db.tbdailydate.aggregate([ {$match:{"activity_no":"3VSWM91OVOZS6LP0Z9YWE11O","create_time":{$gte:new Date("2023-05-01T00:00:00.000Z")}}, {$group:{_id:"$create_time",count:{$sum:"$use_num"}}} ])转成Aggregation类方式

Sure, 以下是将该聚合查询转化为Aggregation类方式的代码：

from datetime import datetime

pipeline = [
    {
        '$match': {
            'activity_no': '3VSWM91OVOZS6LP0Z9YWE11O',
            'create_time': {'$gte': datetime(2023, 5, 1)}
        }
    },
    {
        '$group': {
            '_id': '$create_time',
            'count': {'$sum': '$use_num'}
        }
    }
]

result = db.tbdailydate.aggregate(pipeline)

请注意，需要在代码中导入datetime模块来创建日期对象并将其传递给聚合管道。还需要将`new Date()`转换为Python datetime对象。

相关问题

db.getCollection("tbdailydate").aggregate([ {$match:{'activity_no':'3VSWM91OVOZS6LP0Z9YWE11O',"create_time":{$gte:new Date("2023-05-12T00:00:00.000Z"),$lte:new Date("2023-05-21T23:59:59.000Z")}}}, {$group :{_id:"$create_time", usenum : {$sum : { $cond: [ {$and: [ {$gte: ["$create_time", new Date("2023-05-12T00:00:00.000Z")]}, {$lte: ["$create_time", new Date("2023-05-12T23:59:59.000Z")]}, ]}, "$use_num", 0 ] } }, weekend : { $sum : { $cond: [ {$and: [ {$gte: ["$create_time", new Date("2023-05-19T00:00:00.000Z")]}, {$lte: ["$create_time", new Date("2023-05-21T23:59:59.000Z")]}, ]}, "$use_num", 0 ] } } }} ])转成java格式的Aggregation方式

Sure! Here's the equivalent Java code for the given MongoDB aggregation pipeline:

import org.bson.Document;
import com.mongodb.client.AggregateIterable;
import com.mongodb.client.MongoCollection;
import static com.mongodb.client.model.Aggregates.*;
import static com.mongodb.client.model.Filters.*;
import static com.mongodb.client.model.Accumulators.*;

// Assuming you have a MongoClient instance and have connected to the database

// Get the collection
MongoCollection<Document> collection = database.getCollection("tbdailydate");

// Create the pipeline
List<Document> pipeline = Arrays.asList(
    match(and(eq("activity_no", "3VSWM91OVOZS6LP0Z9YWE11O"), 
              gte("create_time", new Date("2023-05-12T00:00:00.000Z")), 
              lte("create_time", new Date("2023-05-21T23:59:59.000Z")))),
    group("$create_time",
        sum("usenum",
            cond(and(gte("create_time", new Date("2023-05-12T00:00:00.000Z")), 
                      lte("create_time", new Date("2023-05-12T23:59:59.000Z")))),
                "$use_num", 0)),
        sum("weekend",
            cond(and(gte("create_time", new Date("2023-05-19T00:00:00.000Z")), 
                      lte("create_time", new Date("2023-05-21T23:59:59.000Z")))),
                "$use_num", 0))
    );

// Execute the aggregation
AggregateIterable<Document> result = collection.aggregate(pipeline);

Please make sure to import the necessary MongoDB Java driver classes and adjust the code according to your specific setup.

给出下面MongoDB查询表达式的等价SQL语句 db. stud.aggregate([ {$match:{ssex: "女"}}, {$group:{_id:"$sdept",count:{$sum:1},asage:{$avg:"$sage"}}}, {$project : {_id: 0,院系: "$_id",人数: "$count",平均年龄: "$asage"}} ]);

下面是MongoDB查询表达式的等价SQL语句：

SELECT sdept AS 院系, COUNT(*) AS 人数, AVG(sage) AS 平均年龄
FROM stud
WHERE ssex = '女'
GROUP BY sdept

其中，`db.stud.aggregate([ {$match:{ssex: "女"}}, {$group:{_id:"$sdept",count:{$sum:1},asage:{$avg:"$sage"}}}, {$project : {_id: 0,院系: "$_id",人数: "$count",平均年龄: "$asage"}} ])` 表示在 `stud` 集合中查询 `ssex` 字段为 "女" 的文档，对其按照 `sdept` 字段进行分组，并对每一组进行统计计算，最后输出 `院系`、`人数`、`平均年龄` 三个字段的值，而相应的 SQL 查询语句则是上面的语句。