perf(profiling): avoid FFI call on allocation hot path

[realFlowControl]

Description

Read executor_globals.current_execute_data directly on NTS builds instead of calling through the C FFI wrapper for every allocation and reallocation. Keep the existing wrapper on ZTS builds.

A 60-second macOS sample reduced get_current_execute_data self time from 0.412% (207/50,210 main-thread samples) to 0.002% (1/49,737). Known allocation-hook self time fell from 4.184% to 3.601%, a 13.9% relative reduction. Repeated end-to-end throughput remained within system scheduling noise.

Reviewer checklist

• Test coverage seems ok.
• Appropriate labels assigned.

https://datadoghq.atlassian.net/browse/PROF-15505

[datadog-official]

 

⚠️ Warnings

🚦 2 Pipeline jobs failed

DataDog/apm-reliability/dd-trace-php | appsec integration tests: [test8.4-release]     

🧪 1 Test failed

All test failures are known flaky.

❄️ Known flaky: extended heartbeat re-emits configuration, dependencies and integrations() from com.datadog.appsec.php.integration.TelemetryExtendedHeartbeatTests  

java.lang.AssertionError: phpredis not emitted via app-started/app-integrations-change; saw: []. Expression: (phpredis in flushed). Values: flushed = []

java.lang.AssertionError: phpredis not emitted via app-started/app-integrations-change; saw: []. Expression: (phpredis in flushed). Values: flushed = []
	at org.codehaus.groovy.runtime.InvokerHelper.createAssertError(InvokerHelper.java:416)
	at com.datadog.appsec.php.integration.TelemetryExtendedHeartbeatTests.extended heartbeat re-emits configuration, dependencies and integrations(TelemetryExtendedHeartbeatTests.groovy:70)
	at java.base/java.lang.reflect.Method.invoke(Method.java:569)
	at java.base/java.util.ArrayList.forEach(ArrayList.java:1511)
	at java.base/java.util.ArrayList.forEach(ArrayList.java:1511)

Not introduced in this PR.

DataDog/apm-reliability/dd-trace-php | publish docker image for system tests     

ℹ️ Info

No other issues found (see more)

❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 59.64% (-1.02%)

Useful? React with 👍 / 👎

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 241f72a | Docs | Datadog PR Page | Give us feedback!}

[pr-commenter]

Benchmarks [ profiler ]

Benchmark execution time: 2026-07-27 15:06:26

Comparing candidate commit 241f72a in PR branch florian/allocation-profiler-fast-execute-data with baseline commit 0aa19a2 in branch master.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 28 metrics, 8 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 ----------------------------------'

[bwoebi]

Looks good. Now we need to also inline the ZTS path :-)