$expMovingAvg 运算符计算指定字段值的指数移动平均线。
Syntax
{
$expMovingAvg: {
input: < field to use for calculation >,
N: < number of recent documents with the highest weight
}
}
参数
| 参数 | Description |
|---|---|
input |
其值用于计算指数移动平均线的字段 |
N |
计算指数移动平均线时,具有最高权重的先前文档的数量 |
例子
请考虑商店集合中的这个示例文档。
{
"_id": "0fcc0bf0-ed18-4ab8-b558-9848e18058f4",
"name": "First Up Consultants | Beverage Shop - Satterfieldmouth",
"location": {
"lat": -89.2384,
"lon": -46.4012
},
"staff": {
"totalStaff": {
"fullTime": 8,
"partTime": 20
}
},
"sales": {
"totalSales": 75670,
"salesByCategory": [
{
"categoryName": "Wine Accessories",
"totalSales": 34440
},
{
"categoryName": "Bitters",
"totalSales": 39496
},
{
"categoryName": "Rum",
"totalSales": 1734
}
]
},
"promotionEvents": [
{
"eventName": "Unbeatable Bargain Bash",
"promotionalDates": {
"startDate": {
"Year": 2024,
"Month": 6,
"Day": 23
},
"endDate": {
"Year": 2024,
"Month": 7,
"Day": 2
}
},
"discounts": [
{
"categoryName": "Whiskey",
"discountPercentage": 7
},
{
"categoryName": "Bitters",
"discountPercentage": 15
},
{
"categoryName": "Brandy",
"discountPercentage": 8
},
{
"categoryName": "Sports Drinks",
"discountPercentage": 22
},
{
"categoryName": "Vodka",
"discountPercentage": 19
}
]
},
{
"eventName": "Steal of a Deal Days",
"promotionalDates": {
"startDate": {
"Year": 2024,
"Month": 9,
"Day": 21
},
"endDate": {
"Year": 2024,
"Month": 9,
"Day": 29
}
},
"discounts": [
{
"categoryName": "Organic Wine",
"discountPercentage": 19
},
{
"categoryName": "White Wine",
"discountPercentage": 20
},
{
"categoryName": "Sparkling Wine",
"discountPercentage": 19
},
{
"categoryName": "Whiskey",
"discountPercentage": 17
},
{
"categoryName": "Vodka",
"discountPercentage": 23
}
]
}
]
}
示例 1 - 计算总销售额的指数移动平均线
要检索 First Up Consultants 公司内所有商店总销售额的指数移动平均线,请先运行查询来筛选公司。 然后,按开业日期的升序对结果中的文档进行排序。 最后,为最近两个文档分配了最高权重,以计算总销售额的指数移动平均线。
db.stores.aggregate(
[{
"$match": {
"company": {
"$in": [
"First Up Consultants"
]
}
}
},
{
"$setWindowFields": {
"partitionBy": "$company",
"sortBy": {
"storeOpeningDate": 1
},
"output": {
"expMovingAvgForSales": {
"$expMovingAvg": {
"input": "$sales.totalSales",
"N": 2
}
}
}
}
},
{
"$project": {
"company": 1,
"name": 1,
"sales.totalSales": 1,
"storeOpeningDate": 1,
"expMovingAvgForSales": 1
}
}])
此查询返回的两个结果是:
[
{
"_id": "2cf3f885-9962-4b67-a172-aa9039e9ae2f",
"sales": {
"revenue": 37701
},
"company": "First Up Consultants",
"storeOpeningDate": {
"$date": 1633219200000
},
"name": "First Up Consultants | Bed and Bath Center - South Amir",
"expMovingAvgForSales": 37701
},
{
"_id": "8e7a259b-f7d6-4ec5-a521-3bed53adc587",
"name": "First Up Consultants | Drone Stop - Lake Joana",
"sales": {
"revenue": 14329
},
"company": "First Up Consultants",
"storeOpeningDate": {
"$date": 1706958339311
},
"expMovingAvgForSales": 22119.666666666668
}
]
示例 2 - 使用 alpha 参数计算总销售额的指数移动平均线
要检索 First Up Consultants 公司内所有商店总销售额的指数移动平均线,请先运行查询来筛选公司。 然后,按开业日期的升序对结果中的文档进行排序。 最后,指定衰减率 (alpha),计算总销售额的指数移动平均线。 较高的 alpha 值会降低先前文档在计算中的权重。
db.stores.aggregate(
[{
"$match": {
"company": {
"$in": [
"First Up Consultants"
]
}
}
},
{
"$setWindowFields": {
"partitionBy": "$company",
"sortBy": {
"storeOpeningDate": 1
},
"output": {
"expMovingAvgForSales": {
"$expMovingAvg": {
"input": "$sales.totalSales",
"alpha": 0.75
}
}
}
}
},
{
"$project": {
"company": 1,
"name": 1,
"sales.totalSales": 1,
"storeOpeningDate": 1,
"expMovingAvgForSales": 1
}
}
])
此查询返回的前两个结果为:
[
{
"_id": "2cf3f885-9962-4b67-a172-aa9039e9ae2f",
"sales": {
"revenue": 37701
},
"company": "First Up Consultants",
"storeOpeningDate": "2021-10-03T00:00:00.000Z",
"name": "First Up Consultants | Bed and Bath Center - South Amir",
"expMovingAvgForSales": 37701
},
{
"_id": "8e7a259b-f7d6-4ec5-a521-3bed53adc587",
"name": "First Up Consultants | Drone Stop - Lake Joana",
"sales": {
"revenue": 14329
},
"company": "First Up Consultants",
"storeOpeningDate": "2024-09-02T00:05:39.311Z",
"expMovingAvgForSales": 20172
}
]
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