接触Python有一段时间了，经常用来做一些好玩的事，前几天跟领导聊天说到，要是能够实现全国各地访问流量的显示，那就最好了，刚好要申请一些大屏幕来，所以就想到了做这个。确实稍微大点的公司都有这类东西，确实很酷炫了，自己也搞一个这样的。下面说一下实现过程：
1、首先是数据：采集的话我选择的是读取nginx日志。可以提供的思路是，我们选择同一收集的方式，收集到大数据分析机器(同意收集的工具还是很多的，比如rsyslog)，然后通过正则或者一类东西筛选出我们需要的IP（当然日志肯定是按天切割的）:
2、收集到的数据，通过调用公网的IP查询接口进行筛选，筛选，找出对应的城市、入库
3、最后就是前端展示了，这里我选用的是百度的echarts，百度已经提供了比较完善的API和说明文档，自己去阅读就好。
给一下前端的代码：
<!DOCTYPE html>
<head>
<meta charset="utf-8">
<title>ECharts</title>
</head>
<body>
<!-- 为ECharts准备一个具备大小（宽高）的Dom -->
<div id="main" style="height:800px" ></div>
<!-- ECharts单文件引入 -->
<script src="http://echarts.baidu.com/build/dist/echarts.js"></script>
<script type="text/javascript">
// 路径配置
require.config({
paths: {
echarts: ‘http://echarts.baidu.com/build/dist‘
}
});
// 使用
require(
[
‘echarts‘,
‘echarts/chart/map‘
],
function (ec) {
// 基于准备好的dom，初始化echarts图表
var myChart = ec.init(document.getElementById(‘main‘));
var option = {
backgroundColor: ‘#1b1b1b‘,
color: [‘gold‘,‘aqua‘,‘lime‘],
title : {
text: ‘众划算‘,
subtext:‘全国各地访问数据‘,
x:‘center‘,
textStyle : {
color: ‘#fff‘
}
},
tooltip : {
trigger: ‘item‘,
formatter: ‘{b}‘
},
legend: {
orient: ‘vertical‘,
x:‘left‘,
data:[‘北京 Top10‘],
selectedMode: ‘single‘,
selected:{
‘上海 Top10‘ : false,
‘广州 Top10‘ : false
},
textStyle : {
color: ‘#fff‘
}
},
toolbox: {
show : true,
orient : ‘vertical‘,
x: ‘right‘,
y: ‘center‘,
feature : {
mark : {show: true},
dataView : {show: true, readOnly: false},
restore : {show: true},
saveAsImage : {show: true}
}
},
dataRange: {
min : 0,
max : 100,
calculable : true,
color: [‘#ff3333‘, ‘orange‘, ‘yellow‘,‘lime‘,‘aqua‘],
textStyle:{
color:‘#fff‘
}
},
series : [
{
name: ‘全国‘,
type: ‘map‘,
roam: true,
hoverable: false,
mapType: ‘china‘,
itemStyle:{
normal:{
borderColor:‘rgba(100,149,237,1)‘,
borderWidth:0.5,
areaStyle:{
color: ‘#1b1b1b‘
}
}
},
data:[],
markLine : {
smooth:true,
symbol: [‘none‘, ‘circle‘],
symbolSize : 1,
itemStyle : {
normal: {
color:‘#fff‘,
borderWidth:1,
borderColor:‘rgba(30,144,255,0.5)‘
}
},
data : [
[{name:‘北京‘},{name:‘内蒙古‘}],
[{name:‘北京‘},{name:‘北海‘}],
[{name:‘北京‘},{name:‘广东‘}],
[{name:‘北京‘},{name:‘河南‘}],
[{name:‘北京‘},{name:‘吉林‘}],
[{name:‘北京‘},{name:‘长治‘}],
[{name:‘北京‘},{name:‘重庆‘}],
[{name:‘北京‘},{name:‘湖南‘}],
[{name:‘北京‘},{name:‘常州‘}],
[{name:‘北京‘},{name:‘丹东‘}],
[{name:‘北京‘},{name:‘辽宁‘}],
[{name:‘北京‘},{name:‘东营‘}],
[{name:‘北京‘},{name:‘延安‘}],
[{name:‘北京‘},{name:‘福建‘}],
[{name:‘北京‘},{name:‘海口‘}],
[{name:‘北京‘},{name:‘呼和浩特‘}],
[{name:‘北京‘},{name:‘安徽‘}],
[{name:‘北京‘},{name:‘杭州‘}],
[{name:‘北京‘},{name:‘黑龙江‘}],
[{name:‘北京‘},{name:‘舟山‘}],
[{name:‘北京‘},{name:‘银川‘}],
[{name:‘北京‘},{name:‘衢州‘}],
[{name:‘北京‘},{name:‘江西‘}],
[{name:‘北京‘},{name:‘云南‘}],
[{name:‘北京‘},{name:‘贵州‘}],
[{name:‘北京‘},{name:‘甘肃‘}],
[{name:‘北京‘},{name:‘广西‘}],
[{name:‘北京‘},{name:‘拉萨‘}],
[{name:‘北京‘},{name:‘连云港‘}],
[{name:‘北京‘},{name:‘临沂‘}],
[{name:‘北京‘},{name:‘柳州‘}],
[{name:‘北京‘},{name:‘宁波‘}],
[{name:‘北京‘},{name:‘南京‘}],
[{name:‘北京‘},{name:‘南宁‘}],
[{name:‘北京‘},{name:‘江苏‘}],
[{name:‘北京‘},{name:‘上海‘}],
[{name:‘北京‘},{name:‘沈阳‘}],
[{name:‘北京‘},{name:‘陕西‘}],
[{name:‘北京‘},{name:‘汕头‘}],
[{name:‘北京‘},{name:‘深圳‘}],
[{name:‘北京‘},{name:‘青岛‘}],
[{name:‘北京‘},{name:‘山东‘}],
[{name:‘北京‘},{name:‘山西‘}],
[{name:‘北京‘},{name:‘乌鲁木齐‘}],
[{name:‘北京‘},{name:‘潍坊‘}],
[{name:‘北京‘},{name:‘威海‘}],
[{name:‘北京‘},{name:‘浙江‘}],
[{name:‘北京‘},{name:‘无锡‘}],
[{name:‘北京‘},{name:‘厦门‘}],
[{name:‘北京‘},{name:‘西宁‘}],
[{name:‘北京‘},{name:‘徐州‘}],
[{name:‘北京‘},{name:‘烟台‘}],
[{name:‘北京‘},{name:‘盐城‘}],
[{name:‘北京‘},{name:‘珠海‘}],
[{name:‘北京‘},{name:‘香港‘}],
[{name:‘北京‘},{name:‘湖北‘}],
],
},
geoCoord: {
‘上海‘: [121.4648,31.2891],
‘东莞‘: [113.8953,22.901],
‘东营‘: [118.7073,37.5513],
‘中山‘: [113.4229,22.478],
‘临汾‘: [111.4783,36.1615],
‘临沂‘: [118.3118,35.2936],
‘丹东‘: [124.541,40.4242],
‘丽水‘: [119.5642,28.1854],
‘乌鲁木齐‘: [87.9236,43.5883],
‘佛山‘: [112.8955,23.1097],
‘河北‘: [115.0488,39.0948],
‘甘肃‘: [103.5901,36.3043],
‘内蒙古‘: [110.3467,41.4899],
‘北京‘: [116.4551,40.2539],
‘北海‘: [109.314,21.6211],
‘南京‘: [118.8062,31.9208],
‘南宁‘: [108.479,23.1152],
‘广西‘: [108.479,23.1154],
‘江西‘: [116.0046,28.6633],
‘江苏‘: [121.1023,32.1625],
‘厦门‘: [118.1689,24.6478],
‘台州‘: [121.1353,28.6688],
‘安徽‘: [117.29,32.0581],
‘呼和浩特‘: [111.4124,40.4901],
‘咸阳‘: [108.4131,34.8706],
‘黑龙江‘: [127.9688,45.368],
‘唐山‘: [118.4766,39.6826],
‘嘉兴‘: [120.9155,30.6354],
‘大同‘: [113.7854,39.8035],
‘辽宁‘: [122.2229,39.4409],
‘天津‘: [117.4219,39.4189],
‘山西‘: [112.3352,37.9413],
‘威海‘: [121.9482,37.1393],
‘宁波‘: [121.5967,29.6466],
‘宝鸡‘: [107.1826,34.3433],
‘宿迁‘: [118.5535,33.7775],
‘常州‘: [119.4543,31.5582],
‘广东‘: [113.5107,23.2196],
‘香港‘: [113.5107,31.3569],
‘廊坊‘: [116.521,39.0509],
‘延安‘: [109.1052,36.4252],
‘张家口‘: [115.1477,40.8527],
‘徐州‘: [117.5208,34.3268],
‘德州‘: [116.6858,37.2107],
‘惠州‘: [114.6204,23.1647],
‘四川‘: [103.9526,30.7617],
‘扬州‘: [119.4653,32.8162],
‘承德‘: [117.5757,41.4075],
‘拉萨‘: [91.1865,30.1465],
‘无锡‘: [120.3442,31.5527],
‘日照‘: [119.2786,35.5023],
‘云南‘: [102.9199,25.4663],
‘杭州‘: [119.5313,29.8773],
‘枣庄‘: [117.323,34.8926],
‘柳州‘: [109.3799,24.9774],
‘株洲‘: [113.5327,27.0319],
‘湖北‘: [114.3896,30.6628],
‘汕头‘: [117.1692,23.3405],
‘江门‘: [112.6318,22.1484],
‘沈阳‘: [123.1238,42.1216],
‘沧州‘: [116.8286,38.2104],
‘河源‘: [114.917,23.9722],
‘泉州‘: [118.3228,25.1147],
‘泰安‘: [117.0264,36.0516],
‘泰州‘: [120.0586,32.5525],
‘山东‘: [117.1582,36.8701],
‘济宁‘: [116.8286,35.3375],
‘海口‘: [110.3893,19.8516],
‘淄博‘: [118.0371,36.6064],
‘淮安‘: [118.927,33.4039],
‘深圳‘: [114.5435,22.5439],
‘清远‘: [112.9175,24.3292],
‘浙江‘: [120.498,27.8119],
‘渭南‘: [109.7864,35.0299],
‘湖州‘: [119.8608,30.7782],
‘湘潭‘: [112.5439,27.7075],
‘滨州‘: [117.8174,37.4963],
‘潍坊‘: [119.0918,36.524],
‘烟台‘: [120.7397,37.5128],
‘玉溪‘: [101.9312,23.8898],
‘珠海‘: [113.7305,22.1155],
‘盐城‘: [120.2234,33.5577],
‘盘锦‘: [121.9482,41.0449],
‘石家庄‘: [114.4995,38.1006],
‘福建‘: [119.4543,25.9222],
‘秦皇岛‘: [119.2126,40.0232],
‘绍兴‘: [120.564,29.7565],
‘聊城‘: [115.9167,36.4032],
‘肇庆‘: [112.1265,23.5822],
‘舟山‘: [122.2559,30.2234],
‘苏州‘: [120.6519,31.3989],
‘莱芜‘: [117.6526,36.2714],
‘菏泽‘: [115.6201,35.2057],
‘营口‘: [122.4316,40.4297],
‘葫芦岛‘: [120.1575,40.578],
‘衡水‘: [115.8838,37.7161],
‘衢州‘: [118.6853,28.8666],
‘西宁‘: [101.4038,36.8207],
‘陕西‘: [109.1162,34.2004],
‘贵州‘: [106.6992,26.7682],
‘连云港‘: [119.1248,34.552],
‘邢台‘: [114.8071,37.2821],
‘邯郸‘: [114.4775,36.535],
‘河南‘: [113.4668,34.6234],
‘鄂尔多斯‘: [108.9734,39.2487],
‘重庆‘: [107.7539,30.1904],
‘金华‘: [120.0037,29.1028],
‘铜川‘: [109.0393,35.1947],
‘银川‘: [106.3586,38.1775],
‘镇江‘: [119.4763,31.9702],
‘吉林‘: [125.8154,44.2584],
‘湖南‘: [113.0823,28.2568],
‘长治‘: [112.8625,36.4746],
‘阳泉‘: [113.4778,38.0951],
‘青岛‘: [120.4651,36.3373],
‘韶关‘: [113.7964,24.7028]
}
},
{
name: ‘北京 Top10‘,
type: ‘map‘,
mapType: ‘china‘,
data:[],
markLine : {
smooth:true,
effect : {
show: true,
scaleSize: 1,
period: 30,
color: ‘#fff‘,
shadowBlur: 10
},
itemStyle : {
normal: {
borderWidth:1,
lineStyle: {
type: ‘solid‘,
shadowBlur: 10
}
}
},
data : [
{%for x in all_data%}
[{name:‘北京‘}, {name:‘{{x.city}}‘,value:{{x.num}}}],
{%endfor%}
]
},
markPoint : {
symbol:‘emptyCircle‘,
symbolSize : function (v){
return 10 + v/10
},
effect : {
show: true,
shadowBlur : 0
},
itemStyle:{
normal:{
label:{show:false}
},
emphasis: {
label:{position:‘top‘}
}
},
data :
[
{%for i in all_data%}
{name:‘{{i.city}}‘,value:{{i.num}}},
{%endfor%}
]
}
},
]
};
// 为echarts对象加载数据
myChart.setOption(option);
}
);
</script>
</body>
后端的操作，其实也可以是用saltstack远程分析nginx脚本，性能不影响web服务器的话，直接远程入库就好。
时间： 2024-08-09 23:53:45