0Pricing
MongoDB Academy · Leçon

$unwind : décomposer les champs de tableau

Les apprenants transformeront un champ de tableau en documents individuels avec $unwind et le combineront avec $group pour effectuer des analyses par élément.

$unwind : décomposer les champs de tableau est une leçon MongoDB Academy gratuite sur CoddyKit. Ceci est la leçon 2 sur 4. Tu peux lire la leçon complète ci-dessous gratuitement — puis la pratiquer en direct dans le navigateur avec un éditeur de code intégré et un tuteur IA 24/7. Elle fait partie du parcours d'apprentissage MongoDB Academy, et ta progression se synchronise sur le web et l'application CoddyKit. Le cours MongoDB Academy comprend 4 leçons au total.

Certaines parties de cette leçon n'ont pas encore été traduites et s'affichent en anglais.

What Does $unwind Do?

The $unwind stage deconstructs an array field in a document into multiple output documents—one per array element. Each output document is a copy of the original document with the array field replaced by a single element from the array. This is essential for per-element analytics, like counting how many times each tag appears across all articles in a collection.

// Input document:
// { title: 'MongoDB Guide', tags: ['nosql', 'database', 'mongodb'] }

db.articles.aggregate([
  { $unwind: '$tags' }
]);
// Output: THREE documents:
// { title: 'MongoDB Guide', tags: 'nosql' }
// { title: 'MongoDB Guide', tags: 'database' }
// { title: 'MongoDB Guide', tags: 'mongodb' }

Basic $unwind Syntax

The simplest form of $unwind is a string with the dollar-prefixed field path: { $unwind: '$arrayField' }. The extended object syntax allows additional options like preserving empty arrays and including the array index. For most cases, the simple string form is sufficient.

// Simple string form
db.products.aggregate([
  { $unwind: '$reviews' }
]);

// Extended object form with options
db.products.aggregate([
  { $unwind: {
    path: '$reviews',
    includeArrayIndex: 'reviewIndex',   // add index field
    preserveNullAndEmptyArrays: true    // keep docs with no reviews
  }}
]);

The Missing Array Problem

By default, $unwind removes documents where the specified field is missing, null, or an empty array. This behavior is like an inner join: only documents with non-empty arrays pass through. To preserve documents with missing or empty arrays, set preserveNullAndEmptyArrays: true. This is important when the array field is optional and you don't want to lose the parent document.

// Default: documents without 'tags' are DROPPED
db.articles.aggregate([{ $unwind: '$tags' }]);
// Article with no tags: { title: 'Untitled' } -> EXCLUDED

// preserveNullAndEmptyArrays: documents are KEPT
db.articles.aggregate([{
  $unwind: {
    path: '$tags',
    preserveNullAndEmptyArrays: true  // keep docs with no tags
  }
}]);
// { title: 'Untitled' } -> INCLUDED with tags: null

includeArrayIndex for Element Position

The includeArrayIndex option adds a new field to each output document containing the 0-based index of the element in the original array. This is useful when the position in the array carries meaning—for example, the order of steps in a process, the rank of a search result, or the sequence of events in a log.

// Track which position each tag appeared at in the original array
db.articles.aggregate([{
  $unwind: {
    path: '$tags',
    includeArrayIndex: 'tagPosition'
  }
}]);
// { title: 'Guide', tags: 'nosql', tagPosition: 0 }
// { title: 'Guide', tags: 'database', tagPosition: 1 }
// { title: 'Guide', tags: 'mongodb', tagPosition: 2 }

$unwind Followed by $group

The most common pattern in MongoDB aggregation is $unwind + $group: first flatten the array to get one document per element, then group to compute per-element statistics. For example, unwinding the tags array and then grouping by tag name gives you the count of articles per tag.

// Count how many articles use each tag
db.articles.aggregate([
  { $unwind: '$tags' },      // one doc per tag
  { $group: {
    _id: '$tags',            // group by tag value
    count: { $sum: 1 }       // count articles per tag
  }},
  { $sort: { count: -1 } }, // most popular first
  { $limit: 20 }            // top 20 tags
]);

$unwind With $lookup Results

After a $lookup, the joined field is always an array. When the join is one-to-one (joining by a unique _id), you typically $unwind immediately to flatten the single-element array into an embedded object. This is such a common pattern that you'll see it in almost every pipeline that uses $lookup.

db.orders.aggregate([
  { $lookup: {
    from: 'customers',
    localField: 'customerId',
    foreignField: '_id',
    as: 'customer'
  }},
  // Flatten the single-element array
  { $unwind: '$customer' },
  // Now access customer fields as objects
  { $project: {
    orderId: '$_id',
    amount: 1,
    'customer.name': 1,
    'customer.email': 1
  }}
]);

Flattening Nested Arrays

For documents with nested arrays (arrays inside arrays), you can use multiple consecutive $unwind stages. The first $unwind deconstructs the outer array, and the second deconstructs the inner array. Each stage multiplies the number of output documents, so be cautious with deep nesting to avoid explosive document expansion.

// Document: { course: 'Math', modules: [{ name: 'Algebra', lessons: ['L1', 'L2'] }] }

db.courses.aggregate([
  { $unwind: '$modules' },      // deconstruct modules array
  { $unwind: '$modules.lessons' }  // deconstruct nested lessons array
]);
// Output:
// { course: 'Math', modules: { name: 'Algebra', lessons: 'L1' } }
// { course: 'Math', modules: { name: 'Algebra', lessons: 'L2' } }

Calculating Array Element Statistics

A powerful use case for $unwind is computing statistics on individual array elements. For example, in an e-commerce database with orders containing line items (an array of sub-documents), unwinding line items lets you compute total revenue, average quantity, or top-selling items across all line items in all orders—not just per order.

// Total revenue and quantity per product across all orders
db.orders.aggregate([
  { $unwind: '$lineItems' },  // deconstruct line items
  { $group: {
    _id: '$lineItems.productId',
    totalRevenue: { $sum: { $multiply: ['$lineItems.price', '$lineItems.qty'] } },
    totalQty: { $sum: '$lineItems.qty' },
    orderCount: { $sum: 1 }  // how many orders contained this product
  }},
  { $sort: { totalRevenue: -1 } }
]);

Avoiding $unwind When Possible

$unwind can dramatically multiply the number of documents in the pipeline (a document with a 100-element array becomes 100 documents after unwind). This increases memory usage and processing time for all subsequent stages. Always ask: 'Can I achieve this with an array expression operator like $size, $filter, or $map in a $project stage instead of unwinding?' Array expression operators are often faster because they don't expand the document count.

// Better: use $size in $project instead of $unwind + $count
// Avoid:
db.articles.aggregate([
  { $unwind: '$tags' },
  { $group: { _id: '$_id', tagCount: { $sum: 1 } } }  // slow
]);

// Better:
db.articles.aggregate([{
  $project: {
    title: 1,
    tagCount: { $size: { $ifNull: ['$tags', []] } }  // fast
  }
}]);

$unwind Performance Impact

Because $unwind multiplies documents, it is usually the most expensive stage in a pipeline when applied to large arrays. Performance tips: apply $match and $project before $unwind to reduce the input document size and count; add a $match immediately after $unwind if you only care about specific elements; and limit the number of documents entering the unwind with an early $limit when applicable.

// Optimized order for $unwind pipelines:
db.orders.aggregate([
  { $match: { status: 'completed', createdAt: { $gte: thisMonth } } },  // 1. filter first
  { $project: { lineItems: 1, _id: 0 } },  // 2. project only needed fields
  { $unwind: '$lineItems' },               // 3. expand (smaller docs now)
  { $match: { 'lineItems.qty': { $gt: 5 } } },  // 4. filter expanded results
  { $group: { _id: '$lineItems.productId', count: { $sum: 1 } } }
]);

Reconstructing Arrays After $unwind

After a $unwind + $group pipeline, you can use $push in the $group stage to rebuild an array from the processed elements. This pattern is useful for filtering or transforming array elements: unwind to get individual elements, apply per-element transformations or filters, then push back into a new array. This is more flexible than using $filter or $map for complex per-element logic.

// Filter out low-rated reviews per product, keep only rating >= 4
db.products.aggregate([
  { $unwind: '$reviews' },
  { $match: { 'reviews.rating': { $gte: 4 } } },  // per-element filter
  { $group: {
    _id: '$_id',
    name: { $first: '$name' },
    goodReviews: { $push: '$reviews' }  // rebuild filtered array
  }}
]);
// Each product now has only its 4+ star reviews in goodReviews

Quick Check

Test your understanding of $unwind in the aggregation pipeline.

Lesson Recap

In this lesson you learned: $unwind creates one output document per array element, preserveNullAndEmptyArrays: true keeps documents with missing or empty arrays, and the classic $unwind + $group pattern enables per-element analytics like tag frequency counts. Next up we explore $addFields, $replaceRoot, and $mergeObjects.

Questions Fréquemment Posées

La leçon « $unwind : décomposer les champs de tableau » est-elle gratuite ?

Oui — le texte complet de « $unwind : décomposer les champs de tableau » est gratuit à lire ici sur le web. Pour la pratiquer de manière interactive (un éditeur de code intégré et un tuteur IA 24/7) et déverrouiller le reste du cours MongoDB Academy, passe à CoddyKit PRO. Le cours MongoDB Academy comprend 4 leçons au total.

Qu'est-ce que j'apprendrai dans « $unwind : décomposer les champs de tableau » ?

Les apprenants transformeront un champ de tableau en documents individuels avec $unwind et le combineront avec $group pour effectuer des analyses par élément. Tu pratiques MongoDB Academy avec du code pratique que tu exécutes directement dans le navigateur, et un tuteur IA 24/7 répond à tes questions au fur et à mesure que tu avances dans la leçon.

Dois-je avoir de l'expérience pour commencer MongoDB Academy ?

Aucune expérience préalable n'est requise. MongoDB Academy sur CoddyKit est structuré pour les débutants jusqu'aux apprenants avancés, donc tu peux commencer ici ou depuis le début et avancer à ton rythme. Ceci est la leçon 2 sur 4.

Combien de temps prend la leçon « $unwind : décomposer les champs de tableau » ?

La plupart des leçons CoddyKit prennent environ 5–10 minutes. Chacune est courte et interactive, tu progresses régulièrement et tu repiques exactement où tu t'es arrêté sur le web et l'app.

Peux-tu écrire et exécuter du code dans cette leçon MongoDB Academy ?

Oui. Chaque leçon MongoDB Academy inclut un éditeur de code intégré, tu écris et exécutes du vrai code directement dans ton navigateur et tu reçois des retours IA instantanés — aucune configuration locale requise.

Toutes les leçons de ce cours

  1. $lookup : joindre des collections dans la chaîne de traitement
  2. $unwind : décomposer les champs de tableau
  3. $addFields, $replaceRoot et $mergeObjects
  4. $out et $merge : écrire les résultats de la chaîne de traitement
← Retour à MongoDB Academy