CN109271396B - Processing method, device and equipment for call chain data and storage medium - Google Patents
Processing method, device and equipment for call chain data and storage medium Download PDFInfo
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Abstract
The application discloses a processing method of call chain data, which is applied to a server and comprises the following steps: receiving call chain data reported by a client according to a preset sampling rate; storing the reported call chain data; the preset sampling rate is periodically updated by the server according to the historical report records of the client, and then is periodically inquired and acquired by the client so as to keep the reported data volume of the call chain data stable. The method and the device ensure the stability of the reported data volume of the call chain data, avoid the performance consumption of the server due to frequent reporting, avoid the omission of important data due to low-frequency reporting, and effectively improve the processing efficiency and the reporting quality of the call chain data. The application also discloses a processing device, equipment and a computer readable storage medium for the call chain data, and the beneficial effects are also achieved.
Description
Technical Field
The present application relates to the field of big data, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for processing call chain data.
Background
The call chain data is important data recording information such as path, time and the like of a message processing process in the network application service, and one call chain comprises all intermediate links from a source request (such as a front-end webpage request, a wireless client request and the like) to a final bottom-layer device (such as a database, a distributed cache and the like), and is commonly used for quickly positioning a network problem or optimizing time difference between a message request and a response and the like.
Some existing distributed trace systems, such as Dapper and Zipkin, implement call chain data tracing. Generally, a client filters call chain data according to a fixed sampling rate and reports the filtered call chain data to a server. If the sampling rate is too high, the workload of the server side in analyzing the received call chain data is increased; if the sampling rate is too low, some call chain data recorded with important information are easily missed. In fact, the frequency of calls made by the application program in different time periods is different, and the total amount of call chain data generated is also different. Therefore, reporting with a fixed sampling rate will affect the service performance of the server.
Therefore, what kind of processing technology of the call chain data is adopted to dynamically adjust the sampling rate when reporting the call chain data according to the actual running condition of the client, so as to effectively improve the processing efficiency and the reporting quality of the call chain data reporting, which is a technical problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
The present application aims to provide a processing method, an apparatus, a device, and a computer-readable storage medium for call chain data, so as to dynamically adjust a sampling rate when the call chain data is reported according to an actual operating condition of a client, thereby effectively improving the processing efficiency and reporting quality of the call chain data reporting.
In order to solve the above technical problem, the present application provides a processing method for call chain data, which is applied to a server and includes:
receiving call chain data reported by a client according to a preset sampling rate;
storing the reported call chain data;
and after the preset sampling rate is periodically updated by the server according to the historical report record of the client, the preset sampling rate is periodically inquired and acquired by the client so as to keep the reported data volume of the call chain data stable.
Optionally, the periodically updating, by the server, the preset sampling rate according to the historical report record of the client includes:
the server side periodically updates the preset sampling rate of each application service of the client side according to the historical report record of the client side so as to ensure that the reported data volume of the call chain data of each application service is respectively kept stable;
and the calling chain data carries an ID code of a corresponding application service, and the ID code is generated by the server in a handshake operation when the server is connected with the client for the first time and is sent to the client.
Optionally, the preset sampling rate is updated by the server periodically according to the following steps:
acquiring a record value of the reported data volume of the call chain data in the latest preset period according to the historical report record;
determining a predicted value of the reported data volume of the call chain data in the next preset period according to the record value;
judging whether the predicted value is higher than a first preset threshold value or not;
if so, reducing the preset sampling rate;
if not, judging whether the reported data volume is lower than a second preset threshold value, wherein the first preset threshold value is higher than the second preset threshold value;
and if so, increasing the preset sampling rate.
Optionally, the determining, according to the record value, a predicted value of a reported data volume of the call chain data in a next preset period includes:
determining a predicted value of the reported data volume of the call chain data in the next preset period according to the recorded value by adopting any one of the following time sequence prediction methods:
simple time-series average method, weighted moving average method, exponential smoothing method, seasonal trend prediction method.
Optionally, before the storing the reported call chain data, the method further includes:
judging whether the current warehousing rate of the call chain data exceeds a preset warehousing rate or not;
if not, executing the step of storing the reported call chain data;
and if so, discarding the reported call chain data.
Optionally, the method further comprises:
and updating the preset warehousing rate periodically according to the historical report record so as to keep the warehousing data volume of the call chain data stable.
The application also provides a processing method of the call chain data, which is applied to the client and comprises the following steps:
the server is inquired periodically to obtain an updated preset sampling rate, and the preset sampling rate is updated periodically by the server according to the historical report record of the client so as to keep the reported data volume of the call chain data stable;
and reporting the calling chain data to the server according to the preset sampling rate.
The application also provides a processing apparatus for call chain data, which is applied to a server and comprises:
the receiving module is used for receiving call chain data reported by the client according to a preset sampling rate;
the storage module is used for storing the reported call chain data;
and the updating module is used for periodically updating the preset sampling rate according to the historical report record of the client, and the client periodically inquires and acquires the updated sampling rate so as to keep the reported data volume of the calling chain data stable.
The present application further provides a processing device for call chain data, including:
a memory: for storing a computer program;
a processor: for executing said computer program to implement the steps of any of the call chain data processing methods described above.
The present application also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the steps of any one of the call chain data processing methods described above.
The processing method of the call chain data provided by the application is applied to a server and comprises the following steps: receiving call chain data reported by a client according to a preset sampling rate; storing the reported call chain data; and after the preset sampling rate is periodically updated by the server according to the historical report record of the client, the preset sampling rate is periodically inquired and acquired by the client so as to keep the reported data volume of the call chain data stable.
Therefore, compared with the prior art, in the processing method of the call chain data provided by the application, the preset sampling rate adopted when the client reports the call chain data is not constant, but is continuously updated and changed according to the quantity of the call chain data generated by the client. The server predicts the call chain data volume to be generated by the client by analyzing the historical report record of the call chain data of the client, thereby determining an updated value of a preset sampling rate which can ensure that the reported data volume of the client is stable, so that the client reports the call chain data to the server by adopting the updated preset sampling rate. The method and the device avoid performance consumption of the server caused by frequent reporting and avoid omission of important data caused by low-frequency reporting, so that the processing efficiency and the reporting quality of the call chain data can be effectively improved. The processing device, the equipment and the computer readable storage medium for the call chain data provided by the application can realize the processing method for the call chain data, and also have the beneficial effects.
Drawings
In order to more clearly illustrate the technical solutions in the prior art and the embodiments of the present application, the drawings that are needed to be used in the description of the prior art and the embodiments of the present application will be briefly described below. Of course, the following description of the drawings related to the embodiments of the present application is only a part of the embodiments of the present application, and it will be obvious to those skilled in the art that other drawings can be obtained from the provided drawings without any creative effort, and the obtained other drawings also belong to the protection scope of the present application.
Fig. 1 is an application scenario diagram of a processing method for call chain data provided in the present application;
fig. 2 is a flowchart of a processing method of call chain data provided in the present application;
fig. 3 is a flowchart of a method for updating a predetermined sampling rate according to the present application;
FIG. 4 is a flowchart of another method for processing call chain data provided in the present application;
fig. 5 is a block diagram illustrating a structure of a call chain data processing apparatus according to the present application;
fig. 6 is a block diagram illustrating a structure of another call chain data processing apparatus provided in the present application.
Detailed Description
The core of the application is to provide a processing method, a device, equipment and a computer readable storage medium for call chain data, so as to dynamically adjust the sampling rate when the call chain data is reported according to the actual running condition of a client, thereby effectively improving the processing efficiency and the reporting quality of the call chain data reporting.
In order to more clearly and completely describe the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is an application scenario diagram of a processing method for call chain data provided in the present application.
The client shown in fig. 1 has various application services available to the user, and they generate call chain data in the process of providing the corresponding services to the user. The client can filter the generated call chain data according to a certain proportion, namely a preset sampling rate, and then send the call chain data to the server, and the call chain data are stored by the server so as to be analyzed and managed.
Referring to fig. 2, fig. 2 is a flowchart of a processing method of call chain data provided in the present application, which is applied to a server and mainly includes the following steps:
s21: receiving call chain data reported by a client according to a preset sampling rate; the preset sampling rate is periodically updated by the server according to the historical report records of the client, and then is periodically inquired and acquired by the client so as to keep the reported data volume of the call chain data stable.
S22: and storing the reported call chain data.
As described above, the client uses some tools like probes embedded in the application service programs to collect call chain information of each application program, and after call chain data is generated, the call chain data is filtered according to a preset sampling rate and reported to the server.
Specifically, in the processing method of call chain data provided by the present application, the client reports the call chain data to the server according to a preset sampling rate, specifically, the server predicts the generation condition of the call chain data of the client according to the history report record of the client, and updates and adjusts the call chain data continuously, which is not a fixed value used in the prior art.
In the present application, the purpose of dynamically adjusting the preset sampling rate value is to ensure the stability of the data amount of the call chain data reported to the server when the application service is frequently called and rarely called, thereby further ensuring the efficiency and stability when the server processes the call chain data. Specifically, when the server updates the preset sampling rate, the generation condition of the client call chain data is predicted according to the historical report record of the client, if the client is going to generate a large amount of call chain data, the preset sampling rate can be reduced, and if the client is only going to generate a small amount of call chain data, the preset sampling rate can be increased, so that the stability of the data volume reported by the client is ensured.
It is easy to understand that the server may update and adjust the preset sampling rate periodically at intervals, for example, the server may update every 10 minutes, and the corresponding client queries the server every 10 minutes to obtain the updated preset sampling rate. It should be noted that, if the performance consumption of the server does not need to be considered, the updated preset sampling rate may also be actively pushed to the client by the server, and a person skilled in the art may select the setting by himself.
Therefore, in the processing method of the call chain data provided by the application, the preset sampling rate adopted when the client reports the call chain data is not constant, but is continuously updated and changed according to the quantity of the call chain data generated by the client. The server predicts the call chain data volume to be generated by the client by analyzing the historical report record of the call chain data of the client, thereby determining an updated value of a preset sampling rate which can ensure that the reported data volume of the client is stable, so that the client reports the call chain data to the server by adopting the updated preset sampling rate. The method and the device avoid performance consumption of the server caused by frequent reporting and avoid omission of important data caused by low-frequency reporting, so that the processing efficiency and the reporting quality of the call chain data can be effectively improved.
The processing method of the call chain data provided by the application is based on the embodiment as follows:
as a preferred embodiment, the step of periodically updating, by the server, the preset sampling rate according to the history report record of the client includes:
the server side periodically updates the preset sampling rate of each application service of the client side according to the historical report record of the client side, so that the reported data volume of the call chain data of each application service is kept stable;
the calling chain data carries an ID code of the corresponding application service, and the ID code is generated by the server in the handshake operation when the server is connected with the client for the first time and is sent to the client.
Specifically, different from a scheme that a uniform preset sampling rate is adopted for all application services in the prior art, in the call chain data processing method provided by the application, call chain data from different application services reported by the client respectively adopt respective corresponding preset sampling rates, and are respectively updated by the server.
It is easy to understand that some application services in the client are frequently called and used by the user, and some application services are rarely called and used by the user, so that different preset sampling rates are respectively adopted for different application services, the call chain data of each application service can be effectively guaranteed to be reasonably reported, the reported data volume is not too large, and important information is not missed.
In order to distinguish different application services, when the server side performs handshake operation when being connected with the client side for the first time, the server side can allocate the ID codes to the different application services of the client side, and the corresponding application services can be uniquely identified according to the ID codes. Specifically, the ID code may be carried in the call chain data reported by the client, so that the service end can identify which application service the reported call chain data corresponds to. Similarly, when sending the updated preset sampling rate to the client, the server may also carry the ID code, which indicates that the preset sampling rate is updated for the application service corresponding to the ID code.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for updating a predetermined sampling rate according to the present disclosure. As shown in fig. 3, as a preferred embodiment, the preset sampling rate is specifically updated by the server periodically according to the following steps:
s31: and acquiring a recorded value of the reported data volume of the calling chain data in the latest preset period according to the historical reported record.
S32: and determining a predicted value of the reported data volume of the calling link data in the next preset period according to the recorded value.
S33: judging whether the predicted value is higher than a first preset threshold value; if yes, go to S34; if not, the process proceeds to S35.
S34: the preset sampling rate is reduced.
S35: judging whether the reported data volume is lower than a second preset threshold value or not, wherein the first preset threshold value is higher than the second preset threshold value; if yes, the process proceeds to S36.
S36: the preset sampling rate is increased.
Specifically, when the server periodically updates the value of the preset sampling rate, the reporting condition of the call chain data in the next preset period can be predicted according to the reporting condition of the call chain data in the latest preset period. The preset period can be selected and set by a person skilled in the art, for example, 10 minutes or half an hour, and the like, which is not limited in the present application.
After a predicted value of the reported data volume of the call chain data in the next preset period is obtained, an update value of the preset sampling rate can be determined according to the size of the predicted value. If the predicted value is too large, for example, greater than a first preset threshold, the preset sampling rate may be reduced; if the predicted value is too small, for example, smaller than a second preset threshold, the preset sampling rate may be increased; if the predicted value is between the first preset threshold and the second preset threshold, the original value of the preset sampling rate can be maintained. Of course, as for the specific reduction or increase of the preset sampling rate, a person skilled in the art can select and set the preset sampling rate according to the actual application, and the application does not limit this.
As a preferred embodiment, determining the predicted value of the reported data amount of the call chain data in the next preset period according to the recorded value includes:
determining a predicted value of the reported data volume of the call chain data in the next preset period according to the recorded value by adopting any one of the following time sequence prediction methods:
simple time-series average method, weighted moving average method, exponential smoothing method, seasonal trend prediction method.
Specifically, when predicting the reporting condition of the call chain data in the next preset period, various time sequence prediction methods can be specifically adopted. The simple time-series average method is also called an arithmetic average method, namely, statistics of a plurality of historical periods are used as observed values, an arithmetic average is obtained and used as a next-period predicted value, and the method is suitable for trend prediction with little change of things.
The weighted sequence time average method is to weight the historical data of each period according to the influence degree of the near period and the far period and then calculate the average value as the next period predicted value.
The weighted moving average method is to weight the arithmetic mean of several periods obtained by successive moving calculations. In determining the weights, the weights of recent observations should be greater and the weights of distant observations should be less.
The exponential smoothing method is to predict the actual number and the predicted value of the history data by an exponential weighting method. The method can save a lot of data and time for processing the data, reduces the storage capacity of the data, is simple and convenient, and is a short-term prediction method widely used abroad.
The seasonal trend prediction method is used for predicting the seasonal variation trend of economic things according to the periodic seasonal variation indexes of the economic things which repeatedly appear every year.
As a preferred embodiment, the step of periodically updating, by the server, the preset sampling rate according to the history report record of the client includes:
and after receiving the call chain data reported by the client each time, the server updates the preset sampling rate according to the historical report record of the client.
Specifically, the update of the preset sampling rate by the server may also be performed after receiving the call chain data reported by the client each time, that is, the update of the preset sampling rate is performed once every time the call chain data is received by the server, and a person skilled in the art may select the setting by himself.
As a preferred embodiment, before storing the reported call chain data, the method further includes:
judging whether the current warehousing rate of the call chain data exceeds a preset warehousing rate or not;
if not, executing the step of storing the reported call chain data;
if so, discarding the reported call chain data.
Specifically, when the server side puts the reported call chain data into a database, that is, stores the call chain data, the server side may further perform a double screening to further control the storage amount of the call chain data. Specifically, a threshold value, that is, a preset warehousing rate, may be set for the warehousing rate, where the warehousing rate is a ratio of the size of the call chain data stored in the warehouse to the size of the reported call chain data, and if the current warehousing rate already exceeds the preset warehousing rate, the call chain data received currently should be discarded; and if the current warehousing rate does not exceed the preset warehousing rate, continuously storing the currently received call chain data. By examining the storage of the call chain data, the warehousing of a large amount of high-frequency call chain data can be effectively avoided, and further, the full writing of a disk or a database of a server can be avoided.
As a preferred embodiment, further comprising:
and updating the preset warehousing rate periodically according to the historical report record so as to keep the warehousing data volume of the call chain data stable.
Specifically, since the frequencies of the application services being invoked in different time periods may be greatly different, the preset warehousing rate may also be dynamically adjusted, so as to meet the application requirements of the user in different time periods.
Any of the above-described call chain data processing methods can be specifically used in a distributed tracking system such as Zipkin in a server.
Referring to fig. 4, fig. 4 is a flowchart of another call chain data processing method provided in the present application, applied to a client, including the following steps:
s41: the server is regularly inquired to obtain an updated preset sampling rate, and the preset sampling rate is regularly updated by the server according to the historical report record of the client so as to keep the reported data volume of the call chain data stable;
s42: and reporting the call chain data to a server according to a preset sampling rate.
As can be seen, in the processing method of call chain data applied to the client provided in this embodiment, the preset sampling rate adopted when the client reports the call chain data is not constant, but is continuously updated and changed according to the amount of the call chain data generated by the client. The server predicts the call chain data volume to be generated by the client by analyzing the historical report record of the call chain data of the client, thereby determining an updated value of a preset sampling rate which can ensure that the reported data volume of the client is stable, so that the client reports the call chain data to the server by adopting the updated preset sampling rate. The method and the device avoid performance consumption of the server caused by frequent reporting and avoid omission of important data caused by low-frequency reporting, so that the processing efficiency and the reporting quality of the call chain data can be effectively improved.
The following describes a processing apparatus for call chain data provided in the present application.
Referring to fig. 5, fig. 5 is a block diagram of a call chain data processing apparatus applied to a server, and includes a receiving module 51, a storing module 52, and an updating module 53;
the receiving module 51 is configured to receive call chain data reported by a client according to a preset sampling rate;
the storage module 52 is configured to store the reported call chain data;
the updating module 53 is configured to update the preset sampling rate according to the historical report record of the client, and the client periodically queries and acquires the updated sampling rate, so as to keep the reported data volume of the call chain data stable. Therefore, according to the processing device for the call chain data provided by the application, the preset sampling rate adopted when the client reports the call chain data is not constant, but is continuously updated and changed according to the quantity of the call chain data generated by the client. The server predicts the call chain data volume to be generated by the client by analyzing the historical report record of the call chain data of the client, thereby determining an updated value of a preset sampling rate which can ensure that the reported data volume of the client is stable, so that the client reports the call chain data to the server by adopting the updated preset sampling rate. The method and the device avoid performance consumption of the server caused by frequent reporting and avoid omission of important data caused by low-frequency reporting, so that the processing efficiency and the reporting quality of the call chain data can be effectively improved.
The processing device for call chain data provided by the application is based on the above embodiment:
as a preferred embodiment, the updating module 53 is specifically configured to:
according to the historical report record of the client, respectively updating the preset sampling rate of each application service of the client so as to respectively keep the reported data volume of the call chain data of each application service stable;
the calling chain data carries an ID code of the corresponding application service, and the ID code is generated by the server in the handshake operation when the server is connected with the client for the first time and is sent to the client.
As a preferred embodiment, the updating module 53 specifically includes:
the acquisition unit is used for acquiring a recorded value of the reported data volume of the calling chain data in the latest preset period according to the historical reported record;
the prediction unit is used for determining a predicted value of the reported data volume of the calling chain data in the next preset period according to the record value;
the adjusting unit is used for judging whether the predicted value is higher than a first preset threshold value or not; if yes, reducing the preset sampling rate; if not, judging whether the reported data volume is lower than a second preset threshold, wherein the first preset threshold is higher than the second preset threshold; if yes, increasing the preset sampling rate.
As a preferred embodiment, the prediction unit is specifically configured to:
determining a predicted value of the reported data volume of the call chain data in the next preset period according to the recorded value by adopting any one of the following time sequence prediction methods:
simple time-series average method, weighted moving average method, exponential smoothing method, seasonal trend prediction method.
As a preferred embodiment, further comprising:
the judging module is configured to judge whether the current entry rate of the call chain data exceeds a preset entry rate before the storage module 52 stores the reported call chain data; if so, discarding the reported call chain data; if not, the memory module 52 stores the reported call chain data.
As a preferred embodiment, the updating module 53 is further configured to:
and updating the preset warehousing rate according to the historical reported record so as to keep the warehousing data volume of the call chain data stable.
Referring to fig. 6, fig. 6 is a block diagram of another structure of a call chain data processing apparatus provided in the present application, which is applied to a client and includes a query module 61 and a reporting module 62;
the query module 61 is configured to periodically query the server to obtain an updated preset sampling rate, where the preset sampling rate is periodically updated by the server according to a history report record of the client, so as to keep a reported data volume of the call chain data stable;
the reporting module 62 is configured to report the call chain data to the server according to a preset sampling rate.
The present application further provides a processing device for call chain data, including:
a memory: for storing a computer program;
a processor: for executing said computer program to implement the steps of any of the call chain data processing methods described above.
The present application also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the steps of any one of the call chain data processing methods described above.
The specific embodiments of the processing apparatus, the device, and the computer-readable storage medium for call chain data provided in the present application and the processing method for call chain data described above may be referred to correspondingly, and are not described herein again.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, throughout this document, relational terms such as "first" and "second" are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Furthermore, 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, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The technical solutions provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (7)
1. A processing method of call chain data is applied to a server and comprises the following steps:
receiving call chain data respectively reported by a client according to a preset sampling rate of each application service; the calling chain data carries an ID code of a corresponding application service, and the ID code is generated by the server in a handshake operation when the server is connected with the client for the first time and is sent to the client;
judging whether the current warehousing rate of the call chain data exceeds a preset warehousing rate or not; the warehousing rate is the ratio of the size of the call chain data stored in the warehouse to the size of the call chain data reported, and the preset warehousing rate is periodically updated by the server according to historical report records so as to keep the warehousing data volume of the call chain data stable;
if not, storing the reported call chain data;
if so, discarding the reported call chain data; after the preset sampling rate is periodically and respectively updated by the server according to the historical report records of each application service of the client, the client periodically inquires and acquires the preset sampling rate or the preset sampling rate is actively pushed to the client by the server so as to respectively keep the report data volume of the call chain data of each application service stable; and respectively adopting the corresponding preset sampling rates for the call chain data from different application services.
2. The processing method according to claim 1, wherein the preset sampling rate is updated periodically by the server according to the following steps:
acquiring a record value of the reported data volume of the call chain data in the latest preset period according to the historical report record;
determining a predicted value of the reported data volume of the call chain data in the next preset period according to the record value;
judging whether the predicted value is higher than a first preset threshold value or not;
if so, reducing the preset sampling rate;
if not, judging whether the reported data volume is lower than a second preset threshold value, wherein the first preset threshold value is higher than the second preset threshold value;
and if so, increasing the preset sampling rate.
3. The processing method according to claim 2, wherein the determining a predicted value of the reported data amount of the call chain data in a next preset period according to the recorded value comprises:
determining a predicted value of the reported data volume of the call chain data in the next preset period according to the recorded value by adopting any one of the following time sequence prediction methods:
simple time-series average method, weighted moving average method, exponential smoothing method, seasonal trend prediction method.
4. A processing method of call chain data is applied to a client and comprises the following steps:
the method comprises the steps that a server is inquired periodically or push information of the server is received to obtain updated preset sampling rate of each application service, and the preset sampling rate is updated periodically by the server according to historical report records of each application service of a client so that the reported data volume of call chain data of each application service is kept stable; respectively adopting corresponding preset sampling rates for call chain data from different application services;
respectively reporting the call chain data of each application service to the server according to the preset sampling rate; the calling chain data carries an ID code of a corresponding application service, and the ID code is generated by the server in a handshake operation when the server is connected with the client for the first time and is sent to the client; so that the server side judges whether the current warehousing rate of the call chain data exceeds a preset warehousing rate; the warehousing rate is the ratio of the size of the call chain data stored in the warehouse to the size of the call chain data reported, and the preset warehousing rate is periodically updated by the server according to historical report records so as to keep the warehousing data volume of the call chain data stable; if not, storing the reported call chain data; and if so, discarding the reported call chain data.
5. A processing device for call chain data is applied to a server and comprises:
the receiving module is used for receiving call chain data which are respectively reported by the client according to the preset sampling rate of each application service; the calling chain data carries an ID code of a corresponding application service, and the ID code is generated by the server in a handshake operation when the server is connected with the client for the first time and is sent to the client;
the storage module is used for judging whether the current warehousing rate of the calling chain data exceeds a preset warehousing rate; if not, storing the reported call chain data; if so, discarding the reported call chain data; the warehousing rate is the ratio of the size of the call chain data stored in the warehouse to the size of the reported call chain data;
the updating module is used for respectively updating the preset sampling rate periodically according to the historical report records of each application service of the client, and the client periodically inquires and acquires the preset sampling rate or the preset sampling rate is actively pushed to the client by the server so as to respectively keep the reported data volume of the call chain data of each application service stable; respectively adopting corresponding preset sampling rates for call chain data from different application services; and updating the preset warehousing rate periodically according to the historical report record so as to keep the warehousing data volume of the call chain data stable.
6. A call chain data processing apparatus, comprising:
a memory: for storing a computer program;
a processor: steps of a processing method for executing said computer program to implement call chain data according to any one of claims 1 to 4.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method for processing call chain data according to any one of claims 1 to 4.
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