一.项目说明
1.1 项目目的
1.盘活服务器资源，提高资源的使用率；资源是公司的资产，只有尽可能发挥其价值，才能创造更多的价值。所以，有必要知道，公司整体（或某业务、产品）所属的 DB Server的资源使用情况。主要从CPU、内存、Disk的平均数和中位数来反映。实现更合理的资源分配和集中性的管理，节省资源成本。
2.慢查询的次数，既可以说明程序的性能和Server的压力，说明了待确认和优化的情况，也说明了资源的紧张性。
3.此类历史数据的积累，可以生成一个变化趋势图，说明资源使用趋势。
4.之前的监控大部分诊断具体的一个DB Server或应用，这个是针对公司整体（或某业务、产品）所属的 DB Server；是监控体系的一个完善和补充。
即:资源盘活、充分利用、降本增效、监控补充。
1.2 部署环境及架构
现有的监控数据已收集到InfluxDB 和 elasticsearch 中，本次要实现的功能是将数据计算聚合到MySQL中，然后通过邮件发送给相关人员。存储到MySQL 数据库中，一是因为 此类数据有一定的价值（具有追溯性和便于历史趋势分析），二是 InfluxDB 、elasticsearch 数据都有过期时间，数据保留的天数不是太长。
二.表的创建
2.1 存储DB资源使用情况的表
表名定义为weekly_dbperformance，具体的脚本如下：
CREATE TABLE `weekly_dbperformance` ( `id` int(11) NOT NULL AUTO_INCREMENT, `cpu_mean` varchar(255) NOT NULL DEFAULT '', `cpu_median` varchar(255) NOT NULL DEFAULT '', `mem_mean` varchar(255) NOT NULL DEFAULT '', `mem_median` varchar(255) NOT NULL DEFAULT '', `disk_mean` varchar(255) NOT NULL DEFAULT '', `disk_median` varchar(255) NOT NULL DEFAULT '', `datetime_created` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '数据行创建时间', PRIMARY KEY (`id`) ) ENGINE=InnoDB AUTO_INCREMENT=3740 DEFAULT CHARSET=utf8mb4;
在记录数据生成的时间字段添加个索引
create index idx_datetime on weekly_dbperformance (datetime_created);
2.2 存储DB 实例慢查询情况的表
表名定义为weekly_dbslowqty，具体的脚本如下：
CREATE TABLE `weekly_dbslowqty` ( `id` int(11) NOT NULL AUTO_INCREMENT, `qindex_name` varchar(50) NOT NULL DEFAULT '', `qstartdate` varchar(50) NOT NULL DEFAULT '', `qenddate` varchar(50) NOT NULL DEFAULT '', `slowqty` varchar(20) NOT NULL DEFAULT '', `datetime_created` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '数据行创建时间', PRIMARY KEY (`id`) ) ENGINE=InnoDB AUTO_INCREMENT=3740 DEFAULT CHARSET=utf8mb4;
在记录查询的开始时间的字段上添加个索引
create index idx_qstartdate on weekly_dbslowqty (qstartdate);
三.主要功能代码
3.1 统计DB Server资源使用率
可执行文件为collect_dbperformance.py
从InfluxDB中查询DB Server的资源使用情况。包括CPU的平均数、CPU的中位数、内存使用的平均数、内存使用的中位数、磁盘平均使用率、磁盘使用的中位数。
拉取计算的是过去7天的数据。
#!/usr/bin/python # -*- coding: UTF-8 -*- from influxdb import InfluxDBClient import pytz import time import dateutil.parser import datetime import db_monitor_conn mysqldb = db_monitor_conn.db # use cursor cursor = mysqldb.cursor() class DBApi(object): """ 通过infludb获取数据 """ def __init__(self, ip, port): """ 初始化数据 :param ip:influxdb地址 :param port: 端口 """ self.db_name = 'telegraf' self.use_cpu_table = 'cpu' # cpu使用率表 self.phy_mem_table = 'mem'# 物理内存表 self.disk_table = 'disk'# 磁盘表 self.client = InfluxDBClient(ip, port, '用*户*名', '密*码', self.db_name) # 连接influxdb数据库 print ('test link influxdb') def get_use_dbperformance(self, s_time, e_time): """ 获取磁盘io使用率 :param host: 查询的主机host (telegraf 配置参数中的host栏位) :param s_time: 开始时间 :param e_time: 结束时间 :return: """ response = {} ### 时间还需转换，否则报错 TypeError: Tuple or struct_time argument required #s = time.strptime(s_time, '%Y-%m-%d %H:%M:%S') #e = time.strptime(e_time, '%Y-%m-%d %H:%M:%S') s = time.strptime(s_time, '%Y-%m-%d') e = time.strptime(e_time, '%Y-%m-%d') start_time = int(time.mktime(s)) * 1000 * 1000 * 1000 end_time = int(time.mktime(e)) * 1000 * 1000 * 1000 #start_time = s_time #end_time = e_time cpu_mean_list = cpu_median_list = mem_mean_list = mem_median_list = disk_mean_list = disk_median_list = ['0.0'] ##print('开始查询CPU使用率的平均数') cpu_mean_list = self.client.query( "select mean(usage_user) from cpu where time>=%s and time<=%s and cpu = 'cpu-total' AND host != 'qqlog_XXX_XXX' ;" % ( start_time, end_time)) ##print(cpu_mean_list) ### cpu_mean_list的格式 ResultSet({'('cpu', None)': [{'time': '2018-06-21T16:00:00Z', 'mean': 1.7141865567279297}]}) cpu_mean_points = list(cpu_mean_list.get_points(measurement='cpu')) ##print(cpu_mean_points) ### cpu_mean_points的格式[{'time': '2018-06-21T16:00:00Z', 'mean': 1.7141865567279297}] cpu_mean = cpu_mean_points[0]['mean'] ##print(cpu_mean) ### cpu_mean 的格式1.7141865567279297 ##print('查询CPU使用率的平均数结束') ##print('开始查询CPU使用率的中位数') cpu_median_list = self.client.query( "SELECT median(usage_user) from cpu where time>=%s and time<=%s and cpu = 'cpu-total' AND host != 'qqlog_XXX_XXX';" % ( start_time, end_time)) ##print(cpu_median_list) #### cpu_median_list的格式为ResultSet({'('cpu', None)': [{'time': '2018-06-21T16:00:00Z', 'median': 0.726817042581142}]}) cpu_median_points = list(cpu_median_list.get_points(measurement='cpu')) cpu_median = cpu_median_points[0]['median'] ##print(cpu_median) ##print('开始查询mem使用率的平均数') mem_mean_list = self.client.query( "SELECT mean(used) /mean(total) from mem where time>=%s and time<=%s and host != 'qqlog_XXX_XXX';" % ( start_time, end_time)) print(mem_mean_list) ### mem_mean_list的格式为ResultSet({'('mem', None)': [{'time': '2018-06-21T16:00:00Z', 'mean_mean': 0.729324184536873}]}) mem_mean_points = list(mem_mean_list.get_points(measurement='mem')) mem_mean = mem_mean_points[0]['mean_mean'] ##print(mem_mean) ##print('开始查询mem使用率的中位数') mem_median_list = self.client.query( "SELECT median(used) /median(total) from mem where time>=%s and time<=%s AND host != 'qqlog_XXX_XXX' ;" % ( start_time, end_time)) ##print(mem_median_list) ###mem_median_list的格式为ResultSet({'('mem', None)': [{'time': '2018-06-21T16:00:00Z', 'median_median': 0.8698493636354012}]}) mem_median_points = list(mem_median_list.get_points(measurement='mem')) mem_median = mem_median_points[0]['median_median'] ##print('开始查询disk使用率的平均数') disk_mean_list = self.client.query( "SELECT mean(used) /mean(total) from disk where time>=%s and time<=%s AND host != 'qqlog_XXX_XXX';" % ( start_time, end_time)) ##print (disk_mean_list) ###disk_mean_list的格式为esultSet({'('disk', None)': [{'time': '2018-06-21T16:00:00Z', 'mean_mean': 0.31204798557786284}]}) disk_mean_points = list(disk_mean_list.get_points(measurement='disk')) disk_mean = disk_mean_points[0]['mean_mean'] ##print(disk_mean) ##print('开始查询disk使用率的中位数') disk_median_list = self.client.query( "SELECT median(used) /median(total) from disk where time>=%s and time<=%s and host != 'qqlog_XXX_XXX';" % ( start_time, end_time)) ##print (disk_median_list) ###disk_median_list的格式ResultSet({'('disk', None)': [{'time': '2018-06-21T16:00:00Z', 'median_median': 0.08009824336938143}]}) disk_median_points = list(disk_median_list.get_points(measurement='disk')) ##print(disk_median_points) disk_median = disk_median_points[0]['median_median'] ##print(disk_median) ### 将计算统计的结果放到MySQl中，以便汇总发送Report sql_insert = "insert into weekly_dbperformance(cpu_mean,cpu_median,mem_mean,mem_median,disk_mean,disk_median) " "values('%s','%s','%s','%s','%s','%s')" % (cpu_mean,cpu_median,mem_mean,mem_median,disk_mean,disk_median) cursor.execute(sql_insert) mysqldb.commit() def change_time(self, params): """ 时间转换 :param params: :return: """ item = dateutil.parser.parse(params).astimezone(pytz.timezone('Asia/Shanghai')) result = str(item).split("+")[0] response = time.strptime(result, '%Y-%m-%d %H:%M:%S') param = time.strftime('%Y-%m-%d %H:%M:%S', response) return param # 连接 influxdb # INFLUXDB_IP influxdb所在主机 # INFLUXDB_PROT influxdb端口 db = DBApi(ip='XXX.110.119.XXX', port='?????') ###查询的时间范围 ### TypeError: strptime() argument 0 must be str, not <class 'datetime.datetime'> ##e_time = datetime.datetime.now() e_time = datetime.datetime.now().strftime('%Y-%m-%d') ##s_time = e_time + datetime.timedelta(-7) s_time = (datetime.datetime.now() + datetime.timedelta(-7)).strftime('%Y-%m-%d') print('打印查询范围----时间参数如下:') print(e_time) print(s_time) db.get_use_dbperformance(s_time,e_time) #print(disk_points)
注意：此份代码的运行环境是Python 3.6.8；此外还要注意下influxdb的query返回值的处理；可执行文件可以通过crontab设置定时任务，周期性抓取数据。
3.2 统计DB实例的慢查询
可执行文件为count_dbslow.py
从elasticsearch中读取慢查询的数据，主要是统计符合条件的个数。
需要说明的是某产品线下的数据库慢查询放到Index命名一样。本例中mysql-slow-qqorders-*，是查询mysql-slow-qqorders-开通的所有慢查询的个数，qqorders是具体的产品线代码，*是日期的模糊匹配。
#coding:utf8 import os import time from datetime import date ### 导入模块 timedelta ，否则date.today()+ timedelta(days = -2) 报错： AttributeError: 'datetime.date' object has no attribute 'timedelta' from datetime import timedelta from os import walk ###导入模块的from datetime import datetime改成import datetime;否则在day = datetime.datetime.now()报错：AttributeError: type object 'datetime.datetime' has no attribute 'datetime' ##from datetime import datetime import datetime from elasticsearch import Elasticsearch from elasticsearch.helpers import bulk import db_monitor_conn mysqldb = db_monitor_conn.db # use cursor cursor = mysqldb.cursor() ###数据收集前，清除之前收集的数据 ##sql_delete = "delete from weekly_dbslowqty " ##cursor.execute(sql_delete) ##mysqldb.commit() class ElasticObj: def __init__(self, index_name,index_type,ip ="ES实例所在的ServerIP"): ''' :param index_name: 索引名称 :param index_type: 索引类型,默认为_doc ''' self.index_name =index_name self.index_type = index_type # 无用户名密码状态 #self.es = Elasticsearch([ip]) #用户名密码状态 self.es = Elasticsearch([ip],http_auth=('ES用*户*名', 'ES用*户*密*码'),port=ES端口号) #### 获取数据量 def Get_SlowQty_By_Indexname(self,dstart,dend): doc = { "query": { "bool": { "must": [ {"exists":{"field": "query_time"}}, {"range":{ "@timestamp": { "gte": dstart.strftime('%Y-%m-%d %H:%M:%S'), "lte": dend.strftime('%Y-%m-%d %H:%M:%S'), "format": "yyyy-MM-dd HH:mm:SS", "time_zone": "+08:00" } }} ], "must_not": [ ## 排除不符合条件的server,例如 排除 XXX.XXX.XXX.XXX {"term": {"fields.db_host": "XXX.110.119.XXX"}} ] } } } _slowqty = self.es.count(index=self.index_name, doc_type=self.index_type, body=doc) print(_slowqty) #### _slowqty 的返回格式是字典类型，如下{'count': 2374, '_shards': {'total': 16, 'successful': 16, 'skipped': 0, 'failed': 0}} slowqty = _slowqty['count'] print(slowqty) #### 将数据保存到mysql中，以便发送报表 sql_insert = "insert into weekly_dbslowqty(qindex_name,qstartdate,qenddate,slowqty) " "values('%s','%s','%s','%s')" % (self.index_name,dstart,dend,slowqty) cursor.execute(sql_insert) mysqldb.commit() obj =ElasticObj("mysql-slow-qqorders-*","_doc",ip ="ES 所在机器的 ServerIP") ###时间参数 ##day = datetime.datetime.now() ##start = datetime.datetime.strptime('20180628 00:00:00','%Y%m%d %H:%M:%S') ##end = datetime.datetime.strptime('20180629 00:00:00','%Y%m%d %H:%M:%S') ##dstart = (datetime.datetime.now() + datetime.timedelta(-2)) ##dend = (datetime.datetime.now() + datetime.timedelta(-1)) today = date.today() dstart = (date.today()+ timedelta(days = -2)).strftime('%Y-%m-%d') dend = (date.today()+ timedelta(days = -1)).strftime('%Y-%m-%d') ####print(dstart) ####print(dend) ###添加.strftime('%Y-%m-%d')，，否则报错TypeError: strptime() argument 1 must be str, not datetime.date dstart = datetime.datetime.strptime(dstart,'%Y-%m-%d') dend = datetime.datetime.strptime(dend,'%Y-%m-%d') print(dstart) print(dend) obj.Get_SlowQty_By_Indexname(dstart,dend)
注意：此份代码的运行环境也是Python 3.6.8
3.3 发送Server资源性能周报
可执行文件为dbperformance_report_weekly.py
#!/usr/bin/python # -*- coding: UTF-8 -*- import sys reload(sys) sys.setdefaultencoding( "utf-8" ) import db_monitor_conn import os import time import smtp_config_dbperformance from email.mime.text import MIMEText from email.header import Header def send_mail(mail_msg): # 调用send_mail函数 mail_body = """ <style type="text/css"> table.gridtable { font-family: verdana,arial,sans-serif; font-size:11px; color:#333333; border- 1px; border-color: #666666; border-collapse: collapse; } table.gridtable th { border- 1px; padding: 8px; border-style: solid; border-color: #666666; background-color: #dedede; } table.gridtable td { border- 1px; padding: 8px; border-style: solid; border-color: #666666; background-color: #ffffff; } </style> <!-- Table goes in the document BODY --> <table class="gridtable"> <tr> <th>CPU平均数</th><th>CPU中位数据</th><th>内存平均数</th><th>内存中位数据</th> <th>Disk平均数</th><th>Disk中位数</th><th>统计时间</th> </tr> """ mail_body = mail_body + mail_msg + "</table>" message = MIMEText(mail_body, 'html', 'utf-8') subject = 'DB服务器性能周报[资源性能]' message['Subject'] = Header(subject, 'utf-8') smtp_config_dbperformance.send_mail(message) return #定义邮件体变量 mail_msg = "" # 获取数据库连接 db = db_monitor_conn.db print(db) # 使用cursor()方法获取操作游标 cursor = db.cursor() # SQL 查询语句 # 备份日报 sql_dbper_report = " select ROUND(cpu_mean,2) as cpu_mean,ROUND(cpu_median,2) as cpu_median ,ROUND(mem_mean *100 ,2)as mem_mean , " " ROUND(mem_median *100,2) as mem_median ,ROUND(disk_mean * 100,2) as disk_mean,ROUND(disk_median *100,2) as disk_median,date_format(datetime_created, '%Y-%m-%d') as datetime_created " " FROM weekly_dbperformance " " where 1=1" " order by datetime_created limit 1 " try: # 执行SQL语句 cursor.execute(sql_dbper_report) # 获取所有记录列表 results = cursor.fetchall() for row in results: cpu_mean = str(row[0]) cpu_median = str(row[1]) mem_mean = str(row[2]) mem_median = str(row[3]) disk_mean = str(row[4]) disk_median = str(row[5]) rdatetime = str(row[6]) # 生成邮件内容 注意邮件列数和参数的个数一直(<type 'exceptions.Exception'> not all arguments converted during string formatting) mail_msg_single = """ <tr> <td align="center">%s</td><td>%s</td><td align="right">%s</td> <td>%s</td><td align="right">%s</td><td align="right">%s</td> <td align="right">%s</td> </tr> """ % (cpu_mean, cpu_median, mem_mean, mem_median, disk_mean, disk_median, rdatetime) mail_msg = mail_msg + mail_msg_single # 发送邮件 send_mail(mail_msg) except Exception as e: print str(Exception) print str(e) # 关闭游标 cursor.close() # 关闭数据库连接 db.close()
注意：此份代码的运行环境是Python 2.7.5
2.4 发送DB 慢查询周报
可执行文件为dbslowlog_report_weekly.py
#!/usr/bin/python # -*- coding: UTF-8 -*- import sys reload(sys) sys.setdefaultencoding( "utf-8" ) import db_monitor_conn import os import time import smtp_config_dbperformance from email.mime.text import MIMEText from email.header import Header def send_mail(mail_msg): # 调用send_mail函数 mail_body = """ <style type="text/css"> table.gridtable { font-family: verdana,arial,sans-serif; font-size:11px; color:#333333; border- 1px; border-color: #666666; border-collapse: collapse; } table.gridtable th { border- 1px; padding: 8px; border-style: solid; border-color: #666666; background-color: #dedede; } table.gridtable td { border- 1px; padding: 8px; border-style: solid; border-color: #666666; background-color: #ffffff; } </style> <!-- Table goes in the document BODY --> <table class="gridtable"> <tr> <th>统计时间开始参数</th><th>时间结束参数</th><th>DB慢查询个数</th> </tr> """ mail_body = mail_body + mail_msg + "</table>" message = MIMEText(mail_body, 'html', 'utf-8') subject = 'DB服务器性能周报[DB慢查询]' message['Subject'] = Header(subject, 'utf-8') smtp_config_dbperformance.send_mail(message) return #定义邮件体变量 mail_msg = "" # 获取数据库连接 db = db_monitor_conn.db print(db) # 使用cursor()方法获取操作游标 cursor = db.cursor() # SQL 查询语句 # 备份日报 sql_dbslow_report = " select distinct qstartdate,qenddate,slowqty " " FROM weekly_dbslowqty " " where qindex_name ='mysql-slow-qqorders-*' and qstartdate >= date_sub(curdate(),interval 8 day) and qstartdate < date_sub(curdate(),interval 1 day) " " order by datetime_created asc " try: # 执行SQL语句 cursor.execute(sql_dbslow_report) # 获取所有记录列表 results = cursor.fetchall() for row in results: qstartdate = str(row[0]) qenddate = str(row[1]) slowqty = str(row[2]) # 生成邮件内容 注意邮件列数和参数的个数一直(<type 'exceptions.Exception'> not all arguments converted during string formatting) mail_msg_single = """ <tr> <td align="center">%s</td><td align="right">%s</td> <td align="right">%s</td> </tr> """ % (qstartdate, qenddate, slowqty) mail_msg = mail_msg + mail_msg_single # 发送邮件 send_mail(mail_msg) except Exception as e: print str(Exception) print str(e) # 关闭游标 cursor.close() # 关闭数据库连接 db.close()
注意：此份代码的运行环境也是Python 2.7.5
3.5 其他模块
mysql的连接模块:db_monitor_conn
相应的代码可在《通过Python将监控数据由influxdb写入到MySQL》一文中查看，参阅db_conn.py的编写，在此不再赘述。
短信发送的模块：smtp_config_dbperformance
请参阅前面的分享《MySQL数据归档小工具推荐及优化--mysql_archiver》，github地址：https://github.com/dbarun/mysql_archiver 下载的代码，有发送邮件的模块smtp_config.py,在此不再赘述。
四 实现
4.1 DBServer资源报告示样
下图是通过邮件的形式发送某业务线下面DB Server资源使用率的邮件。
4.2 慢查询报告示样
下图是通过邮件的形式发送某业务线下面所有DB 实例的一周的SQL慢查询的个数。
这是个简单的Demo，项目规划是随着DB资源的监控指标清晰、完善，数据丰富，整合到一个Dashboard上。
五 题外话--DAS
阿里云的DAS（Database Autonomy Service）是一种基于机器学习和专家经验实现数据库自感知、自修复、自优化、自运维及自安全的云服务，帮助用户消除数据库管理的复杂性及人工操作引发的服务故障，有效保障数据库服务的稳定、安全及高效，解决方案架构 如下图。
个人认为， DAS 要实现的目标（自感知、自修复、自优化、自运维及自安全）是我们DBA的努力的方向。