MongoDB $facet: One Query, Multiple Results

Introduction MongoDB's aggregation framework offers powerful data processing capabilities, and the MongoDB $facet stage is one of its most versatile features. This guide explores how to effectively use $facet to run multiple aggregation pipelines simultaneously, saving you time and improving application performance. What is the MongoDB $facet Stage? The $facet stage lets you process the same input documents through multiple aggregation pipelines within a single operation. Instead of making several database queries, you can execute multiple analyses at once and receive results in a structured document. { $facet: { "outputName1": [ ], "outputName2": [ ], // More pipelines as needed } } Without $facet, you have to send two requests to get outputName1 and outputName2. Key Benefits of Using $facet Reduced database round trips. Parallel processing of the same dataset. Simplified client-side code. More efficient resource utilization. Cleaner API responses Sample Dataset: E-commerce Products Collection To make the examples concrete and easy to follow, we'll use a single e-commerce products collection throughout this guide. You can create this sample dataset in your local MongoDB instance to test the examples yourself. You can simply follow up with me using MongoDB Compass. Data seeding You can use MongoDB Compass to insert the following products in your products collection. [ { "name": "Smartphone X1", "price": 699.99, "category": "Electronics", "brand": "TechCo", "tags": ["smartphone", "5G", "camera"], "stock": 123, "rating": 4.5, "reviews": 382, "createdAt": "2023-06-15T00:00:00.000Z", "updatedAt": "2023-12-01T00:00:00.000Z", "description": "Latest flagship smartphone with advanced camera system", "featured": true, "discountPercentage": 5, "attributes": { "color": "Black", "size": "6.4 inch", "weight": 189 } }, { "name": "Wireless Earbuds Pro", "price": 149.99, "category": "Electronics", "brand": "AudioPhile", "tags": ["wireless", "earbuds", "noise-cancelling"], "stock": 78, "rating": 4.7, "reviews": 204, "createdAt": "2023-08-21T00:00:00.000Z", "updatedAt": "2023-11-15T00:00:00.000Z", "description": "Premium wireless earbuds with active noise cancellation", "featured": true, "discountPercentage": 0, "attributes": { "color": "White", "size": "Standard", "weight": 58 } }, { "name": "Cotton T-Shirt", "price": 19.99, "category": "Clothing", "brand": "FashionBasics", "tags": ["t-shirt", "cotton", "casual"], "stock": 243, "rating": 4.2, "reviews": 119, "createdAt": "2023-04-10T00:00:00.000Z", "updatedAt": "2023-09-20T00:00:00.000Z", "description": "Comfortable everyday cotton t-shirt", "featured": false, "discountPercentage": 15, "attributes": { "color": "Blue", "size": "M", "weight": 150 } }, { "name": "Smart Watch SE", "price": 299.99, "category": "Electronics", "brand": "TechCo", "tags": ["smartwatch", "fitness", "bluetooth"], "stock": 42, "rating": 4.4, "reviews": 157, "createdAt": "2023-07-08T00:00:00.000Z", "updatedAt": "2023-10-30T00:00:00.000Z", "description": "Track your fitness and stay connected with this smart watch", "featured": true, "discountPercentage": 10, "attributes": { "color": "Silver", "size": "One Size", "weight": 48 } }, { "name": "Bluetooth Speaker", "price": 89.99, "category": "Electronics", "brand": "AudioPhile", "tags": ["speaker", "bluetooth", "portable"], "stock": 65, "rating": 4.3, "reviews": 93, "createdAt": "2023-03-25T00:00:00.000Z", "updatedAt": "2023-08-15T00:00:00.000Z", "description": "Portable bluetooth speaker with rich sound", "featured": false, "discountPercentage": 0, "attributes": { "color": "Red", "size": "Medium", "weight": 540 } }, { "name": "Running Shoes", "price": 129.99, "category": "Footwear", "brand": "SportElite", "tags": ["shoes", "running", "sports"], "stock": 85, "rating": 4.6, "reviews": 214, "createdAt": "2023-05-12T00:00:00.000Z", "updatedAt": "2023-11-05T00:00:00.000Z", "description": "Lightweight professional running shoes", "featured": true, "discountPercentage": 0, "attributes": { "color": "Black/Green", "size": "42", "weight": 305 } }, { "name": "Coffee Maker", "price": 149.99, "category": "Appliances", "brand": "HomeComfort", "tags": ["coffee", "kitchen", "automatic"], "stock": 31, "rating": 4.8, "reviews": 176, "createdAt": "2023-02-18T00:00:00.000Z", "updatedAt": "2023-09-10T00:00:00.000Z", "description": "Automatic coffe

Apr 18, 2025 - 12:47

MongoDB $facet: One Query, Multiple Results

Introduction

MongoDB's aggregation framework offers powerful data processing capabilities, and the MongoDB $facet stage is one of its most versatile features.

This guide explores how to effectively use $facet to run multiple aggregation pipelines simultaneously, saving you time and improving application performance.

What is the MongoDB $facet Stage?

The $facet stage lets you process the same input documents through multiple aggregation pipelines within a single operation.

Instead of making several database queries, you can execute multiple analyses at once and receive results in a structured document.

{
  $facet: {
    "outputName1": [ 1> ],
    "outputName2": [ 2> ],
    // More pipelines as needed
  }
}

Without $facet, you have to send two requests to get outputName1 and outputName2.

Key Benefits of Using $facet

Reduced database round trips.
Parallel processing of the same dataset.
Simplified client-side code.
More efficient resource utilization.
Cleaner API responses

Sample Dataset: E-commerce Products Collection

To make the examples concrete and easy to follow, we'll use a single e-commerce products collection throughout this guide.

You can create this sample dataset in your local MongoDB instance to test the examples yourself. You can simply follow up with me using MongoDB Compass.

Data seeding

You can use MongoDB Compass to insert the following products in your products collection.

[
  {
    "name": "Smartphone X1",
    "price": 699.99,
    "category": "Electronics",
    "brand": "TechCo",
    "tags": ["smartphone", "5G", "camera"],
    "stock": 123,
    "rating": 4.5,
    "reviews": 382,
    "createdAt": "2023-06-15T00:00:00.000Z",
    "updatedAt": "2023-12-01T00:00:00.000Z",
    "description": "Latest flagship smartphone with advanced camera system",
    "featured": true,
    "discountPercentage": 5,
    "attributes": {
      "color": "Black",
      "size": "6.4 inch",
      "weight": 189
    }
  },
  {
    "name": "Wireless Earbuds Pro",
    "price": 149.99,
    "category": "Electronics",
    "brand": "AudioPhile",
    "tags": ["wireless", "earbuds", "noise-cancelling"],
    "stock": 78,
    "rating": 4.7,
    "reviews": 204,
    "createdAt": "2023-08-21T00:00:00.000Z",
    "updatedAt": "2023-11-15T00:00:00.000Z",
    "description": "Premium wireless earbuds with active noise cancellation",
    "featured": true,
    "discountPercentage": 0,
    "attributes": {
      "color": "White",
      "size": "Standard",
      "weight": 58
    }
  },
  {
    "name": "Cotton T-Shirt",
    "price": 19.99,
    "category": "Clothing",
    "brand": "FashionBasics",
    "tags": ["t-shirt", "cotton", "casual"],
    "stock": 243,
    "rating": 4.2,
    "reviews": 119,
    "createdAt": "2023-04-10T00:00:00.000Z",
    "updatedAt": "2023-09-20T00:00:00.000Z",
    "description": "Comfortable everyday cotton t-shirt",
    "featured": false,
    "discountPercentage": 15,
    "attributes": {
      "color": "Blue",
      "size": "M",
      "weight": 150
    }
  },
  {
    "name": "Smart Watch SE",
    "price": 299.99,
    "category": "Electronics",
    "brand": "TechCo",
    "tags": ["smartwatch", "fitness", "bluetooth"],
    "stock": 42,
    "rating": 4.4,
    "reviews": 157,
    "createdAt": "2023-07-08T00:00:00.000Z",
    "updatedAt": "2023-10-30T00:00:00.000Z",
    "description": "Track your fitness and stay connected with this smart watch",
    "featured": true,
    "discountPercentage": 10,
    "attributes": {
      "color": "Silver",
      "size": "One Size",
      "weight": 48
    }
  },
  {
    "name": "Bluetooth Speaker",
    "price": 89.99,
    "category": "Electronics",
    "brand": "AudioPhile",
    "tags": ["speaker", "bluetooth", "portable"],
    "stock": 65,
    "rating": 4.3,
    "reviews": 93,
    "createdAt": "2023-03-25T00:00:00.000Z",
    "updatedAt": "2023-08-15T00:00:00.000Z",
    "description": "Portable bluetooth speaker with rich sound",
    "featured": false,
    "discountPercentage": 0,
    "attributes": {
      "color": "Red",
      "size": "Medium",
      "weight": 540
    }
  },
  {
    "name": "Running Shoes",
    "price": 129.99,
    "category": "Footwear",
    "brand": "SportElite",
    "tags": ["shoes", "running", "sports"],
    "stock": 85,
    "rating": 4.6,
    "reviews": 214,
    "createdAt": "2023-05-12T00:00:00.000Z",
    "updatedAt": "2023-11-05T00:00:00.000Z",
    "description": "Lightweight professional running shoes",
    "featured": true,
    "discountPercentage": 0,
    "attributes": {
      "color": "Black/Green",
      "size": "42",
      "weight": 305
    }
  },
  {
    "name": "Coffee Maker",
    "price": 149.99,
    "category": "Appliances",
    "brand": "HomeComfort",
    "tags": ["coffee", "kitchen", "automatic"],
    "stock": 31,
    "rating": 4.8,
    "reviews": 176,
    "createdAt": "2023-02-18T00:00:00.000Z",
    "updatedAt": "2023-09-10T00:00:00.000Z",
    "description": "Automatic coffee maker with timer",
    "featured": true,
    "discountPercentage": 5,
    "attributes": {
      "color": "Silver/Black",
      "size": "Standard",
      "weight": 2500
    }
  },
  {
    "name": "Backpack",
    "price": 59.99,
    "category": "Accessories",
    "brand": "TravelPro",
    "tags": ["backpack", "travel", "waterproof"],
    "stock": 112,
    "rating": 4.5,
    "reviews": 88,
    "createdAt": "2023-06-30T00:00:00.000Z",
    "updatedAt": "2023-10-15T00:00:00.000Z",
    "description": "Durable waterproof backpack for travel",
    "featured": false,
    "discountPercentage": 0,
    "attributes": {
      "color": "Navy Blue",
      "size": "25L",
      "weight": 780
    }
  },
  {
    "name": "Yoga Mat",
    "price": 29.99,
    "category": "Fitness",
    "brand": "SportElite",
    "tags": ["yoga", "fitness", "exercise"],
    "stock": 95,
    "rating": 4.2,
    "reviews": 67,
    "createdAt": "2023-04-05T00:00:00.000Z",
    "updatedAt": "2023-08-25T00:00:00.000Z",
    "description": "Non-slip yoga mat for comfortable exercise",
    "featured": false,
    "discountPercentage": 0,
    "attributes": {
      "color": "Purple",
      "size": "Standard",
      "weight": 900
    }
  },
  {
    "name": "4K Smart TV",
    "price": 799.99,
    "category": "Electronics",
    "brand": "ViewTech",
    "tags": ["tv", "4k", "smart"],
    "stock": 28,
    "rating": 4.7,
    "reviews": 153,
    "createdAt": "2023-05-20T00:00:00.000Z",
    "updatedAt": "2023-12-10T00:00:00.000Z",
    "description": "55-inch 4K smart TV with HDR",
    "featured": true,
    "discountPercentage": 8,
    "attributes": {
      "color": "Black",
      "size": "55 inch",
      "weight": 15200
    }
  }
]

Essential Examples Using Our Products Collection

Now that we have our products collection, let's explore how to use $facet with this dataset.

Example 1: Product Dashboard Analytics

To create a comprehensive product dashboard with multiple metrics in one query, use the following aggregation pipelines:

[
  {
    $facet: {
      "categoryCounts": [
        { $group: { _id: "$category", count: { $sum: 1 } } },
        { $sort: { count: -1 } }
      ],
      "brandAnalysis": [
        { $group: { _id: "$brand", productCount: { $sum: 1 } } },
        { $sort: { productCount: -1 } }
      ],
      "priceStats": [
        { $group: {
            _id: null,
            avgPrice: { $avg: "$price" },
            minPrice: { $min: "$price" },
            maxPrice: { $max: "$price" },
            totalProducts: { $sum: 1 }
          }
        }
      ],
      "featuredProducts": [
        { $match: { featured: true } },
        { $sort: { price: -1 } },
        { $limit: 3 },
        { $project: {
            _id: 1,
            name: 1,
            price: 1,
            category: 1,
            brand: 1
          }
        }
      ]
    }
  }
]

I think things become more clear now. Again without $facet, to get categoryCounts, brandAnalysis, priceStats, and featuredProducts, you have to send 4 different requests, but with $facet, you do it in one request.

This query produces a result with:

Distribution of products across categories
Brand distribution analysis
Price statistics across all products
Top 3 featured products by price

Example 2: Inventory and Pricing Analysis

To analyze inventory status and pricing in a single query, use the following aggregation pipelines:

[
  {
    $facet: {
      "stockStatus": [
        {
          $bucket: {
            groupBy: "$stock",
            boundaries: [0, 30, 50, 100, 200],
            default: "200+",
            output: {
              count: { $sum: 1 },
              products: { $push: { id: "$_id", name: "$name", stock: "$stock" } }
            }
          }
        }
      ],
      "priceRanges": [
        {
          $bucket: {
            groupBy: "$price",
            boundaries: [0, 50, 100, 300, 500],
            default: "500+",
            output: {
              count: { $sum: 1 },
              avgRating: { $avg: "$rating" }
            }
          }
        }
      ],
      "discountedProducts": [
        { $match: { discountPercentage: { $gt: 0 } } },
        { $sort: { discountPercentage: -1 } },
        { $project: {
            name: 1,
            originalPrice: "$price",
            discountPercentage: 1,
            finalPrice: {
              $multiply: [
                "$price",
                { $subtract: [1, { $divide: ["$discountPercentage", 100] }] }
              ]
            }
          }
        }
      ]
    }
  }
]

If you're not familiar with MongoDB's $bucket stage, think of it as a way to group documents based on a specified range of values---like creating buckets for price ranges. For instance, if your product prices span from 0 to 500, you can define boundaries like [0, 50, 100, 300, 500]. The $bucket stage will then group documents falling within each of those ranges, allowing you to perform separate aggregations or analyses for each price segment.

Anyway, this single query provides:

Products grouped by stock level
Products grouped by price range with average rating
All discounted products with calculated final prices

Example 3: Product Search with Faceted Navigation

To create a powerful product search with faceted navigation options, use the following aggregation pipelines:

[
  {
    $match: {
      $or: [
        { name: { $regex: "smart", $options: "i" } },
        { description: { $regex: "smart", $options: "i" } },
        { tags: "smart" }
      ]
    }
  },
  {
    $facet: {
      "searchResults": [
        { $sort: { rating: -1, reviews: -1 } },
        { $limit: 5 },
        { $project: {
            name: 1,
            price: 1,
            rating: 1,
            description: 1,
            discountPercentage: 1,
            category: 1,
            brand: 1
          }
        }
      ],
      "categoryFilters": [
        { $group: { _id: "$category", count: { $sum: 1 } } },
        { $sort: { count: -1 } }
      ],
      "brandFilters": [
        { $group: { _id: "$brand", count: { $sum: 1 } } },
        { $sort: { count: -1 } }
      ],
      "priceStats": [
        { $group: {
            _id: null,
            avgPrice: { $avg: "$price" },
            minPrice: { $min: "$price" },
            maxPrice: { $max: "$price" }
          }
        }
      ]
    }
  }
]

This provides a complete search experience with:

Top 5 search results for "smart" products
Category filter options with counts
Brand filter options with counts
Price statistics for the search results

Example 4: Products Pagination with Total Count

To implement efficient pagination that includes the total count, use the following aggregation pipelines:

// Use these variables in your JSON before trying
const pageSize = 3;
const pageNumber = 2;
const skip = pageSize * (pageNumber - 1);

// JSON
[
  {
    $facet: {
      "paginatedProducts": [
        { $sort: { createdAt: -1 } },
        { $skip: skip },
        { $limit: pageSize },
        { $project: {
            name: 1,
            price: 1,
            category: 1,
            brand: 1,
            rating: 1,
            createdAt: 1
          }
        }
      ],
      "totalCount": [
        { $count: "count" }
      ]
    }
  },
  {
    $project: {
      products: "$paginatedProducts",
      total: { $arrayElemAt: ["$totalCount.count", 0] },
      page: { $literal: pageNumber },
      pageSize: { $literal: pageSize },
      totalPages: {
        $ceil: {
          $divide: [
            { $arrayElemAt: ["$totalCount.count", 0] },
            pageSize
          ]
        }
      }
    }
  }
]

This query provides the following results in one request:

Page 2 of products (items 4-6 when sorted by creation date)
Total product count
Current page number
Page size
Total number of pages

Optimization Best Practices

Using our products collection as context, here are key optimization strategies:

1. Filter Early

Always place a $match stage before $facet to reduce the document set:

[
  { $match: { stock: { $gt: 0 } } }, // Process only in-stock products
  {
    $facet: {
      "categories": [ /* pipeline */ ],
      "brands": [ /* pipeline */ ]
    }
  }
]

2. Project Only Necessary Fields

Reduce memory usage by keeping only needed fields:

[
  {
    $project: {
      name: 1,
      price: 1,
      category: 1,
      brand: 1,
      stock: 1
    }
  },
  {
    $facet: {
      /* pipelines using only these fields */
    }
  }
]

Keep in mind, $facet can't exceed 100MB output per sub-pipeline, have a look here for more considerations.

3. Handle Empty Results

Transform empty arrays into usable values:

[
  {
    $facet: {
      "outOfStock": [
        { $match: { stock: 0 } },
        { $count: "count" }
      ]
    }
  },
  {
    $project: {
      outOfStockCount: {
        $cond: {
          if: { $eq: [{ $size: "$outOfStock" }, 0] },
          then: 0,
          else: { $arrayElemAt: ["$outOfStock.count", 0] }
        }
      }
    }
  }
]

When to Use MongoDB $facet vs. Multiple Queries

From previous examples, you might think, "$facet is awesome. OK, I will use it in everything".

Actually, our previous examples are very good candidates to use $facet. However, you should not use it blindly.

Use $facet When:

You need multiple aggregations on the same dataset: $facet lets you run several pipelines in parallel on the same input data, reducing duplication.
Atomicity is important: All facets run in a single aggregation, ensuring consistency (all data is from the same snapshot).
You want all results returned in a single response: Convenient when you need structured, grouped data (e.g. counts, lists, stats) in one JSON document.
You want to minimize round trips: Fewer database calls = lower latency and easier error handling compared to multiple individual queries.
You're building dashboards or reports: $facet is great for generating multiple data views (e.g. filters, charts, summaries) in one call.
Input filtering is expensive: You can filter once and reuse the result across multiple facets instead of repeating the same filter logic.

Use Multiple Queries When:

Performance is a concern: If $facet is causing performance issues due to processing multiple pipelines in a single aggregation.
Queries are unrelated: When different parts of the data are not logically connected, and running them separately is cleaner.
You need to run them in parallel: Multiple queries can be executed in parallel in your application to reduce potential latency.
Results don't need to be in the same document: If you don't need all the results bundled together, separate queries can be simpler and more modular.
Query complexity is too high for one pipeline: Large and complex $facet operations can become hard to manage; splitting them improves readability and maintainability.
Each query uses different indexes: Separate queries can take better advantage of indexing, while $facet runs on a single pipeline input.
Different queries have different execution frequencies: You may not need to run all the logic every time---multiple queries give you more control.

Conclusion

MongoDB's $facet operator provides an elegant solution for executing multiple aggregation pipelines on your data in parallel.

By understanding its capabilities and limitations, you can create more efficient queries, reduce database load, and simplify your application architecture.

Think Of It

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If you're interested in exploring more, check out these articles.

Thanks for sticking with me until the end--I hope you found this article valuable and enjoyable!