MongoDB Academy · Pelajaran

Konsep Alur: Tahapan, Operator, dan Ekspresi

Peserta didik akan memahami cara data mengalir melalui tahapan alur dan membedakan operator tahapan dari operator ekspresi.

Konsep Alur: Tahapan, Operator, dan Ekspresi adalah pelajaran MongoDB Academy gratis di CoddyKit. Ini adalah pelajaran 1 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.

What Is the Aggregation Pipeline?

The aggregation pipeline is MongoDB's server-side data transformation engine. Instead of retrieving raw documents and processing them in application code, you define a sequence of stages that transform the data stream step by step—filtering, reshaping, grouping, computing, and sorting—all within the database engine. This keeps heavy computation close to the data and dramatically reduces what you send over the network.

// A simple aggregation pipeline
db.orders.aggregate([
  { $match: { status: 'completed' } },   // stage 1: filter
  { $group: { _id: '$userId', total: { $sum: '$amount' } } },  // stage 2: group
  { $sort: { total: -1 } },             // stage 3: sort
  { $limit: 10 }                        // stage 4: take top 10
]);

How Documents Flow Through Stages

Think of the pipeline as a conveyor belt: each document enters stage 1, is transformed (or filtered out), and the results flow into stage 2, then stage 3, and so on. At each stage, the set of documents can shrink (filtering), expand (unwind), or be completely replaced by computed summaries (group). Documents that fail a filter condition are dropped from the pipeline and never reach later stages.

// Data flow visualization:
// Input: 10,000 orders
// Stage 1 ($match: status='completed'):  8,000 docs
// Stage 2 ($group by userId):            1,200 docs (one per user)
// Stage 3 ($sort by total DESC):         1,200 docs (reordered)
// Stage 4 ($limit 10):                      10 docs
// Output to client: 10 docs

Stage Operators vs Expression Operators

The aggregation framework distinguishes between two kinds of operators: stage operators (prefixed with $) that define what a pipeline stage does—like $match, $group, $project—and expression operators (also prefixed with $) that compute values within a stage—like $sum, $avg, $concat. Stages are the building blocks; expressions are the calculations inside them.

db.sales.aggregate([
  // $group is a STAGE operator
  { $group: {
    _id: '$region',
    // $sum and $avg are EXPRESSION operators (accumulators here)
    totalRevenue: { $sum: '$amount' },
    avgOrder: { $avg: '$amount' },
    // $concat is an expression operator
    label: { $concat: ['Region: ', '$region'] }
  }}
]);

Field References With the $ Sign

Inside aggregation expressions, a string prefixed with $ is a field reference—it refers to the value of that field in the current document. Without the prefix, a string is treated as a literal value. This distinction is crucial: '$price' means 'the value of the price field' while 'price' means the literal string 'price'.

db.products.aggregate([
  { $project: {
    name: 1,
    // '$price' references the 'price' field value
    discountedPrice: { $multiply: ['$price', 0.9] },
    // 'price' without $ is a literal string
    label: 'price',  // all docs get the string 'price', not the field value
    // $$ROOT is a special variable for the whole document
    original: '$$ROOT'
  }}
]);

System Variables: $$ROOT, $$NOW, $$CURRENT

The aggregation pipeline provides system variables prefixed with $$ that give access to special values: $$ROOT refers to the entire current document, $$NOW is the current datetime (useful in $project for computed fields), $$CURRENT is the current document field path context, and $$REMOVE conditionally removes a field when assigned as a value.

db.orders.aggregate([
  { $project: {
    _id: 1,
    orderDate: 1,
    daysOld: {
      $divide: [
        { $subtract: ['$$NOW', '$orderDate'] },  // $$NOW = current time
        1000 * 60 * 60 * 24  // convert ms to days
      ]
    },
    // Conditionally remove a field:
    sensitiveField: { $cond: { if: '$isAdmin', then: '$secret', else: '$$REMOVE' } }
  }}
]);

Common Pipeline Stages Overview

MongoDB provides dozens of pipeline stages. The most essential ones to know are: $match (filter documents), $project (reshape/compute fields), $group (aggregate by key), $sort (order results), $limit and $skip (pagination), $lookup (join another collection), and $unwind (flatten arrays). Master these and you can express almost any analytical query.

// Quick reference of the most-used stages:
// $match   - { $match: { field: condition } }
// $project - { $project: { keep: 1, drop: 0, computed: expr } }
// $group   - { $group: { _id: '$field', agg: { $sum: '$val' } } }
// $sort    - { $sort: { field: 1 or -1 } }
// $limit   - { $limit: N }
// $skip    - { $skip: N }
// $lookup  - { $lookup: { from, localField, foreignField, as } }
// $unwind  - { $unwind: '$arrayField' }

Pipeline Optimization: Stage Order Matters

MongoDB's query optimizer automatically reorders some pipeline stages for efficiency—for example, moving $match before $sort and $group when possible. However, you should always place $match as early as possible yourself: early filtering reduces the number of documents subsequent stages must process, and if $match is the first stage, MongoDB can use an index for it.

// BAD: expensive group before filter
db.orders.aggregate([
  { $group: { _id: '$userId', total: { $sum: '$amount' } } },
  { $match: { total: { $gt: 1000 } } }  // too late to use index
]);

// GOOD: filter first, then group
db.orders.aggregate([
  { $match: { status: 'completed', createdAt: { $gte: thisYear } } },
  { $group: { _id: '$userId', total: { $sum: '$amount' } } },
  { $match: { total: { $gt: 1000 } } }
]);

Arithmetic Expression Operators

Arithmetic expressions let you compute new values from existing fields within a stage. The most common ones are: $add, $subtract, $multiply, $divide, $mod, and $abs. These operators work on field references, literals, or the result of other expressions—enabling complex calculations that previously required application-side processing.

db.invoices.aggregate([
  { $project: {
    subtotal: '$subtotal',
    taxAmount: { $multiply: ['$subtotal', 0.08] },  // 8% tax
    total: { $add: ['$subtotal', { $multiply: ['$subtotal', 0.08] }] },
    discount: { $subtract: ['$listPrice', '$salePrice'] },
    margin: { $divide: [{ $subtract: ['$price', '$cost'] }, '$price'] }
  }}
]);

Conditional Expressions: $cond and $ifNull

Conditional expressions allow if/else logic inside aggregation stages. $cond evaluates a boolean expression and returns one of two values (like a ternary operator). $ifNull returns a fallback value when a field is null or missing. These are essential for handling optional fields and computing derived categories without modifying the stored data.

db.products.aggregate([
  { $project: {
    name: 1,
    price: 1,
    // Ternary: label products as budget or premium
    tier: {
      $cond: {
        if: { $lt: ['$price', 100] },
        then: 'budget',
        else: 'premium'
      }
    },
    // Fallback for missing description
    description: { $ifNull: ['$description', 'No description available'] }
  }}
]);

String Expression Operators

String operators let you manipulate text fields within the pipeline: $concat joins strings, $toUpper/$toLower change case, $trim/$ltrim/$rtrim strip whitespace, $substr extracts a substring, and $split splits on a delimiter. These are useful for normalising data during aggregation without needing to update the stored documents.

db.users.aggregate([
  { $project: {
    // Combine first and last name
    fullName: { $concat: ['$firstName', ' ', '$lastName'] },
    // Normalise email to lowercase
    emailNorm: { $toLower: '$email' },
    // Extract domain from email
    domain: {
      $arrayElemAt: [{ $split: ['$email', '@'] }, 1]
    }
  }}
]);

Date Expression Operators

Date operators extract components from date fields for grouping and computation: $year, $month, $dayOfMonth, $hour, $minute, $dayOfWeek, and $dateToString. These are indispensable for time-series analytics like 'total orders per month' or 'average revenue by day of week'.

// Group orders by year and month
db.orders.aggregate([
  { $group: {
    _id: {
      year: { $year: '$createdAt' },
      month: { $month: '$createdAt' }
    },
    count: { $sum: 1 },
    revenue: { $sum: '$amount' }
  }},
  { $sort: { '_id.year': 1, '_id.month': 1 } }
]);

Quick Check

Test your understanding of aggregation pipeline concepts from this lesson.

Lesson Recap

In this lesson you learned: pipeline stages transform a stream of documents sequentially, stage operators define what a stage does while expression operators compute values within stages, and $ prefixes field references while $$ prefixes system variables. Next up we focus on $match and $project, the filter and reshape workhorses.

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Konsep Alur: Tahapan, Operator, dan Ekspresi” gratis?

Ya — teks lengkap “Konsep Alur: Tahapan, Operator, dan Ekspresi” 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 “Konsep Alur: Tahapan, Operator, dan Ekspresi”?

Peserta didik akan memahami cara data mengalir melalui tahapan alur dan membedakan operator tahapan dari operator ekspresi. 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.

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Semua pelajaran dalam kursus ini

Konsep Alur: Tahapan, Operator, dan Ekspresi
$match dan $project: Menyaring dan Membentuk Ulang
$group: Mengagregasi dan Menghitung Total
$sort, $limit, dan $skip dalam Alur

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