CN113138906A - Call chain data acquisition method, device, equipment and storage medium - Google Patents
Call chain data acquisition method, device, equipment and storage mediumDownload PDFInfo
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Abstract
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a call chain data acquisition method, a call chain data acquisition device, call chain data acquisition equipment and a storage medium.
Background
The call chain data refers to that in the process of completing one service call by the system, call information (time, interface, hierarchy and result) among services is dotted into a log, and then all dotted data are connected into a tree chain, namely one call chain data is generated. The method comprises the steps of collecting a certain amount of call chain data from a large amount of call chain data generated in a service calling process, analyzing and processing the collected call chain data subsequently, carrying out statistical analysis on services according to different dimensions, identifying abnormal service calling, rapidly analyzing and positioning abnormal services, and meanwhile, carrying out statistical analysis on system performance bottlenecks and the like according to data.
In the call chain data acquisition scheme in the related art, a technician is usually required to manually select a call chain data acquisition mode corresponding to a service and set a call chain data acquisition rule, and the technician is required to pay attention to relevant information of call chain data at any time so as to change the current call chain data acquisition mode into a more appropriate call chain data acquisition mode in time. The call chain data collection scheme in the related art relies on the analysis of an engineer.
Disclosure of Invention
Embodiments of the present invention provide a call chain data acquisition method, device, apparatus, and storage medium, which can automatically select a suitable call chain data acquisition rule and acquire call chain data for service analysis of a service.
In a first aspect, an embodiment of the present invention provides a call chain data acquisition method, including:
acquiring all call chain data of the service according to the call information associated with the service;
determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service;
acquiring target call chain data corresponding to the service from all call chain data of the service according to a call chain data acquisition rule corresponding to the service, and storing the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
In a second aspect, an embodiment of the present invention further provides a call chain data acquisition apparatus, including:
the data acquisition module is used for acquiring all calling chain data of the service according to calling information associated with the service;
the rule determining module is used for determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service;
the data acquisition module is used for acquiring target call chain data corresponding to the service from all the call chain data of the service according to a call chain data acquisition rule corresponding to the service, and storing the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the call chain data collection method according to the embodiment of the present invention when executing the computer program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the call chain data collection method according to the embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, all calling chain data of the service are obtained according to the calling information associated with the service; then, determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service; and finally, acquiring target call chain data corresponding to the service from all the call chain data of the service according to the call chain data acquisition rule corresponding to the service, storing the target call chain data corresponding to the service, automatically selecting a proper call chain data acquisition rule according to the service analysis scene type of the service, acquiring the call chain data for service analysis of the service, and meeting the requirements of different service analysis scenes.
Drawings
Fig. 1 is a flowchart of a call chain data acquisition method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a call chain data acquisition method according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a call chain data acquisition device according to a third embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention.
It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of a call chain data acquisition method according to an embodiment of the present invention. The embodiment of the invention can be suitable for acquiring a certain amount of call chain data from a large amount of call chain data generated in the service call process, and is convenient for the subsequent service analysis of the service. For example in a server. As shown in fig. 1, the method of the embodiment of the present invention specifically includes:
Optionally, the service is any service in the system, for example, a registration service, a login service, or a payment service, and the embodiment of the present invention does not limit the type of the service.
Optionally, the call chain data is used to indicate a plurality of nodes in the service call process and call relationships between the plurality of nodes. The call chain data may include identifications of a plurality of nodes. The identity of the node is used to indicate the corresponding node. Illustratively, the identification of the node may be an identification number (ID), name, number, etc. of the node. The call chain data may be a linked list in which each node holds a pointer or reference to the previous or next node, thereby indicating the parent or child of the node via the pointer or reference.
Optionally, the node in the service invocation process may be software, such as an application, a service, a microservice, a module, a submodule, a class, or a function. The nodes in the service invocation process may also be hardware, for example, any one server or a cluster of multiple servers. The embodiment of the invention does not limit the concrete form of the node in the service calling process.
Optionally, the call information associated with the service is call information generated by multiple nodes in the service call process when the service is processed. The call information generated by each node when processing the service is stored in the log information of each node. And acquiring calling information associated with the service from the log information of each node in the service calling process.
Optionally, the calling information is information for describing a calling process. The invocation information may include at least one of a link identification, an identification of the invocation information, an identification of the node, an identification of the parent node, a timestamp, a duration of the invocation, and a status code. The link identification is information for identifying call chain data to which the call information belongs. The identification of the calling information is information for identifying the calling information, and may be a hash value of the identification of the node that generated the calling information. The identifier of the node in the call information is an identifier of the node that generated the call information, and may be a name of the node that generated the call information. The timestamp is the point in time at which the node performed the call action. The calling duration is the time consumed by the node to execute the calling action, and may be the time difference between the time point when the node starts to call and the time point when the node finishes calling. The status code is information indicating the completion status of the call action and may include success and failure.
Optionally, obtaining all call chain data of the service according to the call information associated with the service may include: acquiring service-associated calling information from log information of each node in a service calling process, wherein the service-associated calling information is a plurality of calling information; grouping the calling information associated with the service according to the link identification of each calling information to obtain a plurality of calling information groups, wherein each calling information comprises a plurality of calling information belonging to the same calling chain data; and aiming at each calling information group, constructing calling chain data according to the father node identification of each calling information in the calling information group.
Optionally, the method for grouping the call information associated with the service according to the link identifier of each call information to obtain a plurality of call information groups, where each call information group includes a plurality of call information belonging to the same call chain data, and includes: judging whether the link identifications of any two pieces of calling information in the plurality of pieces of calling information are the same; and if the link identifications of the two pieces of calling information are the same, indicating that the two pieces of calling information correspond to the same calling chain, dividing the two pieces of calling information into the same calling information group. Therefore, the calling information related to the service can be grouped according to the link identification, and the link identification of the calling information in each calling information group is the same, so that the calling information in each calling information group corresponds to the same calling chain data.
Optionally, for each calling information group, a piece of calling chain data is constructed according to the parent node identifier of each calling information in the calling information group. The method can comprise the following steps: for each calling information group, selecting calling information of which the father node identifier is empty or the father node identifier is a preset identifier from the calling information group, and determining a node generating the calling information as a first node in calling chain data; selecting calling information of which the identifier of the father node is the identifier of the first node from the calling information group, and determining the node generating the calling information as a second node in the calling chain data; and analogizing in turn, for any calling information in the calling information group, determining the node generating the calling information and the father node of the node according to the identifier of the node in the calling information and the identifier of the father node, and organizing each calling information in the group through the identifier of the node in each calling information and the identifier of the father node, thereby connecting a piece of calling chain data in series.
And step 102, determining a call chain data acquisition rule corresponding to the service according to the service analysis scene type of the service.
Optionally, the service analysis scenario is a scenario in which statistical analysis is performed on the service, performance of service processing is diagnosed, and a problem of service processing is located. The service analysis scenario types may include: an offline analysis scenario and an online diagnosis scenario.
Optionally, the service analysis scenario is divided into two types according to the timeliness requirement of statistical analysis for the service: an offline analysis scenario and an online diagnosis scenario. The offline analysis scene is a service analysis scene with low timeliness requirement. The online diagnosis scene is a business analysis scene with high requirement on time efficiency.
Optionally, the user may set the service analysis scenario type of the service according to the timeliness requirement for performing statistical analysis on the service, and may modify the service analysis scenario type of the service at any time after the timeliness requirement is changed.
Optionally, the determining, according to the service analysis scenario type of the service, a call chain data acquisition rule corresponding to the service includes: and determining a calling chain data acquisition rule corresponding to the service according to the corresponding relation between the preset service analysis scene type and the calling chain data acquisition rule.
And presetting a corresponding relation between the service analysis scene type and the call chain data acquisition rule. The corresponding relationship between the service analysis scenario type and the call chain data acquisition rule may include: the service analysis system comprises a plurality of service analysis scene types and call chain data acquisition rules corresponding to the service analysis scene types.
The call chain data collection rule is a rule for collecting call chain data used for service analysis of a service from all call chain data of the service. And inquiring the service analysis scene type of the service in the corresponding relation between the preset service analysis scene type and the call chain data acquisition rule. And when a service analysis scene type identical to the service analysis scene type of the service is inquired, acquiring a call chain data acquisition rule corresponding to the service analysis scene type as a call chain data acquisition rule corresponding to the service.
In one embodiment, the plurality of business analysis scenario types includes: an offline analysis scenario and an online diagnosis scenario. The call chain data acquisition rule corresponding to the offline analysis scenario is as follows: and acquiring target call chain data corresponding to the service from all call chain data of the service according to a preset offline acquisition proportion. The call chain data acquisition rule corresponding to the online diagnosis scene is as follows: clustering all call chain data of the service based on the structure of all call chain data of the service to obtain at least one class; determining the acquisition proportion corresponding to each class; and acquiring target call chain data corresponding to the service in each class according to the acquisition proportion corresponding to each class.
And the target call chain data is call chain data used for performing service analysis on the service.
Optionally, the service analysis scenario type of the service is an offline analysis scenario; the acquiring, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service in all call chain data of the service includes: and acquiring target call chain data corresponding to the service from all call chain data of the service according to a preset offline acquisition proportion.
Optionally, the preset offline acquisition proportion is a call chain data acquisition proportion in an offline analysis scene. And presetting an off-line acquisition proportion according to actual needs.
Optionally, acquiring target call chain data corresponding to the service from all call chain data of the service according to a preset offline acquisition ratio may include: and sampling all the call chain data of the service according to a preset offline acquisition proportion, wherein the extracted call chain data is target call chain data corresponding to the service.
In one embodiment, the preset off-line collection ratio may be set to 50%. And sampling all the call chain data of the service according to a proportion of 50%, wherein the extracted call chain data is target call chain data corresponding to the service.
Optionally, the service analysis scenario type of the service is an online diagnosis scenario; the acquiring, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service in all call chain data of the service includes: clustering all call chain data of the service based on the structure of all call chain data of the service to obtain at least one class; determining the acquisition proportion corresponding to each class; and acquiring target call chain data corresponding to the service in each class according to the acquisition proportion corresponding to each class.
Optionally, clustering all call chain data of the service based on the structure of all call chain data of the service to obtain at least one class, including: respectively representing all the call chain data of the service as trees, and acquiring the similarity between the call chain data and the call chain data through the similarity between the trees; for any two pieces of call chain data in all the call chain data of the service, acquiring the similarity between trees corresponding to the two pieces of call chain data as the similarity between the two pieces of call chain data; and clustering all the call chain data of the service according to the similarity among all the call chain data of the service by using a preset clustering algorithm to obtain at least one class.
Optionally, each tree is used to represent a piece of call chain data. Each tree may include a plurality of nodes, and any node in the tree may refer to a node in the call chain data. For example, the root node in the tree may refer to the first node of the call chain data and the leaf node in the tree may refer to the last node of the call chain data. Parent-child relationships between different nodes in the tree may refer to call relationships between different nodes in the call chain data. In the process of generating the tree, the identifier of the corresponding node in the corresponding call chain data may be written into each node in the tree, so that each node in the tree carries the identifier of the corresponding node in the corresponding call chain data. For any two pieces of call chain data, if the call relations of the two pieces of call chain data are similar, the structures of the trees generated based on the two pieces of call chain data are also similar.
Optionally, obtaining the similarity between the call chain data and the call chain data through the similarity between the trees includes: acquiring the same nodes and the same logic nodes of the two trees; generating the same subtrees of the two trees based on the same node and the same logic node, wherein the same subtrees comprise the same node and the same logic node; and according to the same subtree, acquiring the similarity of the two trees as the similarity between the data of the two call chains.
Optionally, the preset clustering algorithm may be any one of a partition-based clustering algorithm, a hierarchy-based clustering algorithm, a density-based clustering algorithm, a network-based clustering algorithm, a model-based clustering algorithm, a fuzzy-based clustering algorithm, a constraint-based clustering algorithm, a granularity-based clustering algorithm, a spectral clustering algorithm, a kernel clustering algorithm, and a quantum clustering algorithm.
Optionally, a class is also called a cluster, and refers to a set of similar objects. In the embodiment of the invention, the class comprises at least one piece of call chain data, the structures of different call chain data in the same class are similar, and the structures of the call chain data in different classes are not similar.
Therefore, according to the structure of the call chain data, the embodiment of the invention can gather a large amount of call chain data of the same service into different classes, the structures of the different call chain data in the same class are similar, and the difference of the structures of the call chain data in the different classes is larger.
Optionally, determining the acquisition proportions corresponding to the respective classes may include: selecting a class with the largest calling chain data quantity from a plurality of classes as a clustering center, and setting the acquisition proportion corresponding to the clustering center as a first acquisition proportion; calculating the distance between each class except the clustering center and the clustering center according to a distance measurement formula; selecting a class which is farthest from the clustering center from the classes except the clustering center as a target class, and setting the acquisition proportion corresponding to the target class as a second acquisition proportion; setting the acquisition proportion corresponding to the classes except the clustering center and the target class as a third acquisition proportion; wherein the second acquisition proportion is greater than the third acquisition proportion, which is greater than the first acquisition proportion.
In a specific example, the first acquisition proportion is 20%, the second acquisition proportion is 60%, and the third acquisition proportion is 40%. The collection ratio corresponding to the cluster center is set to 20%, the collection ratio corresponding to the target class is set to 60%, and the collection ratio corresponding to a class other than the cluster center and the target class is set to 40%.
The large amount of call chain data contained in the cluster should be common call chain data. The call chain data contained in the target class farthest from the cluster center should be rare call chain data. Through the flow, the sampling proportion corresponding to the class far away from the clustering center can be increased, so that the rare calling chain data can not be sampled and discarded.
Optionally, in a case where only one class is obtained, the acquisition ratio corresponding to the class is directly set as the first acquisition ratio.
Optionally, in the case that only two classes are obtained, one class with a large number of call chain data is used as a clustering center, and the acquisition proportion corresponding to the clustering center is set as a first acquisition proportion; and taking another class except the clustering center as a target class, and setting the acquisition proportion corresponding to the target class as a second acquisition proportion.
Optionally, acquiring target call chain data corresponding to the service in each class according to an acquisition ratio corresponding to each class, including: sampling is carried out in all the call chain data of the service according to the acquisition proportion corresponding to each class; and determining the extracted call chain data in each class as the extracted call chain data as the target call chain data corresponding to the service.
In one embodiment, three classes are obtained: a first class, a second class, and a third class. The acquisition proportion corresponding to the first category is 20%, the acquisition proportion corresponding to the second category is 40%, and the acquisition proportion corresponding to the third category is 60%. The sampling is done in the call chain data in the first class at a 20% ratio. The sampling is done in the call chain data in the second class, at a rate of 40%. The sampling is done in 60% proportion in the call chain data in the third class. And summarizing the extracted 3 groups of data into 1 group of data to be used as target call chain data corresponding to the service.
Optionally, storing the target call chain data corresponding to the service may include: and storing the target call chain data corresponding to the service into a preset database, so that the service can be conveniently subjected to statistical analysis subsequently according to the target call chain data corresponding to the service.
The embodiment of the invention can directly extract the call chain data for service analysis from all call chain data generated in the service call process according to a fixed acquisition proportion when the service analysis scene of the service is an off-line analysis scene with low requirement on timeliness.
According to the embodiment of the invention, when the service analysis scene of the service is an online diagnosis scene with high requirement on timeliness, all the calling chain data of the service are gathered into different classes based on the structure of the calling chain data, the calling chain data of each class are sampled respectively according to the acquisition proportion corresponding to each class, the sampling proportion of common calling chain data is reduced, and the sampling proportion of rare calling chain data is improved, so that the rare calling chain data can be ensured not to be sampled and discarded, the problem of service processing is convenient to position, the accuracy of the subsequent service analysis process is improved, the number of common calling chain data in the calling chain data for service analysis of the service can be reduced, the storage cost of the calling chain data is greatly reduced, the efficiency of the subsequent service analysis process is improved, and the timeliness requirement of the service analysis scene is met.
The embodiment of the invention provides a call chain data acquisition method, which comprises the steps of acquiring all call chain data of a service according to call information associated with the service; then, determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service; and finally, acquiring target call chain data corresponding to the service from all the call chain data of the service according to the call chain data acquisition rule corresponding to the service, storing the target call chain data corresponding to the service, automatically selecting a proper call chain data acquisition rule according to the service analysis scene type of the service, acquiring the call chain data for service analysis of the service, and meeting the requirements of different service analysis scenes.
Example two
Fig. 2 is a flowchart of a call chain data acquisition method according to a second embodiment of the present invention. In this embodiment of the present invention, after obtaining all call chain data of the service according to the call information associated with the service, the method further includes: and acquiring abnormal call chain data corresponding to the service from all the call chain data of the service according to a preset abnormal data acquisition rule, and storing the abnormal call chain data corresponding to the service.
As shown in fig. 2, the method of the embodiment of the present invention specifically includes:
Optionally, the preset abnormal data collection rule is a rule for collecting abnormal call chain data from all call chain data of the service.
Optionally, the acquiring, according to a preset abnormal data acquisition rule, abnormal call chain data corresponding to the service in all call chain data of the service includes: acquiring expected call chain data corresponding to the service; comparing each call chain data of the service with the expected call chain data in sequence to obtain the similarity between each call chain data of the service and the expected call chain data; and acquiring call chain data of which the similarity with the expected call chain data is not within a preset similarity range from all the call chain data of the service as abnormal call chain data corresponding to the service.
Optionally, the obtaining of expected call chain data corresponding to the service includes: inputting the calling relation of the service into a machine learning model trained in advance to obtain expected calling chain data corresponding to the service; the input of the machine learning model is the calling relation of the service, and the output is normal calling chain data in the service calling process of the service.
And the expected call chain data corresponding to the service is normal call chain data in the service call process of the service. The calling relation of the service is the calling relation among a plurality of nodes in the calling process of the service.
Optionally, acquiring a preset number of calling relations of different services and normal calling chain data in a service calling process as training samples; and training a machine learning model according to the training samples. The input of the machine learning model is the calling relation of the service, and the output is normal calling chain data in the service calling process of the service.
Optionally, comparing each call chain data of the service with the expected call chain data in sequence to obtain a similarity between each call chain data of the service and the expected call chain data, including: representing the expected call chain data as a tree; acquiring a piece of call chain data in all the call chain data of the service in sequence as the current processing call chain data; representing the current processing call chain data as a tree, and acquiring the similarity between the current processing call chain data and the expected call chain data through the similarity between the tree and the tree; and returning to execute the operation of sequentially acquiring a piece of call chain data from all the call chain data of the service as the current processing call chain data until the processing of all the call chain data of the service is completed, and obtaining the similarity between each call chain data of the service and the expected call chain data.
And setting a preset similarity range in advance according to actual needs. The call chain data with the similarity between the call chain data and the expected call chain data within the preset similarity range is similar to the expected call chain data, and can be regarded as normal call chain data of the service. The call chain data with the similarity between the call chain data and the expected call chain data not within the preset similarity range has a larger difference from the expected call chain data, and can be regarded as abnormal call chain data of the service.
In one embodiment, the predetermined similarity range is 60% or more. And acquiring call chain data of which the similarity with the expected call chain data is not within a preset similarity range, namely the call chain data of which the similarity with the expected call chain data is less than 60%, from all the call chain data of the service as abnormal call chain data corresponding to the service.
The abnormal call chain data usually only occupies a small part of the whole call chain data, and the abnormal call chain data with very low occurrence frequency can be collected only by selecting a proper mode. The embodiment of the invention can acquire abnormal call chain data with extremely low occurrence probability from a large amount of call chain data of the service.
And 203, determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service.
And 204, acquiring target call chain data corresponding to the service from all call chain data of the service according to a call chain data acquisition rule corresponding to the service.
And the target call chain data is call chain data used for performing service analysis on the service.
And step 205, storing the abnormal call chain data and the target call chain data corresponding to the service.
Optionally, the abnormal call chain data and the target call chain data corresponding to the service are stored in a preset database, so that the service is subjected to statistical analysis subsequently according to the abnormal call chain data and the target call chain data corresponding to the service.
The embodiment of the invention provides a call chain data acquisition method, which is characterized in that after all call chain data of a service are acquired according to call information associated with the service, according to a preset abnormal data acquisition rule, abnormal call chain data corresponding to the service are acquired in all the call chain data of the service, and the abnormal call chain data corresponding to the service are stored, so that abnormal call chain data with extremely low probability of occurrence can be acquired from a large amount of call chain data of the service according to the preset abnormal data acquisition rule, the efficiency of a subsequent service analysis process is improved, and the abnormality is positioned quickly.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a call chain data acquisition device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: adata acquisition module 301, arule determination module 302, and adata acquisition module 303.
Thedata obtaining module 301 is configured to obtain all call chain data of a service according to call information associated with the service; arule determining module 302, configured to determine, according to a service analysis scene type of the service, a call chain data acquisition rule corresponding to the service; adata acquisition module 303, configured to acquire, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service from all call chain data of the service, and store the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
The embodiment of the invention provides a call chain data acquisition device, which is used for acquiring all call chain data of a service according to call information associated with the service; then, determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service; and finally, acquiring target call chain data corresponding to the service from all the call chain data of the service according to the call chain data acquisition rule corresponding to the service, storing the target call chain data corresponding to the service, automatically selecting a proper call chain data acquisition rule according to the service analysis scene type of the service, acquiring the call chain data for service analysis of the service, and meeting the requirements of different service analysis scenes.
In an optional implementation manner of the embodiment of the present invention, optionally, the call chain data acquisition device further includes: and the abnormal data acquisition module is used for acquiring abnormal call chain data corresponding to the service from all the call chain data of the service according to a preset abnormal data acquisition rule and storing the abnormal call chain data corresponding to the service.
In an optional implementation manner of the embodiment of the present invention, optionally, when executing an operation of acquiring, according to a preset abnormal data acquisition rule, abnormal call chain data corresponding to the service in all call chain data of the service, the abnormal data acquisition module is specifically configured to: acquiring expected call chain data corresponding to the service; comparing each call chain data of the service with the expected call chain data in sequence to obtain the similarity between each call chain data of the service and the expected call chain data; and acquiring call chain data of which the similarity with the expected call chain data is not within a preset similarity range from all the call chain data of the service as abnormal call chain data corresponding to the service.
In an optional implementation manner of the embodiment of the present invention, optionally, when therule determining module 302 performs an operation of determining, according to the service analysis scenario type of the service, a call chain data acquisition rule corresponding to the service, the operation is specifically configured to: and determining a calling chain data acquisition rule corresponding to the service according to the corresponding relation between the preset service analysis scene type and the calling chain data acquisition rule.
In an optional implementation manner of the embodiment of the present invention, optionally, the service analysis scenario type includes: an offline analysis scenario and an online diagnosis scenario.
In an optional implementation manner of the embodiment of the present invention, optionally, the service analysis scenario type of the service is an offline analysis scenario; thedata acquisition module 303 is specifically configured to, when executing an operation of acquiring, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service from all call chain data of the service: and acquiring target call chain data corresponding to the service from all call chain data of the service according to a preset offline acquisition proportion.
In an optional implementation manner of the embodiment of the present invention, optionally, the service analysis scenario type of the service is an online diagnosis scenario; thedata acquisition module 303 is specifically configured to, when executing an operation of acquiring, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service from all call chain data of the service: clustering all call chain data of the service based on the structure of all call chain data of the service to obtain at least one class; determining the acquisition proportion corresponding to each class; and acquiring target call chain data corresponding to the service in each class according to the acquisition proportion corresponding to each class.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The call chain data acquisition device can execute the call chain data acquisition method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the call chain data acquisition method.
Example four
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of anexemplary computer device 12 suitable for use in implementing embodiments of the present invention. Thecomputer device 12 shown in FIG. 4 is only one example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4,computer device 12 is in the form of a general purpose computing device. The components ofcomputer device 12 may include, but are not limited to: one ormore processors 16, amemory 28, and a bus 18 connecting the various business system components (including thememory 28 and the processors 16).
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Thememory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/orcache memory 32.Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only,storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces.Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) ofprogram modules 42 may be stored, for example, inmemory 28,such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment.Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Theprocessor 16 executes various functional applications and data processing by running the program stored in thememory 28, so as to implement the call chain data acquisition method provided by the embodiment of the present invention: acquiring all call chain data of the service according to the call information associated with the service; determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service; acquiring target call chain data corresponding to the service from all call chain data of the service according to a call chain data acquisition rule corresponding to the service, and storing the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
EXAMPLE five
The fifth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for acquiring call chain data provided in the fifth embodiment of the present invention is implemented: acquiring all call chain data of the service according to the call information associated with the service; determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service; acquiring target call chain data corresponding to the service from all call chain data of the service according to a call chain data acquisition rule corresponding to the service, and storing the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or computer device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A call chain data acquisition method is characterized by comprising the following steps:
acquiring all call chain data of the service according to the call information associated with the service;
determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service;
acquiring target call chain data corresponding to the service from all call chain data of the service according to a call chain data acquisition rule corresponding to the service, and storing the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
2. The method according to claim 1, wherein after obtaining all call chain data of the service according to the call information associated with the service, the method further comprises:
and acquiring abnormal call chain data corresponding to the service from all the call chain data of the service according to a preset abnormal data acquisition rule, and storing the abnormal call chain data corresponding to the service.
3. The method according to claim 2, wherein the acquiring, according to a preset abnormal data acquisition rule, abnormal call chain data corresponding to the service from all call chain data of the service includes:
acquiring expected call chain data corresponding to the service;
comparing each call chain data of the service with the expected call chain data in sequence to obtain the similarity between each call chain data of the service and the expected call chain data;
and acquiring call chain data of which the similarity with the expected call chain data is not within a preset similarity range from all the call chain data of the service as abnormal call chain data corresponding to the service.
4. The method according to claim 1, wherein the determining, according to the service analysis scenario type of the service, the call chain data collection rule corresponding to the service includes:
and determining a calling chain data acquisition rule corresponding to the service according to the corresponding relation between the preset service analysis scene type and the calling chain data acquisition rule.
5. The method of claim 1, wherein the traffic analysis scenario type comprises: an offline analysis scenario and an online diagnosis scenario.
6. The method of claim 5, wherein the service analysis scenario type of the service is an offline analysis scenario;
the acquiring, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service in all call chain data of the service includes:
and acquiring target call chain data corresponding to the service from all call chain data of the service according to a preset offline acquisition proportion.
7. The method of claim 5, wherein the business analysis scenario type of the business is an online diagnosis scenario;
the acquiring, according to a call chain data acquisition rule corresponding to the service, target call chain data corresponding to the service in all call chain data of the service includes:
clustering all call chain data of the service based on the structure of all call chain data of the service to obtain at least one class;
determining the acquisition proportion corresponding to each class;
and acquiring target call chain data corresponding to the service in each class according to the acquisition proportion corresponding to each class.
8. A call chain data acquisition device, comprising:
the data acquisition module is used for acquiring all calling chain data of the service according to calling information associated with the service;
the rule determining module is used for determining a calling chain data acquisition rule corresponding to the service according to the service analysis scene type of the service;
the data acquisition module is used for acquiring target call chain data corresponding to the service from all the call chain data of the service according to a call chain data acquisition rule corresponding to the service, and storing the target call chain data corresponding to the service; and the target call chain data is call chain data used for performing service analysis on the service.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the call chain data collection method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the call chain data acquisition method according to any one of claims 1 to 7.
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