MongoDB $project
The $project stage reshapes pipeline output. It can select fields, rename values and construct expressions.
How it works
A public kit card uses id and title rather than exposing the whole stored document. References such as $_id read a field value; an unprefixed string would be a literal. _id is explicitly excluded because the output already contains the renamed id field.
Run in a separate MongoDB lab
Start a disposable lab automatically: in a separate empty learning folder, save package.json, run-lab.mjs and this lesson’s source with its displayed filename. Run npm install --ignore-scripts, then node run-lab.mjs project.mongosh.js. mongosh lessons also need mongosh installed on PATH. The launcher downloads the pinned MongoDB server on first use, starts one authenticated loopback replica-set member, supplies private child-process credentials, runs the lesson and stops that server. Its cache, generated data and diagnostic files stay in the private .bhylu-mongodb-lab folder; it installs no service. Review downloaded code before executing it.
The following manual connection route is an alternative for readers who already have a suitable separate lab instance.
The reviewed baseline is MongoDB 8.0.12, mongosh 2.14.0, Node.js 22.15.1 and driver 6.17.0. These are tested versions, not a newest-release claim. Use a private authenticated learning server on 127.0.0.1 and an explicit non-default port, not your normal database or an Atlas project. These scripts intentionally refuse port 27017 and remote hosts. Bhylu has no MongoDB browser runner; a plain JavaScript playground cannot provide this database.
Supply your own lab connection through the child process environment variable BHYLU_MONGO_URI and set BHYLU_MONGO_LAB=YES. Keep credentials private; do not save them in source or public commands. The lab role needs permission to create and drop its generated databases and collections. No PHP, Apache or normal XAMPP service change is needed.
Save the complete download as project.mongosh.js and run mongosh --nodb --norc --quiet --file project.mongosh.js in a separate child process. It reads its private connection from the environment. The file uses actual mongosh APIs; it is not a Node.js module.
Every run generates a new bhylu_mongo_ database name, refuses an existing collection set, writes its own ownership marker, seeds the complete fictional dataset and finally removes only that owned database after checking the marker. Do not change the generated name to a valuable existing database. Closing the child process releases its client connections.
Example
// Bhylu MongoDB 8.0.12 lab: $project
// Run only against your separate authenticated loopback learning instance.
async function main() {
if (process.env.BHYLU_MONGO_LAB !== "YES")
throw new Error("Set BHYLU_MONGO_LAB=YES for a private lab");
const raw = process.env.BHYLU_MONGO_URI;
if (!raw)
throw new Error("Set BHYLU_MONGO_URI privately; do not commit credentials");
const address = new URL(raw);
if (
address.protocol !== "mongodb:" ||
address.hostname !== "127.0.0.1" ||
!address.port ||
address.port === "27017"
)
throw new Error("Use a private loopback MongoDB on a non-default port");
const marker = new ObjectId().toString(),
dbName = "bhylu_mongo_" + marker;
const connection = new Mongo(raw);
const db = connection.getDB(dbName);
let owned = false;
const emit = (value) => print(JSON.stringify(value));
try {
if ((await db.getCollectionNames()).length)
throw new Error("Refusing an existing database");
await db.createCollection("_bhylu_owner");
await db.getCollection("_bhylu_owner").insertOne({ _id: marker });
owned = true;
await db.getCollection("kits").insertMany([
{
_id: "K1",
label: "Birdwatch pack",
category: "observation",
available: 3,
condition: "ready",
location: { room: "North", shelf: 2 },
tags: ["outdoor", "compact"],
notes: null,
checks: [
{ kind: "lens", passed: true },
{ kind: "strap", passed: false },
],
},
{
_id: "K2",
label: "Stream sampler",
category: "sampling",
available: 1,
condition: "ready",
location: { room: "South", shelf: 1 },
tags: ["outdoor", "washable"],
checks: [
{ kind: "lens", passed: false },
{ kind: "strap", passed: true },
],
},
{
_id: "K3",
label: "Soil survey set",
category: "sampling",
available: 0,
condition: "repair",
location: { room: "North", shelf: 3 },
tags: ["washable"],
notes: "Replace pouch",
checks: [],
},
{
_id: "K4",
label: "Weather logger",
category: "observation",
available: 2,
condition: "ready",
location: { room: "South", shelf: 2 },
tags: [],
notes: null,
checks: [],
},
]);
await db.getCollection("members").insertMany([
{ _id: "M1", name: "Asha", area: "North" },
{ _id: "M2", name: "Dev", area: "South" },
{ _id: "M3", name: "Noor", area: "West" },
]);
await db.getCollection("loans").insertMany([
{
_id: "L1",
kitId: "K1",
memberId: "M1",
quantity: 1,
status: "returned",
openedAt: new Date("2026-01-10T09:00:00Z"),
},
{
_id: "L2",
kitId: "K1",
memberId: "M2",
quantity: 2,
status: "open",
openedAt: new Date("2026-01-11T09:00:00Z"),
},
{
_id: "L3",
kitId: "K2",
memberId: "M1",
quantity: 1,
status: "open",
openedAt: new Date("2026-01-11T10:00:00Z"),
},
{
_id: "L4",
kitId: "K3",
memberId: "M2",
quantity: 1,
status: "returned",
openedAt: new Date("2026-01-10T11:00:00Z"),
},
{
_id: "L5",
kitId: "K4",
memberId: null,
quantity: 1,
status: "planned",
openedAt: new Date("2026-01-12T09:00:00Z"),
},
]);
const kits = db.getCollection("kits"),
loans = db.getCollection("loans"),
members = db.getCollection("members");
// Lesson commands begin here.
emit(
await kits
.aggregate([
{ $match: { _id: "K2" } },
{
$project: {
_id: 0,
id: "$_id",
title: "$label",
units: "$available",
},
},
])
.toArray(),
);
} finally {
if (owned) {
const owner = await db
.getCollection("_bhylu_owner")
.findOne({ _id: marker });
if (!owner || !/^bhylu_mongo_[a-f0-9]{24}$/.test(dbName))
throw new Error("Refusing cleanup without matching ownership marker");
await db.dropDatabase();
}
// The noninteractive mongosh process closes its connection when it exits.
}
}
main().catch((error) => {
console.error(error.message);
exit(1);
});
Focus on the lesson operations
This excerpt omits connection guards, seed and cleanup. Use the complete download to run it.
emit(await kits.aggregate([
{$match:{_id:'K2'}},{$project:{_id:0,id:'$_id',title:'$label',units:'$available'}}
]).toArray());
Expected result
[{"id":"K2","title":"Stream sampler","units":1}]The script emits JSON so these small results are unambiguous. Interactive shell formatting may look different. Object IDs and owned database names are not hard-coded expected values.
Projection changes the result shape without renaming stored fields. In aggregation, expressions and literal values have their own syntax; use $literal where a value beginning with a dollar sign must remain ordinary data.