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使用 $setWindowFields 进行窗口函数计算

您将使用 $setWindowFields 阶段，在有序分区上计算累计总和、排名和移动平均值。

使用 $setWindowFields 进行窗口函数计算 是 CoddyKit 上的免费 MongoDB Academy 课时。这是第 4 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MongoDB Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MongoDB Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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 order

Practical 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.

常见问题解答

「使用 $setWindowFields 进行窗口函数计算」课时是免费的吗？

是的 — 「使用 $setWindowFields 进行窗口函数计算」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MongoDB Academy 课程的其余内容，请升级到 CoddyKit PRO。 MongoDB Academy 课程共包含 4 节课。

「使用 $setWindowFields 进行窗口函数计算」这节课中我会学到什么？

您将使用 $setWindowFields 阶段，在有序分区上计算累计总和、排名和移动平均值。你通过在浏览器中直接运行的动手代码来练习 MongoDB Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

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