Optimizing MongoDB Query Performance with Aggregation Framework for Large Collections
I'm updating my dependencies and I'm currently working on a Node.js application that uses MongoDB (version 4.2) to store user activity logs... As the collection has grown to over 1 million documents, I've started noticing significant slowdowns when running aggregate queries, particularly when filtering by date ranges and grouping by user ID. For example, this query: ```javascript const { MongoClient } = require('mongodb'); async function fetchUserActivities(startDate, endDate) { const client = new MongoClient('mongodb://localhost:27017'); await client.connect(); const db = client.db('myDatabase'); const collection = db.collection('activityLogs'); const pipeline = [ { $match: { date: { $gte: new Date(startDate), $lte: new Date(endDate) } } }, { $group: { _id: '$userId', totalActivities: { $sum: 1 } } } ]; const results = await collection.aggregate(pipeline).toArray(); await client.close(); return results; } ``` When I run this function, it takes over 15 seconds to return results, which is unacceptable for our use case. I've tried creating indexes on the `date` and `userId` fields, but the performance hasn't improved. The indexes seem to be effective for simple queries, but the aggregation still lags. I've also looked into using `.explain('executionStats')` to analyze the query execution but I'm struggling to interpret the results. The output shows that the query is scanning many documents and not utilizing the index effectively for the `$match` stage. Could anyone provide guidance on optimizing this aggregation pipeline further? Are there specific best practices for handling large collections in MongoDB, or should I consider restructuring my data? Any help or insights would be greatly appreciated! I'm on CentOS using the latest version of Javascript.