将查询结果写入 SSMS
Wtriting query results into SSMS
我试图将 python 中的查询 (API) 的结果写入 SQL 服务器数据库，但我迷失在什么是显然是一个菜鸟错误。 “字符串索引超出范围”。
是的，我不是开发人员，我的代码是数小时阅读、社区帮助和 google 研究的结果。
你们能帮助我了解我的错误在哪里以及如何解决吗？
from re import X
from typing import ItemsView
import requests
import json
import hashlib
import base64
import time
import hmac
import pandas as pd
import datetime
import pyodbc
#Account Info
AccessId = ''
AccessKey = ''
Company = ''
#Request Info
httpVerb ='GET'
resourcePath = '/alert/alerts'
queryParams ='?size=1000&sort=-startEpoch&filter=cleared:*'
#queryParams ='?size=1000&filter=cleared:False'
#queryParams ='?size=1000&filter=cleared:%252A'
#queryParams ='?v=2&size=1000&offset=9000&filter=cleared:"*"'
data = ''
#Construct URL
url = 'https://'+ Company +'.logicmonitor.com/santaba/rest' + resourcePath + queryParams
print(url)
#Get current time in milliseconds
epoch = str(int(time.time() * 1000))
#Concatenate Request details
requestVars = httpVerb + epoch + data + resourcePath
#Construct signature
hmac1 = hmac.new(AccessKey.encode(),msg=requestVars.encode(),digestmod=hashlib.sha256).hexdigest()
signature = base64.b64encode(hmac1.encode())
#Construct headers
auth = 'LMv1 ' + AccessId + ':' + signature.decode() + ':' + epoch
headers = {'Content-Type':'application/json','Authorization':auth}
#Make request
response = requests.get(url, data=data, headers=headers)
data = response.json()
alerts_df = pd.DataFrame(data['data']['items'])
#alerts_df = alerts_df.groupby(['id'], as_index=False).first()
alerts_df = alerts_df[['id','internalId','rule','monitorObjectName','startEpoch','endEpoch','cleared','resourceTemplateName']]
alerts_df['startEpoch'] = pd.to_datetime(alerts_df['startEpoch'],unit='s')
alerts_df['endEpoch'] = alerts_df['endEpoch'].apply(lambda x: pd.to_datetime(x,unit='s') if x !=0 else x)
alerts_df = alerts_df.loc[alerts_df["rule"] == "Critical"]
print(alerts_df)
#print(alerts_df.shape)
#alerts_df.to_csv("alerts_no_groupedby_03.csv")
#alerts_df.to_sql("critical_alerts_00", sqlite3, if_exists='replace', index = False)
server = '999999\SQLEXPRESS'
database = 'LM_Critical_Alerts'
# define connection string
cnxn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server}; \
SERVER=' + server + '; \
DATABASE=' + database +'; \
Trusted_Connection=yes;')
# create the connection cursor
cursor = cnxn.cursor()
# define insert query
insert_query = '''INSERT INTO critical_alerts (row, id, internalId, alertType, monitorObjectName, startEpoch, endEpoch, cleared, resourceTemplateName)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?);'''
# loop through each row in the matrix
for row in alerts_df:
# define the values to insert
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
# insert the data into the database
cursor.execute(insert_query, values)
# commit the inserts
cnxn.commit()
# grab all the rows in the database table
cursor.execute('SELECT * FROM critical_alerts')
# loop through the results
for row in cursor:
print(row)
错误信息和数据结构：
id internalId rule monitorObjectName startEpoch endEpoch cleared resourceTemplateName
13 DS25095821 LMD1815378 Critical MYSERVER 2022-04-20 01:24:55 0 False CPU
120 DS25095744 LMD4168355 Critical MYSERVER 2022-04-20 00:34:40 2022-04-20 00:38:40 True Memory and Processes
149 DS25095716 LMD1815378 Critical MYSERVER 2022-04-20 00:16:08 2022-04-20 01:05:54 True CPU
178 DS25095682 LMD10870447 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
179 DS25095683 LMD10863782 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
180 DS25095684 LMD10866820 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
181 DS25095685 LMD10868897 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
207 DS25095665 LMD12336087 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
208 DS25095666 LMD12335870 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
209 DS25095667 LMD12336893 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
210 DS25095668 LMD12335901 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
270 DS25095620 LMD10866820 Critical MYSERVER 2022-04-19 23:33:14 2022-04-19 23:45:14 True SQL Server Databases
272 DS25095619 LMD12474939 Critical MYSERVER 2022-04-19 23:32:14 2022-04-19 23:35:14 True Elekta MOSAIQ App Services
274 DS25095617 LMD1815378 Critical MYSERVER 2022-04-19 23:31:48 2022-04-19 23:52:36 True CPU
400 DS25095526 LMD1815378 Critical MYSERVER 2022-04-19 22:52:45 2022-04-19 23:02:12 True CPU
436 DS25095501 LMD12006224 Critical MYSERVER 2022-04-19 22:37:37 2022-04-19 22:39:42 True CPU
455 DS25095490 LMD1815378 Critical MYSERVER 2022-04-19 22:30:18 2022-04-19 22:40:45 True CPU
473 DS25095472 LMD10863472 Critical MYSERVER 2022-04-19 22:20:46 2022-04-19 22:44:37 True SQL Server Databases
510 DS25095449 LMD12006813 Critical MYSERVER 2022-04-19 22:06:59 2022-04-19 22:42:32 True CPU
555 DS25095424 LMD12006224 Critical MYSERVER 2022-04-19 21:47:22 2022-04-19 22:26:12 True CPU
628 DS25095366 LMD12006813 Critical MYSERVER 2022-04-19 21:14:52 2022-04-19 21:49:43 True CPU
697 DS25095328 LMD3090757 Critical MYSERVER 2022-04-19 20:51:41 2022-04-19 21:11:40 True Memory and Processes
737 DS25095293 LMD12006813 Critical MYSERVER 2022-04-19 20:38:47 2022-04-19 20:43:34 True CPU
851 DS25095220 LMD12006224 Critical MYSERVER 2022-04-19 19:56:31 2022-04-19 21:25:58 True CPU
894 DS25095183 LMD12006224 Critical MYSERVER 2022-04-19 19:34:23 2022-04-19 19:44:31 True CPU
916 DS25095165 LMD12006813 Critical MYSERVER 2022-04-19 19:25:53 2022-04-19 20:26:38 True CPU
Traceback (most recent call last):
File "c:\Users\Nelson.Silva\OneDrive - Computer & Network Solutions Limited\PBI\LM_Python_Queries\query_alerts.py", line 91, in <module>
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
IndexError: string index out of range
当你这样做时：
for row in alerts_df:
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
您没有遍历行。
试试这个：
for index, row in alerts_df.iterrows():
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
我试图将 python 中的查询 (API) 的结果写入 SQL 服务器数据库，但我迷失在什么是显然是一个菜鸟错误。 “字符串索引超出范围”。
是的，我不是开发人员，我的代码是数小时阅读、社区帮助和 google 研究的结果。
你们能帮助我了解我的错误在哪里以及如何解决吗？
from re import X
from typing import ItemsView
import requests
import json
import hashlib
import base64
import time
import hmac
import pandas as pd
import datetime
import pyodbc
#Account Info
AccessId = ''
AccessKey = ''
Company = ''
#Request Info
httpVerb ='GET'
resourcePath = '/alert/alerts'
queryParams ='?size=1000&sort=-startEpoch&filter=cleared:*'
#queryParams ='?size=1000&filter=cleared:False'
#queryParams ='?size=1000&filter=cleared:%252A'
#queryParams ='?v=2&size=1000&offset=9000&filter=cleared:"*"'
data = ''
#Construct URL
url = 'https://'+ Company +'.logicmonitor.com/santaba/rest' + resourcePath + queryParams
print(url)
#Get current time in milliseconds
epoch = str(int(time.time() * 1000))
#Concatenate Request details
requestVars = httpVerb + epoch + data + resourcePath
#Construct signature
hmac1 = hmac.new(AccessKey.encode(),msg=requestVars.encode(),digestmod=hashlib.sha256).hexdigest()
signature = base64.b64encode(hmac1.encode())
#Construct headers
auth = 'LMv1 ' + AccessId + ':' + signature.decode() + ':' + epoch
headers = {'Content-Type':'application/json','Authorization':auth}
#Make request
response = requests.get(url, data=data, headers=headers)
data = response.json()
alerts_df = pd.DataFrame(data['data']['items'])
#alerts_df = alerts_df.groupby(['id'], as_index=False).first()
alerts_df = alerts_df[['id','internalId','rule','monitorObjectName','startEpoch','endEpoch','cleared','resourceTemplateName']]
alerts_df['startEpoch'] = pd.to_datetime(alerts_df['startEpoch'],unit='s')
alerts_df['endEpoch'] = alerts_df['endEpoch'].apply(lambda x: pd.to_datetime(x,unit='s') if x !=0 else x)
alerts_df = alerts_df.loc[alerts_df["rule"] == "Critical"]
print(alerts_df)
#print(alerts_df.shape)
#alerts_df.to_csv("alerts_no_groupedby_03.csv")
#alerts_df.to_sql("critical_alerts_00", sqlite3, if_exists='replace', index = False)
server = '999999\SQLEXPRESS'
database = 'LM_Critical_Alerts'
# define connection string
cnxn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server}; \
SERVER=' + server + '; \
DATABASE=' + database +'; \
Trusted_Connection=yes;')
# create the connection cursor
cursor = cnxn.cursor()
# define insert query
insert_query = '''INSERT INTO critical_alerts (row, id, internalId, alertType, monitorObjectName, startEpoch, endEpoch, cleared, resourceTemplateName)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?);'''
# loop through each row in the matrix
for row in alerts_df:
# define the values to insert
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
# insert the data into the database
cursor.execute(insert_query, values)
# commit the inserts
cnxn.commit()
# grab all the rows in the database table
cursor.execute('SELECT * FROM critical_alerts')
# loop through the results
for row in cursor:
print(row)
错误信息和数据结构：
id internalId rule monitorObjectName startEpoch endEpoch cleared resourceTemplateName
13 DS25095821 LMD1815378 Critical MYSERVER 2022-04-20 01:24:55 0 False CPU
120 DS25095744 LMD4168355 Critical MYSERVER 2022-04-20 00:34:40 2022-04-20 00:38:40 True Memory and Processes
149 DS25095716 LMD1815378 Critical MYSERVER 2022-04-20 00:16:08 2022-04-20 01:05:54 True CPU
178 DS25095682 LMD10870447 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
179 DS25095683 LMD10863782 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
180 DS25095684 LMD10866820 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
181 DS25095685 LMD10868897 Critical MYSERVER 2022-04-20 00:03:13 2022-04-20 00:08:40 True SQL Server Databases
207 DS25095665 LMD12336087 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
208 DS25095666 LMD12335870 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
209 DS25095667 LMD12336893 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
210 DS25095668 LMD12335901 Critical MYSERVER 2022-04-19 23:56:08 2022-04-20 00:02:10 True SQL Server Databases
270 DS25095620 LMD10866820 Critical MYSERVER 2022-04-19 23:33:14 2022-04-19 23:45:14 True SQL Server Databases
272 DS25095619 LMD12474939 Critical MYSERVER 2022-04-19 23:32:14 2022-04-19 23:35:14 True Elekta MOSAIQ App Services
274 DS25095617 LMD1815378 Critical MYSERVER 2022-04-19 23:31:48 2022-04-19 23:52:36 True CPU
400 DS25095526 LMD1815378 Critical MYSERVER 2022-04-19 22:52:45 2022-04-19 23:02:12 True CPU
436 DS25095501 LMD12006224 Critical MYSERVER 2022-04-19 22:37:37 2022-04-19 22:39:42 True CPU
455 DS25095490 LMD1815378 Critical MYSERVER 2022-04-19 22:30:18 2022-04-19 22:40:45 True CPU
473 DS25095472 LMD10863472 Critical MYSERVER 2022-04-19 22:20:46 2022-04-19 22:44:37 True SQL Server Databases
510 DS25095449 LMD12006813 Critical MYSERVER 2022-04-19 22:06:59 2022-04-19 22:42:32 True CPU
555 DS25095424 LMD12006224 Critical MYSERVER 2022-04-19 21:47:22 2022-04-19 22:26:12 True CPU
628 DS25095366 LMD12006813 Critical MYSERVER 2022-04-19 21:14:52 2022-04-19 21:49:43 True CPU
697 DS25095328 LMD3090757 Critical MYSERVER 2022-04-19 20:51:41 2022-04-19 21:11:40 True Memory and Processes
737 DS25095293 LMD12006813 Critical MYSERVER 2022-04-19 20:38:47 2022-04-19 20:43:34 True CPU
851 DS25095220 LMD12006224 Critical MYSERVER 2022-04-19 19:56:31 2022-04-19 21:25:58 True CPU
894 DS25095183 LMD12006224 Critical MYSERVER 2022-04-19 19:34:23 2022-04-19 19:44:31 True CPU
916 DS25095165 LMD12006813 Critical MYSERVER 2022-04-19 19:25:53 2022-04-19 20:26:38 True CPU
Traceback (most recent call last):
File "c:\Users\Nelson.Silva\OneDrive - Computer & Network Solutions Limited\PBI\LM_Python_Queries\query_alerts.py", line 91, in <module>
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
IndexError: string index out of range
当你这样做时：
for row in alerts_df:
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])
您没有遍历行。
试试这个：
for index, row in alerts_df.iterrows():
values = (row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])