總的而言,分三部分:
1.監控器(monitor.py
): 每秒獲取系統的四個cpu的使用率,存入數據庫。
2.路由器(app.py
): 響應頁面的ajax,獲取最新的一條或多條數據。
3.頁面(index.html
): 發出ajax請求,更新echarts圖表javascript
使用了psutil庫,對系統進行監控。html
import psutil import sqlite3 import time ''' 說明:四個cpu使用率,顯然是臨時數據,因此最好用內存數據庫,如Redis等 可是這裏強行使用sqlite3,無論了,哪一個叫他是內置的呢?! ''' db_name = 'mydb.db' def create_db(): # 鏈接 conn = sqlite3.connect(db_name) c = conn.cursor() # 建立表 c.execute('''DROP TABLE IF EXISTS cpu''') # 刪除舊錶,若是存在(由於這是臨時數據) c.execute('''CREATE TABLE cpu (id INTEGER PRIMARY KEY AUTOINCREMENT, insert_time text,cpu1 float, cpu2 float, cpu3 float, cpu4 float)''') # 關閉 conn.close() def save_to_db(data): '''參數data格式:['00:01',3.5, 5.9, 0.7, 29.6]''' # 創建鏈接 conn = sqlite3.connect(db_name) c = conn.cursor() # 插入數據 c.execute('INSERT INTO cpu(insert_time,cpu1,cpu2,cpu3,cpu4) VALUES (?,?,?,?,?)', data) # 提交!!! conn.commit() # 關閉鏈接 conn.close() # 建立表 create_db() # 經過循環,對系統進行監控 while True: # 獲取系統cpu使用率(每隔1秒) cpus = psutil.cpu_percent(interval=1, percpu=True) # 獲取系統時間(只取分:秒) t = time.strftime('%M:%S', time.localtime()) # 保存到數據庫 save_to_db((t, *cpus))
import sqlite3 from flask import Flask, request, render_template, jsonify app = Flask(__name__) def get_db(): db = sqlite3.connect('mydb.db') db.row_factory = sqlite3.Row return db def query_db(query, args=(), one=False): db = get_db() cur = db.execute(query, args) db.commit() rv = cur.fetchall() db.close() return (rv[0] if rv else None) if one else rv @app.route("/", methods=["GET"]) def index(): return render_template("index.html") @app.route("/cpu", methods=["POST"]) def cpu(): if request.method == "POST": res = query_db("SELECT * FROM cpu WHERE id>=(?)", args=(int(request.form['id'])+1,)) #返回1+個數據 return jsonify(insert_time = [x[1] for x in res], cpu1 = [x[2] for x in res], cpu2 = [x[3] for x in res], cpu3 = [x[4] for x in res], cpu4 = [x[5] for x in res]) # 返回json格式 if __name__ == "__main__": app.run(debug=True)
<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <title>ECharts3 Ajax</title> <script src="{{ url_for('static', filename='jquery-3.1.1.js') }}"></script> <script src="{{ url_for('static', filename='echarts.js') }}"></script> </head> <body> <!--爲ECharts準備一個具有大小(寬高)的Dom--> <div id="main" style="height:500px;border:1px solid #ccc;padding:10px;"></div> <script type="text/javascript"> //--- 折柱 --- var myChart = echarts.init(document.getElementById('main')); myChart.setOption({ title: { text: '服務器系統監控' }, tooltip: {}, legend: { data:['cpu1','cpu2','cpu3','cpu4'] }, xAxis: { data: [] }, yAxis: {}, series: [{ name: 'cpu1', type: 'line', data: [] },{ name: 'cpu2', type: 'line', data: [] },{ name: 'cpu3', type: 'line', data: [] },{ name: 'cpu4', type: 'line', data: [] }] }); // 六個全局變量:插入時間、cpu一、cpu二、cpu三、cpu四、 哨兵(用於POST) var insert_time = ["","","","","","","","","",""], cpu1 = [0,0,0,0,0,0,0,0,0,0], cpu2 = [0,0,0,0,0,0,0,0,0,0], cpu3 = [0,0,0,0,0,0,0,0,0,0], cpu4 = [0,0,0,0,0,0,0,0,0,0], lastID = 0; // 哨兵,記錄上次數據表中的最後id +1(下次查詢只要>=lastID) //準備好統一的 callback 函數 var update_mychart = function (data) { //data是json格式的response對象 myChart.hideLoading(); // 隱藏加載動畫 dataLength = data.insert_time.length; //取回的數據長度 lastID += dataLength; //哨兵,相應增長。 // 切片是能統一的關鍵!! insert_time = insert_time.slice(dataLength).concat(data.insert_time); // 數組,先切片、再拼接 cpu1 = cpu1.slice(dataLength).concat(data.cpu1.map(parseFloat)); //注意map方法 cpu2 = cpu2.slice(dataLength).concat(data.cpu2.map(parseFloat)); cpu3 = cpu3.slice(dataLength).concat(data.cpu3.map(parseFloat)); cpu4 = cpu4.slice(dataLength).concat(data.cpu4.map(parseFloat)); // 填入數據 myChart.setOption({ xAxis: { data: insert_time }, series: [{ name: 'cpu1', // 根據名字對應到相應的系列 data: cpu1 },{ name: 'cpu2', data: cpu2 },{ name: 'cpu3', data: cpu3 },{ name: 'cpu4', data: cpu4 }] }); if (dataLength == 0){clearInterval(timeTicket);} //若是取回的數據長度爲0,中止ajax } myChart.showLoading(); // 首次顯示加載動畫 // 異步加載數據 (首次,get,顯示6個數據) $.get('/cpu').done(update_mychart); // 異步更新數據 (之後,定時post,取回1個數據) var timeTicket = setInterval(function () { $.post('/cpu',{id: lastID}).done(update_mychart); }, 3000); </script> </body> </html>