MongoDB Aggregation Pipelines: A Visual Guide
MongoDB aggregation pipelines are incredibly powerful, but they can be intimidating at first. Let's break them down.
What Are Aggregation Pipelines?
Think of them as an assembly line for your data. Documents enter, get transformed through stages, and come out the other end in a new shape.
Each stage takes input documents, processes them, and passes the output to the next stage.
The Key Stages
$match - Filter First
Always filter as early as possible. It reduces the documents flowing through subsequent stages. This is like a WHERE clause in SQL. Put it early to reduce the data set.
$group - Aggregate Data
This is where the magic happens. Group by a field and compute aggregates like sum, average, count.
$project - Shape Output
Control exactly what fields appear in your output. You can rename, compute, and exclude fields.
$lookup - Join Collections
Yes, MongoDB can do joins. They're just called lookups.
Performance Tips
- **Index your $match fields** - This is crucial for performance
- **$match before $lookup** - Filter before joining
- **Use $limit early** - If you only need 10 results, say so early
- **Avoid $unwind on large arrays** - It can explode your document count
- **Use explain()** - Always check your query plans
Conclusion
Master aggregations, and MongoDB becomes incredibly powerful. Start with simple pipelines and gradually add complexity as you understand each stage.
The key is to think of your data transformation as a series of steps, not one big query.