Today’s new knowledge #2
kishor sutradhar
Posted on November 11, 2024
Today's Overview:
Hello again, everyone! Yesterday, I learned the basics of MongoDB CRUD operations and some field filtering operators. Today, I didn’t do as much since I watched a few episodes of Blue Lock season 2. After that, I decided to update my resume, so I started working on it in Figma. If anyone has suggestions or tips for improving my resume, I’d love to hear them!
I still wanted to learn something new today. here’s what I covered. the first thing i learned is mongodb aggregation framework.
Mongodb Aggregation Framework
The aggregation framework operates on collections of documents and processes them through stages in a pipeline, with each stage applying a specific transformation. you have to use *db.collection.aggregate() *
You can use db.collection.find() for simple queries and filtering, and db.collection.aggregate() for complex queries and filtering. Here are some of the most used aggregate operators.
Mongodb Aggregation operators
$match, $project, $addFields, $out, $merge
Example:
Imagine we have a sales collection with the following documents:
[
{ "_id": 1, "product": "laptop", "category": "electronics", "quantity": 2, "price": 800, "storeLocation": "New York", "date": "2024-11-01" },
{ "_id": 2, "product": "smartphone", "category": "electronics", "quantity": 5, "price": 500, "storeLocation": "Los Angeles", "date": "2024-11-02" },
{ "_id": 3, "product": "laptop", "category": "electronics", "quantity": 1, "price": 800, "storeLocation": "Chicago", "date": "2024-11-03" },
{ "_id": 4, "product": "desk", "category": "furniture", "quantity": 3, "price": 150, "storeLocation": "New York", "date": "2024-11-04" },
{ "_id": 5, "product": "chair", "category": "furniture", "quantity": 10, "price": 75, "storeLocation": "Los Angeles", "date": "2024-11-05" }
]
$match: Filters documents based on specified criteria (similar to the find operation)
db.sales.aggregate([
{ $match: { category: "electronics" } }
])
//output
[
{ "_id": 1, "product": "laptop", "category": "electronics", "quantity": 2, "price": 800, "storeLocation": "New York", "date": "2024-11-01" },
{ "_id": 2, "product": "smartphone", "category": "electronics", "quantity": 5, "price": 500, "storeLocation": "Los Angeles", "date": "2024-11-02" },
{ "_id": 3, "product": "laptop", "category": "electronics", "quantity": 1, "price": 800, "storeLocation": "Chicago", "date": "2024-11-03" }
]
The $project stage allows you to control which fields to include or exclude.
db.sales.aggregate([
{
$project: { product: 1, quantity: 1, price: 1,}}
}
])
/// output
[
{ "_id": 1, "product": "laptop", "quantity": 2, "price": 800, },
{ "_id": 2, "product": "smartphone", "quantity": 5, "price": 500, },
{ "_id": 3, "product": "laptop", "quantity": 1, "price": 800, },
{ "_id": 4, "product": "desk", "quantity": 3, "price": 150, },
{ "_id": 5, "product": "chair", "quantity": 10, "price": 75, }
]
We can use $addFields to add a new field to the document. The special thing is that $addFields does not modify the original document.
db.sales.aggregate([
{
$addFields: {
totalPrice: { $multiply: ["$price", "$quantity"] }
}
}
])
//output
[
{ "_id": 1, "product": "laptop", "category": "electronics", "quantity": 2, "price": 800, "storeLocation": "New York", "date": "2024-11-01", "totalPrice": 1600 },
{ "_id": 2, "product": "smartphone", "category": "electronics", "quantity": 5, "price": 500, "storeLocation": "Los Angeles", "date": "2024-11-02", "totalPrice": 2500 },
{ "_id": 3, "product": "laptop", "category": "electronics", "quantity": 1, "price": 800, "storeLocation": "Chicago", "date": "2024-11-03", "totalPrice": 800 },
{ "_id": 4, "product": "desk", "category": "furniture", "quantity": 3, "price": 150, "storeLocation": "New York", "date": "2024-11-04", "totalPrice": 450 },
{ "_id": 5, "product": "chair", "category": "furniture", "quantity": 10, "price": 75, "storeLocation": "Los Angeles", "date": "2024-11-05", "totalPrice": 750 }
]
New field totalPrice is added to each document in the pipeline
$multiply is an aggregation operator used in MongoDB's aggregation pipeline to multiply numeric values.
The $out stage in MongoDB aggregation pipelines writes the results of a pipeline to a specified collection. The $out stage replaces the target collection if it exists, so use it carefully.
db.sales.aggregate([
{
$addFields: {
totalPrice: { $multiply: ["$price", "$quantity"] }
}
},
{
$out: "salesWithTotalPrice"
}
])
$addFields: Adds the totalPrice field to each document by multiplying price and quantity.
$out: Writes the transformed documents to the salesWithTotalPrice collection.
The $merge stage in MongoDB is similar to $out but more flexible. While $out replaces the entire target collection, $merge allows you to merge the results of an aggregation pipeline into an existing collection, with options to update, insert, or keep the existing documents.
db.sales.aggregate([
{
$addFields: {
totalPrice: { $multiply: ["$price", "$quantity"]}
}
},
{
$merge: {
into: "salesWithTotalPrice",
whenMatched: "merge", // Update existing documents by merging fields
whenNotMatched: "insert" // Insert new documents if no match is found
}
}
])
If using all default options for $merge, including writing to a collection in the same database, you can use the simplified form
db.sales.aggregate([
{
$addFields: {
totalPrice: { $multiply: ["$price", "$quantity"]}
}
},
{ $merge: "salesWithTotalPrice" }
}
])
That's all I learnd today. talk to you next time.
Posted on November 11, 2024
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