$avg

Amazon DocumentDB $avg 中的彙總運算子會計算輸入階段的文件中指定表達式的平均值。此運算子有助於計算一組文件間數值欄位或表達式的平均值。

參數

• expression：用來計算平均值的表達式。這可以是欄位路徑 （例如 "$field") 或表達式 （例如 { $multiply: ["$field1", "$field2"] })。

範例 (MongoDB Shell)

下列範例示範如何使用 $avg 運算子來計算一組學生文件的平均分數。

建立範例文件

db.students.insertMany([
  { name: "John", score: 85 },
  { name: "Jane", score: 92 },
  { name: "Bob", score: 78 },
  { name: "Alice", score: 90 }
]);

查詢範例

db.students.aggregate([
  { $group: { 
    _id: null,
    avgScore: { $avg: "$score" }
  }}
]);

輸出

[
  {
    "_id": null,
    "avgScore": 86.25
  }
]

視窗運算子用量範例 (MongoDB Shell)

8.0.2 版的新功能。

該$avg運算子也可以用作$setWindowFields階段中的視窗運算子。在這種情況下，它會傳回每個視窗中文件的指定表達式平均值。您可以在 output 欄位下指定運算子，並選擇性地使用window文件定義視窗邊界。

建立範例文件

db.readings.insertMany([
  { _id: 1, city: "Denver", day: 1, temp: 70 },
  { _id: 2, city: "Denver", day: 2, temp: 80 },
  { _id: 3, city: "Seattle", day: 1, temp: 60 },
  { _id: 4, city: "Seattle", day: 2, temp: 64 },
  { _id: 5, city: "Seattle", day: 3, temp: 68 }
]);

查詢範例

下列範例會依 分割文件city、依 排序每個分割區day，並傳回temp從分割區開始到目前文件的執行平均值 。

db.readings.aggregate([
  {
    $setWindowFields: {
      partitionBy: "$city",
      sortBy: { day: 1 },
      output: {
        runningAvg: {
          $avg: "$temp",
          window: { documents: ["unbounded", "current"] }
        }
      }
    }
  }
]);

輸出

[
  { "_id": 1, "city": "Denver", "day": 1, "temp": 70, "runningAvg": 70 },
  { "_id": 2, "city": "Denver", "day": 2, "temp": 80, "runningAvg": 75 },
  { "_id": 3, "city": "Seattle", "day": 1, "temp": 60, "runningAvg": 60 },
  { "_id": 4, "city": "Seattle", "day": 2, "temp": 64, "runningAvg": 62 },
  { "_id": 5, "city": "Seattle", "day": 3, "temp": 68, "runningAvg": 64 }
]

每個文件都會以 擴增runningAvg，這是其分割區temp內直到並包含目前文件的平均值。

程式碼範例

若要檢視使用 $avg 運算子的程式碼範例，請選擇您要使用的語言標籤。下列範例顯示累積器用量 （在 中$group) 和視窗運算子用量 （在 中$setWindowFields)：

Node.js

const { MongoClient } = require('mongodb');

async function calculateAvgScore() {
  const client = await MongoClient.connect('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false');
  const db = client.db('test');

  // Accumulator usage: average across grouped documents
  const students = db.collection('students');
  await students.insertMany([
    { name: "John", score: 85 },
    { name: "Jane", score: 92 },
    { name: "Bob", score: 78 },
    { name: "Alice", score: 90 }
  ]);
  const accumulatorResult = await students.aggregate([
    { $group: {
      _id: null,
      avgScore: { $avg: '$score' }
    }}
  ]).toArray();
  console.log('Accumulator result:', accumulatorResult);

  // Window operator usage: running average within each partition
  const readings = db.collection('readings');
  await readings.insertMany([
    { _id: 1, city: "Denver", day: 1, temp: 70 },
    { _id: 2, city: "Denver", day: 2, temp: 80 },
    { _id: 3, city: "Seattle", day: 1, temp: 60 },
    { _id: 4, city: "Seattle", day: 2, temp: 64 },
    { _id: 5, city: "Seattle", day: 3, temp: 68 }
  ]);
  const windowResult = await readings.aggregate([
    {
      $setWindowFields: {
        partitionBy: "$city",
        sortBy: { day: 1 },
        output: {
          runningAvg: {
            $avg: "$temp",
            window: { documents: ["unbounded", "current"] }
          }
        }
      }
    }
  ]).toArray();
  console.log('Window result:', windowResult);

  await client.close();
}

calculateAvgScore();

Python

from pymongo import MongoClient

def calculate_avg_score():
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')
    db = client.test

    # Accumulator usage: average across grouped documents
    students = db.students
    students.insert_many([
        { 'name': 'John', 'score': 85 },
        { 'name': 'Jane', 'score': 92 },
        { 'name': 'Bob', 'score': 78 },
        { 'name': 'Alice', 'score': 90 }
    ])
    accumulator_result = list(students.aggregate([
        { '$group': {
            '_id': None,
            'avgScore': { '$avg': '$score' }
        }}
    ]))
    print('Accumulator result:', accumulator_result)

    # Window operator usage: running average within each partition
    readings = db.readings
    readings.insert_many([
        { '_id': 1, 'city': 'Denver', 'day': 1, 'temp': 70 },
        { '_id': 2, 'city': 'Denver', 'day': 2, 'temp': 80 },
        { '_id': 3, 'city': 'Seattle', 'day': 1, 'temp': 60 },
        { '_id': 4, 'city': 'Seattle', 'day': 2, 'temp': 64 },
        { '_id': 5, 'city': 'Seattle', 'day': 3, 'temp': 68 }
    ])
    window_result = list(readings.aggregate([
        {
            '$setWindowFields': {
                'partitionBy': '$city',
                'sortBy': { 'day': 1 },
                'output': {
                    'runningAvg': {
                        '$avg': '$temp',
                        'window': { 'documents': ['unbounded', 'current'] }
                    }
                }
            }
        }
    ]))
    print('Window result:', window_result)

    client.close()

calculate_avg_score()