CN116248538A

CN116248538A - Statistical method and device for flow index

Info

Publication number: CN116248538A
Application number: CN202211538069.0A
Authority: CN
Inventors: 陈超
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2022-12-01
Filing date: 2022-12-01
Publication date: 2023-06-09

Abstract

The specification provides a statistical method and device for flow indexes. The method comprises the following steps: aiming at a target service flow processed by a target detection service node in a preset time period, acquiring flow related data of the target service flow; determining a target service link according to the flow related data, wherein the target service link is formed by each service node participating in processing the target service flow; determining a service scene of each service node in the target service link when participating in processing the target service flow according to the flow related data; and counting flow indexes of the target service flow in each service scene according to the determined service scene.

Description

Statistical method and device for flow index

Technical Field

The embodiment of the specification belongs to the technical field of Internet, and particularly relates to a flow index statistical method and device.

Background

At present, rich network services such as video watching, online payment and the like can be provided for users through APP (Application) applications or Web applications and the like. To improve service quality and efficiency, service providers often deploy a plurality of service nodes to provide the network service for users, such as splitting an application into micro services for implementing services, and deploying service nodes for providing each micro service, etc. In the process of enjoying network services, users tend to pay more and more attention to their own privacy.

The provider of the network service may need to analyze the operation status of the service node, for example, to evaluate whether a service parameter change of a certain service node affects the service success rate of the node, how much traffic a certain designated service node successfully processes in a certain time, and so on. Taking the change of the operation parameters as an example, at present, operation data such as the CPU occupancy rate, the memory usage amount, the error reporting information and the like of the service equipment where the service node is located are generally collected and analyzed to determine whether the change is abnormal.

However, the analysis method has a relatively coarse analysis granularity, and only the influence of the change abnormality on the overall operation parameters of the machine can be found, but the specific influence of the change abnormality on a certain service or even a certain operation scene cannot be perceived. If the operation parameters of a certain service node are changed, the overall service parameters of the service device where the node is located may not be changed significantly, but the service success rate of the node in a certain service scenario is reduced greatly, and at this time, the analysis mode is often difficult to find such anomalies.

Disclosure of Invention

The invention aims to provide a flow index statistical method and a flow index statistical device.

According to a first aspect of one or more embodiments of the present disclosure, a method for counting a traffic index is provided, including:

Aiming at a target service flow processed by a target detection service node in a preset time period, acquiring flow related data of the target service flow;

determining a target service link according to the flow related data, wherein the target service link is formed by each service node participating in processing the target service flow; determining a service scene of each service node in the target service link when participating in processing the target service flow according to the flow related data;

and counting flow indexes of the target service flow in each service scene according to the determined service scene.

According to a second aspect of one or more embodiments of the present disclosure, there is provided a flow index statistics apparatus, including:

the acquisition unit is used for acquiring flow related data of target service flow processed by the target detection service node in a preset time period;

the determining unit is used for determining a target service link according to the flow related data, wherein the target service link is formed by each service node participating in processing the target service flow; determining a service scene of each service node in the target service link when participating in processing the target service flow according to the flow related data;

And the statistics unit is used for counting the flow index of the target service flow under each service scene according to the determined service scene.

According to a third aspect of one or more embodiments of the present specification, there is provided an electronic device comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor implements the method of any of the first aspects by executing the executable instructions.

According to a fourth aspect of one or more embodiments of the present description, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method as in any of the first aspects.

In the embodiment of the present disclosure, firstly, flow related data of a target service flow processed by a target detection service node in a preset time period is obtained; then determining a target service link formed by each service node participating in processing the target service flow according to the data, and determining a service scene to which each service node in the target service link belongs when participating in processing the target service flow; and finally, counting the flow index of the target service flow in each service scene according to the service scenes.

It can be understood that, because the traffic related data generated by the target detection service node processing the target traffic can determine the service scenario to which the target service link and each service node belong when participating in processing the target traffic, the traffic index of the target traffic under each service scenario, which is counted in the above manner, can accurately reflect the traffic situation of the target detection service node under different service scenarios. For example, in the case where the target detection service node includes service nodes before and after the service parameter is changed, the influence of the change on each service scenario may be determined by the traffic index counted in the above manner. It can be seen that the solution can analyze the operation condition of the target detection service node in more detail and accurately from the granularity of the traffic scenario.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a method for counting traffic indexes according to an exemplary embodiment.

Fig. 2 is a schematic diagram of a process for a target service link according to an exemplary embodiment.

Fig. 3 is a schematic diagram of a clipping effect of a target service link according to an exemplary embodiment.

Fig. 4 is a schematic diagram of a clipping effect of a further target service link provided by an exemplary embodiment.

Fig. 5 is a schematic diagram of an apparatus according to an exemplary embodiment.

Fig. 6 is a block diagram of a flow index statistics apparatus provided by an exemplary embodiment.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.

In order to realize finer granularity running state analysis aiming at the service node, the specification provides a flow index statistical method which is used for counting the flow index of the service node at the service scene level, and the flow index can realize running state analysis on the target detection service node at the service scene level. The scheme is described in detail below with reference to the accompanying drawings.

Referring to fig. 1, fig. 1 is a flowchart of a flow index statistics method according to an exemplary embodiment. As shown in fig. 1, the method may be applied to a management device having an analysis function for a service node, and the method includes the following steps 102-106.

Step 102, obtaining flow related data of a target service flow processed by a target detection service node in a preset time period.

The Service node described in the present specification has various forms, such as an independent APP application or Web application (hereinafter referred to as an application program), a functional component in an application program, and a micro Service (Microservice) created based on an SOA (Service-Oriented Architecture) architecture. The micro-service may be a lightweight service divided from the application program, and any micro-service may be constructed around a specific service. Any of the micro-services may run in separate processes, and different micro-services may communicate with each other using a lightweight communication mechanism, which may be, for example, a RESTful API (Application Programming Interface ) mechanism based on HTTP (Hyper Text Transfer Protocol, hypertext transfer protocol). By the method, different micro services can be mutually coordinated and matched to process the service flow so as to provide corresponding service value for the user. The micro-service may be constructed based on the standard of opentelemet.

In addition, the service nodes described in the present specification may be deployed independently into corresponding service devices, for example, multiple containers may be created in the same service device, and the same or different service nodes may be deployed in the respective containers. The service nodes may be distributed or centralized, and may be deployed in a production environment for processing real traffic in the production environment, or may be deployed in a simulation environment for processing test traffic in the simulation environment (e.g., a copy of the real traffic), which is not limited by the embodiments of the present invention.

The service nodes may form a service cluster at a logic level, where the service cluster may be used to process each service request received by the service platform, a processing procedure of any service request may correspond to at least one service traffic, and any service traffic may be participated in processing by at least one service node. Any service node may have a corresponding traffic scenario when participating in processing traffic, hereinafter referred to as belonging to a certain traffic scenario.

The service scenario is a concept of a service layer, and specifically, the service scenario may correspond to a service function implemented by a service node, for example, the service node for implementing an online payment function may belong to an online payment scenario when processing service traffic, the service node for implementing an online lottery function may belong to an online lottery scenario when processing service traffic, and so on. The application scenario may also be determined by a preset method called in the process of processing the service traffic, for example, a certain service node calls an identity verification method in the process of processing the service traffic, and then considers that the service node belongs to an online verification scenario, another service node calls a record query method in the process of processing the service traffic, and then considers that the service node belongs to a record query scenario, and so on. It can be understood that, in the process that a plurality of service nodes cooperate with each other to process a certain service flow, some or all of the service nodes may not belong to an application scenario (i.e., the service nodes do not have a corresponding application scenario), for example, a certain service node may not call any method; in the case where the plurality of service nodes respectively belong to respective application scenarios, the application scenarios to which any two service nodes belong may be the same or different, which is not limited by the embodiment of the present invention.

Before acquiring the flow related data of the target service flow, the management device needs to determine a target detection service node, where the target detection service node may be any target detection service node in the service cluster. For example, the service parameters of any service node may be changed, for example, the service device where the service node is located may change the service parameters of the service node according to a preset rule or in response to an operation of an operation and maintenance personnel. Illustratively, the change may include updating the price of the item from 100 to 120, updating the discount for the item from 9 to 7, updating the deadline for the discount for the item, and so on. For the above-described service parameter change, the management device may determine a service node before the service parameter change (hereinafter referred to as an original service node) and/or a service node after the change (hereinafter referred to as a changed service node) as the target detection service node. For another example, the operator may designate some service nodes as target detection service nodes through service identifiers, so that the management device may determine the designated service node characterized by the designated service identifier as the target detection service node, where the service identifier may be a name of a service instance or a Socket (Socket), etc. For another example, for a service node belonging to an application program (for example, the service node is divided from the application program), an operator may specify some application programs through an application program identifier, where the service node belonging to the specified application program (hereinafter referred to as a specified application service node) may be a target detection service node, and the application program identifier may be a name or a pre-allocated number of the application program. In summary, the object detection service node described in the present specification may include at least one of the following: the service parameter modification method comprises the steps of original service nodes with unchanged service parameters, changed service nodes with changed service parameters, designated service nodes characterized by designated service identifiers and designated application service nodes belonging to application programs characterized by designated application program identifiers.

Corresponding to the process of at least one service node handling any traffic, the at least one service node may constitute a service link for that traffic. Wherein, in the case that any service flow is only participated in processing by one service node, the service link of the flow only comprises the service node; in the case that any service traffic is mutually matched and participated in processing by a plurality of service nodes, the service link of the traffic comprises the plurality of service nodes, and the connection relationship among the service nodes may satisfy a linear structure, a tree structure, a ring structure and the like. Corresponding flow related data, such as node information of each service node, connection information among a plurality of service nodes, intermediate data transferred between adjacent service nodes, method call parameters used by each service node in the process of processing the service flow, and/or processing results of the corresponding data, etc., can be generated in the process of processing any service flow by at least one service node. In addition, the traffic related data generated by any service node for any service traffic can also include the traffic identifier of the service traffic, and each service node participating in processing the any service traffic can be accurately positioned through the identifier respectively maintained by each service node.

After determining the target detection service node, the management device may acquire corresponding traffic related data for the target traffic handled by the target detection service node in a preset time period. It should be noted that, the management device may determine one or more target detection service nodes, where in the case of determining one target detection service node, the management device may further determine that the node references the processed target traffic and traffic related data thereof in the preset time period; under the condition that a plurality of target detection service nodes are determined, the target service flows which are respectively participated in processing in the same or different preset time periods by the target detection service nodes can be respectively determined, and flow related data of the target service flows are respectively obtained. In addition, any one of the target traffic flows may be engaged in processing by at least one service node, where the traffic is engaged in processing by a plurality of service nodes, the plurality of service nodes may include at least one target detection service node. Taking a certain changed service node A with changed service parameters as an example, the node can cooperate with a service node B to participate in processing a certain target service flow, and the service node B can be either a changed service node with changed service parameters or any service node with unchanged service parameters.

104, determining a target service link according to the flow related data, wherein the target service link is formed by each service node participating in processing the target service flow; and determining the service scene of each service node in the target service link when participating in processing the target service flow according to the flow related data.

After acquiring the flow related data of the target service flow, the management device may determine, according to the data, a target service link of the target service flow and service scenes to which each service node in the link belongs when participating in processing the target service flow. Wherein the target service link of the target traffic flow is formed by each service node (including the target detection service node) participating in processing the flow. The service nodes forming the target service link may have corresponding service scenes respectively, or at least one service node may not have corresponding service scenes.

In an embodiment, the management device may determine the target service link according to a traffic identifier in the traffic related data. For example, the management device may first obtain flow related data of each service flow processed by the candidate service node in the preset period of time; the service node candidates may be part or all of the service nodes in the foregoing service cluster, the traffic related data of any service traffic processed by any service node candidate may include a traffic identifier of any service traffic, a forward node identifier of a previous service node and a backward node identifier of a subsequent service node for processing any service traffic of any service node candidate, where the forward node identifier and/or the backward node identifier respectively represent forward nodes and backward nodes adjacent to any service node candidate. For example, in the case where the service nodes A, B and C sequentially process the same traffic, the service nodes a and C are respectively a forward node and a backward node adjacent to the service node B, and accordingly, the service node a and the service node C are respectively the forward node identifier and/or the backward node identifier. Based on this, the management component may query the candidate service nodes whose traffic related data includes the same traffic identifier, and determine, according to the queried forward node identifier and/or backward node identifier of each candidate service node, a connection relationship between each candidate service node, where the connection relationship corresponds to a target service link represented by the traffic identifier, in other words, the connection relationship may be used to represent the target service link.

Fig. 2 is a schematic diagram of a process for a target service link according to an exemplary embodiment. As shown in fig. 2, the management device may respectively obtain traffic related data of each service traffic processed by a plurality of candidate service nodes including A, B, C, D in a certain period of time, where the traffic related data of any service traffic processed by any candidate service node includes a traffic identifier of the traffic and a corresponding forward node identifier and/or backward node identifier. Taking the service flow 1 as an example, firstly, according to the flow related data respectively maintained by each node, the service nodes A-D can be determined to be the service nodes participating in the processing of the flow; secondly, the backward node identifier included in the traffic related data of the node a may be the node identifier of the node B, the forward node identifier and the backward node identifier included in the traffic related data of the node B may be the node identifiers of the nodes a and C, the forward node identifier and the backward node identifier included in the traffic related data of the node C may be the node identifiers of the nodes B and D, and the forward node identifier included in the traffic related data of the node D may be the node identifier of the node C, and the target service links which are involved in the formation of the service nodes a to D may be determined as a→b→c→d according to the forward node identifier and/or the backward node identifier maintained by each node, as shown in traceId1 in a of fig. 2. Similarly, according to the flow related data of the flows 2 and 3 maintained by the service nodes a-D respectively, it can be determined that the target service link corresponding to the flow 2 is a→b→c→d, as shown in traceId2 in a of fig. 2; and determining that the target service link corresponding to the flow 3 is A- & gt B- & gt C- & gt D, as shown in traceId3 in a of fig. 2.

In an embodiment, the management device may determine, through a plurality of devices, a service scenario to which any service node belongs. For example, the traffic related data of any service node may include a traffic scenario of the node, where the traffic scenario may be determined as a traffic scenario to which the any service node belongs when participating in processing the target traffic. With the foregoing embodiment shown in fig. 2, if the traffic related data of the traffic 1 maintained by the service node C includes its own scenario 1 (e.g., an online payment scenario), the management device may determine the scenario 1 as a traffic scenario to which the node C belongs when participating in processing the traffic 1. For another example, the traffic related data of any service node also includes a method call parameter of the node, where a corresponding called method may be determined according to the method call parameter, and a service scenario to which the method belongs is determined as a service scenario to which the any service node belongs when participating in processing the target traffic. As shown in a of fig. 2, if the flow related data for the flow 1 maintained by the service node D includes a method call parameter of a certain method, and the method belongs to the scenario 3, the scenario 3 may be determined as a service scenario to which the node D belongs when participating in processing the flow 1. By the method, the service scene of any service node when participating in processing the target service flow can be determined more accurately according to the service scene or the method call parameter directly contained in the flow related data.

In addition, different service nodes participating in processing the same service traffic may belong to different service scenarios respectively, for example, service nodes C and D participating in processing traffic 1 belong to scenarios 1 and 3 respectively; in addition, the same service node may belong to the same or different service scenarios in participating in processing different service flows, e.g. service node C belongs to scenario 1 when participating in processing flows 1 and 2 and to scenario 2 when participating in processing flow 3; of course, the same service node may belong to a certain service scenario during participation in processing certain service traffic, but may not belong to any service scenario during participation in processing other service traffic, for example, the service node B does not belong to any service scenario when participating in processing traffic 1, and belongs to scenario 4 when participating in processing traffic 2. The embodiment of the present specification is not limited, and should be determined according to the actual situation of the related data of the corresponding traffic, where, in the process of participating in processing any traffic, whether there is a service scenario to which any service node belongs, whether there is the same service scenario to which the service node belongs as the service scenario to which the service node belongs when participating in processing other traffic.

In an embodiment, the target detection service node may include an original service node in which the service parameter is not changed and a changed service node in which the service parameter is changed, and any original service node and any changed service node may respectively belong to different target service links. In this case, the management device may acquire first traffic related data of the first target traffic handled by the original service node, and determine, according to the first traffic related data, a pre-change target service link in which the original service node is located; and acquiring second flow related data of the second target service flow processed by the change service node, and determining a changed target service link where the change service node is located according to the second flow related data. It can be seen that the first target service link at least includes the original service node, and the second target service link at least includes the changed service node. It can be understood that, for the same service node, the service node may be used as an original service node to participate in processing any first target traffic before the service parameter is changed, and may be used as a changed service node to participate in processing any second target traffic after the service parameter is changed.

Referring to fig. 2, as can be seen from the foregoing determination, any of the target service links shown in fig. 2 includes a target detection service node corresponding to the link, and may further include a service node different from the target detection service node. As shown in a of fig. 2, the target detection service nodes in each target service link are respectively unchanged service nodes, and each target service link is respectively changed target service link: the target detection service node in traceId1 is a node B, which has both change 1 and change 2; the target detection service node in traceId2 comprises a node B and a node D, which are respectively changed 1 and changed 3; the target detection service node in traceId3 is the node B, which has changed 2. It can be seen that each traffic shown in fig. 2 is participated in processing by the change service node, in other words, each change service node participates in processing the corresponding traffic after the service parameter is changed. Similar to the change service nodes shown in fig. 2 as the target detection service nodes, the management node may further determine a corresponding target service link according to flow related data related to the processing of the target traffic flow by the corresponding original service node in the preset time period, so as to serve as the target service link before the change.

And step 106, counting flow indexes of the target service flow in each service scene according to the determined service scene.

By the method, each target service flow processed by the target detection service node in the preset time period can be determined, and further, the target service links corresponding to each target service flow respectively and the service scene of each service node in each target service link when the corresponding target service flow is processed are determined according to the corresponding flow related data. On the basis, the management device can count the flow index of the target service flow in each service scene according to the service scenes.

With the foregoing embodiment, when the pre-change target service link and the post-change target service link are determined, the management device may count the first traffic index of the first target traffic in each traffic scenario according to the traffic scenario to which each service node in the pre-change target service link belongs, and may count the second traffic index of the second target traffic in each traffic scenario according to the traffic scenario to which each service node in the post-change target service link belongs. Then, the flow rate change index corresponding to the parameter change may be determined by comparing the first flow rate index and the second flow rate index.

Taking the number of payment per minute in the online payment scenario as an example, for the target service link before modification, the first number of payment of the first target service flow in the online payment scenario may be counted according to the service scenario to which each service node in the target service link before modification respectively belongs, and the second number of payment of the second target service flow in the online payment scenario may be counted according to the service scenario to which each service node in the target service link after modification respectively belongs. Further, the change rate (or change amount, etc.) of the payment amount corresponding to the parameter change may be determined by comparing the first payment amount with the second payment amount, and the change rate may be used to reflect the influence of the parameter change on the payment amount, for example, an increase in discount strength may cause an increase in payment amount (i.e., an increase in volume), an increase in unit price may cause a decrease in payment amount (i.e., a decrease in volume), etc.

It can be understood that, in the case that any target service link includes a plurality of target detection service nodes (i.e., original service nodes or changed service nodes), the parameter change corresponding to each target detection service node may affect the traffic index of the target traffic corresponding to the link. In this regard, in order to more accurately determine the possible impact of the parameter change on the traffic index of the target traffic and facilitate analysis of the link topology of the target service link, the management device may further split the target service link according to the foregoing parameter change. For example, when any target service link includes a plurality of target detection service nodes, the pre-change target service link may be split into first single-change links corresponding to each original service node, and the post-change target service link may be split into second single-change links corresponding to each changed service node. Each single-change link after splitting according to the parameter change comprises a target detection service node corresponding to the primary parameter change, for example, for any target service link comprising N original service nodes, the target detection service node can be split into N single-change links, wherein any single-change link only comprises the target detection service node before the primary parameter change; for any target service link containing M change service nodes, the target service link can be split into M single change service links, wherein any single change service link contains only one target detection service node after parameter change.

As shown in B of fig. 2, the target detection service node in the traceId1 is a node B, and the node makes two parameter changes, namely a change 1 and a change 2, based on which the traceId1 can be split into two single-change links of a→b→c→d: the node B in the first single change link only changes 1, the node B in the first single change link only changes 2, and other nodes except the node B in the two single change links are the same and have no change. In addition, since the two single-variant links split according to the parameter change are used to represent the node connection relationship of a→b→c→d, the two single-variant links are still labeled "traceId1", respectively, which is described in this way. Similar to traceId1, traceId2 can also be split into two single-change links a→b→c→d: the node B in the first single change link is changed 1, and the node D in the first single change link is changed 3; only node B in traceId3 changes 2 once, i.e. traceId3 is already a single change link, so it does not need to be split according to the parameter changes. It will be appreciated that the above-described splitting by parameter change corresponds to the number of parameter changes that occur: one parameter change of any target detection service node (before or after occurrence) corresponds to a changed link obtained after splitting.

Under the condition that the changed links are obtained by changing and splitting according to the parameters, the management equipment can count first flow indexes of the first target service flow in each service scene according to each split first single-change link; and counting second traffic indexes of the second target traffic under each traffic scene according to each split second single-change link. In the mode, the number of changes can be reflected through the number of single links, so that the influence possibly caused by each parameter change on the flow index of the target service flow can be accurately analyzed, and the accuracy of the flow index statistical result can be improved.

The number of service nodes involved in handling certain traffic flows may be large, making the target service link length for such flows too large. It will be appreciated that such lengthy links tend to introduce more noise into the statistics of the traffic indicators, which may result in less accurate statistics. In this regard, in one embodiment, the target service link may be trimmed prior to statistics and traffic metrics may be counted based on the trimmed core link. For example, the management device may first determine a preamble hierarchy and a successor hierarchy of the target detection service node, the preamble hierarchy and the successor hierarchy being used to characterize a number of consecutive active service nodes in the target service link that are located before and after the target detection service node, respectively. And then, the target detection service node is taken as a center, and the target service link is cut into a core link according to the preamble level and the subsequent level, wherein the core link can be composed of the target detection service node and the corresponding effective service node. The specific values of the preamble level and the subsequent level may be the same or different, any level may be a preset value, and the preset value may be set according to a service scenario to which the service node belongs or a traffic type of the target service traffic; alternatively, any hierarchy may be a predetermined proportion of the target service link length, for example, 50%, 20% or 60% of the total number of service nodes included in the link.

After the clipping is completed, the flow index of the target service flow under each service scene can be counted according to the clipped core link. In this way, for all service nodes in the target service link, the core link may be obtained by clipping with the target detection service node as the center, and the service nodes forming the core link include the target detection service node, and a preceding hierarchical node located before the target detection service node and a subsequent hierarchical node located after the target detection service node.

The splitting according to the parameter change may be performed, that is, the target detection service node to be cut may be a single-change link obtained by splitting. Taking the clipping single change link as an example, refer to fig. 2 c, it is not just assumed that the preamble level and the subsequent level of the target detection service node are both 1, that is, in the clipping process, any target detection service node is taken as a center, and the previous service node and the next service node of the target detection service node are reserved. The structures of the single change links shown in B in fig. 2 are a→b→c→d, wherein in the two single change links of the traceId1, the first single change link of the traceId2 and one single change link of the traceId3, the target detection service node where the parameter change occurs is a node B, so that the nodes a and C in the single change links are respectively reserved, and the core link a→b→c after clipping can be obtained. In the second single-change link of traceId2, since node D is the last node of the link (i.e., node D does not have a subsequent node), only the previous node (i.e., node C) is required to be reserved, and the clipped core link C→D can be obtained.

Of course, the clipping described above may also be performed directly for the target service link. For example, if the preamble level and the subsequent level of the target detection service node are both 1, for the node B in the traceId1, the node a before and the node C after the preceding node B may be reserved, and the core link after clipping is a→b→c. For node B in traceId2, it may be reserved for node a before it and node C after it; for the node D in the traceId2, the previous node C can be reserved, so that clipping is not needed at this time, and the core link of the traceId2 is A- & gtB- & gtC- & gtD. For node B in traceId3, it may be reserved that its previous node A and subsequent node C get the clipped core link A→B→C. The above clipping, which is directly performed for the target service link, is not shown in fig. 2.

According to whether the target detection service node has branches or not, the management equipment can realize the clipping in a corresponding mode. As an exemplary embodiment, in the case that the target service link does not have a branch, in the target service link, a first valid service node may be determined according to a preamble level of the first target detection service node, and a last valid service node may be determined according to a subsequent level of the last target detection service node; and deleting service nodes before the first effective service node and after the last effective service node in the target service link, and determining links formed by the rest service nodes as the core links. It can be understood that the target service link has no branch, that is, the target detection service node is a linear single link. From the above analysis, it is clear that each of the single variant links obtained by splitting the traceids 1 to 3 shown in fig. 2 is a linear single link without branches.

As shown in fig. 3, nodes 1 to 8 for processing the same target traffic are sequentially connected to form a target detection service node, where node 1 is assigned to an application App1 running in Server1, the IP address of the node is IP1, and other nodes are similar and will not be described again. It should be noted that, the information such as the application program to which any service node belongs, the server to which any service node belongs, the IP address in the running process, etc. described in the present specification is not fixed, and the embodiment of the present invention is not limited to this. If two service nodes participating in processing the same target service flow can be assigned to the same application program, or can be assigned to different application programs respectively, and the like, which will not be described again. It may be assumed that the target detection service nodes in the target service link shown in fig. 3 are node 2, node 5 and node 6, respectively, and the preamble level of the target detection service node is 0 and the subsequent level is 1. It can be seen that the first target detection service node is node 2, and the last target detection service node is node 6, and at this time, the management device may determine the first effective service node corresponding to node 2, that is, node 2 itself; and determines the last valid serving node corresponding to node 6, node 7. Thereafter, nodes located before node 2 (i.e., node 1) and nodes located after node 7 (i.e., node 8) in the link may all be deleted, with the remaining nodes (i.e., nodes 2-7) constituting the pruned core link. It can be seen that the clipping essentially takes as a core link a part of the links formed by all the service nodes of the target detection service node between the first and last valid service nodes (including both service nodes).

As another exemplary embodiment, in a case where branches exist in the target service link and each branch satisfies a tree structure, determining a branch link from a root node to each leaf node, and clipping each branch link as a core branch link, respectively; in any branch link, a first effective branch service node can be determined according to a preamble level of a first target detection service node, and a last effective branch service node can be determined according to a subsequent level of a last target detection service node; and deleting the service nodes before the first effective branch service node and after the last effective branch service node in any branch link, and determining the link formed by the rest service nodes as a core branch link after cutting any branch link.

As shown in fig. 4, the nodes 1 to 6 for processing the same target traffic are connected to form two target detection service nodes, namely traceId1 and traceId2. Wherein, the nodes 1-4 form a traceId1, specifically, the nodes 1-2-3-4; nodes 1 and 5-6 constitute traceId2, specifically node 1→node 5→node 6. It is assumed that the target detection service node in traceId1 is node 2, the target detection service node in traceId2 is node 5, and the preamble level of the target detection service node is 0 and the subsequent level is 1. At this time, for the traceId1, the first active branch service node is node 2 itself, the last active branch service node is node 3, and at this time, the node before node 1 (i.e., node 1) and the node after node 3 (i.e., node 4) in the traceId1 may be deleted, and the link node 2→node 3 formed by the remaining service nodes (i.e., nodes 2 and 3) is the core branch link after clipping the branch link of traceId 1. Similarly, for traceId2, the first active branch service node is node 5, the last active branch service node is node 6, and at this time, the node before node 5 (i.e. node 1) and the node after node 6 (where no such node exists) in traceId2 may be deleted, and the link node 5→node 6 formed by the remaining service nodes (i.e. nodes 5 and 6) is the core branch link after the branch link of traceId2 is cut.

In another embodiment, in order to more clearly and accurately count the flow index of the target service flow in different service scenes, the target service link may be split according to an application scene. For example, the target service link may be split into at least one single scenario link according to the service scenario, where the number of service nodes in the existence service scenario included in any single scenario link is one. Through link splitting, the management device can clearly know which links respectively correspond to each service scene. The splitting according to the service scenario may be performed for the target detection service node, or may be performed for the core link after the clipping. Such as splitting for the core link as shown in fig. 2 d. With respect to fig. 2 c, the link structure shown in fig. 2 d is unchanged, except that fig. 2 c focuses on the change of parameters, and fig. 2 d focuses on the difference of service scenarios.

As shown in d of fig. 2, for two core links of traceId1 and one core link of traceId3, only one service node (i.e. node C) in any core link belongs to the corresponding traffic scenario, so these three links are already singleton Jing Lianlu by themselves, and need not be split again according to traffic. Whereas in the first core link of traceId1, node B belongs to scenario 4 and node C belongs to scenario 1, for which the link can be split into two single scenario links A→B→C: only node C in the first single scene link belongs to scene 1, and other nodes do not have the affiliated service scene; only node B in the second single scene link belongs to scene 4, and no other node exists the service scene to which the node B belongs. Similarly, in the second core link of traceId2, node C belongs to scenario 1 and node D belongs to scenario 3, for which the link can be split into two single scenario links C→D: only node C in the first single scene link belongs to scene 1, and node D does not exist to belong to a service scene; only node D in the second single scene link belongs to scene 3, and node C does not exist the service scene to which it belongs. It can be seen that, in any single scenario link obtained by splitting, only one node has a corresponding service scenario, and the other nodes do not have the service scenario to which the node belongs.

On the basis, the management device can count the number of single-scenario links containing any target detection service node corresponding to any service scenario, and determine the number as the calling times of any target detection service node in any service scenario. It can be understood that, in any single scenario link shown in fig. 2 e, a target detection service node for changing a service parameter is included, and a node belonging to a corresponding service scenario is included (the node and the target detection service node for changing the service parameter may be the same node or different nodes), so that the number of single scenario links may be counted to be the number of times corresponding to the change and the scenario dimension.

As shown in f of fig. 2, the result obtained by counting the number of single-scenario links including the node B where the change 1 occurs corresponding to the scenario 1 is 2, that is, it indicates that the change 1 occurring in the node B affects the two-item target traffic including the scenario 1. In addition, the statistics times of each change and the corresponding service scenario can be referred to f in fig. 2, and will not be described again.

Of course, the traffic index may be counted according to multiple dimensions, for example, based on each single scenario link shown in e of fig. 2, or the number of called times corresponding to different changes that occur in each scenario may be counted. If the number of calls corresponding to the change 1 and the change 2 in the scene 1 is 2 times and 1 time, the number of calls corresponding to the change 2 and the change 3 in the scene 2 is 1 time, the number of calls corresponding to the change 3 in the scene 3 is 1 time, the number of calls corresponding to the change 1 in the scene 4 is 1 time, and the like, which will not be described again. Of course, besides the called times, flow indexes such as flow processing times, processing success rate, average processing time length and the like in unit time length can also be counted, and the embodiment of the invention is not limited to the flow indexes.

In an embodiment, after the traffic index is obtained through statistics, the traffic or the traffic may be analyzed based on the traffic index. For example, in the case that the traffic index satisfies an anomaly rule, alarm information for the traffic index may be generated, where the alarm information is used to indicate that the target detection service node has a traffic anomaly. By the method, the user can be timely informed of the abnormal flow so as to perform corresponding processing.

For example, in the case that the target traffic is a real traffic in the production environment (i.e., the traffic is deployed in each service node of the production environment to participate in the processing), if the user wants to change at least one service parameter of the service node participating in the processing of the traffic, the service parameter of the corresponding node deployed in the simulation environment may be modified, and the modified node participates in processing the corresponding test traffic. After that, the management device may respectively count the flow indexes of the real service flow and the test service flow according to the flow related data corresponding to the service flow, and compare and analyze the flow indexes to determine whether the parameter change has an adverse effect on the test service flow, if so, whether the flow processing success rate is reduced, whether the success rate reduction amplitude is acceptable, and so on. Furthermore, a change suggestion may be provided to the user according to the analysis result, for example, in the case that the success rate decreases by an extent exceeding the acceptable threshold, it may be suggested to the user that the parameter change is not performed, so as to avoid that the change has a great adverse effect on the real traffic.

Fig. 5 is a schematic block diagram of an apparatus according to an exemplary embodiment. Referring to fig. 5, at the hardware level, the device includes a processor 502, an internal bus 504, a network interface 506, a memory 508, and a nonvolatile memory 510, although other hardware may be included as needed for other services. One or more embodiments of the present description may be implemented in a software-based manner, such as by the processor 502 reading a corresponding computer program from the non-volatile storage 510 into the memory 508 and then running. Of course, in addition to software implementation, one or more embodiments of the present disclosure do not exclude other implementation manners, such as a logic device or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but may also be hardware or a logic device.

Fig. 6 is a block diagram of a flow index statistics device according to an exemplary embodiment of the present disclosure, where the device may be applied to an apparatus shown in fig. 5 to implement the technical solution of the present disclosure. The device comprises:

an obtaining unit 601, configured to obtain, for a target service flow handled by a target detection service node in a preset time period, flow related data of the target service flow;

A determining unit 602, configured to determine a target service link according to the traffic related data, where the target service link is configured by service nodes participating in processing the target traffic; determining a service scene of each service node in the target service link when participating in processing the target service flow according to the flow related data;

and the statistics unit 603 is configured to count, according to the determined service scenarios, flow indexes of the target service flow in each service scenario.

Optionally, the target detection service node includes at least one of:

the service parameter modification method comprises the steps of original service nodes with unchanged service parameters, changed service nodes with changed service parameters, designated service nodes characterized by designated service identifiers and designated application service nodes belonging to application programs characterized by designated application program identifiers.

Optionally, in the case that the target detection service node includes an original service node whose service parameter is not changed and a changed service node whose service parameter is changed, and any original service node and any changed service node respectively belong to different target service links,

The acquisition unit 601 is further configured to: acquiring first flow related data of a first target service flow processed by the original service node, and acquiring second flow related data of a second target service flow processed by the changed service node;

the determining unit 602 is further configured to: determining a target service link before modification where the original service node is located according to the first flow related data; determining a changed target service link where the changed service node is located according to the second flow related data;

the statistics unit 603 is further configured to: counting first traffic indexes of a first target traffic under each traffic scene according to traffic scenes to which each service node in the target service link before changing belongs respectively, and counting second traffic indexes of a second target traffic under each traffic scene according to traffic scenes to which each service node in the target service link after changing belongs respectively; and determining a flow change index corresponding to the parameter change by comparing the first flow index with the second flow index.

Alternatively to this, the method may comprise,

the system further comprises a change splitting unit 604, configured to split, when any target service link includes a plurality of target detection service nodes, the target service link before the change into first single change links corresponding to each original service node, and split the target service link after the change into second single change links corresponding to each changed service node, where each split single change link includes one target detection service node corresponding to a parameter change;

The statistics unit 603 is further configured to: counting first traffic indexes of the first target traffic under each traffic scene according to each split first single-change link; and counting second traffic indexes of the second target traffic under each traffic scene according to each split second single-change link.

Alternatively to this, the method may comprise,

the data obtaining unit 605 is configured to obtain flow related data of each service flow processed by the candidate service node in the preset time period; wherein, the traffic related data of any traffic handled by any candidate service node includes: the traffic identifier of any service traffic, and the forward node identifier of the previous service node and/or the backward node identifier of the subsequent service node of any candidate service node for participating in processing the any service traffic;

the determining unit 602 is further configured to: and inquiring candidate service nodes with the same flow identification in the flow related data in the candidate service nodes, and determining the connection relation among the candidate service nodes according to the inquired forward node identification and/or backward node identification of each candidate service node, wherein the connection relation corresponds to the target service link represented by the flow identification.

Optionally, the determining unit 602 is further configured to:

determining the service scene as the service scene to which the any service node belongs when participating in processing the target service flow under the condition that the flow related data comprises the service scene of the any service node; or alternatively, the process may be performed,

and under the condition that the flow related data comprises the device calling parameters of any service node, determining a corresponding called device according to the device calling parameters, and determining a service scene to which the called device belongs as the service scene to which the any service node belongs when participating in processing the target service flow.

Alternatively to this, the method may comprise,

further comprising a hierarchy determining unit 606 for determining a preamble hierarchy and a subsequent hierarchy of the target detection service node, the preamble hierarchy and the subsequent hierarchy being used for characterizing the number of consecutive valid service nodes in the target service link before and after the target detection service node, respectively; cutting the target service link into a core link by taking the target detection service node as a center according to the preamble level and the postamble level, wherein the core link is composed of the target detection service node and the corresponding effective service node;

The statistics unit 603 is further configured to: and counting the flow index of the target service flow in each service scene according to the cut core link.

Optionally, the hierarchy determining unit 606 is further configured to:

under the condition that the target service link does not have branches, determining a first effective service node according to the preamble level of the first target detection service node and determining a last effective service node according to the subsequent level of the last target detection service node in the target service link; deleting service nodes before the first effective service node and after the last effective service node in the target service link, and determining links formed by the rest service nodes as the core links; or alternatively, the process may be performed,

determining branch links from a root node to each leaf node under the condition that branches exist in the target service link and each branch meets a tree structure, and cutting each branch link into core branch links respectively; in any branch link, determining a first effective branch service node according to a preamble level of the first target detection service node, and determining a last effective branch service node according to a subsequent level of the last target detection service node; and deleting the service nodes before the first effective branch service node and after the last effective branch service node in any branch link, and determining the link formed by the rest service nodes as a core branch link after cutting any branch link.

Optionally, the traffic indicator includes a number of calls of the target detection service node,

the apparatus further includes a scenario splitting unit 607, configured to split the target service link into at least one single scenario link according to the service scenario, where the number of service nodes in which a service scenario exists in any single scenario link is one;

the statistics unit 603 is further configured to: counting the number of single-scene links which correspond to any business scene and comprise any target detection service node, and determining the number as the calling times of the any target detection service node in the any business scene.

Optionally, the method further comprises:

and the alarm unit 608 is configured to generate alarm information for the traffic index, where the alarm information is used to indicate that the target detection service node has traffic difference when the traffic index meets an abnormal rule.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD, such as a field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation device is a server system. Of course, the invention does not exclude that as future computer technology advances, the computer implementing the functions of the above-described embodiments may be, for example, a personal computer, a laptop computer, a car-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although one or more embodiments of the present description provide method operational steps as described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented in an actual device or end product, the instructions may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment) as illustrated by the embodiments or by the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. For example, if first, second, etc. words are used to indicate a name, but not any particular order.

For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when one or more of the present description is implemented, the functions of each module may be implemented in the same piece or pieces of software and/or hardware, or a module that implements the same function may be implemented by a plurality of sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage, graphene storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

One skilled in the relevant art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

One or more embodiments of the present specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

The foregoing is merely an example of one or more embodiments of the present specification and is not intended to limit the one or more embodiments of the present specification. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of the present specification, should be included in the scope of the claims.

Claims

1. A statistical method of traffic metrics, comprising:

2. The method of claim 1, the target detection service node comprising at least one of:

3. The method of claim 1, wherein in the case that the target detection service node includes an original service node in which the service parameter is not changed and a changed service node in which the service parameter is changed, and any original service node and any changed service node belong to different target service links respectively,

the obtaining the flow related data of the target service flow, and determining the target service link according to the flow related data includes: acquiring first flow related data of a first target service flow processed by the original service node, and determining a target service link before modification where the original service node is located according to the first flow related data; acquiring second flow related data of a second target service flow processed by the change service node, and determining a changed target service link where the change service node is located according to the second flow related data;

The step of counting the flow index of the target service flow in each service scene according to the determined service scene comprises the following steps: counting first traffic indexes of a first target traffic under each traffic scene according to traffic scenes to which each service node in the target service link before changing belongs respectively, and counting second traffic indexes of a second target traffic under each traffic scene according to traffic scenes to which each service node in the target service link after changing belongs respectively; and determining a flow change index corresponding to the parameter change by comparing the first flow index with the second flow index.

4. A method according to claim 3,

further comprises: splitting the target service link before modification into first single-change links corresponding to original service nodes respectively under the condition that any target service link comprises a plurality of target detection service nodes, splitting the target service link after modification into second single-change links corresponding to modified service nodes respectively, wherein each split single-change link comprises one target detection service node corresponding to primary parameter modification;

The counting the first flow index of the first target service flow in each service scene comprises the following steps: counting first traffic indexes of the first target traffic under each traffic scene according to each split first single-change link;

the statistics of the second traffic index of the second target traffic in each traffic scene includes: and counting second traffic indexes of the second target traffic under each traffic scene according to each split second single-change link.

5. The method according to claim 1,

further comprises: acquiring flow related data of each service flow processed by the candidate service node in the preset time period; wherein, the traffic related data of any traffic handled by any candidate service node includes: the traffic identifier of any service traffic, and the forward node identifier of the previous service node and/or the backward node identifier of the subsequent service node of any candidate service node for participating in processing the any service traffic;

the determining the target service link according to the traffic related data comprises the following steps: and inquiring candidate service nodes with the same flow identification in the flow related data in the candidate service nodes, and determining the connection relation among the candidate service nodes according to the inquired forward node identification and/or backward node identification of each candidate service node, wherein the connection relation corresponds to the target service link represented by the flow identification.

6. The method of claim 1, determining, from the traffic-related data, a traffic scenario to which any service node in the target service link belongs when participating in processing the target traffic, comprising:

and under the condition that the flow related data comprises the method calling parameters of any service node, determining a corresponding called method according to the method calling parameters, and determining a service scene to which the called method belongs as the service scene to which the any service node belongs when participating in processing the target service flow.

7. The method according to claim 1,

further comprises: determining a preamble hierarchy and a successor hierarchy of the target detection service node, the preamble hierarchy and the successor hierarchy being used to characterize a number of consecutive valid service nodes in the target service link that are located before and after the target detection service node, respectively; cutting the target service link into a core link by taking the target detection service node as a center according to the preamble level and the postamble level, wherein the core link is composed of the target detection service node and the corresponding effective service node;

The statistics of the flow index of the target service flow in each service scene comprises the following steps: and counting the flow index of the target service flow in each service scene according to the cut core link.

8. The method of claim 7, the centering on the target detection service node, clipping the target service link to a core link in the preamble tier and the successor tier, comprising:

9. The method of claim 1, wherein the traffic indicator comprises a number of calls of the target detection service node,

the method further comprises the steps of: splitting the target service link into at least one single scene link according to the service scene, wherein the number of service nodes of the existence service scene contained in any single scene link is one;

the step of counting the flow index of the target service flow in each service scene according to the determined service scene comprises the following steps: counting the number of single-scene links which correspond to any business scene and comprise any target detection service node, and determining the number as the calling times of the any target detection service node in the any business scene.

10. The method of claim 1, further comprising:

and under the condition that the flow index meets an abnormal rule, generating alarm information aiming at the flow index, wherein the alarm information is used for indicating that the target detection service node has flow abnormality.

11. A flow index statistical device, comprising:

12. An electronic device, comprising:

a processor; a memory for storing processor-executable instructions;

wherein the processor is configured to implement the method of any of claims 1-10 by executing the executable instructions.

13. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of claims 1-10.