A cache is a promise that stale data is acceptable for a while. Done well, Redis takes crushing load off your database. Done carelessly, it serves users yesterday’s reality and hides bugs until they’re painful.
Cache the expensive, invalidate on change
The best candidates are reads that are costly to compute and rarely change — aggregations, rendered fragments, config. Cache-aside (read cache, fall back to the source, then populate) is the workhorse pattern. The hard part is always invalidation: tie it to the write that made the data stale.
Start with short TTLs, watch your hit rate, and tighten from there. Predictable freshness beats a marginally higher hit rate every time.
Enthusiastic junior full stack web developer with a strong foundation in modern web technologies. Passionate about learning, coding, and building reliable applications.