我有此示例mongodb文档-
{
_id: 5db85ee97d9fb13ead4fc54c
applId: 5d48f34f7d9fb10ce171f905
fileId: "dd386cf7-4139-45c2-9853-cbb126621b51"
job: Object
country: "US"
fullName: "abcd xyz"
htmlWordCount: 2766
textWordCount: 1867
rchilliTextWordCount: 2840
deleted: 0
dateEntered: 2019-10-29 15:46:49.237
dateModified: 2019-10-29 15:46:49.237
}
我想在指南针中建立查询,以便在输出中具有以下字段-
{
_id: 5db85ee97d9fb13ead4fc54c
country: "US"
fullName: "abcd xyz"
htmlWordCount: 2766
textWordCount: 1867
rchilliTextWordCount: 2840
winner: "rchilliTextWordCount"
}
[请注意,它有一个称为“ winner”的新字段,该字段总是返回最大字数(3 "htmlWordCount", "textWordCount", "rchilliTextWordCount"
列中的最大列)。此新列"winner"
将在运行时根据查询生成。同样,此查询在country = "US"
上进行过滤。
我该如何在MongoDB Compass中执行此操作,或者聚合管道应该是什么样?
db.collection.aggregate([
{
$match: {
country: "US"
}
},
{
$project: {
country: 1,
fullName: 1,
htmlWordCount: 1,
textWordCount: 1,
rchilliTextWordCount: 1,
winner: {
$switch: {
branches: [
{
case: {
$and: [
{
$gt: [
"$htmlWordCount",
"$textWordCount"
]
},
{
$gt: [
"$htmlWordCount",
"$rchilliTextWordCount"
]
}
]
},
then: "htmlWordCount"
},
{
case: {
$and: [
{
$gt: [
"$textWordCount",
"$htmlWordCount"
]
},
{
$gt: [
"$textWordCount",
"$rchilliTextWordCount"
]
}
]
},
then: "textWordCount"
},
{
case: {
$and: [
{
$gt: [
"$rchilliTextWordCount",
"$htmlWordCount"
]
},
{
$gt: [
"$rchilliTextWordCount",
"$textWordCount"
]
}
]
},
then: "rchilliTextWordCount"
}
],
default: "No winners"
}
}
}
}
])
这是获得结果的另一种方法:
[ "htmlWordCount", "textWordCount", "rchilliTextWordCount" ]
的字段的最大值。通常,从数组中找到最大值是一种归约;因此在这种情况下,我使用了$reduce
。注意,代码更简单。如果要添加另一个用于计算最大值的字段,只需将其添加到数组中即可。
db.winner.aggregate([
{ $match: { country: "US"} },
{ $addFields: { fieldNameValues: { "$objectToArray": "$$ROOT" } } },
{ $project: { _id: 1, country: 1, fullName: 1, htmlWordCount: 1, textWordCount: 1, rchilliTextWordCount: 1,
winner: {
$reduce: {
input: "$fieldNameValues",
initialValue: { },
in: {
$cond: [
{ $and: [
{ $in: [ "$$this.k", [ "htmlWordCount", "textWordCount", "rchilliTextWordCount" ] ] },
{ $gt: [ "$$this.v", "$$value.v"] } ]
},
"$$this",
"$$value"
]
}
}
}
} },
{ $addFields: { winner: "$winner.k" } }
] )
样本数据和结果:
{
"_id" : 1,
"fileId" : "dd386cf7-4139-45c2-9853-cbb126621b51",
"job" : { },
"country" : "US",
"fullName" : "abcd xyz",
"htmlWordCount" : 2766,
"textWordCount" : 1867,
"rchilliTextWordCount" : 2840
}
{
"_id" : 2,
"fileId" : "dd386cf7-4139-45c2-9853-cbb126621b51",
"job" : { },
"country" : "US",
"fullName" : "lmn opqrs",
"htmlWordCount" : 5,
"textWordCount" : 9,
"rchilliTextWordCount" : 2
}
输出:
{
"_id" : 1,
"country" : "US",
"fullName" : "abcd xyz",
"htmlWordCount" : 2766,
"textWordCount" : 1867,
"rchilliTextWordCount" : 2840,
"winner" : "rchilliTextWordCount"
}
{
"_id" : 2,
"country" : "US",
"fullName" : "lmn opqrs",
"htmlWordCount" : 5,
"textWordCount" : 9,
"rchilliTextWordCount" : 2,
"winner" : "textWordCount"
}