j3f*_*ffz 12 database database-design mongodb
我正在为我的垃圾收集公司开发路线追踪/优化软件,并希望对我目前的数据结构/情况提供一些反馈.
这是我的MongoDB结构的简化版本:
数据库:数据
类别:
"客户" - 包含所有客户数据的数据收集.
[
{
"cust_id": "1001",
"name": "Customer 1",
"address": "123 Fake St",
"city": "Boston"
},
{
"cust_id": "1002",
"name": "Customer 2",
"address": "123 Real St",
"city": "Boston"
},
{
"cust_id": "1003",
"name": "Customer 3",
"address": "12 Elm St",
"city": "Boston"
},
{
"cust_id": "1004",
"name": "Customer 4",
"address": "16 Union St",
"city": "Boston"
},
{
"cust_id": "1005",
"name": "Customer 5",
"address": "13 Massachusetts Ave",
"city": "Boston"
}, { ... }, { ... }, ...
]
"卡车" - 包含所有卡车数据的数据收集.
[
{
"truckid": "21",
"type": "Refuse",
"year": "2011",
"make": "Mack",
"model": "TerraPro Cabover",
"body": "Mcneilus Rear Loader XC",
"capacity": "25 cubic yards"
},
{
"truckid": "22",
"type": "Refuse",
"year": "2009",
"make": "Mack",
"model": "TerraPro Cabover",
"body": "Mcneilus Rear Loader XC",
"capacity": "25 cubic yards"
},
{
"truckid": "12",
"type": "Dump",
"year": "2006",
"make": "Chevrolet",
"model": "C3500 HD",
"body": "Rugby Hydraulic Dump",
"capacity": "15 cubic yards"
}
]
"drivers" - 包含所有驱动程序数据的数据收集.
[
{
"driverid": "1234",
"name": "John Doe"
},
{
"driverid": "4321",
"name": "Jack Smith"
},
{
"driverid": "3421",
"name": "Don Johnson"
}
]
"route-lists" - 包含所有预定路由列表的数据收集.
[
{
"route_name": "monday_1",
"day": "monday",
"truck": "21",
"stops": [
{
"cust_id": "1001"
},
{
"cust_id": "1010"
},
{
"cust_id": "1002"
}
]
},
{
"route_name": "friday_1",
"day": "friday",
"truck": "12",
"stops": [
{
"cust_id": "1003"
},
{
"cust_id": "1004"
},
{
"cust_id": "1012"
}
]
}
]
"routes" - 包含所有活动和已完成路径的数据的数据集合.
[
{
"routeid": "1",
"route_name": "monday1",
"start_time": "04:31 AM",
"status": "active",
"stops": [
{
"customerid": "1001",
"status": "complete",
"start_time": "04:45 AM",
"finish_time": "04:48 AM",
"elapsed_time": "3"
},
{
"customerid": "1010",
"status": "complete",
"start_time": "04:50 AM",
"finish_time": "04:52 AM",
"elapsed_time": "2"
},
{
"customerid": "1002",
"status": "incomplete",
"start_time": "",
"finish_time": "",
"elapsed_time": ""
},
{
"customerid": "1005",
"status": "incomplete",
"start_time": "",
"finish_time": "",
"elapsed_time": ""
}
]
}
]
这是迄今为止的过程:
每天司机都以"开始新路线"开始.在开始新路由之前,驱动程序必须首先输入数据:
正确输入所有数据后,将开始启动新路线:
当驾驶员继续他的每日停止/任务时,"路线"集合将相应地更新.
完成所有任务后,驱动程序将能够通过简单地将"状态"字段从"路径"集合中的"完成"更改为"活动"来完成路径处理.
这总结了一下.任何反馈,意见,评论,链接,优化策略都非常感谢.
在此先感谢您的时间.
And*_*ich 16
您将数据库模式看作是"经典"关系数据库模式.Mongodb非常适合数据非规范化.我想当你显示路线时,你加载所有相关的客户,司机,卡车.
如果你想让你的系统真正快速,你可以将所有东西都嵌入到路线收集中.
所以我建议您对架构进行以下修改:
路由表:
嵌入有关客户的数据而不是参考.还嵌入卡车.在这种情况下,架构将是:
{
"route_name": "monday_1",
"day": "monday",
"truck": {
_id = 1,
// here will be all truck data
},
"stops": [{
"customer": {
_id = 1,
//here will be all customer data
}
}, {
"customer": {
_id = 2,
//here will be all customer data
}
}]
}
路线:
当驾驶员从路线列表开始新的路线复制路线时,另外嵌入了驾驶员信息:
{
//copy all route-list data (just make new id for the current route and leave reference to routes-list. In this case you will able to sync route with route-list.)
"_id": "1",
route_list_id: 1,
"start_time": "04:31 AM",
"status": "active",
driver: {
//embedd all driver data here
},
"stops": [{
"customer": {
//all customer data
},
"status": "complete",
"start_time": "04:45 AM",
"finish_time": "04:48 AM",
"elapsed_time": "3"
}]
}
我猜你问自己,如果主要集合中的驱动程序,客户或其他非规范化数据发生了变化,该怎么办？是的,您需要更新其他集合中的所有非规范化数据.您可能需要更新数十亿个文档(取决于您的系统大小),这没关系.如果花费很多时间,你可以做异步.
以上数据结构有什么好处？
您可能会问自己在某些情况下您的数据可能不同步,但要解决此问题,您只需构建一些单元测试以确保正确更新您的denormolized数据.
从文档数据库的角度来看,希望以上将帮助您从非关系方面看世界.