Window Functions With $setWindowFields
Learners will compute running totals, rank, and moving averages over ordered partitions using the $setWindowFields stage.
Window Functions With $setWindowFields is a free MongoDB Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MongoDB Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What Are Window Functions?
Window functions compute values over a set of documents related to the current document—a 'window'—without collapsing them into a single group like $group does. They originated in SQL (SQL:2003) and were added to MongoDB in version 5.0 through the $setWindowFields pipeline stage. Common use cases include running totals, moving averages, rank, and cumulative metrics.
The $setWindowFields Stage Structure
The $setWindowFields stage has three main configuration keys: partitionBy defines how to divide documents into independent windows (like GROUP BY in SQL), sortBy orders documents within each partition, and output specifies the new fields to compute along with their window operator and window bounds.
db.dailySales.aggregate([
{
$setWindowFields: {
partitionBy: '$region', // separate window per region
sortBy: { saleDate: 1 }, // order by date within each region
output: {
runningTotal: {
$sum: '$amount',
window: { documents: ['unbounded', 'current'] }
}
}
}
}
])Document-Based Window Bounds
Window bounds define which documents contribute to the computation for each row. Document-based bounds use the documents key with a two-element array: the start position and end position relative to the current document. 'unbounded' means 'from the beginning (or to the end)', 'current' means the current document, and numeric offsets like -1 mean 'one document before'. Common patterns: ['unbounded', 'current'] for a running total, [-1, 1] for a 3-document moving window.
// Running total: all documents from the start up to the current row
window: { documents: ['unbounded', 'current'] }
// 3-document moving window: previous, current, and next document
window: { documents: [-1, 1] }
// Cumulative (all documents from start to end)
window: { documents: ['unbounded', 'unbounded'] }Range-Based Window Bounds
Range-based bounds define the window using value ranges on the sort key rather than document offsets. This is especially useful for time-series data where you want 'the last 7 days' rather than 'the last 7 documents'. Use the range key with a unit for date fields. This correctly handles gaps in data where days might be missing.
db.temperatures.aggregate([
{
$setWindowFields: {
partitionBy: '$station',
sortBy: { readingDate: 1 },
output: {
sevenDayAvgTemp: {
$avg: '$temperature',
window: {
range: [-6, 0], // 6 days before up to current day
unit: 'day'
}
}
}
}
}
])Computing Running Totals
A running total (cumulative sum) is computed by setting the window to span from the first document in the partition to the current document. As MongoDB processes each document in sort order, it adds that document's value to all previous values. This produces a monotonically increasing total per partition, useful for cumulative revenue or progressive download counts.
db.transactions.aggregate([
{
$setWindowFields: {
partitionBy: '$accountId',
sortBy: { date: 1 },
output: {
runningBalance: {
$sum: '$amount',
window: { documents: ['unbounded', 'current'] }
}
}
}
},
{ $project: { accountId: 1, date: 1, amount: 1, runningBalance: 1 } }
])Moving Averages for Smoothing Data
A moving average smooths out short-term fluctuations in time-series data to reveal underlying trends. Configure the window to span a fixed number of periods in both directions (or only backwards for a 'trailing' average). Moving averages are common in financial charts, performance monitoring dashboards, and IoT sensor analysis.
db.stockPrices.aggregate([
{
$setWindowFields: {
partitionBy: '$ticker',
sortBy: { date: 1 },
output: {
movingAvg5Day: {
$avg: '$closePrice',
window: { documents: [-4, 0] } // current + 4 previous = 5 day average
},
movingAvg10Day: {
$avg: '$closePrice',
window: { documents: [-9, 0] } // 10-day trailing average
}
}
}
}
])Ranking With $rank and $denseRank
The $rank operator assigns a rank number to each document within its partition based on the sort order. Tied documents receive the same rank, and the next rank skips accordingly (1, 2, 2, 4). $denseRank assigns consecutive ranks without gaps for ties (1, 2, 2, 3). Neither takes a window specification—they always rank across the full partition.
db.leaderboard.aggregate([
{
$setWindowFields: {
partitionBy: '$gameId',
sortBy: { score: -1 }, // highest score = rank 1
output: {
rank: { $rank: {} },
denseRank: { $denseRank: {} }
}
}
},
{ $match: { rank: { $lte: 10 } } } // top 10 per game
])$documentNumber: Row Numbering Within Partition
$documentNumber assigns a sequential integer starting from 1 to each document within its partition, in sort order. Unlike $rank, it never repeats numbers—every document gets a unique number. This is useful for pagination, sequence numbering, or when you need to identify which row within a partition a document occupies.
db.orders.aggregate([
{
$setWindowFields: {
partitionBy: '$customerId',
sortBy: { orderDate: 1 },
output: {
orderSequence: { $documentNumber: {} } // 1st order, 2nd order, etc.
}
}
},
// Find customers' 3rd orders
{ $match: { orderSequence: 3 } }
])$shift: Accessing Adjacent Documents
$shift returns the value of an expression from a document at a specified offset relative to the current document within the partition. Use by: -1 to access the previous document's value (e.g., yesterday's price), by: 1 for the next document, and specify a default for when the offset falls outside the partition boundary.
db.dailyMetrics.aggregate([
{
$setWindowFields: {
partitionBy: '$metricName',
sortBy: { date: 1 },
output: {
previousValue: {
$shift: {
output: '$value',
by: -1,
default: null
}
},
// Compute day-over-day change using $shift
dayOverDayChange: {
$subtract: [
'$value',
{ $shift: { output: '$value', by: -1, default: '$value' } }
]
}
}
}
}
])Performance and Index Usage
$setWindowFields benefits from indexes on the partition and sort fields. An index that covers both the partition key and the sort key allows MongoDB to efficiently retrieve each partition's documents in sorted order without a full collection scan. Without an index, MongoDB must sort in-memory (up to allowDiskUse limits). For large collections, ensure indexes align with your window functions' partition and sort specifications.
// For this $setWindowFields:
// partitionBy: '$accountId', sortBy: { date: 1 }
// Create a compound index:
db.transactions.createIndex({ accountId: 1, date: 1 })
// MongoDB can now efficiently scan per-partition in date orderPractical Example: Sales Performance Report
A real-world sales report might need each salesperson's transactions enriched with their running total, their rank within their team, and the team cumulative total—all computed in a single pipeline without multiple joins or application-side computation. This is exactly the kind of analytical query $setWindowFields was built for.
db.sales.aggregate([
{
$setWindowFields: {
partitionBy: '$teamId',
sortBy: { amount: -1 },
output: {
rankInTeam: { $rank: {} },
runningTeamTotal: {
$sum: '$amount',
window: { documents: ['unbounded', 'current'] }
},
teamTotal: {
$sum: '$amount',
window: { documents: ['unbounded', 'unbounded'] }
}
}
}
}
])Quick Check
Test your understanding of MongoDB & NoSQL Databases concepts from this lesson.
Lesson Recap
In this lesson you learned: $setWindowFields computes values over a sliding window of related documents without collapsing them like $group, document-based and range-based window bounds control which documents contribute to each computation, and operators like $rank, $denseRank, $documentNumber, and $shift enable ranking, numbering, and cross-row comparisons. Next up we explore ACID guarantees in MongoDB's distributed document store.
Frequently asked questions
Is the “Window Functions With $setWindowFields” lesson free?
Yes — the full text of “Window Functions With $setWindowFields” is free to read here on the web, and the MongoDB Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MongoDB Academy course, upgrade to CoddyKit PRO.
What will I learn in “Window Functions With $setWindowFields”?
Learners will compute running totals, rank, and moving averages over ordered partitions using the $setWindowFields stage. You practise MongoDB Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MongoDB Academy?
No prior experience is required. MongoDB Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Window Functions With $setWindowFields” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MongoDB Academy lesson?
Yes. Every MongoDB Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- $sum, $avg, $min, $max: Numeric Aggregation
- $push and $addToSet: Building Arrays in Groups
- $first, $last, and $top/$bottom Accumulators
- Window Functions With $setWindowFields