A PostgreSQL community member recently sparked a lively discussion on Reddit about database caching strategies. The genuine frustrations expressed in the responses highlighted the need for simpler, more reliable solutions. This article addresses the key concerns raised and suggests how modern SQL caching architectures, such as Readyset, can solve these longstanding problems. 

Traditional Caching 

Traditional database caching typically relies on Key-Value (KV) stores such as Redis or Memcached. Developers manually manage these caches by explicitly setting keys and values based on database queries, a process outlined in guides like this AWS Redis caching tutorial or Readyset's overview of KV cache complexity. This approach creates several pain points:

• Manual cache management: Developers must explicitly set keys, handle TTLs, and convert database responses into KV pairs
• Invalidation complexity: Detecting when data changes and updating caches accordingly often leads to stale data or over-invalidation
• Application coupling: Cache logic becomes deeply intertwined with business logic, making systems harder to maintain
• Consistency challenges: Keeping cached data synchronized with the database requires careful coordination

Readyset's Streaming Approach

Readyset takes a fundamentally different approach by acting as a transparent proxy that sits between applications and databases. Instead of requiring manual cache management, it uses streaming dataflow (based on the Noria research) to maintain query results in memory and update them incrementally as data changes.

The key innovation is direct subscription to database replication streams and using this for cache maintenance. When underlying data changes, Readyset automatically and atomically updates cached query results, eliminating manual invalidation while maintaining consistency.

Addressing Community Concerns

"When do you actually need caching?"

"How do you know when you really have to add a cache? Are there rules of thumb?" — u/compy3

"I would advise NOT to add a cache until you really feel that you have to, don't do it just cause." — u/illuminanze

The community is right to advocate for restraint. Premature caching optimization often creates more problems than it solves. Clear indicators that caching is needed include:

• Consistent query latency above acceptable thresholds
• Database CPU or I/O bottlenecks during normal traffic
• Slow response times during traffic spikes
• Expensive queries identified through tools like pg_stat_statements or APM solutions like Datadog or New Relic

Readyset addresses the "when to cache" question by monitoring production traffic automatically. In an upcoming release, it will identify expensive or high-throughput queries without manual instrumentation, eliminating guesswork about what needs caching.

"Caching breaks everything"

"Adding a cache will always be adding extra complexity and possibility for data to be out of sync." — u/illuminanze

"Imagine in a few years getting customer service tickets due to cache sync issues that nobody can replicate." — u/pjstanfield

"Because of eviction, you need to pay attention to memory leaks if you use an in-memory map." — u/Embarrassed_Soft_153

"Handling hydration and invalidations… always worth discussion." — u/nursestrangeglove

This concern reflects real pain from traditional caching approaches. The complexity stems from the fundamental gap between cache and database state. In traditional setups, the cache and database operate as separate systems with no built-in coordination mechanism. Developers must bridge this gap with custom logic that inevitably becomes a source of problems:

Inconsistent invalidation becomes a nightmare in distributed systems where multiple services might update related data. A user profile update in one service might need to invalidate cached data in three other services, requiring careful coordination.

Cache stampedes occur when a popular cache entry expires under high load, causing dozens of requests to suddenly rush to regenerate the same expensive query, thereby overwhelming the database. Developers typically implement complex locking mechanisms or probabilistic early expiration to prevent this.

Memory management issues arise when cache eviction policies don't align with actual data access patterns, leading to either memory bloat or frequent cache misses for essential data.

Readyset eliminates the inconsistent invalidation problem entirely by maintaining a direct connection to the database replication stream. There's no separate cache state to manage, just materialized views that update automatically. For cache stampedes and memory management, Readyset implements state-of-the-art approaches built into its core architecture. Rather than forcing developers to implement these complex mitigations in application code, Readyset handles them transparently with proven algorithms designed for high-performance caching systems.

"Just fix your queries instead"

"I'm removing Redis cache usage that is slower than if we had tuned the underlying queries." — u/dektol "

Fix your schema and query pattern so that you get the performance you need directly from the DB." — u/hamiltop

Query optimization absolutely remains essential. Well-designed schemas, proper indexing, and efficient queries form the foundation of database performance. Readyset never advocates skipping these fundamentals. Bad schemas and poorly written queries will create problems that caching cannot solve. Proper database design should always be the first line of defense. However,

• Complex queries with joins and aggregations can be expensive, regardless of optimization
• High-traffic applications may overwhelm even well-tuned databases
• Development teams often need immediate performance improvements while planning longer-term optimizations

Readyset complements rather than replaces query optimization. It provides a performance safety net that allows teams to deliver features while working on underlying improvements, automatically identifying which queries benefit most from caching.

"Redis isn't faster than Postgres anyway"

“Redis, memcached… are not caches (if they’re on a different host). It’s not any faster than the DB.” — u/angrynoah

This observation is often correct for simple, well-indexed queries. PostgreSQL can execute many queries faster than the round-trip time to an external cache. The real issue isn't query execution speed; it's the additional network hop and serialization overhead that traditional caches introduce. Even when Redis itself is fast, you're adding latency rather than removing it.

However, even fast queries can become resource-intensive under high load. Consider Reddit's front page, which fetches stories for hundreds of millions of users repeatedly. While each query might be quick, executing the same operation thousands of times per second consumes substantial CPU and I/O resources. Caching provides significant offload benefits even when latency improvements are minimal, since caches do minimal processing before retrieving results, unlike databases, which must parse, plan, and execute every query.

Readyset's intelligent monitoring identifies which queries actually benefit from caching, avoiding the overhead of caching queries that are already fast. By acting as a proxy rather than a separate service, it also minimizes additional network latency.

"Keep it simple"

"Don't do it unless it's brutally simple." — u/chock-a-block

“There are no silver bullets.” — u/illuminanze

The community's emphasis on simplicity reflects hard-won experience with complex caching implementations. Every KV-Caching deployment starts simple but eventually demands its own operational overhead: monitoring cache hit rates, debugging inconsistent invalidation patterns, and constantly tuning TTL values that never quite feel right. What begins as a performance optimization becomes its own maintenance burden.

Readyset delivers architectural simplicity. While it implements sophisticated algorithms for incremental view maintenance and stream processing under the hood, what it presents to users is clean: standard SQL interception through familiar deployment patterns like proxy or read-replica configurations that most applications already know how to handle. Unlike KV caching approaches that entwine cache logic throughout your application code, Readyset keeps caching concerns completely separate from business logic. Applications continue using standard SQL queries without modification, while caching happens automatically based on observed traffic patterns.

Conclusion

Readyset's caching architecture represents a substantial advancement over traditional caching approaches by addressing core concerns of complexity, consistency, and performance through automated synchronization, real-time updates, and simplified management. It complements rather than replaces essential database optimizations, providing an effective and robust caching solution.