Wednesday, May 27, 2026 · 9:41 AM
ok can you explain caches are speed with memory problems like i actually need to build it
caches make repeated reads cheap by keeping useful data near the caller, but every cache creates freshness and invalidation questions
mental picture: it is a sticky note on your monitor. fast, useful, and dangerous if the real answer changed downstairs
good, now do the real thing
not the fake diagram version
client, CDN, reverse proxy, application, database, and distributed caches all sit in different places with different risks
cache-aside reads from cache first, then database, then fills cache on miss
so the first move is making the invisible numbers visible
exactly
write-through updates cache and database together, trading write latency for fresher reads
what changes when this gets real traffic?
write-behind updates cache first and persists later, which is fast but risky if the cache dies
eviction policies like LRU, LFU, TTL, and size limits decide what disappears first
wait, i thought the better answer was just adding the serious-sounding tool
that is the trap
a cache miss can be worse than no cache if thousands of requests stampede the database at once
ok so what bill shows up later?
long TTLs improve hit rate but risk stale data
short TTLs reduce staleness but increase misses
distributed caches add capacity but bring network and serialization overhead
where do people usually mess this up?
caching negative results, permissions, or personalized responses without a precise invalidation story
if i were designing this tomorrow, what should i write down first?
the read path
the write path
the thing that is allowed to be stale
the thing that absolutely is not
that is annoyingly practical
yeah. most system design is boring on purpose
cache data that is expensive, popular, and safe to be stale for a known amount of time
got it
less architecture cosplay, more pressure map
perfect
draw the pressure, then choose the machinery
Read Wed, May 27 · 9:58 AM