Advanced Aggregation Case Studies in MongoDB

Aggregation in MongoDB is a powerful tool that allows users to process data records and return computed results. This lesson explores advanced case studies that demonstrate the application of aggregation pipelines in real-world scenarios.

In this case study, we analyze sales data to calculate total sales per product category and the average sales per transaction.

db.sales.aggregate([
    {
        $group: {
            _id: "$category",
            totalSales: { $sum: "$amount" },
            averageSales: { $avg: "$amount" }
        }
    },
    {
        $sort: { totalSales: -1 }
    }
])
            

The above aggregation pipeline consists of two stages:

• Group by product category and calculate total and average sales.
• Sort the results by total sales in descending order.

This case study focuses on analyzing user engagement metrics across various platforms to identify trends.

db.userEngagement.aggregate([
    {
        $group: {
            _id: { platform: "$platform", month: { $month: "$date" } },
            totalClicks: { $sum: "$clicks" },
            totalImpressions: { $sum: "$impressions" }
        }
    },
    {
        $project: {
            platform: "$_id.platform",
            month: "$_id.month",
            clickThroughRate: {
                $divide: ["$totalClicks", "$totalImpressions"]
            }
        }
    },
    {
        $sort: { month: 1 }
    }
])
            

This pipeline contains:

• Group by platform and month to aggregate clicks and impressions.
• Calculate the click-through rate.
• Sort by month for trend analysis.

To optimize the performance of aggregation queries in MongoDB, consider the following best practices:

1. Use indexes to speed up query performance.
2. Limit the amount of data processed by using the $match stage early in the pipeline.
3. Use $project to exclude fields that are not needed.
4. Monitor query performance using the explain() method.