一.项目说明
1.1 项目目的
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。
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。
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。
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 = """
CPU平均数 CPU中位数据 内存平均数 内存中位数据
Disk平均数 Disk中位数 统计时间
"""
mail_body = mail_body + mail_msg + "
"
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( not all arguments converted during string formatting)
mail_msg_single = """
%s %s %s
%s %s %s
%s
""" %
(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 = """
统计时间开始参数 时间结束参数 DB慢查询个数
"""
mail_body = mail_body + mail_msg + "
"
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( not all arguments converted during string formatting)
mail_msg_single = """
%s %s
%s
""" %
(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。
