本文档主要为您介绍如何使用日志服务数据加工功能对复杂的JSON数据进行加工。

多子键为数组的复杂JSON数据加工

程序构建的日志会以一种统计性质的JSON格式写入,通常包含一个基础信息以及多个子健为数组的数据形式。例如一个服务器每隔1分钟写入一条日志,包含当前信息状态,以及相关服务器和客户端节点的统计状态信息。
  • 日志样例
    __source__:  1.2.3.4
    __topic__:  
    content:{
         "service": "search_service",
         "overal_status": "yellow",
         "servers": [
             {
                 "host": "1.2.3.4",
                 "status": "green"
             },
             {
                 "host": "1.2.3.5",
                 "status": "green"
             }
         ],
         "clients": [
             {
                 "host": "1.2.3.6",
                 "status": "green"
             },
             {
                 "host": "1.2.3.7",
                 "status": "red"
             }
         ]
    }
  • 加工需求
    1. 对原始日志进行topic分裂,分别是overall_typeclient_statusserver_status
    2. 对不同的topic保存不同的信息。
      • overall_type:保留server、client数量、overal_status颜色和service信息。
      • client_status:保留host地址、status状态和service信息。
      • server_status:保留host地址、status状态和service信息。
  • 期望结果
    __source__:  1.2.3.4
    __topic__:  overall_type
    client_count:  2
    overal_status:  yellow
    server_count:  2
    service:  search_service
    
    
    __source__:  1.2.3.4
    __topic__:  client_status
    host:  1.2.3.7
    status:  red
    service:  search_service
    
    
    __source__:  1.2.3.4
    __topic__:  client_status
    host:  1.2.3.6
    status:  green
    service:  search_service
    
    
    __source__:  1.2.3.4
    __topic__:  server_status
    host:  1.2.3.4
    status:  green
    service:  search_service
    
    
    __source__:  1.2.3.4
    __topic__:  server_status
    host:  1.2.3.5
    status:  green
    service:  search_service
  • 解决方案
    1. 将一条日志拆分成三条日志,给主题赋予三个不同值再进行分裂,经过分裂后会分成除topic不同,其他信息相同的三条日志。
      e_set("__topic__", "server_status,client_status,overall_type")
      e_split("__topic__")
      处理后日志格式如下:
      __source__:  1.2.3.4
      __topic__:  server_status         // 另外2条是client_status和overall_type, 其他一样
      content:  {
          ...如上...
      }
    2. 基于content的JSON内容在第一层展开,并删除content字段。
      e_json('content',depth=1)
      e_drop_fields("content")
      处理后的日志格式如下:
      __source__:  1.2.3.4
      __topic__:  overall_type              // 另外2条是client_status和overall_type, 其他一样
      clients:  [{"host": "1.2.3.6", "status": "green"}, {"host": "1.2.3.7", "status": "red"}]
      overal_status:  yellow
      servers:  [{"host": "1.2.3.4", "status": "green"}, {"host": "1.2.3.5", "status": "green"}]
      service:  search_service
    3. 对主题是overall_type的日志,统计client_countserver_count
      e_if(e_search("__topic__==overall_type"), 
           e_compose(
              e_set("client_count", json_select(v("clients"), "length([*])", default=0)), 
              e_set("server_count", json_select(v("servers"), "length([*])", default=0))
        ))
      处理后的日志为:
      __topic__:  overall_type
      server_count:  2
      client_count:  2
    4. 丢弃相关字段:
      e_if(e_search("__topic__==overall_type"), e_drop_fields("clients", "servers"))
    5. 对主题是server_status的日志,进行进一步分裂。
      e_if(e_search("__topic__==server_status"), 
           e_compose(
              e_split("servers"), 
              e_json("servers", depth=1)
        ))
      处理后的日志为如下两条:
      __topic__:  server_status
      servers:  {"host": "1.2.3.4", "status": "green"}
      host: 1.2.3.4
      status: green
      __topic__:  server_status
      servers:  {"host": "1.2.3.5", "status": "green"}
      host: 1.2.3.5
      status: green
    6. 保留相关字段:
      e_if(e_search("__topic__==overall_type"), e_drop_fields("servers"))
    7. 对主题是client_status的日志进行进一步分裂,再删除多余字段。
      e_if(e_search("__topic__==client_status"), 
           e_compose(
              e_split("clients"), 
              e_json("clients", depth=1),
              e_drop_fields("clients")
        ))
      处理后的日志为如下两个日志:
      __topic__:  client_status
      host: 1.2.3.6
      status: green
      __topic__:  clients
      host: 1.2.3.7
      status: red
    8. 综上LOG DSL规则:
      # 总体分裂
      e_set("__topic__", "server_status,client_status,overall_type")
      e_split("__topic__")
      e_json('content',depth=1)
      e_drop_fields("content")
      
      # 处理overall_type日志
      e_if(e_search("__topic__==overall_type"), 
           e_compose(
              e_set("client_count" json_select(v("clients"), "length([*])", default=0)), 
              e_set("server_count" json_select(v("servers"), "length([*])", default=0))
        ))
      
      # 处理server_status日志
      e_if(e_search("__topic__==server_status"), 
           e_compose(
              e_split("servers"), 
              e_json("servers", depth=1)
        ))
      e_if(e_search("__topic__==overall_type"), e_drop_fields("servers"))
      
      
      # 处理client_status日志
      e_if(e_search("__topic__==client_status"), 
           e_compose(
              e_split("clients"), 
              e_json("clients", depth=1),
              e_drop_fields("clients")
        ))

方案优化

上述方案对content.serverscontent.servers为空时的处理有一些问题。假设原始日志是:
__source__:  1.2.3.4
__topic__:  
content:{
            "service": "search_service",
            "overal_status": "yellow",
            "servers": [ ],
            "clients": [ ]
}
按照上述方案分裂为三条日志,其中主题为client_statusserver_status的日志内容是空的。
__source__:  1.2.3.4
__topic__:  overall_type
client_count:  0
overal_status:  yellow
server_count:  0
service:  search_service


__source__:  1.2.3.4
__topic__:  client_status
service:  search_service
__source__:  1.2.3.4


__topic__:  server_status
host:  1.2.3.4
status:  green
service:  search_service
  • 方案1
    可以在初始分裂后,处理server_statusclient_status日志前分别判断并丢弃空的相关事件。
    # 处理server_status: 空的丢弃(非空保留)
    e_keep(op_and(e_search("__topic__==server_status"), json_select(v("servers"), "length([*])")))
    
    # 处理client_status: 空的丢弃(非空保留)
    e_keep(op_and(e_search("__topic__==client_status"), json_select(v("clients"), "length([*])")))
    综上LOG DSL规则是:
    # 总体分裂
    e_set("__topic__", "server_status,client_status,overall_type")
    e_split("__topic__")
    e_json('content',depth=1)
    e_drop_fields("content")
    
    # 处理overall_type日志
    e_if(e_search("__topic__==overall_type"), 
         e_compose(
            e_set("client_count" json_select(v("clients"), "length([*])", default=0)), 
            e_set("server_count" json_select(v("servers"), "length([*])", default=0))
      ))
    
    # 新增: 预处理server_status: 空的丢弃(非空保留) 
    e_keep(op_and(e_search("__topic__==server_status"), json_select(v("servers"), "length([*])")))
    
    # 处理server_status日志
    e_if(e_search("__topic__==server_status"), 
         e_compose(
            e_split("servers"), 
            e_json("servers", depth=1)
      ))
    e_if(e_search("__topic__==overall_type"), e_drop_fields("servers"))
    
    
    # 新增: 预处理client_status: 空的丢弃(非空保留) 
    e_keep(op_and(e_search("__topic__==client_status"), json_select(v("clients"), "length([*])")))
    
    # 处理client_status日志
    e_if(e_search("__topic__==client_status"), 
         e_compose(
            e_split("clients"), 
            e_json("clients", depth=1),
            e_drop_fields("clients")
      ))
  • 方案2
    在初始分裂时进行判断,如果对应数据为空就进行分裂。
    # 初始主题
    e_set("__topic__", "server_status")
    
    # 如果content.servers非空, 则从server_status分裂出1条日志
    e_if(json_select(v("content"), "length(servers[*])"),
       e_compse(
          e_set("__topic__", "server_status,overall_type"),
          e_split("__topic__")
       ))
    
    # 如果content.clients非空, 则从overall_type再分裂出1条日志
    e_if(op_and(e_search("__topic__==overall_type"), json_select(v("content"), "length(clients[*])")),
       e_compse(
          e_set("__topic__", "client_status,overall_type"),
          e_split("__topic__")
       ))
    综上LOG DSL规则是:
    # 总体分裂
    e_set("__topic__", "server_status")
    
    # 如果content.servers非空, 则从server_status分裂出1条日志
    e_if(json_select(v("content"), "length(servers[*])"),
       e_compse(
          e_set("__topic__", "server_status,overall_type"),
          e_split("__topic__")
       ))
    
    # 如果content.clients非空, 则从server_status分裂出1条日志
    e_if(op_and(e_search("__topic__==overall_type"), json_select(v("content"), "length(clients[*])")),
       e_compse(
          e_set("__topic__", "client_status,overall_type"),
          e_split("__topic__")
       ))
    
    # 处理overall_type日志
    e_if(e_search("__topic__==overall_type"), 
         e_compose(
            e_set("client_count" json_select(v("clients"), "length([*])", default=0)), 
            e_set("server_count" json_select(v("servers"), "length([*])", default=0))
      ))
    
    # 处理server_status日志
    e_if(e_search("__topic__==server_status"), 
         e_compose(
            e_split("servers"), 
            e_json("servers", depth=1)
      ))
    e_if(e_search("__topic__==overall_type"), e_drop_fields("servers"))
    
    
    # 处理client_status日志
    e_if(e_search("__topic__==client_status"), 
         e_compose(
            e_split("clients"), 
            e_json("clients", depth=1),
            e_drop_fields("clients")
      ))
方案对比
  • 方案1在分裂出日志后再删除为空的日志,逻辑上有些多余,但规则简单易维护。默认推荐该方案。
  • 方案2会在分裂前进行判断,处理效率会高一些,但规则略微冗余,仅在特定场景例如初始分裂可能导致大量额外事件产生时推荐。

多层数组对象嵌套的复杂JSON数据加工

以一个复杂的保护多层数组嵌套的对象为示例,将users下的每个对象中的login_histories的每个登录信息都拆成一个登录事件。
  • 原始日志
    __source__:  1.2.3.4
    __topic__:  
    content:{
      "users": [
        {
            "name": "user1",
            "login_historis": [
              {
                "date": "2019-10-10 0:0:0",
                "login_ip": "1.1.1.1"
              },
              {
                "date": "2019-10-10 1:0:0",
                "login_ip": "1.1.1.1"
              },
          {
          ...更多登录信息...
          }
            ]
        },
        {
            "name": "user2",
            "login_historis": [
              {
                "date": "2019-10-11 0:0:0",
                "login_ip": "1.1.1.2"
              },
              {
                "date": "2019-10-11 1:0:0",
                "login_ip": "1.1.1.3"
              },
          {
          ...更多登录信息...
          }     
            ]
        },
      {
        ....更多user....
      }
      ]
    }
  • 期望分裂出的日志
    __source__:  1.2.3.4
    name:  user1
    date:  2019-10-11 1:0:0
    login_ip:  1.1.1.1
    
    __source__: 1.2.3.4
    name:  user1
    date:  2019-10-11 0:0:0
    login_ip:  1.1.1.1
    
    __source__:  1.2.3.4
    name:  user2
    date:  2019-10-11 0:0:0
    login_ip:  1.1.1.2
    
    __source__: 1.2.3.4
    name:  user2
    date:  2019-10-11 1:0:0
    login_ip:  1.1.1.3  
    
    ....更多日志....
  • 解决方案
    1. content中的users进行分裂和展开操作。
      e_split("content", jmes='users[*]', output='item')
      e_json("item",depth=1)
      处理后返回的日志:
      __source__:  1.2.3.4
      __topic__:  
      content:{...如前...}
      item:  {"name": "user1", "login_histories": [{"date": "2019-10-10 0:0:0", "login_ip": "1.1.1.1"}, {"date": "2019-10-10 1:0:0", "login_ip": "1.1.1.1"}]}
      login_histories:  [{"date": "2019-10-10 0:0:0", "login_ip": "1.1.1.1"}, {"date": "2019-10-10 1:0:0", "login_ip": "1.1.1.1"}]
      name:  user1
      
      __source__:  1.2.3.4
      __topic__:  
      content:{...如前...}
      item:  {"name": "user2", "login_histories": [{"date": "2019-10-11 0:0:0", "login_ip": "1.1.1.2"}, {"date": "2019-10-11 1:0:0", "login_ip": "1.1.1.3"}]}
      login_histories:  [{"date": "2019-10-11 0:0:0", "login_ip": "1.1.1.2"}, {"date": "2019-10-11 1:0:0", "login_ip": "1.1.1.3"}]
      name:  user2
    2. login_histories先分裂再展开。
      e_split("login_histories")
      e_json("login_histories", depth=1)
      处理后返回的日志:
      __source__:  1.2.3.4
      __topic__: 
      content: {...如前...}
      date:  2019-10-11 0:0:0
      item:  {"name": "user2", "login_histories": [{"date": "2019-10-11 0:0:0", "login_ip": "1.1.1.2"}, {"date": "2019-10-11 1:0:0", "login_ip": "1.1.1.3"}]}
      login_histories:  {"date": "2019-10-11 0:0:0", "login_ip": "1.1.1.2"}
      login_ip:  1.1.1.2
      name:  user2
      
      __source__:  1.2.3.4
      __topic__: 
      content: {...如前...}
      date:  2019-10-11 1:0:0
      item:  {"name": "user2", "login_histories": [{"date": "2019-10-11 0:0:0", "login_ip": "1.1.1.2"}, {"date": "2019-10-11 1:0:0", "login_ip": "1.1.1.3"}]}
      login_histories:  {"date": "2019-10-11 1:0:0", "login_ip": "1.1.1.3"}
      login_ip:  1.1.1.3
      name:  user2
      
      __source__: 1.2.3.4
      __topic__:  
      content: {...如前...}
      date:  2019-10-10 1:0:0
      item:  {"name": "user1", "login_histories": [{"date": "2019-10-10 0:0:0", "login_ip": "1.1.1.1"}, {"date": "2019-10-10 1:0:0", "login_ip": "1.1.1.1"}]}
      login_histories:  {"date": "2019-10-10 1:0:0", "login_ip": "1.1.1.1"}
      login_ip:  1.1.1.1
      name:  user1
      
      __source__: 1.2.3.4
      __topic__:  
      content: {...如前...}
      date:  2019-10-10 0:0:0
      item:  {"name": "user1", "login_histories": [{"date": "2019-10-10 0:0:0", "login_ip": "1.1.1.1"}, {"date": "2019-10-10 1:0:0", "login_ip": "1.1.1.1"}]}
      login_histories:  {"date": "2019-10-10 0:0:0", "login_ip": "1.1.1.1"}
      login_ip:  1.1.1.1
      name:  user1
    3. 删除无关字段。
      e_drop_fields("content", "item", "login_histories")
      处理后返回的日志:
      __source__: 1.2.3.4
      __topic__:
      name:  user1
      date:  2019-10-11 1:0:0
      login_ip:  1.1.1.1
      
      __source__:  1.2.3.4
      __topic__:
      name:  user1
      date:  2019-10-11 0:0:0
      login_ip:  1.1.1.1
      
      __source__:  1.2.3.4
      __topic__:
      name:  user2
      date:  2019-10-11 0:0:0
      login_ip:  1.1.1.2
      
      __source__: 1.2.3.4
      __topic__:
      name:  user2
      date:  2019-10-11 1:0:0
      login_ip:  1.1.1.3
    4. 综上LOG DSL规则可以如以下形式:
      e_split("content", jmes='users[*]', output='item')
      e_json("item",depth=1)
      e_split("login_histories")
      e_json("login_histories", depth=1)
      e_drop_fields("content", "item", "login_histories")
总结:针对以上类似的需求,首先进行分裂,然后再做展开操作,最后删除无关信息。