Kafka-分布式消息队列实战

0. 背景

 

因为实际项目中碰到这样一个场景：

将不同的轨迹数据发布到不同的处理者进行处理，并在前端展示，想来符合消息队列的场景，开始使用redis实现了一个简单的消息队列：

# -*- coding: utf-8 -*-
import redis

def singleton(cls, *arg, **kw):
    instances = {}
    def _singleton(*arg, **kw):
        if arg not in instances:
            instances[arg] = cls(*arg, **kw)
        return instances[arg]
    return _singleton


class RedisConfig:
    HOST = '127.0.0.1'
    PORT = 6379
    DBID = 0


@singleton
class RedisQueue(object):
    def __init__(self, trail_id, namespace='data_queue'):
        '''
        RedisQueue设计为单例:
        trail_id不同则为不同的单例，即同一trail_id应保持为只有一个实例
        '''
        self.__pool = redis.ConnectionPool(host=RedisConfig.HOST, 
                                            port=RedisConfig.PORT, 
                                            db=RedisConfig.DBID)
        self.__db = redis.Redis(connection_pool=self.__pool)
        self.key = '%s:%s' % (namespace, trail_id)

    @staticmethod
    def create_pool():
        RedisQueue.pool = redis.ConnectionPool(
            host=RedisConfig.HOST, 
            port=RedisConfig.PORT, 
            db=RedisConfig.DBID)
    
    def qsize(self):
        return self.__db.llen(self.key)

    def empty(self):
        return self.qsize() == 0

    def put(self, item):
        self.__db.rpush(self.key, item)

    def get(self, trail_id, block=False, timeout=None):
        key = 'data_queue:%s' %  trail_id
        if block:
            item = self.__db.blpop(key, timeout=timeout)
        else:
            item = self.__db.lpop(key)
        return item

 

这样的实现，虽然满足了现在的项目需求，但是却存在很多问题：

1）数据pop完了会发生什么？

2）设计成了单例模式，使得一条轨迹就使用一个redis实例，但是万一有上万条轨迹，可能无法保证性能

 

针对这些问题，想使用kafka进行改进优化

 

 

 

1. Kafka核心概念与原理

1）topic 和 日志

每个topic通常对应一个业务，对每个topic，kafka对它进行了分区，如下图所示：

 

 

kafka可以在多个consumer并发的情况下提供有序性和负载均衡。

 

 

 

 

 

2. 实战

使用kafka, 仿真端有新的数据就将数据发布到指定的queue，订阅端收到订阅队列的数据直接通过websocket推送到前端

 

直接上代码：

 

# -*- coding: utf-8 -*-
import json
import logging, time
import collections
import threading
import multiprocessing
import tornado.websocket
from kafka import KafkaConsumer, KafkaProducer
from Utils import DBUtils, RedisUtil
from Conf import conf

'''
todo: tornado api开发: 实现restful接口
'''

class Consumer(threading.Thread):
    daemon = True

    def __init__(self, kafka_consumer):
        self._consumer = kafka_consumer
        super(Consumer, self).__init__()

    def run(self):
        for message in self._consumer:
            WebSocketHandler.write_message_to_all(message)


class NewConsumer(multiprocessing.Process):
    def __init__(self):
        multiprocessing.Process.__init__(self)
        self.stop_event = multiprocessing.Event()
        
    def stop(self):
        self.stop_event.set()
        
    def run(self):
        consumer = KafkaConsumer(bootstrap_servers=conf['BOOTSTRAP_SERVERS'],
                                 auto_offset_reset='earliest',
                                 consumer_timeout_ms=1000)
        #订阅某个topic
        consumer.subscribe(['trails'])

        while not self.stop_event.is_set():
            for message in consumer:
                WebSocketHandler.write_message_to_all(message)
                if self.stop_event.is_set():
                    break

        consumer.close()


class Producer(threading.Thread):
    '''
    目前模拟数据产生，后续应该部署到仿真端，由仿真端生产数据
    '''
    def __init__(self):
        threading.Thread.__init__(self)
        self.stop_event = threading.Event()
    
    def stop(self):
        self.stop_event.set()

    def run(self):
        producer = KafkaProducer(bootstrap_servers=conf['BOOTSTRAP_SERVERS'])
        while not self.stop_event.is_set():
            with open(conf['trail_data_fpath'], 'r') as f:
                lines = [line.strip() for line in f.readlines()]
            for line in lines:
                data = {}
                fields = line.split()
                data['coordinate_x'] = float(fields[0].strip()) + 1.0
                data['coordinate_y'] = float(fields[1].strip()) + 1.0
                data['trail_id'] = trail_id
                data['timestamp'] = time.time()
                producer.send(data)

        producer.close()


class WebSocketHandler(tornado.websocket.WebSocketHandler):
    open_sockets = set()

    def __init__(self, *args, **kwargs):
        self._consumer = NewConsumer()
        self._producer = Producer()
        super(WebSocketHandler, self).__init__(*args, **kwargs)

    def open(self, *args):
        self._consumer.start()
        self._producer.start()
        self.set_nodelay(True)
        type(self).open_sockets.add(self)

    def on_message(self, message):
        pass

    @classmethod
    def write_message_to_all(cls, message):
        '''
        需要解析数据格式
        '''
        removable_ws = set()
        for ws in cls.open_sockets:
            if not ws.ws_connection or not ws.ws_connection.stream.socket:
                removable_ws.add(ws)
            else:
                ws.write_message(message)

        for ws in removable_ws:
            cls.open_sockets.remove(ws)

    def on_close(self):
        self._consumer.stop()
        self._producer.stop()    
        print("WebSocket closed")

 

 

 

web框架是使用的tornado，因为是实时性web应用，需要很多websocket，tornado更适用