feat(ffe): expose observeFullEvaluationData config-level FFI getter

[vjfridge]

Summary

Adds the top-level UFC observeFullEvaluationData boolean end-to-end through libdatadog and exposes a config-level FFI getter so SDKs can read it without re-parsing the raw JSON string. This is the libdatadog half of the "Protecting PII in flagevaluations track" initiative; the server-side UFC field ships via ddoghq/dd-source#22826 and the RC schema via ddoghq/dd-go#3627.

• When true: SDK emits unhashed targeting_key + full evaluation context (today's behavior).
• When false (default): SDK emits a SHA-256 hex hash of the targeting key and omits the context.

Default is false (privacy-preserving). A fresh deploy is a no-op for customers until an SDK ships the read-side change and someone toggles the field on for an env.

Changes

• UniversalFlagConfigWire (libdd-ffe): new #[serde(default)] bool observe_full_evaluation_data field, a sibling of environment at the UFC top level. #[serde(default)] keeps older configurations (without the key) parsing to false.
• CompiledFlagsConfig: carries the field through the wire -> compiled transform.
• Configuration::observe_full_evaluation_data(): Rust getter, mirroring the existing created_at() / environment() accessors.
• ddog_ffe_configuration_get_observe_full_evaluation_data (libdd-ffe-ffi): new #[no_mangle] C accessor returning bool. The datadog/ffe.h header regenerates via cbindgen.
• examples/ffi/ffe.c: prints the flag after ddog_ffe_configuration_new to demonstrate usage.

Test plan

• cargo check -p libdd-ffe -p libdd-ffe-ffi
• cargo +nightly-2026-07-26 fmt --all -- --check
• cargo +nightly-2026-07-26 clippy -p libdd-ffe -p libdd-ffe-ffi --all-targets -- -D warnings
• cargo nextest run -p libdd-ffe -p libdd-ffe-ffi (incl. 4 new unit tests: field defaults to false when absent, parses true when present, exposed via Configuration true/false)
• cargo test -p libdd-ffe --doc
• cargo ffi-test — ffe C example compiles, links against the regenerated header (new symbol present), and runs green against the ffe-system-test-data fixture.

Refs: FFL-3038

[vjfridge]

Note to self: Follow-ups to propagate to SDK repos

This PR adds the ddog_ffe_configuration_get_observe_full_evaluation_data symbol to libdatadog, but it only takes effect once SDKs wrap it so they dont need to re-parsing the raw JSON. Required follow-ups, in merge order:

1. libdatadog release Nothing downstream can consume the getter until a release goes out for this PR.
2. datadog/libdatadog-rb bump the vendored libdatadog to the release from step 1, then add observe_full_evaluation_data to the Core::FeatureFlags::Configuration C extension wrapping ddog_ffe_configuration_get_observe_full_evaluation_data
3. dd-trace-rb consume the new libdatadog-rb release and read observe_full_evaluation_data from the C extension instead of parsing the config string.

Other language SDKs to check

The same getter should be exposed by the other libdatadog FFI consumers once they pick up the release:

• Python (dd-trace-py / libdatadog Python bindings) — wrap the new symbol if it ships FFE bindings.
• Any other SDK whose C extension links libdd-ffe-ffi and currently parses the UFC JSON for this field.

[datadog-datadog-prod-us1-2]

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 99.08%
• Overall Coverage: 76.62% (+0.20%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: f1baf07 | Docs | View more details | Give us feedback!}

[pr-commenter]

Benchmarks

Comparison

Benchmark execution time: 2026-08-20 15:11:49

Comparing candidate commit f1baf07 in PR branch vickie/ffl-3038-add-observe_full_evaluation_data-accessor with baseline commit 24022a9 in branch main.

Found 0 performance improvements and 3 performance regressions! Performance is the same for 11 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/payloads/scale/2500flags_500users_20fields

• 🟥 execution_time [+846.814µs; +866.901µs] or [+4.026%; +4.121%]

scenario:flagevaluation_evp/payloads/stress/10flags_1000users_250fields

• 🟥 execution_time [+6.991ms; +7.117ms] or [+7.299%; +7.431%]
• 🟥 throughput [-722.479op/s; -709.797op/s] or [-6.920%; -6.799%]

Candidate

Candidate benchmark details

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	f1baf07	1787237900	vickie/ffl-3038-add-observe_full_evaluation_data-accessor

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	5.399ms	5.415ms ± 0.007ms	5.415ms ± 0.004ms	5.419ms	5.426ms	5.430ms	5.432ms	0.31%	0.001	-0.398	0.12%	0.000ms	1	200
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	460270.402op/s	461691.308op/s ± 558.215op/s	461681.988op/s ± 380.399op/s	462063.100op/s	462652.001op/s	462846.579op/s	463023.542op/s	0.29%	0.004	-0.399	0.12%	39.472op/s	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	7.938ms	7.951ms ± 0.008ms	7.950ms ± 0.003ms	7.954ms	7.960ms	7.992ms	8.008ms	0.73%	3.625	19.694	0.11%	0.001ms	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	124880.323op/s	125771.118op/s ± 132.557op/s	125792.778op/s ± 53.677op/s	125840.196op/s	125909.280op/s	125967.047op/s	125983.009op/s	0.15%	-3.598	19.476	0.11%	9.373op/s	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	180.008µs	185.268µs ± 38.810µs	182.406µs ± 0.252µs	182.746µs	183.505µs	184.004µs	731.321µs	300.93%	14.031	194.913	20.90%	2.744µs	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	136738.860op/s	545824.351op/s ± 29125.388op/s	548226.955op/s ± 757.517op/s	548838.721op/s	549492.275op/s	553253.855op/s	555530.248op/s	1.33%	-13.959	193.559	5.32%	2059.476op/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	[5.414ms; 5.416ms] or [-0.017%; +0.017%]	None	None	None
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	[461613.944op/s; 461768.671op/s] or [-0.017%; +0.017%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	[7.950ms; 7.952ms] or [-0.015%; +0.015%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	[125752.747op/s; 125789.489op/s] or [-0.015%; +0.015%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	[179.889µs; 190.646µs] or [-2.903%; +2.903%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	[541787.853op/s; 549860.850op/s] or [-0.740%; +0.740%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	f1baf07	1787237900	vickie/ffl-3038-add-observe_full_evaluation_data-accessor

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	156.723ns	159.792ns ± 2.413ns	159.289ns ± 1.318ns	160.848ns	164.147ns	166.964ns	169.920ns	6.67%	1.227	1.697	1.51%	0.171ns	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	[159.457ns; 160.126ns] or [-0.209%; +0.209%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	f1baf07	1787237900	vickie/ffl-3038-add-observe_full_evaluation_data-accessor

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	21.791ms	21.892ms ± 0.040ms	21.892ms ± 0.024ms	21.914ms	21.966ms	21.981ms	22.003ms	0.51%	0.183	0.053	0.18%	0.003ms	1	200
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	113621.456op/s	114196.084op/s ± 208.615op/s	114197.228op/s ± 124.331op/s	114323.732op/s	114564.847op/s	114663.358op/s	114726.658op/s	0.46%	-0.172	0.051	0.18%	14.751op/s	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	102.187ms	102.839ms ± 0.346ms	102.844ms ± 0.233ms	103.062ms	103.349ms	103.491ms	104.780ms	1.88%	0.797	3.624	0.34%	0.024ms	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	9543.828op/s	9724.067op/s ± 32.599op/s	9723.460op/s ± 21.940op/s	9746.972op/s	9773.054op/s	9783.643op/s	9785.970op/s	0.64%	-0.748	3.341	0.33%	2.305op/s	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	596.601µs	599.242µs ± 1.526µs	598.780µs ± 1.246µs	600.891µs	601.550µs	601.901µs	602.036µs	0.54%	0.221	-1.428	0.25%	0.108µs	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	166103.071op/s	166878.567op/s ± 424.834op/s	167006.185op/s ± 347.878op/s	167238.423op/s	167451.833op/s	167554.974op/s	167616.267op/s	0.37%	-0.217	-1.428	0.25%	30.040op/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	[21.887ms; 21.898ms] or [-0.025%; +0.025%]	None	None	None
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	[114167.172op/s; 114224.996op/s] or [-0.025%; +0.025%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	[102.791ms; 102.887ms] or [-0.047%; +0.047%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	[9719.549op/s; 9728.585op/s] or [-0.046%; +0.046%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	[599.030µs; 599.454µs] or [-0.035%; +0.035%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	[166819.689op/s; 166937.444op/s] or [-0.035%; +0.035%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	f1baf07	1787237900	vickie/ffl-3038-add-observe_full_evaluation_data-accessor

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	142.671µs	145.905µs ± 5.834µs	144.610µs ± 0.676µs	145.645µs	150.917µs	168.367µs	204.827µs	41.64%	7.253	61.899	3.99%	0.412µs	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	[145.096µs; 146.713µs] or [-0.554%; +0.554%]	None	None	None

Baseline

Baseline benchmark details

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	24022a9	1787233149	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	5.394ms	5.414ms ± 0.008ms	5.414ms ± 0.004ms	5.418ms	5.425ms	5.431ms	5.454ms	0.74%	0.596	3.409	0.14%	0.001ms	1	200
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	458337.573op/s	461764.085op/s ± 649.141op/s	461749.716op/s ± 359.687op/s	462119.904op/s	462808.942op/s	463224.213op/s	463446.685op/s	0.37%	-0.574	3.319	0.14%	45.901op/s	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	7.958ms	7.973ms ± 0.007ms	7.973ms ± 0.004ms	7.976ms	7.982ms	8.005ms	8.010ms	0.47%	1.936	7.490	0.09%	0.000ms	1	200
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	124842.735op/s	125417.734op/s ± 109.556op/s	125430.847op/s ± 58.044op/s	125485.028op/s	125550.964op/s	125601.165op/s	125656.687op/s	0.18%	-1.921	7.410	0.09%	7.747op/s	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	181.249µs	183.830µs ± 0.548µs	183.702µs ± 0.210µs	184.002µs	184.937µs	185.309µs	186.316µs	1.42%	0.518	5.159	0.30%	0.039µs	1	200
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	536723.998op/s	543984.343op/s ± 1619.989op/s	544358.857op/s ± 622.529op/s	544878.338op/s	545342.718op/s	548185.545op/s	551726.878op/s	1.35%	-0.456	5.228	0.30%	114.551op/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	[5.413ms; 5.415ms] or [-0.019%; +0.019%]	None	None	None
flagevaluation_evp/coalescer/scale/2500flags_500users_20fields	throughput	[461674.121op/s; 461854.050op/s] or [-0.019%; +0.019%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	execution_time	[7.972ms; 7.974ms] or [-0.012%; +0.012%]	None	None	None
flagevaluation_evp/coalescer/stress/10flags_1000users_250fields	throughput	[125402.551op/s; 125432.917op/s] or [-0.012%; +0.012%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	execution_time	[183.754µs; 183.906µs] or [-0.041%; +0.041%]	None	None	None
flagevaluation_evp/coalescer/typical/100flags_50users_10fields	throughput	[543759.828op/s; 544208.858op/s] or [-0.041%; +0.041%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	24022a9	1787233149	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	155.856ns	159.741ns ± 2.613ns	159.260ns ± 1.656ns	161.317ns	164.691ns	167.195ns	168.960ns	6.09%	0.890	0.322	1.63%	0.185ns	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	[159.379ns; 160.103ns] or [-0.227%; +0.227%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	24022a9	1787233149	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	20.914ms	21.035ms ± 0.060ms	21.025ms ± 0.036ms	21.066ms	21.145ms	21.247ms	21.278ms	1.20%	1.192	2.284	0.29%	0.004ms	1	200
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	117491.967op/s	118848.336op/s ± 340.261op/s	118905.482op/s ± 205.072op/s	119081.403op/s	119270.048op/s	119394.142op/s	119539.227op/s	0.53%	-1.168	2.195	0.29%	24.060op/s	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	95.334ms	95.784ms ± 0.297ms	95.854ms ± 0.153ms	95.974ms	96.059ms	96.110ms	98.208ms	2.46%	2.454	20.303	0.31%	0.021ms	1	200
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	10182.420op/s	10440.205op/s ± 32.100op/s	10432.499op/s ± 16.628op/s	10473.142op/s	10481.122op/s	10484.165op/s	10489.463op/s	0.55%	-2.307	18.824	0.31%	2.270op/s	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	577.619µs	580.269µs ± 1.252µs	580.372µs ± 0.942µs	581.146µs	582.405µs	583.108µs	583.547µs	0.55%	0.092	-0.624	0.22%	0.089µs	1	200
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	171365.691op/s	172334.603op/s ± 371.778op/s	172303.193op/s ± 279.636op/s	172626.423op/s	172913.165op/s	173075.221op/s	173124.356op/s	0.48%	-0.083	-0.630	0.22%	26.289op/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	[21.027ms; 21.044ms] or [-0.040%; +0.040%]	None	None	None
flagevaluation_evp/payloads/scale/2500flags_500users_20fields	throughput	[118801.179op/s; 118895.493op/s] or [-0.040%; +0.040%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	execution_time	[95.743ms; 95.826ms] or [-0.043%; +0.043%]	None	None	None
flagevaluation_evp/payloads/stress/10flags_1000users_250fields	throughput	[10435.756op/s; 10444.653op/s] or [-0.043%; +0.043%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	execution_time	[580.096µs; 580.443µs] or [-0.030%; +0.030%]	None	None	None
flagevaluation_evp/payloads/typical/100flags_50users_10fields	throughput	[172283.079op/s; 172386.128op/s] or [-0.030%; +0.030%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	24022a9	1787233149	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	140.028µs	143.702µs ± 5.713µs	142.448µs ± 0.637µs	143.286µs	148.373µs	167.353µs	203.346µs	42.75%	7.393	65.500	3.97%	0.404µs	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	[142.910µs; 144.494µs] or [-0.551%; +0.551%]	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.so	8.20 MB	8.26 MB	+.76% (+64.00 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	89.47 MB	89.51 MB	+.04% (+41.45 KB) 🔍

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	100.65 MB	100.69 MB	+.04% (+41.53 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.06 MB	11.06 MB	+.01% (+1.82 KB) 🔍

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	26.62 MB	26.64 MB	+.05% (+16.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	94.56 KB	94.96 KB	+.42% (+414 B) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	180.69 MB	180.74 MB	+.03% (+56.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	772.58 MB	772.82 MB	+.03% (+247.01 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	8.72 MB	8.73 MB	+.06% (+5.50 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	94.56 KB	94.96 KB	+.42% (+414 B) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	25.64 MB	25.65 MB	+.03% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	51.06 MB	51.09 MB	+.04% (+25.26 KB) 🔍

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	23.20 MB	23.22 MB	+.06% (+14.50 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	96.04 KB	96.45 KB	+.42% (+420 B) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	185.60 MB	185.66 MB	+.02% (+56.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	759.08 MB	759.15 MB	+0% (+68.95 KB) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	6.74 MB	6.74 MB	+.06% (+4.50 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	96.04 KB	96.45 KB	+.42% (+420 B) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	27.58 MB	27.59 MB	+.05% (+16.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	48.60 MB	48.62 MB	+.04% (+24.72 KB) 🔍

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	79.74 MB	79.77 MB	+.04% (+35.69 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	9.16 MB	9.16 MB	+.04% (+4.00 KB) 🔍

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	95.38 MB	95.41 MB	+.03% (+35.98 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.16 MB	11.17 MB	+.04% (+5.48 KB) 🔍

[chatgpt-codex-connector]

💡 Codex Review

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Reviewed commit: 5dd17bc9b7

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[pavlokhrebto]

lgtm, please address Codex review comments

[Aaalibaba42]

lgtm o/