$Facet in MongoDB for multi-output aggregation.

 

---

💡 Introduction: What is $facet in MongoDB?

When working with large-scale data, you often need to perform multiple analyses or aggregations in parallel. MongoDB’s $facet stage in the aggregation pipeline is a powerful operator that allows you to generate multiple outputs from a single aggregation query, efficiently and in a well-organized manner.

---

🔍 Understanding $facet: The Power of Multi-Pipeline Aggregation

• $facet is an aggregation stage that allows you to run multiple sub-pipelines in parallel.

• Each sub-pipeline operates on the same input data and produces a separate result.

• The final output is a single document with fields corresponding to each pipeline.

🔸 Use cases:

• Pagination + total count in one query

• Summary statistics + filtered results

• Charts and reports generation from the same dataset

---

🧱 Syntax of $facet

{
  $facet: {
    <outputField1>: [ <stage1>, <stage2>, ... ],
    <outputField2>: [ <stage1>, <stage2>, ... ],
    ...
  }
}

Each key in the $facet object defines a sub-pipeline and its stages.

---

🧰 Step-by-Step: Using $facet in Practice

📌 Step 1: Sample Data

Let’s say we have a products collection:

{
  "_id": 1,
  "category": "Electronics",
  "price": 199.99,
  "rating": 4.5
}

📌 Step 2: Create an Aggregation Pipeline with $facet

🎯 Goal:

• Get average price by category

• Get top 3 high-rated products

🔧 Aggregation Query:

db.products.aggregate([
  {
    $facet: {
      "AveragePriceByCategory": [
        { $group: { _id: "$category", avgPrice: { $avg: "$price" } } }
      ],
      "TopRatedProducts": [
        { $sort: { rating: -1 } },
        { $limit: 3 }
      ]
    }
  }
])

🖼️ Output:

{
  "AveragePriceByCategory": [
    { "_id": "Electronics", "avgPrice": 199.99 }
  ],
  "TopRatedProducts": [
    { "_id": 2, "category": "Books", "price": 19.99, "rating": 5.0 },
    ...
  ]
}

🖼️ (Optional: Insert a diagram showing the data flow through multiple pipelines inside $facet)

---

🚀 Future Scope 

• Dashboards & Analytics: Use $facet to generate multiple chart data in a single query.

• Performance Optimization: Instead of firing multiple queries, $facet runs everything in one pass.

• Combined Filtering and Reporting: Apply filters before the $facet to limit processed data.

• Integration with BI Tools: Efficient multi-result pipelines help in real-time analytics.

---

👩‍🎓 Name: Sarthak Kulkarni

🏫 College: Sri Balaji University, Pune

🏫 School: School of Computer Studies

🎓 Class: TY-BCA(E)

📸 Instagram - https://www.instagram.com/sbup.scs/

🔗 LinkedIn - https://www.linkedin.com/company/sbup-schools-ug/posts/?feedView=all

🌐 Official Website - https://scs.sbup.edu.in/