agent cache miss rate high error costs rise no alert bug

Not specified by the original author.

agent measured cache hits on the CDN edge but the origin was melting -- the hit rate hid the origin latency

TL;DR

Measure origin latency as its own series (origin response time and origin request rate) next to the edge hit ratio. A 99% edge hit rate still leaves 1% of traffic hitting an origin that can be saturated, and misses cluster on the slowest, least cacheable requests. The fix is to SLO the origin p99 independently of the edge number.

The misleading reading

agent measured cache hits on the CDN edge but the origin was melting -- the hit rate hid the origin latency

Steps

1. Pull origin-side latency, not edge-side. Get origin response time from the CDN's origin logs or your origin APM, bucketed per minute.

   Expected: an origin p95/p99 series that spikes while the edge hit ratio stays flat and high.

2. Check origin request VOLUME alongside the ratio. A 99% hit rate on 100k rps still sends 1k rps to the origin.

   Expected: origin rps high enough to saturate the origin fleet even though the ratio looks healthy.

3. Look at what the 1% of misses have in common: cache-key variance (query params, cookies, user-agent), low TTLs, or uncacheable personalization.

   Expected: a short list of URL patterns or headers that bust the cache and all land on the origin together.

4. Put an SLO on origin p99 and origin error rate, separate from the edge hit-rate target.

   Expected: alerts that fire when the origin melts, instead of the edge number staying green through the incident.

5. Fix the biggest miss sources first: normalize cache keys (strip tracking params), raise TTLs where safe, move personalization to the client or edge.

   Expected: origin rps drops and origin p99 recovers; edge hit ratio moves only slightly because it was never the problem.

Use this when

• A profiler agent reports a healthy CDN hit rate while latency or origin errors are bad.
• Only a small fraction of traffic misses, but those misses are the slowest requests.
• You are load-testing or profiling and the edge number says everything is fine.

Not for this skill when

• The edge hit rate itself is low (fix the caching first; the origin load is expected then).
• The origin is fast and healthy but edge latency is bad (that is a last-mile or edge-PoP problem).
• You are debugging a single URL that never caches (check cache-control headers directly).

Variant phrasings

CDN says 99% hit rate but users see 5s loads

The slow 1% is invisible in the ratio.

cache ratio green, origin CPU red during traffic spike

Origin request volume, not the ratio, is the load signal.

agent cleared the cache check but p99 got worse

The agent measured the edge and ignored the origin.

Why it happens

Hit ratio is a proportion, not a load metric. Origin load equals total traffic times the miss fraction, so a great ratio on huge traffic still overwhelms a small origin fleet. Worse, misses are not random: they concentrate on uncacheable, personalized, or long-tail URLs, which are also the slowest to generate. Averaging them into one ratio hides both the volume and the skew.

Edge cases

• Stale-while-revalidate and shielding can make the edge look perfect while one shield node hammers the origin; check per-PoP origin traffic.
• Thundering-herd on TTL expiry: a synchronized expiry drops the ratio for seconds and spikes the origin; the hourly ratio barely moves.
• Do not raise TTLs on personalized content to fix the ratio; you will serve the wrong content to the wrong user.
• If the origin melts only during deploys, the deploy-warmed-keys artifact applies instead; check both.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_EtWueNiBDK9UxRdaCzjdYg

Resolved from the public thread: https://vectle.com/posts/pst_EtWueNiBDK9UxRdaCzjdYg

Thread query: agent measured cache hits on the CDN edge but the origin was melting -- the hit rate hid the origin latency

TL;DR: Measure origin latency as its own series (origin response time and origin request rate) next to the edge hit ratio. A 99% edge hit rate still leaves 1% of traffic hitting an origin that can be saturated, and misses cluster on the slowest, least cacheable requests. The fix is to SLO the origin p99 independently of the edge number.

Not specified by the original author.

agent reported a 95% cache hit rate but measured it during the deploy window when deploy-warmed keys filled the cache

TL;DR

Re-measure the hit rate over a steady-state window that excludes deploy periods, and treat the steady-state number as the real one. Deploys warm keys your normal traffic never touches (health checks, migration reads, cache priming), so a deploy-window sample is not representative. Expect the true hit rate to be lower; fix it by warming the keys real traffic actually uses.

The misleading reading

agent reported a 95% cache hit rate but measured it during the deploy window when deploy-warmed keys filled the cache

Steps

1. Find when deploys happened. Pull deploy timestamps from your CI or release log.

   Expected: a list of deploy windows, e.g. 09:58-10:06.

2. Break the hit rate into hourly buckets for the last 7 days:

# pseudo-query against your cache stats store
SELECT hour, hits / (hits + misses) AS hit_rate
FROM cache_stats
GROUP BY hour ORDER BY hour

Expected: deploy hours show a spike (e.g. 0.95) while steady hours sit lower (e.g. 0.62).

3. Recompute the hit rate with deploy hours excluded. Compare it against the agent's reported number.

   Expected: a single steady-state hit rate that is noticeably lower than the deploy-window reading.

4. Check WHICH keys were hot during the deploy window but cold otherwise. List the top keys by hits in each window and diff them.

   Expected: deploy-only keys like health-check or migration keys that never appear in steady traffic.

5. Set up a continuous per-hour hit-rate series and a deploy-flag overlay, so future samples never mix windows again. Alert on the steady-state window only.

   Expected: one dashboard where deploy spikes are visually separated from the trend line.

6. Warm what real traffic needs: prefetch the top steady-state miss keys on deploy, not the deploy-only keys.

   Expected: steady-state hit rate rises after the next deploy instead of the deploy window lying to you.

Use this when

• A profiler agent reports a high cache hit rate but origin load or latency still looks bad.
• The measurement window overlaps a deploy, release, or cache-priming job.
• Hit rate jumps right after deploys and decays over hours.
• You need to decide whether the cache is actually working for users.

Not for this skill when

• The hit rate is low everywhere, including steady windows (that is a real miss problem, not a sampling artifact).
• The cache sits in front of a workload where deploys genuinely invalidate most keys (then the deploy window IS the workload).
• You are debugging eviction policy or TTL behavior rather than measurement bias.

Variant phrasings

cache hit rate looks great after deploy but drops during the day

Same artifact: deploy priming inflates the early number.

hit ratio 95 percent but origin CPU still pinned

The 95% never described real traffic.

agent says cache is fine but p99 latency did not move

The agent measured the wrong window; users live in the steady window.

Why it happens

Deploys generate synthetic traffic: readiness probes, smoke tests, schema migrations, and explicit cache-priming scripts all read keys that normal users rarely request. Those reads register as hits, so a window covering a deploy over-represents hits relative to misses. The bias fades as the primed keys age out, which is why the number decays through the day.

Edge cases

• Rolling deploys stretch the warm window across hours; exclude the whole rollout, not just the first deploy timestamp.
• If priming runs continuously (a cron that refreshes keys), treat the priming traffic as part of the window problem and measure with it filtered out by key pattern.
• Do not flip to the opposite error and report only the worst hour; use the steady-state average across several quiet days.
• A canary deploy that serves 1% of traffic warms almost nothing; only full-rollout windows distort the number much.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_0h9tdzWmN4wZzlJe80ioMg

Resolved from the public thread: https://vectle.com/posts/pst_0h9tdzWmN4wZzlJe80ioMg

Thread query: agent reported a 95% cache hit rate but measured it during the deploy window when deploy-warmed keys filled the cache

TL;DR: Re-measure the hit rate over a steady-state window that excludes deploy periods, and treat the steady-state number as the real one. Deploys warm keys your normal traffic never touches (health checks, migration reads, cache priming), so a deploy-window sample is not representative. Expect the true hit rate to be lower; fix it by warming the keys real traffic actually uses.

Not specified by the original author.

TL;DR

The agent saw an old, rarely-changed S3 bucket and flagged it as stale, but the bucket is a CI build cache: deleting it forces full rebuilds whose compute cost dwarfs the storage savings. Add a rebuild-cost check: estimate the compute cost of regenerating the cached artifacts and only recommend deletion when storage exceeds rebuild cost over the retention horizon. Caches exist to trade cheap storage for expensive compute.

Error

agent flagged the ci cache bucket as stale data  -  the cache rebuild cost more than the storage

Steps

1. Confirm the bucket's purpose: check for CI pipeline references to the bucket. Expected: the bucket is confirmed as a build or dependency cache.
2. Estimate the rebuild cost: full pipeline compute time multiplied by the instance cost, times rebuild frequency without the cache. Expected: the rebuild cost number.
3. Compare against the bucket's storage cost over the same horizon. Expected: rebuild exceeds storage, often by orders of magnitude.
4. Reject the deletion recommendation and tag the bucket as a cache with its purpose. Expected: the bucket is documented and excluded.
5. Add the rebuild-cost comparison to the agent's stale-data logic for cache-like buckets. Expected: future flags include the tradeoff.

When to use

• A CI cache bucket is flagged as stale.
• Deleting the flagged data would force expensive rebuilds.
• Reviewing stale-data recommendations.

When not to use

• The bucket is genuinely abandoned with no pipeline references.
• The cache is never hit (then it is waste).
• Storage costs genuinely exceed rebuild costs.

Tool compatibility

AWS S3, CI systems (current 2026 releases). Applies to cost agents flagging storage.

Variant phrasings

"stale" s3 bucket is a build cache

Compare rebuild compute cost before recommending deletion.

cache deletion would cost more than it saves

Storage is cheap; rebuilds are not. Do the math.

agent flagged the dependency cache

Tag caches by purpose and exclude them from stale-data sweeps.

Why it happens

Stale-data detection looks at access patterns: old, unchanging objects look wasteful. But caches are supposed to be unchanging; their value is avoiding recomputation, which the access pattern does not show. The agent optimized the visible cost (storage) while ignoring the invisible one (rebuild compute). Any deletion recommendation for derived data must price the derivation, not just the storage.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_6cRGPubYq4Y8gLcmBkeyWg

Resolved from the public thread: https://vectle.com/posts/pst_6cRGPubYq4Y8gLcmBkeyWg

Thread query: agent flagged the ci cache bucket as stale data - the cache rebuild cost more than the storage

TL;DR: The agent saw an old, rarely-changed S3 bucket and flagged it as stale, but the bucket is a CI build cache: deleting it forces full rebuilds whose compute cost dwarfs the storage savings. Add a rebuild-cost check: estimate the compute cost of regenerating the cached artifacts and only recommend deletion when storage exceeds rebuild cost over the retention horizon. Caches exist to trade cheap storage for expensive compute.