mongodb聚合
2016-07-16 20:44
253 查看
1.
或者
或者
2. group:
group做的聚合有些复杂。先选定分组所依据的键,此后MongoDB就会将集合依据选定键值的不同分成若干组。然后可以通过聚合每一组内的文档,产生一个结果文档。
--这里是准备的测试数据
> db.test.remove()
> db.test.insert({"day" : "2012-08-20", "time" : "2012-08-20 03:20:40", "price" : 4.23})
> db.test.insert({"day" : "2012-08-21", "time" : "2012-08-21 11:28:00", "price" : 4.27})
> db.test.insert({"day" : "2012-08-20", "time" : "2012-08-20 05:00:00", "price" : 4.10})
> db.test.insert({"day" : "2012-08-22", "time" : "2012-08-22 05:26:00", "price" : 4.30})
> db.test.insert({"day" : "2012-08-21", "time" : "2012-08-21 08:34:00", "price" : 4.01})
--这里将用day作为group的分组键,然后取出time键值为最新时间戳的文档,同时也取出该文档的price键值。
> db.test.group( {
... "key" : {"day":true}, --如果是多个字段,可以为{"f1":true,"f2":true}
... "initial" : {"time" : "0"}, --initial表示$reduce函数参数prev的初始值。每个组都有一份该初始值。
... "$reduce" : function(doc,prev) { --reduce函数接受两个参数,doc表示正在迭代的当前文档,prev表示累加器文档。
... if (doc.time > prev.time) {
... prev.day = doc.day
... prev.price = doc.price;
... prev.time = doc.time;
... }
... } } )
[
{
"day" : "2012-08-20",
"time" : "2012-08-20 05:00:00",
"price" : 4.1
},
{
"day" : "2012-08-21",
"time" : "2012-08-21 11:28:00",
"price" : 4.27
},
{
"day" : "2012-08-22",
"time" : "2012-08-22 05:26:00",
"price" : 4.3
}
]
--下面的例子是统计每个分组内文档的数量。
> db.test.group( {
... key: { day: true},
... initial: {count: 0},
... reduce: function(obj,prev){ prev.count++;},
... } )
[
{
"day" : "2012-08-20",
"count" : 2
},
{
"day" : "2012-08-21",
"count" : 2
},
{
"day" : "2012-08-22",
"count" : 1
}
]
--最后一个是通过完成器修改reduce结果的例子。
> db.test.group( {
... key: { day: true},
... initial: {count: 0},
... reduce: function(obj,prev){ prev.count++;},
... finalize: function(out){ out.scaledCount = out.count * 10 } --在结果文档中新增一个键。
... } )
[
{
"day" : "2012-08-20",
"count" : 2,
"scaledCount" : 20
},
{
"day" : "2012-08-21",
"count" : 2,
"scaledCount" : 20
},
{
"day" : "2012-08-22",
"count" : 1,
"scaledCount" : 10
}
]
aggregation
1.1 $project{ "_id" : 1, title: "abc123", isbn: "0001122223334", author: { last: "zzz", first: "aaa" }, copies: 5 }
db.books.aggregate( [ { $project : { title : 1 , author : 1 } } ] )
{ "_id" : 1, "title" : "abc123", "author" : { "last" : "zzz", "first" : "aaa" } }
或者
{ _id: 1, user: "1234", stop: { title: "book1", author: "xyz", page: 32 } } { _id: 2, user: "7890", stop: [ { title: "book2", author: "abc", page: 5 }, { title: "book3", author: "ijk", page: 100 } ] }
db.bookmarks.aggregate( [ { $project: { stop: { title: 1 } } } ] )
{ "_id" : 1, "stop" : { "title" : "book1" } } { "_id" : 2, "stop" : [ { "title" : "book2" }, { "title" : "book3" } ] }
或者
{ "_id" : 1, title: "abc123", isbn: "0001122223334", author: { last: "zzz", first: "aaa" }, copies: 5 }
db.books.aggregate( [ { $project: { title: 1, isbn: { prefix: { $substr: [ "$isbn", 0, 3 ] }, group: { $substr: [ "$isbn", 3, 2 ] }, publisher: { $substr: [ "$isbn", 5, 4 ] }, title: { $substr: [ "$isbn", 9, 3 ] }, checkDigit: { $substr: [ "$isbn", 12, 1] } }, lastName: "$author.last", copiesSold: "$copies" } } ] )
{ "_id" : 1, "title" : "abc123", "isbn" : { "prefix" : "000", "group" : "11", "publisher" : "2222", "title" : "333", "checkDigit" : "4" }, "lastName" : "zzz", "copiesSold" : 5 }
$group
"_id" : 1, "item" : "abc", "price" : 10, "quantity" : 2, "date" : ISODate("2014-03-01T08:00:00Z") } { "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1, "date" : ISODate("2014-03-01T09:00:00Z") } { "_id" : 3, "item" : "xyz", "price" : 5, "quantity" : 10, "date" : ISODate("2014-03-15T09:00:00Z") } { "_id" : 4, "item" : "xyz", "price" : 5, "quantity" : 20, "date" : ISODate("2014-04-04T11:21:39.736Z") } { "_id" : 5, "item" : "abc", "price" : 10, "quantity" : 10, "date" : ISODate("2014-04-04T21:23:13.331Z") }
db.sales.aggregate( [ { $group : { _id : { month: { $month: "$date" }, day: { $dayOfMonth: "$date" }, year: { $year: "$date" } }, totalPrice: { $sum: { $multiply: [ "$price", "$quantity" ] } }, averageQuantity: { $avg: "$quantity" }, count: { $sum: 1 } } } ] )
{ "_id" : { "month" : 3, "day" : 15, "year" : 2014 }, "totalPrice" : 50, "averageQuantity" : 10, "count" : 1 } { "_id" : { "month" : 4, "day" : 4, "year" : 2014 }, "totalPrice" : 200, "averageQuantity" : 15, "count" : 2 } { "_id" : { "month" : 3, "day" : 1, "year" : 2014 }, "totalPrice" : 40, "averageQuantity" : 1.5, "count" : 2 }
2. group:
group做的聚合有些复杂。先选定分组所依据的键,此后MongoDB就会将集合依据选定键值的不同分成若干组。然后可以通过聚合每一组内的文档,产生一个结果文档。
--这里是准备的测试数据
> db.test.remove()
> db.test.insert({"day" : "2012-08-20", "time" : "2012-08-20 03:20:40", "price" : 4.23})
> db.test.insert({"day" : "2012-08-21", "time" : "2012-08-21 11:28:00", "price" : 4.27})
> db.test.insert({"day" : "2012-08-20", "time" : "2012-08-20 05:00:00", "price" : 4.10})
> db.test.insert({"day" : "2012-08-22", "time" : "2012-08-22 05:26:00", "price" : 4.30})
> db.test.insert({"day" : "2012-08-21", "time" : "2012-08-21 08:34:00", "price" : 4.01})
--这里将用day作为group的分组键,然后取出time键值为最新时间戳的文档,同时也取出该文档的price键值。
> db.test.group( {
... "key" : {"day":true}, --如果是多个字段,可以为{"f1":true,"f2":true}
... "initial" : {"time" : "0"}, --initial表示$reduce函数参数prev的初始值。每个组都有一份该初始值。
... "$reduce" : function(doc,prev) { --reduce函数接受两个参数,doc表示正在迭代的当前文档,prev表示累加器文档。
... if (doc.time > prev.time) {
... prev.day = doc.day
... prev.price = doc.price;
... prev.time = doc.time;
... }
... } } )
[
{
"day" : "2012-08-20",
"time" : "2012-08-20 05:00:00",
"price" : 4.1
},
{
"day" : "2012-08-21",
"time" : "2012-08-21 11:28:00",
"price" : 4.27
},
{
"day" : "2012-08-22",
"time" : "2012-08-22 05:26:00",
"price" : 4.3
}
]
--下面的例子是统计每个分组内文档的数量。
> db.test.group( {
... key: { day: true},
... initial: {count: 0},
... reduce: function(obj,prev){ prev.count++;},
... } )
[
{
"day" : "2012-08-20",
"count" : 2
},
{
"day" : "2012-08-21",
"count" : 2
},
{
"day" : "2012-08-22",
"count" : 1
}
]
--最后一个是通过完成器修改reduce结果的例子。
> db.test.group( {
... key: { day: true},
... initial: {count: 0},
... reduce: function(obj,prev){ prev.count++;},
... finalize: function(out){ out.scaledCount = out.count * 10 } --在结果文档中新增一个键。
... } )
[
{
"day" : "2012-08-20",
"count" : 2,
"scaledCount" : 20
},
{
"day" : "2012-08-21",
"count" : 2,
"scaledCount" : 20
},
{
"day" : "2012-08-22",
"count" : 1,
"scaledCount" : 10
}
]
相关文章推荐
- MongoDB 基本使用
- nodejs中使用mongodb
- nodejs操作mongodb
- MongoDB操作
- ubuntu下安装mongoDB教程
- CentOS6.3安装MongoDB2.2 及 安装PHP的MongoDB客户端
- Ubuntu 14.04 安装 MongoDB
- MongoDB常用操作命令大全
- 安装mongodb以及设置为windows服务 详细步骤
- mongodb安装与使用
- Nodejs——搭建电影展示平台(Express+MongoDB)
- mongodb 分片集群中加入新的config servers
- Python Web 7 —— python调用mongodb优化,使用mongoengine
- nodejs对mongodb数据库的增删改查操作
- deepin 系统中MongoDB的安装
- MongoDB聚合查询
- MongoDB集群卡死问题
- Mongodb ReplSet 小结
- MongoDB的选举过程
- mongoDB如何复制collection里的数据到另一个collection方法总结