[leoromanovsky]

What does this PR do?

Adds Disabled, Immediate, and FutureExperimentsOnly evaluation modes using dd_experimentation_opt_out_at. SDK wrappers resolve provider configuration; the evaluator reads no process settings.

• Immediate mode skips exposure-logging allocations before targeting and randomization.
• Future mode retains activation strictly before the cutoff. Equal, later, missing, or null activation excludes the allocation.
• Invalid activation returns per-flag PARSE_ERROR only for future mode with a valid cutoff, including unreachable allocations.
• Cutoff and activation share the documented ASCII timestamp grammar: valid Gregorian fields, lowercase t/z, nanosecond truncation, and normalized UTC years 0000–9999. Whitespace, named zones, compact offsets and leap seconds are rejected; allocation schedules retain their existing parser.
• Ordinary allocations and retained exposure eligibility remain intact. Existing C entry points retain their default and boolean-mode behavior.

Motivation

Customers need to exclude subjects from experimentation immediately or from experiments activated at or after their opt-out time.

Additional Notes

The parser refinement follows the review feedback and the scoped D005/D006/D007 contract. The cross-SDK lowercase disagreement remains separately tracked.

How to test the change?

Native tests cover all 90 shared policy cases, malformed input, unreachable allocations, valid neighboring flags, timestamp boundaries, inactive exclusion calls, and panic containment. Shared fixtures use the merged #52 commit.

Tested commit	Validation	Result	Evidence
DataDog/libdatadog@ead1d3db2aa16e494f23e47b946ff12a7a1be53a	Packaged C integration: mode/cutoff/error boundaries, allocation metadata, exposure eligibility, and legacy APIs; macOS/arm64	252 cases and 504 calls passed; 1 FFE example passed. 9 null/array/object attribute cases cannot be represented by the C API.	Native receipt

Downstream SDK runtime and telemetry delivery were not tested. Shared decisions and scoped adoption.

Parser follow-up at 975c8e4fe: all 50 timestamp cases pass through both production paths across 464 evaluations, including all seven reviewer examples. The new adapter fails against the prior parser. All 110 native tests pass, including the unchanged 90 policy cases and FFI tests; check, formatting, Clippy, docs and shared-model verification pass. The C integration receipt above remains historical evidence for its stated commit, not this follow-up. Parser validation and publication receipt.

[datadog-prod-us1-4]

 

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 94.51%
• Overall Coverage: 78.57% (-3.84%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 975c8e4 | Docs | View more details | Give us feedback!}

[pr-commenter]

Benchmarks

Comparison

Benchmark execution time: 2026-10-26 19:49:43

Comparing candidate commit 975c8e4 in PR branch leoromanovsky/experimentation-opt-out-immediate with baseline commit 1adb233 in branch main.

📊 Benchmarking dashboard

Found 4 performance improvements and 0 performance regressions! Performance is the same for 10 metrics, 0 unstable metrics.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

• 🟩 = significantly better candidate vs. baseline
• 🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

scenario:flagevaluation_evp/coalescer/stress/10flags_1000users_250fields

• 🟩 execution_time [-12.060ms; -11.431ms] or [-5.987%; -5.675%]
• 🟩 throughput [+299.438op/s; +316.061op/s] or [+6.031%; +6.366%]

scenario:flagevaluation_evp/payloads/stress/10flags_1000users_250fields

• 🟩 execution_time [-12.678ms; -12.527ms] or [-6.504%; -6.426%]
• 🟩 throughput [+352.441op/s; +356.705op/s] or [+6.870%; +6.953%]

Candidate

Candidate benchmark details

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	975c8e4	1793043480	leoromanovsky/experimentation-opt-out-immediate

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	execution_time	34.101ms	34.378ms ± 0.114ms	34.375ms ± 0.078ms	34.462ms	34.558ms	34.657ms	34.683ms	0.90%	-0.019	-0.226	0.33%	0.008ms	1	200
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	72081.055op/s	72721.093op/s ± 241.823op/s	72727.889op/s ± 164.948op/s	72876.593op/s	73131.037op/s	73277.228op/s	73312.631op/s	0.80%	0.037	-0.228	0.33%	17.099op/s	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	181.357ms	182.330ms ± 0.384ms	182.321ms ± 0.237ms	182.593ms	182.961ms	183.139ms	183.183ms	0.47%	-0.032	-0.415	0.21%	0.027ms	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	5459.030op/s	5484.593op/s ± 11.563op/s	5484.825op/s ± 7.128op/s	5491.664op/s	5504.089op/s	5509.199op/s	5513.992op/s	0.53%	0.042	-0.413	0.21%	0.818op/s	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	834.082µs	835.551µs ± 0.696µs	835.437µs ± 0.459µs	836.037µs	836.826µs	837.328µs	838.270µs	0.34%	0.572	0.639	0.08%	0.049µs	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	119293.297op/s	119681.560op/s ± 99.588op/s	119697.768op/s ± 65.792op/s	119749.355op/s	119823.066op/s	119875.671op/s	119892.226op/s	0.16%	-0.566	0.626	0.08%	7.042op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	execution_time	[34.362ms; 34.394ms] or [-0.046%; +0.046%]	None	None	None
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	[72687.578op/s; 72754.607op/s] or [-0.046%; +0.046%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	[182.276ms; 182.383ms] or [-0.029%; +0.029%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	[5482.990op/s; 5486.195op/s] or [-0.029%; +0.029%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	[835.455µs; 835.648µs] or [-0.012%; +0.012%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	[119667.758op/s; 119695.362op/s] or [-0.012%; +0.012%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	975c8e4	1793043480	leoromanovsky/experimentation-opt-out-immediate

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
single_flag_killswitch/rules-based	execution_time	155.677ns	159.080ns ± 2.894ns	158.695ns ± 1.615ns	160.177ns	164.386ns	168.938ns	170.651ns	7.53%	1.421	2.645	1.81%	0.205ns	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
single_flag_killswitch/rules-based	execution_time	[158.679ns; 159.481ns] or [-0.252%; +0.252%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	975c8e4	1793043480	leoromanovsky/experimentation-opt-out-immediate

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
sdk_test_data/rules-based	execution_time	260.065µs	266.301µs ± 12.818µs	263.085µs ± 1.319µs	265.314µs	278.067µs	314.771µs	397.950µs	51.26%	6.919	59.835	4.80%	0.906µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sdk_test_data/rules-based	execution_time	[264.525µs; 268.077µs] or [-0.667%; +0.667%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	975c8e4	1793043480	leoromanovsky/experimentation-opt-out-immediate

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	execution_time	30.423ms	30.629ms ± 0.076ms	30.625ms ± 0.046ms	30.672ms	30.747ms	30.831ms	30.951ms	1.06%	0.493	1.893	0.25%	0.005ms	1	200
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	80771.822op/s	81622.313op/s ± 201.953op/s	81631.853op/s ± 121.889op/s	81742.971op/s	81942.720op/s	82041.520op/s	82175.494op/s	0.67%	-0.466	1.824	0.25%	14.280op/s	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	188.086ms	189.688ms ± 1.916ms	188.831ms ± 0.305ms	189.469ms	194.191ms	195.843ms	196.887ms	4.27%	1.845	2.202	1.01%	0.135ms	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	5079.060op/s	5272.330op/s ± 52.283op/s	5295.750op/s ± 8.571op/s	5302.476op/s	5308.476op/s	5312.431op/s	5316.727op/s	0.40%	-1.822	2.092	0.99%	3.697op/s	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	682.521µs	684.675µs ± 1.112µs	684.659µs ± 0.489µs	685.104µs	685.745µs	686.331µs	696.605µs	1.74%	6.118	64.436	0.16%	0.079µs	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	143553.342op/s	146055.094op/s ± 234.895op/s	146058.170op/s ± 104.344op/s	146169.340op/s	146324.643op/s	146408.290op/s	146515.543op/s	0.31%	-5.978	62.511	0.16%	16.610op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	execution_time	[30.619ms; 30.640ms] or [-0.034%; +0.034%]	None	None	None
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	[81594.325op/s; 81650.302op/s] or [-0.034%; +0.034%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	[189.423ms; 189.954ms] or [-0.140%; +0.140%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	[5265.084op/s; 5279.576op/s] or [-0.137%; +0.137%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	[684.521µs; 684.829µs] or [-0.023%; +0.023%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	[146022.540op/s; 146087.648op/s] or [-0.022%; +0.022%]	None	None	None

Baseline

Baseline benchmark details

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	1adb233	1793041762	main

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	execution_time	35.075ms	35.317ms ± 0.098ms	35.316ms ± 0.059ms	35.366ms	35.471ms	35.614ms	35.692ms	1.06%	0.594	1.366	0.28%	0.007ms	1	200
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	70043.990op/s	70787.054op/s ± 195.130op/s	70788.617op/s ± 118.665op/s	70913.634op/s	71065.700op/s	71193.967op/s	71275.793op/s	0.69%	-0.570	1.316	0.27%	13.798op/s	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	193.943ms	194.932ms ± 0.386ms	194.871ms ± 0.261ms	195.193ms	195.583ms	195.955ms	196.225ms	0.69%	0.417	0.089	0.20%	0.027ms	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	5096.184op/s	5130.020op/s ± 10.149op/s	5131.602op/s ± 6.862op/s	5137.627op/s	5143.671op/s	5151.207op/s	5156.164op/s	0.48%	-0.405	0.074	0.20%	0.718op/s	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	851.041µs	853.426µs ± 0.953µs	853.384µs ± 0.658µs	854.049µs	854.983µs	855.865µs	856.128µs	0.32%	0.160	-0.027	0.11%	0.067µs	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	116804.917op/s	117174.891op/s ± 130.884op/s	117180.568op/s ± 90.289op/s	117258.478op/s	117401.081op/s	117460.451op/s	117503.176op/s	0.28%	-0.154	-0.031	0.11%	9.255op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	execution_time	[35.304ms; 35.331ms] or [-0.038%; +0.038%]	None	None	None
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	[70760.011op/s; 70814.097op/s] or [-0.038%; +0.038%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	[194.878ms; 194.985ms] or [-0.027%; +0.027%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	[5128.613op/s; 5131.426op/s] or [-0.027%; +0.027%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	[853.294µs; 853.558µs] or [-0.015%; +0.015%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	[117156.752op/s; 117193.030op/s] or [-0.015%; +0.015%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	1adb233	1793041762	main

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
single_flag_killswitch/rules-based	execution_time	158.213ns	161.662ns ± 2.671ns	161.278ns ± 1.642ns	163.042ns	166.188ns	171.062ns	172.930ns	7.22%	1.376	2.788	1.65%	0.189ns	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
single_flag_killswitch/rules-based	execution_time	[161.292ns; 162.032ns] or [-0.229%; +0.229%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	1adb233	1793041762	main

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
sdk_test_data/rules-based	execution_time	257.305µs	262.931µs ± 12.274µs	259.948µs ± 1.043µs	261.537µs	273.363µs	309.580µs	391.179µs	50.48%	7.247	64.389	4.66%	0.868µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sdk_test_data/rules-based	execution_time	[261.230µs; 264.632µs] or [-0.647%; +0.647%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	1adb233	1793041762	main

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	execution_time	31.500ms	31.705ms ± 0.109ms	31.694ms ± 0.063ms	31.754ms	31.930ms	31.985ms	32.162ms	1.47%	0.853	1.175	0.34%	0.008ms	1	200
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	77732.195op/s	78852.133op/s ± 270.016op/s	78878.231op/s ± 156.406op/s	79034.945op/s	79241.032op/s	79319.143op/s	79364.079op/s	0.62%	-0.828	1.101	0.34%	19.093op/s	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	200.682ms	201.434ms ± 1.220ms	201.148ms ± 0.133ms	201.289ms	202.275ms	206.686ms	207.007ms	2.91%	3.907	13.918	0.60%	0.086ms	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	4830.765op/s	4964.581op/s ± 29.377op/s	4971.459op/s ± 3.294op/s	4974.385op/s	4979.155op/s	4981.582op/s	4983.003op/s	0.23%	-3.896	13.852	0.59%	2.077op/s	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	694.737µs	696.615µs ± 0.772µs	696.543µs ± 0.511µs	697.137µs	697.901µs	698.342µs	698.561µs	0.29%	0.155	-0.353	0.11%	0.055µs	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	143151.509op/s	143551.554op/s ± 159.073op/s	143566.211op/s ± 105.330op/s	143651.269op/s	143810.857op/s	143877.646op/s	143939.438op/s	0.26%	-0.150	-0.354	0.11%	11.248op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	execution_time	[31.690ms; 31.720ms] or [-0.048%; +0.048%]	None	None	None
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	[78814.711op/s; 78889.555op/s] or [-0.047%; +0.047%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	[201.265ms; 201.603ms] or [-0.084%; +0.084%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	[4960.510op/s; 4968.652op/s] or [-0.082%; +0.082%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	[696.508µs; 696.722µs] or [-0.015%; +0.015%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	[143529.508op/s; 143573.600op/s] or [-0.015%; +0.015%]	None	None	None

[dd-octo-sts]

Artifact Size Benchmark Report

aarch64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	96.56 MB	96.60 MB	+.05% (+49.61 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so	9.08 MB	9.08 MB	0% (0 B) 👌

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	12.29 MB	12.29 MB	+.01% (+1.71 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	108.05 MB	108.10 MB	+.04% (+51.13 KB) 🔍

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	29.24 MB	29.26 MB	+.08% (+24.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	99.56 KB	100.21 KB	+.65% (+664 B) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	193.18 MB	193.38 MB	+.10% (+208.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	822.80 MB	823.46 MB	+.08% (+675.49 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	9.81 MB	9.81 MB	+.05% (+6.00 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	99.56 KB	100.21 KB	+.65% (+664 B) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	27.60 MB	27.61 MB	+.02% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	55.88 MB	55.91 MB	+.05% (+30.93 KB) 🔍

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	25.68 MB	25.70 MB	+.09% (+24.00 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	101.12 KB	101.78 KB	+.65% (+674 B) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	198.82 MB	199.00 MB	+.09% (+184.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	809.61 MB	809.85 MB	+.02% (+245.10 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	7.60 MB	7.60 MB	+.07% (+5.50 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	101.12 KB	101.78 KB	+.65% (+674 B) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	29.73 MB	29.76 MB	+.07% (+24.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	52.81 MB	52.84 MB	+.05% (+28.75 KB) 🔍

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	86.56 MB	86.61 MB	+.05% (+45.57 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	10.12 MB	10.13 MB	+.03% (+4.00 KB) 🔍

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	102.39 MB	102.43 MB	+.04% (+47.21 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	12.37 MB	12.37 MB	+.04% (+5.28 KB) 🔍

[chatgpt-codex-connector]

Codex Review Summary

This comment shows the latest Codex review activity on this pull request.

Review	Status	Commit	Review trigger
📝 Code Review	✅ Completed 2026-10-21T00:16:20.968041Z	0200047	Draft marked ready
🔒 Security Review	✅ Completed 2026-10-21T00:19:05.152147Z	0200047	Draft marked ready

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[yannham]

Can't say much about the logic, but LGTM from common component's side

[danyal002]

The exclusion logic matches the shared contract. It runs before targeting and randomization. doLog=false allocations are never excluded, immediate mode skips every logging allocation, and future mode skips activations at or after the cutoff, and missing ones. Disabled mode and the existing C entry points keep their current behavior, and an invalid cutoff returns INVALID_CONTEXT. One item to fix and one question, below.

Review by Claude on Danyal's behalf.