AWS documentdb documentation change
Summary
Removed entire documentation page for the $near operator, including all content, examples, and code samples.
Security assessment
The change removes documentation for a geospatial query operator ($near) but provides no evidence of addressing a security vulnerability, weakness, or incident. The removal appears to be a routine documentation update, possibly consolidating or moving content elsewhere, with no security context mentioned in the diff.
Diff
diff --git a/documentdb/latest/developerguide/near.md b/documentdb/latest/developerguide/near.md index 4861a1238..8b1378917 100644 --- a//documentdb/latest/developerguide/near.md +++ b//documentdb/latest/developerguide/near.md @@ -1 +0,0 @@ -[](/pdfs/documentdb/latest/developerguide/developerguide.pdf#near "Open PDF") @@ -3,185 +1,0 @@ -[Documentation](/index.html)[Amazon DocumentDB](/documentdb/index.html)[Developer Guide](what-is.html) - -Example (MongoDB Shell)Code examples - -# $near - -The `$near` operator in Amazon DocumentDB is used to find documents that are geographically near a specified point. It returns documents ordered by distance, with the closest documents first. This operator requires a 2dsphere geospatial index and is useful for proximity queries on location data. - -**Parameters** - - * `$geometry`: A GeoJSON Point object that defines the center point for the near query. - - * `$maxDistance`: (optional) The maximum distance in meters from the specified point that a document can be to match the query. - - * `$minDistance`: (optional) The minimum distance in meters from the specified point that a document can be to match the query. - - - - -**Index Requirements** - - * `2dsphere index`: Required for geospatial queries on GeoJSON Point data. - - - - -## Example (MongoDB Shell) - -The following example demonstrates how to use the `$near` operator to find the nearest restaurants to a specific location in Seattle, Washington. - -**Create sample documents** - - - db.usarestaurants.insert([ - { - "name": "Noodle House", - "city": "Seattle", - "state": "Washington", - "rating": 4.8, - "location": { "type": "Point", "coordinates": [-122.3517, 47.6159] } - }, - { - "name": "Pike Place Grill", - "city": "Seattle", - "state": "Washington", - "rating": 4.2, - "location": { "type": "Point", "coordinates": [-122.3403, 47.6062] } - }, - { - "name": "Lola", - "city": "Seattle", - "state": "Washington", - "rating": 4.5, - "location": { "type": "Point", "coordinates": [-122.3407, 47.6107] } - } - ]); - -**Create 2dsphere index** - - - db.usarestaurants.createIndex({ "location": "2dsphere" }); - -**Query example with GeoJSON Point** - - - db.usarestaurants.find({ - location: { - $near: { - $geometry: { - type: "Point", - coordinates: [-122.3516, 47.6156] - }, - $maxDistance: 100, - $minDistance: 10 - } - } - }); - -**Output** - - - { - "_id" : ObjectId("69031ec9ea1c2922a1ce5f4a"), - "name" : "Noodle House", - "city" : "Seattle", - "state" : "Washington", - "rating" : 4.8, - "location" : { - "type" : "Point", - "coordinates" : [ -122.3517, 47.6159 ] - } - } - -## Code examples - -To view a code example for using the `$near` command, choose the tab for the language that you want to use: - -Node.js - - - - const { MongoClient } = require('mongodb'); - - async function findNearbyRestaurants() { - 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'); - const restaurants = db.collection('usarestaurants'); - - // Create 2dsphere index - await restaurants.createIndex({ "location": "2dsphere" }); - - const result = await restaurants.find({ - location: { - $near: { - $geometry: { - type: "Point", - coordinates: [-122.3516, 47.6156] - }, - $maxDistance: 100, - $minDistance: 10 - } - } - }).toArray(); - - console.log(result); - - client.close(); - } - - findNearbyRestaurants(); - -Python - - - - from pymongo import MongoClient - - def find_nearby_restaurants(): - client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false') - db = client['test'] - restaurants = db['usarestaurants'] - - # Create 2dsphere index - restaurants.create_index([("location", "2dsphere")]) - - result = list(restaurants.find({ - 'location': { - '$near': { - '$geometry': { - 'type': 'Point', - 'coordinates': [-122.3516, 47.6156] - }, - '$maxDistance': 100, - '$minDistance': 10 - } - } - })) - - print(result) - - client.close() - - find_nearby_restaurants() - - **Javascript is disabled or is unavailable in your browser.** - -To use the Amazon Web Services Documentation, Javascript must be enabled. Please refer to your browser's Help pages for instructions. - -[Document Conventions](/general/latest/gr/docconventions.html) - -$minDistance - -$nearSphere - -Did this page help you? - Yes - -Thanks for letting us know we're doing a good job! - -If you've got a moment, please tell us what we did right so we can do more of it. - -Did this page help you? - No - -Thanks for letting us know this page needs work. We're sorry we let you down. - -If you've got a moment, please tell us how we can make the documentation better.