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2025-01-16 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Development >
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This article mainly introduces the relevant knowledge of "case analysis of call chain tracking technology in distributed micro service system". The editor shows you the operation process through an actual case, and the operation method is simple, fast and practical. I hope this article "case analysis of call chain tracking technology in distributed micro service system" can help you solve the problem.
1. Interviewer: with so many machines and long call chain in the distributed micro-service environment, how do you locate the problem?
Problem analysis: this problem, if you have used the micro-service framework, you must know this technology for service governance, if as a rookie of the micro-service architecture, you only know some basic knowledge, and you are suddenly asked this question. I'm really confused. With so many machine clusters, how do I know which machine the service hit each time, and how do I know which link threw an exception?
Me: in view of the above problems in distributed systems, we are in urgent need of a link tracking (Trace) system to solve these pain points. the main task of this system is to collect logs of various services, report logs, analyze logs, and save displays. Its key core is the call chain, which generates a globally unique ID (Traceld) for each request, and correlates the "isolated" call information of different systems together through Traceld to restore more valuable data.
(if you don't understand what's going on, just take a look at the finished product.)
Query a request through a Trace, this Trace is the only one in the show. Through this link tracking system, you can clearly know the depth of service invocation, the number of services involved, the time and status of each service call, and which service is abnormal. Specific to the method name, when looking for a time-consuming link, you can click the word "time-consuming" on the query result page to arrange the data in ascending or descending order. It is clear at a glance that all the above problems have been solved.
2. Interviewer: what mature open source tools do you know of the call chain?
Google Dapper
Dapper started as a self-contained tracking tool, but later developed into a monitoring platform with high performance, low code intrusiveness and support for cluster expansion.
Dapper processing logs are divided into three phases:
Each service writes span data to the local log
The dapper daemon pulls the log files and reads them into the dapper collector
The dapper collector writes the results to bigtable, and the trace is recorded as one line at a time.
Alibaba's distributed call tracking system-Hawkeye (EagleEye)
EagleEye is a monitoring system with call chain tracking technology as the core. By collecting, storing and analyzing the call event parameters in the distributed system, cooperating with developers for fault location, capacity estimation, performance bottleneck location, system request link carding and so on, the development of EagleEye is also based on the design idea of Google Dapper.
Meituan distributed session tracking system-MTrace
MTrace is a distributed session tracking system within Meituan Dianping, which also draws lessons from the dapper of Google in 2010. It concatenates the same request distributed on each service node through a global ID to restore the original call relationship, track system problems, analyze call data, and count system indicators. MTrace supports Meituan's internal RPC middleware, HTTP middleware, MySQL,Tair,MQ and other middleware data burying points.
This is the end of the content of "case analysis of call chain tracking technology in distributed micro-service system". Thank you for your reading. If you want to know more about the industry, you can follow the industry information channel. The editor will update different knowledge points for you every day.
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