本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。
$avg
Amazon DocumentDB 中的$avg聚合运算符计算输入到该阶段的文档中指定表达式的平均值。此运算符可用于计算一组文档中数值字段或表达式的平均值。
参数
示例（MongoDB Shell）
以下示例演示如何使用$avg运算符计算一组学生文档的平均分数。
创建示例文档
db.students.insertMany([
{ name: "John", score: 85 },
{ name: "Jane", score: 92 },
{ name: "Bob", score: 78 },
{ name: "Alice", score: 90 }
]);
查询示例
db.students.aggregate([
{ $group: {
_id: null,
avgScore: { $avg: "$score" }
}}
]);
输出
[
{
"_id": null,
"avgScore": 86.25
}
]
窗口运算符用法示例（MongoDB Shell）
8.0.2 版中的新内容。
该$avg操作员还可以在$setWindowFields舞台中用作窗口操作员。在这种情况下，它返回每个窗口中文档的指定表达式的平均值。您可以在output字段下指定运算符，也可以选择使用window文档定义窗口边界。
创建示例文档
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 }
]);
查询示例
以下示例按文档分区city，按分区排序day，并返回temp从分区开始到当前文档的运行平均值。
db.readings.aggregate([
{
$setWindowFields: {
partitionBy: "$city",
sortBy: { day: 1 },
output: {
runningAvg: {
$avg: "$temp",
window: { documents: ["unbounded", "current"] }
}
}
}
}
]);
输出
[
{ "_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 }
]
每个文档都增加了runningAvg，即其分区temp内直至包括当前文档在内的平均值。
代码示例
要查看使用$avg运算符的代码示例，请选择要使用的语言的选项卡。以下示例显示了累加器的用法（输入$group）和窗口运算符的用法（输入$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()
