数据库服务器资源使用情况周报

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。