MongoDB's aggregation framework is a versatile tool for transforming and analyzing data. Combining $unwind with other aggregation stages allow you to perform even more complex queries and gain deeper insights from your data.
Understanding $unwind
The $unwind stage in MongoDB's aggregation pipeline deconstructs an array field from the input documents to output a document for each element of the array. This effectively "flattens" the array, creating multiple documents from a single document that contains an array.
{
$unwind: {
path: <arrayFieldPath>,
includeArrayIndex: <string>, // Optional
preserveNullAndEmptyArrays: <boolean> // Optional
}
}
How $unwind Works
Consider the following collection users:
[
{ "_id": 1, "name": "Alice", "hobbies": ["reading", "cycling", "swimming"] },
{ "_id": 2, "name": "Bob", "hobbies": ["painting", "drawing"] },
{ "_id": 3, "name": "Charlie", "hobbies": [] },
{ "_id": 4, "name": "David" }
]
Applying the $unwind stage on the hobbies field:
{ $unwind: "$hobbies" }
[
{ "_id": 1, "name": "Alice", "hobbies": "reading" },
{ "_id": 1, "name": "Alice", "hobbies": "cycling" },
{ "_id": 1, "name": "Alice", "hobbies": "swimming" },
{ "_id": 2, "name": "Bob", "hobbies": "painting" },
{ "_id": 2, "name": "Bob", "hobbies": "drawing" }
]
To retain documents with empty or non-existent arrays, you can use the preserveNullAndEmptyArrays option:
{ $unwind: { path: "$hobbies", preserveNullAndEmptyArrays: true } }
[
{ "_id": 1, "name": "Alice", "hobbies": "reading" },
{ "_id": 1, "name": "Alice", "hobbies": "cycling" },
{ "_id": 1, "name": "Alice", "hobbies": "swimming" },
{ "_id": 2, "name": "Bob", "hobbies": "painting" },
{ "_id": 2, "name": "Bob", "hobbies": "drawing" },
{ "_id": 3, "name": "Charlie", "hobbies": null },
{ "_id": 4, "name": "David", "hobbies": null }
]
Practical Use Cases for $unwind
1. E-Commerce Order Processing
Consider an e-commerce database where each order document contains an array of items purchased:
{
"_id": 101,
"customer": "John Doe",
"items": [
{ "product": "Laptop", "quantity": 1, "price": 1200 },
{ "product": "Mouse", "quantity": 2, "price": 40 }
]
}
Using $unwind, we can flatten the items array to analyze sales data more effectively:
{ $unwind: "$items" }
Result:
[
{ "_id": 101, "customer": "John Doe", "items": { "product": "Laptop", "quantity": 1, "price": 1200 } },
{ "_id": 101, "customer": "John Doe", "items": { "product": "Mouse", "quantity": 2, "price": 40 } }
]
In a social media platform, each user document might contain an array of posts. To generate reports on individual posts, $unwind can be used to flatten the posts array:
{
"_id": 202,
"username": "janedoe",
"posts": [
{ "post_id": 1, "content": "Hello World!" },
{ "post_id": 2, "content": "MongoDB is awesome!" }
]
}
Using $unwind on the posts array:
{ $unwind: "$posts" }
Result:
[
{ "_id": 202, "username": "janedoe", "posts": { "post_id": 1, "content": "Hello World!" } },
{ "_id": 202, "username": "janedoe", "posts": { "post_id": 2, "content": "MongoDB is awesome!" } }
]
Combining $unwind with $match, $group, and $project
To demonstrate the combined power of these stages, let's work with an example collection called orders. This collection stores customer orders, each containing an array of items.
Sample Data
[
{
"_id": 1,
"customer": "John Doe",
"orderDate": "2023-05-01",
"items": [
{ "product": "Laptop", "quantity": 1, "price": 1200 },
{ "product": "Mouse", "quantity": 2, "price": 25 }
]
},
{
"_id": 2,
"customer": "Jane Smith",
"orderDate": "2023-05-02",
"items": [
{ "product": "Keyboard", "quantity": 1, "price": 100 },
{ "product": "Monitor", "quantity": 1, "price": 300 },
{ "product": "Mouse", "quantity": 1, "price": 25 }
]
}
]
Step-by-Step Aggregation
1. $unwind
First, we use $unwind to deconstruct the items array in each order document. This will create separate documents for each item in an order.
{ $unwind: "$items" }
Output:
[
{ "_id": 1, "customer": "John Doe", "orderDate": "2023-05-01", "items": { "product": "Laptop", "quantity": 1, "price": 1200 } },
{ "_id": 1, "customer": "John Doe", "orderDate": "2023-05-01", "items": { "product": "Mouse", "quantity": 2, "price": 25 } },
{ "_id": 2, "customer": "Jane Smith", "orderDate": "2023-05-02", "items": { "product": "Keyboard", "quantity": 1, "price": 100 } },
{ "_id": 2, "customer": "Jane Smith", "orderDate": "2023-05-02", "items": { "product": "Monitor", "quantity": 1, "price": 300 } },
{ "_id": 2, "customer": "Jane Smith", "orderDate": "2023-05-02", "items": { "product": "Mouse", "quantity": 1, "price": 25 } }
]
2. $match
Next, we use $match to filter documents. For example, let's find all orders containing the product "Mouse".
{ $match: { "items.product": "Mouse" } }
Output:
[
{ "_id": 1, "customer": "John Doe", "orderDate": "2023-05-01", "items": { "product": "Mouse", "quantity": 2, "price": 25 } },
{ "_id": 2, "customer": "Jane Smith", "orderDate": "2023-05-02", "items": { "product": "Mouse", "quantity": 1, "price": 25 } }
]
3. $group
To aggregate data, we can use $group. Let's sum up the total quantity and revenue for each product.
{
$group: {
_id: "$items.product",
totalQuantity: { $sum: "$items.quantity" },
totalRevenue: { $sum: { $multiply: ["$items.quantity", "$items.price"] } }
}
}
Output:
[
{ "_id": "Mouse", "totalQuantity": 3, "totalRevenue": 75 },
{ "_id": "Keyboard", "totalQuantity": 1, "totalRevenue": 100 },
{ "_id": "Monitor", "totalQuantity": 1, "totalRevenue": 300 },
{ "_id": "Laptop", "totalQuantity": 1, "totalRevenue": 1200 }
]
4. $project
Finally, we use $project to format the output. Let's create a more readable output that renames fields and calculates the average price per unit.
{
$project: {
_id: 0,
product: "$_id",
totalQuantity: 1,
totalRevenue: 1,
avgPrice: { $divide: ["$totalRevenue", "$totalQuantity"] }
}
}
Output:
[
{ "product": "Mouse", "totalQuantity": 3, "totalRevenue": 75, "avgPrice": 25 },
{ "product": "Keyboard", "totalQuantity": 1, "totalRevenue": 100, "avgPrice": 100 },
{ "product": "Monitor", "totalQuantity": 1, "totalRevenue": 300, "avgPrice": 300 },
{ "product": "Laptop", "totalQuantity": 1, "totalRevenue": 1200, "avgPrice": 1200 }
]
Full Aggregation Pipeline
Combining all these stages, the complete aggregation pipeline looks like this:
[
{ $unwind: "$items" },
{ $match: { "items.product": "Mouse" } },
{
$group: {
_id: "$items.product",
totalQuantity: { $sum: "$items.quantity" },
totalRevenue: { $sum: { $multiply: ["$items.quantity", "$items.price"] } }
}
},
{
$project: {
_id: 0,
product: "$_id",
totalQuantity: 1,
totalRevenue: 1,
avgPrice: { $divide: ["$totalRevenue", "$totalQuantity"] }
}
}
]
Conclusion
Using $unwind in conjunction with other aggregation stages like $match, $group, and $project allows you to perform sophisticated data transformations and analysis in MongoDB. Whether you are analyzing e-commerce data, social media interactions, or any other type of array-based data, these techniques can help you unlock deeper insights and create more meaningful reports.
