02：MongoDB操作

1.1 MongoDB 增加

　　1、插入数据

　　　　　　1. 插入文档: insert 如果插入数据的时候，collection还不存在，自动创建集合
　　　　　　2. insertOne: 插入一条数据
　　　　　　3. insertMany: 接收数组，插入多条文档

#1、插入单条数据
db.student1.insertOne({_id:"stu001","name":"Tom","age":25,grade:{"chinese":80,"math":90,"english":88}})

#2、插入多条数据
db.student1.insertMany([
{_id:"stu002","name":"Mary","age":23,grade:{"chinese":80,"math":90}},
{_id:"stu003","name":"Mike","age":23,grade:{"chinese":81,"math":90,"english":88}}
]);

　　2、更新文档: updateOne和updateMany

#1、更新_id=7839的薪水 ---> 8000
db.emp.updateOne({"_id":7839},{$set:{"sal":8000}})
db.emp.find({"_id":7839})  # 查询id=7893的文档

#2、更新多条数据：更新10号部门的员工薪水，加100块钱
错误：db.emp.updateMany({"deptno":{$eq:10}},{$set:{"sal":"sal"+100}})  ---> 不对
正确：db.emp.updateMany({"deptno":{$eq:10}},{$inc:{"sal",100}})

　　3、删除文档: deleteOne和deleteMany

　　　　　　db.emp.deleteOne({"_id":7839})

　　4、批处理

　　　　　　注: 为了提高效率，db.collection.bulkWrite，支持：insert  update  remove 同时也支持insertMany

db.mystudents.bulkWrite([
{insertOne:{"document":{"_id":100,"name":"Tom","age":25}}},
{insertOne:{"document":{"_id":101,"name":"Mary","age":24}}},
{updateOne:{"filter":{"_id":100},"update":{$set:{"name":"Tom123"}}}}
]);

1.2 MongoDB查询操作

　　1、基本查询

db.emp.insert(
[
{_id:7369,ename:'SMITH' ,job:'CLERK'    ,mgr:7902,hiredate:'17-12-80',sal:800,comm:0,deptno:20},
{_id:7499,ename:'ALLEN' ,job:'SALESMAN' ,mgr:7698,hiredate:'20-02-81',sal:1600,comm:300 ,deptno:30},
{_id:7521,ename:'WARD'  ,job:'SALESMAN' ,mgr:7698,hiredate:'22-02-81',sal:1250,comm:500 ,deptno:30},
{_id:7566,ename:'JONES' ,job:'MANAGER'  ,mgr:7839,hiredate:'02-04-81',sal:2975,comm:0,deptno:20},
{_id:7654,ename:'MARTIN',job:'SALESMAN' ,mgr:7698,hiredate:'28-09-81',sal:1250,comm:1400,deptno:30},
{_id:7698,ename:'BLAKE' ,job:'MANAGER'  ,mgr:7839,hiredate:'01-05-81',sal:2850,comm:0,deptno:30},
{_id:7782,ename:'CLARK' ,job:'MANAGER'  ,mgr:7839,hiredate:'09-06-81',sal:2450,comm:0,deptno:10},
{_id:7788,ename:'SCOTT' ,job:'ANALYST'  ,mgr:7566,hiredate:'19-04-87',sal:3000,comm:0,deptno:20},
{_id:7839,ename:'KING'  ,job:'PRESIDENT',mgr:0,hiredate:'17-11-81',sal:5000,comm:0,deptno:10},
{_id:7844,ename:'TURNER',job:'SALESMAN' ,mgr:7698,hiredate:'08-09-81',sal:1500,comm:0,deptno:30},
{_id:7876,ename:'ADAMS' ,job:'CLERK'    ,mgr:7788,hiredate:'23-05-87',sal:1100,comm:0,deptno:20},
{_id:7900,ename:'JAMES' ,job:'CLERK'    ,mgr:7698,hiredate:'03-12-81',sal:950,comm:0,deptno:30},
{_id:7902,ename:'FORD'  ,job:'ANALYST'  ,mgr:7566,hiredate:'03-12-81',sal:3000,comm:0,deptno:20},
{_id:7934,ename:'MILLER',job:'CLERK'    ,mgr:7782,hiredate:'23-01-82',sal:1300,comm:0,deptno:10}
]
);

#1、查询所有的员工信息
db.emp.find()

#2、查询职位值经理的员工
db.emp.find({"job":"MANAGER"})

#3、操作符：$in和$or
#查询职位是MANAGER或者是CLERK员工信息
db.emp.find({"job":{$in:["MANAGER","CLERK"]}})
db.emp.find({$or:[{"job":"MANAGER"},{"job":"CLERK"}]})

#4、查询10号部门工资大于2000的员工
db.emp.find({"sal":{$gt:2000},"deptno":10})

　　2、查询嵌套文档

db.student2.insertMany([
{_id:"stu0001",name:"Mary",age:25,grade:{chinese:80,math:85,english:90}},
{_id:"stu0002",name:"Tom",age:25,grade:{chinese:86,math:82,english:95}},
{_id:"stu0003",name:"Mike",age:25,grade:{chinese:81,math:90,english:88}},
{_id:"stu0004",name:"Jerry",age:25,grade:{chinese:95,math:87,english:89}}
])

#1、查询语文是81分，英语成绩是88分的文档
db.student2.find({grade:{chinese:81,english:88}})   ---> 得不到结果

#2、查询语文是81分，数学90分，英语成绩是88分的文档
db.student2.find({grade:{chinese:81,math:90,english:88}})  ---> 得到结果
# { "_id" : "stu0003", "name" : "Mike", "age" : 25, "grade" : { "chinese" : 81, "math" : 90, "english" : 88 } }
小结：如果是相等查询，保证匹配所有的field，顺序也要一样
    
#3、查询嵌套文档中的一个列：查询数学成绩是82分的文档
db.student2.find({"grade.math":82})
    
#4、使用比较运算符：查询英语成绩大于88分文档
db.student2.find({"grade.english":{$gt:88}})
    
#5、使用AND运算符：查询英语成绩大于88分，语文成绩大于85分的文档
db.student2.find({"grade.english":{$gt:88},"grade.chinese":{$gt:85}})

　　3、查询数组文档

db.studentbook.insert([
{_id:"stu001",name:"Tom",books:["Hadoop","Java","NoSQL"]},
{_id:"stu002",name:"Mary",books:["C++","Java","Oracle"]},
{_id:"stu003",name:"Mike",books:["Java","MySQL","PHP"]},
{_id:"stu004",name:"Jerry",books:["Hadoop","Spark","Java"]},
{_id:"stu005",name:"Jone",books:["C","Python"]}
])

#1、查询所有有Hadoop和Java的文档
错误：db.studentbook.find({books:["Hadoop","Java"]})  ---> 没有结果
正确：db.studentbook.find({books:{$all:["Hadoop","Java"]}})
'''
{ "_id" : "stu001", "name" : "Tom", "books" : [ "Hadoop", "Java", "NoSQL" ] }
{ "_id" : "stu004", "name" : "Jerry", "books" : [ "Hadoop", "Spark", "Java" ] }
'''

#2、根查询嵌套的文档一样，匹配每个元素，顺序也要一致
db.studentbook.find({books:["Hadoop","Java","NoSQL"]})
'''
{ "_id" : "stu001", "name" : "Tom", "books" : [ "Hadoop", "Java", "NoSQL" ] }
'''

　　4、查询数组中嵌套的文档

db.studentbook1.insertMany([
{_id:"stu001",name:"Tome",books:[{"bookname":"Hadoop", quantity:2},{"bookname":"Java", quantity:3},{"bookname":"NoSQL", quantity:4}]},
{_id:"stu002",name:"Mary",books:[{"bookname":"C++", quantity:4},   {"bookname":"Java", quantity:3},{"bookname":"Oracle", quantity:5}]},
{_id:"stu003",name:"Mike",books:[{"bookname":"Java", quantity:4},  {"bookname":"MySQL", quantity:1},{"bookname":"PHP", quantity:1}]},
{_id:"stu004",name:"Jone",books:[{"bookname":"Hadoop", quantity:3},{"bookname":"Spark", quantity:2},{"bookname":"Java", quantity:4}]},
{_id:"stu005",name:"Jane",books:[{"bookname":"C", quantity:1},     {"bookname":"Python", quantity:5}]}])

#1、查询Java有4本的文档
db.studentbook1.find({books:{"bookname":"Java","quantity":4}})
'''
{
    "_id": "stu003",
    "name": "Mike",
    "books": [{
        "bookname": "Java",
        "quantity": 4
    }, {
        "bookname": "MySQL",
        "quantity": 1
    }, {
        "bookname": "PHP",
        "quantity": 1
    }]
}
'''

#2、指定查询的条件：查询数组中第一个元素大于3本的文档
db.studentbook1.find({"books.0.quantity":{$gt:3}})
'''
{
    "_id": "stu002",
    "name": "Mary",
    "books": [{
        "bookname": "C++",
        "quantity": 4
    }, {
        "bookname": "Java",
        "quantity": 3
    }, {
        "bookname": "Oracle",
        "quantity": 5
    }]
}
'''
                
#3、如果不知道field的位置: 查询文档中至少有一个quantity的值大于3
db.studentbook1.find({"books.quantity":{$gt:3}})

#4、查询Java等于4本的文档
db.studentbook1.find({"books":{$elemMatch:{"bookname":"Java","quantity":4}}})
'''
{
    "_id": "stu003",
    "name": "Mike",
    "books": [{
        "bookname": "Java",
        "quantity": 4
    }, {
        "bookname": "MySQL",
        "quantity": 1
    }, {
        "bookname": "PHP",
        "quantity": 1
    }]
}
'''

　　5、查询空值null或者缺失的列

db.student3.insertMany([
   { _id: 1,name:"Tom",age:null },
   { _id: 2,name:"Mary"}
])

#1、查询值为null的文档
db.student3.find({age:null})  ---> 返回两条记录
'''
{ "_id" : 1, "name" : "Tom", "age" : null }
{ "_id" : 2, "name" : "Mary" }
'''

#2、只返回null的记录：BSON表示null：10
db.student3.find({"age":{$type:10}})
'''
{ "_id" : 1, "name" : "Tom", "age" : null }
'''
                
#3、检查是否缺失某个列
db.student3.find({age:{$exists:false}})
db.student3.find({age:{$exists:true}})

　　6、使用游标

#1、定义游标
var mycursor = db.emp.find()
mycursor

#2、使用游标访问文档（打印json格式数据）
var mycursor = db.emp.find()
while(mycursor.hasNext()){
printjson(mycursor.next())
}    

#3、游标和数组
var mycursor = db.emp.find()                       # 定义一个游标
var myarray = mycursor.toArray()                   # 将查询结果转换成数组
var mydoc = myarray[3]                             # 取出数组中第3条数据

#4、分页操作
第一页： limit表示查询过滤出前5条数据
var mycursor = db.emp.find().limit(5)
第二页： skip(5)表示跳过多少条数据
var mycursor = db.emp.find().limit(5).skip(5)

 1.3 聚合操作：aggregation

　　1、聚合操作说明

　　　　　　1. Pipeline:速度快于MapReduce，单个的聚合操作耗费的内存不能超过20%，返回的结果集：限制在16M

　　　　　　2. MapReduce：多个Server上并行计算

 　　2、Pipeline聚合操作

#1、$match和$project
$match: 过滤进入PipeLine的数据
$project:指定提取的列，其中： 1表示提取列  0不提取
#查询部门id=10，只显示ename、sal、deptno
db.emp.aggregate(
{$match:{"deptno":{$eq:10}}},
{$project:{"ename":1,"sal":1,"deptno":1}}
);    
'''
{ "_id" : 7782, "ename" : "CLARK", "sal" : 2450, "deptno" : 10 }
{ "_id" : 7839, "ename" : "KING", "sal" : 8000, "deptno" : 10 }
{ "_id" : 7934, "ename" : "MILLER", "sal" : 1300, "deptno" : 10 }
'''

db.emp.aggregate(
{$project:{"sal":1,"deptno":1}},
{$group:{"_id":"$deptno",salTotal:{$sum:"$sal"}}}
);

'''
{ "_id" : 10, "salTotal" : 11750 }
{ "_id" : 30, "salTotal" : 9400 }
{ "_id" : 20, "salTotal" : 10875 }
'''

#3、按照部门，不同的职位求工资总额
#select deptno,job,sum(sal) from emp group by deptno,job;
db.emp.aggregate(
{$project:{"job":1,"sal":1,"deptno":1}},
{$group:{"_id":{"deptno":"$deptno","job":"$job"},salTotal:{$sum:"$sal"}}}
);    
'''
{ "_id" : { "deptno" : 20, "job" : "ANALYST" }, "salTotal" : 6000 }
{ "_id" : { "deptno" : 30, "job" : "SALESMAN" }, "salTotal" : 5600 }
{ "_id" : { "deptno" : 20, "job" : "CLERK" }, "salTotal" : 1900 }
'''

 

 

 

 

 

 

 

 

 

 

 

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