MongoDB系列--03、mongoDB常用命令

约定不等于承诺〃 2022-11-22 05:28 301阅读 0赞

### MongoDB系列--03、mongoDB常用命令 ###

*   *  数据库操作命令
     *   *  创建数据库
         *  查看数据库
         *  删除数据库
     *  集合操作命令
     *   *  创建集合
         *  显示集合
         *  删除集合
     *  文档的操作命令
     *   *  插入文档
         *  查询文档
         *  删除文档
         *  文档排序
     *  limit()
     *  skip()
     *  mongoDB 索引
     *   *  创建索引
         *  查看集合索引
         *  查看集合索引大小
         *  删除集合所有索引
         *  删除集合指定索引
     *  聚合查询
     *   *  aggregate
         *  $ group与$sum
         *  $avg
         *  $ min与$max
         *  $match
         *  $sort
         *  $limit
         *  $project
         *  $lookup

## 数据库操作命令 ##

### 创建数据库 ###

use DATABASE_NAME	# 如果数据库不存在，则创建数据库，否则切换到指定数据库

> use christy
    switched to db christy
    > db
    christy

### 查看数据库 ###

> show dbs
    admin   0.000GB
    config  0.000GB
    local   0.000GB

可以看到上面没有我们刚才使用的数据库`christy`，如果要显示它需要向数据库中插入一些数据

> db.first_collection.insert({
       "name":"christy","age":18})
    {
       "t":{
       "$date":"2020-11-02T20:22:35.364+08:00"},"s":"I",  "c":"STORAGE",  "id":20320,   "ctx":"conn1","msg":"createCollection","attr":{
       "namespace":"christy.first_collection","uuidDisposition":"generated","uuid":{
       "uuid":{
       "$uuid":"0a3a0f06-6d16-4546-8428-155c884262d9"}},"options":{}}}
    {
       "t":{
       "$date":"2020-11-02T20:22:35.400+08:00"},"s":"I",  "c":"INDEX",    "id":20345,   "ctx":"conn1","msg":"Index build: done building","attr":{
       "buildUUID":null,"namespace":"christy.first_collection","index":"_id_","commitTimestamp":{
       "$timestamp":{
       "t":0,"i":0}}}}
    WriteResult({ "nInserted" : 1 })
    > show dbs
    admin    0.000GB
    christy  0.000GB
    config   0.000GB
    local    0.000GB

我们向数据库`christy`中的`first_collection`中插入了一条数据`{"name":"christy","age":18}`，再次执行`show dbs`就可以看到我们的数据库了

### 删除数据库 ###

db.dropDatabase()

这个命令是删除当前数据库，所以再删除数据库之前你需要使用`use`命令切换到你要删除的数据库。

> `db`是查看当前数据库命令

> use christy
    switched to db christy
    > db.dropDatabase()
    {
       "t":{
       "$date":"2020-11-02T20:29:59.612+08:00"},"s":"I",  "c":"COMMAND",  "id":20337,   "ctx":"conn2","msg":"dropDatabase - starting","attr":{
       "db":"christy"}}
    {
       "t":{
       "$date":"2020-11-02T20:29:59.612+08:00"},"s":"I",  "c":"COMMAND",  "id":20338,   "ctx":"conn2","msg":"dropDatabase - dropping collection","attr":{
       "db":"christy","namespace":"christy.first_collection"}}
    {
       "t":{
       "$date":"2020-11-02T20:29:59.613+08:00"},"s":"I",  "c":"COMMAND",  "id":20336,   "ctx":"conn2","msg":"dropDatabase","attr":{
       "db":"christy","numCollectionsDropped":1}}
    { "dropped" : "christy", "ok" : 1 }

上述操作执行完毕后数据库`christy` 已经被删除，再执行`show dbs`时已经看不到`christy`数据库了

> show dbs
    admin   0.000GB
    config  0.000GB
    local   0.000GB

## 集合操作命令 ##

mongoDB中的集合相当于MySQL中的table

### 创建集合 ###

mongoDB 中使用 createCollection() 方法来创建集合

db.createCollection(name,options)
    # name：要创建的集合（mysql中对应table）名称
    # options：可选参数，指定有关内存大小及索引的选项

options可以是以下参数

<table> 
 <thead> 
 <tr> 
 <th>字段</th> 
 <th>类型</th> 
 <th>描述</th> 
 </tr> 
 </thead> 
 <tbody> 
 <tr> 
 <td>capped</td> 
 <td>布尔</td> 
 <td>（可选）如果为 true，则创建固定集合。固定集合是指有着固定大小的集合，当达到最大值时，它会自动覆盖最早的文档。 当该值为 true 时，必须指定 size 参数</td> 
 </tr> 
 <tr> 
 <td>autoIndexId</td> 
 <td>布尔</td> 
 <td>（可选）如为 true，自动在 _id 字段创建索引。默认为 false</td> 
 </tr> 
 <tr> 
 <td>size</td> 
 <td>数值</td> 
 <td>（可选）为固定集合指定一个最大值（以字节计）。 如果 capped 为 true，也需要指定该字段</td> 
 </tr> 
 <tr> 
 <td>max</td> 
 <td>数值</td> 
 <td>（可选）指定固定集合中包含文档的最大数量</td> 
 </tr> 
 </tbody> 
</table>

> 在插入文档时，mongoDB 首先检查固定集合的 size 字段，然后检查 max 字段。

use christy
    switched to db christy
    > db.createCollection("first_collection")
    {
       "t":{
       "$date":"2020-11-02T20:40:05.920+08:00"},"s":"I",  "c":"STORAGE",  "id":20320,   "ctx":"conn3","msg":"createCollection","attr":{
       "namespace":"christy.first_collection","uuidDisposition":"generated","uuid":{
       "uuid":{
       "$uuid":"5a74c819-fc93-4950-9182-7d2d67e59e27"}},"options":{}}}
    {
       "t":{
       "$date":"2020-11-02T20:40:05.926+08:00"},"s":"I",  "c":"INDEX",    "id":20345,   "ctx":"conn3","msg":"Index build: done building","attr":{
       "buildUUID":null,"namespace":"christy.first_collection","index":"_id_","commitTimestamp":{
       "$timestamp":{
       "t":0,"i":0}}}}
    { "ok" : 1 }

### 显示集合 ###

执行`show collections`可以看到上面我们创建的集合

> show collections
    first_collection

### 删除集合 ###

集合的删除语法也很简单

db.collection.drop()	#还是一个函数，collection是要删除的集合的名称
    
    > show collections
    first_collection
    > db.first_collection.drop()
    {
       "t":{
       "$date":"2020-11-02T20:45:47.498+08:00"},"s":"I",  "c":"COMMAND",  "id":518070,  "ctx":"conn3","msg":"CMD: drop","attr":{
       "namespace":"christy.first_collection"}}
    {
       "t":{
       "$date":"2020-11-02T20:45:47.498+08:00"},"s":"I",  "c":"STORAGE",  "id":23879,   "ctx":"conn3","msg":"About to abort all index builders","attr":{
       "namespace":"christy.first_collection","uuid":{
       "uuid":{
       "$uuid":"5a74c819-fc93-4950-9182-7d2d67e59e27"}},"reason":"Collection christy.first_collection(5a74c819-fc93-4950-9182-7d2d67e59e27) is being dropped"}}
    {
       "t":{
       "$date":"2020-11-02T20:45:47.499+08:00"},"s":"I",  "c":"STORAGE",  "id":20318,   "ctx":"conn3","msg":"Finishing collection drop","attr":{
       "namespace":"christy.first_collection","uuid":{
       "uuid":{
       "$uuid":"5a74c819-fc93-4950-9182-7d2d67e59e27"}}}}
    true

最后返回`true`说明删除成功，这时候再去`show collections`，发现已经为空

> show collections
    >

## 文档的操作命令 ##

mongoDB中的文档相当于MySQL中的行或者说记录

### 插入文档 ###

mongoDB中使用insert或者save方法向集合中插入文档

db.collection_name.insert(document);

> var document = {
       "name":"christy","age":18}
    > db.first_collection.insert(document)
    {
       "t":{
       "$date":"2020-11-02T20:54:19.158+08:00"},"s":"I",  "c":"STORAGE",  "id":20320,   "ctx":"conn3","msg":"createCollection","attr":{
       "namespace":"christy.first_collection","uuidDisposition":"generated","uuid":{
       "uuid":{
       "$uuid":"358a36e2-0731-410c-8930-81d321702ebb"}},"options":{}}}
    {
       "t":{
       "$date":"2020-11-02T20:54:19.184+08:00"},"s":"I",  "c":"INDEX",    "id":20345,   "ctx":"conn3","msg":"Index build: done building","attr":{
       "buildUUID":null,"namespace":"christy.first_collection","index":"_id_","commitTimestamp":{
       "$timestamp":{
       "t":0,"i":0}}}}
    WriteResult({ "nInserted" : 1 })

上述方法先是定义了一个document，然后执行的插入操作。

### 查询文档 ###

db.collection.find(query, projection)

> query ：可选，使用查询操作符指定查询条件  
> projection ：可选，使用投影操作符指定返回的键。查询时返回文档中所有键值， 只需省略该参数即可（默认省略）

如果你需要以易读的方式来读取数据，可以使用 pretty() 方法，语法格式如下：

db.mycollection.find().pretty()

> db.first_collection.find().pretty()
    {
            "_id" : ObjectId("5fa0017b1453bd91e72a6101"),
            "name" : "christy",
            "age" : 18
    }
    {
            "_id" : ObjectId("5fa003741453bd91e72a6102"),
            "name" : "Tide",
            "age" : 19
    }

### 删除文档 ###

MongoDB的remove()函数是用来移除集合中的数据。在执行remove()函数前先执行find()命令来判断执行的条件是否正确，这是一个比较好的习惯

db.collection.remove(
 	<query>,
 {
 		justOne: <boolean>,
 		writeConcern: <document>
 	}
 )

> query :（可选）删除的文档的条件  
> justOne : （可选）如果设为 true 或 1，则只删除一个文档，如果不设置该参数，或使用默认值 false，则删除所有匹配条件的文档  
> writeConcern :（可选）抛出异常的级别

我们现在移除数据库`christy`的集合`first_collection`中name为Tide的数据

> db.first_collection.remove({
       "name":"Tide"})
    WriteResult({ "nRemoved" : 1 })

返回结果先是移除一条数据

如果你想一次性删除集合中的所有数据，只需执行

> db.collection_name.remove({})
    # 这个就类似于MySQL中的truncate

### 文档排序 ###

在 MongoDB 中使用 sort() 方法对数据（文档）进行排序，sort() 方法可以通过参数指定排序的字段，并使用 1 和 -1来指定排序的方式，**其中 1 为升序排列，而 -1 是用于降序排列**。

> db.collection_name.find().sort({
       KEY:1})

比如我们对数据库`christy`的集合`first_collection`中的文档按照age字段升序

> db.first_collection.find().sort({
       "age":1})
    { "_id" : ObjectId("5fa0072e1453bd91e72a6104"), "name" : "baby", "age" : 4 }
    { "_id" : ObjectId("5fa0017b1453bd91e72a6101"), "name" : "christy", "age" : 18 }
    { "_id" : ObjectId("5fa0071b1453bd91e72a6103"), "name" : "Tide", "age" : 19 }

## limit() ##

如果你需要在MongoDB中读取指定数量的数据记录，可以使用MongoDB的Limit方法，limit()方法接受一个数字参数，该参数指定从MongoDB中读取的记录条数

> db.collection_name.find(<query>).limit(number)

目前我们数据库中有以下数据

> db.first_collection.find().pretty()
    { 
            "_id" : ObjectId("5fa0017b1453bd91e72a6101"),
            "name" : "christy",
            "age" : 18
    }
    { 
            "_id" : ObjectId("5fa0071b1453bd91e72a6103"),
            "name" : "Tide",
            "age" : 19
    }
    {  
        	"_id" : ObjectId("5fa0072e1453bd91e72a6104"), 
        	"name" : "baby", 
        	"age" : 4 
    }

目前我们的需求是查找文档中的两条记录，那么我们可以这么写

> db.first_collection.find({},{
       "_id":0,"name":1,"age":1}).limit(2)
    { "name" : "christy", "age" : 18 }
    { "name" : "Tide", "age" : 19 }

> find函数这里说明一下
> 
> \{\}：第一个花括号是查询条件，为空则说明查询所有的记录
> 
> \{“\_id”:0,“name”:1,“age”:1\}：指的是查询结果id不显示，name和age显示

## skip() ##

我们除了可以使用limit()方法来读取指定数量的数据外，还可以使用skip()方法来跳过指定数量的数据，skip方法同样接受一个数字参数作为跳过的记录条数

> db.collection_name.find().limit(NUMBER).skip(NUMBER)

> db.first_collection.find({},{
       "_id":0,"name":1,"age":1}).limit(1).skip(1)
    { "name" : "Tide", "age" : 19 }

> skip()方法默认参数为0

## mongoDB 索引 ##

### 创建索引 ###

MongoDB使用 createIndex() 方法来创建索引

> db.collection_name.createIndex(keys, options)

> 语法中 Key 值为你要创建的索引字段，1 为指定按升序创建索引，如果你想按降序来创建索引指定为 -1 即可

createIndex() 方法中你也可以设置使用多个字段创建索引（关系型数据库中称作复合索引）

> db.col.createIndex({
       "title":1,"description":-1})

createIndex() 接收可选参数，可选参数列表如下：

<table> 
 <thead> 
 <tr> 
 <th align="left">Parameter</th> 
 <th align="left">Type</th> 
 <th align="left">Description</th> 
 </tr> 
 </thead> 
 <tbody> 
 <tr> 
 <td align="left">background</td> 
 <td align="left">Boolean</td> 
 <td align="left">建索引过程会阻塞其它数据库操作，background可指定以后台方式创建索引，即增加 “background” 可选参数。 “background” 默认值为false。</td> 
 </tr> 
 <tr> 
 <td align="left">unique</td> 
 <td align="left">Boolean</td> 
 <td align="left">建立的索引是否唯一。指定为true创建唯一索引。默认值为false.</td> 
 </tr> 
 <tr> 
 <td align="left">name</td> 
 <td align="left">string</td> 
 <td align="left">索引的名称。如果未指定，MongoDB的通过连接索引的字段名和排序顺序生成一个索引名称。</td> 
 </tr> 
 <tr> 
 <td align="left">dropDups</td> 
 <td align="left">Boolean</td> 
 <td align="left">**3.0+版本已废弃。**在建立唯一索引时是否删除重复记录,指定 true 创建唯一索引。默认值为 false.</td> 
 </tr> 
 <tr> 
 <td align="left">sparse</td> 
 <td align="left">Boolean</td> 
 <td align="left">对文档中不存在的字段数据不启用索引；这个参数需要特别注意，如果设置为true的话，在索引字段中不会查询出不包含对应字段的文档.。默认值为 false.</td> 
 </tr> 
 <tr> 
 <td align="left">expireAfterSeconds</td> 
 <td align="left">integer</td> 
 <td align="left">指定一个以秒为单位的数值，完成 TTL设定，设定集合的生存时间。</td> 
 </tr> 
 <tr> 
 <td align="left">v</td> 
 <td align="left">index version</td> 
 <td align="left">索引的版本号。默认的索引版本取决于mongod创建索引时运行的版本。</td> 
 </tr> 
 <tr> 
 <td align="left">weights</td> 
 <td align="left">document</td> 
 <td align="left">索引权重值，数值在 1 到 99,999 之间，表示该索引相对于其他索引字段的得分权重。</td> 
 </tr> 
 <tr> 
 <td align="left">default_language</td> 
 <td align="left">string</td> 
 <td align="left">对于文本索引，该参数决定了停用词及词干和词器的规则的列表。 默认为英语</td> 
 </tr> 
 <tr> 
 <td align="left">language_override</td> 
 <td align="left">string</td> 
 <td align="left">对于文本索引，该参数指定了包含在文档中的字段名，语言覆盖默认的language，默认值为 language.</td> 
 </tr> 
 </tbody> 
</table>

例如，我们对数据库`christy`的集合`first_collection`中的文档字段name创建一个升序的索引

> db.first_collection.createIndex({name:1})
    {
       "t":{
       "$date":"2020-11-02T21:58:42.747+08:00"},"s":"I",  "c":"INDEX",    "id":20438,   "ctx":"conn4","msg":"Index build: registering","attr":{
       "buildUUID":{
       "uuid":{
       "$uuid":"25cb9cb7-a2c6-4e91-8352-02de9c600e61"}},"namespace":"christy.first_collection","collectionUUID":{
       "uuid":{
       "$uuid":"358a36e2-0731-410c-8930-81d321702ebb"}},"indexes":1,"firstIndex":{
       "name":"name_1"}}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.774+08:00"},"s":"I",  "c":"INDEX",    "id":20384,   "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: starting","attr":{
       "namespace":"christy.first_collection","buildUUID":null,"properties":{
       "v":2,"key":{
       "name":1.0},"name":"name_1"},"method":"Hybrid","maxTemporaryMemoryUsageMB":200}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.774+08:00"},"s":"I",  "c":"INDEX",    "id":20440,   "ctx":"conn4","msg":"Index build: waiting for index build to complete","attr":{
       "buildUUID":{
       "uuid":{
       "$uuid":"25cb9cb7-a2c6-4e91-8352-02de9c600e61"}},"deadline":{
       "$date":{
       "$numberLong":"9223372036854775807"}}}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.774+08:00"},"s":"I",  "c":"INDEX",    "id":20391,   "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: collection scan done","attr":{
       "buildUUID":null,"totalRecords":3,"durationMillis":0}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.775+08:00"},"s":"I",  "c":"INDEX",    "id":20685,   "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: inserted keys from external sorter into index","attr":{
       "namespace":"christy.first_collection","index":"name_1","keysInserted":3,"durationMillis":0}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.776+08:00"},"s":"I",  "c":"STORAGE",  "id":3856203, "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: waiting for next action before completing final phase","attr":{
       "buildUUID":{
       "uuid":{
       "$uuid":"25cb9cb7-a2c6-4e91-8352-02de9c600e61"}}}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.776+08:00"},"s":"I",  "c":"STORAGE",  "id":3856204, "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: received signal","attr":{
       "buildUUID":{
       "uuid":{
       "$uuid":"25cb9cb7-a2c6-4e91-8352-02de9c600e61"}},"action":"Single-phase commit"}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.776+08:00"},"s":"I",  "c":"INDEX",    "id":20345,   "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: done building","attr":{
       "buildUUID":null,"namespace":"christy.first_collection","index":"name_1","commitTimestamp":{
       "$timestamp":{
       "t":0,"i":0}}}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.776+08:00"},"s":"I",  "c":"STORAGE",  "id":20663,   "ctx":"IndexBuildsCoordinatorMongod-0","msg":"Index build: completed successfully","attr":{
       "buildUUID":{
       "uuid":{
       "$uuid":"25cb9cb7-a2c6-4e91-8352-02de9c600e61"}},"namespace":"christy.first_collection","uuid":{
       "uuid":{
       "$uuid":"358a36e2-0731-410c-8930-81d321702ebb"}},"indexesBuilt":1,"numIndexesBefore":1,"numIndexesAfter":2}}
    {
       "t":{
       "$date":"2020-11-02T21:58:42.777+08:00"},"s":"I",  "c":"INDEX",    "id":20447,   "ctx":"conn4","msg":"Index build: completed","attr":{
       "buildUUID":{
       "uuid":{
       "$uuid":"25cb9cb7-a2c6-4e91-8352-02de9c600e61"}}}}
    {
            "createdCollectionAutomatically" : false,
            "numIndexesBefore" : 1,
            "numIndexesAfter" : 2,
            "ok" : 1
    }

结果蹦出来那么一大坨东西，我们的索引到底有没有创建成功呢？我们来看下查询索引的命令

### 查看集合索引 ###

> db.first_collection.getIndexes()
    [
            {
                    "v" : 2,
                    "key" : {
                            "_id" : 1
                    },
                    "name" : "_id_"
            },
            {
                    "v" : 2,
                    "key" : {
                            "name" : 1
                    },
                    "name" : "name_1"
            }
    ]

可以看到集合`first_collection`中有两个索引：\_id是自动生成的，这个不管；另外一个就是我们刚才自己新增的`name`索引

### 查看集合索引大小 ###

db.collection_name.totalIndexSize()

### 删除集合所有索引 ###

db.collection_name.dropIndexes()

### 删除集合指定索引 ###

db.collection_name.dropIndex("索引名称")

## 聚合查询 ##

MongoDB中聚合(aggregate)主要用于处理数据(诸如统计平均值,求和等)，并返回计算后的数据结果

> db.collection_name.aggregate(AGGREGATE_OPERATION)

<table> 
 <thead> 
 <tr> 
 <th align="left">表达式</th> 
 <th>SQL操作</th> 
 <th align="left">描述</th> 
 <th align="left">实例</th> 
 </tr> 
 </thead> 
 <tbody> 
 <tr> 
 <td align="left">$group</td> 
 <td>group by</td> 
 <td align="left">分组</td> 
 <td align="left"></td> 
 </tr> 
 <tr> 
 <td align="left">$sum</td> 
 <td>count(),sum()</td> 
 <td align="left">计算总和</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", num_tutorial : { s u m : " sum : " sum:"likes"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$avg</td> 
 <td>avg()</td> 
 <td align="left">计算平均值</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", num_tutorial : { a v g : " avg : " avg:"likes"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$min</td> 
 <td>min()</td> 
 <td align="left">获取集合中所有文档对应值得最小值。</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", num_tutorial : { m i n : " min : " min:"likes"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$max</td> 
 <td>max()</td> 
 <td align="left">获取集合中所有文档对应值得最大值。</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", num_tutorial : { m a x : " max : " max:"likes"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$match</td> 
 <td>where,having</td> 
 <td align="left">查询条件</td> 
 <td align="left"></td> 
 </tr> 
 <tr> 
 <td align="left">$sort</td> 
 <td>order by</td> 
 <td align="left">排序</td> 
 <td align="left"></td> 
 </tr> 
 <tr> 
 <td align="left">$limit</td> 
 <td>limit</td> 
 <td align="left">取条数</td> 
 <td align="left"></td> 
 </tr> 
 <tr> 
 <td align="left">$project</td> 
 <td>select</td> 
 <td align="left">选择</td> 
 <td align="left"></td> 
 </tr> 
 <tr> 
 <td align="left">$lookup</td> 
 <td>join</td> 
 <td align="left">连接</td> 
 <td align="left"></td> 
 </tr> 
 <tr> 
 <td align="left">$push</td> 
 <td></td> 
 <td align="left">在结果文档中插入值到一个数组中</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", url : { p u s h : " push: " push:"url"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$addToSet</td> 
 <td></td> 
 <td align="left">在结果文档中插入值到一个数组中，但不创建副本</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", url : { a d d T o S e t : " addToSet : " addToSet:"url"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$first</td> 
 <td></td> 
 <td align="left">根据资源文档的排序获取第一个文档数据</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", first_url : { f i r s t : " first : " first:"url"}}}])</td> 
 </tr> 
 <tr> 
 <td align="left">$last</td> 
 <td></td> 
 <td align="left">根据资源文档的排序获取最后一个文档数据</td> 
 <td align="left">db.mycol.aggregate([{ KaTeX parse error: Expected '}', got 'EOF' at end of input: …roup : {_id : "by_user", last_url : { l a s t : " last : " last:"url"}}}])</td> 
 </tr> 
 </tbody> 
</table>

> 本表参考文章：
> 
> https://www.cnblogs.com/zhoujie/p/mongo1.html
> 
> https://www.runoob.com/mongodb/mongodb-aggregate.html

开始聚合查询之前我们先准备一组数据

> var document = [{
       "name":"zhangsan","age":12,"addr":"HF"},{
       "name":"lisi","age":13,"addr":"HF"},{
       "name":"wangwu","age":14,"addr":"WH"},{
       "name":"zhaoliu","age":15,"addr":"WH"},{
       "name":"qianqi","age":16,"addr":"BB"},{
       "name":"zhangba","age":17,"addr":"BB"},{
       "name":"Christy","age":18,"addr":"AH"},{
       "name":"Tide","age":19,"addr":"AH"}]
    > db.second_collection.insert(document)

插入完成后我们可以查看一下完整的数据如下

> db.second_collection.find().pretty()
    {
            "_id" : ObjectId("5fa018db41c583a81e394185"),
            "name" : "zhangsan",
            "age" : 12,
            "addr" : "HF"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e394186"),
            "name" : "lisi",
            "age" : 13,
            "addr" : "HF"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e394187"),
            "name" : "wangwu",
            "age" : 14,
            "addr" : "WH"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e394188"),
            "name" : "zhaoliu",
            "age" : 15,
            "addr" : "WH"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e394189"),
            "name" : "qianqi",
            "age" : 16,
            "addr" : "BB"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e39418a"),
            "name" : "zhangba",
            "age" : 17,
            "addr" : "BB"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e39418b"),
            "name" : "Christy",
            "age" : 18,
            "addr" : "AH"
    }
    {
            "_id" : ObjectId("5fa018db41c583a81e39418c"),
            "name" : "Tide",
            "age" : 19,
            "addr" : "AH"
    }

### aggregate ###

> db.second_collection.aggregate([])
    {  "_id" : ObjectId("5fa018db41c583a81e394185"), "name" : "zhangsan", "age" : 12, "addr" : "HF" }
    {  "_id" : ObjectId("5fa018db41c583a81e394186"), "name" : "lisi", "age" : 13, "addr" : "HF" }
    {  "_id" : ObjectId("5fa018db41c583a81e394187"), "name" : "wangwu", "age" : 14, "addr" : "WH" }
    {  "_id" : ObjectId("5fa018db41c583a81e394188"), "name" : "zhaoliu", "age" : 15, "addr" : "WH" }
    {  "_id" : ObjectId("5fa018db41c583a81e394189"), "name" : "qianqi", "age" : 16, "addr" : "BB" }
    {  "_id" : ObjectId("5fa018db41c583a81e39418a"), "name" : "zhangba", "age" : 17, "addr" : "BB" }
    {  "_id" : ObjectId("5fa018db41c583a81e39418b"), "name" : "Christy", "age" : 18, "addr" : "AH" }
    {  "_id" : ObjectId("5fa018db41c583a81e39418c"), "name" : "Tide", "age" : 19, "addr" : "AH" }

### $ group与$sum ###

> db.second_collection.aggregate([{$group:{_id:"$addr",snum:{$sum:"$age"}}}])
    { "_id" : "WH", "snum" : 29 }
    { "_id" : "HF", "snum" : 25 }
    { "_id" : "BB", "snum" : 33 }
    { "_id" : "AH", "snum" : 37 }

上面的意思是根据籍贯分组并计算每个地方人数的年龄之和，类似于mysql中的

select addr,sum(age) from second_collection group by addr

再比如：根据籍贯求人员个数

> db.second_collection.aggregate([{$group:{_id:"$addr",scount:{$sum:1}}}])
    { "_id" : "WH", "scount" : 2 }
    { "_id" : "HF", "scount" : 2 }
    { "_id" : "BB", "scount" : 2 }
    { "_id" : "AH", "scount" : 2 }

### $avg ###

需求：根据籍贯分组对年龄求平均

> db.second_collection.aggregate([{$group:{_id:"$addr",avg:{$avg:"$age"}}}])
    { "_id" : "WH", "avg" : 14.5 }
    { "_id" : "HF", "avg" : 12.5 }
    { "_id" : "BB", "avg" : 16.5 }
    { "_id" : "AH", "avg" : 18.5 }

### $ min与$max ###

需求：按照籍贯分组求每个籍贯的最小与最大年龄

> db.second_collection.aggregate([{$group:{_id:"$addr",min:{$min:"$age"}}}])
    { "_id" : "WH", "min" : 14 }
    { "_id" : "HF", "min" : 12 }
    { "_id" : "BB", "min" : 16 }
    { "_id" : "AH", "min" : 18 }
    > db.second_collection.aggregate([{$group:{_id:"$addr",max:{$max:"$age"}}}])
    { "_id" : "WH", "max" : 15 }
    { "_id" : "HF", "max" : 13 }
    { "_id" : "BB", "max" : 17 }
    { "_id" : "AH", "max" : 19 }

### $match ###

需求：取年龄大于15且小于18的记录

> db.second_collection.aggregate([{ $match:{ age:{ $gt:15,$lte:18}}}])
    {  "_id" : ObjectId("5fa018db41c583a81e394189"), "name" : "qianqi", "age" : 16, "addr" : "BB" }
    {  "_id" : ObjectId("5fa018db41c583a81e39418a"), "name" : "zhangba", "age" : 17, "addr" : "BB" }
    {  "_id" : ObjectId("5fa018db41c583a81e39418b"), "name" : "Christy", "age" : 18, "addr" : "AH" }

再进一步，将上述结果按照城市分组并求个数

> db.second_collection.aggregate([{$match:{age:{$gt:15,$lte:18}}},{$group:{_id:"$addr",scount:{$sum:1}}}])
    { "_id" : "AH", "scount" : 1 }
    { "_id" : "BB", "scount" : 2 }

### $sort ###

需求：按照年龄倒序排序

> db.second_collection.aggregate([{$sort:{age:-1}}])
    { "_id" : ObjectId("5fa018db41c583a81e39418c"), "name" : "Tide", "age" : 19, "addr" : "AH" }
    { "_id" : ObjectId("5fa018db41c583a81e39418b"), "name" : "Christy", "age" : 18, "addr" : "AH" }
    { "_id" : ObjectId("5fa018db41c583a81e39418a"), "name" : "zhangba", "age" : 17, "addr" : "BB" }
    { "_id" : ObjectId("5fa018db41c583a81e394189"), "name" : "qianqi", "age" : 16, "addr" : "BB" }
    { "_id" : ObjectId("5fa018db41c583a81e394188"), "name" : "zhaoliu", "age" : 15, "addr" : "WH" }
    { "_id" : ObjectId("5fa018db41c583a81e394187"), "name" : "wangwu", "age" : 14, "addr" : "WH" }
    { "_id" : ObjectId("5fa018db41c583a81e394186"), "name" : "lisi", "age" : 13, "addr" : "HF" }
    { "_id" : ObjectId("5fa018db41c583a81e394185"), "name" : "zhangsan", "age" : 12, "addr" : "HF" }

### $limit ###

需求：取上述倒序的结果得前3条数据

> db.second_collection.aggregate([{$sort:{age:-1}},{$limit:3}])
    { "_id" : ObjectId("5fa018db41c583a81e39418c"), "name" : "Tide", "age" : 19, "addr" : "AH" }
    { "_id" : ObjectId("5fa018db41c583a81e39418b"), "name" : "Christy", "age" : 18, "addr" : "AH" }
    { "_id" : ObjectId("5fa018db41c583a81e39418a"), "name" : "zhangba", "age" : 17, "addr" : "BB" }

### $project ###

需求：将上面三条数据不显示“\_id”字段

> db.second_collection.aggregate([{$project:{_id:0,name:1,age:1,addr:1}},{$sort:{age:-1}},{$limit:3}])
    { "name" : "Tide", "age" : 19, "addr" : "AH" }
    { "name" : "Christy", "age" : 18, "addr" : "AH" }
    { "name" : "zhangba", "age" : 17, "addr" : "BB" }

### $lookup ###

这个命令是用于多个文档关联的，使用这个命令之前需要特别处理一下数据

> var document = [{
       "_id":1,"name":"male"},{
       "_id":2,"name":"female"}]
    > db.code_collection.insert(document)
    {
       "t":{
       "$date":"2020-11-03T19:14:24.471+08:00"},"s":"I",  "c":"STORAGE",  "id":20320,   "ctx":"conn3","msg":"createCollection","attr":{
       "namespace":"christy.code_collection","uuidDisposition":"generated","uuid":{
       "uuid":{
       "$uuid":"4daa6ed6-2b40-4755-945b-221c4eecb124"}},"options":{}}}
    {
       "t":{
       "$date":"2020-11-03T19:14:24.477+08:00"},"s":"I",  "c":"INDEX",    "id":20345,   "ctx":"conn3","msg":"Index build: done building","attr":{
       "buildUUID":null,"namespace":"christy.code_collection","index":"_id_","commitTimestamp":{
       "$timestamp":{
       "t":0,"i":0}}}}
    BulkWriteResult({
            "writeErrors" : [ ],
            "writeConcernErrors" : [ ],
            "nInserted" : 2,
            "nUpserted" : 0,
            "nMatched" : 0,
            "nModified" : 0,
            "nRemoved" : 0,
            "upserted" : [ ]
    })
    > db.code_collection.find()
    { "_id" : 1, "name" : "male" }
    { "_id" : 2, "name" : "female" }

新建一个代码集的集合，里面有两个类别`male`与`female`。另外，给我们的`second_collection`中的文档新增一个`sex`字段

> db.second_collection.update({},{$set:{sex:1}},{multi:1})
    WriteResult({ "nMatched" : 8, "nUpserted" : 0, "nModified" : 8 })
    > db.second_collection.find()
    { "_id" : ObjectId("5fa018db41c583a81e394185"), "name" : "zhangsan", "age" : 12, "addr" : "HF", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e394186"), "name" : "lisi", "age" : 13, "addr" : "HF", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e394187"), "name" : "wangwu", "age" : 14, "addr" : "WH", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e394188"), "name" : "zhaoliu", "age" : 15, "addr" : "WH", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e394189"), "name" : "qianqi", "age" : 16, "addr" : "BB", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e39418a"), "name" : "zhangba", "age" : 17, "addr" : "BB", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e39418b"), "name" : "Christy", "age" : 18, "addr" : "AH", "sex" : 1 }
    { "_id" : ObjectId("5fa018db41c583a81e39418c"), "name" : "Tide", "age" : 19, "addr" : "AH", "sex" : 1 }

需求：关联`second_collection a`与`code_collection b`，将a的”sex”与b的“\_id”相关联，在a中显示sex对应的name

> db.second_collection.aggregate([{$lookup:{
       from:"code_collection",localField:"sex",foreignField:"_id",as:"code_name"}},{$project:{
       "_id":0}}])
    { "name" : "zhangsan", "age" : 12, "addr" : "HF", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "lisi", "age" : 13, "addr" : "HF", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "wangwu", "age" : 14, "addr" : "WH", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "zhaoliu", "age" : 15, "addr" : "WH", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "qianqi", "age" : 16, "addr" : "BB", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "zhangba", "age" : 17, "addr" : "BB", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "Christy", "age" : 18, "addr" : "AH", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }
    { "name" : "Tide", "age" : 19, "addr" : "AH", "sex" : 1, "code_name" : [ { "_id" : 1, "name" : "male" } ] }

> from：需要关联的表【code\_collection】  
> localField: 【second\_collection】表需要关联的键。  
> foreignField：【code\_collection】的matching key 主键  
> as：对应的外键集合的数据，【因为是一对多的】