Write-Through Caching Lab (Interactive)
Tune write rate, commit latency, and unread-write share to compare Write-Through and Cache-Aside second by second. Quantify the Write-Through bargain: synchronous RAM+disk dual writes buy zero read misses on fresh data, at higher write latency and wasted RAM.
Write-Through vs Cache-Aside Trade-off Lab
Pay the synchronous RAM+DB dual write on every PUT — and collect zero read misses on freshly written data.
Workload Inputs
Interview line: “Write-Through trades +15ms of write latency for zero read misses on new data — right for Discord-style hot channels, wrong for telemetry ingestion.”
How It Works Under the Hood
Write-Through makes the cache the application’s only interface: the provider updates RAM, then synchronously persists to the database, acknowledging only after both confirm. Every write pays RAM + disk commit (~0.2 + 15ms), but newly written entities are guaranteed warm, so read cold-starts vanish — ideal when data is read seconds after creation, like Discord or Slack channel messages. The hazard is memory pollution: writes that are never read (audit logs, telemetry) still enter RAM and, via LRU, evict genuinely hot items, which is why Write-Through must be paired with aggressive TTLs and eviction policies.
Core Architectural Principles
- Write latency = RAM write + synchronous DB commit; success is acknowledged only after both.
- Zero read misses on newly written data — the cache is always the freshest system of record view.
- Cold writes that are never read still consume RAM and evict hot keys without LRU + TTL safeguards.
Contrast Write-Through with Cache-Aside along two axes: write latency (higher, dual synchronous write) versus read freshness (zero misses on new data). Recommend it for chat channels, collaborative documents, and notifications — anything written then immediately read. Pair the answer with the memory caveat: run LRU eviction and TTLs so write-once-never-read records cannot exhaust the cache.
Always-fresh cached reads and simpler application code in exchange for slower writes and RAM spent on data that may never be read.