What are the differences between MongoDB's aggregation pipeline and MapReduce?

What are the differences between MongoDB's aggregation pipeline and MapReduce?

The aggregation pipeline is a framework for data processing in MongoDB that allows you to perform operations on data in stages, while MapReduce is a more traditional method for processing large data sets using a map function to filter and a reduce function to aggregate.

Which one is more efficient for large datasets?

Generally, the aggregation pipeline is more efficient for large datasets because it can optimize the execution plan and reduce the amount of data processed at each stage.

Can you give an example of when to use each?

Use the aggregation pipeline for tasks like filtering and transforming data in a single query. MapReduce is better for complex aggregations that require custom logic.

Are there any limitations to using the aggregation pipeline?

Yes, the aggregation pipeline has some limitations, such as not supporting certain operations that MapReduce can handle, like complex joins.