Eliminate Redundant Work with Targeted Caching

Most database performance problems aren't caused by slow queries, but by doing the same work repeatedly. At scale, redundancy becomes the dominant cost even when individual queries are "fast." Targeted caching removes that waste without turning the application into a maze of cache keys and invalidation logic.

1. Start with repetition, not latency: Look for queries that run thousands of times per second with identical shapes and similar parameters. If a query's result doesn't meaningfully change between requests, recomputing it every time is pure overhead. Caching pays off fastest when it eliminates repeated work, not when it shaves milliseconds.
2. Cache at the query boundary, not deep in application code: Application-level caches tend to sprawl as teams cache the same data with different TTLs and assumptions, creating inconsistency and operational risk. Caching at the SQL query level centralizes logic, preserves clear semantics, and avoids duplicated effort across services.
3. Be selective! Cache the hot paths, not everything: Over-caching is just as dangerous as under-caching. Focus on the small set of queries that dominate traffic or CPU usage. If a query isn't hot or repeatable, caching it won't move the needle.
4. Treat caching as a performance primitive, not a workaround: Caching shouldn't be a last-resort patch. At scale, it's a foundational tool for controlling load. Eliminating redundant work reduces CPU pressure, stabilizes tail latency, and creates headroom for growth without re-architecting the system.

Targeted caching shifts the focus from micro-optimizations to eliminating redundant work, which is where the real gains show up at scale.

🔍 What We're Watching in the Database Ecosystem

The Biggest Cybersecurity and Cyberattack Stories of 2025

This roundup underscores that most major breaches still originate from basic failures: exposed systems, overbroad permissions, and neglected infrastructure. For database teams, system simplicity matters. Eliminating redundant services, shadow caches, and ad-hoc data copies doesn't just improve performance, it meaningfully shrinks the attack surface when things go wrong.

2025 LLM Year in Review

2025 was less about bigger models and more about how LLMs are trained and used. Longer reinforcement learning runs and agent-style tools like Cursor and Claude Code drove most of the real capability gains. LLMs are getting more powerful and more uneven at the same time, which puts pressure on systems and infrastructure to manage that jaggedness.

DeepSeek's New Training Approach Shows How AI Scaling Is Hitting Constraints

DeepSeek's work reinforces a broader shift away from brute-force scaling toward efficiency. The same shift is happening in databases, where efficiency increasingly comes from eliminating wasted computation rather than throwing more hardware at the problem, especially redundant reads that add cost without adding value.

In Case You Missed It

We're kicking off 2026 at FOSDEM!

We will be hosting a happy hour on Saturday January 31 (more details coming soon) and co-sponsoring the Belgian MySQL, MariaDB, and Friends community dinner on Friday, January 30 (registration here).

We would love to connect if you're attending or are in the area!