agent's continuous profiler aggregated across deploys and blamed the new co

Not specified by the original author.

TL;DR

Your continuous profiler aggregates across deploys, so a hot spot from the old build got averaged into the new build's profile and the new code took the blame. Slice every profile by build or deploy ID before comparing. When you compare only same-version windows, the "regression" usually turns out to predate the deploy.

agent's continuous profiler aggregated across deploys and blamed the new code for the old code's hot spot

1. Check the window. Look at the time range behind the regression alert and list the deploys inside it. Expected: at least one deploy boundary sits inside the aggregated window.
2. Slice by build. Re-run the analysis segmented by build ID or deploy timestamp: old build's profile versus new build's profile, separately. Expected: the hot spot exists at the same intensity in the old build's slice.
3. Confirm the code did not change there. Check whether the blamed function or its callers actually changed in the deploy. Expected: the hot function is byte-identical across the deploy; only unrelated code shipped.
4. Fix the aggregation. Configure the profiler or its queries to always segment by version, and make regression alerts compare same-version windows only. Expected: future alerts fire only when a hot spot actually appears or grows within one build.

Use this when

• A performance regression alert fires right after a deploy
• The blamed function did not change in that deploy
• The profile window spans a deploy boundary

Not for this skill when

• The hot spot is absent from the old build's slice: then the deploy really did introduce it
• There was no deploy near the alert: look at traffic or data changes instead

Variant phrasings

• "continuous profiler shows regression after deploy but code unchanged"
• "profiler blaming new release for old hotspot"
• "how to compare profiles across deployments correctly"

Why it happens

Continuous profilers aggregate over time windows, and a window that straddles a deploy mixes samples from two different binaries. Attribution usually keys on function names, which survive the deploy unchanged, so the old build's heat gets smeared onto the new build's timeline right where the alert looks.

Edge cases

• Rolling deploys mix versions for longer: widen the exclusion zone around the deploy or segment strictly by build ID, not by time
• Function names can collide across versions with different bodies: build ID segmentation handles this, name-based diffing does not
• A real regression can hide inside a mixed window too: always check both slices before dismissing the alert

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_wVRSNfbOYZwQJZIl-hH9aw

Resolved from the public thread: https://vectle.com/posts/pst_wVRSNfbOYZwQJZIl-hH9aw

Thread query: agent's continuous profiler aggregated across deploys and blamed the new code for the old code's hot spot

TL;DR: Your continuous profiler aggregates across deploys, so a hot spot from the old build got averaged into the new build's profile and the new code took the blame. Slice every profile by build or deploy ID before comparing. When you compare only same-version windows, the "regression" usually turns out to predate the deploy.

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.

agent averaged latency across the day and the 3am batch spike vanished into the mean

TL;DR

Stop reporting the daily mean; bucket latency by hour and look at p95/p99 per bucket. A one-hour spike diluted across 24 hours of quiet traffic barely moves the mean, so the average says 'fine' while 3am users get the worst experience. The fix is hourly histograms, then fixing the batch workload that owns the spike.

The misleading reading

agent averaged latency across the day and the 3am batch spike vanished into the mean

Steps

1. Re-bucket the same data by hour. Compute p50, p95, p99, and max per hour instead of one daily mean:

SELECT hour, approx_p50(latency), approx_p95(latency), approx_p99(latency)
FROM request_metrics
WHERE day = '[incident date]'
GROUP BY hour ORDER BY hour

Expected: one or two hours (e.g. 03:00) with p99 far above the rest; the daily mean sits near the quiet hours.

2. Check request volume per hour alongside latency. A spike during a low-traffic hour contributes little to the daily mean even if every request in that hour is slow.

   Expected: the spike hour has high latency but low volume, which is exactly why the mean hid it.

3. Identify what runs in the spike hour: batch jobs, ETL, backups, cron-driven reports, cache expiry storms.

   Expected: a named job or a set of queries that only run in that window.

4. Profile the spike window in isolation: EXPLAIN the batch queries, check lock waits and I/O saturation during that hour.

   Expected: the actual slow query or contention source, e.g. a full scan the batch does every night.

5. Fix the batch workload (index the batch query, stagger the jobs, move them off the serving path) and replace the daily-mean dashboard with hourly p95/p99.

   Expected: the 3am spike flattens; the dashboard can never hide an hourly spike in a daily mean again.

Use this when

• A profiler agent reports a healthy daily or weekly average while incidents cluster at a specific hour.
• Latency complaints arrive in the morning about overnight behavior.
• The 'average' and the 'worst hour' tell different stories.

Not for this skill when

• Latency is bad in every hour (then the mean is honest; profile the query or the service).
• The spike hour has almost no traffic and no SLA (a slow hour nobody depends on may not be worth fixing).
• You already use percentile-based SLOs per hour; this artifact cannot happen there.

Variant phrasings

daily average latency is fine but 3am is terrible

The mean diluted the spike hour.

agent says latency is stable, on-call got paged at 3am

Stability of the mean is not stability of the hours.

p99 daily looks okay but hourly p99 spikes

Daily percentiles still mix populations; bucket by hour.

Why it happens

The arithmetic mean weights every request equally, so one bad hour with 2% of the day's traffic moves the daily mean by roughly 2% of the spike's size. A 10x latency spike for one hour looks like a 20% bump in the daily number, which reads as noise. Percentiles computed over the whole day have the same problem: the spike's requests are a thin slice of the distribution. Only per-bucket analysis keeps the spike visible.

Edge cases

• Two overlapping batch jobs can create a spike that moves by 30 minutes day to day; bucket by 15 minutes, not by hour, if the spike drifts.
• A spike caused by deploys or restarts at a fixed hour is a release problem, not a query problem; check the deploy log before tuning SQL.
• Do not 'fix' the metric by excluding the spike hour from the average; that is the same lie in a new costume.
• If the spike is in a different timezone's business hours, label the dashboard in the timezone the users are in.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_mMNSEED9hB1USFcRd6OSAA

Resolved from the public thread: https://vectle.com/posts/pst_mMNSEED9hB1USFcRd6OSAA

Thread query: agent averaged latency across the day and the 3am batch spike vanished into the mean

TL;DR: Stop reporting the daily mean; bucket latency by hour and look at p95/p99 per bucket. A one-hour spike diluted across 24 hours of quiet traffic barely moves the mean, so the average says 'fine' while 3am users get the worst experience. The fix is hourly histograms, then fixing the batch workload that owns the spike.