极客笔记Mongodb $group运算符
$group运算符在MongoDB中非常关键，因为它可以帮助进行各种数据转换。$group运算符通过一定的指定表达式对相似的数据进行分组，并且为每个不同的分组分组文档。
假设数据库中有50名学生，他们都喜欢板球。如果我们想要统计所有喜欢板球的学生数量，那么$group运算符就是这样一个任务的优雅解决方案。
语法：
{
$group:
{
_id: <expression>, // Group By Expression
<field1>: { <accumulator1> : <expression1> },
...
}
}
重要点：
_id：此字段是用于分组的必选字段。如果您将_id字段的值指定为null或常量，则$group运算符将累加所有输入文档的值作为一个整体进行计算。- field：这是一个可选字段，可以使用运算符对其进行计算。
示例
在以下示例中，我们将使用：
Database: JavaTpoint
Collection: invoice
Document: Ten documents that contain the details of the invoice
{
"_id" : A1,
"item_name" : "Blue box",
"price" : 10,
"qty" : 15,
"date_of_bill" : "13/04/2015"
}
{
"_id" : A2,
"item_name" : "Light Red box",
"price" : 15,
"qty" : 20,
"date_of_bill" : "05/12/2014"
}
{
"_id" : null,
"item_name" : "Green box",
"price" : 10,
"qty" : 30,
"date_of_bill" : "17/12/2014"
}
{
"_id" : A3,
"item_name" : "White box",
"price" : 8,
"qty" : 25,
"date_of_bill" : "07/02/2014"
}
{
"_id" : A4,
"item_name" : "Blue box",
"price" : 15,
"qty" : 20,
"date_of_bill" : "13/04/2015"
}
{
"_id" : A5,
"item_name" : "Red box",
"price" : 12,
"qty" : 10,
"date_of_bill" : "05/12/2014"
}
{
"_id" : A6,
"item_name" : "Black box",
"price" : 10,
"qty" : 30,
"date_of_bill" : "22/04/2020"
}
{
"_id" : A7,
"item_name" : "Red box",
"price" : 8,
"qty" : 15,
"date_of_bill" : "05/12/2014"
}
{
"_id" : A8,
"item_name" : "Green box",
"price" : 20,
"qty" : 10,
"date_of_bill" : "17/12/2014"
}
{
"_id" : A9,
"item_name" : "Green box",
"price" : 10,
"qty" : 30,
"date_of_bill" : "17/12/2014"
}
示例1：$group
在此示例中，我们将按账单日期分组，并显示以下字段（总金额、平均数量以及同一日期内的账单数）。
db.invoice.aggregate(
[
{
group : {_id : "date_of_bill ",
Total_price: { sum: {multiply: [ "price", "qty" ] } },
Average_qty: { avg: "qty" },
count: { $sum: 1 }
}
}
]
).pretty();
输出：
{ "_id" : "13/04/2015", "Total_price" : 875, "Average_qty" : 17.5, "count" : 2 }
{ "_id" : "05/12/2014", "Total_price" : 1575, "Average_qty" : 15, "count" : 3 }
{ "_id" : "17/12/2014", "Total_price" : 2800, "Average_qty" : 28.3333333, "count" : 3 }
{ "_id" : "07/02/2014", "Total_price" : 200, "Average_qty" : 25, "count" : 1 }
{ "_id" : "22/04/2020", "Total_price" : 300, "Average_qty" : 30, "count" : 1 }
这里的结果显示，根据账单日期文件进行分组，并显示该日期的总价格、平均数量以及已完成账单的数量。
示例2：按多个键进行分组
在这个示例中，我们将按照账单日期和商品名称字段进行分组，并显示这些字段（总价格、平均数量以及同一日期的账单数量）。
db.invoice.aggregate(
[
{
group : {_id : { date_of_bill : "date_of_bill ", item : "item_name"
Total_price: {sum: { multiply: [ "price", "qty" ] } },
Average_qty: {avg: "qty" },
count: {sum: 1 }
}
}
]
).pretty();
输出：
{ "_id" : {
"date_of_bill" : "13/04/2015",
"item" : "Blue box"
}
"Total_price" : 875,
"Average_qty" : 17.5,
"count" : 2
}
{ "_id" : {
"date_of_bill" : "05/12/2014",
"item" : "Light Red box"
}
"Total_price" : 300,
"Average_qty" : 20,
"count" : 1
}
{ "_id" : {
"date_of_bill" : "05/12/2014",
"item" : "Red box"
}
"Total_price" : 500,
"Average_qty" : 12.5,
"count" : 2
}
{ "_id" : {
"date_of_bill" : "17/12/2014",
"item" : "Green box"
}
"Total_price" : 2800,
"Average_qty" : 28.3333333,
"count" : 3
}
{ "_id" : {
"date_of_bill" : "07/02/2014",
"item" : "White box"
}
"Total_price" : 200,
"Average_qty" : 25,
"count" : 1
}
{ "_id" : {
"date_of_bill" : "22/04/2020",
"item" : "Black box"
}
"Total_price" : 300,
"Average_qty" : 30,
"count" : 1
}
示例3：通过多个键进行分组并使用$match
在这个示例中，我们将通过账单日期和项目名称字段进行分组，并为账单日期为05/12/2014的文档显示这些字段（总价、平均数量以及相同日期的账单数量）。
db.invoice.aggregate(
[
{
match : { date_of_bill : "05/12/2014"}
},
{group : {_id : { date_of_bill : "date_of_bill ", item : "item_name"
Total_price: { sum: {multiply: [ "price", "qty" ] } },
Average_qty: { avg: "qty" },
count: { $sum: 1 }
}
}
]
).pretty();
输出：
{ "_id" : {
"date_of_bill" : "05/12/2014",
"item" : "Light Red box"
}
"Total_price" : 300,
"Average_qty" : 20,
"count" : 1
}
{ "_id" : {
"date_of_bill" : "05/12/2014",
"item" : "Red box"
}
"Total_price" : 500,
"Average_qty" : 12.5,
"count" : 2
}