以京东商品评论为目标网站，架构采用爬虫+Flume+Kafka+Spark Streaming+Mysql，实现数据动态实时的采集、分析、展示数据。

#-*- codeing =utf-8 -*-
#@Time : 2022/3/16 20:15
#@Author : huaobin
#@File : new.py
#@Software: PyCharm
# -*- coding: utf-8 -*-
import gzip
import urllib.request
import json
import time
import random
import demjson as dj
import requests
import itertools

headers = {
    "Cookie": "",
 "Accept": "*/*",
    "Accept-Encoding": "gzip, deflate, br",
    "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
    "Connection": "keep-alive",
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36 Edg/99.0.1150.39"
}
headers2 = {
    "accept": "*/*",
    "accept-encoding": "gzip, deflate, br",
    "accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
    "cookie": ""
"referer": "https://search.jd.com/"
}
def crawlProductComment(url):
    # 读取原始数据(注意选择gbk编码方式)
    try:
        req = requests.get(url=url, headers=headers2).text
        reqs = req.replace("fetchJSON_comment98(", "").strip(');')
        print(reqs)
        jsondata = json.loads(reqs)
        # 遍历商品评论列表
        comments = jsondata['comments']
        return comments
    except IOError:
        print("Error: gbk不合适")
    # 从原始数据中提取出JSON格式数据(分别以'{'和'}'作为开始和结束标志)



def getProduct(url):
    ids = []
    req = requests.get(url=url, headers=headers2).text
    reqs = req.replace("jQuery1544821(", "").strip(')')
    jsondata = json.loads(reqs)['291']
    for i in range(0, len(jsondata)):
        ids.append(jsondata[i]['sku_id'])
    print(ids)
    return ids




import paramiko

#服务器信息，主机名（IP地址）、端口号、用户名及密码
hostname = "hadoop102"
port = 22
username = "root"
password = "123456"


client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
client.connect(hostname, port, username, password, compress=True)
sftp_client = client.open_sftp()
remote_file = sftp_client.open("/opt/a.log",'a+')  # 文件路径自己设置
ids = []
for i in range(2,3):
    product_id = getProduct(
        "https://search-x.jd.com/Search?callback=jQuery1544821&area=5&enc=utf-8&keyword=%E7%94%B7%E5%A3%AB%E8%BF%90%E5%8A%A8%E9%9E%8B&adType=7&page="+str(i)+"&ad_ids=291%3A33&xtest=new_search&_=1647325621019")

    time.sleep(random.randint(1, 3))
    ids.append(product_id)

data = []
count = 0
for k in list(set(itertools.chain.from_iterable(ids))):
    for i in range(0, 100):
        url = 'https://club.jd.com/comment/productPageComments.action?callback=fetchJSON_comment98&productId=' + str(
            k) + '&score=0&sortType=5&page=' \
              + str(i) + '&pageSize=10&isShadowSku=0&fold=1'
        comments = crawlProductComment(url)
        if len(comments) <= 0:
            break
        print(comments)
        remote_file.writelines(str(len(comments))+"\n")
        data.extend(comments)
        # 设置休眠时间
        time.sleep(random.randint(1, 5))
        print('-------', i)

    print("这是第{}类商品".format(count))
    count += 1

　　python爬取京东商品并向虚拟机上传

flume编写conf文件配置agent（连接kafka）

a1.sources=r1
a1.sinks=k1 k2
a1.channels=c1 c2
#define source
a1.sources.r1.type=exec
a1.sources.r1.channels=c1 c2
a1.sources.r1.command=tail -F /training/testData/test-flume.log//监控的目录（爬去信息不断上传的目录）
a1.sources.r1.shell=/bin/sh -c
a1.sources.r1.selector.type=replicating
#sink1toKafka
a1.sinks.k1.type =org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = test02//监听的kafka的topic(在idea代码中会自动创建)
a1.sinks.k1.brokerList= 192.168.63.139:9092,192.168.63.140:9092,192.168.63.134:9092//非集群模式可以只设主机ip地址（这里的中文在运行时可能导致报错，把中文注释删除即可）
a1.sinks.k1.kafka.bootstrap.servers = 192.168.63.139:9092,192.168.63.140:9092,192.168.63.134:9092//这里也是一样可以只设主机ip地址
a1.sinks.k1.producer.requiredAcks = 1
a1.sinks.k1.batchSize = 5
a1.sinks.k1.channel=c1
#sink2toHDFS
a1.sinks.k2.type=hdfs
a1.sinks.k2.channel=c2
a1.sinks.k2.hdfs.path=hdfs://192.168.63.139:9000/DataTest/datatest/test02//hdfs上，你想输出文件的目录
#channel1
a1.channels.c1.type=memory
#channel2
a1.channels.c2.type=memory

flume启动命令：

 bin/flume-ng agent --name a1 --conf conf  --conf-file conf/flume.conf  -Dflume.root.logger=INFO,console

idea部分编写接受数据并存储到Mysql中

package com.niit.spark

import java.sql.{Connection, DriverManager, PreparedStatement}


import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent


object BigWork{

  def main(args:Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("BigWork").setMaster("local[2]")
    val streamingContext = new StreamingContext(sparkConf, Seconds(2))

    val kafkaParams = Map[String, Object](
      "bootstrap.servers" -> "flume:9092",//修个成自己的虚拟机主机
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> "use_a_separate_group_id_for_each_stream",
      "auto.offset.reset" -> "latest",
      "enable.auto.commit" -> (false: java.lang.Boolean)
    )

    val topics = Array("test02", "t100")//kafka监听目录
    val stream = KafkaUtils.createDirectStream[String, String](
      //监听
      streamingContext,
      PreferConsistent,
      Subscribe[String, String](topics, kafkaParams)
    )
    val mapDStream: DStream[(String, String)] = stream.map(record => (record.key, record.value))

    val resultRDD: DStream[(String, Int)] = mapDStream.map(_._2.split(",")(2)).map(word=>(word,1)).reduceByKey(_+_)//    处理数据

    resultRDD.print()//打印
    
    //转化成rdd，连接并存储到myql中
    resultRDD.foreachRDD(rdd => {
      def func(records: Iterator[(String,Int)]) {
        var conn: Connection = null
        var stmt: PreparedStatement = null
        try {

          val url = "jdbc:mysql://localhost:3306/bigjob?useUnicode=true&characterEncoding=UTF-8"
          val user = "root"
          val password = "123456"
          conn = DriverManager.getConnection(url, user, password)

          records.foreach(p => {
            val sql = "insert into bigwork(name,count) values (?,?)"
            stmt = conn.prepareStatement(sql);
            stmt.setString(1, p._1.trim)
            stmt.setInt(2,p._2.toInt)
            stmt.executeUpdate()
          })
        } catch {
          case e: Exception => e.printStackTrace()
        } finally {
          if (stmt != null) {
            stmt.close()
          }
          if (conn != null) {
            conn.close()
          }
        }
      }

      val repartitionedRDD = rdd.repartition(3)
      repartitionedRDD.foreachPartition(func)
    })

    streamingContext.start()

  streamingContext.awaitTermination()
  }
}

最后在数据库中读取数据可视化展示即可