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MongoDB Academy · Pelajaran

Menggabungkan Sort, Skip, Limit, dan Proyeksi

Peserta didik akan menyusun rangkaian kueri lengkap dengan penyaringan, proyeksi, pengurutan, dan paginasi untuk mendukung titik akhir daftar yang realistis.

Menggabungkan Sort, Skip, Limit, dan Proyeksi adalah pelajaran MongoDB Academy gratis di CoddyKit. Ini adalah pelajaran 4 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar MongoDB Academy, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus MongoDB Academy mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

The Complete Query Chain

A production-quality MongoDB list query combines four components: a filter to select matching documents, a projection to select which fields to return, a sort to order the results, and pagination (skip/limit or keyset) to return one page at a time. Understanding how these compose and in what order MongoDB executes them is essential for writing correct and performant queries.

MongoDB's Internal Execution Order

Regardless of the order you chain methods in the driver, MongoDB always executes query components in this fixed sequence: 1) Filter → 2) Sort → 3) Skip → 4) Limit. Projection is applied as documents are read from the storage engine. This order matters: skip operates on the sorted result set, and limit caps the final output after skipping. You cannot change this execution order.

// These two are identical — order of chaining doesn't matter
db.products.find({}).sort({ price: -1 }).skip(20).limit(10);
db.products.find({}).limit(10).skip(20).sort({ price: -1 });
// MongoDB executes: filter -> sort -> skip -> limit

Building a List API Endpoint

Put it all together in a realistic list endpoint. The example below fetches page 2 of active products in the 'electronics' category, sorted by rating descending, returning only the fields needed for a product card UI. Each piece of the chain serves a specific purpose.

const filter = { category: 'electronics', isActive: true };
const projection = { _id: 1, name: 1, price: 1, thumbnailUrl: 1, rating: 1 };
const PAGE = 2;
const PAGE_SIZE = 20;

const products = await db.collection('products')
  .find(filter)                     // 1. filter
  .project(projection)              // projection
  .sort({ rating: -1, _id: -1 })   // 2. sort
  .skip((PAGE - 1) * PAGE_SIZE)    // 3. skip
  .limit(PAGE_SIZE)                 // 4. limit
  .toArray();

The Supporting Index

For the complete query chain to be efficient, you need an index that covers the filter and sort. The filter uses category and isActive (equality), and the sort uses rating and _id. The ideal compound index places equality fields first, then sort fields: { category: 1, isActive: 1, rating: -1, _id: -1 }.

// Index supporting the list API query
db.products.createIndex({ category: 1, isActive: 1, rating: -1, _id: -1 });

// Verify: explain should show IXSCAN and no SORT stage
db.products.find({ category: 'electronics', isActive: true })
  .sort({ rating: -1, _id: -1 })
  .skip(20)
  .limit(20)
  .explain('executionStats');

The Complete Mongoose Query Chain

Mongoose's fluent API makes the same query chain readable and type-safe. Chain .find(), .select(), .sort(), .skip(), and .limit(), then call .lean() for a plain POJO result (much faster than full Mongoose documents when you don't need lifecycle hooks or virtuals).

const products = await Product
  .find({ category: 'electronics', isActive: true })
  .select({ name: 1, price: 1, thumbnailUrl: 1, rating: 1, _id: 1 })
  .sort({ rating: -1, _id: -1 })
  .skip((PAGE - 1) * PAGE_SIZE)
  .limit(PAGE_SIZE)
  .lean();  // returns plain objects, not Mongoose Documents

console.log('Products on page:', products.length);

Returning Pagination Metadata

A well-designed list API response includes pagination metadata alongside the data array: the current page, page size, total document count, total page count, and whether there is a next/previous page. This lets clients render pagination controls without making a separate count request. Always return metadata and data together in one response object.

const [total, items] = await Promise.all([
  Product.countDocuments({ category: 'electronics', isActive: true }),
  Product.find({ category: 'electronics', isActive: true })
    .select({ name: 1, price: 1, thumbnailUrl: 1, rating: 1 })
    .sort({ rating: -1 })
    .skip((PAGE - 1) * PAGE_SIZE)
    .limit(PAGE_SIZE)
    .lean()
]);

res.json({
  data:       items,
  page:       PAGE,
  pageSize:   PAGE_SIZE,
  total:      total,
  totalPages: Math.ceil(total / PAGE_SIZE),
  hasNext:    PAGE * PAGE_SIZE < total,
  hasPrev:    PAGE > 1
});

Applying Default Values and Input Validation

Always validate and sanitise the page, limit, and sort query parameters before using them in a MongoDB query. Unchecked values can cause skip(-5) errors, enormous limit(999999) calls that exhaust memory, or sort injections if you pass user-provided sort keys directly. Whitelist allowed sort fields and enforce sane min/max bounds on page and limit.

const ALLOWED_SORT_FIELDS = new Set(['rating', 'price', 'createdAt']);

function parseListParams(query) {
  const page  = Math.max(1, parseInt(query.page)  || 1);
  const limit = Math.min(100, Math.max(1, parseInt(query.limit) || 20));
  const sortField = ALLOWED_SORT_FIELDS.has(query.sort) ? query.sort : 'createdAt';
  const sortDir   = query.order === 'asc' ? 1 : -1;
  return { page, limit, sort: { [sortField]: sortDir, _id: sortDir } };
}

Switching Between Offset and Keyset Pagination

You can offer both pagination modes from the same endpoint by checking for a cursor parameter (keyset) vs a page parameter (offset). When cursor is provided, apply the range filter and omit skip(). When only page is provided, use skip(). This lets you migrate clients gradually from offset to keyset pagination without breaking backward compatibility.

async function listItems(req, res) {
  const limit = 20;
  let query = Product.find({ isActive: true }).sort({ createdAt: -1, _id: -1 }).limit(limit);

  if (req.query.cursor) {
    const { createdAt, _id } = decodeCursor(req.query.cursor);
    query = query.where('$or').equals([
      { createdAt: { $lt: new Date(createdAt) } },
      { createdAt: new Date(createdAt), _id: { $lt: _id } }
    ]);
  } else if (req.query.page) {
    const page = Math.max(1, parseInt(req.query.page) || 1);
    query = query.skip((page - 1) * limit);
  }

  const items = await query.lean();
  res.json({ items, nextCursor: items.length === limit ? encodeCursor(items.at(-1)) : null });
}

Testing the Query Chain

Test your complete query chain with both unit tests (mock the driver) and integration tests (real MongoDB via an in-memory mongod or testcontainers). Seed the test collection with enough documents to verify that pagination boundaries are correct: that page 1 and page 2 together return exactly 2×pageSize distinct documents with no duplicates or gaps.

Caching Paginated Results

For read-heavy paginated APIs, consider caching responses at the HTTP level (Redis or a CDN) keyed by the full query string including page, sort, and filters. Cache TTLs of 10-60 seconds reduce database load dramatically for popular queries. Be aware that cached responses may return slightly stale data—acceptable for most use cases but not for financial or real-time dashboards.

Verifying the Full Chain With explain()

Run your complete find chain with explain('executionStats') to validate the full query plan. The ideal plan shows: IXSCAN for the filter, the same index scan serving the sort (no separate SORT stage), and nReturned equal to your limit value. Any unexpected COLLSCAN or SORT stage indicates a missing or ineffective index.

const stats = await db.collection('products')
  .find({ category: 'electronics', isActive: true })
  .sort({ rating: -1, _id: -1 })
  .skip(20)
  .limit(20)
  .explain('executionStats');

const stage = stats.executionStats.executionStages;
console.log('Stage:', stage.stage);        // should be LIMIT
console.log('Docs examined:', stats.executionStats.totalDocsExamined);
console.log('Docs returned:', stats.executionStats.nReturned);

Quick Check

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

Lesson Recap

In this lesson you learned: MongoDB always executes filter → sort → skip → limit regardless of method chaining order, the supporting index should place equality fields first then sort fields, and pagination metadata (total, hasNext, hasPrev) should be returned alongside the data array. Next up we explore importing and exporting data with mongoimport, mongoexport, and seed scripts.

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Menggabungkan Sort, Skip, Limit, dan Proyeksi” gratis?

Ya — teks lengkap “Menggabungkan Sort, Skip, Limit, dan Proyeksi” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus MongoDB Academy, upgrade ke CoddyKit PRO. Kursus MongoDB Academy mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Menggabungkan Sort, Skip, Limit, dan Proyeksi”?

Peserta didik akan menyusun rangkaian kueri lengkap dengan penyaringan, proyeksi, pengurutan, dan paginasi untuk mendukung titik akhir daftar yang realistis. Kamu berlatih MongoDB Academy dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai MongoDB Academy?

Tidak diperlukan pengalaman sebelumnya. MongoDB Academy di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 4 dari 4.

Berapa lama pelajaran “Menggabungkan Sort, Skip, Limit, dan Proyeksi” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran MongoDB Academy ini?

Ya. Setiap pelajaran MongoDB Academy menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Pengurutan dengan sort() dan Beberapa Kunci
  2. Skip dan Limit: Paginasi Berbasis Offset
  3. Paginasi Keyset dengan Kueri Rentang
  4. Menggabungkan Sort, Skip, Limit, dan Proyeksi
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