A detailed analysis demonstrates how MongoDB schema choices dramatically impact application performance. The study tests four application versions, from a naive `appV1` baseline to an optimized `appV4`. Each iteration addresses a specific design flaw, revealing how seemingly minor changes to document structure and indexing can accumulate into major performance gains.

The journey starts with `appV1`, which embeds `key` and `date` fields in the `_id` field, creating redundant indexes and large storage overhead. Subsequent versions progressively optimize by moving fields out of `_id`, shortening field names, and converting string keys to binary data. These changes reduce document size and index footprint.

The final `appV4` version achieves the most significant storage efficiency by concatenating the `key` and `date` fields directly into the `_id` field. This eliminates the need for a separate compound index, leveraging MongoDB's default `_id` index for all queries. While storage per event dropped 35%, the article notes performance gains were more modest, suggesting architectural limits for the current document-per-day model.