Designing the schema for a high-volume social feed requires a delicate balance between normalization and read-performance. In a read-heavy system (where fetching the feed happens 100x more often than creating a post), over-normalized databases can lead to devastating JOIN penalties. We have strategically denormalized specific aggregate counts—such as storing TotalLikes on the Post table or using indexed views—to prevent massive aggregate recalculations on every page load. Furthermore, we implemented clustered indexes on our chronological sorting columns (CreatedAt) and non-clustered indexes covering our foreign keys (AuthorId, SiteId) to guarantee sub-millisecond seek times as our row counts push into the tens of millions.

Designing the schema for a high-volume social feed requires a delicate balance between normalization and read-performance. In a read-heavy system (where fetching the feed happens 100x more often than creating a post), over-normalized databases can lead to devastating JOIN penalties. We have strategically denormalized specific aggregate counts—such as storing TotalLikes on the Post table or using indexed views—to prevent massive aggregate recalculations on every page load. Furthermore, we implemented clustered indexes on our chronological sorting columns (CreatedAt) and non-clustered indexes covering our foreign keys (AuthorId, SiteId) to guarantee sub-millisecond seek times as our row counts push into the tens of millions.