MongoDB $unwind
The $unwind stage emits an output for array elements. That changes cardinality and can multiply later calculations.
How it works
The tag report expands each kit’s tags, groups identical strings and counts how many expanded entries they contribute. K4’s empty array supplies no row under the default unwind behavior. If every original kit must be retained, choose preserveNullAndEmptyArrays deliberately and define how its missing tag should be reported.
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 unwind.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 unwind.mongosh.js and run mongosh --nodb --norc --quiet --file unwind.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: $unwind
// 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([
{ $unwind: "$tags" },
{ $group: { _id: "$tags", kits: { $sum: 1 } } },
{ $sort: { _id: 1 } },
])
.toArray(),
);
} finally {
if (owned) {
const owner = await db
.getCollection("_bhylu_owner")
.findOne({ _id: marker });
if (!owner || !/^bhylu_mongo_[a-f0-9]{24}$/.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([
{$unwind:'$tags'},{$group:{_id:'$tags',kits:{$sum:1}}},{$sort:{_id:1}}
]).toArray());
Expected result
[{"_id":"compact","kits":1},{"_id":"outdoor","kits":2},{"_id":"washable","kits":2}]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.
The total expanded entries exceed the number of kit documents. If tags contained duplicates, this count would count occurrences rather than automatically deduplicate one kit’s tag contribution. State which meaning your report requires.