Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
APPLIES TO:
MongoDB vCore
The aggregate
command is used to process data records and return computed results. It performs operations on the data, such as filtering, grouping, and sorting, and can transform the data in various ways. The aggregate
command is highly versatile and is commonly used for data analysis and reporting.
Syntax
db.collection.aggregate(pipeline, options)
- pipeline: An array of aggregation stages that process and transform the data.
- options: Optional. Specifies more options for the aggregation, such as
explain
,allowDiskUse
, andcursor
.
Examples
Example 1: Calculate total sales by category
This example demonstrates how to calculate the total sales for each category in the stores
collection.
db.stores.aggregate([
{
$unwind: "$sales.salesByCategory"
},
{
$group: {
_id: "$sales.salesByCategory.categoryName",
totalSales: { $sum: "$sales.salesByCategory.totalSales" }
}
}
])
Sample output
[mongos] StoreData> db.stores.aggregate([
... {
... $unwind: "$sales.salesByCategory"
... },
... {
... $group: {
... _id: "$sales.salesByCategory.categoryName",
... totalSales: { $sum: "$sales.salesByCategory.totalSales" }
... }
... }
... ])
[
{ _id: 'Christmas Trees', totalSales: 3147281 },
{ _id: 'Nuts', totalSales: 3002332 },
{ _id: 'Camping Tables', totalSales: 4431667 }
]
Example 2: Find stores with full-time staff greater than 10
This example shows how to filter stores where the number of full-time staff is greater than 10.
db.stores.aggregate([
{
$match: {
"staff.totalStaff.fullTime": { $gt: 10 }
}
}
])
Sample output
[mongos] StoreData> db.stores.aggregate([
... {
... $match: {
... "staff.totalStaff.fullTime": { $gt: 10 }
... }
... }
... ])
[
{
_id: '7954bd5c-9ac2-4c10-bb7a-2b79bd0963c5',
name: "Lenore's DJ Equipment Store",
location: { lat: -9.9399, lon: -0.334 },
staff: { totalStaff: { fullTime: 18, partTime: 7 } },
sales: {
totalSales: 35911,
salesByCategory: [ { categoryName: 'DJ Headphones', totalSales: 35911 } ]
},
promotionEvents: [
{
discounts: [
{ categoryName: 'DJ Turntables', discountPercentage: 18 },
{ categoryName: 'DJ Mixers', discountPercentage: 15 }
]
}
],
tag: [ '#SeasonalSale', '#FreeShipping', '#MembershipDeals' ]
}
]
Example 3: List all promotion events with discounts greater than 15%
This example lists all promotion events where any discount is greater than 15%.
db.stores.aggregate([
{
$unwind: "$promotionEvents"
},
{
$unwind: "$promotionEvents.discounts"
},
{
$match: {
"promotionEvents.discounts.discountPercentage": { $gt: 15 }
}
},
{
$group: {
_id: "$promotionEvents.eventName",
discounts: { $push: "$promotionEvents.discounts" }
}
}
])
Sample output
[mongos] StoreData> db.stores.aggregate([
... {
... $unwind: "$promotionEvents"
... },
... {
... $unwind: "$promotionEvents.discounts"
... },
... {
... $match: {
... "promotionEvents.discounts.discountPercentage": { $gt: 20 }
... }
... },
... {
... $group: {
... _id: "$promotionEvents.eventName",
... discounts: { $push: "$promotionEvents.discounts" }
... }
... }
... ])
[
{
[
{ categoryName: 'Basketball Gear', discountPercentage: 23 },
{ categoryName: 'Wool Carpets', discountPercentage: 22 },
{
categoryName: 'Portable Bluetooth Speakers',
discountPercentage: 24
}
]
}
]
Related content
- Review options for Migrating from MongoDB to Azure Cosmos DB for MongoDB (vCore)
- Read more about Feature compatibility with MongoDB