refactor(libdd-data-pipeline): feature gate telemetry

[hoolioh]

What does this PR do?

This PR adds a new feature flag to libdd-data-pipeline in order to trim down telemetry for cases where is not needed (e.g. serverless)

[github-actions]

📚 Documentation Check Results

⚠️ 877 documentation warning(s) found

📦 libdd-data-pipeline - 877 warning(s)

---

Updated: 2026-04-07 11:37:12 UTC | Commit: 4966130 | missing-docs job results

[github-actions]

Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

• Base Branch: origin/main
• PR Branch: origin/julio/opt-in-telemetry

Summary by Rule

Rule	Base Branch	PR Branch	Change
unwrap_used	5	4	✅ -1 (-20.0%)
Total	5	4	✅ -1 (-20.0%)

Annotation Counts by File

File	Base Branch	PR Branch	Change
libdd-data-pipeline/src/stats_exporter.rs	1	1	No change (0%)
libdd-data-pipeline/src/telemetry/mod.rs	1	0	✅ -1 (-100.0%)
libdd-data-pipeline/src/trace_exporter/error.rs	1	1	No change (0%)
libdd-data-pipeline/src/trace_exporter/mod.rs	2	2	No change (0%)

Annotation Stats by Crate

Crate	Base Branch	PR Branch	Change
clippy-annotation-reporter	5	5	No change (0%)
datadog-ffe-ffi	1	1	No change (0%)
datadog-ipc	21	21	No change (0%)
datadog-live-debugger	6	6	No change (0%)
datadog-live-debugger-ffi	10	10	No change (0%)
datadog-profiling-replayer	4	4	No change (0%)
datadog-remote-config	3	3	No change (0%)
datadog-sidecar	55	55	No change (0%)
libdd-common	10	10	No change (0%)
libdd-common-ffi	12	12	No change (0%)
libdd-data-pipeline	5	4	✅ -1 (-20.0%)
libdd-ddsketch	2	2	No change (0%)
libdd-dogstatsd-client	1	1	No change (0%)
libdd-profiling	13	13	No change (0%)
libdd-telemetry	19	19	No change (0%)
libdd-tinybytes	4	4	No change (0%)
libdd-trace-normalization	2	2	No change (0%)
libdd-trace-obfuscation	8	8	No change (0%)
libdd-trace-utils	15	15	No change (0%)
Total	196	195	✅ -1 (-0.5%)

About This Report

This report tracks Clippy allow annotations for specific rules, showing how they've changed in this PR. Decreasing the number of these annotations generally improves code quality.

[github-actions]

🔒 Cargo Deny Results

⚠️ 1 issue(s) found, showing only errors (advisories, bans, sources)

📦 libdd-data-pipeline - 1 error(s)

Show output

error[vulnerability]: Denial of Service via Stack Exhaustion
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:269:1
    │
269 │ time 0.3.41 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0009
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0009
    ├ ## Impact
      
      When user-provided input is provided to any type that parses with the RFC 2822 format, a denial of
      service attack via stack exhaustion is possible. The attack relies on formally deprecated and
      rarely-used features that are part of the RFC 2822 format used in a malicious manner. Ordinary,
      non-malicious input will never encounter this scenario.
      
      ## Patches
      
      A limit to the depth of recursion was added in v0.3.47. From this version, an error will be returned
      rather than exhausting the stack.
      
      ## Workarounds
      
      Limiting the length of user input is the simplest way to avoid stack exhaustion, as the amount of
      the stack consumed would be at most a factor of the length of the input.
    ├ Announcement: https://github.com/time-rs/time/blob/main/CHANGELOG.md#0347-2026-02-05
    ├ Solution: Upgrade to >=0.3.47 (try `cargo update -p time`)
    ├ time v0.3.41
      └── tracing-appender v0.2.3
          └── libdd-log v1.0.0
              └── (dev) libdd-data-pipeline v3.0.1

advisories FAILED, bans ok, sources ok

---

Updated: 2026-04-07 11:37:06 UTC | Commit: 4966130 | dependency-check job results

[codecov-commenter]

Codecov Report

❌ Patch coverage is 95.58824% with 12 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.52%. Comparing base (5b6dffc) to head (36290ab).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1778      +/-   ##
==========================================
+ Coverage   71.46%   71.52%   +0.06%     
==========================================
  Files         426      427       +1     
  Lines       67374    67409      +35     
==========================================
+ Hits        48148    48216      +68     
+ Misses      19226    19193      -33     

Components	Coverage Δ
libdd-crashtracker	65.99% <ø> (+0.05%)	⬆️
libdd-crashtracker-ffi	34.09% <ø> (ø)
libdd-alloc	98.77% <ø> (ø)
libdd-data-pipeline	86.86% <95.58%> (+0.20%)	⬆️
libdd-data-pipeline-ffi	75.88% <ø> (+1.02%)	⬆️
libdd-common	79.16% <ø> (ø)
libdd-common-ffi	73.87% <ø> (ø)
libdd-telemetry	62.80% <ø> (ø)
libdd-telemetry-ffi	16.75% <ø> (ø)
libdd-dogstatsd-client	82.64% <ø> (ø)
datadog-ipc	71.00% <ø> (ø)
libdd-profiling	82.13% <ø> (+0.01%)	⬆️
libdd-profiling-ffi	64.94% <ø> (ø)
datadog-sidecar	30.67% <ø> (+0.35%)	⬆️
datdog-sidecar-ffi	9.44% <ø> (+1.64%)	⬆️
spawn-worker	54.69% <ø> (ø)
libdd-tinybytes	93.16% <ø> (ø)
libdd-trace-normalization	81.71% <ø> (ø)
libdd-trace-obfuscation	87.24% <ø> (ø)
libdd-trace-protobuf	68.25% <ø> (ø)
libdd-trace-utils	88.73% <ø> (ø)
datadog-tracer-flare	86.88% <ø> (ø)
libdd-log	74.69% <ø> (ø)

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
• 📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

[datadog-official]

✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 95.59%
• Overall Coverage: 71.53% (-0.00%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 36290ab | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!}

[pr-commenter]

Benchmarks

Comparison

Benchmark execution time: 2026-03-25 08:47:41

Comparing candidate commit c00625e in PR branch julio/opt-in-telemetry with baseline commit c664ed7 in branch main.

Found 6 performance improvements and 12 performance regressions! Performance is the same for 42 metrics, 1 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:credit_card/is_card_number/ 378282246310005

• 🟥 execution_time [+16.047µs; +16.201µs] or [+23.607%; +23.834%]
• 🟥 throughput [-2831615.604op/s; -2808754.556op/s] or [-19.248%; -19.092%]

scenario:credit_card/is_card_number/378282246310005

• 🟥 execution_time [+5.405µs; +5.523µs] or [+8.327%; +8.507%]
• 🟥 throughput [-1207238.806op/s; -1183550.073op/s] or [-7.837%; -7.683%]

scenario:credit_card/is_card_number/37828224631000521389798

• 🟥 execution_time [+7.453µs; +7.475µs] or [+16.321%; +16.368%]
• 🟥 throughput [-3081244.660op/s; -3071624.412op/s] or [-14.071%; -14.027%]

scenario:credit_card/is_card_number/x371413321323331

• 🟩 execution_time [-397.701ns; -395.813ns] or [-5.817%; -5.790%]
• 🟩 throughput [+8990028.733op/s; +9033649.223op/s] or [+6.146%; +6.176%]

scenario:credit_card/is_card_number_no_luhn/ 378282246310005

• 🟥 execution_time [+5.570µs; +5.637µs] or [+10.424%; +10.549%]
• 🟥 throughput [-1785634.579op/s; -1766418.329op/s] or [-9.541%; -9.439%]

scenario:credit_card/is_card_number_no_luhn/378282246310005

• 🟥 execution_time [+5.356µs; +5.410µs] or [+10.653%; +10.759%]
• 🟥 throughput [-1931704.928op/s; -1914394.448op/s] or [-9.713%; -9.626%]

scenario:credit_card/is_card_number_no_luhn/37828224631000521389798

• 🟥 execution_time [+7.458µs; +7.479µs] or [+16.333%; +16.380%]
• 🟥 throughput [-3083423.542op/s; -3073894.013op/s] or [-14.079%; -14.035%]

scenario:credit_card/is_card_number_no_luhn/x371413321323331

• 🟩 execution_time [-400.799ns; -398.713ns] or [-5.861%; -5.830%]
• 🟩 throughput [+9054345.905op/s; +9101714.771op/s] or [+6.192%; +6.224%]

scenario:sql/obfuscate_sql_string

• 🟩 execution_time [-198.947µs; -198.760µs] or [-69.577%; -69.512%]

scenario:write only interface

• 🟩 execution_time [-2.507µs; -2.059µs] or [-45.679%; -37.516%]

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	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
sql/obfuscate_sql_string	execution_time	86.889µs	87.083µs ± 0.259µs	87.060µs ± 0.064µs	87.123µs	87.218µs	87.441µs	90.461µs	3.91%	11.172	142.295	0.30%	0.018µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sql/obfuscate_sql_string	execution_time	[87.047µs; 87.119µs] 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	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
receiver_entry_point/report/2598	execution_time	3.408ms	3.442ms ± 0.021ms	3.437ms ± 0.010ms	3.449ms	3.485ms	3.506ms	3.527ms	2.64%	1.422	2.404	0.60%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
receiver_entry_point/report/2598	execution_time	[3.439ms; 3.445ms] or [-0.084%; +0.084%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	execution_time	185.992µs	186.370µs ± 0.209µs	186.353µs ± 0.146µs	186.501µs	186.738µs	186.854µs	186.889µs	0.29%	0.381	-0.445	0.11%	0.015µs	1	200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	5350776.150op/s	5365688.064op/s ± 6008.398op/s	5366158.485op/s ± 4220.077op/s	5370312.950op/s	5375026.735op/s	5376292.599op/s	5376561.606op/s	0.19%	-0.376	-0.449	0.11%	424.858op/s	1	200
normalization/normalize_name/normalize_name/bad-name	execution_time	17.814µs	17.899µs ± 0.041µs	17.891µs ± 0.021µs	17.918µs	17.968µs	18.001µs	18.164µs	1.53%	1.803	8.337	0.23%	0.003µs	1	200
normalization/normalize_name/normalize_name/bad-name	throughput	55054640.574op/s	55868836.247op/s ± 127881.149op/s	55894444.986op/s ± 66776.322op/s	55944371.557op/s	56040585.251op/s	56099786.436op/s	56135173.498op/s	0.43%	-1.755	7.991	0.23%	9042.563op/s	1	200
normalization/normalize_name/normalize_name/good	execution_time	10.198µs	10.459µs ± 0.219µs	10.305µs ± 0.055µs	10.692µs	10.769µs	10.836µs	10.848µs	5.27%	0.378	-1.710	2.08%	0.015µs	1	200
normalization/normalize_name/normalize_name/good	throughput	92183993.622op/s	95654688.959op/s ± 1983640.989op/s	97040739.097op/s ± 522334.740op/s	97357345.657op/s	97635255.486op/s	97804983.567op/s	98060424.684op/s	1.05%	-0.369	-1.723	2.07%	140264.599op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	execution_time	[186.341µs; 186.399µs] or [-0.016%; +0.016%]	None	None	None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	[5364855.358op/s; 5366520.770op/s] or [-0.016%; +0.016%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	execution_time	[17.893µs; 17.905µs] or [-0.032%; +0.032%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	throughput	[55851113.150op/s; 55886559.344op/s] or [-0.032%; +0.032%]	None	None	None
normalization/normalize_name/normalize_name/good	execution_time	[10.428µs; 10.489µs] or [-0.290%; +0.290%]	None	None	None
normalization/normalize_name/normalize_name/good	throughput	[95379775.396op/s; 95929602.522op/s] or [-0.287%; +0.287%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

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	144.092µs	146.339µs ± 1.828µs	146.019µs ± 0.511µs	146.610µs	148.029µs	152.176µs	163.838µs	12.20%	6.078	49.380	1.25%	0.129µs	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	[146.085µs; 146.592µs] or [-0.173%; +0.173%]	None	None	None

Group 5

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
profile_add_sample_timestamped_x1000	execution_time	4.164ms	4.169ms ± 0.008ms	4.168ms ± 0.001ms	4.170ms	4.172ms	4.176ms	4.275ms	2.56%	11.917	154.950	0.19%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
profile_add_sample_timestamped_x1000	execution_time	[4.168ms; 4.170ms] or [-0.026%; +0.026%]	None	None	None

Group 6

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
concentrator/add_spans_to_concentrator	execution_time	12.979ms	13.009ms ± 0.015ms	13.008ms ± 0.009ms	13.017ms	13.032ms	13.051ms	13.088ms	0.62%	1.125	3.481	0.11%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
concentrator/add_spans_to_concentrator	execution_time	[13.007ms; 13.011ms] or [-0.016%; +0.016%]	None	None	None

Group 7

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
two way interface	execution_time	18.160µs	26.497µs ± 9.862µs	18.446µs ± 0.228µs	34.947µs	44.622µs	45.726µs	69.934µs	279.12%	0.880	0.475	37.13%	0.697µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
two way interface	execution_time	[25.130µs; 27.864µs] or [-5.158%; +5.158%]	None	None	None

Group 8

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching string interning on wordpress profile	execution_time	160.624µs	161.212µs ± 0.302µs	161.173µs ± 0.124µs	161.306µs	161.641µs	162.129µs	163.866µs	1.67%	4.016	29.859	0.19%	0.021µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching string interning on wordpress profile	execution_time	[161.170µs; 161.253µs] or [-0.026%; +0.026%]	None	None	None

Group 9

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	execution_time	495.299µs	496.163µs ± 0.742µs	496.068µs ± 0.261µs	496.366µs	496.860µs	497.270µs	504.762µs	1.75%	7.910	88.433	0.15%	0.052µs	1	200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	1981131.266op/s	2015470.597op/s ± 2979.200op/s	2015851.762op/s ± 1061.290op/s	2016844.574op/s	2018044.208op/s	2018603.567op/s	2018981.567op/s	0.16%	-7.786	86.495	0.15%	210.661op/s	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	370.757µs	371.511µs ± 0.312µs	371.494µs ± 0.233µs	371.736µs	371.994µs	372.240µs	372.916µs	0.38%	0.475	1.129	0.08%	0.022µs	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	2681568.414op/s	2691709.400op/s ± 2260.882op/s	2691830.050op/s ± 1688.770op/s	2693398.057op/s	2695040.696op/s	2696102.736op/s	2697181.563op/s	0.20%	-0.468	1.106	0.08%	159.868op/s	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	168.528µs	168.901µs ± 0.165µs	168.890µs ± 0.101µs	168.988µs	169.223µs	169.346µs	169.391µs	0.30%	0.463	0.139	0.10%	0.012µs	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	5903507.040op/s	5920636.236op/s ± 5794.224op/s	5921003.079op/s ± 3530.761op/s	5924504.862op/s	5929428.098op/s	5931651.171op/s	5933718.160op/s	0.21%	-0.457	0.133	0.10%	409.713op/s	1	200
normalization/normalize_service/normalize_service/[empty string]	execution_time	36.823µs	37.017µs ± 0.111µs	37.027µs ± 0.101µs	37.103µs	37.179µs	37.270µs	37.283µs	0.69%	0.109	-1.164	0.30%	0.008µs	1	200
normalization/normalize_service/normalize_service/[empty string]	throughput	26821987.927op/s	27014790.460op/s ± 81111.286op/s	27007511.195op/s ± 73656.130op/s	27099271.650op/s	27123531.983op/s	27135712.746op/s	27156573.419op/s	0.55%	-0.101	-1.173	0.30%	5735.434op/s	1	200
normalization/normalize_service/normalize_service/test_ASCII	execution_time	46.208µs	46.299µs ± 0.098µs	46.286µs ± 0.027µs	46.319µs	46.372µs	46.443µs	47.527µs	2.68%	9.959	121.654	0.21%	0.007µs	1	200
normalization/normalize_service/normalize_service/test_ASCII	throughput	21040532.340op/s	21598953.798op/s ± 44809.371op/s	21604692.151op/s ± 12741.646op/s	21616653.664op/s	21629507.821op/s	21636380.565op/s	21641063.748op/s	0.17%	-9.804	119.027	0.21%	3168.501op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	execution_time	[496.060µs; 496.266µs] or [-0.021%; +0.021%]	None	None	None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	[2015057.709op/s; 2015883.486op/s] or [-0.020%; +0.020%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	[371.468µs; 371.555µs] or [-0.012%; +0.012%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	[2691396.063op/s; 2692022.736op/s] or [-0.012%; +0.012%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	[168.878µs; 168.924µs] or [-0.014%; +0.014%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	[5919833.212op/s; 5921439.260op/s] or [-0.014%; +0.014%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	execution_time	[37.002µs; 37.032µs] or [-0.042%; +0.042%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	throughput	[27003549.216op/s; 27026031.704op/s] or [-0.042%; +0.042%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	execution_time	[46.285µs; 46.312µs] or [-0.029%; +0.029%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	throughput	[21592743.651op/s; 21605163.946op/s] or [-0.029%; +0.029%]	None	None	None

Group 10

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
credit_card/is_card_number/	execution_time	3.900µs	3.915µs ± 0.002µs	3.915µs ± 0.001µs	3.916µs	3.919µs	3.920µs	3.922µs	0.18%	-0.841	6.080	0.06%	0.000µs	1	200
credit_card/is_card_number/	throughput	254998112.715op/s	255449817.160op/s ± 159150.157op/s	255451183.296op/s ± 94077.487op/s	255546586.152op/s	255681755.211op/s	255721532.091op/s	256427884.299op/s	0.38%	0.854	6.163	0.06%	11253.616op/s	1	200
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	79.159µs	79.874µs ± 0.424µs	79.806µs ± 0.287µs	80.099µs	80.645µs	81.097µs	81.830µs	2.54%	1.018	1.721	0.53%	0.030µs	1	200
credit_card/is_card_number/ 3782-8224-6310-005	throughput	12220517.492op/s	12520046.788op/s ± 66162.050op/s	12530330.746op/s ± 45221.124op/s	12574443.101op/s	12607918.531op/s	12619855.910op/s	12632759.271op/s	0.82%	-0.976	1.547	0.53%	4678.363op/s	1	200
credit_card/is_card_number/ 378282246310005	execution_time	82.962µs	84.098µs ± 0.532µs	84.067µs ± 0.389µs	84.464µs	85.141µs	85.414µs	85.486µs	1.69%	0.390	-0.270	0.63%	0.038µs	1	200
credit_card/is_card_number/ 378282246310005	throughput	11697841.414op/s	11891326.003op/s ± 75084.033op/s	11895246.709op/s ± 54923.086op/s	11947976.819op/s	12001208.072op/s	12038982.524op/s	12053765.059op/s	1.33%	-0.361	-0.301	0.63%	5309.243op/s	1	200
credit_card/is_card_number/37828224631	execution_time	3.895µs	3.916µs ± 0.003µs	3.916µs ± 0.002µs	3.918µs	3.921µs	3.924µs	3.925µs	0.23%	-0.683	5.547	0.09%	0.000µs	1	200
credit_card/is_card_number/37828224631	throughput	254794169.193op/s	255365278.621op/s ± 223275.111op/s	255382843.121op/s ± 142262.222op/s	255514366.295op/s	255652233.281op/s	255727732.216op/s	256718772.372op/s	0.52%	0.702	5.660	0.09%	15787.935op/s	1	200
credit_card/is_card_number/378282246310005	execution_time	69.787µs	70.383µs ± 0.420µs	70.319µs ± 0.302µs	70.660µs	71.201µs	71.443µs	71.457µs	1.62%	0.680	-0.422	0.60%	0.030µs	1	200
credit_card/is_card_number/378282246310005	throughput	13994489.522op/s	14208542.813op/s ± 84445.118op/s	14220812.871op/s ± 61438.616op/s	14280571.348op/s	14312206.124op/s	14320499.623op/s	14329229.590op/s	0.76%	-0.661	-0.460	0.59%	5971.172op/s	1	200
credit_card/is_card_number/37828224631000521389798	execution_time	53.052µs	53.129µs ± 0.041µs	53.123µs ± 0.025µs	53.155µs	53.186µs	53.231µs	53.371µs	0.47%	1.704	7.232	0.08%	0.003µs	1	200
credit_card/is_card_number/37828224631000521389798	throughput	18736908.329op/s	18822142.870op/s ± 14371.930op/s	18824282.149op/s ± 8800.702op/s	18832455.046op/s	18840503.480op/s	18844851.485op/s	18849579.260op/s	0.13%	-1.689	7.134	0.08%	1016.249op/s	1	200
credit_card/is_card_number/x371413321323331	execution_time	6.428µs	6.440µs ± 0.005µs	6.440µs ± 0.004µs	6.444µs	6.449µs	6.452µs	6.453µs	0.21%	0.209	-0.681	0.08%	0.000µs	1	200
credit_card/is_card_number/x371413321323331	throughput	154958306.842op/s	155283688.224op/s ± 129051.028op/s	155289992.570op/s ± 100909.293op/s	155385867.709op/s	155473278.447op/s	155515815.467op/s	155569461.815op/s	0.18%	-0.206	-0.682	0.08%	9125.286op/s	1	200
credit_card/is_card_number_no_luhn/	execution_time	3.898µs	3.916µs ± 0.003µs	3.915µs ± 0.002µs	3.918µs	3.921µs	3.923µs	3.925µs	0.25%	-0.510	3.824	0.08%	0.000µs	1	200
credit_card/is_card_number_no_luhn/	throughput	254770741.745op/s	255377526.312op/s ± 209622.655op/s	255398327.142op/s ± 136343.761op/s	255517902.358op/s	255660399.075op/s	255725833.089op/s	256551222.300op/s	0.45%	0.524	3.893	0.08%	14822.560op/s	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	64.936µs	65.168µs ± 0.153µs	65.122µs ± 0.091µs	65.246µs	65.478µs	65.641µs	65.716µs	0.91%	1.081	0.827	0.23%	0.011µs	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	15217101.097op/s	15345029.175op/s ± 35883.549op/s	15355795.272op/s ± 21413.392op/s	15371943.901op/s	15385825.658op/s	15393965.686op/s	15399886.041op/s	0.29%	-1.069	0.791	0.23%	2537.350op/s	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	58.525µs	59.037µs ± 0.237µs	58.993µs ± 0.155µs	59.171µs	59.445µs	59.583µs	59.775µs	1.33%	0.508	-0.216	0.40%	0.017µs	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	16729372.290op/s	16938894.581op/s ± 67988.112op/s	16951229.623op/s ± 44372.038op/s	16991279.655op/s	17031831.733op/s	17063458.101op/s	17086824.993op/s	0.80%	-0.490	-0.240	0.40%	4807.486op/s	1	200
credit_card/is_card_number_no_luhn/37828224631	execution_time	3.893µs	3.915µs ± 0.003µs	3.915µs ± 0.001µs	3.916µs	3.919µs	3.920µs	3.921µs	0.17%	-2.162	18.687	0.07%	0.000µs	1	200
credit_card/is_card_number_no_luhn/37828224631	throughput	255030819.801op/s	255445383.690op/s ± 179896.332op/s	255457989.936op/s ± 93848.783op/s	255542170.847op/s	255649158.502op/s	255732038.735op/s	256883419.970op/s	0.56%	2.196	19.002	0.07%	12720.592op/s	1	200
credit_card/is_card_number_no_luhn/378282246310005	execution_time	55.333µs	55.665µs ± 0.189µs	55.638µs ± 0.125µs	55.773µs	56.028µs	56.237µs	56.395µs	1.36%	1.098	1.475	0.34%	0.013µs	1	200
credit_card/is_card_number_no_luhn/378282246310005	throughput	17732127.838op/s	17964889.766op/s ± 60862.807op/s	17973285.409op/s ± 40412.207op/s	18012562.412op/s	18038598.090op/s	18054405.670op/s	18072245.609op/s	0.55%	-1.075	1.393	0.34%	4303.650op/s	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	53.061µs	53.128µs ± 0.042µs	53.120µs ± 0.023µs	53.150µs	53.198µs	53.240µs	53.414µs	0.55%	2.004	9.998	0.08%	0.003µs	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	18721564.766op/s	18822307.622op/s ± 14738.147op/s	18825263.703op/s ± 8222.076op/s	18831873.814op/s	18840956.212op/s	18844963.097op/s	18846318.147op/s	0.11%	-1.986	9.838	0.08%	1042.144op/s	1	200
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	6.429µs	6.439µs ± 0.005µs	6.439µs ± 0.004µs	6.443µs	6.449µs	6.450µs	6.452µs	0.20%	0.314	-0.670	0.08%	0.000µs	1	200
credit_card/is_card_number_no_luhn/x371413321323331	throughput	154996791.347op/s	155302146.082op/s ± 124513.060op/s	155311798.439op/s ± 95780.778op/s	155407875.295op/s	155476669.433op/s	155512467.254op/s	155553033.074op/s	0.16%	-0.311	-0.673	0.08%	8804.403op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
credit_card/is_card_number/	execution_time	[3.914µs; 3.915µs] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number/	throughput	[255427760.479op/s; 255471873.842op/s] or [-0.009%; +0.009%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	[79.815µs; 79.933µs] or [-0.074%; +0.074%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	throughput	[12510877.365op/s; 12529216.212op/s] or [-0.073%; +0.073%]	None	None	None
credit_card/is_card_number/ 378282246310005	execution_time	[84.024µs; 84.172µs] or [-0.088%; +0.088%]	None	None	None
credit_card/is_card_number/ 378282246310005	throughput	[11880920.078op/s; 11901731.928op/s] or [-0.088%; +0.088%]	None	None	None
credit_card/is_card_number/37828224631	execution_time	[3.915µs; 3.916µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/37828224631	throughput	[255334334.838op/s; 255396222.404op/s] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/378282246310005	execution_time	[70.324µs; 70.441µs] or [-0.083%; +0.083%]	None	None	None
credit_card/is_card_number/378282246310005	throughput	[14196839.532op/s; 14220246.094op/s] or [-0.082%; +0.082%]	None	None	None
credit_card/is_card_number/37828224631000521389798	execution_time	[53.123µs; 53.135µs] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/37828224631000521389798	throughput	[18820151.059op/s; 18824134.681op/s] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number/x371413321323331	execution_time	[6.439µs; 6.441µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/x371413321323331	throughput	[155265802.993op/s; 155301573.455op/s] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number_no_luhn/	execution_time	[3.915µs; 3.916µs] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number_no_luhn/	throughput	[255348474.628op/s; 255406577.995op/s] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	[65.147µs; 65.189µs] or [-0.032%; +0.032%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	[15340056.061op/s; 15350002.290op/s] or [-0.032%; +0.032%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	[59.004µs; 59.070µs] or [-0.056%; +0.056%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	[16929472.083op/s; 16948317.080op/s] or [-0.056%; +0.056%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	execution_time	[3.914µs; 3.915µs] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	throughput	[255420451.789op/s; 255470315.592op/s] or [-0.010%; +0.010%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	execution_time	[55.639µs; 55.691µs] or [-0.047%; +0.047%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	throughput	[17956454.766op/s; 17973324.766op/s] or [-0.047%; +0.047%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	[53.123µs; 53.134µs] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	[18820265.057op/s; 18824350.188op/s] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	[6.438µs; 6.440µs] or [-0.011%; +0.011%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	throughput	[155284889.769op/s; 155319402.395op/s] or [-0.011%; +0.011%]	None	None	None

Group 11

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
normalization/normalize_trace/test_trace	execution_time	239.437ns	251.910ns ± 14.547ns	245.160ns ± 4.177ns	256.548ns	284.180ns	292.028ns	298.543ns	21.77%	1.505	1.160	5.76%	1.029ns	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
normalization/normalize_trace/test_trace	execution_time	[249.894ns; 253.926ns] or [-0.800%; +0.800%]	None	None	None

Group 12

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

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	190.789ns	193.149ns ± 2.105ns	192.851ns ± 1.413ns	194.075ns	197.015ns	200.123ns	202.595ns	5.05%	1.429	2.852	1.09%	0.149ns	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	[192.857ns; 193.441ns] or [-0.151%; +0.151%]	None	None	None

Group 13

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
write only interface	execution_time	1.187µs	3.205µs ± 1.616µs	2.966µs ± 0.027µs	2.992µs	3.646µs	14.158µs	18.730µs	531.47%	7.758	62.895	50.29%	0.114µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
write only interface	execution_time	[2.981µs; 3.429µs] or [-6.987%; +6.987%]	None	None	None

Group 14

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
profile_add_sample2_frames_x1000	execution_time	732.897µs	734.300µs ± 0.650µs	734.208µs ± 0.423µs	734.682µs	735.488µs	735.966µs	736.766µs	0.35%	0.588	0.556	0.09%	0.046µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
profile_add_sample2_frames_x1000	execution_time	[734.210µs; 734.390µs] or [-0.012%; +0.012%]	None	None	None

Group 15

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
redis/obfuscate_redis_string	execution_time	35.280µs	35.563µs ± 0.428µs	35.385µs ± 0.047µs	35.452µs	36.375µs	36.416µs	38.556µs	8.96%	2.717	11.075	1.20%	0.030µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
redis/obfuscate_redis_string	execution_time	[35.504µs; 35.623µs] or [-0.167%; +0.167%]	None	None	None

Group 16

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
ip_address/quantize_peer_ip_address_benchmark	execution_time	4.946µs	5.020µs ± 0.048µs	5.021µs ± 0.048µs	5.049µs	5.108µs	5.113µs	5.114µs	1.85%	0.258	-0.938	0.95%	0.003µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
ip_address/quantize_peer_ip_address_benchmark	execution_time	[5.014µs; 5.027µs] or [-0.132%; +0.132%]	None	None	None

Group 17

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
profile_add_sample_frames_x1000	execution_time	4.133ms	4.159ms ± 0.009ms	4.161ms ± 0.001ms	4.163ms	4.166ms	4.168ms	4.169ms	0.18%	-2.057	2.819	0.21%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
profile_add_sample_frames_x1000	execution_time	[4.158ms; 4.160ms] or [-0.029%; +0.029%]	None	None	None

Group 18

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
tags/replace_trace_tags	execution_time	2.276µs	2.362µs ± 0.018µs	2.364µs ± 0.005µs	2.371µs	2.380µs	2.385µs	2.388µs	1.02%	-2.639	7.682	0.76%	0.001µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
tags/replace_trace_tags	execution_time	[2.359µs; 2.364µs] or [-0.106%; +0.106%]	None	None	None

Group 19

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching deserializing traces from msgpack to their internal representation	execution_time	48.579ms	48.841ms ± 1.018ms	48.687ms ± 0.055ms	48.767ms	49.003ms	51.143ms	59.947ms	23.13%	9.223	89.432	2.08%	0.072ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching deserializing traces from msgpack to their internal representation	execution_time	[48.699ms; 48.982ms] or [-0.289%; +0.289%]	None	None	None

Group 20

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	c00625e	1774427381	julio/opt-in-telemetry

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching serializing traces from their internal representation to msgpack	execution_time	13.922ms	13.974ms ± 0.030ms	13.969ms ± 0.013ms	13.983ms	14.028ms	14.086ms	14.144ms	1.25%	2.589	10.016	0.21%	0.002ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching serializing traces from their internal representation to msgpack	execution_time	[13.970ms; 13.978ms] or [-0.030%; +0.030%]	None	None	None

Baseline

Omitted due to size.

[dd-octo-sts]

Artifact Size Benchmark Report

aarch64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	82.89 MB	82.92 MB	+.03% (+32.12 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so	7.63 MB	7.63 MB	0% (0 B) 👌

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	98.86 MB	98.90 MB	+.04% (+44.00 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	10.08 MB	10.08 MB	+0% (+688 B) 👌

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	24.98 MB	24.99 MB	+.02% (+7.50 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	77.85 KB	77.85 KB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	182.37 MB	182.46 MB	+.05% (+96.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	904.68 MB	905.61 MB	+.10% (+954.76 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	7.85 MB	7.85 MB	+.03% (+3.00 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	77.85 KB	77.85 KB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	23.51 MB	23.51 MB	+.03% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	45.85 MB	45.86 MB	+.02% (+11.91 KB) 🔍

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	21.48 MB	21.48 MB	--.02% (-4.50 KB) 💪
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	79.06 KB	79.06 KB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	186.38 MB	186.37 MB	-0% (-8.00 KB) 👌
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	889.47 MB	890.28 MB	+.09% (+824.52 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	6.09 MB	6.10 MB	+.03% (+2.00 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	79.06 KB	79.06 KB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	25.16 MB	25.16 MB	+.03% (+8.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	43.33 MB	43.34 MB	+.02% (+9.09 KB) 🔍

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	74.07 MB	74.09 MB	+.03% (+24.65 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	8.49 MB	8.49 MB	+.04% (+4.00 KB) 🔍

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	91.32 MB	91.36 MB	+.04% (+42.27 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	10.13 MB	10.14 MB	+.03% (+4.08 KB) 🔍

[bantonsson]

Is there a similar test somewhere?

[hoolioh]

Yes, there are test in libdd-telemetry that check specifically for that

[paullegranddc]

Not sure if we should feature gate it, instead of making it a no-op since it's part of the API

You could imagine that we have a language where we want to build a shared library.
The code in the language would probably look like this

if (!serverless) {
  ddog_trace_exporter_config_enable_telemetry();
}

if we feature gate this, we can't have this switch at rutime and force them to use macros

[hoolioh]

I was unsure what to do here. In order to maintain ABI compatibility it would be better to write no-op functions but, since most of the data-pipeline crate users are migrating to building their own version from source, I think it'd be cleaner to reduce the API surface as much as possible. At the end of the day they're going to control the features they want to introduce.
Anyway it's an interesting topic to discuss internally because our position is to encourage every consumer to build their own thing but we didn't discuss what to do with the prebuilt binary. I'll bring this up next common standup.

[paullegranddc]

I think that for this file where there are a lot of telemetry/not(telemetry) feature gate, doing something like this would be cleaner

#[cfg(feature = "telemetry")]
mod inner {
  //telemetry only imports, items
  struct SendPayloadTelemetry
  struct TelemetryClient
}

#[cfg(not(feature = "telemetry"))]
mod inner {
 // placeholder
}

use inner::*;

[hoolioh]

I'll try that. LMK how it looks.

[VianneyRuhlmann]

Is it possible to feature gate the whole telemetry module in libdd-data-pipeline and it's use in trace_exporter rather than creating no-op functions ?

[hoolioh]

The fist try was using that approach but looking at the changes it seemed that the trace exporter was cluttered with #[cfg(feature)] statements which in the long run would be very hard to maintain, specially if we keep gating features. Also it keeps most of the changes in the module affected and lastly since feature statements only apply to the following block sometimes adding a new statement requires refactoring the original code.
Anyway I wanted to test this approach in order to discuss how it feels. Do you think is incovenient?