Swift Proxy Memcache & Rate-Limit Tuning Prompt
Tune the Swift proxy-server pipeline (memcache, ratelimit, container/account caching) to stop 503 storms and cache stampedes under load.
- Target user
- Advanced Swift operators running multi-node proxy tiers
- Difficulty
- Advanced
- Tools
- Claude, ChatGPT
The prompt
You are a senior Swift operator who tunes proxy-server pipelines for throughput and latency. I will provide: - Swift version and `proxy-server.conf` (full pipeline + ratelimit/memcache/cache sections) - `memcache.conf` and the memcached topology (which nodes, RAM allotted, connection counts) - The symptom: 503s, slow listings, container-server overload, or memcache evictions - Proxy logs around the incident and `swift-recon` output (async pendings, load, mem) - Workload shape: object size distribution, request rate, hot containers Your job: 1. **Pipeline audit** — read the WSGI pipeline order and flag misordered or missing middlewares (ratelimit before/after auth, cache placement). 2. **Memcache sizing** — assess whether evictions or connection exhaustion are causing container/account info cache misses that hammer the backend. 3. **Ratelimit policy** — evaluate `ratelimit` config (account/container limits, `max_sleep_time_seconds`, blacklist) against the observed hot-spot. 4. **503 attribution** — determine whether 503s originate from ratelimit (intended), backend overload, or handoff exhaustion. 5. **Tuning plan** — concrete config deltas (cache TTLs, memcache pool size, ratelimit thresholds) with the expected effect on each symptom. 6. **Validation** — what to watch in swift-recon and proxy logs after the change to confirm improvement without masking a real backend problem. Output as: (a) a pipeline + middleware table with issues flagged, (b) a sized memcache/ratelimit recommendation, (c) a rollout and validation plan. Apply tuning to one proxy node, observe under real load, then roll to the tier; never raise ratelimit thresholds to hide a saturated backend.
Why this prompt works
Swift 503 storms are almost always a feedback loop, not a single broken component: a hot container overwhelms the container-server, memcache misses force the proxy to re-resolve container existence on every request, and ratelimit either fires too aggressively or not at all. Operators tend to grab one knob — usually loosening ratelimit — and accidentally move the overload downstream. This prompt forces the model to attribute 503s to a specific origin (ratelimit vs backend vs handoff) before recommending any knob, which is the discipline that breaks the loop instead of relocating it.
The pipeline audit step is what separates this from generic “tune Swift” advice. Middleware ordering in the WSGI pipeline genuinely changes behavior — ratelimit before auth rate-limits anonymous probes, cache placement changes what gets memoized — and a misordered pipeline produces symptoms that look like a sizing problem. Making the model emit the pipeline as a table with issues flagged gives you something you can diff against the documented reference order.
Finally, the one-node-at-a-time rollout with a validation step keeps you from the classic mistake of tuning the whole proxy tier at once and then being unable to tell whether you helped or hurt. Pairing the change with specific swift-recon and log signals to watch means you confirm you fixed the cause rather than masked it.