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MongoDB
[
{ "_id": 1, "name": "John", "age": 25 },
{ "_id": 2, "name": "Jane", "age": 30 },
{ "_id": 3, "name": "Bob", "age": 25 },
{ "_id": 4, "name": "Alice", "age": 35 }
]
To count the number of documents in the “users” collection, you can simply execute the count() command without any parameters:
db.users.count()
The output will be:
4
Now, let’s say you want to count the number of users who are 25 years old. You can pass a query object to the count() command to filter the documents based on certain criteria:
db.users.count({ age: 25 })
The output will be:
2
In this case, the count() command returns the count of documents where the “age” field is equal to 25.
Note that starting from MongoDB version 4.0, the count() command has been deprecated in favor of the countDocuments() method. It provides similar functionality but is more versatile and offers additional options.
[
{ "_id": 1, "name": "John", "age": 25 },
{ "_id": 2, "name": "Jane", "age": 30 },
{ "_id": 3, "name": "Bob", "age": 25 },
{ "_id": 4, "name": "Alice", "age": 35 }
]
To retrieve the distinct values of the “age” field from the “users” collection, you can use the distinct() command as follows:
db.users.distinct("age")
The output will be an array of distinct values:
[25, 30, 35]
In this example, the distinct() command returns the unique values present in the “age” field of the “users” collection.
You can also specify a query object to filter the documents before retrieving the distinct values. For instance, to get the distinct names of users who are older than 25:
db.users.distinct("name", { age: { $gt: 25 } })
The output will be:
["Jane", "Alice"]
In this case, the distinct() command returns the distinct names of users whose “age” field is greater than 25.
The distinct() command can be helpful when you want to obtain unique values for a particular field in your collection, which can be useful for analysis or data exploration purposes.
[
{ "_id": 1, "product": "A", "quantity": 5, "price": 10 },
{ "_id": 2, "product": "B", "quantity": 3, "price": 15 },
{ "_id": 3, "product": "A", "quantity": 2, "price": 12 },
{ "_id": 4, "product": "C", "quantity": 7, "price": 8 },
{ "_id": 5, "product": "B", "quantity": 4, "price": 13 }
]
To calculate the total quantity and revenue for each product, you can use the $group stage as follows:
db.sales.aggregate([
{
$group: {
_id: "$product",
totalQuantity: { $sum: "$quantity" },
totalRevenue: { $sum: { $multiply: ["$quantity", "$price"] } }
}
}
])
The output will be:
[
{ "_id": "A", "totalQuantity": 7, "totalRevenue": 74 },
{ "_id": "B", "totalQuantity": 7, "totalRevenue": 107 },
{ "_id": "C", "totalQuantity": 7, "totalRevenue": 56 }
]
In this example, the $group stage groups the documents based on the “product” field and calculates the total quantity and revenue for each product using the $sum and $multiply aggregation operators.
The aggregation framework provides a wide range of stages and operators that allow you to perform complex transformations, aggregations, and analyses on your data. It is a powerful tool for data manipulation and analysis in MongoDB.
[
{ "_id": 1, "product": "A", "quantity": 5, "price": 10 },
{ "_id": 2, "product": "B", "quantity": 3, "price": 15 },
{ "_id": 3, "product": "A", "quantity": 2, "price": 12 },
{ "_id": 4, "product": "C", "quantity": 7, "price": 8 },
{ "_id": 5, "product": "B", "quantity": 4, "price": 13 }
]
Let’s use mapReduce() to calculate the total revenue for each product:
var mapFunction = function() {
emit(this.product, this.quantity * this.price);
};
var reduceFunction = function(key, values) {
return Array.sum(values);
};
db.sales.mapReduce(
mapFunction,
reduceFunction,
{ out: "product_revenue" }
)
In this example, the mapFunction calculates the revenue for each document by multiplying the “quantity” and “price” fields and emits the product as the key. The reduceFunction receives the key (product) and the array of revenue values for that product and sums them using Array.sum().
The result is stored in a new collection called “product_revenue”. To retrieve the result, you can query the “product_revenue” collection:
db.product_revenue.find()
The output will be:
[
{ "_id": "A", "value": 74 },
{ "_id": "B", "value": 107 },
{ "_id": "C", "value": 56 }
]
MapReduce is a powerful tool for complex data processing and analysis in MongoDB. However, it is important to note that the mapReduce() command has some limitations, such as slower performance compared to the aggregation framework and requiring JavaScript execution. Therefore, for most scenarios, it is recommended to use the aggregation framework, which provides a more efficient and flexible way to perform data processing and analysis in MongoDB.
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