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基于hive分析Flask为后端框架echarts为前端框架的招聘网站可视化大屏项目
基于hive分析Flask为后端框架echarts为前端框架的招聘网站可视化大屏项目
1. 项目概述
项目目标是构建一个大数据分析系统，包含以下核心模块：
1、数据爬取：通过request请求获取猎聘网的就业数据。
2、数据存储和分析：使用 Hive 进行数据存储和分析。
3、数据迁移：使用sqoop将hive数据导入mysql。
4、后端服务：使用 Flask 搭建数据接口，将分析结果提供给前端。
5、数据可视化：使用 ECharts 制作大屏展示，实现数据的图形化呈现。2. 项目环境准备
在开始之前，需要搭建如下环境：
Hive：作为数据仓库，用于存储和分析数据。
Flask：轻量级 Python Web 框架，用于构建后端 RESTful API。
ECharts：JavaScript 图表库，用于前端数据可视化。
MySQL：用于保存一些系统配置或小规模数据。
Sqoop：数据同步工具，将hive数据同步到mysql。3、数据爬取
通过python获取猎聘网的照片信息，存储到csv文件里
import csv import time import requests import execjs from storage.csv2mysql import sync_data2db f = open('../storage/data.csv', mode='a', encoding='utf-8') csv_writer = csv.DictWriter(f,fieldnames=[ '职位', '城市', '薪资', '经验', '标签', '公司', '公司领域', '公司规模']) csv_writer.writeheader() def read_js_code(): f= open('/Users/shareit/workspace/chart_show/demo.js',encoding='utf-8') txt = f.read() js_code = execjs.compile(txt) ckId = js_code.call('r',32) return ckId def post_data(): read_js_code() url = "https://api-c.liepin.com/api/com.liepin.searchfront4c.pc-search-job" headers = { 'Accept': 'application/json, text/plain, */*', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9', 'Connection': 'keep-alive', 'Sec-Ch-Ua-Platform':'macOS', 'Content-Length': '398', 'Content-Type': 'application/json;charset=UTF-8;', 'Host': 'api-c.liepin.com', 'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36', 'Origin': 'https://www.liepin.com', 'Referer': 'https://www.liepin.com/', 'Sec-Ch-Ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"', 'Sec-Ch-Ua-Mobile': '?0', 'Sec-Fetch-Dest': 'empty', 'Sec-Fetch-Mode': 'cors', 'Sec-Fetch-Site': 'same-site', 'X-Client-Type': 'web', 'X-Fscp-Bi-Stat': '{"location": "https://www.liepin.com/zhaopin"}', 'X-Fscp-Fe-Version': '', 'X-Fscp-Std-Info': '{"client_id": "40108"}', 'X-Fscp-Trace-Id': '52262313-e6ca-4cfd-bb67-41b4a32b8bb5', 'X-Fscp-Version': '1.1', 'X-Requested-With': 'XMLHttpRequest', } list = ["H01$H0001","H01$H0002", "H01$H0003","H01$H0004","H01$H0005", "H01$H0006","H01$H0007","H01$H0008", "H01$H0009","H01$H00010","H02$H0018","H02$H0019","H03$H0022", "H03$H0023","H03$H0024","H03$H0025","H04$H0030","H04$H0031", "H04$H0032","H05$H05","H06$H06","H07$H07","H08$H08"] for name in list: print("-------{}---------".format(name)) for i in range(10): print("------------第{}页-----------".format(i)) data = {"data": {"mainSearchPcConditionForm": {"city": "410", "dq": "410", "pubTime": "", "currentPage": i, "pageSize": 40, "key": "", "suggestTag": "", "workYearCode": "1", "compId": "", "compName": "", "compTag": "", "industry": name, "salary": "", "jobKind": "", "compScale": "", "compKind": "", "compStage": "", "eduLevel": ""}, "passThroughForm": {"scene": "page", "skId": "z33lm3jhwza7k1xjvcyn8lb8e9ghxx1b", "fkId": "z33lm3jhwza7k1xjvcyn8lb8e9ghxx1b", "ckId": read_js_code(), 'sfrom': 'search_job_pc'}}} response = requests.post(url=url, json=data, headers=headers) time.sleep(2) parse_data(response) def parse_data(response): try: jobCardList = response.json()['data']['data']['jobCardList'] except Exception as e: return4、加载hive数据进行分析
1、将storage下的data.csv上传到虚拟机上
2、创建work_base表，并将data.csv数据加载到hive表里CREATE TABLE work_base ( id INT COMMENT 'id', title STRING COMMENT '标题', city STRING COMMENT '城市', salary STRING COMMENT '薪资', campus_job_kind STRING COMMENT '经验', labels STRING COMMENT '标签', compName STRING COMMENT '公司', compIndustry STRING COMMENT '公司领域', compScale STRING COMMENT '公司规模' ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE; LOAD local DATA INPATH './data.csv' OVERWRITE INTO TABLE flask_work.work_base;3、创建hive ads层数仓表进行分析
-- 4. 热门公司分析 CREATE TABLE top_companies ( company_name STRING COMMENT '公司名称', job_count INT COMMENT '职位数量' ) STORED AS TEXTFILE; INSERT INTO top_companies SELECT compName, COUNT(*) AS job_count FROM work_base GROUP BY compName ORDER BY job_count DESC LIMIT 10; -- 5. 岗位分布情况分析 CREATE TABLE job_distribution ( job_title STRING COMMENT '岗位名称', job_count INT COMMENT '职位数量' ) STORED AS TEXTFILE; INSERT INTO job_distribution SELECT title, COUNT(*) AS job_count FROM work_base GROUP BY title; -- 6. 学历要求分析 CREATE TABLE education_requirements ( education_level STRING COMMENT '学历要求', job_count INT COMMENT '职位数量' ) STORED AS TEXTFILE; INSERT INTO education_requirements SELECT CASE WHEN labels LIKE '%博士%' THEN '博士' WHEN labels LIKE '%硕士%' THEN '硕士' WHEN labels LIKE '%本科%' THEN '本科' WHEN labels LIKE '%大专%' THEN '大专' ELSE '其他' END AS education_level, COUNT(*) AS job_count FROM work_base GROUP BY education_level; -- 7. 薪资待遇分析（各个城市的平均薪资） CREATE TABLE city_salary_analysis ( city STRING COMMENT '城市', avg_salary DOUBLE COMMENT '平均薪资' ) STORED AS TEXTFILE; INSERT INTO city_salary_analysis SELECT city, AVG(CAST(salary AS DOUBLE)) AS avg_salary FROM work_base WHERE salary RLIKE '^[0-9]+$' GROUP BY city;5、将hive分析的结果数据导入mysql
使用sqoop迁移数据
sqoop export \ --connect jdbc:mysql://localhost:3306/flask_work \ --username root --password '123456' \ --table city_job_count \ --export-dir /hive/warehouse/flask_work.db/flask_work.city_job_count \ --input-fields-terminated-by '\001' \ --input-lines-terminated-by '\n'; sqoop export \ --connect jdbc:mysql:// localhost:3306/flask_work \ --username root --password 123456 \ --table job_salary_analysis \ --export-dir /user/hive/warehouse/flask_work.db/flask_work.job_salary_analysis \ --input-fields-terminated-by '\001' \ --input-lines-terminated-by '\n' sqoop export \ --connect jdbc:mysql:// localhost:3306/flask_work \ --username root --password 123456 \ --table top_companies \ --export-dir /user/hive/warehouse/flask_work.db/flask_work.top_companies \ --input-fields-terminated-by '\001' \ --input-lines-terminated-by '\n' sqoop export \ --connect jdbc:mysql:// localhost:3306/flask_work \ --username root --password 123456 \ --table job_distribution \ --export-dir /user/hive/warehouse/flask_work.db/flask_work.job_distribution \ --input-fields-terminated-by '\001' \ --input-lines-terminated-by '\n'6. 后端服务（Flask）
使用 Flask 构建后端服务，编写rest api，读取mysql数据提供给前端页面进行展示
app.pyfrom flask import Flask, render_template, request, flash, redirect, url_for from data import * from service.task_service import get_user, register_user app = Flask(__name__) app.secret_key = 'b6b52fae-5618-4805-b368-501c62c6d1df' @app.after_request def add_header(response): response.cache_control.max_age = 0 return response @app.route('/', methods=['GET', 'POST']) def login(): if request.method == 'POST': username = request.form['username'] password = request.form['password'] user = get_user(username, password) # 检查用户是否存在 if user is not None: data = SourceData() return render_template('index.html', form=data, title=data.title) else: # 用户名或密码错误，显示错误消息 flash('用户名或密码错误') return redirect(url_for('login')) # 重定向回登录页面 # 如果是 GET 请求，则直接返回登录页面 return render_template('login.html') @app.route('/register', methods=['GET', 'POST']) def register(): if request.method == 'POST': username = request.form.get('username') password = request.form.get('password') if username and password: register_user(username, password) # 确保此函数已定义 return "注册成功！" flash('用户名和密码不能为空') return redirect(url_for('register')) return render_template('register.html') if __name__ == "__main__": app.run(host='127.0.0.1', debug=False)task_service.py
import pymysql db_config = { 'host': '127.0.0.1', 'user': 'root', 'password': '12345678', 'database': 'flask_work', 'charset': 'utf8mb4', 'cursorclass': pymysql.cursors.DictCursor } connection = pymysql.connect(**db_config) def get_user(username,password): try: with connection.cursor() as cursor: select_query = "select * from user where username = %s and password = %s" cursor.execute(select_query,(username,password)) result = cursor.fetchall() return result[0] except Exception as e: print(e) return None def get_title_count(): try: with connection.cursor() as cursor: select_query = "select count(distinct(city)) city,count(distinct(compName)) compName from work_base;" cursor.execute(select_query) result = cursor.fetchall() a=result[0]['city'] b=result[0]['compName'] return a,b except Exception as e: print(e) return None def work_count_by_city(): try: with connection.cursor() as cursor: select_query = "select city,job_count from city_job_count order by job_count desc limit 10" cursor.execute(select_query) result = cursor.fetchall() re_list = [] for re in result: re_list.append({"name": re['city'], "value": re['job_count']}) print(re_list) return re_list except Exception as e: print(e) return None def work_avg_salary(): try: with connection.cursor() as cursor: select_query = "select job_title,avg_salary from job_salary_analysis order by avg_salary desc limit 10;" cursor.execute(select_query) result = cursor.fetchall() re_list = [] for re in result: re_list.append({"name": re['job_title'][0:8], "value": int(re['avg_salary'])}) print(re_list) return re_list except Exception as e: print(e) return None def top_companies(): try: with connection.cursor() as cursor: select_query = "select company_name,job_count from top_companies limit 3;" cursor.execute(select_query) result = cursor.fetchall() re_list = [] for re in result: re_list.append({"name": re['company_name'], "value": re['job_count']}) print(re_list) return re_list except Exception as e: print(e) return None def job_distribution_count(): try: with connection.cursor() as cursor: select_query = "select job_title,job_count from job_distribution order by job_count desc limit 10;" cursor.execute(select_query) result = cursor.fetchall() re_list = [] for re in result: re_list.append({"name": re['job_title'][0:6], "value": re['job_count'],"value2": 20, "color": "01", "radius": ['59%', '70%']}) print(re_list) return re_list except Exception as e: print(e) return None def register_user(username,password): try: with connection.cursor() as cursor: select_query = "insert into user(username,password) values(%s,%s)" cursor.execute(select_query,(username,password)) connection.commit() except Exception as e: print(e) return None def education_requirements(): try: with connection.cursor() as cursor: select_query = "select education_level,job_count from education_requirements;" cursor.execute(select_query) result = cursor.fetchall() re_list = [] for re in result: re_list.append({"name": re['education_level'], "value": int(re['job_count'])}) return re_list except Exception as e: print(e) return None def city_salary_analysis(): try: with connection.cursor() as cursor: select_query = "select city,avg_salary from city_salary_analysis order by avg_salary desc limit 10;" cursor.execute(select_query) result = cursor.fetchall() re_list = [] for re in result: re_list.append({"name": re['city'], "value": int(re['avg_salary'])}) return re_list except Exception as e: print(e) return None if __name__ == '__main__': print(city_salary_analysis())7 页面设计
前端采用 ECharts 制作一个招聘网站大数据分析的可视化大屏。
使用 ECharts 渲染数据doctype html> <html> <head> <meta charset="utf-8"> <title>indextitle> <script type="text/javascript" src="../static/js/jquery.js">script> <script type="text/javascript" src="../static/js/echarts.min.js">script> <script type="text/javascript" src="../static/js/china.js">script> <link rel="stylesheet" href="../static/css/comon0.css"> head> <script> $(window).load(function(){ $(".loading").fadeOut() }) /****/ $(document).ready(function(){ var whei=$(window).width() $("html").css({fontSize:whei/20}) $(window).resize(function(){ var whei=$(window).width() $("html").css({fontSize:whei/20}) }); }); script> <script type="text/javascript" src="../static/js/echarts.min.js">script> <script type="text/javascript" src="../static/js/china.js">script> <body> <div class="canvas" style="opacity: .2"> <iframe frameborder="0" src="../static/js/index.html" style="width: 100%; height: 100%">iframe> div> <div class="loading"> <div class="loadbox"><img src="../static/picture/loading.gif"> 页面加载中...div> div> <div class="head"> <h1>{{title}}h1> <div class="weather"> <span id="showTime">span> div> <script> var t = null; t = setTimeout(time,1000);//開始运行 function time() { clearTimeout(t);//清除定时器 dt = new Date(); var y=dt.getFullYear(); var mt=dt.getMonth()+1; var day=dt.getDate(); var h=dt.getHours();//获取时 var m=dt.getMinutes();//获取分 var s=dt.getSeconds();//获取秒 document.getElementById("showTime").innerHTML = y+"年"+mt+"月"+day+"日"+"-"+h+"时"+m+"分"+s+"秒"; t = setTimeout(time,1000); //设定定时器，循环运行 } script> div> <div class="mainbox"> <ul class="clearfix"> <li> <div class="boxall" style="height: 3.2rem"> <div class="alltitle">{{form.echart1.title}}div> <div class="allnav" id="echart1">div> <div class="boxfoot">div> div> <div class="boxall" style="height: 3.2rem"> <div class="alltitle">{{form.echart2.title}}div> <div class="allnav" id="echart2">div> <div class="boxfoot">div> div> <div class="boxall" style="height: 3.2rem"> <div style="height:100%; width: 100%;"> <div class="alltitle">{{form.echart3.title}}div> <div class="allnav" id="echart3">div> div> <div class="boxfoot"> div> div> li> <li> <div class="bar"> <div class="barbox"> <ul class="clearfix"> <li class="pulll_left counter">{{form.counter.value}}li> <li class="pulll_left counter">{{form.counter2.value}}li> ul> div> <div class="barbox2"> <ul class="clearfix"> <li class="pulll_left">{{form.counter.name}}li> <li class="pulll_left">{{form.counter2.name}}li> ul> div> div> <div class="map"> <div class="map1"><img src="../static/picture/lbx.png">div> <div class="map2"><img src="../static/picture/jt.png">div> <div class="map3"><img src="../static/picture/map.png">div> <div class="map4" id="map_1">div> div> li> <li> <div class="boxall" style="height:3.4rem"> <div class="alltitle">{{form.echart4.title}}div> <div class="allnav" id="echart4">div> <div class="boxfoot">div> div> <div class="boxall" style="height: 3.2rem"> <div class="alltitle">{{form.echart5.title}}div> <div class="allnav" id="echart5">div> <div class="boxfoot">div> div> <div class="boxall" style="height: 3rem"> <div class="alltitle">{{form.echart6.title}}div> <div class="allnav" id="echart6">div> <div class="boxfoot">div> div> li> ul> div> <div class="back">div> <script> $(function echarts_1() { // 基于准备好的dom，初始化echarts实例 var myChart = echarts.init(document.getElementById('echart1')); option = { // backgroundColor: '#00265f', tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } }, grid: { left: '0%', top:'10px', right: '0%', bottom: '4%', containLabel: true }, xAxis: [{ type: 'category', data: {{form.echart1.xAxis|safe}}, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, axisTick: { show: false, }, axisLabel: { interval: 0, // rotate:50, show: true, splitNumber: 15, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '12', }, }, }], yAxis: [{ type: 'value', axisLabel: { //formatter: '{value} %' show:true, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '12', }, }, axisTick: { show: false, }, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1 )", width: 1, type: "solid" }, }, splitLine: { lineStyle: { color: "rgba(255,255,255,.1)", } } }], series: [ { type: 'bar', data: {{form.echart1.series|safe}}, barWidth:'35%', //柱子宽度 // barGap: 1, //柱子之间间距 itemStyle: { normal: { color:'#2f89cf', opacity: 1, barBorderRadius: 5, } } } ] }; // 使用刚指定的配置项和数据显示图表。 myChart.setOption(option); window.addEventListener("resize",function(){ myChart.resize(); }); }) script> <script> $(function echarts_2() { // 基于准备好的dom，初始化echarts实例 var myChart = echarts.init(document.getElementById('echart2')); option = { tooltip: { trigger: 'item' // 修改为 'item' 以适应散点图 }, grid: { left: '0%', top: '10px', right: '0%', bottom: '4%', containLabel: true }, xAxis: [{ type: 'category', data: {{form.echart2.xAxis|safe}}, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, axisTick: { show: false, }, axisLabel: { interval: 0, rotate: 45, // 将标签旋转90度 show: true, splitNumber: 15, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '8', }, }, }], yAxis: [{ type: 'value', axisLabel: { show: true, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '12', }, }, axisTick: { show: false, }, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, splitLine: { lineStyle: { color: "rgba(255,255,255,.1)", } } }], series: [{ type: 'scatter', // 将类型更改为 'scatter' data: {{form.echart2.series|safe}}, symbolSize: 10, // 设置散点的大小 itemStyle: { normal: { color: '#27d08a', opacity: 1, } } }] }; // 使用刚指定的配置项和数据显示图表。 myChart.setOption(option); window.addEventListener("resize", function() { myChart.resize(); }); }) script> <script> $(function echarts_3() { // 基于准备好的dom，初始化echarts实例 var myChart = echarts.init(document.getElementById('echart3')); // 配置项 var option = { tooltip: { trigger: 'item', formatter: '{a}
{b}: {c} ({d}%)' // 在悬停提示框中显示公司名称、数值和百分比 }, legend: { top: '5%', left: 'center', textStyle: { color: "rgba(255,255,255,.6)" } }, series: [ { name: '公司数据', // 扇形图系列名称 type: 'pie', radius: ['40%', '70%'], // 内外半径，形成环形图 avoidLabelOverlap: false, itemStyle: { borderRadius: 10, borderColor: '#fff', borderWidth: 2 }, labelLine: { show: true, length: 6, length2: 8, lineStyle: { color: "rgba(255,255,255,.6)" } }, data:{{form.echart3.data|safe}}, } ] }; // 使用刚指定的配置项和数据显示图表。 myChart.setOption(option); window.addEventListener("resize", function() { myChart.resize(); }); }); script> <script> $(function echarts_4() { // 基于准备好的dom，初始化echarts实例 var myChart = echarts.init(document.getElementById('echart4')); option = { tooltip: { trigger: 'item' // 修改为 'item' 以适应散点图 }, grid: { left: '0%', top: '10px', right: '0%', bottom: '4%', containLabel: true }, xAxis: [{ type: 'category', data: {{form.echart4.xAxis|safe}}, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, axisTick: { show: false, }, axisLabel: { interval: 0, rotate: 45, // 将标签旋转90度 show: true, splitNumber: 15, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '8', }, }, }], yAxis: [{ type: 'value', axisLabel: { show: true, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '12', }, }, axisTick: { show: false, }, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, splitLine: { lineStyle: { color: "rgba(255,255,255,.1)", } } }], series: [{ type: 'scatter', // 将类型更改为 'scatter' data: {{form.echart4.series|safe}}, symbolSize: 10, // 设置散点的大小 itemStyle: { normal: { color: '#27d08a', opacity: 1, } } }] }; // 使用刚指定的配置项和数据显示图表。 myChart.setOption(option); window.addEventListener("resize", function() { myChart.resize(); }); }) script> <script> $(function echarts_5() { // 基于准备好的dom，初始化echarts实例 var myChart = echarts.init(document.getElementById('echart5')); option = { tooltip: { trigger: 'axis', axisPointer: { type: 'line' // 修改为'line'以匹配折线图的样式 } }, grid: { left: '0%', top: '10px', right: '0%', bottom: '2%', containLabel: true }, xAxis: [{ type: 'category', data: {{form.echart5.xAxis|safe}}, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, axisTick: { show: false, }, axisLabel: { rotate: 90, interval: 0, show: true, splitNumber: 15, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '12', }, }, }], yAxis: [{ type: 'value', axisLabel: { show: true, textStyle: { color: "rgba(255,255,255,.6)", fontSize: '12', }, }, axisTick: { show: false, }, axisLine: { show: true, lineStyle: { color: "rgba(255,255,255,.1)", width: 1, type: "solid" }, }, splitLine: { lineStyle: { color: "rgba(255,255,255,.1)", } } }], series: [{ type: 'line', // 修改为'line'类型 data: {{form.echart5.series|safe}}, smooth: true, // 可选：让折线平滑 itemStyle: { color: '#2f89cf', opacity: 1, }, lineStyle: { width: 2, // 线条宽度 type: 'solid' // 线条类型 } }] }; // 使用刚指定的配置项和数据显示图表。 myChart.setOption(option); window.addEventListener("resize", function() { myChart.resize(); }); }) script> <script> $(function echarts_6() { var myChart = echarts.init(document.getElementById('echart6')); var option = { tooltip: {}, radar: { indicator: {{form.echart6.data|safe}} }, series: [{ type: 'radar', data: [ { value: [5500, 7000, 11064, 9500, 7469, 6250, 0, 7500, 6250, 7000], name: '城市指标' } ] }] }; myChart.setOption(option); window.addEventListener("resize", function() { myChart.resize(); }); }); script> <script> $(function map() { // 基于准备好的dom，初始化echarts实例 var myChart = echarts.init(document.getElementById('map_1')); var data = {{form.map_1.data|safe}}; var geoCoordMap = { '海门':[121.15,31.89], '鄂尔多斯':[109.781327,39.608266], '招远':[120.38,37.35], '舟山':[122.207216,29.985295], '齐齐哈尔':[123.97,47.33], '盐城':[120.13,33.38], '赤峰':[118.87,42.28], '青岛':[120.33,36.07], '乳山':[121.52,36.89], '金昌':[102.188043,38.520089], '泉州':[118.58,24.93], '莱西':[120.53,36.86], '日照':[119.46,35.42], '胶南':[119.97,35.88], '南通':[121.05,32.08], '拉萨':[91.11,29.97], '云浮':[112.02,22.93], '梅州':[116.1,24.55], '文登':[122.05,37.2], '上海':[121.48,31.22], '攀枝花':[101.718637,26.582347], '威海':[122.1,37.5], '承德':[117.93,40.97], '厦门':[118.1,24.46], '汕尾':[115.375279,22.786211], '潮州':[116.63,23.68], '丹东':[124.37,40.13], '太仓':[121.1,31.45], '曲靖':[103.79,25.51], '烟台':[121.39,37.52], '福州':[119.3,26.08], '瓦房店':[121.979603,39.627114], '即墨':[120.45,36.38], '抚顺':[123.97,41.97], '玉溪':[102.52,24.35], '张家口':[114.87,40.82], '阳泉':[113.57,37.85], '莱州':[119.942327,37.177017], '湖州':[120.1,30.86], '汕头':[116.69,23.39], '昆山':[120.95,31.39], '宁波':[121.56,29.86], '湛江':[110.359377,21.270708], '揭阳':[116.35,23.55], '荣成':[122.41,37.16], '连云港':[119.16,34.59], '葫芦岛':[120.836932,40.711052], '常熟':[120.74,31.64], '东莞':[113.75,23.04], '河源':[114.68,23.73], '淮安':[119.15,33.5], '泰州':[119.9,32.49], '南宁':[108.33,22.84], '营口':[122.18,40.65], '惠州':[114.4,23.09], '江阴':[120.26,31.91], '蓬莱':[120.75,37.8], '韶关':[113.62,24.84], '嘉峪关':[98.289152,39.77313], '广州':[113.23,23.16], '延安':[109.47,36.6], '太原':[112.53,37.87], '清远':[113.01,23.7], '中山':[113.38,22.52], '昆明':[102.73,25.04], '寿光':[118.73,36.86], '盘锦':[122.070714,41.119997], '长治':[113.08,36.18], '深圳':[114.07,22.62], '珠海':[113.52,22.3], '宿迁':[118.3,33.96], '咸阳':[108.72,34.36], '铜川':[109.11,35.09], '平度':[119.97,36.77], '佛山':[113.11,23.05], '海口':[110.35,20.02], '江门':[113.06,22.61], '章丘':[117.53,36.72], '肇庆':[112.44,23.05], '大连':[121.62,38.92], '临汾':[111.5,36.08], '吴江':[120.63,31.16], '石嘴山':[106.39,39.04], '沈阳':[123.38,41.8], '苏州':[120.62,31.32], '茂名':[110.88,21.68], '嘉兴':[120.76,30.77], '长春':[125.35,43.88], '胶州':[120.03336,36.264622], '银川':[106.27,38.47], '张家港':[120.555821,31.875428], '三门峡':[111.19,34.76], '锦州':[121.15,41.13], '南昌':[115.89,28.68], '柳州':[109.4,24.33], '三亚':[109.511909,18.252847], '自贡':[104.778442,29.33903], '吉林':[126.57,43.87], '阳江':[111.95,21.85], '泸州':[105.39,28.91], '西宁':[101.74,36.56], '宜宾':[104.56,29.77], '呼和浩特':[111.65,40.82], '成都':[104.06,30.67], '大同':[113.3,40.12], '镇江':[119.44,32.2], '桂林':[110.28,25.29], '张家界':[110.479191,29.117096], '宜兴':[119.82,31.36], '北海':[109.12,21.49], '西安':[108.95,34.27], '金坛':[119.56,31.74], '东营':[118.49,37.46], '牡丹江':[129.58,44.6], '遵义':[106.9,27.7], '绍兴':[120.58,30.01], '扬州':[119.42,32.39], '常州':[119.95,31.79], '潍坊':[119.1,36.62], '重庆':[106.54,29.59], '台州':[121.420757,28.656386], '南京':[118.78,32.04], '滨州':[118.03,37.36], '贵阳':[106.71,26.57], '无锡':[120.29,31.59], '本溪':[123.73,41.3], '克拉玛依':[84.77,45.59], '渭南':[109.5,34.52], '马鞍山':[118.48,31.56], '宝鸡':[107.15,34.38], '焦作':[113.21,35.24], '句容':[119.16,31.95], '北京':[116.46,39.92], '徐州':[117.2,34.26], '衡水':[115.72,37.72], '包头':[110,40.58], '绵阳':[104.73,31.48], '乌鲁木齐':[87.68,43.77], '枣庄':[117.57,34.86], '杭州':[120.19,30.26], '淄博':[118.05,36.78], '鞍山':[122.85,41.12], '溧阳':[119.48,31.43], '库尔勒':[86.06,41.68], '安阳':[114.35,36.1], '开封':[114.35,34.79], '济南':[117,36.65], '德阳':[104.37,31.13], '温州':[120.65,28.01], '九江':[115.97,29.71], '邯郸':[114.47,36.6], '临安':[119.72,30.23], '兰州':[103.73,36.03], '沧州':[116.83,38.33], '临沂':[118.35,35.05], '南充':[106.110698,30.837793], '天津':[117.2,39.13], '富阳':[119.95,30.07], '泰安':[117.13,36.18], '诸暨':[120.23,29.71], '郑州':[113.65,34.76], '哈尔滨':[126.63,45.75], '聊城':[115.97,36.45], '芜湖':[118.38,31.33], '唐山':[118.02,39.63], '平顶山':[113.29,33.75], '邢台':[114.48,37.05], '德州':[116.29,37.45], '济宁':[116.59,35.38], '荆州':[112.239741,30.335165], '宜昌':[111.3,30.7], '义乌':[120.06,29.32], '丽水':[119.92,28.45], '洛阳':[112.44,34.7], '秦皇岛':[119.57,39.95], '株洲':[113.16,27.83], '石家庄':[114.48,38.03], '莱芜':[117.67,36.19], '常德':[111.69,29.05], '保定':[115.48,38.85], '湘潭':[112.91,27.87], '金华':[119.64,29.12], '岳阳':[113.09,29.37], '长沙':[113,28.21], '衢州':[118.88,28.97], '廊坊':[116.7,39.53], '菏泽':[115.480656,35.23375], '合肥':[117.27,31.86], '武汉':[114.31,30.52], '大庆':[125.03,46.58] }; var convertData = function (data) { var res = []; for (var i = 0; i < data.length; i++) { var geoCoord = geoCoordMap[data[i].name]; if (geoCoord) { res.push({ name: data[i].name, value: geoCoord.concat(data[i].value) }); } } return res; }; option = { tooltip : { trigger: 'item', formatter: function (params) { if(typeof(params.value)[2] == "undefined"){ return params.name + ' : ' + params.value; }else{ return params.name + ' : ' + params.value[2]; } } }, geo: { map: 'china', label: { emphasis: { show: false } }, roam: false,//禁止其放大缩小 itemStyle: { normal: { areaColor: '#4c60ff', borderColor: '#002097' }, emphasis: { areaColor: '#293fff' } } }, series : [ { name: '消费金额', type: 'scatter', coordinateSystem: 'geo', data: convertData(data), symbolSize: function (val) { return val[2] / {{form.map_1.symbolSize}}; }, label: { normal: { formatter: '{b}', position: 'right', show: false }, emphasis: { show: true } }, itemStyle: { normal: { color: '#ffeb7b' } } } ] }; myChart.setOption(option); window.addEventListener("resize",function(){ myChart.resize(); }); } ) script> body> html>
8、总结
通过 request爬虫获取数据，使用Hive 进行大数据分析、通过sqoop进行数据迁移，最后使用Flask 构建后端接口获取mysql数据提供给前端，再结合 ECharts 前端可视化，能够构建一个完整的大数据展示系统。
如有遇到问题可以找小编沟通交流哦。另外小编帮忙辅导大课作业，学生毕设等。不限于MapReduce， MySQL, python，java，大数据，模型训练等。 hadoop hdfs yarn spark Django flask flink kafka flume datax sqoop seatunnel echart可视化 机器学习等
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- 原文地址：https://blog.csdn.net/lhyandlwl/article/details/143334809