Today’s new knowledge #2

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.