What are the potential issues with embedding data in MongoDB?

Embedding data in MongoDB is a strategy that can improve performance through denormalization, but it also has potential drawbacks. When embedding documents within one another, the size of the resulting documents can become quite large, especially if the embedded documents contain substantial amounts of data. This scenario can negatively impact performance because MongoDB has a document size limit of 16 MB. If documents approach this limit, it can lead to issues like increased latency during reads and writes or even encounters with the document size constraint, ultimately complicating operations.

When documents become large, they may not only increase the time it takes to retrieve or write the data but also consume more memory during these operations. The overhead associated with managing large documents can, therefore, lead to an overall performance bottleneck in data access times and resource utilization.

While other potential issues related to data embedding exist, such as data duplication or query complexity, the primary concern centers around the performance impact stemming from excessively large documents, making option B particularly relevant.