Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke
Sie verstehen, wie Daten durch Pipeline-Stufen fließen, und unterscheiden zwischen Stufenoperatoren und Ausdrucksoperatoren.
Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke ist eine kostenlose MongoDB Academy-Lektion auf CoddyKit. Dies ist Lektion 1 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des MongoDB Academy-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der MongoDB Academy-Kurs umfasst insgesamt 4 Lektionen.
Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.
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 docsStage 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.
Häufig gestellte Fragen
Ist die Lektion „Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke“ kostenlos?
Ja — der vollständige Text von „Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des MongoDB Academy-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der MongoDB Academy-Kurs umfasst insgesamt 4 Lektionen.
Was lerne ich in „Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke“?
Sie verstehen, wie Daten durch Pipeline-Stufen fließen, und unterscheiden zwischen Stufenoperatoren und Ausdrucksoperatoren. Du übst MongoDB Academy mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.
Brauche ich Erfahrung, um MongoDB Academy zu starten?
Keine Vorkenntnisse erforderlich. MongoDB Academy auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 1 von 4.
Wie lange dauert die Lektion „Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke“?
Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.
Kann ich in dieser MongoDB Academy-Lektion Code schreiben und ausführen?
Ja. Jede MongoDB Academy-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.
Alle Lektionen in diesem Kurs
- Konzepte von Pipelines: Stufen, Operatoren und Ausdrücke
- $match und $project: Filtern und Umformen
- $group: Aggregieren und Summen berechnen
- $sort, $limit und $skip in der Pipeline