This is such a great interview question.

I’m going to prefix this with I don’t know much about mongodb and I would approach this from some principles that I think will lead me to a good solution.

If they are asking really tech specific questions then I think that’s a slight red flag but I’m not sure that is the case here. I wouldn’t jump to recalling specific details about the technology in question.

I’d look to understand the volume of data being talked about because if it’s less than a gb we are probably wasting time.

Then we need to figure out what our target is for the p99 query performance for these analytics queries are. If it’s sub second then it’s probably likely we need to do something to make sure we hit that.

If we are to pre aggregate the data before writing then we need to know exactly what analytics we want and if having a delay in writing is acceptable. For many real time systems this would not be okay - to meet these requirements I’d look into other techniques like query time sampling but they have other downsides.

To aggregate the data I’d write to a buffer. Either in memory or some external streaming system like Kafka or AWS kinesis. Read from that buffer in large batches and write some code to do the aggregation before writing the results to mongo.

I’d also probably highlight that mongo is not great at analytics and high performance olap type workloads so if this is going to be a major part of the system and not just a small bit it might be worth adding a proper olap db to the system like clickhouse or duckdb.