python连接clickhouse数据库的两种方式小结
目录
python连接clickhouse数据库主要针对clickhouse_driver的使用进行简要介绍python将数据写入clickhousepython连接clickhouse数据库
在Python中获取系统信息的一个好办法是使用psutil这个第三方模块。
顾名思义,psutil = process and system utilities,它不仅可以通过一两行代码实现系统监控,还可以跨平台使用。
主要针对clickhouse_driver的使用进行简要介绍
第一步:
通过pip install clickhouse_driver 安装 clickhouse_driver第二步:
方法一:使用clickhouse_driver 包中的Client类,通过实例化一个客户端进行对数据库的增删改查操作from clickhouse_driver import Client from datetime import datetime import psutil host_name = "192.168.50.94" client = Client(host=host_name,database="default",user="default",password="自己设的密码",send_receive_timeout=20,port=55666) now = datetime.now() time_stamp = now.strftime("%a %b %d %H:%M:%S CST %Y")# Tue Apr 06 15:32:55 CST 2021方法二:使用clickhouse_driver 包中的connect函数,通过实例化一个客户端进行对数据库的增删改查操作create_at = datetime.now().strftime("%Y-%m-%d %H:%M:%S") disk_io = psutil.disk_io_counters() net_io = psutil.net_io_counters() chart_name = ["磁盘IO","网络IO"] metric_name1 = ["读(数量)","写(数量)", "读(字节)", "写(字节)", "读(时间)", "写(时间)"] metric_name2 = ["发送字节数","接收字节数","发送包数","接收包"] metric_value1 = [disk_io.read_count,disk_io.write_count,disk_io.read_bytes,disk_io.write_bytes,disk_io.read_time,disk_io.write_time] metric_value2 = [net_io.bytes_sent,net_io.bytes_recv,net_io.packets_sent,net_io.packets_recv] try: for i in chart_name: if i is "磁盘IO": for j in metric_name1: sql = "insert into clickhouse_host_metrics777(time_stamp,host_name, chart_name, metric_name,metric_value,create_at) " \ "values("%s","%s","%s","%s","%s","%s")" % \ (time_stamp, host_name, i, j, metric_value1[metric_name1.index(j)], create_at) res = client.execute(sql) elif i is "网络IO": for j in metric_name2: sql = "insert into clickhouse_host_metrics777(time_stamp,host_name, chart_name, metric_name,metric_value,create_at) " \ "values("%s","%s","%s","%s","%s","%s")" % \ (time_stamp, host_name, i, j, metric_value2[metric_name2.index(j)], create_at) res = client.execute(sql) print("成功写入数据") except Exception as e: print(str(e))
from datetime import datetime import psutil from clickhouse_driver import connect host_name = "192.168.50.94" #账号:密码@主机名:端口号/数据库 conn = connect("clickhouse://default:自己设的密码@"+host_name+":55666/default") cursor = conn.cursor() now = datetime.now() time_stamp = now.strftime("%a %b %d %H:%M:%S CST %Y")# Tue Apr 06 15:32:55 CST 2021create_at = datetime.now().strftime("%Y-%m-%d %H:%M:%S") disk_io = psutil.disk_io_counters() net_io = psutil.net_io_counters() chart_name = ["磁盘IO","网络IO"] metric_name1 = ["读(数量)","写(数量)", "读(字节)", "写(字节)", "读(时间)", "写(时间)"] metric_name2 = ["发送字节数","接收字节数","发送包数","接收包"] metric_value1 = [disk_io.read_count,disk_io.write_count,disk_io.read_bytes,disk_io.write_bytes,disk_io.read_time,disk_io.write_time] metric_value2 = [net_io.bytes_sent,net_io.bytes_recv,net_io.packets_sent,net_io.packets_recv] try: for i in chart_name: if i is "磁盘IO": for j in metric_name1: sql = "insert into clickhouse_host_metrics777(time_stamp,host_name, chart_name, metric_name,metric_value,create_at) values("%s","%s","%s","%s","%s","%s")" % \ (time_stamp, host_name, i, j, metric_value1[metric_name1.index(j)], create_at) # res = client.execute(sql) res = cursor.execute(sql) elif i is "网络IO": for j in metric_name2: sql = "insert into clickhouse_host_metrics777(time_stamp,host_name, chart_name, metric_name,metric_value,create_at) values("%s","%s","%s","%s","%s","%s")" % \ (time_stamp, host_name, i, j, metric_value2[metric_name2.index(j)], create_at) res = cursor.execute(sql) cursor.close() print("成功写入数据") except Exception as e: print(str(e))
python将数据写入clickhouse
from clickhouse_driver import Client # connect ClickHouse client = Client(host= ,port= ,user= ,database= , password=) # 得到table1中查询的数据导入table2中(database2中应该事先建立对应的table2表) query_ck_sql = """ SELECT * FROM database1.table1 WHERE date = today() """ # 导入数据到临时表 try: # 导入数据 client.execute("insert into {official_table_db}.{official_all_table_name} \ {query_ck_sql}".format( official_table_db = database2, official_table_name = table2, query_ck_sql = query_ck_sql) ,types_check = True) except Exception as e: print str(e)
以上为个人经验,希望能给大家一个参考,也希望大家多多支持脚本之家。
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