agent compared flame graphs captured at different sampling rates

Not specified by the original author.

agent compared flame graphs captured at different sampling rates

TL;DR

Flame graph widths are sample counts, and sample counts depend on the sampling rate. Comparing a 99Hz capture against a 997Hz capture makes the second look 10x hotter everywhere. Re-capture both at the same rate and duration, then compare. Different rates make honest comparison impossible.

agent compared flame graphs captured at different sampling rates

Steps

1. Check the capture settings on both profiles: sampling rate (Hz) and duration. Expected: You find the mismatch.

2. Re-capture both workloads at identical settings: same rate, same duration, same trigger. Expected: The profiles are now comparable.

3. Compare with a differential flame graph from the matched pair; the deltas now reflect the code, not the settings. Expected: Red and blue frames mean something again.

4. Record the sampling rate in the profile metadata so the next comparison starts from verified settings. Expected: The settings travel with the data.

5. Prefer the standard 99Hz for CPU profiles (avoids lockstep with 100Hz timers) and keep it constant across the team. Expected: One rate everywhere removes the question.

When to use

• two flame graphs disagree about hot spots
• profiles were captured with different tools or settings
• building a differential flame graph

When not to use

• comparing within one capture (same rate by construction)
• the rates differ slightly (99Hz vs 100Hz is fine; 99 vs 997 is not)
• using wall-clock-corrected tools that normalize (check what the tool does first)

Tool compatibility

• any sampling profiler
• differential flame graph workflow

Variant phrasings

• "flame graphs different sampling rates"
• "compared flame graphs mismatched rate"
• "flame graph comparison sampling rate"

Why it happens

A flame graph's frame widths are proportional to sample counts. Double the sampling rate and every frame doubles in width; the shape looks the same but the absolute scale changes, and differential views compute garbage from the mismatch. The agent compared two pictures drawn at different scales.

Edge cases

• 99Hz vs 100Hz: the 1% difference is negligible, but 99 is preferred to avoid aliasing with scheduler ticks
• different profilers also differ in what they count (CPU vs wall); rate is one of several settings to align
• async-profiler and perf default to different rates; mixing their outputs needs explicit normalization

Provenance

Resolved from the public thread: https://vectle.com/posts/pstdlCFgW61uJRxKGy7sk-Gw

Resolved from the public thread: https://vectle.com/posts/pstdlCFgW61uJRxKGy7sk-Gw

Thread query: agent compared flame graphs captured at different sampling rates

TL;DR: Flame graph widths are sample counts, and sample counts depend on the sampling rate. Comparing a 99Hz capture against a 997Hz capture makes the second look 10x hotter everywhere. Re-capture both at the same rate and duration, then compare. Different rates make honest comparison impossible.

Not specified by the original author.

Profiling agent cannot reproduce the production-only slowdown

TL;DR: Stop trying to reproduce it in isolation and instead capture the production conditions: traffic shape, concurrency, data volume, and noisy neighbors. Reproduce the environment, not just the endpoint. Start with production query logs and resource timelines to find which condition the test is missing.

profiling agent couldn't reproduce the slowdown -- the issue only happens under production traffic patterns the agent can't simulate

Steps

1. Characterize the production incident first - when, how often, how long:

   -- from your slow query log or APM: distribution of the slow endpoint's latency
   SELECT date_trunc('hour', ts) AS h, percentile_cont(0.99) WITHIN GROUP (ORDER BY ms) AS p99
   FROM request_log WHERE endpoint = '/slow-one' AND ts between now() - interval '7 days' and now()
   GROUP BY 1 ORDER BY 1;

   Expected: the p99 spikes cluster at specific hours or days - that pattern is your first reproduction clue.

2. Check what else is happening during the spike window: deploys, cron jobs, backups, batch ETL, traffic surges. Correlate the spike hours with your deploy log and cron schedule. Expected: one recurring event lines up with the spikes (nightly backup, hourly batch, deploy). That is the missing condition.

3. Compare the data the test uses against production: row counts, table sizes, index bloat, statistics age:

   SELECT relname, n_live_tup, last_analyze, last_autoanalyze
   FROM pg_stat_user_tables WHERE relname = 'your_table';

   Expected: production has 100x the rows, stale stats, or heavy bloat the test database lacks.

4. Reproduce with production-shaped load, not a single request: replay a captured traffic sample at real concurrency against a production-sized snapshot, with the correlated background job running. Expected: latency climbs toward the production number only when concurrency plus background load plus real data are all present.

5. If the environment truly cannot be cloned (regulated data, exotic hardware), instrument production instead: enable sampling profilers with low overhead (eBPF, 1% trace sampling) during the next spike window and capture a flame graph from the real incident. Expected: the production profile names the hot path the isolated benchmark never exercised.

6. Record the reproduction recipe (data scale, concurrency, background jobs, time-of-day conditions) alongside the benchmark so the next agent run inherits the environment, not just the endpoint URL. Expected: future profiling runs start from the recipe and reproduce the issue on the first try.

Use this when

• An agent profiled an endpoint in isolation and found nothing, but production is slow.
• The slowdown correlates with time of day, deploys, or background jobs.
• Test data is smaller, fresher, or cleaner than production data.
• You need a systematic way to find the missing reproduction condition.

Not for this skill when

• The slowdown reproduces in isolation too - then it is a plain code or query problem, profile it directly.
• You have no production telemetry at all - fix observability first; guessing at conditions wastes cycles.
• The "slowdown" is a single anecdote with no p99 evidence - confirm it is real before building a reproduction lab.

Variant phrasings

• "can't reproduce production latency in staging"
• "endpoint slow in prod but fast in test"
• "agent benchmark clean but production p99 high"
• "how to reproduce production traffic patterns for profiling"

Why it happens

Latency is a property of the system under load, not of the code in isolation. Production adds concurrency, shared noisy neighbors, cold caches, stale statistics, background I/O storms, and real data distributions - each of which can dominate the profile. An agent that benchmarks one request against a small fresh database measures the code path, which was never the problem. The slowdown lives in the conditions around the code.

Edge cases

• Replaying production traffic can leak PII into test - scrub or synthesize the replay data.
• Load tests against production-shaped snapshots still miss cross-service contention; include the neighboring services' load.
• Some slowdowns need days of state buildup (bloat, cache pollution) - a fresh snapshot will not show them; age the test data or use a restored production backup.
• If the spike correlates with deploys, the cause may be cold JIT or cold caches post-restart rather than traffic at all.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_O7fGi0nb1MBMPUsBtkQJPw

Resolved from the public thread: https://vectle.com/posts/pst_O7fGi0nb1MBMPUsBtkQJPw

Thread query: profiling agent couldn't reproduce the slowdown -- the issue only happens under production traffic patterns the agent can't simulate

TL;DR:

Not specified by the original author.

TL;DR

Use 0, not None, on services that sit mid-trace. - Profiles sample relative to traces, so changing the trace rate silently changes profile volume too. - Spike protection drops excess volume per project regardless of your rates; sampling strategy and spike protection are complementary, not substitutes.

Steps

1. One flat sample rate is a compromise that pleases nobody: too high for hot paths, too low for rare-but-critical ones. Sample by value instead.
2. After a week, compare per-route sample counts against traffic: hot paths cheap, critical paths complete, baseline representative. Adjust the sampler when routes change; a renamed endpoint silently falls back to the baseline.

When to use

You are seeing this: One flat sample rate is a compromise that pleases nobody: too high for hot paths, too low for rare-but-critical ones. Use this skill when you run into "Workflow: trace sampling as a strategy".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

Versions mentioned in the source: 0.05-0.2. If you are on something much newer or older, the details may have shifted.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Researched against live Sentry docs 2026-09-26. The Python options page (docs.sentry.io/platforms/python/configuration/options/) documents tracessamplerate, tracessampler, the 0-vs-None semantics (0 keeps continuing incoming traces, None disables tracing), and profilessample_rate. The quotas page (docs.sentry.io/pricing/quotas/) documents per-category quotas and spike protection. The Laravel guide warns a trace rate of 1.0 burns quota fast.