聚合查詢示例，對工作和學習很有用
聚合用於在 mongo 查詢中執行復雜的資料搜尋操作，這在普通的查詢查詢中無法完成。
建立一些虛擬資料：
db.employees.insert({"name":"Adma","dept":"Admin","languages":["german","french","english","hindi"],"age":30, "totalExp":10});
db.employees.insert({"name":"Anna","dept":"Admin","languages":["english","hindi"],"age":35, "totalExp":11});
db.employees.insert({"name":"Bob","dept":"Facilities","languages":["english","hindi"],"age":36, "totalExp":14});
db.employees.insert({"name":"Cathy","dept":"Facilities","languages":["hindi"],"age":31, "totalExp":4});
db.employees.insert({"name":"Mike","dept":"HR","languages":["english", "hindi", "spanish"],"age":26, "totalExp":3});
db.employees.insert({"name":"Jenny","dept":"HR","languages":["english", "hindi", "spanish"],"age":25, "totalExp":3});
主題示例：
1.匹配： 用於匹配文件（如 SQL where 子句）
db.employees.aggregate([{$match:{dept:"Admin"}}]);
Output:
{ "_id" : ObjectId("54982fac2e9b4b54ec384a0d"), "name" : "Adma", "dept" : "Admin", "languages" : [ "german", "french", "english", "hindi" ], "age" : 30, "totalExp" : 10 }
{ "_id" : ObjectId("54982fc92e9b4b54ec384a0e"), "name" : "Anna", "dept" : "Admin", "languages" : [ "english", "hindi" ], "age" : 35, "totalExp" : 11 }
2.專案： 用於填充特定欄位的值
除非你指定禁用，否則專案階段將自動包含_id 欄位。
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1}}]);
Output:
{ "_id" : ObjectId("54982fac2e9b4b54ec384a0d"), "name" : "Adma", "dept" : "Admin" }
{ "_id" : ObjectId("54982fc92e9b4b54ec384a0e"), "name" : "Anna", "dept" : "Admin" }
db.employees.aggregate({$project: {'_id':0, 'name': 1}})
Output:
{ "name" : "Adma" }
{ "name" : "Anna" }
{ "name" : "Bob" }
{ "name" : "Cathy" }
{ "name" : "Mike" }
{ "name" : "Jenny" }
3.組： $ group 用於按特定欄位對文件進行分組，此處文件按 dept 欄位的值分組。另一個有用的功能是你可以按 null 分組，這意味著所有文件將聚合為一個。
db.employees.aggregate([{$group:{"_id":"$dept"}}]);
{ "_id" : "HR" }
{ "_id" : "Facilities" }
{ "_id" : "Admin" }
db.employees.aggregate([{$group:{"_id":null, "totalAge":{$sum:"$age"}}}]);
Output:
{ "_id" : null, "noOfEmployee" : 183 }
4. Sum： $ sum 用於對組內的值進行計數或求和。
db.employees.aggregate([{$group:{"_id":"$dept", "noOfDept":{$sum:1}}}]);
Output:
{ "_id" : "HR", "noOfDept" : 2 }
{ "_id" : "Facilities", "noOfDept" : 2 }
{ "_id" : "Admin", "noOfDept" : 2 }
5.平均值： 計算每組特定欄位值的平均值。
db.employees.aggregate([{$group:{"_id":"$dept", "noOfEmployee":{$sum:1}, "avgExp":{$avg:"$totalExp"}}}]);
Output:
{ "_id" : "HR", "noOfEmployee" : 2, "totalExp" : 3 }
{ "_id" : "Facilities", "noOfEmployee" : 2, "totalExp" : 9 }
{ "_id" : "Admin", "noOfEmployee" : 2, "totalExp" : 10.5 }
6.最小值： 查詢每個組中欄位的最小值。
db.employees.aggregate([{$group:{"_id":"$dept", "noOfEmployee":{$sum:1}, "minExp":{$min:"$totalExp"}}}]);
Output:
{ "_id" : "HR", "noOfEmployee" : 2, "totalExp" : 3 }
{ "_id" : "Facilities", "noOfEmployee" : 2, "totalExp" : 4 }
{ "_id" : "Admin", "noOfEmployee" : 2, "totalExp" : 10 }
7.最大值： 查詢每個組中欄位的最大值。
db.employees.aggregate([{$group:{"_id":"$dept", "noOfEmployee":{$sum:1}, "maxExp":{$max:"$totalExp"}}}]);
Output:
{ "_id" : "HR", "noOfEmployee" : 2, "totalExp" : 3 }
{ "_id" : "Facilities", "noOfEmployee" : 2, "totalExp" : 14 }
{ "_id" : "Admin", "noOfEmployee" : 2, "totalExp" : 11 }
8.從每組的第一個和最後一個文件中獲取特定欄位的值： 在對結果進行排序時執行良好。
db.employees.aggregate([{$group:{"_id":"$age", "lasts":{$last:"$name"}, "firsts":{$first:"$name"}}}]);
Output:
{ "_id" : 25, "lasts" : "Jenny", "firsts" : "Jenny" }
{ "_id" : 26, "lasts" : "Mike", "firsts" : "Mike" }
{ "_id" : 35, "lasts" : "Cathy", "firsts" : "Anna" }
{ "_id" : 30, "lasts" : "Adma", "firsts" : "Adma" }
9.最大最小值：
db.employees.aggregate([{$group:{"_id":"$dept", "noOfEmployee":{$sum:1}, "maxExp":{$max:"$totalExp"}, "minExp":{$min: "$totalExp"}}}]);
Output:
{ "_id" : "HR", "noOfEmployee" : 2, "maxExp" : 3, "minExp" : 3 }
{ "_id" : "Facilities", "noOfEmployee" : 2, "maxExp" : 14, "minExp" : 4 }
{ "_id" : "Admin", "noOfEmployee" : 2, "maxExp" : 11, "minExp" : 10 }
10. Push 和 addToSet： Push 將組中每個文件的欄位值新增到用於以陣列格式投影資料的陣列中，addToSet 類似於推送，但它省略了重複值。
db.employees.aggregate([{$group:{"_id":"dept", "arrPush":{$push:"$age"}, "arrSet": {$addToSet:"$age"}}}]);
Output:
{ "_id" : "dept", "arrPush" : [ 30, 35, 35, 35, 26, 25 ], "arrSet" : [ 25, 26, 35, 30 ] }
11.展開： 用於為指定陣列型別欄位中的每個值建立多個記憶體中文件，然後我們可以根據這些值進行進一步聚合。
db.employees.aggregate([{$match:{"name":"Adma"}}, {$unwind:"$languages"}]);
Output:
{ "_id" : ObjectId("54982fac2e9b4b54ec384a0d"), "name" : "Adma", "dept" : "HR", "languages" : "german", "age" : 30, "totalExp" : 10 }
{ "_id" : ObjectId("54982fac2e9b4b54ec384a0d"), "name" : "Adma", "dept" : "HR", "languages" : "french", "age" : 30, "totalExp" : 10 }
{ "_id" : ObjectId("54982fac2e9b4b54ec384a0d"), "name" : "Adma", "dept" : "HR", "languages" : "english", "age" : 30, "totalExp" : 10 }
{ "_id" : ObjectId("54982fac2e9b4b54ec384a0d"), "name" : "Adma", "dept" : "HR", "languages" : "hindi", "age" : 30, "totalExp" : 10 }
12.排序：
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1}}, {$sort: {name: 1}}]);
Output:
{ "_id" : ObjectId("57ff3e553dedf0228d4862ac"), "name" : "Adma", "dept" : "Admin" }
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin" }
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1}}, {$sort: {name: -1}}]);
Output:
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin" }
{ "_id" : ObjectId("57ff3e553dedf0228d4862ac"), "name" : "Adma", "dept" : "Admin" }
13.跳過：
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1}}, {$sort: {name: -1}}, {$skip:1}]);
Output:
{ "_id" : ObjectId("57ff3e553dedf0228d4862ac"), "name" : "Adma", "dept" : "Admin" }
14.限制：
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1}}, {$sort: {name: -1}}, {$limit:1}]);
Output:
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin" }
15.投影中的比較運算子：
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1, age: {$gt: ["$age", 30]}}}]);
Output:
{ "_id" : ObjectId("57ff3e553dedf0228d4862ac"), "name" : "Adma", "dept" : "Admin", "age" : false }
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin", "age" : true }
16.匹配中的比較運算子：
db.employees.aggregate([{$match:{dept:"Admin", age: {$gt:30}}}, {$project:{"name":1, "dept":1}}]);
Output:
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin" }
比較運算子列表：$ cmp，$ eq，$ gt，$ gte，$ lt，$ lte 和$ ne
17.投影中的布林聚合 opertor：
db.employees.aggregate([{$match:{dept:"Admin"}}, {$project:{"name":1, "dept":1, age: { $and: [ { $gt: [ "$age", 30 ] }, { $lt: [ "$age", 36 ] } ] }}}]);
Output:
{ "_id" : ObjectId("57ff3e553dedf0228d4862ac"), "name" : "Adma", "dept" : "Admin", "age" : false }
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin", "age" : true }
18.匹配中的布林聚合 opertor：
db.employees.aggregate([{$match:{dept:"Admin", $and: [{age: { $gt: 30 }}, {age: {$lt: 36 }} ] }}, {$project:{"name":1, "dept":1, age: { $and: [ { $gt: [ "$age", 30 ] }, { $lt: [ "$age", 36 ] } ] }}}]);
Output:
{ "_id" : ObjectId("57ff3e5e3dedf0228d4862ad"), "name" : "Anna", "dept" : "Admin", "age" : true }
布林聚合 opertors 列表：$和，$或，$ not。
完整的參考： https ： //docs.mongodb.com/v3.2/reference/operator/aggregation/