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Add force jmethodID experimental feature support#11549

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Add force jmethodID experimental feature support#11549
zhengyu123 wants to merge 5 commits into
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zgu/experimental_reduce_jmethod

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What Does This Do

This PR adds an experimental flag profiling.experimental.ddprof.force_jmethodID to enable ddprof feature that aims to reduce pre-loading jmethodIDs for all classes.

The flag is off (true) by default to preserve current behavior.

Motivation

Preloading all jmethodIDs is proved to be a memory footprint issue. In an extreme case, it consumed 9GB native memory.

ddprof is experimenting a way to lessen dependency on jmethod IDs: none lambda and hidden classes that are loaded by system class loaders (bootstrap, platform and application class loaders), never unloaded. Instead of storing jmethod IDs for the methods of the classes, ddprof stores raw method pointers and resolves at dumping time. Therefore, ddprof does not preload jmethod IDs of none-lambda/hidden classes from those class loaders.

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Jira ticket: [PROJ-IDENT]

@zhengyu123 zhengyu123 added type: enhancement Enhancements and improvements comp: profiling Profiling labels Jun 3, 2026
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dd-octo-sts Bot commented Jun 3, 2026

🟢 Java Benchmark SLOs — All performance SLOs passed

Suite Status
Startup 🟢 pass

SLO thresholds are defined here based on automatically generated metrics. A warning is raised when results are within 5% of the threshold.

PR vs. master results
Scenario Candidate master Δ (95% CI of mean)
startup:insecure-bank:iast:Agent 14.07 s 14.02 s [-0.6%; +1.4%] (no difference)
startup:insecure-bank:tracing:Agent 12.96 s 12.93 s [-1.1%; +1.6%] (no difference)
startup:petclinic:appsec:Agent 16.58 s 16.34 s [-0.0%; +3.0%] (no difference)
startup:petclinic:iast:Agent 16.52 s 15.67 s [-3.2%; +14.0%] (unstable)
startup:petclinic:profiling:Agent 16.48 s 16.35 s [-0.5%; +2.2%] (no difference)
startup:petclinic:tracing:Agent 15.82 s 14.91 s [-2.6%; +14.9%] (unstable)

Commit: 0c12bd9e · CI Pipeline · Benchmarking Platform UI


Load and DaCapo benchmarks can be triggered manually in the GitLab pipeline. Results will appear in the Benchmarking Platform UI after completion.

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