mongo聚合查询操作
Mongo的aggregate查询操作
如果你想做的就不仅仅是将数据从数据里面取出来，那么就需要使用聚合查询
一般聚合操作 由一连串的构件组成
主要有筛选（
关键字
-
- Mongo的aggregate查询操作
-
- $ match
- $ project
- $ group $sum $first $last $push $slice $cond
- $ sort
- $ limit $skip
- $ unwind
$ match
用于对文档数据进行筛选
db.temperature.aggregate([{$match:{}}])
//效果相当于
db.temperature.find({})
至于查询条件我就不介绍了 可以使用所有的常规操作符 举个例子
{ "_id" : ObjectId("5f1950202706fa2a11e6af75"), "project" : "5f192d51cb387a69229e28d0", "account" : "robot_qkjx8034", "robot" : "ZZPR1120B000SZZH010720200707", "face" : "5f19501edf138e4a43e5eaa9", "person" : { "type" : "stranger", "id" : "unknown", "name" : "unknown", "idcard" : "" }, "status" : "normal", "temperature" : 34.9, "description" : "1", "createtime" : 1595494429 }
{ "_id" : ObjectId("5f1950ae2706fa2a11e6af76"), "project" : "5f192d51cb387a69229e28d0", "account" : "robot_qkjx8034", "robot" : "ZZPR1120B000SZZH010720200707", "face" : "5f1950ac24518c4a444638a2", "person" : { "type" : "stranger", "id" : "unknown", "name" : "unknown", "idcard" : "" }, "status" : "normal", "temperature" : 35, "description" : "1", "createtime" : 1595494570 }
{ "_id" : ObjectId("5f1951312706fa2a11e6af77"), "project" : "5f192d51cb387a69229e28d0", "account" : "robot_qkjx8034", "robot" : "ZZPR1120B000SZZH010720200707", "face" : "5f195130d8faef4a4ab8021d", "person" : { "type" : "stranger", "id" : "unknown", "name" : "unknown", "idcard" : "" }, "status" : "normal", "temperature" : 34.8, "description" : "1", "createtime" : 1595494702 }
{ "_id" : ObjectId("5f1951392706fa2a11e6af78"), "project" : "5f192d51cb387a69229e28d0", "account" : "robot_qkjx8034", "robot" : "ZZPR1120B000SZZH010720200707", "face" : "5f1951383cecd84a60c25e7c", "person" : { "type" : "stranger", "id" : "unknown", "name" : "unknown", "idcard" : "" }, "status" : "normal", "temperature" : 34.8, "description" : "1", "createtime" : 1595494710 }
{ "_id" : ObjectId("5f19535a2706fa2a11e6af79"), "project" : "5f192d51cb387a69229e28d0", "account" : "robot_qkjx8034", "robot" : "ZZPR1120B000SZZH010720200707", "face" : "5f195357df138e4a43e5eaaa", "person" : { "type" : "stranger", "id" : "unknown", "name" : "unknown", "idcard" : "" }, "status" : "normal", "temperature" : 34.9, "description" : "1", "createtime" : 1595495254 }
我要查询 createtime 字段为 1595495254的数据
db.temperature.find({createtime:1595495254})
db.temperature.aggregate([{$match:{createtime:1595495254}}])
效果是一样的
db.temperature.aggregate([{$addFields:{id:{$toString:'$_id'}}},{$match:{}},{$project:{_id:0}}])
将objectid变id(很实用)
$ project
用于从$match 筛选结果中提取字段
例如 只想取用结果中的 project 和 createtime
db.temperature.aggregate([{$match:{createtime:1595495254}},{$project:{project:1,createtime:1,_id:0}}])
注明 字段名：0代表过滤 ：1 代表 选中的字段
改字段名操作
db.temperature.aggregate([{$match:{createtime:1595495254}},{$project:{project_xxx:'$project',createtime:1,_id:0}}])
还可以使用一些其他字符串表达式，下面列举一些我平时用过的一些表达式（手动狗头）
$ add :[…]
表示将这些字段相加 注明字段要加上$
db.xxx.aggregate([{$match:{}},{$project:{totol:{$add:[$fields1,$fields]}}}])
//表示将 fields1 ，fields2 两个字段值相加 得到结果赋值给 totol 再返回
$substract:[expr1,expr2]
表示用第一个字段减去第二个字段 作为结果
$multiply:[expr1,expr2]…
表示多个字段相乘 作为结果
以上操作前提是字段类型必须是数值
$ group $sum $first $last $push $slice $cond
单个字段分组
直接 _id:$字段名
多个字段分组
_id:{'project':'$project',createtime:'$createtime'}
以project 和 createtime 分组
分组后想加入字段 操作如下
db.temperature.aggregate([{$match:{createtime:1595495254}},{$group:{_id:{'project':'$project',createtime:'$createtime'},account:{$first:'$account'}}}])
$first 表示每个分组第一个数据的信息，可以取用里面的值
上面查询语句的意思就是 每组信息返回第一个数据的account字段
$last 表示最后一个数据
{ "_id" : "KWPR1120A000SZZH011119200043", "account" : "zhaccd1026", "area" : "", "begin" : 1583078400, "description" : "", "end" : 1901203200, "group" : "", "mfrs" : "ciot", "model" : "PR1120A", "name" : "智慧广场2F-43", "projectid" : "5e0b17c8fd79d3137850ee3a", "type" : "PR", "version" : "V2.0.18_beta", "x" : 0, "y" : 0, "alias" : "", "user" : "robot_zhaccd1026", "status" : "activate" }
{ "_id" : "KWPR1120A000SZZH011119200044", "account" : "szkszn9904", "area" : "5e62040bfac083309479986c", "begin" : 1583482631, "description" : "", "end" : 1930406400, "group" : "", "mfrs" : "ciot", "model" : "PR1120A", "name" : "深圳科松智 能-1.4-44号机", "projectid" : "5e62040b42066924c4e362bc", "type" : "PR", "version" : "V2.0.18_beta", "x" : 0, "y" : 0, "alias" : "", "user" : "robot_szkszn9904", "status" : "activate" }
{ "_id" : "KWPR1120A000SZZH280419200001", "account" : "lhkss3363", "alias" : "", "area" : "", "begin" : 1583337600, "description" : "", "end" : 1901203200, "group" : "", "mfrs" : "ciot", "model" : "PR1120A", "name" : "罗湖看守所", "projectid" : "5e5e4a9b42066924c4e362a2", "type" : "PR", "version" : "V2.0.18_beta", "x" : 0, "y" : 0, "user" : "robot_lhkss3363", "status" : "activate" }
{ "_id" : "KWPR1120A000SZZH280419200002", "account" : "shnx6035", "alias" : "", "area" : "", "begin" : 1589246218, "description" : "", "end" : 1903795200, "group" : "", "mfrs" : "ciot", "model" : "PR1120A", "name" : "上海南晓-002号机", "projectid" : "5e623f0a42066924c4e362c0", "type" : "gate", "version" : "2005072", "x" : 0, "y" : 0, "user" : "robot_shnx6035", "status" : "activate" }
{ "_id" : "KWPR1120A000SZZH280419200003", "account" : "yfcs6761", "alias" : "", "area" : "", "begin" : 1589539509, "description" : "", "end" : 1779984000, "group" : "", "mfrs" : "ciot", "model" : "PR1120A", "name" : "new", "projectid" : "5e5ce3d942066924c4e3629a", "type" : "gate", "version" : "V2.0.18_2003311_beta", "x" : 0, "y" : 0, "user" : "robot_yfcs6761", "status" : "activate" }
{ "_id" : "KWPR1120A000SZZH280419200007", "account" : "zhaccd1026", "alias" : "", "area" : "5e0b17c8cdfc6a13a4a7f7a5", "begin" : 1577955.902, "description" : "", "end" : 2182659555, "group" : "", "mfrs" : "ciot", "model" : "PR1120A", "name" : "智能访客机器人007-公司7楼", "projectid" : "5e0b17c8fd79d3137850ee3a", "type" : "gate", "version" : "V2.0.3_beta", "x" : 0, "y" : 0, "user" : "robot_zhaccd1026", "status" : "activate" }
db.robot.aggregate([{$group:{_id:'$account',arrs:{$push:'$_id'}}}])
{ "_id" : "azyhyy8017", "arrs" : [ "ZZPR1120BOOOSZZH010420200220" ] }
{ "_id" : "jbfp1351", "arrs" : [ "ZZPR1120B000SZZH010720200704" ] }
{ "_id" : "kppu0583", "arrs" : [ "ZZPR1120B000SZZH010720200699", "ZZPR1120B000SZZH010720200700" ] }
{ "_id" : "apwc1927", "arrs" : [ "ZZPR1120B000SZZH010720200686" ] }
{ "_id" : "vygw3264", "arrs" : [ "ZZPR1120B000SZZH010720200672", "ZZPR1120B000SZZH010720200673" ] }
{ "_id" : "hrwj6457", "arrs" : [ "ZZPR1120B000SZZH010620200669" ] }
{ "_id" : "buec6187", "arrs" : [ "ZZPR1120B000SZZH010620200662" ] }
{ "_id" : "ftobafwyxgs5049", "arrs" : [ "ZZPR1120B000SZZH010620200616" ] }
{ "_id" : "lyshdl8986", "arrs" : [ "ZZPR1120B000SZZH010620200610" ] }
{ "_id" : "sm73hysshkj0345", "arrs" : [ "ZZPR1120B000SZZH010620200607" ] }
{ "_id" : "adgydxwsxomzb3129", "arrs" : [ "ZZPR1120B000SZZH010620200606" ] }
{ "_id" : "adsdermyy4889", "arrs" : [ "ZZPR1120B000SZZH010620200604", "ZZPR1120B000SZZH010620200605" ] }
{ "_id" : "yuxk6186", "arrs" : [ "ZZPR1120B000SZZH010620200601" ] }
{ "_id" : "gjqxzx2998", "arrs" : [ "ZZPR1120B000SZZH010620200598" ] }
{ "_id" : "xyyxtzfx7564", "arrs" : [ "ZZPR1120B000SZZH010620200597" ] }
{ "_id" : "jzjqr4976", "arrs" : [ "ZZPR1120B000SZZH010620200591" ] }
{ "_id" : "uuof9002", "arrs" : [ "ZZPR1120B000SZZH010720200691", "ZZPR1120B000SZZH010720200693" ] }
{ "_id" : "nqhm6353", "arrs" : [ "ZZPR1120B000SZZH010620200590" ] }
{ "_id" : "lystytdfylbm9564", "arrs" : [ "ZZPR1120B000SZZH010620200583" ] }
{ "_id" : "szgzjtkjyxgs1110", "arrs" : [ "ZZPR1120B000SZZH010620200579" ] }
$push : expr
表示 不管expr 是什么值 都将它加入到数组中
上面查询表示 将每组中的主键id 都存到arrs 再返回
$slice 对数据中的数组做切割处理
db.render_queue.aggregate([
{
$match: {
template_user_id: { $in: [85,86,84] },
type:'render'
}
},
{ $group: { _id: '$type', data: { $push:'$$CURRENT' } } },//$$CURRENT 表示将数据全部字段
{$project:{
print:{
$slice:['$data',1,2] // 表示skip 1 后取2条 ['$data',2] 只传一个参数时 表示 skip 0 取两条
}
}}
])
$cond 判断用的 ，可以跟if then else 语句 也是配合 project 使用
计算每组的数据的总量值
$sum 操作符
db.temperature.aggregate([{$match:{createtime:1595495254}},{$group:{_id:{'project':'$project',createtime:'$createtime'},sum:{$sum:1}}}])
$sum: value
value 为 1代表统计数据量 value 为某个数值字段时 表示每组中该字段的总和
$ sort
排序
{$sort:{createtime:1}}
1 为升序 -1 降序
$ limit $skip
接受值都为数值
db.robot.aggregate([{$group:{_id:'$account',arrs:{$push:'$_id'}}},{$skip:1},{$limit:1}])
{ "_id" : "jbfp1351", "arrs" : [ "ZZPR1120B000SZZH010720200704" ] }
总结聚合查询一般按 match project sort group skip limit 顺序 操作
$ unwind
unwind一般用于数组查询
例：对于数组中的字段要进行模糊查询
{ "_id" : ObjectId("5fa92299c0d5068284408769"), "datas" : [ "你好", "好呀" ], "answer" : "good" }
{ "_id" : ObjectId("5fa922b3c0d506828440876a"), "datas" : [ "好么", "才怪" ], "answer" : "good" }
{ "_id" : ObjectId("5fa934f662dbd3acc310ae70"), "datas" : [ "不好" ], "answer" : "bad" }
第一步先使用unwind将数据分组
第二步使用match 进行条件过滤
第三步使用聚合将结果总结
（如果有不懂 或者mongo 聚合查询语句写不出来的 可以私信喔，我看到了就会回复的）
最后 希望能给关注 鼓励一下我这个crud 男孩 哈哈
最后来个黑小虎镇楼
