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perf(trace): cache repeated SQL obfuscation - #2453

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perf(trace): cache repeated SQL obfuscation#2453
BridgeAR wants to merge 1 commit into
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BridgeAR/2026-09-03-agentless-sql-cache

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@BridgeAR

@BridgeAR BridgeAR commented Sep 3, 2026

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What does this PR do?

Adds a payload-scoped two-hit SQL cache keyed by the exact raw resource and parsed DBMS. The agentless exporter reuses obfuscation only after a key repeats.

Motivation

Agentless batches commonly contain adjacent spans with identical SQL resources, which currently repeat the full tokenizer work.

Additional Notes

For 100 production-shaped spans, repeated SQL improved from 138.46 µs to 10.67 µs. Balanced unique-query medians remained equal at 155.74 µs versus 155.08 µs.

How to test the change?

Run cargo test -p libdd-trace-obfuscation -p libdd-data-pipeline-core and cargo bench -p libdd-trace-obfuscation --bench trace_obfuscation -- v04_sql.

@BridgeAR
perf(trace): cache repeated SQL obfuscation
b1d22d9 
Retain a payload-scoped two-hit cache keyed by the exact raw resource and DBMS. Delaying output retention until repetition keeps unique-query batches on the baseline path.

Criterion on production SpanSlice inputs improved 100 repeated SQL spans from 138.46 µs to 10.67 µs (92.3%), while unique medians remained equal at 155.74 µs versus 155.08 µs.
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📚 Documentation Check Results

⚠️ 1658 documentation warning(s) found

📦 libdd-data-pipeline-core - 805 warning(s)

📦 libdd-trace-obfuscation - 853 warning(s)

Updated: 2026-09-03 11:24:38 UTC | Commit: 057b1c7 | missing-docs job results

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🔒 Cargo Deny Results

⚠️ 6 issue(s) found, showing only errors (advisories, bans, sources)

📦 libdd-data-pipeline-core - 3 error(s)

Show output 
error[vulnerability]: Invalid pointer dereference in `fmt::Pointer` impl for `Atomic` and `Shared` when the underlying pointer is invalid
   ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:64:1
   │
64 │ crossbeam-epoch 0.9.18 registry+https://github.com/rust-lang/crates.io-index
   │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
   │
   ├ ID: RUSTSEC-2026-0204
   ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0204
   ├ Affected versions of `fmt::Display` dereference the underlying pointer. This causes a invalid pointer dereference e.g., when a pointer created with `Atomic::null` or `Shared::null`. `fmt::Debug` impls and pre-0.9 `fmt::Display` impls, which do not dereference pointers, are not affected by this issue.
   ├ Announcement: https://github.com/crossbeam-rs/crossbeam/pull/1276
   ├ Solution: Upgrade to >=0.9.20 (try `cargo update -p crossbeam-epoch`)
   ├ crossbeam-epoch v0.9.18
     └── crossbeam-deque v0.8.5
         └── rayon-core v1.12.1
             └── rayon v1.10.0
                 └── criterion v0.5.1
                     ├── libdd-common v5.2.0
                     │   ├── libdd-capabilities-impl v4.0.0
                     │   │   └── libdd-trace-utils v11.0.0
                     │   │       ├── libdd-data-pipeline-core v1.0.0
                     │   │       ├── libdd-trace-obfuscation v7.0.0
                     │   │       │   └── libdd-data-pipeline-core v1.0.0 (*)
                     │   │       └── (dev) libdd-trace-utils v11.0.0 (*)
                     │   ├── libdd-data-pipeline-core v1.0.0 (*)
                     │   ├── libdd-trace-obfuscation v7.0.0 (*)
                     │   └── libdd-trace-utils v11.0.0 (*)
                     ├── (dev) libdd-trace-normalization v4.0.0
                     │   └── libdd-trace-utils v11.0.0 (*)
                     ├── (dev) libdd-trace-obfuscation v7.0.0 (*)
                     └── (dev) libdd-trace-utils v11.0.0 (*)

error[vulnerability]: h2 unbounded empty DATA frames
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:101:1
    │
101 │ h2 0.4.6 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0258
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0258
    ├ The h2 crate, used internally by hyper, had a flaw that would accept and queue empty DATA frames without limit.
      If streams were not actively drained, this could lead to unbounded memory usage, or a panic if the length overflows.
      
      Low severity.
      
      Patched in v0.4.16.
    ├ Announcement: https://github.com/hyperium/hyper/security/advisories/GHSA-q83h-524g-xf6h
    ├ Solution: Upgrade to >=0.4.16 (try `cargo update -p h2`)
    ├ h2 v0.4.6
      └── hyper v1.6.0
          ├── httpmock v0.8.0-alpha.1
          │   └── libdd-trace-utils v11.0.0
          │       ├── libdd-data-pipeline-core v1.0.0
          │       ├── libdd-trace-obfuscation v7.0.0
          │       │   └── libdd-data-pipeline-core v1.0.0 (*)
          │       └── (dev) libdd-trace-utils v11.0.0 (*)
          ├── hyper-rustls v0.27.7
          │   └── libdd-common v5.2.0
          │       ├── libdd-capabilities-impl v4.0.0
          │       │   └── libdd-trace-utils v11.0.0 (*)
          │       ├── libdd-data-pipeline-core v1.0.0 (*)
          │       ├── libdd-trace-obfuscation v7.0.0 (*)
          │       └── libdd-trace-utils v11.0.0 (*)
          ├── hyper-util v0.1.17
          │   ├── httpmock v0.8.0-alpha.1 (*)
          │   ├── hyper-rustls v0.27.7 (*)
          │   └── libdd-common v5.2.0 (*)
          ├── libdd-common v5.2.0 (*)
          └── libdd-trace-utils v11.0.0 (*)

error[unsound]: Rand is unsound with a custom logger using `rand::rng()`
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:192:1
    │
192 │ rand 0.8.5 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ unsound advisory detected
    │
    ├ ID: RUSTSEC-2026-0097
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0097
    ├ It has been reported (by [@lopopolo](https://github.com/lopopolo)) that the `rand` library is [unsound](https://rust-lang.github.io/unsafe-code-guidelines/glossary.html#soundness-of-code--of-a-library) (i.e. that safe code using the public API can cause Undefined Behaviour) when all the following conditions are met:
      
      - The `log` and `thread_rng` features are enabled
      - A [custom logger](https://docs.rs/log/latest/log/#implementing-a-logger) is defined
      - The custom logger accesses `rand::rng()` (previously `rand::thread_rng()`) and calls any `TryRng` (previously `RngCore`) methods on `ThreadRng`
      - The `ThreadRng` (attempts to) reseed while called from the custom logger (this happens every 64 kB of generated data)
      - Trace-level logging is enabled or warn-level logging is enabled and the random source (the `getrandom` crate) is unable to provide a new seed
      
      `TryRng` (previously `RngCore`) methods for `ThreadRng` use `unsafe` code to cast `*mut BlockRng<ReseedingCore>` to `&mut BlockRng<ReseedingCore>`. When all the above conditions are met this results in an aliased mutable reference, violating the Stacked Borrows rules. Miri is able to detect this violation in sample code. Since construction of [aliased mutable references is Undefined Behaviour](https://doc.rust-lang.org/stable/nomicon/references.html), the behaviour of optimized builds is hard to predict.
    ├ Announcement: https://github.com/rust-random/rand/pull/1763
    ├ Solution: Upgrade to >=0.10.1 OR <0.10.0, >=0.9.3 OR <0.9.0, >=0.8.6 (try `cargo update -p rand`)
    ├ rand v0.8.5
      ├── (dev) libdd-common v5.2.0
      │   ├── libdd-capabilities-impl v4.0.0
      │   │   └── libdd-trace-utils v11.0.0
      │   │       ├── libdd-data-pipeline-core v1.0.0
      │   │       ├── libdd-trace-obfuscation v7.0.0
      │   │       │   └── libdd-data-pipeline-core v1.0.0 (*)
      │   │       └── (dev) libdd-trace-utils v11.0.0 (*)
      │   ├── libdd-data-pipeline-core v1.0.0 (*)
      │   ├── libdd-trace-obfuscation v7.0.0 (*)
      │   └── libdd-trace-utils v11.0.0 (*)
      ├── (dev) libdd-trace-normalization v4.0.0
      │   └── libdd-trace-utils v11.0.0 (*)
      ├── libdd-trace-utils v11.0.0 (*)
      └── proptest v1.5.0
          ├── (dev) libdd-common v5.2.0 (*)
          └── (dev) libdd-tinybytes v1.1.2
              ├── (dev) libdd-data-pipeline-core v1.0.0 (*)
              ├── (dev) libdd-tinybytes v1.1.2 (*)
              ├── (dev) libdd-trace-obfuscation v7.0.0 (*)
              └── libdd-trace-utils v11.0.0 (*)

advisories FAILED, bans ok, sources ok

📦 libdd-trace-obfuscation - 3 error(s)

Show output 
error[vulnerability]: Invalid pointer dereference in `fmt::Pointer` impl for `Atomic` and `Shared` when the underlying pointer is invalid
   ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:64:1
   │
64 │ crossbeam-epoch 0.9.18 registry+https://github.com/rust-lang/crates.io-index
   │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
   │
   ├ ID: RUSTSEC-2026-0204
   ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0204
   ├ Affected versions of `fmt::Display` dereference the underlying pointer. This causes a invalid pointer dereference e.g., when a pointer created with `Atomic::null` or `Shared::null`. `fmt::Debug` impls and pre-0.9 `fmt::Display` impls, which do not dereference pointers, are not affected by this issue.
   ├ Announcement: https://github.com/crossbeam-rs/crossbeam/pull/1276
   ├ Solution: Upgrade to >=0.9.20 (try `cargo update -p crossbeam-epoch`)
   ├ crossbeam-epoch v0.9.18
     └── crossbeam-deque v0.8.5
         └── rayon-core v1.12.1
             └── rayon v1.10.0
                 └── criterion v0.5.1
                     ├── libdd-common v5.2.0
                     │   ├── libdd-capabilities-impl v4.0.0
                     │   │   └── libdd-trace-utils v11.0.0
                     │   │       ├── libdd-trace-obfuscation v7.0.0
                     │   │       └── (dev) libdd-trace-utils v11.0.0 (*)
                     │   ├── libdd-trace-obfuscation v7.0.0 (*)
                     │   └── libdd-trace-utils v11.0.0 (*)
                     ├── (dev) libdd-trace-normalization v4.0.0
                     │   └── libdd-trace-utils v11.0.0 (*)
                     ├── (dev) libdd-trace-obfuscation v7.0.0 (*)
                     └── (dev) libdd-trace-utils v11.0.0 (*)

error[vulnerability]: h2 unbounded empty DATA frames
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:101:1
    │
101 │ h2 0.4.6 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0258
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0258
    ├ The h2 crate, used internally by hyper, had a flaw that would accept and queue empty DATA frames without limit.
      If streams were not actively drained, this could lead to unbounded memory usage, or a panic if the length overflows.
      
      Low severity.
      
      Patched in v0.4.16.
    ├ Announcement: https://github.com/hyperium/hyper/security/advisories/GHSA-q83h-524g-xf6h
    ├ Solution: Upgrade to >=0.4.16 (try `cargo update -p h2`)
    ├ h2 v0.4.6
      └── hyper v1.6.0
          ├── httpmock v0.8.0-alpha.1
          │   └── libdd-trace-utils v11.0.0
          │       ├── libdd-trace-obfuscation v7.0.0
          │       └── (dev) libdd-trace-utils v11.0.0 (*)
          ├── hyper-rustls v0.27.7
          │   └── libdd-common v5.2.0
          │       ├── libdd-capabilities-impl v4.0.0
          │       │   └── libdd-trace-utils v11.0.0 (*)
          │       ├── libdd-trace-obfuscation v7.0.0 (*)
          │       └── libdd-trace-utils v11.0.0 (*)
          ├── hyper-util v0.1.17
          │   ├── httpmock v0.8.0-alpha.1 (*)
          │   ├── hyper-rustls v0.27.7 (*)
          │   └── libdd-common v5.2.0 (*)
          ├── libdd-common v5.2.0 (*)
          └── libdd-trace-utils v11.0.0 (*)

error[unsound]: Rand is unsound with a custom logger using `rand::rng()`
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:189:1
    │
189 │ rand 0.8.5 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ unsound advisory detected
    │
    ├ ID: RUSTSEC-2026-0097
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0097
    ├ It has been reported (by [@lopopolo](https://github.com/lopopolo)) that the `rand` library is [unsound](https://rust-lang.github.io/unsafe-code-guidelines/glossary.html#soundness-of-code--of-a-library) (i.e. that safe code using the public API can cause Undefined Behaviour) when all the following conditions are met:
      
      - The `log` and `thread_rng` features are enabled
      - A [custom logger](https://docs.rs/log/latest/log/#implementing-a-logger) is defined
      - The custom logger accesses `rand::rng()` (previously `rand::thread_rng()`) and calls any `TryRng` (previously `RngCore`) methods on `ThreadRng`
      - The `ThreadRng` (attempts to) reseed while called from the custom logger (this happens every 64 kB of generated data)
      - Trace-level logging is enabled or warn-level logging is enabled and the random source (the `getrandom` crate) is unable to provide a new seed
      
      `TryRng` (previously `RngCore`) methods for `ThreadRng` use `unsafe` code to cast `*mut BlockRng<ReseedingCore>` to `&mut BlockRng<ReseedingCore>`. When all the above conditions are met this results in an aliased mutable reference, violating the Stacked Borrows rules. Miri is able to detect this violation in sample code. Since construction of [aliased mutable references is Undefined Behaviour](https://doc.rust-lang.org/stable/nomicon/references.html), the behaviour of optimized builds is hard to predict.
    ├ Announcement: https://github.com/rust-random/rand/pull/1763
    ├ Solution: Upgrade to >=0.10.1 OR <0.10.0, >=0.9.3 OR <0.9.0, >=0.8.6 (try `cargo update -p rand`)
    ├ rand v0.8.5
      ├── (dev) libdd-common v5.2.0
      │   ├── libdd-capabilities-impl v4.0.0
      │   │   └── libdd-trace-utils v11.0.0
      │   │       ├── libdd-trace-obfuscation v7.0.0
      │   │       └── (dev) libdd-trace-utils v11.0.0 (*)
      │   ├── libdd-trace-obfuscation v7.0.0 (*)
      │   └── libdd-trace-utils v11.0.0 (*)
      ├── (dev) libdd-trace-normalization v4.0.0
      │   └── libdd-trace-utils v11.0.0 (*)
      ├── libdd-trace-utils v11.0.0 (*)
      └── proptest v1.5.0
          ├── (dev) libdd-common v5.2.0 (*)
          └── (dev) libdd-tinybytes v1.1.2
              ├── (dev) libdd-tinybytes v1.1.2 (*)
              ├── (dev) libdd-trace-obfuscation v7.0.0 (*)
              └── libdd-trace-utils v11.0.0 (*)

advisories FAILED, bans ok, sources ok

Updated: 2026-09-03 11:26:44 UTC | Commit: 057b1c7 | dependency-check job results

@datadog-datadog-prod-us1

datadog-datadog-prod-us1 Bot commented Sep 3, 2026

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Tests

✅ All CI checks and tests passed.

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 77.11% (+0.02%)

This comment will be updated automatically if new data arrives.
🔗 Commit SHA: b1d22d9 | Docs | View more details | Give us feedback!

@pr-commenter

pr-commenter Bot commented Sep 3, 2026 •
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Benchmarks

Comparison

Benchmark execution time: 2026-09-03 11:35:23

Comparing candidate commit b1d22d9 in PR branch BridgeAR/2026-09-03-agentless-sql-cache with baseline commit efbbf4c in branch main.

Found 2 performance improvements and 0 performance regressions! Performance is the same for 23 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:trace_buffer/2_senders/no_delay

  • 🟩 execution_time [-85.873µs; -69.299µs] or [-5.142%; -4.149%]
  • 🟩 throughput [+46487.893op/s; +57515.455op/s] or [+4.310%; +5.332%]

Benchmark execution time: 2026-09-03 11:34:35

Comparing candidate commit b1d22d9 in PR branch BridgeAR/2026-09-03-agentless-sql-cache with baseline commit efbbf4c in branch main.

Found 6 performance improvements and 15 performance regressions! Performance is the same for 14 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:credit_card/is_card_number/

  • 🟥 execution_time [+362.816ns; +365.213ns] or [+9.247%; +9.308%]
  • 🟥 throughput [-21703382.274op/s; -21568645.827op/s] or [-8.516%; -8.463%]

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

  • 🟥 execution_time [+3.909µs; +3.958µs] or [+4.872%; +4.932%]
  • 🟥 throughput [-586025.727op/s; -578839.840op/s] or [-4.702%; -4.644%]

scenario:credit_card/is_card_number/ 378282246310005

  • 🟥 execution_time [+8.084µs; +8.121µs] or [+11.816%; +11.869%]
  • 🟥 throughput [-1551101.297op/s; -1544306.880op/s] or [-10.612%; -10.566%]

scenario:credit_card/is_card_number/37828224631

  • 🟥 execution_time [+368.701ns; +370.546ns] or [+9.401%; +9.448%]
  • 🟥 throughput [-22013422.398op/s; -21905096.494op/s] or [-8.634%; -8.591%]

scenario:credit_card/is_card_number/378282246310005

  • 🟥 execution_time [+8.441µs; +8.491µs] or [+12.978%; +13.054%]
  • 🟥 throughput [-1775522.547op/s; -1765922.952op/s] or [-11.548%; -11.486%]

scenario:credit_card/is_card_number/x371413321323331

  • 🟩 execution_time [-397.492ns; -394.759ns] or [-5.799%; -5.759%]
  • 🟩 throughput [+8917710.966op/s; +8979407.756op/s] or [+6.113%; +6.155%]

scenario:credit_card/is_card_number_no_luhn/

  • 🟥 execution_time [+367.016ns; +369.373ns] or [+9.355%; +9.415%]
  • 🟥 throughput [-21935356.339op/s; -21799422.673op/s] or [-8.606%; -8.553%]

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

  • 🟩 execution_time [-5.899µs; -5.855µs] or [-8.949%; -8.883%]
  • 🟩 throughput [+1479326.250op/s; +1490555.612op/s] or [+9.751%; +9.826%]

scenario:credit_card/is_card_number_no_luhn/37828224631

  • 🟥 execution_time [+367.988ns; +370.591ns] or [+9.381%; +9.447%]
  • 🟥 throughput [-22006768.741op/s; -21857539.332op/s] or [-8.633%; -8.574%]

scenario:credit_card/is_card_number_no_luhn/x371413321323331

  • 🟩 execution_time [-395.894ns; -392.977ns] or [-5.777%; -5.734%]
  • 🟩 throughput [+8878894.498op/s; +8945046.973op/s] or [+6.085%; +6.130%]

scenario:sql/obfuscate_sql_string

  • 🟥 execution_time [+12.248µs; +12.537µs] or [+4.119%; +4.216%]

Candidate

Candidate benchmark details

Group 1

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 9.596ms 9.629ms ± 0.019ms 9.626ms ± 0.012ms 9.640ms 9.665ms 9.674ms 9.693ms 0.69% 0.810 0.297 0.20% 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 [9.627ms; 9.632ms] or [-0.027%; +0.027%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
trace_buffer/1_senders/10us_delay execution_time 60.004ms 60.078ms ± 0.034ms 60.066ms ± 0.007ms 60.080ms 60.142ms 60.212ms 60.247ms 0.30% 2.463 7.028 0.06% 0.002ms 1 200
trace_buffer/1_senders/10us_delay throughput 14938.455op/s 14980.526op/s ± 8.577op/s 14983.414op/s ± 1.789op/s 14984.549op/s 14987.768op/s 14989.164op/s 14999.051op/s 0.10% -2.458 7.000 0.06% 0.606op/s 1 200
trace_buffer/1_senders/1us_delay execution_time 50.192ms 50.566ms ± 0.263ms 50.485ms ± 0.141ms 50.689ms 51.145ms 51.208ms 51.408ms 1.83% 1.041 0.357 0.52% 0.019ms 1 200
trace_buffer/1_senders/1us_delay throughput 17506.872op/s 17799.009op/s ± 92.208op/s 17826.975op/s ± 49.556op/s 17866.239op/s 17907.383op/s 17920.653op/s 17931.110op/s 0.58% -1.021 0.311 0.52% 6.520op/s 1 200
trace_buffer/1_senders/no_delay execution_time 346.487µs 353.263µs ± 2.973µs 353.344µs ± 1.340µs 354.520µs 357.184µs 362.483µs 368.154µs 4.19% 1.064 5.557 0.84% 0.210µs 1 200
trace_buffer/1_senders/no_delay throughput 2444626.458op/s 2547856.420op/s ± 21265.040op/s 2547090.385op/s ± 9659.688op/s 2557525.709op/s 2584661.429op/s 2595705.831op/s 2597502.279op/s 1.98% -0.908 4.886 0.83% 1503.665op/s 1 200
trace_buffer/2_senders/10us_delay execution_time 60.062ms 60.127ms ± 0.041ms 60.122ms ± 0.023ms 60.142ms 60.207ms 60.274ms 60.287ms 0.27% 1.566 2.959 0.07% 0.003ms 1 200
trace_buffer/2_senders/10us_delay throughput 29857.311op/s 29936.478op/s ± 20.309op/s 29939.043op/s ± 11.544op/s 29951.998op/s 29957.937op/s 29962.790op/s 29969.026op/s 0.10% -1.561 2.939 0.07% 1.436op/s 1 200
trace_buffer/2_senders/1us_delay execution_time 50.694ms 51.121ms ± 0.130ms 51.138ms ± 0.090ms 51.207ms 51.326ms 51.391ms 51.425ms 0.56% -0.238 -0.041 0.25% 0.009ms 1 200
trace_buffer/2_senders/1us_delay throughput 35002.395op/s 35210.600op/s ± 89.572op/s 35199.055op/s ± 62.004op/s 35269.455op/s 35354.543op/s 35415.784op/s 35507.159op/s 0.88% 0.253 -0.026 0.25% 6.334op/s 1 200
trace_buffer/2_senders/no_delay execution_time 1.499ms 1.593ms ± 0.033ms 1.592ms ± 0.025ms 1.616ms 1.648ms 1.667ms 1.703ms 6.99% 0.321 0.149 2.04% 0.002ms 1 200
trace_buffer/2_senders/no_delay throughput 1057022.425op/s 1130694.309op/s ± 22997.218op/s 1130922.651op/s ± 17531.981op/s 1149351.454op/s 1164159.794op/s 1174204.792op/s 1200783.792op/s 6.18% -0.198 0.040 2.03% 1626.149op/s 1 200
trace_buffer/4_senders/10us_delay execution_time 60.125ms 60.204ms ± 0.047ms 60.193ms ± 0.021ms 60.217ms 60.311ms 60.334ms 60.379ms 0.31% 1.168 1.142 0.08% 0.003ms 1 200
trace_buffer/4_senders/10us_delay throughput 59622.924op/s 59796.428op/s ± 47.124op/s 59807.147op/s ± 20.678op/s 59825.690op/s 59854.748op/s 59862.364op/s 59875.719op/s 0.11% -1.164 1.130 0.08% 3.332op/s 1 200
trace_buffer/4_senders/1us_delay execution_time 51.095ms 51.292ms ± 0.104ms 51.282ms ± 0.058ms 51.343ms 51.497ms 51.556ms 51.762ms 0.94% 1.192 3.149 0.20% 0.007ms 1 200
trace_buffer/4_senders/1us_delay throughput 69549.146op/s 70186.418op/s ± 141.536op/s 70200.100op/s ± 79.987op/s 70275.563op/s 70373.414op/s 70452.567op/s 70457.472op/s 0.37% -1.169 3.055 0.20% 10.008op/s 1 200
trace_buffer/4_senders/no_delay execution_time 3.593ms 3.673ms ± 0.028ms 3.672ms ± 0.017ms 3.690ms 3.717ms 3.742ms 3.755ms 2.27% 0.095 0.385 0.75% 0.002ms 1 200
trace_buffer/4_senders/no_delay throughput 958645.370op/s 980219.847op/s ± 7354.741op/s 980409.058op/s ± 4549.671op/s 984404.530op/s 991487.087op/s 997363.712op/s 1001852.983op/s 2.19% -0.042 0.373 0.75% 520.059op/s 1 200
trace_buffer/8_senders/10us_delay execution_time 60.238ms 60.323ms ± 0.063ms 60.302ms ± 0.020ms 60.335ms 60.443ms 60.549ms 60.606ms 0.50% 1.849 3.795 0.10% 0.004ms 1 200
trace_buffer/8_senders/10us_delay throughput 118801.056op/s 119358.350op/s ± 125.274op/s 119399.383op/s ± 39.698op/s 119432.029op/s 119485.518op/s 119518.533op/s 119526.103op/s 0.11% -1.841 3.759 0.10% 8.858op/s 1 200
trace_buffer/8_senders/1us_delay execution_time 51.349ms 51.767ms ± 0.293ms 51.665ms ± 0.173ms 52.009ms 52.302ms 52.497ms 52.623ms 1.86% 0.815 -0.343 0.56% 0.021ms 1 200
trace_buffer/8_senders/1us_delay throughput 136822.429op/s 139088.309op/s ± 783.993op/s 139360.521op/s ± 469.500op/s 139707.887op/s 139970.426op/s 140172.485op/s 140216.698op/s 0.61% -0.799 -0.381 0.56% 55.437op/s 1 200
trace_buffer/8_senders/no_delay execution_time 7.018ms 7.129ms ± 0.058ms 7.120ms ± 0.039ms 7.162ms 7.230ms 7.312ms 7.347ms 3.19% 0.848 1.170 0.81% 0.004ms 1 200
trace_buffer/8_senders/no_delay throughput 979935.058op/s 1009955.654op/s ± 8143.208op/s 1011217.965op/s ± 5565.300op/s 1016079.374op/s 1021054.759op/s 1024444.439op/s 1025931.609op/s 1.46% -0.789 1.008 0.80% 575.812op/s 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
trace_buffer/1_senders/10us_delay execution_time [60.073ms; 60.083ms] or [-0.008%; +0.008%] None None None
trace_buffer/1_senders/10us_delay throughput [14979.337op/s; 14981.715op/s] or [-0.008%; +0.008%] None None None
trace_buffer/1_senders/1us_delay execution_time [50.529ms; 50.602ms] or [-0.072%; +0.072%] None None None
trace_buffer/1_senders/1us_delay throughput [17786.230op/s; 17811.788op/s] or [-0.072%; +0.072%] None None None
trace_buffer/1_senders/no_delay execution_time [352.851µs; 353.675µs] or [-0.117%; +0.117%] None None None
trace_buffer/1_senders/no_delay throughput [2544909.290op/s; 2550803.550op/s] or [-0.116%; +0.116%] None None None
trace_buffer/2_senders/10us_delay execution_time [60.122ms; 60.133ms] or [-0.009%; +0.009%] None None None
trace_buffer/2_senders/10us_delay throughput [29933.663op/s; 29939.292op/s] or [-0.009%; +0.009%] None None None
trace_buffer/2_senders/1us_delay execution_time [51.103ms; 51.139ms] or [-0.035%; +0.035%] None None None
trace_buffer/2_senders/1us_delay throughput [35198.186op/s; 35223.013op/s] or [-0.035%; +0.035%] None None None
trace_buffer/2_senders/no_delay execution_time [1.588ms; 1.597ms] or [-0.283%; +0.283%] None None None
trace_buffer/2_senders/no_delay throughput [1127507.116op/s; 1133881.502op/s] or [-0.282%; +0.282%] None None None
trace_buffer/4_senders/10us_delay execution_time [60.198ms; 60.211ms] or [-0.011%; +0.011%] None None None
trace_buffer/4_senders/10us_delay throughput [59789.897op/s; 59802.959op/s] or [-0.011%; +0.011%] None None None
trace_buffer/4_senders/1us_delay execution_time [51.278ms; 51.307ms] or [-0.028%; +0.028%] None None None
trace_buffer/4_senders/1us_delay throughput [70166.803op/s; 70206.034op/s] or [-0.028%; +0.028%] None None None
trace_buffer/4_senders/no_delay execution_time [3.669ms; 3.677ms] or [-0.104%; +0.104%] None None None
trace_buffer/4_senders/no_delay throughput [979200.551op/s; 981239.143op/s] or [-0.104%; +0.104%] None None None
trace_buffer/8_senders/10us_delay execution_time [60.314ms; 60.331ms] or [-0.015%; +0.015%] None None None
trace_buffer/8_senders/10us_delay throughput [119340.989op/s; 119375.712op/s] or [-0.015%; +0.015%] None None None
trace_buffer/8_senders/1us_delay execution_time [51.727ms; 51.808ms] or [-0.078%; +0.078%] None None None
trace_buffer/8_senders/1us_delay throughput [138979.656op/s; 139196.963op/s] or [-0.078%; +0.078%] None None None
trace_buffer/8_senders/no_delay execution_time [7.121ms; 7.138ms] or [-0.112%; +0.112%] None None None
trace_buffer/8_senders/no_delay throughput [1008827.084op/s; 1011084.224op/s] or [-0.112%; +0.112%] None None None

Group 1

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 2.082µs 2.100µs ± 0.009µs 2.099µs ± 0.004µs 2.102µs 2.120µs 2.125µs 2.129µs 1.47% 0.981 0.615 0.44% 0.001µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
write only interface execution_time [2.099µs; 2.102µs] or [-0.061%; +0.061%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
v04_sql/cached_repeated execution_time 26.942µs 30.770µs ± 0.711µs 31.046µs ± 0.176µs 31.177µs 31.257µs 31.353µs 33.089µs 6.58% -2.375 8.384 2.31% 0.050µs 1 200
v04_sql/cached_unique execution_time 376.544µs 379.680µs ± 1.013µs 380.026µs ± 0.406µs 380.317µs 380.760µs 381.093µs 383.043µs 0.79% -1.003 1.361 0.27% 0.072µs 1 200
v04_sql/uncached_repeated execution_time 316.339µs 319.031µs ± 1.081µs 319.030µs ± 0.476µs 319.467µs 320.606µs 321.283µs 323.725µs 1.47% 0.202 1.204 0.34% 0.076µs 1 200
v04_sql/uncached_unique execution_time 374.321µs 377.746µs ± 1.155µs 378.046µs ± 0.574µs 378.512µs 379.021µs 379.798µs 381.472µs 0.91% -0.848 1.057 0.30% 0.082µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
v04_sql/cached_repeated execution_time [30.672µs; 30.869µs] or [-0.320%; +0.320%] None None None
v04_sql/cached_unique execution_time [379.540µs; 379.821µs] or [-0.037%; +0.037%] None None None
v04_sql/uncached_repeated execution_time [318.881µs; 319.181µs] or [-0.047%; +0.047%] None None None
v04_sql/uncached_unique execution_time [377.586µs; 377.906µs] or [-0.042%; +0.042%] None None None

Group 3

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 309.063µs 309.772µs ± 0.965µs 309.600µs ± 0.189µs 309.828µs 310.616µs 312.294µs 321.183µs 3.74% 8.818 97.235 0.31% 0.068µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sql/obfuscate_sql_string execution_time [309.638µs; 309.906µs] or [-0.043%; +0.043%] None None None

Group 4

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 4.909µs 4.997µs ± 0.045µs 4.987µs ± 0.036µs 5.033µs 5.071µs 5.074µs 5.075µs 1.77% 0.254 -1.187 0.90% 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 [4.991µs; 5.003µs] or [-0.125%; +0.125%] None None None

Group 5

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 21.034µs 21.639µs ± 0.313µs 21.585µs ± 0.130µs 21.742µs 21.994µs 22.376µs 24.705µs 14.46% 5.012 44.721 1.44% 0.022µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
two way interface execution_time [21.595µs; 21.682µs] or [-0.201%; +0.201%] None None None

Group 6

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 4.271µs 4.288µs ± 0.008µs 4.287µs ± 0.007µs 4.294µs 4.301µs 4.305µs 4.308µs 0.50% 0.274 -0.865 0.18% 0.001µs 1 200
credit_card/is_card_number/ throughput 232114123.706op/s 233223279.792op/s ± 430186.085op/s 233272477.759op/s ± 357658.660op/s 233577654.279op/s 233807056.646op/s 233930144.118op/s 234116119.788op/s 0.36% -0.268 -0.871 0.18% 30418.750op/s 1 200
credit_card/is_card_number/ 3782-8224-6310-005 execution_time 84.009µs 84.171µs ± 0.119µs 84.153µs ± 0.076µs 84.230µs 84.368µs 84.531µs 84.883µs 0.87% 1.791 6.365 0.14% 0.008µs 1 200
credit_card/is_card_number/ 3782-8224-6310-005 throughput 11780919.642op/s 11880553.419op/s ± 16748.679op/s 11883092.771op/s ± 10712.083op/s 11893514.169op/s 11899408.659op/s 11901632.940op/s 11903493.675op/s 0.17% -1.770 6.213 0.14% 1184.310op/s 1 200
credit_card/is_card_number/ 378282246310005 execution_time 76.386µs 76.519µs ± 0.103µs 76.502µs ± 0.062µs 76.571µs 76.691µs 76.816µs 77.290µs 1.03% 2.572 14.514 0.13% 0.007µs 1 200
credit_card/is_card_number/ 378282246310005 throughput 12938235.825op/s 13068724.031op/s ± 17578.769op/s 13071551.076op/s ± 10603.557op/s 13081474.017op/s 13087347.480op/s 13090232.749op/s 13091489.223op/s 0.15% -2.533 14.124 0.13% 1243.007op/s 1 200
credit_card/is_card_number/37828224631 execution_time 4.268µs 4.292µs ± 0.005µs 4.291µs ± 0.003µs 4.295µs 4.299µs 4.304µs 4.307µs 0.37% -0.644 2.731 0.12% 0.000µs 1 200
credit_card/is_card_number/37828224631 throughput 232193475.961op/s 233009439.229op/s ± 284833.068op/s 233045770.197op/s ± 157430.041op/s 233180639.212op/s 233318552.154op/s 233941782.484op/s 234311511.825op/s 0.54% 0.660 2.778 0.12% 20140.739op/s 1 200
credit_card/is_card_number/378282246310005 execution_time 73.162µs 73.506µs ± 0.165µs 73.499µs ± 0.112µs 73.607µs 73.781µs 73.945µs 74.023µs 0.71% 0.421 -0.059 0.22% 0.012µs 1 200
credit_card/is_card_number/378282246310005 throughput 13509316.005op/s 13604395.441op/s ± 30452.494op/s 13605706.333op/s ± 20756.217op/s 13627838.215op/s 13647166.837op/s 13662036.660op/s 13668211.840op/s 0.46% -0.410 -0.078 0.22% 2153.317op/s 1 200
credit_card/is_card_number/37828224631000521389798 execution_time 46.637µs 46.728µs ± 0.053µs 46.718µs ± 0.037µs 46.759µs 46.821µs 46.867µs 46.932µs 0.46% 0.864 0.641 0.11% 0.004µs 1 200
credit_card/is_card_number/37828224631000521389798 throughput 21307516.420op/s 21400544.895op/s ± 24346.262op/s 21405235.810op/s ± 17044.158op/s 21420187.845op/s 21432729.307op/s 21435708.555op/s 21442159.774op/s 0.17% -0.858 0.622 0.11% 1721.541op/s 1 200
credit_card/is_card_number/x371413321323331 execution_time 6.445µs 6.458µs ± 0.007µs 6.456µs ± 0.005µs 6.462µs 6.469µs 6.477µs 6.487µs 0.47% 0.959 1.191 0.10% 0.000µs 1 200
credit_card/is_card_number/x371413321323331 throughput 154165892.851op/s 154841324.492op/s ± 162020.849op/s 154883683.006op/s ± 108165.608op/s 154967480.594op/s 155026743.665op/s 155112073.611op/s 155147506.818op/s 0.17% -0.952 1.166 0.10% 11456.604op/s 1 200
credit_card/is_card_number_no_luhn/ execution_time 4.275µs 4.292µs ± 0.007µs 4.292µs ± 0.005µs 4.297µs 4.302µs 4.306µs 4.308µs 0.38% -0.374 -0.395 0.17% 0.001µs 1 200
credit_card/is_card_number_no_luhn/ throughput 232106499.858op/s 233016056.233op/s ± 388629.386op/s 232984780.310op/s ± 255289.515op/s 233215964.978op/s 233732594.784op/s 233789135.014op/s 233919278.389op/s 0.40% 0.382 -0.394 0.17% 27480.247op/s 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time 59.838µs 60.042µs ± 0.119µs 60.024µs ± 0.083µs 60.108µs 60.295µs 60.420µs 60.449µs 0.71% 1.036 1.068 0.20% 0.008µs 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput 16542939.676op/s 16655209.637op/s ± 32888.162op/s 16659973.589op/s ± 23048.101op/s 16682114.688op/s 16692509.821op/s 16700412.083op/s 16711890.510op/s 0.31% -1.024 1.031 0.20% 2325.544op/s 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time 52.278µs 52.474µs ± 0.184µs 52.395µs ± 0.081µs 52.582µs 52.837µs 53.003µs 53.208µs 1.55% 1.335 1.355 0.35% 0.013µs 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 throughput 18794299.443op/s 19057252.053op/s ± 66551.725op/s 19085763.930op/s ± 29452.644op/s 19107060.537op/s 19121032.179op/s 19124763.471op/s 19128329.055op/s 0.22% -1.319 1.287 0.35% 4705.918op/s 1 200
credit_card/is_card_number_no_luhn/37828224631 execution_time 4.271µs 4.292µs ± 0.008µs 4.293µs ± 0.004µs 4.297µs 4.303µs 4.311µs 4.322µs 0.66% -0.064 0.371 0.19% 0.001µs 1 200
credit_card/is_card_number_no_luhn/37828224631 throughput 231400352.503op/s 232984264.263op/s ± 436706.170op/s 232927699.242op/s ± 238722.927op/s 233262922.377op/s 233732235.179op/s 233797792.158op/s 234155298.829op/s 0.53% 0.078 0.351 0.19% 30879.789op/s 1 200
credit_card/is_card_number_no_luhn/378282246310005 execution_time 49.046µs 49.218µs ± 0.157µs 49.162µs ± 0.076µs 49.285µs 49.573µs 49.696µs 49.745µs 1.19% 1.299 1.074 0.32% 0.011µs 1 200
credit_card/is_card_number_no_luhn/378282246310005 throughput 20102443.629op/s 20317849.906op/s ± 64705.830op/s 20340752.479op/s ± 31689.718op/s 20367950.013op/s 20381481.489op/s 20387994.672op/s 20388936.963op/s 0.24% -1.286 1.030 0.32% 4575.393op/s 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time 46.645µs 46.718µs ± 0.055µs 46.708µs ± 0.038µs 46.751µs 46.821µs 46.891µs 46.946µs 0.51% 1.088 1.257 0.12% 0.004µs 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput 21301233.926op/s 21404913.502op/s ± 25087.652op/s 21409510.572op/s ± 17511.135op/s 21424792.551op/s 21433259.509op/s 21436838.798op/s 21438640.467op/s 0.14% -1.081 1.227 0.12% 1773.965op/s 1 200
credit_card/is_card_number_no_luhn/x371413321323331 execution_time 6.443µs 6.458µs ± 0.008µs 6.456µs ± 0.005µs 6.464µs 6.472µs 6.480µs 6.483µs 0.41% 0.685 0.011 0.12% 0.001µs 1 200
credit_card/is_card_number_no_luhn/x371413321323331 throughput 154250205.584op/s 154835652.289op/s ± 179698.921op/s 154882792.524op/s ± 111092.519op/s 154975752.151op/s 155073748.505op/s 155114619.680op/s 155202971.721op/s 0.21% -0.680 -0.000 0.12% 12706.633op/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 [4.287µs; 4.289µs] or [-0.026%; +0.026%] None None None
credit_card/is_card_number/ throughput [233163660.138op/s; 233282899.446op/s] or [-0.026%; +0.026%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 execution_time [84.155µs; 84.188µs] or [-0.020%; +0.020%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 throughput [11878232.213op/s; 11882874.625op/s] or [-0.020%; +0.020%] None None None
credit_card/is_card_number/ 378282246310005 execution_time [76.504µs; 76.533µs] or [-0.019%; +0.019%] None None None
credit_card/is_card_number/ 378282246310005 throughput [13066287.783op/s; 13071160.279op/s] or [-0.019%; +0.019%] None None None
credit_card/is_card_number/37828224631 execution_time [4.291µs; 4.292µs] or [-0.017%; +0.017%] None None None
credit_card/is_card_number/37828224631 throughput [232969964.105op/s; 233048914.353op/s] or [-0.017%; +0.017%] None None None
credit_card/is_card_number/378282246310005 execution_time [73.483µs; 73.529µs] or [-0.031%; +0.031%] None None None
credit_card/is_card_number/378282246310005 throughput [13600175.018op/s; 13608615.863op/s] or [-0.031%; +0.031%] None None None
credit_card/is_card_number/37828224631000521389798 execution_time [46.720µs; 46.735µs] or [-0.016%; +0.016%] None None None
credit_card/is_card_number/37828224631000521389798 throughput [21397170.737op/s; 21403919.053op/s] or [-0.016%; +0.016%] None None None
credit_card/is_card_number/x371413321323331 execution_time [6.457µs; 6.459µs] or [-0.015%; +0.015%] None None None
credit_card/is_card_number/x371413321323331 throughput [154818869.961op/s; 154863779.023op/s] or [-0.015%; +0.015%] None None None
credit_card/is_card_number_no_luhn/ execution_time [4.291µs; 4.293µs] or [-0.023%; +0.023%] None None None
credit_card/is_card_number_no_luhn/ throughput [232962195.937op/s; 233069916.528op/s] or [-0.023%; +0.023%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time [60.025µs; 60.058µs] or [-0.027%; +0.027%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput [16650651.654op/s; 16659767.620op/s] or [-0.027%; +0.027%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time [52.449µs; 52.500µs] or [-0.049%; +0.049%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 throughput [19048028.624op/s; 19066475.482op/s] or [-0.048%; +0.048%] None None None
credit_card/is_card_number_no_luhn/37828224631 execution_time [4.291µs; 4.293µs] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/37828224631 throughput [232923740.988op/s; 233044787.538op/s] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/378282246310005 execution_time [49.196µs; 49.240µs] or [-0.044%; +0.044%] None None None
credit_card/is_card_number_no_luhn/378282246310005 throughput [20308882.300op/s; 20326817.512op/s] or [-0.044%; +0.044%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time [46.711µs; 46.726µs] or [-0.016%; +0.016%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput [21401436.595op/s; 21408390.409op/s] or [-0.016%; +0.016%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 execution_time [6.457µs; 6.460µs] or [-0.016%; +0.016%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 throughput [154810747.747op/s; 154860556.831op/s] 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 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 2.551µs 2.635µs ± 0.021µs 2.643µs ± 0.007µs 2.648µs 2.654µs 2.662µs 2.674µs 1.17% -1.764 3.247 0.80% 0.001µs 1 200
tags/replace_trace_tags_v04 execution_time 2.606µs 2.792µs ± 0.072µs 2.815µs ± 0.017µs 2.832µs 2.879µs 2.886µs 2.891µs 2.70% -1.225 0.275 2.57% 0.005µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
tags/replace_trace_tags execution_time [2.632µs; 2.638µs] or [-0.110%; +0.110%] None None None
tags/replace_trace_tags_v04 execution_time [2.782µs; 2.802µs] or [-0.357%; +0.357%] None None None

Group 8

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz b1d22d9 1788396784 BridgeAR/2026-09-03-agentless-sql-cache
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 29.120µs 30.022µs ± 1.261µs 29.240µs ± 0.072µs 31.673µs 32.136µs 33.243µs 33.313µs 13.93% 1.008 -0.741 4.19% 0.089µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
redis/obfuscate_redis_string execution_time [29.847µs; 30.197µs] or [-0.582%; +0.582%] None None None

Baseline

Omitted due to size.

@dd-octo-sts

dd-octo-sts Bot commented Sep 3, 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 91.30 MB 91.33 MB +.02% (+28.02 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so 8.45 MB 8.45 MB 0% (0 B) 👌
aarch64-unknown-linux-gnu
Artifact Baseline Commit Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.38 MB 11.38 MB +0% (+520 B) 👌
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a 102.61 MB 102.64 MB +.02% (+27.57 KB) 🔍
libdatadog-x64-windows
Artifact Baseline Commit Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll 27.33 MB 27.34 MB +.03% (+9.50 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib 96.08 KB 96.08 KB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb 185.41 MB 185.46 MB +.02% (+56.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib 805.24 MB 803.78 MB --.18% (-1.45 MB) 💪
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll 9.01 MB 9.01 MB +.02% (+2.00 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib 96.08 KB 96.08 KB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb 26.21 MB 26.22 MB +.02% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib 52.22 MB 52.23 MB +.02% (+14.29 KB) 🔍
libdatadog-x86-windows
Artifact Baseline Commit Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll 23.82 MB 23.83 MB +.02% (+7.00 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib 97.58 KB 97.58 KB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb 190.43 MB 190.49 MB +.03% (+64.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib 788.61 MB 787.78 MB --.10% (-848.24 KB) 💪
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll 6.96 MB 6.96 MB +.01% (+1.00 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib 97.58 KB 97.58 KB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb 28.19 MB 28.19 MB +.02% (+8.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib 49.70 MB 49.71 MB +.02% (+10.82 KB) 🔍
x86_64-alpine-linux-musl
Artifact Baseline Commit Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a 81.45 MB 81.47 MB +.02% (+16.86 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so 9.41 MB 9.41 MB 0% (0 B) 👌
x86_64-unknown-linux-gnu
Artifact Baseline Commit Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a 97.22 MB 97.23 MB +.01% (+17.00 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.46 MB 11.46 MB +0% (+448 B) 👌

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