Master MongoDB Aggregation: How to Use $match and $group (2025 Guide)

Ready to turn raw data into powerful insights? 📊 The combination of $match and $group is the foundation of almost every MongoDB aggregation pipeline. In this essential 2025 guide, we’ll show you how to filter your data and perform complex calculations like sums, averages, and counts with ease.

This tutorial is perfect for developers who need to generate reports, analyze user behavior, or summarize large datasets directly in the database. 🚀

What you will learn in this video:

The $match Stage (The Filter): * Think of it as the WHERE clause in SQL.

Why you should always put $match at the beginning of your pipeline to take advantage of indexes and reduce the data passed to later stages. ⚡

How to use comparison operators like $gt, $lt, and $in within your match stage.

The $group Stage (The Aggregator):

How to define the _id field to group your documents by a specific key (like category, status, or userId). 📁

Using Accumulators: Learn how to use $sum, $avg, $min, $max, and $push to calculate values for each group.

Building a Combined Pipeline: * A real-world example: Filtering a sales collection for the year 2024 and then grouping by product to find the total revenue. 💰

Performance Optimization: * Understanding how the "Pipeline" flow works—where the output of $match becomes the input for $group.

Pro-tip: Using db.collection.explain() to see how your stages are executing. 🔍

Example Syntax:

JavaScript

db.orders.aggregate([
{ $match: { status: "completed" } },
{ $group: { _id: "$customer_id", totalSpent: { $sum: "$amount" } } }
])
By the end of this tutorial, you’ll be able to write efficient aggregation pipelines that do the heavy lifting for your backend, making your apps faster and more scalable!

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