$median - Amazon DocumentDB
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$median

New from version 8.0.1.

The $median operator in Amazon DocumentDB calculates the median value of numeric data. As an accumulator, it computes the median of numeric values across documents within a group in the $group stage of an aggregation pipeline. As an expression, it calculates the median of an array of numbers.

Parameters

  • input: An expression that resolves to a numeric value or an array of numeric values.

  • method: A string specifying the calculation method. Currently only "approximate" is supported, which uses the t-digest algorithm.

Behavior

The "approximate" method uses the t-digest algorithm to calculate an approximate median. The result is an existing value from the dataset rather than an interpolation between values. Precision improves as the number of data points increases.

Example (MongoDB Shell)

The following example demonstrates the use of the $median operator to calculate the median test score per class.

Create sample documents

db.students.insertMany([ { class: "A", score: 72 }, { class: "A", score: 85 }, { class: "A", score: 90 }, { class: "A", score: 68 }, { class: "A", score: 95 }, { class: "B", score: 80 }, { class: "B", score: 75 }, { class: "B", score: 92 }, { class: "B", score: 88 }, { class: "B", score: 70 } ]);

Query example

db.students.aggregate([ { $group: { _id: "$class", medianScore: { $median: { input: "$score", method: "approximate" } } }} ]);

Output

[ { "_id": "A", "medianScore": 85 }, { "_id": "B", "medianScore": 80 } ]

Expression usage example (MongoDB Shell)

The $median operator can also be used as an expression within a $project stage to compute the median of an array field.

Create sample documents

db.surveys.insertMany([ { _id: 1, ratings: [3, 5, 7, 9, 2] }, { _id: 2, ratings: [10, 20, 30, 40, 50] }, { _id: 3, ratings: [1, 1, 2, 3, 5] } ]);

Query example

db.surveys.aggregate([ { $project: { medianRating: { $median: { input: "$ratings", method: "approximate" } } }} ]);

Output

[ { "_id": 1, "medianRating": 5 }, { "_id": 2, "medianRating": 30 }, { "_id": 3, "medianRating": 2 } ]

Code examples

To view a code example for using the $median operator, choose the tab for the language that you want to use. The following examples show both accumulator usage (in $group) and expression usage (in $project):

Node.js
const { MongoClient } = require('mongodb'); async function example() { const uri = 'mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false'; const client = new MongoClient(uri); try { await client.connect(); const db = client.db('test'); // Accumulator usage: median across grouped documents const students = db.collection('students'); const accumulatorResult = await students.aggregate([ { $group: { _id: "$class", medianScore: { $median: { input: "$score", method: "approximate" } } }} ]).toArray(); console.log('Accumulator result:', accumulatorResult); // Expression usage: median of an array field const surveys = db.collection('surveys'); const expressionResult = await surveys.aggregate([ { $project: { medianRating: { $median: { input: "$ratings", method: "approximate" } } }} ]).toArray(); console.log('Expression result:', expressionResult); } finally { await client.close(); } } example();
Python
from pymongo import MongoClient def example(): client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false') try: db = client['test'] # Accumulator usage: median across grouped documents students = db['students'] accumulator_result = list(students.aggregate([ { '$group': { '_id': '$class', 'medianScore': { '$median': { 'input': '$score', 'method': 'approximate' } } }} ])) print('Accumulator result:', accumulator_result) # Expression usage: median of an array field surveys = db['surveys'] expression_result = list(surveys.aggregate([ { '$project': { 'medianRating': { '$median': { 'input': '$ratings', 'method': 'approximate' } } }} ])) print('Expression result:', expression_result) finally: client.close() example()