Microservices now a days are higly used though it has its own challanges. we develop microservices and use downstream and upstream microservices in one another. some times we go through high latency and other issues due other services and some times its very difficult to go though the log and find out the culprit.
the complexity of microservices increase when we use multiple service layers and hit other api's.Each service call go through multiple layers and call multiple other api hits, some thimes its very difficult to identify the issues with other api's or service call and going though log one by one also consumes lot of time. Distributed tracing provides a holistic view of requests transiting through multiple services, allowing for immediate identification of issues
Span – A span is an individual operation.
Trace – A Trace is an end-to-end latency graph, composed of spans.
Tracers – Tracers records spans and passes context required to connect them into a trace.
Zipkin is a distributed tracing system. It helps gather timing data needed to troubleshoot latency problems in microservice architectures. It manages both the collection and lookup of this data. Zipkin’s design is based on the Google Dapper paper.
Applications are instrumented to report timing data to Zipkin. The Zipkin UI also presents a Dependency diagram showing how many traced requests went through each application. If you are troubleshooting latency problems or errors, you can filter or sort all traces based on the application, length of trace, annotation, or timestamp. Once you select a trace, you can see the percentage of the total trace time each span takes which allows you to identify the problem application.
Zipkin UI Zipkin UI provides following features to analyze the distributed tracing details across services:
Search traces by service name, span name etc Trace dependency graph showing trace requests flow across services Provides the details of total trace time for each individual span By analyzing the details provided by Zipkin UI, it becomes easier to find the latency or any particular services issues across the interconnected microservices architecture.
create sample application using spring initializer where you will have your controllers.
after importing the project add following dependencies to your project.
compile('io.zipkin.java:zipkin-server')
compile('io.zipkin.java:zipkin-autoconfigure-ui')add @EnableZipkinServer annotation to your application.
@SpringBootApplication
@EnableZipkinServer
public class ZipkinnApplication {
public static void main(String[] args) {
SpringApplication.run(ZipkinnApplication.class, args);
}
}add blow port to your application.properties file
server.port=9411
open your command prompt got you application folder and run following command
gradle build
building zipkin application
running zipkin application
screen zipkin application
we integrate Sleuth with our services or Controller to track the complex flow. to integrate Sleuth service we have to follow following steps.
create sample application using spring initializer.
add below dependencies to you project.
compile('org.springframework.cloud:spring-cloud-starter-sleuth')
compile('org.springframework.cloud:spring-cloud-sleuth-zipkin')run your Sleuth application ie your applicaiton where you have your complex business.
open your command prompt got you application folder and run following command
gradle build
building sleuth application
running sleuth application
after running you apllication hit some api's sleuth server and track them on zipkin browser console.
Details of a particular service call statistics
One particular transaction overview
