MongoDB Go Driver: Aggregation Pipeline

In this tutorial, you'll learn about MongoDB Go Driver: Aggregation Pipeline. We cover key concepts, practical examples, and best practices.

MongoDB aggregation -- Build aggregation pipelines with the Go driver using bson.D stages for complex data processing.

The Problem

Aggregation pipeline stage order matters. $match should come first, $group before $sort. Wrong order or incorrect field references cause empty or wrong results.

Wrong

pipeline := mongo.Pipeline{
    bson.D{{"$group", bson.D{{"_id", "$status"}},
}

Output:

// Aggregation works but processes all documents. Slow.

Right

pipeline := mongo.Pipeline{
    bson.D{{"$match", bson.D{{"status", "active"}},
    bson.D{{"$group", bson.D{
        {"_id", "$status"},
        {"count", bson.D{{"$sum", 1}},
    }},
    bson.D{{"$sort", bson.D{{"count", -1}},
}
cursor, err := collection.Aggregate(ctx, pipeline)

Output:

// Returns active status counts sorted descending

Prevention

• Use $match first to reduce documents
• bson.D preserves field order (required for stages)
• Use bson.A for arrays in pipeline
• Aggregate returns cursor, iterate with cursor.All()
• Use $lookup for JOIN-like operations

Common Mistakes with mongodb aggregate

1. Using return to exit a function early instead of wrapping a pure value in the monad
2. Mixing let bindings with <- bindings in do notation, producing type errors
3. Overlapping type class instances that cause GHC to reject the program with ambiguous dispatch errors

These mistakes appear frequently in real-world GO code. DodaTech's contributors have identified these patterns through analysis of open-source projects and production systems.

Practice Exercise

Write a pure function that safely divides two integers using Maybe, then test it with edge cases like division by zero and negative numbers.

This exercise reinforces the concepts covered in this guide. Try implementing it before checking online solutions.

FAQ

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