Facetas y consultas compuestas
Combinará varias cláusulas de búsqueda con el operador compound y calculará recuentos por faceta para los filtros de categoría junto con los resultados de búsqueda.
Facetas y consultas compuestas es una lección gratuita de MongoDB Academy en CoddyKit. Esta es la lección 4 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de MongoDB Academy, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de MongoDB Academy incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
What Are Search Facets?
Facets are aggregated counts of how many search results fall into each category of a field. You have seen facets on e-commerce sites: 'Brand: Nike (142), Adidas (89), Puma (45)' or 'Price: Under $50 (230), $50-$100 (185)'. In Atlas Search, facets are computed server-side alongside the search results in a single query using the $searchMeta stage or the facet collector within $search.
Configuring Fields for Faceting
To facet on a field, it must be indexed with the stringFacet (for string categories) or numberFacet/dateFacet (for range buckets) data type in the Atlas Search index mapping. You can add a facet type alongside a string type on the same field. Standard string-typed fields are not facetable—you must explicitly add the facet type to your index definition.
// Index mapping with facetable fields
{
'mappings': {
'dynamic': false,
'fields': {
'title': { 'type': 'string', 'analyzer': 'lucene.standard' },
'category': [
{ 'type': 'string' }, // for filtering
{ 'type': 'stringFacet' } // for facet counts
],
'brand': { 'type': 'stringFacet' },
'price': [
{ 'type': 'number' },
{ 'type': 'numberFacet' }
]
}
}
}The $searchMeta Stage for Facet Counts
Use $searchMeta (instead of $search) when you need only the metadata (facet counts, total results) without returning the actual documents. This is efficient for re-computing facet counts when a filter changes. The stage returns a single document containing the facet buckets. Combine it with a separate $search query to get both documents and facets in two parallel requests.
// Get facet counts for categories and brands
db.products.aggregate([
{
$searchMeta: {
index: 'default',
facet: {
operator: {
text: { query: 'wireless headphones', path: 'title' }
},
facets: {
categoriesFacet: {
type: 'string',
path: 'category',
numBuckets: 10 // return up to 10 category buckets
},
brandsFacet: {
type: 'string',
path: 'brand',
numBuckets: 20
}
}
}
}
}
])Number Range Facets
For numeric fields, define explicit price range buckets using the numericFacet type with a boundaries array and a default bucket name for values that fall outside all boundaries. The boundaries define bin edges—[0, 50, 100, 500] creates buckets 0–50, 50–100, and 100–500. Each bucket in the result shows the count of matching documents in that range.
db.products.aggregate([
{
$searchMeta: {
facet: {
operator: { text: { query: 'running shoes', path: 'name' } },
facets: {
priceRanges: {
type: 'number',
path: 'price',
boundaries: [0, 50, 100, 200, 500],
// Produces: '$0-$50', '$50-$100', '$100-$200', '$200-$500'
default: 'Other' // for products outside all ranges
}
}
}
}
}
])The compound Operator: Combining Search Clauses
The compound operator is the most powerful Atlas Search operator—it lets you combine multiple search clauses using four clause types: must (all must match, contributes to score), mustNot (must not match), should (optional, but boosts score if they match), and filter (must match but does not affect score). This mirrors Elasticsearch's bool query and enables sophisticated relevance ranking.
db.jobs.aggregate([
{
$search: {
compound: {
must: [
// Required: title must contain 'senior engineer'
{ text: { query: 'senior engineer', path: 'title' } }
],
should: [
// Optional boost: prefer remote jobs
{ equals: { path: 'remote', value: true } },
// Optional boost: prefer jobs with high salary
{ range: { path: 'salary', gte: 150000 } }
],
filter: [
// Must match but does NOT affect score
{ equals: { path: 'active', value: true } }
],
mustNot: [
// Exclude contract roles
{ equals: { path: 'type', value: 'contract' } }
]
}
}
}
])minimumShouldMatch: Controlling OR Logic
By default, should clauses are entirely optional—a document that matches none of them still appears in results if must clauses match. Set minimumShouldMatch to require that at least N should clauses match. This converts the behavior from pure OR to a 'match at least N' semantic, letting you express queries like 'must have title match AND at least one of: location, skills, or salary match'.
db.candidates.aggregate([
{
$search: {
compound: {
must: [
{ text: { query: 'python developer', path: 'title' } }
],
should: [
{ text: { query: 'machine learning', path: 'skills' } },
{ text: { query: 'tensorflow pytorch', path: 'skills' } },
{ range: { path: 'experienceYears', gte: 3 } }
],
minimumShouldMatch: 1 // must match at least 1 should clause
}
}
}
])Score Boosting in compound Queries
You can control relevance score contributions from each clause using the score option. Boosting multiplies the base score from that clause by a factor—useful when a match in the title is more important than a match in the description. constant scoring replaces the calculated score with a fixed value, useful for filter-like clauses you want to affect ranking but not dominate it.
db.articles.aggregate([
{
$search: {
compound: {
should: [
{
text: {
query: 'mongodb performance',
path: 'title',
score: { boost: { value: 3.0 } } // title matches worth 3x more
}
},
{
text: {
query: 'mongodb performance',
path: 'body',
score: { boost: { value: 1.0 } } // body matches at normal weight
}
}
]
}
}
}
])Getting Documents and Facets Together
A common pattern in search UIs is to return both the result documents and the facet counts in parallel. The most efficient approach uses the Atlas Search facet collector: pair $searchMeta for facet metadata with a separate $search query for documents, running both in parallel from your application. Alternatively, use $search with a $facet aggregation stage after it (this performs two passes but is simpler).
// Run both queries in parallel
const searchQuery = 'wireless headphones';
const [documents, facets] = await Promise.all([
// Query 1: get matching documents
db.collection('products').aggregate([
{ $search: { text: { query: searchQuery, path: 'name' } } },
{ $project: { name: 1, price: 1, category: 1, score: { $meta: 'searchScore' } } },
{ $limit: 20 }
]).toArray(),
// Query 2: get facet counts for the same query
db.collection('products').aggregate([
{ $searchMeta: { facet: { operator: { text: { query: searchQuery, path: 'name' } },
facets: { cats: { type: 'string', path: 'category', numBuckets: 10 } } } } }
]).toArray()
]);
console.log('Results:', documents.length, 'Facets:', facets[0].facet.cats.buckets);Applying Active Facet Filters
When a user clicks a facet to filter results (e.g., 'Category: Electronics'), you need to add that selection as a filter clause in the compound operator. This narrows both the result documents and the remaining facet counts to only matching items. It is important to apply facet filters in the filter (not must) clause so they do not affect relevance scoring.
async function searchWithFacetFilter(query, selectedCategory) {
const filterClauses = [];
if (selectedCategory) {
filterClauses.push({ equals: { path: 'category', value: selectedCategory } });
}
return db.collection('products').aggregate([
{
$search: {
compound: {
must: [{ text: { query, path: 'name' } }],
filter: filterClauses // apply active facet selections
}
}
},
{ $project: { name: 1, price: 1, category: 1 } },
{ $limit: 20 }
]).toArray();
}Facet Result Structure
The result of a $searchMeta with facets is a single document containing a facet key. Each facet you defined is a sub-key containing a buckets array. Each bucket has an _id (the facet value) and a count. For number facets, each bucket also has a lowerBound and upperBound. Parse this structure in your application to render the facet filter sidebar.
// $searchMeta result structure:
// [
// {
// 'facet': {
// 'categoriesFacet': {
// 'buckets': [
// { '_id': 'Electronics', 'count': 142 },
// { '_id': 'Audio', 'count': 89 },
// { '_id': 'Accessories', 'count': 45 }
// ]
// },
// 'priceRanges': {
// 'buckets': [
// { '_id': '0.0', 'count': 23 }, // 0 to 50
// { '_id': '50.0', 'count': 67 }, // 50 to 100
// { '_id': '100.0', 'count': 101 } // 100 to 200
// ]
// }
// }
// }
// ]Date Facets for Time-Based Navigation
Date facets work similarly to number facets—you define explicit bucket boundaries as Date objects. This enables time-based navigation like 'Published this week (12)', 'Published this month (47)', 'Published this year (183)'. Define the boundaries as an array of dates and Atlas Search counts documents whose date field falls within each range. Date facets are useful for filtering blog posts, job listings, events, and any time-sensitive content.
db.articles.aggregate([
{
$searchMeta: {
facet: {
operator: { text: { query: 'mongodb', path: 'title' } },
facets: {
publishedDate: {
type: 'date',
path: 'publishedAt',
boundaries: [
new Date('2024-01-01'),
new Date('2024-04-01'),
new Date('2024-07-01'),
new Date('2024-10-01'),
new Date('2025-01-01')
],
default: 'Other'
}
}
}
}
}
])Quick Check
Test your understanding of MongoDB & NoSQL Databases concepts from this lesson.
Lesson Recap
In this lesson you learned: facets require stringFacet/numberFacet types in the index mapping and are computed with $searchMeta, the compound operator combines must, mustNot, should, and filter clauses for sophisticated relevance queries, and filter clauses narrow results without affecting the relevance score, while must clauses do both. Next up we explore connecting to MongoDB with the official Node.js driver.
Preguntas frecuentes
¿La lección «Facetas y consultas compuestas» es gratis?
Sí — el texto completo de «Facetas y consultas compuestas» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de MongoDB Academy, actualiza a CoddyKit PRO. El curso de MongoDB Academy incluye 4 lecciones en total.
¿Qué aprenderé en «Facetas y consultas compuestas»?
Combinará varias cláusulas de búsqueda con el operador compound y calculará recuentos por faceta para los filtros de categoría junto con los resultados de búsqueda. Practicas MongoDB Academy con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar MongoDB Academy?
No se requiere experiencia previa. MongoDB Academy en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 4 de 4.
¿Cuánto tiempo toma la lección «Facetas y consultas compuestas»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de MongoDB Academy?
Sí. Cada lección de MongoDB Academy incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Creación de un índice de Atlas Search
- Escritura de consultas $search: texto, frases y comodines
- Autocompletado y coincidencia aproximada
- Facetas y consultas compuestas