What is the primary benefit of using a denormalized pre-computed feed table for fast reads?

What is the primary benefit of using a denormalized pre-computed feed table for fast reads?

• A. To store raw normalized data in large scale.
• B. To support fast reads by pre-joining data for commonly requested queries.
• C. To duplicate data across all shards.
• D. To enforce transactional consistency across multiple updates.

Answer: (B)

The primary benefit is speeding reads by storing the joined data ahead of time. A denormalized, pre-computed feed table materializes the results of common queries (such as user, post, and metadata in one row) so that a read can fetch everything needed without performing multiple joins at query time. This reduces latency, simplifies the query path, and makes high-traffic reads much faster because the data layout matches how requests are actually served. Storing raw normalized data would still require joining tables during reads, which slows things down. Duplicating data across shards is more about distribution and fault tolerance than the fundamental read-optimization goal of denormalization. Enforcing transactional consistency across multiple updates becomes more complex with denormalized data and is not the core benefit when focusing on fast reads.

The primary benefit is speeding reads by storing the joined data ahead of time. A denormalized, pre-computed feed table materializes the results of common queries (such as user, post, and metadata in one row) so that a read can fetch everything needed without performing multiple joins at query time. This reduces latency, simplifies the query path, and makes high-traffic reads much faster because the data layout matches how requests are actually served.

Storing raw normalized data would still require joining tables during reads, which slows things down. Duplicating data across shards is more about distribution and fault tolerance than the fundamental read-optimization goal of denormalization. Enforcing transactional consistency across multiple updates becomes more complex with denormalized data and is not the core benefit when focusing on fast reads.