$replaceWith (aggregation stage)

Example 1

An example collection sales is populated with the following documents:

db.sales.insertMany([
   { "_id" : 1, "item" : "butter", "price" : 10, "quantity": 2, date: ISODate("2019-03-01T08:00:00Z"), status: "C" },
   { "_id" : 2, "item" : "cream", "price" : 20, "quantity": 1, date: ISODate("2019-03-01T09:00:00Z"), status: "A" },
   { "_id" : 3, "item" : "jam", "price" : 5, "quantity": 10, date: ISODate("2019-03-15T09:00:00Z"), status: "C" },
   { "_id" : 4, "item" : "muffins", "price" : 5, "quantity": 10, date: ISODate("2019-03-15T09:00:00Z"), status: "C" }
])

Assume that for reporting purposes, you want to calculate for each completed sale, the total amount as of the current report run time. The following operation finds all the sales with status C and creates new documents using the $replaceWith stage. The $replaceWith calculates the total amount as well as uses the variable NOW to get the current time.

db.sales.aggregate([
   { $match: { status: "C" } },
   { $replaceWith: { _id: "$_id", item: "$item", amount: { $multiply: [ "$price", "$quantity"]}, status: "Complete", asofDate: "$$NOW" } }
])

The operation returns the following documents:

{ "_id" : 1, "item" : "butter", "amount" : 20, "status" : "Complete", "asofDate" : ISODate("2019-06-03T22:47:54.812Z") }
{ "_id" : 3, "item" : "jam", "amount" : 50, "status" : "Complete", "asofDate" : ISODate("2019-06-03T22:47:54.812Z") }
{ "_id" : 4, "item" : "muffins", "amount" : 50, "status" : "Complete", "asofDate" : ISODate("2019-06-03T22:47:54.812Z") }

Example 2

An example collection reportedsales is populated with the reported sales information by quarter and regions:

db.reportedsales.insertMany( [
   { _id: 1, quarter: "2019Q1", region: "A", qty: 400 },
   { _id: 2, quarter: "2019Q1", region: "B", qty: 550 },
   { _id: 3, quarter: "2019Q1", region: "C", qty: 1000 },
   { _id: 4, quarter: "2019Q2", region: "A", qty: 660 },
   { _id: 5, quarter: "2019Q2", region: "B", qty: 500 },
   { _id: 6, quarter: "2019Q2", region: "C", qty: 1200 }
] )

Assume that for reporting purposes, you want to view the reported sales data by quarter; e.g.

{ "_id" : "2019Q1", "A" : 400, "B" : 550, "C" : 1000 }

To view the data grouped by quarter, you can use the following aggregation pipeline:

db.reportedsales.aggregate( [
   { $addFields: { obj:  { k: "$region", v: "$qty" } } },
   { $group: { _id: "$quarter", items: { $push: "$obj" } } },
   { $project: { items2: { $concatArrays: [ [ { "k": "_id", "v": "$_id" } ], "$items" ] } } },
   { $replaceWith: { $arrayToObject: "$items2" } }
] )

First stage:

The $addFields stage adds a new obj document field that defines the key k as the region value and the value v as the quantity for that region. For example:

{ "_id" : 1, "quarter" : "2019Q1", "region" : "A", "qty" : 400, "obj" : { "k" : "A", "v" : 400 } }

Second stage:

The $group stage groups by the quarter and uses $push to accumulate the obj fields into a new items array field. For example:

{ "_id" : "2019Q1", "items" : [ { "k" : "A", "v" : 400 }, { "k" : "B", "v" : 550 }, { "k" : "C", "v" : 1000 } ] }

Third stage:

The $project stage uses $concatArrays to create a new array items2 that includes the _id info and the elements from the items array:

{ "_id" : "2019Q1", "items2" : [ { "k" : "_id", "v" : "2019Q1" }, { "k" : "A", "v" : 400 }, { "k" : "B", "v" : 550 }, { "k" : "C", "v" : 1000 } ] }

Fourth stage:

The $replaceWith uses the $arrayToObject to convert the items2 into a document, using the specified key k and value v pairs and outputs that document to the next stage. For example:

{ "_id" : "2019Q1", "A" : 400, "B" : 550, "C" : 1000 }

The aggregation returns the following document:

{ "_id" : "2019Q1", "A" : 400, "B" : 550, "C" : 1000 }
{ "_id" : "2019Q2", "A" : 660, "B" : 500, "C" : 1200 }