What are the best practices for using MongoDB aggregation pipelines?

What are the best practices for using MongoDB aggregation pipelines?

When using MongoDB aggregation pipelines, consider the following best practices: 1. Use $match early to filter data. 2. Limit the number of documents processed. 3. Use $project to include only necessary fields. 4. Optimize stages for performance.

Can you explain why using $match early is important?

Using $match early reduces the number of documents that subsequent stages need to process, which can significantly improve performance.

What is the role of $project in an aggregation pipeline?

The $project stage reshapes each document in the stream, allowing you to include, exclude, or add new fields, which helps in reducing the amount of data processed.

Are there any common pitfalls to avoid?

Yes, common pitfalls include not indexing fields used in $match, overusing $group, and neglecting to limit the output size.