zipkin微服务调用链分析

1.zipkin的作用

在微服务架构下，一个http请求从发出到响应，中间可能经过了N多服务的调用，或者N多逻辑操作，

如何监控某个服务，或者某个逻辑操作的执行情况，对分析耗时操作，性能瓶颈具有很大价值，

zipkin帮助我们实现了这一监控功能。

2.启动zipkin

下载可执行文件： https://zipkin.io/quickstart.sh | bash -s

java -jar zipkin.jar

运行结果：

zipkin监听9411端口，通过浏览器查看：

3.python中zipkin的实现模块py_zipkin

创建flask项目，新建demo.py

import requests
from flask import Flask
from py_zipkin.zipkin import zipkin_span,create_http_headers_for_new_span
import time

app = Flask(__name__)

app.config.update({
    "ZIPKIN_HOST":"127.0.0.1",
    "ZIPKIN_PORT":"9411",
    "APP_PORT":5000,
    # any other app config-y things
})

def do_stuff():
    time.sleep(2)
    headers = create_http_headers_for_new_span()
    requests.get('http://localhost:6000/service1/', headers=headers)
    return 'OK'

def http_transport(encoded_span):
    # encoding prefix explained in https://github.com/Yelp/py_zipkin#transport
    #body = b"\x0c\x00\x00\x00\x01"+encoded_span
    body=encoded_span
    zipkin_url="http://127.0.0.1:9411/api/v1/spans"
    #zipkin_url = "http://{host}:{port}/api/v1/spans".format(
     #   host=app.config["ZIPKIN_HOST"], port=app.config["ZIPKIN_PORT"])
    headers = {"Content-Type": "application/x-thrift"}

    # You'd probably want to wrap this in a try/except in case POSTing fails
    r=requests.post(zipkin_url, data=body, headers=headers)
    print(type(encoded_span))
    print(encoded_span)
    print(body)
    print(r)
    print(r.content)

@app.route('/')
def index():
    with zipkin_span(
        service_name='webapp',
        span_name='index',
        transport_handler=http_transport,
        port=5000,
        sample_rate=100, #0.05, # Value between 0.0 and 100.0
    ):
        with zipkin_span(service_name='webapp', span_name='do_stuff'):
            do_stuff()
        time.sleep(1)
    return 'OK', 200

if __name__=='__main__':
    app.run(host="0.0.0.0",port=5000,debug=True)

新建server1.py

from flask import request
import requests
from flask import Flask
from py_zipkin.zipkin import zipkin_span,ZipkinAttrs
import time

import MySQLdb

app = Flask(__name__)
app.config.update({
    "ZIPKIN_HOST":"127.0.0.1",
    "ZIPKIN_PORT":"9411",
    "APP_PORT":5000,
    # any other app config-y things
})

def do_stuff():
    time.sleep(2)
    with zipkin_span(service_name='service1', span_name='service1_db_search'):
        db_search()
    return 'OK'

def db_search():
    # 打开数据库连接
    db = MySQLdb.connect("127.0.0.1", "username", "psw", "db", charset='utf8')
    # 使用cursor()方法获取操作游标
    cursor = db.cursor()
    # 使用execute方法执行SQL语句
    cursor.execute("SELECT VERSION()")
    # 使用 fetchone() 方法获取一条数据
    data = cursor.fetchone()
    print("Database version : %s " % data)
    # 关闭数据库连接
    db.close()

def http_transport(encoded_span):
    # encoding prefix explained in https://github.com/Yelp/py_zipkin#transport
    #body = b"\x0c\x00\x00\x00\x01" + encoded_span
    body=encoded_span
    zipkin_url="http://127.0.0.1:9411/api/v1/spans"
    #zipkin_url = "http://{host}:{port}/api/v1/spans".format(
    #    host=app.config["ZIPKIN_HOST"], port=app.config["ZIPKIN_PORT"])
    headers = {"Content-Type": "application/x-thrift"}

    # You'd probably want to wrap this in a try/except in case POSTing fails
    requests.post(zipkin_url, data=body, headers=headers)

@app.route('/service1/')
def index():
    with zipkin_span(
        service_name='service1',
        zipkin_attrs=ZipkinAttrs(
            trace_id=request.headers['X-B3-TraceID'],
            span_id=request.headers['X-B3-SpanID'],
            parent_span_id=request.headers['X-B3-ParentSpanID'],
            flags=request.headers['X-B3-Flags'],
            is_sampled=request.headers['X-B3-Sampled'],
        ),
        span_name='index_service1',
        transport_handler=http_transport,
        port=6000,
        sample_rate=100, #0.05, # Value between 0.0 and 100.0
    ):
        with zipkin_span(service_name='service1', span_name='service1_do_stuff'):
            do_stuff()
    return 'OK', 200

if __name__=='__main__':
    app.run(host="0.0.0.0",port=6000,debug=True)

运行demo.py

运行server1.py

访问5000端口

查看调用链：

可以看到，有webapp和services两个service，5个span标签，可以清楚看到service和service，service和span，span和span之间的关系，和各span耗时情况。

4.py_zipkin代码分析

上例中我们主要使用了py_zipkin的两个对象，zipkin_span和create_http_headers_for_new_span

1)zipkin_span

with zipkin_span(
        service_name='webapp',
        span_name='index',
        transport_handler=http_transport,
        port=5000,
        sample_rate=100, #0.05, # Value between 0.0 and 100.0
    ):

service_name:服务名

span_name：标签名，用来标志服务里面的某个操作

transport_handler:处理函数，post数据到zipkin

port:服务端口号

sample_rate:待研究

需要注意的是，在一个服务里面，只有root-span需要定义transport_handler,port等参数，非root-span只有service_name是必须的，其他参数继承root-span

with zipkin_span(service_name='webapp', span_name='do_stuff'):
    do_stuff()

zip_span还可以是用装饰器的方式，来包裹对应的操作

@zipkin_span(service_name='webapp', span_name='do_stuff')
def do_stuff():
    time.sleep(2)
    headers = create_http_headers_for_new_span()
    requests.get('http://localhost:6000/service1/', headers=headers)
    return 'OK'

但是，我这边试了装饰器的方式，不起作用

transport_handler的定义如下：

def http_transport(encoded_span):
    # encoding prefix explained in https://github.com/Yelp/py_zipkin#transport
    #body = b"\x0c\x00\x00\x00\x01"+encoded_span
    body=encoded_span
    zipkin_url="http://127.0.0.1:9411/api/v1/spans"
    #zipkin_url = "http://{host}:{port}/api/v1/spans".format(
     #   host=app.config["ZIPKIN_HOST"], port=app.config["ZIPKIN_PORT"])
    headers = {"Content-Type": "application/x-thrift"}

    # You'd probably want to wrap this in a try/except in case POSTing fails
    r=requests.post(zipkin_url, data=body, headers=headers)

zipkin会把编码后的span，通过接口post到zipkin，我们通过浏览器就可以看到调用链详情了。

2)create_http_headers_for_new_span

跳到一个新的服务时，通过create_http_headers_for_new_span生成新的span信息，包括trace_id，span_id，parent_span_id等，服务之间就做好关联了

with zipkin_span(
        service_name='service1',
        zipkin_attrs=ZipkinAttrs(
            trace_id=request.headers['X-B3-TraceID'],
            span_id=request.headers['X-B3-SpanID'],
            parent_span_id=request.headers['X-B3-ParentSpanID'],
            flags=request.headers['X-B3-Flags'],
            is_sampled=request.headers['X-B3-Sampled'],
        ),
        span_name='index_service1',
        transport_handler=http_transport,
        port=6000,
        sample_rate=100, #0.05, # Value between 0.0 and 100.0
    ):