Programmer & Inspector
Programmer
Hey, I’ve been stuck on a caching strategy that keeps latency low while still being clean and scalable. Think we can work it out together?
Inspector
Sure thing. Tell me what you’ve got so far: the type of data, traffic patterns, and how strict your consistency needs are. If you’re chasing low latency and clean scalability, a two‑tier cache usually does the trick: an in‑memory L1 cache on each node for instant hits, backed by a distributed L2 like Redis or Memcached for shared data. Use a cache‑aside or write‑through strategy so the cache stays in sync without chasing every write. For read‑heavy workloads, a CDN or edge cache can offload the pressure further. Keep your keys consistent and watch out for stampedes – a simple token bucket or request coalescing can save you. Need help choosing the right eviction policy or sharding scheme? Let me know.
Programmer
Sounds solid. I’m handling user profile data – JSON blobs, not huge, but millions of read ops per second during peak. Write traffic is moderate, a few writes per user per minute. Consistency can’t be eventual – I need to read the latest profile right after a write, so strong read-after-write is required. I’m worried about stampedes on login. Any specific techniques for that?
Inspector
You’ll want a write‑through cache so every update hits the DB first, then the cache. That guarantees the next read sees the fresh blob. For stampedes, use a single‑flight pattern: when a key is missing, let the first request acquire a lightweight lock, fetch from the DB, populate the cache, and release. Subsequent requests hit the cache immediately. If you’re using Redis, the “SETNX” command can act as that lock, or a Lua script that sets a short‑lived key while you pull the data. Another trick is to add a small random expiry buffer to cache entries so not every key expires at the same moment. Also keep a “stale‑while‑revalidate” window: serve a slightly old profile while you refresh in the background; that keeps the UI snappy. If you need zero latency after a write, consider a per‑user in‑memory store on the login server, but that won’t scale if you have thousands of concurrent logins. The best bet is write‑through + single‑flight cache‑aside, with a modest TTL and a retry window. That balances consistency, speed, and avoids the stampede.
Programmer
Got it, write‑through and single‑flight make sense. I’m leaning toward a TTL of about 30 seconds with a random jitter of ±5 seconds to avoid batch expirations. Do you recommend LRU for eviction in the in‑memory L1, or something simpler like FIFO? Also, how do we handle cache misses when the DB is down?
Inspector
LRU is usually the safer bet for L1. It keeps the hot users in memory and naturally drops the ones you’re not seeing. FIFO is a bit too naïve – you could end up evicting someone who’s about to log in again. As for DB outages, you’re better off keeping a read‑only replica in the mix or at least a small in‑memory buffer that holds the last successful blob. If the primary goes down, serve the buffered copy and flag it as stale, then reconcile once you get back online. In short, LRU, a tiny “stale” cache, and a graceful degradation plan.
Programmer
Thanks, LRU it is. I’ll set up a small per‑node buffer for the last successful blob. If the DB is unreachable, the buffer will kick in. Will keep an “isStale” flag in the payload so the front‑end can decide whether to show a warning. One more thing: any idea how to test the single‑flight logic under load without a real cluster? Maybe a mock with a rate limiter?Just spin up a few worker threads that all request the same key at the same time. Mock the DB with a sleep, and log when each thread hits the DB versus reads the cache. If only one thread hits the DB and the rest wait, the single‑flight is working. Use a countdown latch or a simple barrier in your test framework to sync the start. That gives you a controlled, repeatable way to validate the lock logic.Run a quick unit test that launches, say, 20 goroutines or threads all asking for the same profile key at once. Add a small delay in the mock DB call so it’s noticeable. Log the timestamps of each DB hit. If only one hit appears in the log and the others hit the cache, the single‑flight works. It’s a lightweight way to confirm the lock logic before you spin up the whole cluster.
Inspector
Sounds like you’ve got the basics nailed. Just make sure your barrier fires before any of the threads touch the cache, otherwise you’ll get a phantom race. And if the mock DB sleeps a bit longer than your latch releases, you’ll see a clear DB hit per thread—bad news. Keep the log timestamps precise; a few milliseconds can make the whole test look busted when it’s actually fine. When you run it, look for one distinct DB‑hit entry and a flood of cache hits right after. That’ll prove the single‑flight is doing its job. Happy testing.