Question

我有一个mongo / mongoose架构，在查询时会重新编译

等文档

{ "_id" : ObjectId("5907a5850b459d4fdcdf49ac"), "amount" : -33.3, "name" : "RINGGO", "method" : "VIS", "date" : ISODate("2017-04-26T23:00:00Z"), "importDate" : ISODate("2017-05-01T21:15:49.581Z"), "category" : "Not Set", "__v" : 0 }
{ "_id" : ObjectId("5907a5850b459d4fdcdf49ba"), "amount" : -61.3, "name" : "Amazon", "method" : "VIS", "date" : ISODate("2017-03-23T00:00:00Z"), "importDate" : ISODate("2017-05-01T21:15:49.592Z"), "category" : "Not Set", "__v" : 0 }
{ "_id" : ObjectId("5907a5850b459d4fdcdf49ce"), "amount" : -3.3, "name" : "Tesco", "method" : "VIS", "date" : ISODate("2017-03-15T00:00:00Z"), "importDate" : ISODate("2017-05-01T21:15:49.601Z"), "category" : "Not Set", "__v" : 0 }
{ "_id" : ObjectId("5907a5850b459d4fdcdf49cc"), "amount" : -26.3, "name" : "RINGGO", "method" : "VIS", "date" : ISODate("2017-03-16T00:00:00Z"), "importDate" : ISODate("2017-05-01T21:15:49.600Z"), "category" : "Not Set", "__v" : 0 }
{ "_id" : ObjectId("5907a5850b459d4fdcdf49f7"), "amount" : -63.3, "name" : "Sky", "method" : "VIS", "date" : ISODate("2017-03-02T00:00:00Z"), "importDate" : ISODate("2017-05-01T21:15:49.617Z"), "category" : "Not Set", "__v" : 0 }
{ "_id" : ObjectId("5907a5850b459d4fdcdf49be"), "amount" : -3.3, "name" : "RINGGO", "method" : "VIS", "date" : ISODate("2017-03-22T00:00:00Z"), "importDate" : ISODate("2017-05-01T21:15:49.593Z"), "category" : "Not Set", "__v" : 0 }

我想写一个查询，提供每个供应商（"name" : "Amazon"）的年度，月度和每周支出，例如供应商RINGGO：

2017年有三个花费33.3 + 26.3 + 3.3，因此每年的总支出为59.9
2017-03学年有两笔花费，总金额为26.3 + 3.3，所以月总数为26.6
每个花费都在不同的星期，所以每周总数将是（例如）wk12 26.3，wk13 3.3，wk 15 33.3

我可以写一个查询，例如

db.statements.aggregate(
   [        
       { $group : { _id : "$name", amount: { $push: "$amount" } } }
   ]
)

将根据供应商名称汇总所有花费（amount），但我不确定如何按年份，按月，按周解决这个问题。

编辑以回复评论我不完全确定结果可能具有的形状，但理想情况下它将如下所示：

我需要年，月，周等，以便查询可以通过网址驱动（例如domain.com/vendorname/2017，domain.com/vendorname/2017/3，domain.com/vendorname/2017/3/12）

我还希望每个月/月/周的个人花费和总花费，因为我想将这些花费打印到页面上。

{ "_id" : 
    { "year" : 2017, 
      "month" : 3, 
      "week" : 12 }, 
    "name": "RINGGO", //vendor name
    "YearlySpends":[ 33.3, 26.3, 3.3] 
    "totalYearlylyAmount" : [ 59.9] 
    "MonthlySpends":[ 26.3, 3.3] 
    "totalMonthlyAmount" : [ 26.6] 
    "WeeklylySpends":[ 3.3] 
    "totalWeeklylyAmount" : [3.3] 

}

Answer 1

一个好的方法是将聚合流程分成几个步骤，目的是计算每个组的聚合，即每年，每月和每周聚合。

我做了一个微弱的尝试来生成所说的管道，但不确定那是否是你所追求的，但可以给你一些解决方案，更好，但最佳的解决方案。也许其他人可以给出更好的答案。

考虑以下未经测试的管道：

db.statements.aggregate([
    {
        "$group": {
            "_id": {
                "name": "$name",
                "year": { "$year": "$date" },
                "month": { "$month": "$date" },
                "week": { "$week": "$date" }
            },
            "total": { "$sum": "$amount" }
        }
    },
    {
        "$group": {
            "_id": {
                "name": "$_id.name",
                "year": "$_id.year"
            },
            "YearlySpends": { "$push": "$total" },
            "totalYearlyAmount": { "$sum": "$total" },
            "data": { "$push": "$$ROOT" }
        }
    },
    { "$unwind": "$data" },
    {
        "$group": {
            "_id": {
                "name": "$_id.name",
                "month": "$data._id.month"
            },
            "YearlySpends": { "$first": "$YearlySpends" },
            "totalYearlyAmount": { "$first": "$totalYearlyAmount" },
            "MonthlySpends": { "$push": "$data.total" },
            "totalMonthlyAmount": { "$sum": "$data.total" },
            "data": { "$push": "$data" }
        }
    },
    { "$unwind": "$data" },
    {
        "$group": {
            "_id": {
                "name": "$_id.name",
                "week": "$data._id.week"
            },
            "YearlySpends": { "$first": "$YearlySpends" },
            "totalYearlyAmount": { "$first": "$totalYearlyAmount" },
            "MonthlySpends": { "$first": "$MonthlySpends" },
            "totalMonthlyAmount": { "$first": "$totalMonthlyAmount" },
            "WeeklySpends": { "$push": "$data.total" },
            "totalWeeklyAmount": { "$sum": "$data.total" },
            "data": { "$push": "$data" }
        }
    },
    { "$unwind": "$data" },
    {
        "$group": {
            "_id": "$data._id",
            "YearlySpends": { "$first": "$YearlySpends" },
            "totalYearlyAmount": { "$first": "$totalYearlyAmount" },
            "MonthlySpends": { "$first": "$MonthlySpends" },
            "totalMonthlyAmount": { "$first": "$totalMonthlyAmount" },
            "WeeklySpends": { "$first": "$WeeklySpends" },
            "totalWeeklyAmount": { "$first": "$totalWeeklyAmount" }
        }
    }
])

示例输出

/* 1 */
{
    "_id" : {
        "name" : "Tesco",
        "year" : 2017,
        "month" : 3,
        "week" : 11
    },
    "YearlySpends" : [ 
        -3.3
    ],
    "totalYearlyAmount" : -3.3,
    "MonthlySpends" : [ 
        -3.3
    ],
    "totalMonthlyAmount" : -3.3,
    "WeeklySpends" : [ 
        -3.3
    ],
    "totalWeeklyAmount" : -3.3
}

/* 2 */
{
    "_id" : {
        "name" : "RINGGO",
        "year" : 2017,
        "month" : 4,
        "week" : 17
    },
    "YearlySpends" : [ 
        -3.3, 
        -26.3, 
        -33.3
    ],
    "totalYearlyAmount" : -62.9,
    "MonthlySpends" : [ 
        -33.3
    ],
    "totalMonthlyAmount" : -33.3,
    "WeeklySpends" : [ 
        -33.3
    ],
    "totalWeeklyAmount" : -33.3
}

/* 3 */
{
    "_id" : {
        "name" : "RINGGO",
        "year" : 2017,
        "month" : 3,
        "week" : 12
    },
    "YearlySpends" : [ 
        -3.3, 
        -26.3, 
        -33.3
    ],
    "totalYearlyAmount" : -62.9,
    "MonthlySpends" : [ 
        -3.3, 
        -26.3
    ],
    "totalMonthlyAmount" : -29.6,
    "WeeklySpends" : [ 
        -3.3
    ],
    "totalWeeklyAmount" : -3.3
}

/* 4 */
{
    "_id" : {
        "name" : "RINGGO",
        "year" : 2017,
        "month" : 3,
        "week" : 11
    },
    "YearlySpends" : [ 
        -3.3, 
        -26.3, 
        -33.3
    ],
    "totalYearlyAmount" : -62.9,
    "MonthlySpends" : [ 
        -3.3, 
        -26.3
    ],
    "totalMonthlyAmount" : -29.6,
    "WeeklySpends" : [ 
        -26.3
    ],
    "totalWeeklyAmount" : -26.3
}

/* 5 */
{
    "_id" : {
        "name" : "Sky",
        "year" : 2017,
        "month" : 3,
        "week" : 9
    },
    "YearlySpends" : [ 
        -63.3
    ],
    "totalYearlyAmount" : -63.3,
    "MonthlySpends" : [ 
        -63.3
    ],
    "totalMonthlyAmount" : -63.3,
    "WeeklySpends" : [ 
        -63.3
    ],
    "totalWeeklyAmount" : -63.3
}

/* 6 */
{
    "_id" : {
        "name" : "Amazon",
        "year" : 2017,
        "month" : 3,
        "week" : 12
    },
    "YearlySpends" : [ 
        -61.3
    ],
    "totalYearlyAmount" : -61.3,
    "MonthlySpends" : [ 
        -61.3
    ],
    "totalMonthlyAmount" : -61.3,
    "WeeklySpends" : [ 
        -61.3
    ],
    "totalWeeklyAmount" : -61.3
}

更新

如果您希望在汇总操作中包含过滤器，那么我建议您使用 $match 查询作为第一个管道阶段。但是，如果有一个初始的 $match 步骤，那么前面的步骤会稍微改变，因为您将聚合过滤结果，这与最初聚合所有文档然后应用完全不同过滤结果。

如果您要采用 filter-first-then-aggregate 路由，请考虑运行使用 $match 的聚合操作作为过滤的第一步供应商提供的文档，然后是前面的 $redact 管道步骤，以进一步过滤日期字段的月份部分的文档，然后其余部分将是 $group 阶段：

Statements.aggregate([
    { "$match": { "name": req.params.vendor } },
    {
        "$redact": {
            "$cond": [
                { "$eq": [{ "$month": "$date" }, parseInt(req.params.month) ]},
                "$$KEEP",
                "$$PRUNE"
            ]
        }
    },
    .....
    /*
        add the remaining pipeline steps after
    */
], function(err, data){
    if (err) throw err;
    console.log(data);
})

如果要采用 group-first-then-filter 路由，则过滤器将位于最后一个管道上，该管道提供分组结果但应用于不同字段作为该部分的文档流的流程将与原始模式不同。

此路由不具备性能，因为您开始使用集合中的所有文档进行聚合操作，然后进行过滤：

Statements.aggregate([
    .....
    /*
        place the initial pipeline steps from 
        the original query above here
    */
    .....
    { 
        "$match": { 
            "_id.name": req.params.vendor,
            "_id.month": parseInt(req.params.month)
        } 
    }
], function(err, data){
    if (err) throw err;
    console.log(data);
})

对于多个日期过滤器参数， $redact 运算符将为

{
    "$redact": {
        "$cond": [
            {
                "$and": [
                     { "$eq": [{ "$year": "$date" },  parseInt(req.params.year)  ]},
                     { "$eq": [{ "$month": "$date" }, parseInt(req.params.month) ]},
                     { "$eq": [{ "$week": "$date" },  parseInt(req.params.week)  ]}
                ]
            },
            "$$KEEP",
            "$$PRUNE"
        ]
    }
}

Mongo / Mongoose - 按日期汇总

1 个答案:

更新