MongoDB Aggregation Framework | $match, $group, aggregate()

# CHAPTER 15

Aggregation Framework Basics #

1. Introduction #

2. Learning Objectives #

• Understand the concept of an Aggregation Pipeline.

• Use the aggregate() method.

• Filter data flowing into the pipeline using the $match stage.

• Crunch data (Sum, Average, Count) using the $group stage.

• Shape the final output using the $project stage.

3. What is an Aggregation Pipeline? #

1. 1. Stage 1: Filter out the bad ones. (Now we have 500 documents).

1. 2. Stage 2: Group them by category and calculate the total price. (Now we have 5 summary documents).

1. 3. Stage 3: Sort them from highest to lowest.

1. 4. Output: The final analytics report!

4. The $match Stage (The Filter) #

// Pipeline Stage 1: Only allow "Completed" orders onto the conveyor belt
db.orders.aggregate([
    { $match: { status: "Completed" } }
])

5. The $group Stage (The Calculator) #

// Calculate Total Revenue grouped by User
db.orders.aggregate([
    // Stage 1: Group by the user_id (You must prefix the field with a '$' inside strings!)
    { $group: { 
        _id: "$user_id",               // Group by User
        total_spent: { $sum: "$price" } // Calculate the SUM of the price field
    }}
])

*(Notice the syntax: When referencing the value of an existing field inside an aggregation string, you must prefix it with a dollar sign: "$price").*

6. Chaining the Pipeline ($match + $group) #

db.orders.aggregate([
    // Stage 1: Filter. Only allow 2023 orders onto the belt.
    { $match: { year: 2023 } },
    
    // Stage 2: Math. Group the 2023 orders by Category, and sum their totals!
    { $group: {
        _id: "$category",
        annual_revenue: { $sum: "$total" },
        items_sold: { $sum: 1 } // Adding 1 for every document counts the total number of orders!
    }}
])

7. The $project Stage (The Formatter) #

db.users.aggregate([
    // Stage 1: Re-shape the output document
    { $project: {
        _id: 0, // Hide the ID
        full_name: { $concat: ["$first_name", " ", "$last_name"] }, // Combine strings!
        is_adult: { $gte: ["$age", 18] } // Returns a boolean True/False!
    }}
])

8. Mini Project: E-Commerce Analytics Dashboard #

db.sales.aggregate([
    // 1. Filter out non-electronics
    { $match: { category: "Electronics" } },
    
    // 2. Group by product name and sum the revenue
    { $group: {
        _id: "$product_name",
        total_revenue: { $sum: "$price" }
    }},
    
    // 3. Sort by total_revenue descending (Highest first)
    { $sort: { total_revenue: -1 } },
    
    // 4. Limit to the Top 3
    { $limit: 3 }
])

9. Common Mistakes #

• Forgetting the $ Prefix in Grouping: If you write { id: "userid" }, MongoDB will literally group everyone under the text string "userid". You MUST write { id: "$userid" } to tell MongoDB to evaluate the *value* of the userid field.

• Putting $group before $match: If you group and calculate the math on 5 million rows, and THEN filter it, you are wasting massive amounts of server CPU. Always $match first to reduce the pipeline volume as early as possible.

10. Best Practices #

• Indexes work on $match: The $match stage can utilize B-Tree indexes, but ONLY if it is the very first stage in the pipeline!

11. Exercises #

1. 1. What MongoDB method is used to initiate an aggregation pipeline?

1. 2. Inside a $group stage, what operator do you use to add up the values of a specific numeric field across all grouped documents?

12. MongoDB Challenges #

db.employees.aggregate([
    { $group: {
        _id: "$department",
        average_salary: { $avg: "$salary" }
    }}
])

13. MCQ Quiz with Answers #

16. Summary #

17. Next Chapter Recommendation #