Group, project, sort, unwind, lookup, facet, and aggregate analytics data with MongoDB pipelines.
The stages most developers use first.
db.orders.aggregate([
{ $match: { status: "paid" } },
{ $group: { _id: "$country", revenue: { $sum: "$total" }, orders: { $sum: 1 } } },
{ $sort: { revenue: -1 } }
])`$match` early in the pipeline can reduce the amount of work later stages perform.
db.orders.aggregate([
{ $project: { customer_id: 1, total: 1, tax: { $multiply: ["$total", 0.08] } } }
])`$project` is useful for output shaping and lightweight computed fields.
db.orders.aggregate([
{ $unwind: "$items" },
{ $group: { _id: "$items.sku", units: { $sum: "$items.qty" } } },
{ $sort: { units: -1 } }
])`$unwind` is essential for analytics on nested arrays.
db.orders.aggregate([
{
$lookup: {
from: "users",
localField: "user_id",
foreignField: "_id",
as: "user"
}
},
{ $unwind: "$user" }
])`$lookup` is the MongoDB pipeline equivalent of a join.
Useful patterns for dashboards and analytics endpoints.
db.orders.aggregate([
{
$facet: {
totalsByStatus: [
{ $group: { _id: "$status", count: { $sum: 1 } } }
],
recentOrders: [
{ $sort: { created_at: -1 } },
{ $limit: 5 }
]
}
}
])`$facet` is handy when one request needs several summary blocks.
db.orders.aggregate([
{
$bucket: {
groupBy: "$total",
boundaries: [0, 50, 100, 500, 1000],
default: "1000+",
output: { count: { $sum: 1 } }
}
}
])Bucketing is useful for histograms and pricing summaries.
db.orders.aggregate([
{ $match: { status: "paid" } },
{ $count: "paid_orders" }
])`$count` is a concise finishing stage for reporting queries.