the current time period based on the target interface performance indicator data and a system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

[0008] In an exemplary embodiment of the present disclosure, response time data is aggregated and interface performance indicator data corresponding to different time periods are determined based on the aggregation processing results, including: aggregating the response time data according to a preset time window to obtain aggregate data groups corresponding to different time periods; for each aggregate data group, determining the interface performance indicator data corresponding to the aggregate data group based on the response time data in the aggregate data group.

[0009] In an exemplary embodiment of the present disclosure, the response time data of the dependent interface of the application in the previous statistical period is collected, including: for each application in the distributed system, according to the corresponding relationship between the application and the dependent interface, the response time data of the dependent interface of the application in the previous statistical period is collected.

[0010] In an exemplary embodiment of the present disclosure, response time data is the time consumed by an application calling a method that depends on an interface; for each aggregated data group, the interface performance indicator data corresponding to the aggregated data group is determined based on the response time data in the aggregated data group, including: sorting the response time data in the aggregated data group to obtain a data sequence; determining a target percentile based on scenario performance information, and obtaining the interface performance indicator data corresponding to the aggregated data group from the data sequence based on the target percentile; wherein the scenario performance information is used to indicate the tolerance of the current business scenario to interface response delays.

[0011] In an exemplary embodiment of the present disclosure, the system tolerance threshold includes a lower tolerance limit value and an upper tolerance limit value; based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current time period is determined, including: constraining the fuse threshold corresponding to the current time period within the tolerance range constructed by the lower tolerance limit value and the upper tolerance limit value.

[0012] In an exemplary embodiment of the present disclosure, a fuse threshold corresponding to the current time period is determined based on the target interface performance indicator data and the system tolerance threshold, including: if the target interface performance indicator data is less than the tolerance lower limit value, the tolerance lower limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is greater than or equal to the tolerance lower limit value, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined according to the comparison result.

[0013] In an exemplary embodiment of the present disclosure, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined based on the comparison result, including: when the target interface performance indicator data is greater than the tolerance upper limit value, the tolerance upper limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is less than or equal to the tolerance upper limit value, the target interface performance indicator data is determined as the fuse threshold corresponding to the current time period.

[0014] In an exemplary embodiment of the present disclosure, the method further includes: updating a lower tolerance limit value and an upper tolerance limit value according to scene performance information.

[0015] According to one aspect of the present disclosure, there is provided a fuse processing device, including: a data collection module, used to collect response time data of an application's dependent interface in a previous statistical period; a data processing module, used to aggregate the response time data, and determine interface performance indicator data corresponding to different time periods based on the aggregation processing results, the interface performance indicator data being used to describe the distribution of interface response time; a fuse processing module, used to obtain target interface performance indicator data corresponding to a current time period, and determine a fuse threshold corresponding to the current time period based on the target interface performance indicator data and a system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

[0016] According to one aspect of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any one of the above methods is implemented.

[0017] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above methods by executing the executable instructions.

[0018] The fuse processing method in the exemplary embodiment of the present disclosure collects the response time data of the application's dependent interface in the previous statistical period, aggregates the response time data, and determines the interface performance indicator data corresponding to different time periods based on the aggregation processing results. The interface performance indicator data is used to describe the distribution of the interface response time, and then obtains the target interface performance indicator data corresponding to the current time period, and determines the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold. This process can dynamically calculate the interface performance indicator data corresponding to different time periods based on the response time data in the previous statistical period, thereby providing reference evaluation conditions matching the time period for the subsequent acquisition of the target interface performance indicator data corresponding to the current time period, and taking the performance of each time period (interface performance indicator data) into consideration, which is conducive to improving the accuracy of determining the fuse threshold, and dynamically determining the fuse threshold of the current time period based on the target interface performance indicator data and the system tolerance threshold, avoiding the use of a fixed fuse threshold, and also providing a bottom line for dynamically adjusting the fuse threshold corresponding to the current time period through the system tolerance threshold, avoiding the determined tolerance threshold being too large or too small, ensuring the system adaptability of the tolerance threshold, and thus improving the accuracy and effectiveness of the fuse processing as a whole.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present disclosure are shown in an exemplary and non-limiting manner.

[0021] Figure 1 A flow chart of a fusing processing method according to an exemplary embodiment of the present disclosure is shown.

[0022] Figure 2 A flow chart of determining interface performance indicator data corresponding to different time periods according to an exemplary embodiment of the present disclosure is shown.

[0023] Figure 3 A schematic diagram of a flow chart for determining a blown fuse threshold according to an exemplary embodiment of the present disclosure is shown.

[0024] Figure 4 A complete flow chart of a fuse processing according to an exemplary embodiment of the present disclosure is shown.

[0025] Figure 5 A schematic diagram of the composition of a fusing processing device according to an exemplary embodiment of the present disclosure is shown.

[0026] Figure 6 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown.

[0027] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0028] The exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and the concepts of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0029] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known structures, methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor devices and / or microcontroller devices.

[0031] In interdependent applications of distributed architecture systems, if the downstream system has an increased interface time due to network jitter, server resource failure, etc., the thread pool of the distributed system will be full, resulting in service unavailability, and further, the upstream application will be unavailable. This kind of failure of a node gradually develops into a failure of the entire system along the dependency path, which is called service avalanche in the field of distributed architecture. How to reasonably set the fuse threshold to avoid the fuse threshold being too sensitive and causing frequent triggering of the fuse to affect business operations, and to identify system failures as much as possible to protect the current system.

[0032] At present, the average RT (Reaction Time) of a certain time window of the downstream interface can be used to set the fuse threshold by floating a certain proportion. For example, if the average RT of a certain interface on the previous day was 100ms and it was increased by 30%, the fuse threshold would be set to 130ms. Alternatively, the fuse threshold can be uniformly set according to the downstream RT acceptable to this application. For example, if the RT of the downstream interface of the task is within 200ms, the impact on this application is small, and the fuse threshold is uniformly set to 200ms.

[0033] However, by setting the fuse threshold by the average RT floating ratio in a fixed time window, the traffic fluctuations of some interfaces in a specific time window due to scheduled tasks or other reasons will cause inconsistent RT performance. Then, for different time windows, the fuse threshold of an interface cannot be adjusted adaptively, affecting the fuse processing process. The method of setting a fixed fuse threshold cannot take into account the performance of the interface in different time periods, resulting in the problem that the fuse is too sensitive or ineffective.

[0034] Based on this, an exemplary embodiment of the present disclosure provides a fuse processing method, which relies on the performance of the interface in different time periods as an evaluation condition for determining the fuse threshold, and dynamically sets the fuse threshold of the current time period by combining the system tolerance threshold, thereby improving the system adaptability of the fuse threshold and improving the accuracy of fuse processing.

[0035] It should be noted that the fuse processing method of the exemplary embodiment of the present disclosure can be applied to various scenarios where fuse processing is required to achieve system protection. For example, in a distributed architecture, there are scenarios where interdependent applications exist or application scenarios based on a microservice architecture. The exemplary embodiment of the present disclosure does not specifically limit the application scenarios of the fuse processing method.

[0036] refer to Figure 1 FIG. 1 is a flow chart of a fusing processing method according to an exemplary embodiment of the present disclosure. The fusing processing method includes steps S110 to S130, which are specifically as follows:

[0037] In step S110, the response time data of the dependent interface of the application in the previous statistical period is collected.

[0038] In the exemplary embodiments of the present disclosure, the application may be an application in a distributed system architecture or an application in a microservice architecture, without limitation. The dependent interface of the application refers to other interfaces or services that the application needs to rely on at runtime to ensure the normal operation of its functions. These dependent interfaces may be external APIs (Application Programming Interfaces), internal module interfaces, or other service interfaces.

[0039] The response time data is the time it takes for an application to call a method that relies on an interface. It can be the time required for a service to process a request, including the time it takes to receive a request, process a request, and send a response. The response time data can reflect the performance of the dependent interface. The response time data can refer to the RT data mentioned above.

[0040] The statistical period can be flexibly set according to task requirements, for example, the statistical period can be 0.5 days or 1 day. For example, at the end of each day, the response time data of the application's dependent interface on the current day can be collected, so that when determining the fuse threshold corresponding to the current time period on the next day, the response time data is used as the time data in the previous statistical period, and the performance of the dependent interface in each time period of the previous statistical period is used to guide the fuse process of the next day.

[0041] In an exemplary embodiment of the present disclosure, the response time data of the dependent interface of the application in the previous statistical period can be collected by a data collector, and the data collector can be arranged in the application server or can be an independently configured data collector.

[0042] Optionally, when collecting response time data of dependent interfaces, the interface call timestamp is also obtained to indicate the specific time when the call occurred, so as to ensure that the call data is authentic, accurate and traceable.

[0043] Optionally, the response time data of the collected application's dependent interface in the previous statistical period can be stored in the time series service storage. The time series service storage refers to a service that provides time series data management, analysis, storage, and query functions. It is used to process and analyze data arranged in chronological order and provide accurate time series data for subsequent data aggregation processing.

[0044] In step S120, the response time data is aggregated, and interface performance indicator data corresponding to different time periods is determined according to the aggregation processing result.

[0045] In an exemplary embodiment of the present disclosure, the interface performance indicator data is used to describe the distribution of the interface response time. The interface performance indicator data may be the TP (Top Percentile) percentage of the response time, such as TP999, which refers to the 99.9% percentile of the response time, and may also be referred to as the 99.9% response time of the interface. TP999 indicates that among all response times, 99.9% of the request response times are less than or equal to the value represented by TP999. TP999 can evaluate the performance of the interface in most cases. Of course, in addition to selecting the interface performance indicator data as TP999, according to actual needs, the interface performance indicator data may also be selected as other TP fractional digits, such as TP99, TP90, etc.

[0046] The response time data may be aggregated according to different time periods, that is, the response time data of the dependent interface in the previous statistical period may be aggregated and stored according to different time periods according to the time granularity. For example, at a time granularity of half an hour, the response time data may be aggregated and stored according to 0:30 to 1:00, 1:00 to 1:30, ...; at a time granularity of half an hour, the response time data may be aggregated and stored according to 0:00 to 1:00, 1:00 to 2:00, ...

[0047] After obtaining the aggregation processing results corresponding to different time periods, for each aggregation processing result, the interface performance indicator data of the corresponding time period is determined according to the response time data included in the aggregation processing result.

[0048] In step 130, the target interface performance indicator data corresponding to the current period is obtained, and based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current period is determined, so as to perform fuse processing on the dependent interface in the current period based on the fuse threshold.

[0049] In an exemplary embodiment of the present disclosure, after obtaining the interface performance indicator data corresponding to different time periods, the interface performance indicator data corresponding to the current time period can be obtained according to the time period correspondence, as the target interface performance indicator data for the current time period. For example, if the current time period is from 0:30 to 1:00, then the target performance interface indicator data corresponding to the time period from 0:30 to 1:00 is obtained from the interface performance indicator data corresponding to different time periods. It should be understood that when the response time data is aggregated to determine the interface performance indicators corresponding to different time periods, the corresponding method can be used when obtaining the target interface performance indicator data for the current time period, so as to ensure the correspondence between the time periods and thus ensure the accuracy of the target interface performance indicator data.

[0050] Among them, the fuse threshold refers to when certain key indicators (such as request failure rate, response time, etc.) exceed the preset threshold during system operation, the system will automatically take protective measures to temporarily interrupt or limit certain functions to prevent system crashes or data loss. The system tolerance threshold of the exemplary embodiment of the present disclosure is a system-level fuse threshold configured according to the business scenario, including a lower tolerance limit and an upper tolerance limit. The fuse threshold corresponding to the current time period is constrained within the tolerance range constructed by the lower tolerance limit and the upper tolerance limit to avoid the fuse threshold of the current time period being too large or too small. The system-level fuse threshold is used as a bottom-line value to ensure that the fuse threshold of the current time period always meets system requirements.

[0051] For example, in some business scenarios, the upper tolerance limit is 500ms and the lower tolerance limit is 100ms. In some scenarios with higher response time requirements, the upper tolerance limit is 450ms and the lower tolerance limit is 150ms. The system tolerance threshold can be set according to the response time requirements of the business scenario.

[0052] According to the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current period can be dynamically determined, so that the fuse threshold is selected from the target interface performance indicator data, the tolerance lower limit value and the tolerance upper limit value. Then, the dependent interface can be fused in the current period according to the determined fuse threshold.

[0053] The fuse processing method in the exemplary embodiment of the present disclosure collects the response time data of the application's dependent interface in the previous statistical period, aggregates the response time data, and determines the interface performance indicator data corresponding to different time periods based on the aggregation processing results. The interface performance indicator data is used to describe the distribution of the interface response time, and then obtains the target interface performance indicator data corresponding to the current time period, and determines the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold. This process can dynamically calculate the interface performance indicator data corresponding to different time periods based on the response time data in the previous statistical period, thereby providing reference evaluation conditions matching the time period for the subsequent acquisition of the target interface performance indicator data corresponding to the current time period, and taking the performance of each time period (interface performance indicator data) into consideration, which is conducive to improving the accuracy of determining the fuse threshold, and dynamically determining the fuse threshold of the current time period based on the target interface performance indicator data and the system tolerance threshold, avoiding the use of a fixed fuse threshold, and also providing a bottom line for dynamically adjusting the fuse threshold corresponding to the current time period through the system tolerance threshold, avoiding the determined tolerance threshold being too large or too small, ensuring the system adaptability of the tolerance threshold, and thus improving the accuracy and effectiveness of the fuse processing as a whole.

[0054] In an exemplary embodiment, if Figure 2 As shown, performing aggregation processing on the response time data and determining the interface performance indicator data corresponding to different time periods according to the aggregation processing result may include:

[0055] Step S210: Aggregate the response time data according to a preset time window to obtain aggregate data groups corresponding to different time periods.

[0056] As can be seen above, the preset time window is also the time granularity. The response time data is aggregated according to the time granularity to obtain aggregate data groups corresponding to different time periods. For example, the response time data corresponding to 0:30 to 1:00 is used as an aggregate data group, the response time data corresponding to 1:00 to 1:30 is used as an aggregate data group, and so on, to obtain multiple aggregate data groups.

[0057] Step S220: for each aggregated data group, determine the interface performance indicator data corresponding to the aggregated data group according to the response time data in the aggregated data group.

[0058] For each aggregate data group, the interface performance indicator data corresponding to the aggregate data group may be determined according to the response time data belonging to the aggregate data group.

[0059] Optionally, for each aggregated data group, determining the interface performance indicator data corresponding to the aggregated data group according to the response time data in the aggregated data group may include:

[0060] First, the response time data in the aggregated data group is sorted to obtain a data sequence, and then the target percentile is determined according to the scenario performance information, and the interface performance indicator data corresponding to the aggregated data group is obtained from the data sequence based on the target percentile.

[0061] Among them, the scenario performance information is used to indicate the tolerance of the current business scenario to the interface response delay, and the target percentile is determined according to the tolerance level, such as "99.9%", "99%", "90%", "50%", etc. Among them, the larger the target percentile, the higher the performance requirement for the interface, for example, "99.9%" can be selected, and the smaller the target percentile, the lower the performance requirement for the interface, for example, "50%" can be selected. Therefore, the target percentile can be determined according to the tolerance indicated by the scenario performance information. The exemplary embodiment of the present disclosure takes into account that TP999 can generally reflect the RT performance of an interface, and takes the target percentile of TP999 as an example for explanation.

[0062] For example, if the response time data in an aggregate array are 10ms, 1000ms, 100ms and 2ms respectively, the four response time sequences can be sorted in ascending order to obtain data sequences of 2ms, 10ms, 100ms and 1000ms. Taking the target percentile of 99.9% as an example, the number of requests for 99.9% is 4×0.999, which is rounded to 4. The minimum time required to meet all four requests is 1000ms, and the value of TP999 is 1000ms, that is, the interface performance indicator data corresponding to the aggregate data group is 1000ms. Of course, the rounding method can also be directly rounded to 3, and the rounding method can be determined according to the needs.

[0063] The exemplary embodiment of the present disclosure determines the interface performance indicator data according to the response time data for each time period, takes the performance of each time period into consideration, and is used to guide the subsequent determination of the fuse threshold of the current time period. Since the characteristics of the dependent interface are fully considered, the accuracy of determining the fuse threshold can be improved to meet the actual situation of the dependent interface, and to a certain extent, the situation where the fuse is too sensitive or ineffective can be avoided.

[0064] In an exemplary embodiment, collecting response time data of a dependent interface of an application in a previous statistical period includes:

[0065] For each application in the distributed system, based on the correspondence between the application and the dependent interface, the response time data of the dependent interface of the application in the previous statistical period is collected.

[0066] Different applications have their own dependent interfaces. Therefore, according to the correspondence between the application and the dependent interface, the response time data of the dependent interface of the application in the previous statistical period can be collected to ensure the accurate correspondence between the application-interface and the response time data.

[0067] For example, based on the data interface settings (the correspondence between the application and the dependent interface) including "caller application", "callee interface" and "callee interface method", the response time data of the dependent interface of the application can be collected at a preset time. For example, the response time data of the dependent interface can be collected at the minute level.

[0068] In an exemplary embodiment, when setting the fuse threshold, if it depends entirely on the response time data of the dependent interface, it is found in actual implementation that if the response time data (such as RT data) of a dependent interface is high and exceeds the limit of the distributed system, the system will definitely be blown. Therefore, by setting a system tolerance threshold for the system, the fuse threshold is prevented from being too large or too small, thereby ensuring accurate fuse processing.

[0069] Specifically, based on the target interface performance indicator data and the system tolerance threshold, determining the fuse threshold corresponding to the current time period may include:

[0070] If the target interface performance indicator data is less than the tolerance lower limit, the tolerance lower limit is determined as the fuse threshold corresponding to the current period. If the target interface performance indicator data is greater than or equal to the tolerance lower limit, the target interface performance indicator data is compared with the tolerance upper limit, and the fuse threshold corresponding to the current period is determined based on the comparison result.

[0071] When the target interface performance indicator data is greater than the tolerance upper limit value, the tolerance upper limit value is determined as the fuse threshold corresponding to the current period. If the target interface performance indicator data is less than or equal to the tolerance upper limit value, the target interface performance indicator data is determined as the fuse threshold corresponding to the current period.

[0072] That is, by comparing the target interface performance indicator data with the lower and upper tolerance limits, the fuse threshold corresponding to the current time period is constrained within the tolerance range constructed by the lower and upper tolerance limits. The system tolerance threshold is used to provide a safety net for dynamically adjusting the fuse threshold corresponding to the current time period, thereby avoiding the determined tolerance threshold being too large or too small, ensuring the system adaptability of the tolerance threshold, and thus improving the accuracy and effectiveness of the fuse processing as a whole.

[0073] For example, Figure 3 A schematic diagram of a process for determining a fuse threshold is shown below. Figure 3 Determining the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold is explained.

[0074] First, at the end of each day, scan the dependent interfaces of all applications, traverse each dependent interface, aggregate the response time data based on the configured time granularity, and obtain the interface performance indicator data corresponding to different time periods.

[0075] Secondly, the target interface performance indicator data corresponding to the current time period is obtained based on the time period matching relationship.

[0076] Next, based on the target interface performance indicator data (taking TP999 as an example) and the system tolerance threshold, the fuse threshold corresponding to the current time period is determined.

[0077] Among them, first determine whether TP999 is less than the lower limit of tolerance. If so, set the lower limit of tolerance as the fuse threshold of the current period. Otherwise, compare TP999 with the upper limit of tolerance. If TP999 is greater than the upper limit of tolerance, set the upper limit of tolerance as the fuse threshold of the current period. Otherwise, set TP999 as the fuse threshold of the current period.

[0078] In the exemplary embodiments of the present disclosure, while taking the performance of the dependent interface in each time period as a reference condition for determining the fuse threshold, the lower tolerance limit value and the upper tolerance limit value are also used as the bottom-line value of the system tolerance to dynamically and adaptively determine the tolerance threshold of the current time period, thereby improving the accuracy of the fuse threshold and the adaptability of the system, thereby improving the accuracy of the fuse processing based on the fuse threshold. In actual implementation, even if there are large differences in the RT performance of the downstream dependent interface, the long-term stable operation of the system can be protected.

[0079] Based on the aforementioned exemplary embodiments, the lower tolerance limit value and the upper tolerance limit value may also be updated according to the scene performance information.

[0080] As can be seen above, the target percentile can be determined according to the tolerance indicated by the scene performance information, thereby obtaining the interface performance indicator data corresponding to each aggregated data group. Correspondingly, the lower tolerance limit and the upper tolerance limit can also be updated according to the scene performance information. For example, for scenes with high scene performance requirements and low tolerance for request response time, the lower tolerance limit and the upper tolerance limit can be reduced. On the contrary, for scenes with low scene performance requirements and high tolerance for request response time, the lower tolerance limit and the upper tolerance limit can be increased.

[0081] Among them, the correspondence between the scenario performance information (such as the response time requirement within 10ms) and the lower tolerance limit and the upper tolerance limit can be set in advance, and the lower tolerance limit and the upper tolerance limit can be dynamically updated according to the correspondence. Of course, the accuracy of the updated lower tolerance limit and the upper tolerance limit can also be tested through manual intervention and combined with manual experience.

[0082] like Figure 4 A complete flow chart of a fuse processing is shown below. Figure 4 , the fuse processing method of the exemplary embodiment of the present disclosure is described.

[0083] First, the response time data of the application's dependent interfaces in the previous statistical period is collected through the data collector.

[0084] Secondly, the response time data of the dependent interface of the collection application in the previous statistical period is stored in the time series storage.

[0085] Next, the response time data may be aggregated, and interface performance indicator data corresponding to different time periods may be determined according to the aggregation processing result.

[0086] The corresponding relationship between the time period and the interface performance indicator data can be stored.

[0087] Finally, the target interface performance indicator data corresponding to the current period is obtained, and based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current period is determined, so as to perform fuse processing on the dependent interface in the current period based on the fuse threshold.

[0088] Among them, a scheduled task (such as once every 30 minutes) can be used to push the obtained fuse threshold corresponding to the current time period to the fuse system for implementation.

[0089] It should be noted that the specific contents involved in each step have been recorded in the above exemplary embodiments and will not be repeated here.

[0090] The fuse processing method in the exemplary embodiment of the present disclosure collects the response time data of the application's dependent interface in the previous statistical period, aggregates the response time data, and determines the interface performance indicator data corresponding to different time periods based on the aggregation processing results. The interface performance indicator data is used to describe the distribution of the interface response time, and then obtains the target interface performance indicator data corresponding to the current time period, and determines the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold. This process can dynamically calculate the interface performance indicator data corresponding to different time periods based on the response time data in the previous statistical period, thereby providing reference evaluation conditions matching the time period for the subsequent acquisition of the target interface performance indicator data corresponding to the current time period, and taking the performance of each time period (interface performance indicator data) into consideration, which is conducive to improving the accuracy of determining the fuse threshold, and dynamically determining the fuse threshold of the current time period based on the target interface performance indicator data and the system tolerance threshold, avoiding the use of a fixed fuse threshold, and also providing a bottom line for dynamically adjusting the fuse threshold corresponding to the current time period through the system tolerance threshold, avoiding the determined tolerance threshold being too large or too small, ensuring the system adaptability of the tolerance threshold, and thus improving the accuracy and effectiveness of the fuse processing as a whole.

[0091] In an exemplary embodiment of the present disclosure, a fusing processing device is also provided. Figure 5 As shown, the fuse processing device 500 may include a data acquisition module 510, a data processing module 520 and a fuse processing module 530. Specifically:

[0092] The data collection module 510 is used to collect the response time data of the application's dependent interface in the previous statistical period; the data processing module 520 is used to aggregate the response time data and determine the interface performance indicator data corresponding to different time periods based on the aggregation processing results. The interface performance indicator data is used to describe the distribution of the interface response time; the fuse processing module 530 is used to obtain the target interface performance indicator data corresponding to the current time period, and determine the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

[0093] In an exemplary embodiment of the present disclosure, the data processing module 520 is configured to perform: aggregating the response time data according to a preset time window to obtain aggregate data groups corresponding to different time periods; for each aggregate data group, determining the interface performance indicator data corresponding to the aggregate data group based on the response time data in the aggregate data group.

[0094] In an exemplary embodiment of the present disclosure, the data collection module 510 is configured to execute: for each application in the distributed system, according to the correspondence between the application and the dependent interface, collect the response time data of the dependent interface of the application in the previous statistical period.

[0095] In an exemplary embodiment of the present disclosure, the response time data is the time consumed by the application calling a method that depends on the interface; the data processing module 520 is configured to execute: sorting the response time data in the aggregated data group to obtain a data sequence; determining the target percentile according to the scenario performance information, and obtaining the interface performance indicator data corresponding to the aggregated data group from the data sequence based on the target percentile; wherein the scenario performance information is used to indicate the tolerance of the current business scenario to the interface response delay.

[0096] In an exemplary embodiment of the present disclosure, the system tolerance threshold includes a lower tolerance limit and an upper tolerance limit; the fuse processing module 530 is configured to execute: constraining the fuse threshold corresponding to the current time period within the tolerance range constructed by the lower tolerance limit and the upper tolerance limit.

[0097] In an exemplary embodiment of the present disclosure, the fuse processing module 530 is configured to execute: if the target interface performance indicator data is less than the lower tolerance limit, the lower tolerance limit is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is greater than or equal to the lower tolerance limit, the target interface performance indicator data is compared with the upper tolerance limit, and the fuse threshold corresponding to the current time period is determined based on the comparison result.

[0098] In an exemplary embodiment of the present disclosure, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined based on the comparison result, including: when the target interface performance indicator data is greater than the tolerance upper limit value, the tolerance upper limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is less than or equal to the tolerance upper limit value, the target interface performance indicator data is determined as the fuse threshold corresponding to the current time period.

[0099] In an exemplary embodiment of the present disclosure, the fuse processing module 530 is further configured to execute: updating the lower tolerance limit value and the upper tolerance limit value according to the scenario performance information.

[0100] Since the details of the functional modules of the fuse processing device of the exemplary embodiment of the present disclosure have been described in the exemplary embodiment of the fuse processing method described above, they will not be repeated here.

[0101] It should be noted that, although several modules or units of the fuse processing device are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0102] The exemplary embodiments of the present disclosure also provide a computer program product, which includes a computer program, and when the computer program is executed by a processor, the above-mentioned fuse processing method is implemented.

[0103] In one embodiment, the computer program product may be a tangible product containing a computer program, such as a computer-readable storage medium storing a computer program. The readable storage medium may be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory (Flash), mechanical hard disk (HDD), solid-state drive (SSD), and the like. Exemplarily, the computer program product may be implemented as a non-volatile storage medium storing a computer program, such as a read-only memory, a NAND flash memory, and the like.

[0104] In one embodiment, the computer program product may be an intangible product including a computer program. Exemplarily, the computer program product may be implemented as a virtual digital product, such as a digital file storing an executable file, an installation package, etc. of the computer program.

[0105] The code of the computer program can be written in one or more programming languages. Programming languages ​​such as C language, Java, C++, etc. The program code can be executed entirely on the user computing device, or partially on the user computing device, or as a separate software package, or partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, such as a local area network (LAN), a wide area network (WAN), etc., or can be connected to an external computing device (e.g., an Internet connection provided by an operator).

[0106] Computer programs can be carried or transmitted through electrical, magnetic, optical, electromagnetic, infrared and other signals. Electronic devices can convert signals carrying computer programs into digital signals, and then run computer programs. When a computer program runs on an electronic device, its code is used to enable the electronic device to execute (more specifically, the processor of the electronic device can execute) the method steps of various exemplary embodiments of the present disclosure, such as the above-mentioned fuse processing method, which includes the following steps: collecting the response time data of the application's dependent interface in the previous statistical period; performing aggregation processing on the response time data, and determining the interface performance index data corresponding to different time periods based on the aggregation processing results, the interface performance index data is used to describe the distribution of the interface response time; obtaining the target interface performance index data corresponding to the current time period, and determining the fuse threshold corresponding to the current time period based on the target interface performance index data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

[0107] The above method steps are executed by a computer program to collect response time data of the application's dependent interface in the previous statistical period, aggregate the response time data, and determine interface performance indicator data corresponding to different time periods based on the aggregation processing results. The interface performance indicator data is used to describe the distribution of the interface response time. Then, the target interface performance indicator data corresponding to the current time period is obtained, and based on the target interface performance indicator data and the system tolerance threshold, the corresponding fuse threshold of the current time period is determined, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold. This process can dynamically calculate the interface performance indicator data corresponding to different time periods based on the response time data in the previous statistical period, thereby providing reference evaluation conditions matching the time period for the subsequent acquisition of the target interface performance indicator data corresponding to the current time period, and taking the performance of each time period (interface performance indicator data) into consideration, which is conducive to improving the accuracy of determining the fuse threshold, and dynamically determining the fuse threshold of the current time period based on the target interface performance indicator data and the system tolerance threshold, avoiding the use of a fixed fuse threshold, and also providing a bottom line for dynamically adjusting the fuse threshold corresponding to the current time period through the system tolerance threshold, avoiding the determined tolerance threshold being too large or too small, ensuring the system adaptability of the tolerance threshold, and thus improving the accuracy and effectiveness of the fuse processing as a whole.

[0108] In an exemplary embodiment of the present disclosure, response time data is aggregated and interface performance indicator data corresponding to different time periods are determined based on the aggregation processing results, including: aggregating the response time data according to a preset time window to obtain aggregate data groups corresponding to different time periods; for each aggregate data group, determining the interface performance indicator data corresponding to the aggregate data group based on the response time data in the aggregate data group.

[0109] In an exemplary embodiment of the present disclosure, the response time data of the dependent interface of the application in the previous statistical period is collected, including: for each application in the distributed system, according to the corresponding relationship between the application and the dependent interface, the response time data of the dependent interface of the application in the previous statistical period is collected.

[0110] In an exemplary embodiment of the present disclosure, response time data is the time consumed by an application calling a method that depends on an interface; for each aggregated data group, the interface performance indicator data corresponding to the aggregated data group is determined based on the response time data in the aggregated data group, including: sorting the response time data in the aggregated data group to obtain a data sequence; determining a target percentile based on scenario performance information, and obtaining the interface performance indicator data corresponding to the aggregated data group from the data sequence based on the target percentile; wherein the scenario performance information is used to indicate the tolerance of the current business scenario to interface response delays.

[0111] In an exemplary embodiment of the present disclosure, the system tolerance threshold includes a lower tolerance limit value and an upper tolerance limit value; based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current time period is determined, including: constraining the fuse threshold corresponding to the current time period within the tolerance range constructed by the lower tolerance limit value and the upper tolerance limit value.

[0112] In an exemplary embodiment of the present disclosure, a fuse threshold corresponding to the current time period is determined based on the target interface performance indicator data and the system tolerance threshold, including: if the target interface performance indicator data is less than the tolerance lower limit value, the tolerance lower limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is greater than or equal to the tolerance lower limit value, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined according to the comparison result.

[0113] In an exemplary embodiment of the present disclosure, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined based on the comparison result, including: when the target interface performance indicator data is greater than the tolerance upper limit value, the tolerance upper limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is less than or equal to the tolerance upper limit value, the target interface performance indicator data is determined as the fuse threshold corresponding to the current time period.

[0114] In an exemplary embodiment of the present disclosure, the method further includes: updating a lower tolerance limit value and an upper tolerance limit value according to scene performance information.

[0115] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. It will be appreciated by those skilled in the art that various aspects of the present disclosure may be implemented as a system, method, or program product. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which may be collectively referred to herein as a "circuit", "module", or "system".

[0116] Refer to the following Figure 6 hereinafter describes an electronic device 600 according to such an embodiment of the present disclosure. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0117] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0118] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps described in the above "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can execute the following steps: collect the response time data of the application's dependent interface in the previous statistical period; aggregate the response time data, and determine the interface performance indicator data corresponding to different time periods based on the aggregation processing results, and the interface performance indicator data is used to describe the distribution of the interface response time; obtain the target interface performance indicator data corresponding to the current time period, and determine the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

[0119] In an exemplary embodiment of the present disclosure, response time data is aggregated and interface performance indicator data corresponding to different time periods are determined based on the aggregation processing results, including: aggregating the response time data according to a preset time window to obtain aggregate data groups corresponding to different time periods; for each aggregate data group, determining the interface performance indicator data corresponding to the aggregate data group based on the response time data in the aggregate data group.

[0120] In an exemplary embodiment of the present disclosure, the response time data of the dependent interface of the application in the previous statistical period is collected, including: for each application in the distributed system, according to the corresponding relationship between the application and the dependent interface, the response time data of the dependent interface of the application in the previous statistical period is collected.

[0121] In an exemplary embodiment of the present disclosure, response time data is the time consumed by an application calling a method that depends on an interface; for each aggregated data group, the interface performance indicator data corresponding to the aggregated data group is determined based on the response time data in the aggregated data group, including: sorting the response time data in the aggregated data group to obtain a data sequence; determining a target percentile based on scenario performance information, and obtaining the interface performance indicator data corresponding to the aggregated data group from the data sequence based on the target percentile; wherein the scenario performance information is used to indicate the tolerance of the current business scenario to interface response delays.

[0122] In an exemplary embodiment of the present disclosure, the system tolerance threshold includes a lower tolerance limit value and an upper tolerance limit value; based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current time period is determined, including: constraining the fuse threshold corresponding to the current time period within the tolerance range constructed by the lower tolerance limit value and the upper tolerance limit value.

[0123] In an exemplary embodiment of the present disclosure, a fuse threshold corresponding to the current time period is determined based on the target interface performance indicator data and the system tolerance threshold, including: if the target interface performance indicator data is less than the tolerance lower limit value, the tolerance lower limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is greater than or equal to the tolerance lower limit value, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined according to the comparison result.

[0124] In an exemplary embodiment of the present disclosure, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined based on the comparison result, including: when the target interface performance indicator data is greater than the tolerance upper limit value, the tolerance upper limit value is determined as the fuse threshold corresponding to the current time period; if the target interface performance indicator data is less than or equal to the tolerance upper limit value, the target interface performance indicator data is determined as the fuse threshold corresponding to the current time period.

[0125] In an exemplary embodiment of the present disclosure, the method further includes: updating the tolerance lower limit value and the tolerance upper limit value according to the scene performance information. The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 621 and / or a cache storage unit 622, and may further include a read-only storage unit (ROM) 623.

[0126] The storage unit 620 may also include a program / utility 624 having a set (at least one) of program modules 625, such program modules 625 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0127] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0128] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0129] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0130] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0131] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A fusing processing method, characterized in that: include: Collect the response time data of the application's dependent interfaces in the previous statistical period; Aggregate the response time data, and determine interface performance indicator data corresponding to different time periods according to the aggregation processing result, wherein the interface performance indicator data is used to describe the distribution of the interface response time; Obtain target interface performance indicator data corresponding to the current time period, and determine a fuse threshold corresponding to the current time period based on the target interface performance indicator data and a system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

2. The method according to claim 1, characterized in that The aggregating the response time data and determining the interface performance indicator data corresponding to different time periods according to the aggregation processing result includes: Aggregate the response time data according to a preset time window to obtain aggregate data groups corresponding to different time periods; For each of the aggregated data groups, the interface performance indicator data corresponding to the aggregated data group is determined according to the response time data in the aggregated data group.

3. The method according to claim 1 or 2, characterized in that: The response time data of the dependent interface of the collection application in the previous statistical period includes: For each application in the distributed system, according to the corresponding relationship between the application and the dependent interface, the response time data of the dependent interface of the application in the previous statistical period is collected.

4. The method according to claim 2, characterized in that: The response time data is the time consumed by the application to call the method of the dependent interface; The step of determining, for each of the aggregated data groups, interface performance indicator data corresponding to the aggregated data group according to the response time data in the aggregated data group, includes: Sorting the response time data in the aggregated data group to obtain a data sequence; Determine a target percentile according to the scenario performance information, and obtain interface performance indicator data corresponding to the aggregated data group from the data sequence based on the target percentile; The scenario performance information is used to indicate the tolerance of the current business scenario to interface response delay.

5. The method according to claim 1, characterized in that The system tolerance threshold includes a lower tolerance limit and an upper tolerance limit; The determining, based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current time period includes: The fuse threshold corresponding to the current time period is constrained within a tolerance range constructed by the lower tolerance limit value and the upper tolerance limit value.

6. The method according to claim 5, characterized in that The determining, based on the target interface performance indicator data and the system tolerance threshold, the fuse threshold corresponding to the current time period includes: If the target interface performance indicator data is less than the tolerance lower limit value, the tolerance lower limit value is determined as the fuse threshold corresponding to the current time period; If the target interface performance indicator data is greater than or equal to the tolerance lower limit value, the target interface performance indicator data is compared with the tolerance upper limit value, and the fuse threshold corresponding to the current time period is determined according to the comparison result.

7. The method according to claim 6, characterized in that The comparing the target interface performance indicator data with the tolerance upper limit value, and determining the fuse threshold corresponding to the current time period according to the comparison result, includes: When the target interface performance indicator data is greater than the tolerance upper limit value, determining the tolerance upper limit value as the fuse threshold corresponding to the current time period; If the target interface performance indicator data is less than or equal to the tolerance upper limit value, the target interface performance indicator data is determined as the fuse threshold corresponding to the current time period.

8. The method according to claim 5, characterized in that The method further comprises: The lower tolerance limit value and the upper tolerance limit value are updated according to the scene performance information.

9. A fusing processing device, characterized in that: include: The data collection module is used to collect the response time data of the application's dependent interface in the previous statistical period; A data processing module, used to aggregate the response time data and determine interface performance indicator data corresponding to different time periods according to the aggregation processing results, wherein the interface performance indicator data is used to describe the distribution of interface response time; The fuse processing module is used to obtain the target interface performance indicator data corresponding to the current time period, and determine the fuse threshold corresponding to the current time period based on the target interface performance indicator data and the system tolerance threshold, so as to perform fuse processing on the dependent interface in the current time period based on the fuse threshold.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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