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MongoDB Academy · 강의

Node.js 스크립트로 데이터 시드하기

학습자는 JSON 파일을 읽고 로컬 개발을 위해 문서를 MongoDB에 대량 삽입하는 Node.js 시드 스크립트를 작성합니다.

Node.js 스크립트로 데이터 시드하기은(는) CoddyKit의 무료 MongoDB Academy 강의입니다. 이것은 4개 중 4번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 MongoDB Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. MongoDB Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Why Write a Seed Script?

A seed script is a Node.js program that populates a MongoDB database with initial or test data. Unlike mongoimport, a seed script can generate dynamic data (IDs, relationships, timestamps), conditionally insert data that doesn't already exist, and apply business logic while seeding—for example, hashing passwords or computing derived fields. Seed scripts are the backbone of local development environment setup.

Setting Up the MongoDB Client

A seed script connects to MongoDB using the official mongodb Node.js driver. Keep the connection URI in an environment variable or a .env file—never hardcode credentials. Call client.connect() at the start, run all seed operations, and call client.close() in a finally block to ensure the script exits cleanly even if an error occurs.

const { MongoClient, ObjectId } = require('mongodb');

const URI = process.env.MONGO_URI || 'mongodb://localhost:27017';
const DB_NAME = 'myapp';

async function seed() {
  const client = new MongoClient(URI);
  try {
    await client.connect();
    console.log('Connected to MongoDB');
    const db = client.db(DB_NAME);
    await seedUsers(db);
    await seedProducts(db);
    console.log('Seeding complete!');
  } finally {
    await client.close();
  }
}

seed().catch(console.error);

Inserting Documents With bulkWrite

Use collection.bulkWrite() with ordered: false for efficient bulk inserts. Unlike multiple insertOne calls, bulkWrite sends all operations to the server in a single network round trip. Set ordered: false so that duplicate key errors on individual documents don't stop the entire batch—useful when re-running a seed script that uses deterministic IDs.

async function seedUsers(db) {
  const users = [
    { _id: new ObjectId('aaa000000000000000000001'), name: 'Alice', email: 'alice@example.com', role: 'admin' },
    { _id: new ObjectId('aaa000000000000000000002'), name: 'Bob',   email: 'bob@example.com',   role: 'user'  },
    { _id: new ObjectId('aaa000000000000000000003'), name: 'Carol', email: 'carol@example.com', role: 'user'  }
  ];

  const ops = users.map(u => ({ insertOne: { document: u } }));
  const result = await db.collection('users').bulkWrite(ops, { ordered: false });
  console.log('Users inserted:', result.insertedCount);
}

Idempotent Seeding With deleteMany

Make your seed script idempotent—safe to run multiple times—by clearing the target collections before inserting. Call deleteMany({}) (or drop()) at the start of each seed function. This ensures the database starts from a known clean state on every run, which is essential for local development where you want reproducible data without duplicates.

async function seedProducts(db) {
  const col = db.collection('products');

  // Clear existing data first — idempotent
  await col.deleteMany({});
  console.log('Cleared products collection');

  const products = generateProducts(50); // generate 50 sample products
  await col.insertMany(products);
  console.log('Inserted', products.length, 'products');
}

Generating Realistic Fake Data

For development seeds, generate realistic-looking data programmatically using a helper library like @faker-js/faker or by constructing data manually. Generating data in the script (rather than loading static JSON files) lets you easily change the volume—seed 10 documents for unit tests, 10,000 for load testing—with a single parameter change.

function generateProducts(count) {
  const categories = ['electronics', 'clothing', 'tools', 'books'];
  return Array.from({ length: count }, (_, i) => ({
    _id: new ObjectId(),
    sku: 'PROD-' + String(i + 1).padStart(4, '0'),
    name: 'Product ' + (i + 1),
    price: parseFloat((Math.random() * 100 + 1).toFixed(2)),
    category: categories[i % categories.length],
    rating: parseFloat((Math.random() * 2 + 3).toFixed(1)), // 3.0 - 5.0
    isActive: true,
    createdAt: new Date(Date.now() - i * 86400000) // staggered dates
  }));
}

Seeding Relationships Between Collections

When seeding related collections, create parent documents first and use their _id values when creating child documents. Using deterministic ObjectIds (constructed from fixed hex strings) lets you reference specific parent documents reliably across runs without having to query for them after insertion.

const USER_ID_ALICE = new ObjectId('aaa000000000000000000001');
const USER_ID_BOB   = new ObjectId('aaa000000000000000000002');

async function seedOrders(db) {
  await db.collection('orders').deleteMany({});
  const orders = [
    { userId: USER_ID_ALICE, total: 49.99,  status: 'delivered', createdAt: new Date() },
    { userId: USER_ID_BOB,   total: 120.00, status: 'pending',   createdAt: new Date() }
  ];
  await db.collection('orders').insertMany(orders);
  console.log('Orders seeded');
}

Reading Seed Data From JSON Files

For complex seed data that is easier to maintain as JSON (product catalogues, country lists, configuration tables), read the JSON file with fs.readFileSync and pass the parsed array directly to insertMany. Pair this with the idempotent deleteMany approach so the script can be re-run safely after editing the JSON file.

const fs  = require('fs');
const path = require('path');

async function seedFromFile(db, collectionName, filePath) {
  const raw  = fs.readFileSync(path.resolve(filePath), 'utf-8');
  const docs = JSON.parse(raw);

  const col = db.collection(collectionName);
  await col.deleteMany({});
  await col.insertMany(docs);
  console.log('Seeded', docs.length, 'documents into', collectionName);
}

// Usage
await seedFromFile(db, 'countries', './seed-data/countries.json');

Creating Indexes After Seeding

Seed scripts should create the same indexes that production uses. Call collection.createIndex() (or createIndexes()) at the end of each seed function, or in a dedicated ensureIndexes step. Creating indexes after bulk insert is faster than maintaining them during the insert—MongoDB builds the B-tree from the sorted data in one pass.

async function ensureIndexes(db) {
  // Products: fast category lookups and rating sorts
  await db.collection('products').createIndex({ category: 1, rating: -1 });
  await db.collection('products').createIndex({ sku: 1 }, { unique: true });

  // Orders: fast user-based queries
  await db.collection('orders').createIndex({ userId: 1, createdAt: -1 });

  console.log('Indexes created');
}

Running the Seed Script

Run the seed script from the command line with node seed.js or add it to your package.json scripts section. Pass environment-specific URIs via environment variables so the same script works for local, CI, and staging environments without modification. Never run a seed script that calls deleteMany against a production URI.

// package.json scripts
// {
//   "scripts": {
//     "seed": "node scripts/seed.js",
//     "seed:test": "MONGO_URI=mongodb://localhost:27017 node scripts/seed.js"
//   }
// }

// Run with:
// npm run seed
// or
// MONGO_URI='mongodb://localhost:27017' node scripts/seed.js

Using Seed Scripts in CI Pipelines

In a CI pipeline, run the seed script as part of the test setup step before executing integration or end-to-end tests. Spin up a MongoDB Docker container (or use mongodb-memory-server), run the seed script to populate test data, run the tests, and tear down the container. This gives every CI run a clean, reproducible database state.

# GitHub Actions step example
# - name: Start MongoDB
#   run: docker run -d -p 27017:27017 mongo:7

# - name: Seed test data
#   run: node scripts/seed.js
#   env:
#     MONGO_URI: mongodb://localhost:27017

# - name: Run integration tests
#   run: npm test
#   env:
#     MONGO_URI: mongodb://localhost:27017

Handling Seed Errors Gracefully

Wrap your seed functions in try/catch blocks so that a failure in one section doesn't leave the database in a half-seeded state without a clear error message. Log the error, abort the remaining seed steps, and exit with a non-zero code so CI pipelines detect the failure. A partial seed is often worse than no seed because it produces misleading test results.

async function seed() {
  const client = new MongoClient(process.env.MONGO_URI);
  try {
    await client.connect();
    const db = client.db('myapp');
    await seedUsers(db);
    await seedProducts(db);
    await seedOrders(db);
    await ensureIndexes(db);
    console.log('All seed steps completed successfully');
    process.exit(0);
  } catch (err) {
    console.error('Seed failed:', err.message);
    process.exit(1);
  } finally {
    await client.close();
  }
}

Quick Check

Test your understanding of MongoDB & NoSQL Databases concepts from this lesson.

Lesson Recap

In this lesson you learned: seed scripts use the Node.js driver to insert, clear, and regenerate test data programmatically, deterministic ObjectIds make cross-collection relationships reproducible across runs, and calling deleteMany at the start of each seed function makes the script idempotent and safe to re-run. This completes the Importing and Exporting Data course — next we advance to Indexes Fundamentals for production-grade query performance.

자주 묻는 질문

“Node.js 스크립트로 데이터 시드하기” 강의는 무료인가요?

네 — “Node.js 스크립트로 데이터 시드하기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 MongoDB Academy 강의 전체를 잠금 해제할 수 있습니다. MongoDB Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

“Node.js 스크립트로 데이터 시드하기”에서 뭘 배우나요?

학습자는 JSON 파일을 읽고 로컬 개발을 위해 문서를 MongoDB에 대량 삽입하는 Node.js 시드 스크립트를 작성합니다. 브라우저에서 직접 실행하는 실습 코드로 MongoDB Academy을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

MongoDB Academy을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 MongoDB Academy은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 4번째 강의입니다.

“Node.js 스크립트로 데이터 시드하기” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 MongoDB Academy 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 MongoDB Academy 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

  1. mongoimport: JSON 및 CSV 파일 불러오기
  2. mongoexport: 컬렉션을 파일로 내보내기
  3. 전체 백업을 위한 mongodump와 mongorestore
  4. Node.js 스크립트로 데이터 시드하기
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