Python做网站 性能网址查询工具

1, datafram根据相同的key聚合

# 创建df1 ==> json {'key':'A', 'value':1 } {'key':'B', 'value':2 }
data1 = {'key': ['A', 'B'],
'value': [1, 2]}
df1 = pd.DataFrame(data1)# 创建df2 ==> {'key':'A', 'value':11 } {'key':'B', 'value':22 }
data2 = {'key': ['A', 'B'],
'value': [11, 22]}
df2 = pd.DataFrame(data2)# 创建df3 ==>{'key':'A', 'value':111 } {'key':'B', 'value':222 } {'key':'C', 'value':333 }
data3 = {'key': ['A', 'B', 'c'],
'value': [111, 222, 333]}
df3 = pd.DataFrame(data3)#### 聚合两个dataframe  
#==> {'key':'A', 'value_x':1, 'value_y':11 } {'key':'B', 'value_x':2, 'value_y':22 }
>>> mdf1=pd.merge(df1, df2, on='key')
>>> mdf1key  value_x  value_y
0   A        1       11
1   B        2       22
#### 再聚合两个dataframe 
#==> {'key':'A',  'value_x':1, 'value_y':11 , 'value':111 } {'key':'B', 'value_x':2, 'value_y':22 , 'value':222 }
mdf = pd.merge(pd.merge(df1, df2, on='key'), df3, on='key') 
>>> mdf2=pd.merge(mdf1, df3, on='key')
>>> mdf2key  value_x  value_y  value
0   A        1       11    111
1   B        2       22    222

2, 数据合并：获取采集10,20,30分钟es索引数据

[root@localhost ] # cat es-indices-monitor.py
import json
import time
import requests
import os
import sys
import glob
import pandas as pddef deloldfile(workdir):# 获取目录下所有的文件all_files = glob.glob(os.path.join(workdir, '*'))# 将文件名和访问时间存入列表file_list = []for file in all_files:file_list.append((file, os.path.getatime(file)))# 根据访问时间排序file_list.sort(key=lambda x: x[1], reverse=False)# 删除旧文件，只保留最新的文件for file in file_list[:-3]: # 排除最后三个文件，因为它是最新的os.remove(file[0])def createfile(workdir,fileName):if not os.path.exists(workdir):os.makedirs(workdir)#os.system("find {}/*.json   -type f -ctime +1 -delete".format(workdir) )#for fileName in os.listdir(workdir):file=open(workdir+fileName,'w',encoding="utf-8")return filedef readfile(workdir):if not os.path.exists(workdir):os.makedirs(workdir)# 获取目录下所有的文件all_files = glob.glob(os.path.join(workdir, '*'))# 将文件名和访问时间存入列表file_list = []for file in all_files:file_list.append((file, os.path.getatime(file)))# 根据访问时间排序files=[]file_list.sort(key=lambda x: x[1], reverse=False)for file in file_list: # 排除最后两个文件，因为它是最新的files.append(file[0])return filesdef writejson(file,jsonArr):for js in jsonArr:jstr=json.dumps(js)+"\n"file.write(jstr)file.close()#3，json转字符串
def getdata(domain,password):url = "http://"+domain+"/_cat/indices?format=json"# 设置认证信息auth = ('elastic', password)# 发送GET请求，并在请求中添加认证信息response = requests.get(url, auth=auth)# 检查响应状态码，如果成功则打印响应内容if response.status_code == 200:#遍历返回的json数组，提取需要的字段jsonArr=json.loads(response.text)df = pd.json_normalize(jsonArr)dfnew = df.drop(["uuid","docs.deleted"], axis=1)#print(dfnew)#保存_cat/es/indices数据到json文件workdir="/data/es-indices/"workdir_tmp=workdir+"tmp/"f_time = time.strftime("%Y-%m-%d_%H-%M-%S",time.localtime())filename="es-data-{}.json".format(f_time)filename_tmp="tmp-{}.json".format(f_time)file=createfile(workdir_tmp,filename_tmp)writejson(file,jsonArr)#删除旧文件，只保留2个最新的deloldfile(workdir_tmp)deloldfile(workdir)files=readfile(workdir_tmp)#df1=pd.read_json(files[0],lines=True,convert_dates=False)if len(files) > 1:print(files[0])print(files[1])df1=pd.read_json(files[0],lines=True)df2=pd.read_json(files[1],lines=True)#"health","status","index","uuid","pri","rep","docs.count","docs.deleted","store.size","pri.store.size"df1 = df1.drop(["health","status","uuid","pri","rep","docs.deleted","store.size","pri.store.size"], axis=1)df2 = df2.drop(["health","status","uuid","pri","rep","docs.deleted","store.size","pri.store.size"], axis=1)mdf = pd.merge(df1, df2, on='index', how='outer')#print(df1)else:mdf=dfnew#聚合3条数据,查看索引文档数量是否变化: 近10分钟的数量为doc.count, 前10分钟的数量为doc.count_x, 前20分钟的数量为doc.count_y, #print(mdf) mdf2 = pd.merge(dfnew, mdf, on='index', how='outer')mdf2 = mdf2.rename(columns={"docs.count_x":"docs.count_30", "docs.count_y":"docs.count_20"})#print(mdf2) file=createfile(workdir,filename)for idx,row in mdf2.iterrows():jstr=row.to_json()file.write(jstr+"\n")file.close()else:print('请求失败，状态码：', response.status_code)domain="196.1.0.106:9200"
password="123456"
getdata(domain,password)

脚本测试验证

[root@localhost] #  python3 es-indices-monitor.py
/data/es-indices/tmp/tmp-2023-09-28_13-56-12.json
/data/es-indices/tmp/tmp-2023-09-28_14-11-47.json#查看结果
[root@localhost] # /appset/ldm/script # ll /data/es-indices/
total 148
-rw------- 1 root root 46791 Sep 28 13:56 es-data-2023-09-28_13-56-12.json
-rw------- 1 root root 46788 Sep 28 14:11 es-data-2023-09-28_14-11-47.json
-rw------- 1 root root 46788 Sep 28 14:12 es-data-2023-09-28_14-12-07.json
drwx------ 2 root root  4096 Sep 28 14:12 tmp
[root@localhost] # /appset/ldm/script # ll /data/es-indices/tmp/
total 156
-rw------- 1 root root 52367 Sep 28 13:56 tmp-2023-09-28_13-56-12.json
-rw------- 1 root root 52364 Sep 28 14:11 tmp-2023-09-28_14-11-47.json
-rw------- 1 root root 52364 Sep 28 14:12 tmp-2023-09-28_14-12-07.json#核对文档数量
[root@localhost] # /appset/ldm/script # head  -n 2 /data/es-indices/es-data-2023-09-28_13-56-12.json  |grep 2023_09 |grep count
{"health":"green","status":"open","index":"test_2023_09","pri":"3","rep":"1","docs.count":"14393","store.size":"29.7mb","pri.store.size":"13.9mb","docs.count_30":14391.0,"docs.count_20":14393.0}[root@localhost] # /appset/ldm/script # head  -n 2 /data/es-indices/es-data-2023-09-28_14-11-47.json  |grep 2023_09 |grep count
{"health":"green","status":"open","index":"test_2023_09","pri":"3","rep":"1","docs.count":"14422","store.size":"33.5mb","pri.store.size":"15.8mb","docs.count_30":14391.0,"docs.count_20":14393.0}[root@localhost] # /appset/ldm/script # head  -n 2 /data/es-indices/es-data-2023-09-28_14-12-07.json  |grep 2023_09 |grep count
{"health":"green","status":"open","index":"test_2023_09","pri":"3","rep":"1","docs.count":"14427","store.size":"33.5mb","pri.store.size":"15.8mb","docs.count_30":14393.0,"docs.count_20":14422.0}