mongodb 官网示例

1.安装

最新版本为4.0.6，装完可以执行mongo -version查看

下载tar包，解压完

确实够简洁呀，就bin目录，然后在/etc/profile配下环境变量source下就ok！

创建db目录

mkdir -p /data/db

官网还让创建/var/lib/mongo和/var/log/mongo，创建完启动发现这俩目录也没写入数据。

2.启动

临时启动

mongod

看到等待端口连接就ok了，貌似3.2以后不提供web ui界面了，28017端口无法访问了。

可以下载一个RoBo 3T，供可视化。

默认外网无法连接，可以临时启动时，加--bind_ip_all，或者--bind_ip <address>指定ip

 mongod --bind_ip_all

也可以创建配置文件，然后启动的时候

mongod -f 配置文件

后台启动

nohup或者&

3.与mysql对比

官网截的图，mongo的collection就是mysql的table，document文档就是row，

而field字段就是column，都是三层结构。

下面是RoBo 3T的截图

类似json数据，每个文档document都有一个类型为ObjectId的_id。

4.mongo的curd

1.查看数据库

show dbs

2.查看当前数据库

db

3.进入某个数据库/如果不存在,新建

use dbName

库无数据的话，此时show dbs，看不到该库

4.查看库的collection

show collections

5.创建collection

collection刚创建无数据时，show collections，看不到该collection

db.newCollection

6.插入数据

db.newCollection.insertOne({x:1})

7.插入多条

db.zlq.insertMany([// MongoDB adds the _id field with an ObjectId if _id is not present{ item: "journal", qty: 25, status: "A",size: { h: 14, w: 21, uom: "cm" }, tags: [ "blank", "red" ] },{ item: "notebook", qty: 50, status: "A",size: { h: 8.5, w: 11, uom: "in" }, tags: [ "red", "blank" ] },{ item: "paper", qty: 100, status: "D",size: { h: 8.5, w: 11, uom: "in" }, tags: [ "red", "blank", "plain" ] },{ item: "planner", qty: 75, status: "D",size: { h: 22.85, w: 30, uom: "cm" }, tags: [ "blank", "red" ] },{ item: "postcard", qty: 45, status: "A",size: { h: 10, w: 15.25, uom: "cm" }, tags: [ "blue" ] }
]);

8.查collection所有数据

db.zlq.find( {} )

9.过滤

db.zlq.find( { status: "D" } )
//select * from zlq where status="D"

10.匹配嵌入的document/类似json数组的小json

db.zlq.find( { size: { h: 14, w: 21, uom: "cm" } } )

11.匹配嵌入document的字段

db.zlq.find( { "size.uom": "in" } )

12.匹配数组里的字段

db.zlq.find( { tags: "red" } )

13.匹配数组

db.zlq.find( { tags: ["red", "blank"] } )
//这样的话tags:["red","blank","blue"]匹配不到

14.删库/删collection

db.dropDatabase()
db.zlq.drop()

15.删document

db.zlq.deleteOne({status:"D"})//只删除一行，即使多行符合
db.zlq.deleteMany({status:"A"})

5.mongo的aggregation

db.zlq.aggregate([ {$group:{_id:"$status",total:{$sum:"$qty"}}}])
//select status,sum(qty) from zlq group by status

mongo的mapreduce

对于熟悉hadoop的，就很容易理解了。

官网截的图，首先将每条数据map成key,value，hadoop的话，执行reduce相同的key会聚合成

key,Iterator(value)，mongo的话是key,Array(value)，然后执行sum

先过滤，再map-->reduce

distinct，返回一个数组

6.与mysql curd对比

<1>create&alter

sql	mongob
CREATE TABLE people (id MEDIUMINT NOT NULLAUTO_INCREMENT,user_id Varchar(30),age Number,status char(1),PRIMARY KEY (id) )	db.people.insertOne( {user_id: "abc123",age: 55,status: "A"} ) db.createCollection("people")
ALTER TABLE people ADD join_date DATETIME	db.people.updateMany({ },{ $set: { join_date: new Date() } } )
ALTER TABLE people DROP COLUMN join_date	db.people.updateMany({ },{ $unset: { "join_date": "" } } )
CREATE INDEX idx_user_id_asc ON people(user_id)	db.people.createIndex( { user_id: 1 } )
CREATE INDEXidx_user_id_asc_age_desc ON people(user_id, age DESC)	db.people.createIndex( { user_id: 1, age: -1 } )
DROP TABLE people	db.people.drop()

<2>insert

INSERT INTO people(user_id,age,status)
VALUES ("bcd001",45,"A")

db.people.insertOne({ user_id: "bcd001", age: 45, status: "A" }
)

<3>select

SELECT * FROM people	db.people.find()
SELECT id,user_id,status FROM people	db.people.find({ },{ user_id: 1, status: 1 } )
SELECT user_id, status FROM people	db.people.find({ },{ user_id: 1, status: 1, _id: 0 } )
SELECT * FROM people WHERE status = "A"	db.people.find({ status: "A" } )
SELECT user_id, status FROM people WHERE status = "A"	db.people.find({ status: "A" },{ user_id: 1, status: 1, _id: 0 } )
SELECT * FROM people WHERE status != "A"	db.people.find({ status: { $ne: "A" } } )
SELECT * FROM people WHERE status = "A" AND age = 50	db.people.find({ status: "A",age: 50 } )
SELECT * FROM people WHERE status = "A" OR age = 50	db.people.find({ $or: [ { status: "A" } , { age: 50 } ] } )
SELECT * FROM people WHERE age > 25	db.people.find({ age: { $gt: 25 } } )
SELECT * FROM people WHERE age < 25	db.people.find({ age: { $lt: 25 } } )
SELECT * FROM people WHERE age > 25 AND age <= 50	db.people.find({ age: { $gt: 25, $lte: 50 } } )
SELECT * FROM people WHERE user_id like "%bc%"	db.people.find( { user_id: /bc/ } ) -or- db.people.find( { user_id: { $regex: /bc/ } } )
SELECT * FROM people WHERE user_id like "bc%"	db.people.find( { user_id: /^bc/ } ) -or- db.people.find( { user_id: { $regex: /^bc/ } } )
SELECT * FROM people WHERE status = "A" ORDER BY user_id ASC	db.people.find( { status: "A" } ).sort( { user_id: 1 } )
SELECT * FROM people WHERE status = "A" ORDER BY user_id DESC	db.people.find( { status: "A" } ).sort( { user_id: -1 } )
SELECT COUNT(*) FROM people	db.people.count() or db.people.find().count()
SELECT COUNT(user_id) FROM people	db.people.count( { user_id: { $exists: true } } ) or db.people.find( { user_id: { $exists: true } } ).count()
SELECT COUNT(*) FROM people WHERE age > 30	db.people.count( { age: { $gt: 30 } } ) or db.people.find( { age: { $gt: 30 } } ).count()
SELECT DISTINCT(status) FROM people	db.people.aggregate( [ { $group : { _id : "$status" } } ] ) or, for distinct value sets that do not exceed the BSON size limit db.people.distinct( "status" )
SELECT * FROM people LIMIT 1	db.people.findOne() or db.people.find().limit(1)
SELECT * FROM people LIMIT 5 SKIP 10	db.people.find().limit(5).skip(10)
EXPLAIN SELECT * FROM people WHERE status = "A"	db.people.find( { status: "A" } ).explain()

<4>update

UPDATE people
SET status = "C"
WHERE age > 25

db.people.updateMany({ age: { $gt: 25 } },{ $set: { status: "C" } }
)

UPDATE people
SET age = age + 3
WHERE status = "A"

db.people.updateMany({ status: "A" } ,{ $inc: { age: 3 } }
)

<5> delete Records

DELETE FROM people WHERE status = "D"	db.people.deleteMany( { status: "D" } )
DELETE FROM people	db.people.deleteMany({})