nginx日志分析

在nginx.conf中定义的日志格式如下：

           http {
               ...
               log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                                 '$status [$request_body] $body_bytes_sent "$http_referer" '
                                 '"$http_user_agent" "$http_x_forwarded_for"';
               ...
           }
   
  

日志文件如下：

116.2.52.247 - - [26/Oct/2017:15:04:00 +0000] "POST /api/v1/f1_static/ HTTP/1.1" 200 [{\x22user_id\x22:\x229b999d46dd6149f49\x22}] 323 "http://www.abc.com/ProductPerspective/detail/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36" "-"116.2.52.247 - - [26/Oct/2017:15:04:00 +0000] "OPTIONS /api/v1/fund_info/ HTTP/1.1" 200 [-] 31 "http://www.abc.com/ProductPerspective/detail/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36" "-"
  

nginx没有命令直接将日志按天分割，我们写了一个shell脚本，每日0点定时执行。

        #!/bin/bashlogs_path="/mydata/nginx/logs/"
        mv ${logs_path}access-web.log ${logs_path}access-web-$(date -d "yesterday" +"%Y%m%d").logmv ${logs_path}access-api.log ${logs_path}access-api-$(date -d "yesterday" +"%Y%m%d").log
   
  

cron：

0 0 * * * /mydata/nginx/nginx.log.sh
  

从nginx服务器将日志数据传输到日志服务器

       [root@VM_231_116_centos ~]
       root@10.105.83.34's password:
       access-power-20170929.log 100%  126KB 125.8KB/s  00:00
       access-web-20171016.log   100% 2616KB  2.6MB/s  00:00
       access-power-20170907.log  100% 1687KB  1.7MB/s  00:00
       access-api-20170911.log    100% 1209KB  1.2MB/s  00:00
       access-power-20170930.log   100% 1354KB  1.3MB/s  00:00
       access.log   100%  45MB  45.2MB/s  00:00
       access-api-20170907.log  100% 2960KB  2.9MB/s  00:00
       access-power-20170906.log  100%  669KB 669.1KB/s  00:01
       access-api-20170904.log   100% 9186KB  9.0MB/s  00:00
   
  

• 服务器之间文件（夹）复制

       scp local_file remote_username@remote_ip:remote_folder  或者
       scp local_file remote_username@remote_ip:remote_file
        scp -r local_folder remote_username@remote_ip:remote_folder
   
  

主要有几点：

1. 逐行解析

2. 正则匹配

3. 日期的处理

4. 批量写入数据库

       import reimport timeimport osimport arrowimport pandas as pdimport jsonimport io_tosqlimport shutil
       from sqlalchemy import create_engine
       engine_user_info = create_engine(    "mysql+pymysql://{}:{}@{}:{}/{}".format('usr', 'pwd', 'host','port', 'db'),
           connect_args={"charset": "utf8"})
       def parse(filename):
           month_abr = {"Jan":"01", "Feb":"02", "Mar":"03", "Apr":"04", "May":"05", "Jun":"06",                 "Jul":"07", "Aug":"08", "Sep":"09", "Oct":"10", "Nov":"11", "Dec":"12"}
           dfs = []
           try:
               i = 0
               file = open(filename)        for line in file:
                   pattern = "(\d+\.\d+\.\d+\.\d+).*?\[(.*?)\].*?(\w+) (/.*?) .*?\" (\d+) \[(.*?)\] (\d+) \"(.*?)\" \"(.*?)\" \"(.*?)\""
                   s = re.search(pattern, line)            if s:
                       remote_addr = s.group(1)
                       local_time = s.group(2)
                       request_method = s.group(3)
                       request_url = s.group(4)
                       status = s.group(5)
                       request_body = s.group(6)
                       body_bytes_sent = s.group(7)
                       http_referer = s.group(8)
                       http_user_agent = s.group(9)
                       http_x_forwarded_for = s.group(10)
                       for mon in month_abr.keys():                    if mon in local_time:
                               local_time = local_time.replace(mon, month_abr[mon])                        break
                       lt = arrow.get(local_time, "DD/MM/YYYY:HH:mm:ss")
                       lt = lt.shift(hours=8)
                       local_time = str(lt.datetime)
                       i = i+1
                       if request_body != '-':                    try:
                               request_body = request_body.replace(r'\x22', '"').replace("null", '""')
                               request_body_dict = json.loads(request_body)
                               fund_id = request_body_dict.get('fund_id', None)
                               user_id = request_body_dict.get('user_id', None)                        if user_id is None:
                                   user_id = request_body_dict.get('userId', None)                    except Exception as e:
                               print("request_body:{}".format(request_body))
                               print(e)
                               fund_id = None
                               user_id = None
                       else:
                           fund_id = None
                           user_id = None
                       if request_method not in ("GET", "POST"):
                           continue
                       df = pd.DataFrame({"remote_addr": [remote_addr], "request_method": [request_method], "local_time": [local_time],                                                "request_url": [request_url], "status": [status], "request_body": [request_body],                                                "body_bytes_sent": [body_bytes_sent], "http_referer": [http_referer],                                                "http_user_agent": [http_user_agent], "http_x_forwarded_for": [http_x_forwarded_for],                                                "fund_id": [fund_id], "user_id": [user_id]
                                                       })
                       df['create_at'] = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
                       dfs.append(df)
                       if len(dfs) >= 100:
                           df_all = pd.concat(dfs)
                           df_all = df_all.drop_duplicates(subset=['remote_addr', 'request_url','local_time'])
                           df_all.to_sql("log_table", engine, if_exists="append", index=False)
                           print("写入长度为：" + str(len(df_all)))
                           dfs = []
               df_all = pd.concat(dfs)
               df_all = df_all.drop_duplicates(subset=['remote_addr', 'request_url','local_time'])
               df_all.to_sql("log_table", engine, if_exists="append", index=False)
           except Exception as e:
               print(e)
   
  

日志结构化写入数据库后，到前端页面可以多维度展示，下面是展示页面示例：

• 统计每日活跃IP数

• 统计每日API请求次数

• 分类分析