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[WIP] Experimental OTEL profiling support#1754

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danielsn wants to merge 9 commits intomainfrom
dsn/otel-impl-for-python
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[WIP] Experimental OTEL profiling support#1754
danielsn wants to merge 9 commits intomainfrom
dsn/otel-impl-for-python

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@danielsn
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@danielsn danielsn commented Mar 17, 2026 •
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What does this PR do?

This is an experimental branch adding a new "otel" feature to the profiler, which

  1. Uses an "otel shaped" internal representation
  2. adds an otel protobuf definition
  3. has code to generate either pprof or otel

Motivation

OTEL is coming, lets see what it would take to support it.

Additional Notes

Doens't currently support

  1. upscaling
  2. profilers that have more than two sample types at the same time
  3. streaming generation of the otel proto: we build the entire thing then emit it.

How to test the change?

Describe here in detail how the change can be validated.

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dd-octo-sts bot commented Mar 17, 2026 •
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Artifact Size Benchmark Report

aarch64-alpine-linux-musl
Artifact Baseline Commit Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a 91.49 MB 91.51 MB +.02% (+24.43 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so 8.38 MB 8.38 MB 0% (0 B) 👌
aarch64-unknown-linux-gnu
Artifact Baseline Commit Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so 10.75 MB 10.75 MB +0% (+224 B) 👌
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a 106.22 MB 106.23 MB +.01% (+11.93 KB) 🔍
libdatadog-x64-windows
Artifact Baseline Commit Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll 24.97 MB 24.97 MB +.01% (+3.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib 75.94 KB 75.94 KB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb 166.41 MB 166.81 MB +.24% (+416.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib 837.48 MB 838.08 MB +.07% (+615.49 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll 9.55 MB 9.55 MB --.01% (-1.00 KB) 💪
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib 75.94 KB 75.94 KB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb 22.99 MB 23.00 MB +.03% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib 48.35 MB 48.35 MB +0% (+1.58 KB) 👌
libdatadog-x86-windows
Artifact Baseline Commit Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll 21.05 MB 21.05 MB +.01% (+3.50 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib 77.12 KB 77.12 KB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb 170.15 MB 170.19 MB +.02% (+48.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib 823.53 MB 823.41 MB --.01% (-128.53 KB) 💪
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll 7.23 MB 7.23 MB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib 77.12 KB 77.12 KB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb 24.58 MB 24.59 MB +.03% (+8.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib 44.09 MB 44.10 MB +0% (+3.12 KB) 👌
x86_64-alpine-linux-musl
Artifact Baseline Commit Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a 80.04 MB 80.04 MB +0% (+6.69 KB) 👌
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so 9.86 MB 9.86 MB 0% (0 B) 👌
x86_64-unknown-linux-gnu
Artifact Baseline Commit Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a 100.17 MB 100.17 MB +0% (+4.15 KB) 👌
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.43 MB 11.43 MB +0% (+144 B) 👌

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codecov-commenter commented Mar 17, 2026

Codecov Report

❌ Patch coverage is 57.22022% with 237 lines in your changes missing coverage. Please review.
✅ Project coverage is 69.80%. Comparing base (27de9f3) to head (bca3ebd).
⚠️ Report is 68 commits behind head on main.

Additional details and impacted files 
@@            Coverage Diff             @@
##             main    #1754      +/-   ##
==========================================
- Coverage   71.17%   69.80%   -1.38%     
==========================================
  Files         423      429       +6     
  Lines       62063    64056    +1993     
==========================================
+ Hits        44173    44712     +539     
- Misses      17890    19344    +1454
Components Coverage Δ
libdd-crashtracker 63.98% <ø> (+1.43%) ⬆️
libdd-crashtracker-ffi 18.19% <ø> (+2.39%) ⬆️
libdd-alloc 98.77% <ø> (ø)
libdd-data-pipeline 87.94% <ø> (+1.00%) ⬆️
libdd-data-pipeline-ffi 74.85% <ø> (+0.45%) ⬆️
libdd-common 79.73% <ø> (-0.86%) ⬇️
libdd-common-ffi 73.40% <ø> (-0.35%) ⬇️
libdd-telemetry 62.48% <ø> (-0.04%) ⬇️
libdd-telemetry-ffi 16.75% <ø> (ø)
libdd-dogstatsd-client 82.64% <ø> (ø)
datadog-ipc 80.47% <ø> (-0.37%) ⬇️
libdd-profiling 70.27% <57.22%> (-11.27%) ⬇️
libdd-profiling-ffi 63.65% <ø> (-0.02%) ⬇️
datadog-sidecar 33.10% <ø> (+0.32%) ⬆️
datdog-sidecar-ffi 10.79% <ø> (+1.29%) ⬆️
spawn-worker 54.69% <ø> (ø)
libdd-tinybytes 93.16% <ø> (ø)
libdd-trace-normalization 81.71% <ø> (ø)
libdd-trace-obfuscation 91.80% <ø> (-2.41%) ⬇️
libdd-trace-protobuf 68.25% <ø> (+0.25%) ⬆️
libdd-trace-utils 88.98% <ø> (-0.12%) ⬇️
datadog-tracer-flare 90.46% <ø> (+1.50%) ⬆️
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.

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github-actions bot commented Mar 17, 2026

Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

  • Base Branch: origin/main
  • PR Branch: origin/dsn/otel-impl-for-python

Summary by Rule

Rule Base Branch PR Branch Change
expect_used 2 2 No change (0%)
unwrap_used 1 7 ⚠️ +6 (+600.0%)
Total 3 9 ⚠️ +6 (+200.0%)

Annotation Counts by File

File Base Branch PR Branch Change
libdd-profiling/src/internal/location.rs 1 1 No change (0%)
libdd-profiling/src/internal/mapping.rs 1 1 No change (0%)
libdd-profiling/src/internal/otel_style_observation.rs 0 6 ⚠️ +6 (N/A)
libdd-profiling/src/internal/profile/mod.rs 1 1 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 28 27 ✅ -1 (-3.6%)
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 59 59 No change (0%)
libdd-common 10 10 No change (0%)
libdd-common-ffi 12 12 No change (0%)
libdd-crashtracker 0 12 ⚠️ +12 (N/A)
libdd-data-pipeline 5 5 No change (0%)
libdd-ddsketch 2 2 No change (0%)
libdd-dogstatsd-client 1 1 No change (0%)
libdd-profiling 13 19 ⚠️ +6 (+46.2%)
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 9 9 No change (0%)
libdd-trace-utils 15 15 No change (0%)
Total 208 225 ⚠️ +17 (+8.2%)

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.

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github-actions bot commented Mar 17, 2026

📚 Documentation Check Results

⚠️ 645 documentation warning(s) found

📦 libdd-profiling - 645 warning(s)

Updated: 2026-03-17 22:29:50 UTC | Commit: 9de1314 | missing-docs job results

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github-actions bot commented Mar 17, 2026

🔒 Cargo Deny Results

✅ No issues found!

📦 libdd-profiling - ✅ No issues

Updated: 2026-03-17 22:32:58 UTC | Commit: 9de1314 | dependency-check job results

@danielsn danielsn changed the title Dsn/otel impl for python [WIP] Experimental OTEL profiling support Mar 18, 2026
@danielsn danielsn force-pushed the dsn/otel-impl-for-python branch from bca3ebd to d7d685e Compare March 25, 2026 19:49
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pr-commenter bot commented Mar 25, 2026 •
edited
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Benchmarks

Comparison

Benchmark execution time: 2026-03-25 20:07:23

Comparing candidate commit d7d685e in PR branch dsn/otel-impl-for-python with baseline commit b1d5bcf in branch main.

Found 11 performance improvements and 7 performance regressions! Performance is the same for 40 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:benching serializing traces from their internal representation to msgpack

  • 🟥 execution_time [+793.613µs; +806.378µs] or [+5.669%; +5.761%]

scenario:concentrator/add_spans_to_concentrator

  • 🟩 execution_time [-2.635ms; -2.629ms] or [-19.821%; -19.778%]

scenario:credit_card/is_card_number/ 378282246310005

  • 🟥 execution_time [+4.502µs; +4.614µs] or [+6.626%; +6.792%]
  • 🟥 throughput [-935751.211op/s; -914271.976op/s] or [-6.357%; -6.212%]

scenario:credit_card/is_card_number/378282246310005

  • 🟥 execution_time [+4.263µs; +4.370µs] or [+6.568%; +6.733%]
  • 🟥 throughput [-971516.535op/s; -949135.955op/s] or [-6.306%; -6.160%]

scenario:credit_card/is_card_number/x371413321323331

  • 🟩 execution_time [-402.838ns; -401.067ns] or [-5.891%; -5.865%]
  • 🟩 throughput [+9111310.494op/s; +9151579.962op/s] or [+6.231%; +6.258%]

scenario:credit_card/is_card_number_no_luhn/ 3782-8224-6310-005

  • 🟩 execution_time [-3.192µs; -3.021µs] or [-4.859%; -4.598%]
  • 🟩 throughput [+734949.357op/s; +778829.682op/s] or [+4.829%; +5.117%]

scenario:credit_card/is_card_number_no_luhn/x371413321323331

  • 🟩 execution_time [-400.989ns; -399.253ns] or [-5.864%; -5.839%]
  • 🟩 throughput [+9069751.913op/s; +9109504.396op/s] or [+6.202%; +6.229%]

scenario:normalization/normalize_name/normalize_name/good

  • 🟩 execution_time [-655.842ns; -645.228ns] or [-6.188%; -6.088%]
  • 🟩 throughput [+6119377.200op/s; +6220624.919op/s] or [+6.486%; +6.593%]

scenario:normalization/normalize_service/normalize_service/[empty string]

  • 🟥 execution_time [+1.824µs; +1.860µs] or [+4.930%; +5.029%]
  • 🟥 throughput [-1295293.154op/s; -1269164.808op/s] or [-4.792%; -4.695%]

scenario:sql/obfuscate_sql_string

  • 🟩 execution_time [-197.427µs; -197.295µs] or [-68.938%; -68.892%]

scenario:write only interface

  • 🟩 execution_time [-2.447µs; -2.050µs] or [-45.027%; -37.721%]

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 d7d685e 1774468160 dsn/otel-impl-for-python
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 242.285ns 253.638ns ± 14.346ns 245.927ns ± 2.801ns 263.252ns 281.746ns 299.366ns 301.510ns 22.60% 1.506 1.502 5.64% 1.014ns 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 [251.650ns; 255.626ns] or [-0.784%; +0.784%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 4.981µs 5.031µs ± 0.032µs 5.022µs ± 0.011µs 5.032µs 5.092µs 5.095µs 5.098µs 1.52% 0.944 -0.473 0.63% 0.002µs 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 [5.027µs; 5.035µs] or [-0.087%; +0.087%] None None None

Group 3

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 742.260µs 743.226µs ± 0.610µs 743.125µs ± 0.361µs 743.538µs 744.313µs 745.070µs 746.962µs 0.52% 1.854 6.880 0.08% 0.043µs 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 [743.141µs; 743.310µs] or [-0.011%; +0.011%] None None None

Group 4

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 2.293µs 2.358µs ± 0.016µs 2.362µs ± 0.004µs 2.366µs 2.373µs 2.376µs 2.379µs 0.74% -2.553 6.461 0.69% 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 [2.356µs; 2.360µs] or [-0.096%; +0.096%] None None None

Group 5

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 1.245µs 3.186µs ± 1.432µs 3.013µs ± 0.020µs 3.032µs 3.367µs 13.906µs 15.158µs 403.13% 7.513 56.978 44.84% 0.101µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
write only interface execution_time [2.987µs; 3.384µs] or [-6.230%; +6.230%] None None None

Group 6

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 493.261µs 494.088µs ± 0.685µs 494.020µs ± 0.216µs 494.251µs 494.678µs 494.885µs 502.359µs 1.69% 8.885 104.828 0.14% 0.048µs 1 200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput 1990607.209op/s 2023935.159op/s ± 2770.362op/s 2024208.408op/s ± 884.245op/s 2025014.441op/s 2026240.593op/s 2026869.070op/s 2027325.875op/s 0.15% -8.770 102.974 0.14% 195.894op/s 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time 385.242µs 386.003µs ± 0.348µs 385.981µs ± 0.212µs 386.178µs 386.669µs 386.919µs 387.088µs 0.29% 0.472 0.222 0.09% 0.025µs 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput 2583393.341op/s 2590656.877op/s ± 2334.000op/s 2590800.519op/s ± 1423.382op/s 2592291.202op/s 2594327.097op/s 2595333.919op/s 2595774.192op/s 0.19% -0.467 0.214 0.09% 165.039op/s 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time 167.724µs 168.025µs ± 0.137µs 168.018µs ± 0.091µs 168.108µs 168.258µs 168.371µs 168.410µs 0.23% 0.205 -0.210 0.08% 0.010µs 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput 5937898.694op/s 5951514.769op/s ± 4846.873op/s 5951738.323op/s ± 3229.793op/s 5954986.465op/s 5959329.462op/s 5961061.278op/s 5962174.302op/s 0.18% -0.201 -0.213 0.08% 342.726op/s 1 200
normalization/normalize_service/normalize_service/[empty string] execution_time 38.663µs 38.838µs ± 0.064µs 38.829µs ± 0.039µs 38.874µs 38.925µs 39.075µs 39.157µs 0.85% 1.236 4.704 0.17% 0.005µs 1 200
normalization/normalize_service/normalize_service/[empty string] throughput 25537909.421op/s 25748290.682op/s ± 42619.153op/s 25754069.914op/s ± 26131.005op/s 25772334.759op/s 25805974.890op/s 25823699.637op/s 25864745.964op/s 0.43% -1.210 4.590 0.17% 3013.629op/s 1 200
normalization/normalize_service/normalize_service/test_ASCII execution_time 45.701µs 45.805µs ± 0.110µs 45.787µs ± 0.035µs 45.829µs 45.901µs 45.998µs 47.156µs 2.99% 9.288 110.334 0.24% 0.008µs 1 200
normalization/normalize_service/normalize_service/test_ASCII throughput 21206402.601op/s 21831753.236op/s ± 51479.082op/s 21840432.159op/s ± 16777.504op/s 21854562.938op/s 21868842.628op/s 21877983.934op/s 21881518.869op/s 0.19% -9.101 107.230 0.24% 3640.121op/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 [493.993µs; 494.183µs] or [-0.019%; +0.019%] None None None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput [2023551.213op/s; 2024319.104op/s] or [-0.019%; +0.019%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time [385.955µs; 386.051µs] or [-0.012%; +0.012%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput [2590333.407op/s; 2590980.347op/s] or [-0.012%; +0.012%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time [168.006µs; 168.044µs] or [-0.011%; +0.011%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput [5950843.039op/s; 5952186.499op/s] or [-0.011%; +0.011%] None None None
normalization/normalize_service/normalize_service/[empty string] execution_time [38.829µs; 38.847µs] or [-0.023%; +0.023%] None None None
normalization/normalize_service/normalize_service/[empty string] throughput [25742384.077op/s; 25754197.287op/s] or [-0.023%; +0.023%] None None None
normalization/normalize_service/normalize_service/test_ASCII execution_time [45.790µs; 45.820µs] or [-0.033%; +0.033%] None None None
normalization/normalize_service/normalize_service/test_ASCII throughput [21824618.730op/s; 21838887.741op/s] or [-0.033%; +0.033%] None None None

Group 7

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 185.452µs 185.890µs ± 0.252µs 185.870µs ± 0.158µs 186.038µs 186.272µs 186.447µs 187.625µs 0.94% 2.056 10.606 0.14% 0.018µs 1 200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput 5329785.828op/s 5379525.159op/s ± 7278.862op/s 5380100.032op/s ± 4579.531op/s 5384513.131op/s 5388076.988op/s 5390893.862op/s 5392219.243op/s 0.23% -2.022 10.323 0.13% 514.693op/s 1 200
normalization/normalize_name/normalize_name/bad-name execution_time 17.349µs 17.619µs ± 0.163µs 17.595µs ± 0.144µs 17.769µs 17.867µs 17.907µs 17.930µs 1.90% 0.123 -1.354 0.93% 0.012µs 1 200
normalization/normalize_name/normalize_name/bad-name throughput 55773363.298op/s 56763351.455op/s ± 526095.190op/s 56833587.557op/s ± 462252.996op/s 57220038.974op/s 57519446.260op/s 57627535.015op/s 57639846.862op/s 1.42% -0.105 -1.358 0.92% 37200.548op/s 1 200
normalization/normalize_name/normalize_name/good execution_time 9.891µs 9.948µs ± 0.028µs 9.943µs ± 0.020µs 9.967µs 10.003µs 10.020µs 10.028µs 0.86% 0.498 -0.048 0.28% 0.002µs 1 200
normalization/normalize_name/normalize_name/good throughput 99718973.801op/s 100520986.689op/s ± 284782.224op/s 100571696.763op/s ± 202354.837op/s 100721802.565op/s 100908728.974op/s 101074592.931op/s 101097056.238op/s 0.52% -0.483 -0.066 0.28% 20137.144op/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 [185.855µs; 185.925µs] or [-0.019%; +0.019%] None None None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput [5378516.378op/s; 5380533.939op/s] or [-0.019%; +0.019%] None None None
normalization/normalize_name/normalize_name/bad-name execution_time [17.596µs; 17.641µs] or [-0.129%; +0.129%] None None None
normalization/normalize_name/normalize_name/bad-name throughput [56690439.721op/s; 56836263.188op/s] or [-0.128%; +0.128%] None None None
normalization/normalize_name/normalize_name/good execution_time [9.944µs; 9.952µs] or [-0.039%; +0.039%] None None None
normalization/normalize_name/normalize_name/good throughput [100481518.611op/s; 100560454.766op/s] or [-0.039%; +0.039%] None None None

Group 8

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 88.404µs 89.020µs ± 0.155µs 89.035µs ± 0.081µs 89.109µs 89.212µs 89.266µs 90.065µs 1.16% 0.915 10.334 0.17% 0.011µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sql/obfuscate_sql_string execution_time [88.999µs; 89.042µs] or [-0.024%; +0.024%] None None None

Group 9

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 14.762ms 14.798ms ± 0.030ms 14.791ms ± 0.008ms 14.802ms 14.826ms 14.904ms 15.041ms 1.69% 4.412 26.505 0.20% 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 [14.794ms; 14.802ms] or [-0.028%; +0.028%] None None None

Group 10

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 4.263ms 4.271ms ± 0.008ms 4.270ms ± 0.002ms 4.272ms 4.274ms 4.277ms 4.377ms 2.51% 11.853 155.329 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_frames_x1000 execution_time [4.269ms; 4.272ms] or [-0.026%; +0.026%] None None None

Group 11

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 17.930µs 24.767µs ± 8.468µs 18.152µs ± 0.161µs 32.104µs 39.407µs 40.351µs 64.414µs 254.85% 0.970 0.811 34.11% 0.599µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
two way interface execution_time [23.593µs; 25.940µs] or [-4.739%; +4.739%] None None None

Group 12

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 162.030µs 162.944µs ± 0.389µs 162.895µs ± 0.131µs 163.043µs 163.393µs 164.134µs 166.818µs 2.41% 5.459 49.561 0.24% 0.027µs 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 [162.890µs; 162.997µs] or [-0.033%; +0.033%] None None None

Group 13

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 47.950ms 48.254ms ± 0.861ms 48.120ms ± 0.064ms 48.185ms 48.439ms 51.856ms 56.527ms 17.47% 8.008 67.912 1.78% 0.061ms 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 [48.134ms; 48.373ms] or [-0.247%; +0.247%] None None None

Group 14

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 34.109µs 34.748µs ± 1.105µs 34.230µs ± 0.048µs 34.370µs 37.144µs 37.177µs 38.011µs 11.05% 1.695 0.959 3.17% 0.078µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
redis/obfuscate_redis_string execution_time [34.595µs; 34.901µs] or [-0.441%; +0.441%] None None None

Group 15

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 189.570ns 192.133ns ± 2.120ns 191.888ns ± 1.419ns 193.171ns 196.490ns 198.114ns 202.013ns 5.28% 1.195 2.142 1.10% 0.150ns 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 [191.839ns; 192.427ns] or [-0.153%; +0.153%] None None None

Group 16

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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/2597 execution_time 3.525ms 3.552ms ± 0.016ms 3.549ms ± 0.009ms 3.559ms 3.583ms 3.603ms 3.618ms 1.95% 1.262 2.017 0.46% 0.001ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
receiver_entry_point/report/2597 execution_time [3.549ms; 3.554ms] or [-0.064%; +0.064%] None None None

Group 17

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 144.725µs 146.457µs ± 1.913µs 146.157µs ± 0.531µs 146.722µs 148.597µs 154.474µs 166.196µs 13.71% 6.662 59.431 1.30% 0.135µs 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 [146.192µs; 146.722µs] or [-0.181%; +0.181%] None None None

Group 18

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 10.639ms 10.660ms ± 0.013ms 10.658ms ± 0.008ms 10.667ms 10.682ms 10.694ms 10.730ms 0.67% 1.302 3.973 0.12% 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 [10.658ms; 10.662ms] or [-0.017%; +0.017%] None None None

Group 19

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz d7d685e 1774468160 dsn/otel-impl-for-python
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 3.896µs 3.914µs ± 0.002µs 3.914µs ± 0.001µs 3.915µs 3.918µs 3.920µs 3.920µs 0.16% -1.450 11.606 0.06% 0.000µs 1 200
credit_card/is_card_number/ throughput 255083907.131op/s 255500756.965op/s ± 160785.858op/s 255494021.457op/s ± 95603.907op/s 255603449.106op/s 255700982.262op/s 255755021.493op/s 256646465.426op/s 0.45% 1.471 11.774 0.06% 11369.277op/s 1 200
credit_card/is_card_number/ 3782-8224-6310-005 execution_time 78.904µs 80.720µs ± 0.741µs 80.674µs ± 0.459µs 81.161µs 81.900µs 82.566µs 82.794µs 2.63% 0.179 0.170 0.92% 0.052µs 1 200
credit_card/is_card_number/ 3782-8224-6310-005 throughput 12078178.151op/s 12389601.231op/s ± 113620.446op/s 12395541.332op/s ± 70183.249op/s 12459308.246op/s 12566768.255op/s 12662417.651op/s 12673703.843op/s 2.24% -0.120 0.145 0.91% 8034.179op/s 1 200
credit_card/is_card_number/ 378282246310005 execution_time 71.826µs 72.498µs ± 0.400µs 72.463µs ± 0.278µs 72.722µs 73.197µs 73.600µs 73.942µs 2.04% 0.616 0.244 0.55% 0.028µs 1 200
credit_card/is_card_number/ 378282246310005 throughput 13524108.231op/s 13793875.395op/s ± 75865.810op/s 13800070.255op/s ± 52958.189op/s 13854474.198op/s 13900795.338op/s 13912631.835op/s 13922544.777op/s 0.89% -0.585 0.166 0.55% 5364.523op/s 1 200
credit_card/is_card_number/37828224631 execution_time 3.893µs 3.914µs ± 0.003µs 3.914µs ± 0.002µs 3.916µs 3.918µs 3.921µs 3.937µs 0.59% 0.393 16.143 0.09% 0.000µs 1 200
credit_card/is_card_number/37828224631 throughput 254004800.740op/s 255488231.506op/s ± 222178.876op/s 255494915.658op/s ± 115699.836op/s 255605047.289op/s 255771752.378op/s 255859880.260op/s 256891530.040op/s 0.55% -0.347 16.104 0.09% 15710.419op/s 1 200
credit_card/is_card_number/378282246310005 execution_time 68.617µs 69.222µs ± 0.385µs 69.143µs ± 0.277µs 69.495µs 69.822µs 70.335µs 70.664µs 2.20% 0.803 0.404 0.55% 0.027µs 1 200
credit_card/is_card_number/378282246310005 throughput 14151424.931op/s 14446682.103op/s ± 79988.114op/s 14462699.261op/s ± 57696.648op/s 14511046.910op/s 14548061.799op/s 14561702.009op/s 14573633.282op/s 0.77% -0.774 0.315 0.55% 5656.014op/s 1 200
credit_card/is_card_number/37828224631000521389798 execution_time 45.513µs 45.745µs ± 0.081µs 45.748µs ± 0.055µs 45.800µs 45.870µs 45.919µs 45.975µs 0.50% -0.146 -0.008 0.18% 0.006µs 1 200
credit_card/is_card_number/37828224631000521389798 throughput 21750869.513op/s 21860233.569op/s ± 38624.260op/s 21859097.283op/s ± 26212.662op/s 21886870.072op/s 21925137.280op/s 21954317.378op/s 21971656.162op/s 0.51% 0.156 -0.006 0.18% 2731.148op/s 1 200
credit_card/is_card_number/x371413321323331 execution_time 6.428µs 6.437µs ± 0.004µs 6.437µs ± 0.002µs 6.439µs 6.444µs 6.446µs 6.466µs 0.46% 1.517 8.546 0.07% 0.000µs 1 200
credit_card/is_card_number/x371413321323331 throughput 154656519.713op/s 155358917.553op/s ± 106227.177op/s 155363264.356op/s ± 56147.528op/s 155420080.275op/s 155520382.108op/s 155559786.658op/s 155572234.020op/s 0.13% -1.500 8.420 0.07% 7511.396op/s 1 200
credit_card/is_card_number_no_luhn/ execution_time 3.893µs 3.913µs ± 0.003µs 3.913µs ± 0.001µs 3.915µs 3.917µs 3.920µs 3.922µs 0.22% -1.485 14.415 0.07% 0.000µs 1 200
credit_card/is_card_number_no_luhn/ throughput 254991521.407op/s 255542601.357op/s ± 178134.863op/s 255550151.718op/s ± 95901.184op/s 255649868.345op/s 255733583.227op/s 255856999.368op/s 256868311.810op/s 0.52% 1.515 14.645 0.07% 12596.037op/s 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time 60.889µs 62.596µs ± 0.616µs 62.657µs ± 0.341µs 62.986µs 63.414µs 64.019µs 64.617µs 3.13% -0.167 0.276 0.98% 0.044µs 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput 15475827.682op/s 15977064.316op/s ± 157565.423op/s 15959900.731op/s ± 86463.326op/s 16063357.487op/s 16271885.904op/s 16320585.663op/s 16423426.086op/s 2.90% 0.232 0.233 0.98% 11141.558op/s 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time 53.847µs 54.046µs ± 0.067µs 54.049µs ± 0.047µs 54.095µs 54.143µs 54.201µs 54.242µs 0.36% -0.077 0.206 0.12% 0.005µs 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 throughput 18435900.209op/s 18502639.571op/s ± 22771.681op/s 18501817.333op/s ± 16013.496op/s 18517862.171op/s 18539761.540op/s 18556074.636op/s 18571166.407op/s 0.37% 0.085 0.206 0.12% 1610.201op/s 1 200
credit_card/is_card_number_no_luhn/37828224631 execution_time 3.893µs 3.914µs ± 0.003µs 3.914µs ± 0.002µs 3.916µs 3.918µs 3.920µs 3.921µs 0.17% -1.791 13.375 0.07% 0.000µs 1 200
credit_card/is_card_number_no_luhn/37828224631 throughput 255068615.553op/s 255501086.752op/s ± 181408.169op/s 255501376.495op/s ± 109546.375op/s 255612582.533op/s 255727515.969op/s 255854685.942op/s 256844411.172op/s 0.53% 1.817 13.599 0.07% 12827.495op/s 1 200
credit_card/is_card_number_no_luhn/378282246310005 execution_time 50.180µs 50.328µs ± 0.093µs 50.309µs ± 0.056µs 50.375µs 50.457µs 50.671µs 50.842µs 1.06% 1.706 5.562 0.18% 0.007µs 1 200
credit_card/is_card_number_no_luhn/378282246310005 throughput 19668803.054op/s 19869564.353op/s ± 36489.330op/s 19877023.156op/s ± 22085.364op/s 19894780.604op/s 19911618.138op/s 19924059.520op/s 19928284.214op/s 0.26% -1.681 5.407 0.18% 2580.185op/s 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time 45.456µs 45.726µs ± 0.086µs 45.729µs ± 0.063µs 45.792µs 45.861µs 45.906µs 45.986µs 0.56% -0.083 0.015 0.19% 0.006µs 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput 21745837.236op/s 21869500.943op/s ± 41372.455op/s 21867936.139op/s ± 30207.552op/s 21898131.542op/s 21941039.288op/s 21958140.950op/s 21999117.022op/s 0.60% 0.094 0.018 0.19% 2925.474op/s 1 200
credit_card/is_card_number_no_luhn/x371413321323331 execution_time 6.428µs 6.438µs ± 0.005µs 6.437µs ± 0.003µs 6.440µs 6.445µs 6.448µs 6.465µs 0.43% 1.390 5.318 0.07% 0.000µs 1 200
credit_card/is_card_number_no_luhn/x371413321323331 throughput 154681892.999op/s 155335436.656op/s ± 110930.240op/s 155346445.098op/s ± 64610.887op/s 155411052.094op/s 155484268.802op/s 155528420.693op/s 155573875.137op/s 0.15% -1.379 5.245 0.07% 7843.952op/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; 3.914µs] or [-0.009%; +0.009%] None None None
credit_card/is_card_number/ throughput [255478473.591op/s; 255523040.338op/s] or [-0.009%; +0.009%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 execution_time [80.617µs; 80.822µs] or [-0.127%; +0.127%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 throughput [12373854.530op/s; 12405347.932op/s] or [-0.127%; +0.127%] None None None
credit_card/is_card_number/ 378282246310005 execution_time [72.443µs; 72.554µs] or [-0.076%; +0.076%] None None None
credit_card/is_card_number/ 378282246310005 throughput [13783361.124op/s; 13804389.667op/s] or [-0.076%; +0.076%] None None None
credit_card/is_card_number/37828224631 execution_time [3.914µs; 3.915µs] or [-0.012%; +0.012%] None None None
credit_card/is_card_number/37828224631 throughput [255457439.651op/s; 255519023.362op/s] or [-0.012%; +0.012%] None None None
credit_card/is_card_number/378282246310005 execution_time [69.169µs; 69.276µs] or [-0.077%; +0.077%] None None None
credit_card/is_card_number/378282246310005 throughput [14435596.520op/s; 14457767.686op/s] or [-0.077%; +0.077%] None None None
credit_card/is_card_number/37828224631000521389798 execution_time [45.734µs; 45.757µs] or [-0.024%; +0.024%] None None None
credit_card/is_card_number/37828224631000521389798 throughput [21854880.618op/s; 21865586.520op/s] or [-0.024%; +0.024%] None None None
credit_card/is_card_number/x371413321323331 execution_time [6.436µs; 6.437µs] or [-0.009%; +0.009%] None None None
credit_card/is_card_number/x371413321323331 throughput [155344195.488op/s; 155373639.618op/s] or [-0.009%; +0.009%] None None None
credit_card/is_card_number_no_luhn/ execution_time [3.913µs; 3.914µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/ throughput [255517913.578op/s; 255567289.136op/s] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time [62.510µs; 62.681µs] or [-0.136%; +0.136%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput [15955227.264op/s; 15998901.369op/s] or [-0.137%; +0.137%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time [54.037µs; 54.056µs] or [-0.017%; +0.017%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 throughput [18499483.635op/s; 18505795.507op/s] or [-0.017%; +0.017%] None None None
credit_card/is_card_number_no_luhn/37828224631 execution_time [3.913µs; 3.914µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/37828224631 throughput [255475945.324op/s; 255526228.179op/s] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/378282246310005 execution_time [50.316µs; 50.341µs] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/378282246310005 throughput [19864507.282op/s; 19874621.423op/s] or [-0.025%; +0.025%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time [45.714µs; 45.738µs] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput [21863767.119op/s; 21875234.768op/s] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 execution_time [6.437µs; 6.438µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 throughput [155320062.791op/s; 155350810.520op/s] or [-0.010%; +0.010%] None None None

Baseline

Omitted due to size.

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@danielsn @codecov-commenter