Why doesn’t SQL Server implement result-set caching like Redis?

This question is asked by an interview. In Redis we can caching the data but why not we are implementing cache like Redis.

Because result-set caching fundamentally conflicts with SQL Server’s transactional guarantees, isolation semantics, and write-heavy OLTP design. The cost of correctness and invalidation would exceed the cost of re computation. SQL Server doesn’t implement result-set caching because it would break transactional isolation, require impossible cache invalidation, and violate ACID guarantees. Redis can cache results because it delegates correctness and staleness decisions to the application. SQL Server refuses to.

Below are the reasons

Ø  Result-set caching breaks transactional isolation: - SQL Server supports

o   READ COMMITTED

o   REPEATABLE READ

o   SNAPSHOT

o   SERIALIZABLE

o   Each transaction can see different versions of the same data.

Ø  Cache invalidation is impossible at OLTP scale

o   Any INSERT / UPDATE / DELETE:

§  Could affect thousands of cached result sets

§  Across different predicates

§  Across different joins

o   One row change could invalidate:

§  Aggregates

§  Groupings

§  Subqueries

§  Window functions

Ø  Ad-hoc queries make result caching unbounded

o   Each query:

§  Slightly different predicate

§  Slightly different result

§  Slightly different cache key

Ø  Plan cache ≠ Result cache:

o   SQL Server does cache:

§  Execution plans

§  Metadata

§  Data pages (buffer pool)

o   But it never caches:

§  Rows returned by SELECT

Ø  Locks, latches, and versioning make caching unsafe

Ø  Indexed Views prove Microsoft chose storage, not caching

o   Indexed (materialized) views

§  Physically stored

§  Logged

§  Transactionally maintained

§  Deterministic

o   This design:

§  Preserves ACID

§  Keeps results correct

§  Pushes cost to write time, not read time

 

The difference between them.

Aspect	Microsoft SQL Server	Redis
Primary role	Transactional RDBMS	Cache / KV store
Consistency	Strong ACID	Eventually consistent
Query type	Ad-hoc relational	Key-based
Data mutability	High write frequency	Mostly read-heavy
Cache invalidation	Complex	Explicit / TTL-based