CN110806959A - Method for tracking distributed service chain and aggregating logs - Google Patents
Method for tracking distributed service chain and aggregating logs Download PDFInfo
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Abstract
The invention relates to the technical field of service link development, in particular to a distributed service link tracking and log aggregation method. The method integrates a distributed tracking system and a log analysis system, a service layer is a service logic which is distributed and represents a corresponding system, a basic platform layer represents a basic function module supporting the service layer, a data persistence layer stores system configuration data, distributed system tracking data and log data collected by the log system, a user interaction layer is packaged on two system interface layers for micro-aggregation, the distributed tracking system and the log system are logically integrated into a system, and aggregation is realized by mutual association of tracking Ids. The method realizes the correlation search between the service chain and the log; can be used for distributed service chain tracking.
Description
Technical Field
The invention relates to the technical field of service link development, in particular to a distributed service link tracking and log aggregation method.
Background
Traditional single application is gradually transformed to distributed and micro-service due to factors such as low performance, difficult expansion, code bloat and difficult upgrading. Decoupling each functional sub-module from the single application is really an effective way for solving the traditional application at present. However, in the micro-service environment, the modules and the services are realized by remote calling such as rpc and http. When the calling system is abnormal, the positioning problem is a disaster to the operation and maintenance. Therefore, a system capable of tracking and recording the calling situation among the components, modules and sub-services is urgently needed, and a distributed service chain tracking system is born. The system is an indispensable operation and maintenance apparatus in the micro-service environment of software system disputes nowadays. However, the distributed tracking system has the following problems:
firstly, the current distributed tracking system scheme has a plurality of mature schemes, such as skywalk, pinpoint and the like. The solutions are only limited to paying attention to whether the service call is normal or not when tracking the distributed service call chain, the obtained basic information is coarse-grained information, and the service chain tracking and system application log is completely split. However, typically the most detailed error log is the application system's own log record.
At present, the distributed service chain tracking system and the log system commonly adopted in the industry look at two completely independent systems, so that frequent switching between the two systems is often required when positioning is abnormal, which undoubtedly increases the daily maintenance cost of the platform.
Third, usually, a distributed tracking system is used as the main body to locate the abnormal position, and a log system is used to locate the specific error information. In the method, the exception can be positioned only through the call chain, but the exception of the call chain cannot be positioned through the exception log.
And fourthly, the final abnormal information is obtained by checking the log, and the level and experience of the operation and maintenance personnel are greatly depended on, so that a standardized judgment process does not exist, and the final abnormal positioning brings great uncertainty and operation and maintenance cost.
If the distributed tracking system is combined with the log system, the functions of the tracking system can be greatly expanded, and the system abnormity can be more conveniently positioned.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for tracking a distributed service chain and aggregating logs; and logically unifying service chain tracking and application system logs to realize correlation searching between service chains and logs.
The technical scheme for solving the technical problems is as follows:
the method is to micro-aggregate a log system in the existing distributed tracking system; the distributed tracking system and the log analysis system are integrated, a service layer is distributed to represent service logic corresponding to the system, a basic platform layer represents a basic function module supporting the service layer, a data persistence layer stores system configuration data, distributed system tracking data and log data collected by the log system, a user interaction layer is packaged on two system interface layers for micro-aggregation, the distributed tracking system and the log system are logically integrated into a system, and aggregation is realized by mutual association of tracking Ids.
The distributed tracking system is provided with a non-invasive log collection agent plug-in, and the non-invasive log collection plug-in collects each log and adds a unique tracking ID through embedding points in log collection points of the application system.
The log analysis system comprises a log receiving module, a basic log retrieval module and an intelligent log analysis module;
the log receiving module is specially responsible for receiving logs with tracking Id marks, which are sent by a non-invasive acquisition agent plug-in the distributed tracking system, sending the logs into the log processing module and finally storing the logs into a log database;
the basic log retrieval module is used as a log retrieval engine;
the intelligent log analysis module uses an open source-based scimit-learn machine learning framework, and mining the historical abnormal log information of the application system as sample data, extracting abnormal characteristic information, constructing an abnormal decision tree, and labeling each warehousing log.
The method for realizing the log collection comprises the following specific steps:
step 1: editing the entry class of a non-invasive log collection agent to realize a premain method; the method is used for defining a class converter to complete the intervention of the java class loading process;
step 2: defining a log interception point, wherein the class realizes that ClassInstanceMethodEnhancePluginDefine is used for intercepting log writing operation, and specific internal logic is realized in a before method thereof;
and step 3: the agent acquires the tracking Id of the service chain from a tracking chain Carrier, copies an original log and adds a tracking Id mark to the log;
and 4, step 4: the agent sends the log record with the tracking Id identification to the message middleware, and the log without the tracking Id identification does not carry out any treatment on the content of the log;
and 5: a log receiving module reads an application system log with a tracking Id mark in message middleware;
step 6: and the log analysis system carries out structuralization and secondary processing on the log acquired by the receiving end and then sends the log into a back end for storage.
The method for realizing intelligent log analysis comprises the following steps:
step 1: analyzing a sample abnormal log, analyzing abnormal characteristic values of abnormal log contents and specific error contents, and calculating a Pearson correlation coefficient between the abnormal log contents and the specific error contents;
step 2: data preprocessing, namely processing the abnormal values and the redundant values obtained in the step 1, and performing data standardization processing on the abnormal log contents to convert the abnormal log contents into corresponding numerical values;
and step 3: modeling log abnormal information, constructing an abnormal recognition decision tree model by using an ID3 algorithm model of a scimit-leann machine learning library, and sending data obtained in the previous step into the model for training;
and 4, step 4: setting an accuracy threshold, comparing whether the model in the step 3 is more than or equal to the threshold, and otherwise, continuing to sample data and model parameters;
and 5: and sending the numerical values obtained by the classification model into an error type matching feedback knowledge base, and realizing the interconversion between the abnormal type and the error prompt content facing the human natural language by the knowledge base to obtain a final result.
The micro-polymerization implementation steps are as follows:
step 1: the user interaction layer receives a user request;
step 2: the routing gateway matches the path of the request flow guide according to the request url, and if the path accords with the log system url rule, the flow guide is processed in the step 3; if the url rule of the distributed tracking system is met, the flow is guided to the step 4 for processing; if the two are not met, the flow is guided to the step 5 for processing;
and step 3: the Log analysis system Log controller receives and processes the request and returns the result
And 4, step 4: the ApmConroller of the distributed tracking system receives the request and returns the result
And 5: and the log analysis system and the distributed tracking system aggregation layer receive the request and return a result.
The distributed service chain and log aggregation method logically unifies service chain tracking and application system logs, and realizes the correlation search between the service chain and the logs. The log of the application system is collected by using the non-invasive log collection agent which accords with the service chain system plug-in specification, so that the integrity, safety and controllability of the system are ensured. An intelligent log analysis subsystem based on data mining is introduced, the robustness of the system is improved, the abnormal information is identified quickly and efficiently, and an abnormal expression mode oriented to human natural language is introduced in the process, so that the operation and maintenance cost is greatly reduced.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a layered architecture diagram of the present invention;
FIG. 2 is a diagram of the logical architecture of the present invention;
FIG. 3 is a flow chart of log collection in accordance with the present invention;
FIG. 4 is a flow diagram of the intelligent log analysis of the present invention;
FIG. 5 is a flow chart of a method of the present invention for a micro integrated system.
Detailed Description
As shown in fig. 1 to 5, the distributed service chain tracking and log aggregation of the present invention mainly achieves the purpose of tracking chain and log aggregation by micro-integrating the log system in the existing distributed tracking system. The invention divides the whole system framework into three modules or subsystems: firstly, a distributed tracking system with a non-invasive log collection agent plug-in; a log analysis system with a log receiving module, a basic log retrieval module and an intelligent log analysis module; and thirdly, a distributed service chain and log aggregation module.
The layered architecture of the present invention is shown in fig. 1, and includes a user interaction layer, an interface layer, a service layer, a base platform layer, and a data persistence layer. The invention integrates a distributed tracking system and a log analysis system, wherein a service layer is a service logic which is distributed and represents a corresponding system, a basic platform layer represents a basic function module supporting the service layer, and a data persistence layer stores system configuration data, distributed system tracking data and log data collected by a log system.
1. Distributed tracking system
As shown in fig. 2, the distributed tracking system is a distributed tracking system developed based on the Dapper framework published by Google.
The non-intrusive log collection agent plug-in of the distributed tracking system is a non-code intrusive plug-in which java agents and byteudy byte code injection technologies are combined and meets the plug-in specification of the distributed tracking system.
The non-invasive log collection plug-in collects and adds a tracking ID (TraceID) unique identifier to each log by burying points at log collection points of an application system. The non-invasive log collection plug-in does not modify the format and the content of the local log of the application system, only modifies the log sent to the log analysis system, and is realized by adding TraceID identification information.
2. Log analysis system
The log receiving module of the log analysis system is specially responsible for receiving the log with the traceId mark sent by the non-intrusive collection agent plug-in the distributed tracking system, sending the log into the log processing module and finally storing the log into the log database.
The basic log retrieval module is specially used as a log retrieval engine, such as an elastic search.
The intelligent log analysis module is essentially an independent data mining system, and uses an open source-based scimit-leann machine learning framework to mine historical abnormal log information of an application system as sample data, extract abnormal characteristic information, construct an abnormal decision tree, and perform labeling processing on each warehouse-in log. The characteristic information refers to a characteristic value which is associated with the abnormal prompt information based on the natural language of human beings by performing data preprocessing on all original abnormal information by an expert.
3. Distributed service chain and log aggregation module
The distributed service chain tracking system and the log aggregation system are micro aggregation systems, the bottom distributed tracking system and the bottom log analysis system are two completely independent systems with autonomous functions, and micro aggregation is realized by encapsulating a user interaction layer at two system API layers, as shown in FIG. 1.
The distributed service chain and the log aggregation module only logically integrate the distributed tracking system and the log system into one system, and realize aggregation through correlation of traceId.
The distributed service chain and log aggregation module combines the natural language based abnormal prompt information obtained by the intelligent log analysis module of the log analysis system with the distributed tracking chain. The logs can be located through the distributed tracking chain, and the nodes with problems of the whole calling chain can also be traced through the abnormal logs, namely, the logs can be searched logically through the tracking chain, and the abnormal conditions of the chain link points can also be traced through log tracing, so that the nodes are traced mutually.
The following is a detailed description of the secure and non-intrusive log collection, intelligent log analysis based on machine learning framework, and micro-aggregation method of the log analysis system and the distributed tracking system according to the present invention as follows: .
Safe and non-invasive log collection
As shown in fig. 3, the steps for realizing safe and non-intrusive log collection are as follows:
step 1: and editing an entry class LogAgent of a non-invasive log collection agent, wherein the entry class LogAgent needs to realize a prepain method and is used for defining a class converter ClassFileTracsformer and finishing the intervention on the java class loading process.
Step 2: a log interception point LogInvokeInterceptor is defined, the class realizes ClassInstanceMethodMethodEnhancePluginDefine for intercepting log writing operation, and specific internal logic is realized in a before method thereof.
And step 3: and the agent acquires the TraceId of the service chain from the tracking chain Carrier, copies the original log and adds the TraceId identifier to the log.
And 4, step 4: agent sends the log record with the TraceId identifier to MQ (message middleware), and the log without the TraceId identifier does not have any processing on its contents.
And 5: the log receiving module reads an application system log with a TraceId identifier in the MQ;
step 6: and the log analysis system carries out structuralization and secondary processing on the log acquired by the receiving end and then sends the log into a back-end storage system.
The invention only copies one log collected by the Agent before writing the log into the log file of the application system, and adds the TraceId identifier when the log is sent. Thus, the log structure of the original application system is kept unchanged. In view of the performance problem of the application system itself, to avoid the communication overhead, the sending log is not directly communicated with the log receiving end. The log collected by the Agent is firstly stored in a local MQ, and the log receiving terminal acquires the log through communication with the MQ.
Intelligent log analysis based on machine learning framework
As shown in fig. 4, the intelligent log analysis based on the machine learning framework of the present invention is mainly completed by an intelligent log analysis module, which comprises the following specific steps:
step 1: and analyzing the sample abnormal log, analyzing the abnormal characteristic value of the abnormal log content and the specific error content, and calculating a Pearson correlation coefficient between the abnormal log content and the specific error content.
Step 2: and (3) data preprocessing, namely processing the abnormal values and the redundant values obtained in the step (1), and performing data standardization processing on the abnormal log contents to convert the abnormal log contents into corresponding numerical values.
And step 3: and (3) modeling log abnormal information, constructing an abnormal recognition decision tree model by using an ID3 algorithm model of a scimit-leann machine learning library, and sending the data obtained in the previous step into the model for training.
And 4, step 4: and (3) comparing whether the model in the step (3) is more than or equal to the value by setting an accuracy threshold, and otherwise, continuing to sample data and model parameters.
And 5: and sending the numerical values obtained by the classification model into an error type matching feedback knowledge base, and realizing the interconversion between the abnormal type and the error prompt content facing the human natural language by the base to obtain a final result.
The method of the invention does not simply combine the abnormal information and the service chain to display the abnormal information, but focuses more on obtaining the intrinsic feedback content according to the abnormal information. Because the distributed service chain system is an operation and maintenance tool and faces to operation and maintenance personnel, the operation and maintenance need to know what is wrong intuitively; the abnormal information of the abnormal node is not checked, and the judgment is carried out according to the experience of the user (the mode has great artificial uncertainty).
Meanwhile, the invention can only use a log analysis module to realize that the abnormal information uses an expression mode facing to human natural language, and the abnormal expression is associated with the characteristic value in the data preprocessing process of the log mining system. The abnormal prompt oriented to the human natural language expression mode can normalize the positioning process, reduce the interference of human factors, improve the accuracy and lower the abnormal reading threshold.
Third, micro-aggregation method of log analysis system and distributed tracking system
As shown in FIG. 5, the micro-polymerization method of the present invention comprises the following specific steps:
step 1: the user interaction layer receives a user request.
Step 2: and (3) the routing gateway Appgateway matches the path of the request flow guide according to the request url, and if the path accords with the log system url rule, the flow guide is processed in the step 3. And if the url rule of the distributed tracking system is met, the flow is guided to the step 4 for processing, and if the url rule of the distributed tracking system is not met, the flow is guided to the step 5 for processing.
And step 3: the log analysis system LogController receives the request, processes it and returns the result.
And 4, step 4: the distributed tracking system ApmController receives the request and returns the result.
And 5: and the log analysis system and the distributed tracking system aggregation layer receive the request and return a result.
The invention integrates a distributed tracking system and a log analysis system in a micro-integration mode. The service logic of a log analysis system does not need to be integrated in a distributed tracking system, so that the problems of serious coupling, code bloat and difficulty in upgrading and maintaining in the system are avoided.
As shown in fig. 1, the present invention further encapsulates a user interaction layer polytontroller on the ApmController layer of the distributed service chain system and the LogController layer of the log analysis system, and adds an application routing gateway AppGateway. The business logic of the aggregation operation required by the distributed tracking system and the log analysis system is realized in the layer, the service request of the distributed tracking system is routed to the ApmController, and the service request of the log analysis system is guided to the LogController. In this way, the distributed service chain system and the log analysis system are logically formed into a complete system, but the bottom layer is two partitioned autonomous systems. The method can ensure the function requirement of the original independent system, realize the aggregation logic between the systems, and is more suitable for agile development and rapid online of the service.
Claims (7)
1. A method for tracking and aggregating logs of a distributed service chain is characterized in that: the method is to micro-aggregate a log system in the existing distributed tracking system; the distributed tracking system and the log analysis system are integrated, a service layer is distributed to represent service logic corresponding to the system, a basic platform layer represents a basic function module supporting the service layer, a data persistence layer stores system configuration data, distributed system tracking data and log data collected by the log system, a user interaction layer is packaged on two system interface layers for micro-aggregation, the distributed tracking system and the log system are logically integrated into a system, and aggregation is realized by mutual association of tracking Ids.
2. The method of claim 1, wherein: the distributed tracking system is provided with a non-invasive log collection agent plug-in, and the non-invasive log collection plug-in collects each log and adds a unique tracking ID through embedding points in log collection points of the application system.
3. The method of claim 2, wherein: the log analysis system comprises a log receiving module, a basic log retrieval module and an intelligent log analysis module;
the log receiving module is specially responsible for receiving logs with tracking Id marks, which are sent by a non-invasive acquisition agent plug-in the distributed tracking system, sending the logs into the log processing module and finally storing the logs into a log database;
the basic log retrieval module is used as a log retrieval engine;
the intelligent log analysis module uses an open source-based scimit-learn machine learning framework, and mining the historical abnormal log information of the application system as sample data, extracting abnormal characteristic information, constructing an abnormal decision tree, and labeling each warehousing log.
4. The method of claim 3, wherein: the method for realizing the log collection comprises the following specific steps:
step 1: editing the entry class of a non-invasive log collection agent to realize a premain method; the method is used for defining a class converter to complete the intervention of the java class loading process;
step 2: defining a log interception point, wherein the class realizes that ClassInstanceMethodEnhancePluginDefine is used for intercepting log writing operation, and specific internal logic is realized in a before method thereof;
and step 3: the agent acquires the tracking Id of the service chain from a tracking chain Carrier, copies an original log and adds a tracking Id mark to the log;
and 4, step 4: the agent sends the log record with the tracking Id identification to the message middleware, and the log without the tracking Id identification does not carry out any treatment on the content of the log;
and 5: a log receiving module reads an application system log with a tracking Id mark in message middleware;
step 6: and the log analysis system carries out structuralization and secondary processing on the log acquired by the receiving end and then sends the log into a back end for storage.
5. The method according to claim 3 or 4, characterized in that: the method for realizing intelligent log analysis comprises the following steps:
step 1: analyzing a sample abnormal log, analyzing abnormal characteristic values of abnormal log contents and specific error contents, and calculating a Pearson correlation coefficient between the abnormal log contents and the specific error contents;
step 2: data preprocessing, namely processing the abnormal values and the redundant values obtained in the step 1, and performing data standardization processing on the abnormal log contents to convert the abnormal log contents into corresponding numerical values;
and step 3: modeling log abnormal information, constructing an abnormal recognition decision tree model by using an ID3 algorithm model of a scimit-leann machine learning library, and sending data obtained in the previous step into the model for training;
and 4, step 4: setting an accuracy threshold, comparing whether the model in the step 3 is more than or equal to the threshold, and otherwise, continuing to sample data and model parameters;
and 5: and sending the numerical values obtained by the classification model into an error type matching feedback knowledge base, and realizing the interconversion between the abnormal type and the error prompt content facing the human natural language by the knowledge base to obtain a final result.
6. The method according to claim 3 or 4, characterized in that: the micro-polymerization implementation steps are as follows:
step 1: the user interaction layer receives a user request;
step 2: the routing gateway matches the path of the request flow guide according to the request url, and if the path accords with the log system url rule, the flow guide is processed in the step 3; if the url rule of the distributed tracking system is met, the flow is guided to the step 4 for processing; if the two are not met, the flow is guided to the step 5 for processing;
and step 3: the Log analysis system Log controller receives and processes the request and returns the result
And 4, step 4: the ApmConroller of the distributed tracking system receives the request and returns the result
And 5: and the log analysis system and the distributed tracking system aggregation layer receive the request and return a result.
7. The method of claim 5, wherein: the micro-polymerization implementation steps are as follows:
step 1: the user interaction layer receives a user request;
step 2: the routing gateway matches the path of the request flow guide according to the request url, and if the path accords with the log system url rule, the flow guide is processed in the step 3; if the url rule of the distributed tracking system is met, the flow is guided to the step 4 for processing; if the two are not met, the flow is guided to the step 5 for processing;
and step 3: the Log analysis system Log controller receives and processes the request and returns the result
And 4, step 4: the ApmConroller of the distributed tracking system receives the request and returns the result
And 5: and the log analysis system and the distributed tracking system aggregation layer receive the request and return a result.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111290355A (en) * | 2020-02-27 | 2020-06-16 | 中电九天智能科技有限公司 | Method for recording and searching log |
| CN111949481A (en) * | 2020-08-11 | 2020-11-17 | 安徽继远检验检测技术有限公司 | Anomaly tracking detection system based on micro-service |
| CN113157592A (en) * | 2021-05-24 | 2021-07-23 | 杭州笨马网络技术有限公司 | Visual defect positioning method |
| CN115022402A (en) * | 2022-07-01 | 2022-09-06 | 杭州乘云数字技术有限公司 | Agent acquisition method and system based on one-stack integration technology |
| CN116915463A (en) * | 2023-07-17 | 2023-10-20 | 北京优特捷信息技术有限公司 | Call chain data security analysis method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111290355A (en) * | 2020-02-27 | 2020-06-16 | 中电九天智能科技有限公司 | Method for recording and searching log |
| CN111290355B (en) * | 2020-02-27 | 2021-06-22 | 中电九天智能科技有限公司 | Method for recording and searching log |
| CN111949481A (en) * | 2020-08-11 | 2020-11-17 | 安徽继远检验检测技术有限公司 | Anomaly tracking detection system based on micro-service |
| CN113157592A (en) * | 2021-05-24 | 2021-07-23 | 杭州笨马网络技术有限公司 | Visual defect positioning method |
| CN115022402A (en) * | 2022-07-01 | 2022-09-06 | 杭州乘云数字技术有限公司 | Agent acquisition method and system based on one-stack integration technology |
| CN116915463A (en) * | 2023-07-17 | 2023-10-20 | 北京优特捷信息技术有限公司 | Call chain data security analysis method, device, equipment and storage medium |
| CN116915463B (en) * | 2023-07-17 | 2024-03-08 | 北京优特捷信息技术有限公司 | Call chain data security analysis method, device, equipment and storage medium |
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