DI: add re-entrancy guard for method probes on stdlib

[p-datadog]

What does this PR do?

Adds a fiber-local re-entrancy guard to method probes and integration tests exercising it.

When a probe targets a stdlib method that DI calls internally during snapshot building (e.g., String#length via SecureRandom.uuid), the probe would re-enter DI processing recursively. The guard prevents this by skipping DI processing for calls that occur while DI is already processing a probe.

The guard is a split guard: held during DI pre-processing (serialization) and post-processing (notification), but released during super() so that recursive calls and nested probes fire normally during user code execution.

Motivation:

Without this, a non-capture probe on String#length causes SystemStackError via: probe fires → build_snapshot → SecureRandom.uuid → gen_random_urandom → String#length → probe fires → ...

Change log entry

Yes. Dynamic Instrumentation: fix SystemStackError when probes target standard library methods used internally by probe processing.

Additional Notes:

The guard uses Thread.current[:datadog_di_in_probe] (fiber-local, not thread-local) so each fiber tracks independently — correct for fiber-based web servers (Falcon, async).

Line probes are not affected because Ruby's TracePoint is self-disabling during its callback.

How to test the change?

Automated:

bundle exec rspec spec/datadog/di/instrumenter_spec.rb spec/datadog/di/integration/instrumentation_spec.rb spec/datadog/di/integration/stdlib_probe_spec.rb

135 examples, 0 failures.

Gobo (manual verification via Live Debugger UI):

1. Deploy gobo with a dd-trace-rb that includes this PR's branch
2. In the Datadog Live Debugger UI, set a method probe on String class, length method — this is the C-implemented stdlib method that triggered the SystemStackError
3. Set the probe to non-capture (log probe without snapshot) — this is the configuration that caused the crash due to the 5000/sec rate limit allowing recursive invocations
4. Hit any gobo endpoint (e.g., the home page) — String#length is called pervasively by Ruby code
5. Expected with this PR: a snapshot appears in the Live Debugger UI; gobo responds normally
6. Expected without this PR: gobo crashes with SystemStackError: stack level too deep

[github-actions]

Typing analysis

Note: Ignored files are excluded from the next sections.

steep:ignore comments

This PR introduces 4 steep:ignore comments, and clears 2 steep:ignore comments.

steep:ignore comments (+4-2)

❌ Introduced:

lib/datadog/di/base.rb:109
lib/datadog/di/instrumenter.rb:265
lib/datadog/di/instrumenter.rb:381
lib/datadog/di/instrumenter.rb:383

✅ Cleared:

lib/datadog/di/instrumenter.rb:233
lib/datadog/di/instrumenter.rb:341

Untyped methods

This PR introduces 1 partially typed method, and clears 1 partially typed method. It increases the percentage of typed methods from 61.72% to 61.79% (+0.07%).

Partially typed methods (+1-1)

❌ Introduced:

sig/datadog/di/code_tracker.rbs:16
└── def iseqs_for_path_suffix: (String suffix) -> untyped

✅ Cleared:

sig/datadog/di/code_tracker.rbs:14
└── def iseqs_for_path_suffix: (String suffix) -> untyped

If you believe a method or an attribute is rightfully untyped or partially typed, you can add # untyped:accept on the line before the definition to remove it from the stats.

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-04-03 21:09:19

Comparing candidate commit a14bf0d in PR branch di-integration-test-stdlib-probes with baseline commit 609f934 in branch master.

Found 0 performance improvements and 1 performance regressions! Performance is the same for 44 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:method instrumentation

• 🟥 throughput [-12052.961op/s; -11217.031op/s] or [-7.913%; -7.364%]

[datadog-datadog-prod-us1-2]

✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 98.67%
• Overall Coverage: 95.37% (+0.00%)

_{This comment will be updated automatically if new data arrives.
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