chore(deps): bump to libdatadog v42.0.0

[gyuheon0h]

Description

Bump libdatadog to v42.0.0

Testing

Risks

Additional Notes

[gyuheon0h]

• chore(deps): bump to libdatadog v42.0.0 #19892 👈 (View in Graphite)
• main

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[datadog-datadog-prod-us1-2]

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🔄 Datadog auto-retried 1 job - 1 passed on retry

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: cb134e8 | Docs | View more details | Give us feedback!}

[chatgpt-codex-connector]

💡 Codex Review

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[cit-pr-commenter-54b7da]

Codeowners resolved as

Resolved from the full PR diff against main using the target branch CODEOWNERS file.
CODEOWNERS team requests not listed below are not required by the current file set.

src/native/Cargo.lock                                                   @DataDog/apm-core-python
src/native/Cargo.toml                                                   @DataDog/apm-core-python
src/native/py_string.rs                                                 @DataDog/apm-core-python
src/native/rc_shm.rs                                                    @DataDog/apm-core-python
src/native/remote_config.rs                                             @DataDog/apm-core-python

[cit-pr-commenter-54b7da]

Codeowners resolved as

Resolved from the full PR diff against main using the target branch CODEOWNERS file.
CODEOWNERS team requests not listed below are not required by the current file set.

No remaining files require a CODEOWNERS review.

[cit-pr-commenter-54b7da]

Circular import analysis

⚠️ Existing circular imports

There are 3 circular imports that already exist on the base branch and have not been changed by this PR.

ddtrace.llmobs -> ddtrace.llmobs._evaluators -> ddtrace.llmobs._evaluators.format -> ddtrace.llmobs._experiment -> ddtrace.llmobs
ddtrace.errortracking._handled_exceptions.bytecode_injector -> ddtrace.errortracking._handled_exceptions.callbacks -> ddtrace.errortracking._handled_exceptions.collector -> ddtrace.errortracking._handled_exceptions.bytecode_reporting -> ddtrace.errortracking._handled_exceptions.bytecode_injector
ddtrace.appsec._asm_request_context -> ddtrace.appsec._iast._iast_request_context_base -> ddtrace.appsec._iast._iast_env -> ddtrace.appsec._iast.reporter -> ddtrace.appsec._exploit_prevention.stack_traces -> ddtrace.appsec._asm_request_context

[cit-pr-commenter-54b7da]

Dependency direction analysis

⚠️ Existing dependency direction violations

There are 240 dependency direction violations that already exist on the base branch and have not been changed by this PR.

Show existing violations (showing 5 of 240 highest severity)

ddtrace.internal.tracemethods -×-> ddtrace.trace  (internal-core -> product:tracing, score=135)
ddtrace.llmobs._integrations.bedrock -×-> ddtrace.trace  (product:llmobs -> product:tracing, score=133)
ddtrace.llmobs._evaluators.runner -×-> ddtrace.trace  (product:llmobs -> product:tracing, score=133)
ddtrace.llmobs._integrations.pydantic_ai -×-> ddtrace.trace  (product:llmobs -> product:tracing, score=133)
ddtrace.llmobs._integrations.google_adk -×-> ddtrace.trace  (product:llmobs -> product:tracing, score=133)

To see all violations, download the layers-base.json and layers-pr.json artifacts from this CI job and run:

uv run --script scripts/import-analysis/layers.py compare layers-base.json layers-pr.json

[cit-pr-commenter-54b7da]

Dependency direction analysis

⚠️ Existing dependency direction violations

There are 240 dependency direction violations that already exist on the base branch and have not been changed by this PR.

Show existing violations (showing 5 of 240 highest severity)

ddtrace.internal.tracemethods -×-> ddtrace.trace  (internal-core -> product:tracing, score=135)
ddtrace.debugging._signal.tracing -×-> ddtrace.trace  (product:debugging -> product:tracing, score=133)
ddtrace.profiling.scheduler -×-> ddtrace.trace  (product:profiling -> product:tracing, score=133)
ddtrace.appsec._contrib.django -×-> ddtrace.trace  (product:appsec -> product:tracing, score=133)
ddtrace.llmobs._integrations.bedrock_agents -×-> ddtrace.trace  (product:llmobs -> product:tracing, score=133)

To see all violations, download the layers-base.json and layers-pr.json artifacts from this CI job and run:

uv run --script scripts/import-analysis/layers.py compare layers-base.json layers-pr.json

[bwoebi]

Looks right to me.

[brettlangdon]

the from_owned AI comment seems legit something that needs to be addressed ?

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-08-27 15:45:04

Comparing candidate commit cb134e8 in PR branch gyuheon0h/libdatadog-v42 with baseline commit 89fbfa5 in branch main.

📊 Benchmarking dashboard

Found 0 performance improvements and 8 performance regressions! Performance is the same for 574 metrics, 10 unstable metrics, 3 known flaky benchmarks, 15 flaky benchmarks without significant changes.

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:httppropagationextract-tracecontext_headers

• 🟥 execution_time [+2.345µs; +2.603µs] or [+7.107%; +7.888%]

scenario:httppropagationinject-ids_only

• 🟥 execution_time [+2.698µs; +2.824µs] or [+16.199%; +16.956%]

scenario:iastaspects-add_aspect

• 🟥 execution_time [+8.902µs; +11.466µs] or [+8.743%; +11.262%]

scenario:iastaspects-strip_aspect

• 🟥 execution_time [+85.517µs; +91.837µs] or [+30.231%; +32.465%]

scenario:iastaspectsospath-ospathbasename_aspect

• 🟥 execution_time [+140.635µs; +147.546µs] or [+35.500%; +37.245%]

scenario:iastaspectssplit-rsplit_aspect

• 🟥 execution_time [+39.823µs; +45.528µs] or [+28.198%; +32.237%]

scenario:telemetryaddmetric-1-count-metric-1-times

• 🟥 execution_time [+726.489ns; +765.302ns] or [+27.618%; +29.093%]

scenario:tracer-small

• 🟥 execution_time [+31.922µs; +34.498µs] or [+9.897%; +10.696%]

Unstable benchmarks

These benchmarks have a confidence interval too wide to call a change; treat them as noise rather than signal.

scenario:coreapiscenario-context_with_data_listeners

• unstable execution_time [-797.444ns; +675.922ns] or [-7.262%; +6.156%]

scenario:coreapiscenario-core_dispatch_1_listener

• unstable execution_time [-34.535ns; +31.706ns] or [-5.679%; +5.214%]

scenario:coreapiscenario-core_dispatch_50_listeners

• unstable execution_time [-1689.164ns; +1595.576ns] or [-9.910%; +9.361%]

scenario:coreapiscenario-core_dispatch_exception_listeners

• unstable execution_time [-1024.893ns; +1513.316ns] or [-7.892%; +11.653%]

scenario:coreapiscenario-core_dispatch_listeners

• unstable execution_time [-318.015ns; +335.795ns] or [-8.650%; +9.134%]

scenario:coreapiscenario-core_dispatch_no_args_listeners

• unstable execution_time [-250.041ns; +264.907ns] or [-8.533%; +9.040%]

scenario:coreapiscenario-core_dispatch_with_results_1_listener

• unstable execution_time [-30.365ns; +112.420ns] or [-2.679%; +9.919%]

scenario:coreapiscenario-core_dispatch_with_results_50_listeners

• unstable execution_time [-3739.623ns; +4199.710ns] or [-9.208%; +10.341%]

scenario:coreapiscenario-core_dispatch_with_results_listeners

• unstable execution_time [-711.494ns; +833.803ns] or [-8.807%; +10.321%]

scenario:packagesupdateimporteddependencies-import_many_stdlib_cached

• unstable execution_time [-60.147µs; +57.556µs] or [-9.652%; +9.236%]

Known flaky benchmarks

These benchmarks are marked as flaky and will not trigger a failure. Modify FLAKY_BENCHMARKS_REGEX to control which benchmarks are marked as flaky.

scenario:iastaspects-casefold_noaspect

• 🟥 execution_time [+60.958µs; +65.921µs] or [+24.467%; +26.459%]

scenario:iastaspects-translate_noaspect

• 🟥 execution_time [+25.086µs; +29.589µs] or [+7.109%; +8.385%]

scenario:span-start

• 🟥 execution_time [+1.627ms; +1.889ms] or [+11.966%; +13.891%]

Known flaky benchmarks without significant changes:

• scenario:errortrackingflasksqli-baseline
• scenario:flasksimple-iast-get
• scenario:iastaspects-casefold_aspect
• scenario:iastaspects-index_aspect
• scenario:iastaspects-ljust_noaspect
• scenario:iastaspects-lower_aspect
• scenario:iastaspects-replace_aspect
• scenario:iastaspects-swapcase_aspect
• scenario:iastaspects-title_noaspect
• scenario:iastaspects-translate_aspect
• scenario:iastaspects-upper_noaspect
• scenario:packagespackageforrootmodulemapping-cache_off
• scenario:packagespackageforrootmodulemapping-cache_on
• scenario:sethttpmeta-all-enabled
• scenario:telemetryaddmetric-record-100-metrics

[gyuheon0h]

the from_owned AI comment seems legit something that needs to be addressed ?

@brettlangdon The comment is valid, but nothing is calling from_owned at the moment, so I chose not to implement.

As for actually implementing this, it seems like a non-trivial challenge on its own. data must point at a buffer that is immutable and stable for as long as the struct lives, and storage is the only thing that can keep a non-'static buffer alive.

fn from_owned(value: String) -> Self; has no Python token so we can't create Python objects, any Py<PyAny>. Seems like the most viable way is to leak the String into a Box<str> and treat it like a static string with storage: None. Or, we can attach to Python but that takes the GIL and I am 99% sure that is something we don't want to do here

I'm not sure if this is the best way, nor am I too familiar with the usage of this. Thoughts on leaving this unimplemented, since noone is calling it at the moment, and coming up with a confident solution after?

EDIT: didn't see yall's later comments -- following up there

cc @bwoebi