$rand (aggregation)

Generate Random Data Points

This example models charitable donations. The collection starts with a list of donors.

db.donors.insertMany(
   [
     { donorId: 1000, amount: 0, frequency: 1 },
     { donorId: 1001, amount: 0, frequency: 2 },
     { donorId: 1002, amount: 0, frequency: 1 },
     { donorId: 1003, amount: 0, frequency: 2 },
     { donorId: 1004, amount: 0, frequency: 1 }
   ]
)

We use an aggregation pipeline to update each document with a random donation amount.

db.donors.aggregate(
   [
      { $set: { amount: { $multiply: [ { $rand: {} }, 100 ] } } },
      { $set: { amount: { $floor: "$amount" } } },
      { $merge: "donors" }
   ]
)

The first $set stage updates the amount field. An initial value between 0 and 1 is generated using $rand. Then $multiply scales it upward 100 times.

The $floor operator in the second $set stage removes the decimal portion from the amount to leave an integer value.

Finally, $merge writes the random value created in the previous steps to the amount field, updating it for each document in the donors collection.

You can view the results with a projection stage:

db.donors.aggregate(
   [
      { $project: {_id: 0, donorId: 1, amount: 1 } }
   ]
)

The projection shows the scaled amounts are now random values in the range from 0 to 99.

{ "donorId" : 1000, "amount" : 27 }
{ "donorId" : 1001, "amount" : 10 }
{ "donorId" : 1002, "amount" : 88 }
{ "donorId" : 1003, "amount" : 73 }
{ "donorId" : 1004, "amount" : 5 }

Select Random Items From a Collection

You can use $rand in an aggregation pipeline to select random documents from a collection. Consider a collection of voter records:

db.voters.insertMany(
   [
     { name: "Archibald", voterId: 4321, district: 3, registered: true },
     { name: "Beckham", voterId: 4331, district: 3, registered: true },
     { name: "Carolin", voterId: 5321, district: 4, registered: true },
     { name: "Debarge", voterId: 4343, district: 3, registered: false },
     { name: "Eckhard", voterId: 4161, district: 3, registered: false },
     { name: "Faberge", voterId: 4300, district: 1, registered: true },
     { name: "Grimwald", voterId: 4111, district: 3, registered: true },
     { name: "Humphrey", voterId: 2021, district: 3, registered: true },
     { name: "Idelfon", voterId: 1021, district: 4, registered: true },
     { name: "Justo", voterId: 9891, district: 3, registered: false }
   ]
)

Imagine you want to select about half of the voters in District 3 to do some polling.

db.voters.aggregate(
   [
      { $match: { district: 3 } },
      { $match: { $expr: { $lt: [0.5, {$rand: {} } ] } } },
      { $project: { _id: 0, name: 1, registered: 1 } }
   ]
)

The first pipeline stage matches all documents where the voter is from district 3.

The second $match stage uses $rand in a match expression to further refine the selection. For each document, $rand generates a value between 0 and 1. The threshold of 0.5 in the less than ($lt) comparison means that $expr will be true for about half the documents.

In the $project stage the selected documents are filtered to return the name and registered fields. There are 7 voters in District 3, running the code selects about half of them.

{ "name" : "Archibald", "registered" : true }
{ "name" : "Debarge", "registered" : false }
{ "name" : "Humphrey", "registered" : true }