fix(crastracking): strip bootstrap dir for ct receiver

[gyuheon0h]

PROF-15922

Description

The crashtracker receiver subprocess inherits PYTHONPATH from the parent process. When the parent was launched with ddtrace-run, that PYTHONPATH includes the bootstrap dir containing sitecustomize.py, so the receiver's own interpreter re-runs the full ddtrace bootstrap, starting a second, independently-configured copy of ddtrace using the receiver's stripped-down env (no DD_ENV/DD_SERVICE). This was noticed here in this schema check failure for system tests

We can strip the ddtrace-run bootstrap directory out of the inherited PYTHONPATH before spawning the receiver, so it stays an uninstrumented script while still able to locate the ddtrace package using any other PYTHONPATH entries.

Testing

Risks

Additional Notes

[gyuheon0h]

• fix(crastracking): strip bootstrap dir for ct receiver #20084 👈 (View in Graphite)
• main

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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.

No remaining files require a CODEOWNERS review.

[datadog-official]

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 62ca6ac | Docs | View more details | Give us feedback!}

[cit-pr-commenter-54b7da]

Circular import analysis

⚠️ Existing circular imports

There are 2 circular imports that already exist on the base branch and have not been changed by this PR.

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

Show existing violations (showing 5 of 230 highest severity)

ddtrace.internal.tracemethods -×-> ddtrace.trace  (internal-core -> product:tracing, score=134)
ddtrace.internal.opentelemetry.trace -×-> ddtrace.trace  (product:opentelemetry -> product:tracing, score=132)
ddtrace.debugging._signal.model -×-> ddtrace.trace  (product:debugging -> product:tracing, score=132)
ddtrace.llmobs._telemetry -×-> ddtrace.trace  (product:llmobs -> product:tracing, score=132)
ddtrace.profiling.scheduler -×-> ddtrace.trace  (product:profiling -> product:tracing, score=132)

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

[chatgpt-codex-connector]

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[pr-commenter]

Benchmarks

Benchmark execution time: 2026-09-08 17:30:22

Comparing candidate commit 62ca6ac in PR branch gyuheon0h/bootstrap-ct-receiver with baseline commit 3f36032 in branch main.

📊 Benchmarking dashboard

Found 0 performance improvements and 1 performance regressions! Performance is the same for 568 metrics, 10 unstable metrics, 6 known flaky benchmarks, 18 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:samplingrules-high_match

• 🟥 execution_time [+13.958µs; +16.353µs] or [+8.990%; +10.533%]

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 [-708.759ns; +766.442ns] or [-6.416%; +6.938%]

scenario:coreapiscenario-core_dispatch_1_listener

• unstable execution_time [-31.747ns; +34.126ns] or [-5.193%; +5.582%]

scenario:coreapiscenario-core_dispatch_50_listeners

• unstable execution_time [-1677.731ns; +1608.653ns] or [-9.844%; +9.439%]

scenario:coreapiscenario-core_dispatch_exception_listeners

• unstable execution_time [-1408.945ns; +1030.082ns] or [-10.666%; +7.798%]

scenario:coreapiscenario-core_dispatch_listeners

• unstable execution_time [-326.824ns; +321.249ns] or [-8.907%; +8.755%]

scenario:coreapiscenario-core_dispatch_no_args_listeners

• unstable execution_time [-246.406ns; +252.076ns] or [-8.417%; +8.611%]

scenario:coreapiscenario-core_dispatch_with_results_1_listener

• unstable execution_time [-56.237ns; +92.515ns] or [-4.957%; +8.155%]

scenario:coreapiscenario-core_dispatch_with_results_50_listeners

• unstable execution_time [-3782.253ns; +4217.573ns] or [-9.253%; +10.318%]

scenario:coreapiscenario-core_dispatch_with_results_listeners

• unstable execution_time [-697.053ns; +882.704ns] or [-8.643%; +10.945%]

scenario:packagesupdateimporteddependencies-import_many_stdlib_cached

• unstable execution_time [-52.725µs; +58.387µs] or [-9.082%; +10.057%]

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:httppropagationinject-ids_only

• 🟥 execution_time [+1.820µs; +2.036µs] or [+10.537%; +11.784%]

scenario:iastaspectsospath-ospathbasename_aspect

• 🟥 execution_time [+129.300µs; +136.749µs] or [+33.020%; +34.923%]

scenario:iastaspectssplit-rsplit_aspect

• 🟥 execution_time [+16.415µs; +19.653µs] or [+11.435%; +13.691%]

scenario:span-start

• 🟥 execution_time [+1.609ms; +1.755ms] or [+11.389%; +12.424%]

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

• 🟥 execution_time [+339.658ns; +386.338ns] or [+11.768%; +13.385%]

scenario:tracer-small

• 🟥 execution_time [+39.050µs; +41.183µs] or [+12.021%; +12.678%]

Known flaky benchmarks without significant changes:

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

[brettlangdon]

small nit, otherwise lgtm

question: worth injecting an env var when the receiver is running, and we can have our bootstrap exit early or something when in that case?

[taegyunkim]

This overlaps with my earlier PR, #19735.

My main request is to bring over the subprocess regression so we verify both native-module importability and the absence of receiver self-instrumentation, rather than only checking the generated PYTHONPATH