MongoDB查询总结

2017-10-25 本文已影响109人流水不腐小夏

MongoDB查询总结

MongoDB查询总结

介绍

前面写过一篇关于Mongo�db的例子——浅谈MongoDB数据库，当时使用的只是简单的查询，然后后面业务变的有点复杂，原先没有仔细研究过Mongodb的查询，以为就是简单调用下find就可以了，乃衣服。

所以今天特地举例说明一下Mongo中查询问题。

Mongo查询可以�分为2种：

普通查询，类似于Sql中的 select where
聚合查询，类似于Sql中的 group by

普通查询

首先放一下官方文档，普通查询主要用到db.collection.find()函数。

定义下示例数据库，下面是是初始化数据，可以在Mongo中的控制台�执行。

db.inventory.insertMany([
   { item: "journal", qty: 25, size: { h: 14, w: 21, uom: "cm" }, status: "A" },
   { item: "notebook", qty: 50, size: { h: 8.5, w: 11, uom: "in" }, status: "A" },
   { item: "paper", qty: 100, size: { h: 8.5, w: 11, uom: "in" }, status: "D" },
   { item: "planner", qty: 75, size: { h: 22.85, w: 30, uom: "cm" }, status: "D" },
   { item: "postcard", qty: 45, size: { h: 10, w: 15.25, uom: "cm" }, status: "A" }
]);

查询所有

db.inventory.find( {} )

映射Sql语句

SELECT * FROM inventory

条件查询

语法格式

{ <field1>: <value1>, ... }

比如查询status为D记录。

db.inventory.find( { status: "D" } )

映射Sql语句

SELECT * FROM inventory WHERE status = "D"

使用操作符进行条件查询

语法格式

{ <field1>: { <operator1>: <value1> }, ... }

比如查询满足status是�数组[A,D]中的记录

db.inventory.find( { status: { $in: [ "A", "D" ] } } )

映射Sql语句

SELECT * FROM inventory WHERE status in ("A", "D")

AND 条件查询

直接在find函数指定多个字段满足即可，这样就是 and 条件。

比如下面语句就是 status 为 A，qty 小于 30。

db.inventory.find( { status: "A", qty: { $lt: 30 } } )

映射Sql语句

SELECT * FROM inventory WHERE status = "A" AND qty < 30

OR 条件查询

�OR 和 AND 就不一样了，需要用到操作符 $or，如下所示。

db.inventory.find( { $or: [ { status: "A" }, { qty: { $lt: 30 } } ] } )

类似于SQL中的

SELECT * FROM inventory WHERE status = "A" OR qty < 30

OR 和 AND 集合一起

db.inventory.find( {
     status: "A",
     $or: [ { qty: { $lt: 30 } }, { item: /^p/ } ]
} )

表示这样的意思。

SELECT * FROM inventory WHERE status = "A" AND ( qty < 30 OR item LIKE "p%")

查询举例

查询全部

SELECT *
FROM people

db.people.find()

指定字段

SELECT id,
       user_id,
       status
FROM people

db.people.find(
    { },
    { user_id: 1, status: 1 }
)

SELECT user_id, status
FROM people

指定字段，不显示_id

db.people.find(
    { },
    { user_id: 1, status: 1, _id: 0 }
)

条件查询全部

SELECT *
FROM people
WHERE status = "A"

db.people.find(
    { status: "A" }
)

条件查询�指定字段

SELECT user_id, status
FROM people
WHERE status = "A"

db.people.find(
    { status: "A" },
    { user_id: 1, status: 1, _id: 0 }
)

条件查询不等于

SELECT *
FROM people
WHERE status != "A"

db.people.find(
    { status: { $ne: "A" } }
)

条件查询 AND

SELECT *
FROM people
WHERE status = "A"
AND age = 50

db.people.find(
    { status: "A",
      age: 50 }
)

条件查询 OR

SELECT *
FROM people
WHERE status = "A"
OR age = 50

db.people.find(
    { $or: [ { status: "A" } ,
             { age: 50 } ] }
)

条件查询 �>

SELECT *
FROM people
WHERE age > 25

db.people.find(
    { age: { $gt: 25 } }
)

条件查询 �<

SELECT *
FROM people
WHERE age < 25

db.people.find(
   { age: { $lt: 25 } }
)

�复杂的条件查询

SELECT *
FROM people
WHERE age > 25
AND   age <= 50

db.people.find(
   { age: { $gt: 25, $lte: 50 } }
)

条件查询 �LIKE

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/ } } )

排序

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 } )

统计数量

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()

去除重复distinct

SELECT DISTINCT(status)
FROM people

db.people.distinct( "status" )

SELECT *
FROM people
LIMIT 1

限制数量

db.people.findOne()

// or

db.people.find().limit(1)

SELECT *
FROM people
LIMIT 5
SKIP 10

db.people.find().limit(5).skip(10)

EXPLAIN

EXPLAIN SELECT *
FROM people
WHERE status = "A"

db.people.find( { status: "A" } ).explain()

聚合查询

上面�普通查询使用find函数即可，但是聚合查询使用另外一个函数aggregate，这里是官方文档。

初始化数据如下，有2个表 orders 和 order_lineitem �，外键关联order_lineitem.order_id and the orders.id。

{
  cust_id: "abc123",
  ord_date: ISODate("2012-11-02T17:04:11.102Z"),
  status: 'A',
  price: 50,
  items: [ { sku: "xxx", qty: 25, price: 1 },
           { sku: "yyy", qty: 25, price: 1 } ]
}

统计数量

db.orders.aggregate( [
   {
     $group: {
        _id: null,
        count: { $sum: 1 }
     }
   }
] )

映射Sql语句

SELECT COUNT(*) AS count
FROM orders

计算总和

db.orders.aggregate( [
   {
     $group: {
        _id: null,
        total: { $sum: "$price" }
     }
   }
] )

映射Sql语句

SELECT SUM(price) AS total
FROM orders

分组计算总和

db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   }
] )

映射Sql语句

SELECT cust_id,
       SUM(price) AS total
FROM orders
GROUP BY cust_id

分组计算总和并排序

db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   },
   { $sort: { total: 1 } }
] )

映射Sql语句

SELECT cust_id,
       SUM(price) AS total
FROM orders
GROUP BY cust_id
ORDER BY tota

多个字段分组

db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: {
               month: { $month: "$ord_date" },
               day: { $dayOfMonth: "$ord_date" },
               year: { $year: "$ord_date"}
           }
        },
        total: { $sum: "$price" }
     }
   }
] )

映射Sql语句

SELECT cust_id,
       ord_date,
       SUM(price) AS total
FROM orders
GROUP BY cust_id,
         ord_date

条件分组——HAVING

db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        count: { $sum: 1 }
     }
   },
   { $match: { count: { $gt: 1 } } }
] )

映射Sql语句

SELECT cust_id,
       count(*)
FROM orders
GROUP BY cust_id
HAVING count(*) > 1

复杂条件分组统计

db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: {
               month: { $month: "$ord_date" },
               day: { $dayOfMonth: "$ord_date" },
               year: { $year: "$ord_date"}
           }
        },
        total: { $sum: "$price" }
     }
   },
   { $match: { total: { $gt: 250 } } }
] )

映射Sql语句

SELECT cust_id,
       ord_date,
       SUM(price) AS total
FROM orders
GROUP BY cust_id,
         ord_date
HAVING total > 250

复杂条件分组统计示例1

db.orders.aggregate( [
   { $match: { status: 'A' } },
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   }
] )

映射Sql语句

SELECT cust_id,
       SUM(price) as total
FROM orders
WHERE status = 'A'
GROUP BY cust_id

复杂条件分组统计示例2

db.orders.aggregate( [
   { $match: { status: 'A' } },
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   },
   { $match: { total: { $gt: 250 } } }
] )

映射Sql语句

SELECT cust_id,
       SUM(price) as total
FROM orders
WHERE status = 'A'
GROUP BY cust_id
HAVING total > 250

表关联

db.orders.aggregate( [
   { $unwind: "$items" },
   {
     $group: {
        _id: "$cust_id",
        qty: { $sum: "$items.qty" }
     }
   }
] )

映射Sql语句

SELECT cust_id,
       SUM(li.qty) as qty
FROM orders o,
     order_lineitem li
WHERE li.order_id = o.id
GROUP BY cust_id

嵌套查询

db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: {
               month: { $month: "$ord_date" },
               day: { $dayOfMonth: "$ord_date" },
               year: { $year: "$ord_date"}
           }
        }
     }
   },
   {
     $group: {
        _id: null,
        count: { $sum: 1 }
     }
   }
] )

映射Sql语句

SELECT COUNT(*)
FROM (SELECT cust_id,
             ord_date
      FROM orders
      GROUP BY cust_id,
               ord_date)
      as DerivedTable

Map-Reduce

Mongo中聚合查询还有一种叫Map-Reduce，官方文档在这里，在思想上它跟Hadoop一样，从一个单一集合中输入数据，然后将结果输出到一个集合中。通常在使用类似SQL中Group By操作时，Map/Reduce会是一个好的工具。

Map-Reduce

接口方法定义

db.collection.mapReduce(
    <map>,
    <reduce>,
    {
        out: <collection>,
        query: <document>,
        sort: <document>,
        limit: <number>,
        finalize: <function>,
        scope: <document>,
        jsMode: <boolean>,
        verbose: <boolean>,
        bypassDocumentValidation: <boolean>
    }
)

参数说明

mapReduce：要执行Map/Reduce集合的名字
map: map 函数 (下面会详细介绍）
reduce: reduce函数(下面会详细介绍）
out: 存放结果的集合 (下面会详细介绍）
query: 设置查询条件 <可选>
sort: 按某个键来排序 <可选>
limit: 指明从集合检索文档个数的最大值 <可选>
finalize: 对reduce结果做进一步处理 <可选>
scope: 指明通过map/reduce/finalize可以访问到的变量 <可选>
jsMode: 指明Map/Reduce执行过程中文档保持JSON状态 <可选>
verbose: 提供关于任务执行的统计数据 <可选>

示例说明

�举例说明Map-Reduce的用途，�虽然代码比较多，也行用上面的聚合查询，一下子就搞定了，但是这里只是举例。

比如有个订单表，如下所示，我们需要计算每个人的订单总价。

{
     _id: ObjectId("50a8240b927d5d8b5891743c"),
     cust_id: "abc123",
     ord_date: new Date("Oct 04, 2012"),
     status: 'A',
     price: 25,
     items: [ { sku: "mmm", qty: 5, price: 2.5 },
              { sku: "nnn", qty: 5, price: 2.5 } ]
}

首先定义Map方法，就说我们后面的聚合计算需要哪些字段，�由于需要计算每个人的订单总结，那么个人信息和加个肯定是我们需要的。

var mapFunction1 = function() {
    emit(this.cust_id, this.price);
};

然后定义reduce方法，计算每个人的订单价格。

var reduceFunction1 = function(keyCustId, valuesPrices) {
    return Array.sum(valuesPrices);
};

然后存储最后的计算结果。

db.orders.mapReduce(
    mapFunction1,
    reduceFunction1,
    { out: "map_reduce_example" }
)

这样一个简单的Map-Reduce实例就完成了，结果放在map_reduce_example中。

上面示例比较简单，那么我们来一个复杂一点的例子。

一条订单记录中，有sdk的名称、数量、价格，那么要查询出日期大于01/01/2012，所有订单的总数，以及�平均sdk价格。

首先还是定义个map函数。

var mapFunction2 = function() {
    for (var idx = 0; idx < this.items.length; idx++) {
        var key = this.items[idx].sku;
        var value = {
                        count: 1,
                        qty: this.items[idx].qty
                    };
        emit(key, value);
    }
};

然后算出sku的数量，和总价格。

var reduceFunction2 = function(keySKU, countObjVals) {
    reducedVal = { count: 0, qty: 0 };

    for (var idx = 0; idx < countObjVals.length; idx++) {
        reducedVal.count += countObjVals[idx].count;
        reducedVal.qty += countObjVals[idx].qty;
    }

    return reducedVal;
};

总价格出来后，还要计算出平均价格。

var finalizeFunction2 = function (key, reducedVal) {
    reducedVal.avg = reducedVal.qty / reducedVal.count;
    return reducedVal;
};

还有日期的条件过滤，最后得出完整的map-reduce。

db.orders.mapReduce(
    mapFunction2,
    reduceFunction2,
    {
        out: { merge: "map_reduce_example" },
        query: {
            ord_date:{ $gt: new Date('01/01/2012') }
        },
        finalize: finalizeFunction2
    }
)

总结

以上就是我对MongoDB的示例总结，本人是一个初学者，也有很多地方不懂，如果有错误的地方，欢迎指出。

MongoDB查询总结

MongoDB查询总结

介绍

普通查询

查询举例

聚合查询

Map-Reduce

接口方法定义

参数说明

示例说明

总结

相关资料

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