【mongoDB高級篇①】彙集運算之group與aggregate

時間 2019-12-04

標籤 mongoDB高級篇① 彙集運算 group aggregate 欄目 MongoDB 简体版

原文原文鏈接

group

語法

db.collection.group({
    key:{field:1},//按什麼字段進行分組
    
    initial:{count:0},//進行分組前變量初始化,該處聲明的變量能夠在如下回調函數中做爲result的屬性使用
    
    cond:{},//相似mysql中的having,分組後的查詢返回
    
    reduce: function ( curr, result ) { }, //The function takes two arguments: the current document and an aggregation result document for that group.先迭代出分組,而後再迭代分組中的文檔,即curr變量就表明當前分組中此刻迭代到的文檔,result變量就表明當前分組。
   
   keyf：function(doc){},//keyf和key二選一,傳入的參數doc表明當前文檔,若是分組的字段是通過運算後的字段用到,做用相似mysql中的group by left('2015-09-12 14:05:22',10);
   
   finalize:function(result) {}//該result也就是reduce的result,都是表明當前分組,這個函數是在走完當前分組結束後回調;
})

除了分組的key字段外,就只返回有result參數的回調函數中的操做的屬性字段;html

實例

# 表結構以下
{
  _id: ObjectId("5085a95c8fada716c89d0021"),
  ord_dt: ISODate("2012-07-01T04:00:00Z"),
  ship_dt: ISODate("2012-07-02T04:00:00Z"),
  item: { sku: "abc123",
          price: 1.99,
          uom: "pcs",
          qty: 25 }
}

#Example1
SELECT ord_dt, item_sku
FROM orders
WHERE ord_dt > '01/01/2012'
GROUP BY ord_dt, item_sku
↓↓↓↓
db.orders.group(
   {
     key: { ord_dt: 1, 'item.sku': 1 },
     cond: { ord_dt: { $gt: new Date( '01/01/2012' ) } },
     reduce: function ( curr, result ) { },
     initial: { }
   }
)

#Example2
SELECT ord_dt, item_sku, SUM(item_qty) as total
FROM orders
WHERE ord_dt > '01/01/2012'
GROUP BY ord_dt, item_sku
↓↓↓↓
db.orders.group(
   {
     key: { ord_dt: 1, 'item.sku': 1 },
     cond: { ord_dt: { $gt: new Date( '01/01/2012' ) } },
     reduce: function( curr, result ) {
                 result.total += curr.item.qty;
             },
     initial: { total : 0 }
   }
)

#Example3
db.orders.group(
   {
     keyf: function(doc) {
               return { day_of_week: doc.ord_dt.getDay() };
           },
     cond: { ord_dt: { $gt: new Date( '01/01/2012' ) } },
    reduce: function( curr, result ) {
                result.total += curr.item.qty;
                result.count++;
            },
    initial: { total : 0, count: 0 },
    finalize: function(result) {
                  var weekdays = [
                       "Sunday", "Monday", "Tuesday",
                       "Wednesday", "Thursday",
                       "Friday", "Saturday"
                      ];
                  result.day_of_week = weekdays[result.day_of_week];
                  result.avg = Math.round(result.total / result.count);
              }
   }
)

[
  { "day_of_week" : "Sunday", "total" : 70, "count" : 4, "avg" : 18 },
  { "day_of_week" : "Friday", "total" : 110, "count" : 6, "avg" : 18 },
  { "day_of_week" : "Tuesday", "total" : 70, "count" : 3, "avg" : 23 }
]

工做中用到的實例

#查詢每一個欄目最貴的商品價格, max()操做
{
  key:{cat_id:1},
  cond:{},
  reduce:function(curr , result) {
      if(curr.shop_price > result.max) {
          result.max = curr.shop_price;
      }
  },
  initial:{max:0}
}

#查詢每一個欄目下商品的平均價格
{
  key:{cat_id:1},
  cond:{},
  reduce:function(curr , result) {
      result.cnt += 1;
      result.sum += curr.shop_price;
  },
  initial:{sum:0,cnt:0},
  finalize:function(result) {
      result.avg = result.sum/result.cnt; //在每次分組完畢後進行運算
  }
}

group其實略微有點雞肋,由於既然用到了mongodb,那複製集和分片是避無可免的,而group是不支持分片的運算mysql

Aggregation

聚合管道是一個基於數據處理管道概念的框架。經過使用一個多階段的管道，將一組文檔轉換爲最終的聚合結果。sql

語法

參考手冊: http://docs.mongoing.com/manual-zh/core/aggregation-pipeline.htmlmongodb

db.collection.aggregate(pipeline, options);

pipeline Array

# 與mysql中的字段對比說明
$project # 返回哪些字段,select,說它像select實際上是不太準確的,由於aggregate是一個階段性管道操做符,$project是取出哪些數據進入下一個階段管道操做,真正的最終數據返回仍是在group等操做中;

$match # 放在group前至關於where使用,放在group後面至關於having使用

$sort # 排序1升-1降 sort通常放在group後,也就是說獲得結果後再排序,若是先排序再分組沒什麼意義;

$limit # 至關於limit m,不能設置偏移量

$skip # 跳過第幾個文檔

$unwind # 把文檔中的數組元素打開,並造成多個文檔,參考Example1

$group: { _id: <expression>, <field1>: { <accumulator1> : <expression1> }, ...  # 按什麼字段分組,注意全部字段名前面都要加$,不然mongodb就爲覺得不加$的是普一般量,其中accumulator又包括如下幾個操做符
# $sum,$avg,$first,$last,$max,$min,$push,$addToSet
#若是group by null就是 count(*)的效果

$geoNear # 取某一點的最近或最遠,在LBS地理位置中有用

$out # 把結果寫進新的集合中。注意1,不能寫進一個分片集合中。注意2,不能寫進

實例

Example1: unwindexpress

> db.test.insert({ "_id" : 1, "item" : "ABC1", sizes: [ "S", "M", "L"] });
WriteResult({ "nInserted" : 1 })
> db.test.aggregate( [ { $unwind : "$sizes" } ] )
{ "_id" : 1, "item" : "ABC1", "sizes" : "S" }
{ "_id" : 1, "item" : "ABC1", "sizes" : "M" }
{ "_id" : 1, "item" : "ABC1", "sizes" : "L" }

db.test.insert({ "_id" : 2, "item" : "ABC1", sizes: [ "S", "M", "L",["XXL",'XL']] });
WriteResult({ "nInserted" : 1 })
> db.test.aggregate( [ { $unwind : "$sizes" } ] )
{ "_id" : 1, "item" : "ABC1", "sizes" : "S" }
{ "_id" : 1, "item" : "ABC1", "sizes" : "M" }
{ "_id" : 1, "item" : "ABC1", "sizes" : "L" }
{ "_id" : 2, "item" : "ABC1", "sizes" : "S" }
{ "_id" : 2, "item" : "ABC1", "sizes" : "M" }
{ "_id" : 2, "item" : "ABC1", "sizes" : "L" }
{ "_id" : 2, "item" : "ABC1", "sizes" : [ "XXL", "XL" ] } # 只能打散一維數組

Example2

#數據源
{ "_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") }

# 綜合示例
db.sales.aggregate([
  # 由上到下,分階段的進行,注意該數組中的順序是有意義的
  {
    $project:{item:1,price:1,quantity:1} # 1.取出什麼元素待操做;
  },
  {
    $group:{ # 2. 對已取出的元素進行聚合運算;
      _id:"$item", # 根據什麼來分組
      quantityCount:{$sum:'$quantity'},
      priceTotal:{$sum:'$price'}
    }
  },
  {
    $sort:{
      quantityCount:1 #3.升序
    }
  },

  # 4.基於上面的結果,取倒數第二名
  {
    $skip: 2
  },
  {
    $limit:1
  },

  # 5.而後把結果寫到result集合中
  {
    $out:'result'
  }
])

#表達式$month,$dayOfMonth,$year,$sum,$avg
db.sales.aggregate(
   [
      {
        $group : {
           _id : { month: { $month: "$date" }, day: { $dayOfMonth: "$date" }, year: { $year: "$date" } }, #按月日年分組
           totalPrice: { $sum: { $multiply: [ "$price", "$quantity" ] } },
           averageQuantity: { $avg: "$quantity" },
           count: { $sum: 1 }
        }
      }
   ]
)

#結果
{ "_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 }

#
#
# 表達式$push
db.sales.aggregate(
   [
     {
       $group:
         {
           _id: { day: { $dayOfYear: "$date"}, year: { $year: "$date" } },
           itemsSold: { $push:  { item: "$item", quantity: "$quantity" } }
         }
     }
   ]
)

# result
{
   "_id" : { "day" : 46, "year" : 2014 },
   "itemsSold" : [
      { "item" : "abc", "quantity" : 10 },
      { "item" : "xyz", "quantity" : 10 },
      { "item" : "xyz", "quantity" : 5 },
      { "item" : "xyz", "quantity" : 10 }
   ]
}
{
   "_id" : { "day" : 34, "year" : 2014 },
   "itemsSold" : [
      { "item" : "jkl", "quantity" : 1 },
      { "item" : "xyz", "quantity" : 5 }
   ]
}
{
   "_id" : { "day" : 1, "year" : 2014 },
   "itemsSold" : [ { "item" : "abc", "quantity" : 2 } ]
}

#
#
# 表達式$addToSet
db.sales.aggregate(
   [
     {
       $group:
         {
           _id: { day: { $dayOfYear: "$date"}, year: { $year: "$date" } },
           itemsSold: { $addToSet: "$item" }
         }
     }
   ]
)

#result
{ "_id" : { "day" : 46, "year" : 2014 }, "itemsSold" : [ "xyz", "abc" ] }
{ "_id" : { "day" : 34, "year" : 2014 }, "itemsSold" : [ "xyz", "jkl" ] }
{ "_id" : { "day" : 1, "year" : 2014 }, "itemsSold" : [ "abc" ] }

#
#
# 表達式 $first
db.sales.aggregate(
   [
     { $sort: { item: 1, date: 1 } },
     {
       $group:
         {
           _id: "$item",
           firstSalesDate: { $first: "$date" }
         }
     }
   ]
)

# result
{ "_id" : "xyz", "firstSalesDate" : ISODate("2014-02-03T09:05:00Z") }
{ "_id" : "jkl", "firstSalesDate" : ISODate("2014-02-03T09:00:00Z") }
{ "_id" : "abc", "firstSalesDate" : ISODate("2014-01-01T08:00:00Z") }

Example3數組

db.sales.aggregate(
   [
      {
        $group : {
           _id : null, # 若是爲null,就統計出所有
           totalPrice: { $sum: { $multiply: [ "$price", "$quantity" ] } },
           averageQuantity: { $avg: "$quantity" },
           count: { $sum: 1 }
        }
      }
   ]
)

Example4框架

# 數據源
{ "_id" : 8751, "title" : "The Banquet", "author" : "Dante", "copies" : 2 }
{ "_id" : 8752, "title" : "Divine Comedy", "author" : "Dante", "copies" : 1 }
{ "_id" : 8645, "title" : "Eclogues", "author" : "Dante", "copies" : 2 }
{ "_id" : 7000, "title" : "The Odyssey", "author" : "Homer", "copies" : 10 }
{ "_id" : 7020, "title" : "Iliad", "author" : "Homer", "copies" : 10 }

# 根據做者分組,得到其著多少書籍
db.books.aggregate(
   [
     { $group : { _id : "$author", books: { $push: "$title" } } }
   ]
)

# result
{ "_id" : "Homer", "books" : [ "The Odyssey", "Iliad" ] }
{ "_id" : "Dante", "books" : [ "The Banquet", "Divine Comedy", "Eclogues" ] }

# 經過系統變量$$ROOT(當前的根文檔)來分組
db.books.aggregate(
   [
     { $group : { _id : "$author", books: { $push: "$$ROOT" } } }
   ]
)

# result
{
  "_id" : "Homer",
  "books" :
     [
       { "_id" : 7000, "title" : "The Odyssey", "author" : "Homer", "copies" : 10 },
       { "_id" : 7020, "title" : "Iliad", "author" : "Homer", "copies" : 10 }
     ]
}
{
  "_id" : "Dante",
  "books" :
     [
       { "_id" : 8751, "title" : "The Banquet", "author" : "Dante", "copies" : 2 },
       { "_id" : 8752, "title" : "Divine Comedy", "author" : "Dante", "copies" : 1 },
       { "_id" : 8645, "title" : "Eclogues", "author" : "Dante", "copies" : 2 }
     ]
}

郵政編碼數據集的聚合實例: http://docs.mongoing.com/manual-zh/tutorial/aggregation-zip-code-data-set.html函數

對用戶愛好數據作聚合實例:
http://docs.mongoing.com/manual-zh/tutorial/aggregation-with-user-preference-data.html編碼