chore(profiling): run native tests in ci

[taegyunkim]

1. Use pytest-cpp to discover and run profiling C++ tests
   a. pytest-cpp will find executables that start with test_
   b. all tests are put into ddtrace/internal/datadog/profiling/test and tests/profiling_v2/native_test will symlink to it for discovery.
2. DD_PROFILING_NATIVE_TESTS flag is used to build native tests
   a. needed to set this in build_base_venv
   b. setup.py includes native test binaries when the flag is set
   c. gitlab will also propagate those artifacts

Checklist

• PR author has checked that all the criteria below are met
• The PR description includes an overview of the change
• The PR description articulates the motivation for the change
• The change includes tests OR the PR description describes a testing strategy
• The PR description notes risks associated with the change, if any
• Newly-added code is easy to change
• The change follows the library release note guidelines
• The change includes or references documentation updates if necessary
• Backport labels are set (if applicable)

Reviewer Checklist

• Reviewer has checked that all the criteria below are met
• Title is accurate
• All changes are related to the pull request's stated goal
• Avoids breaking API changes
• Testing strategy adequately addresses listed risks
• Newly-added code is easy to change
• Release note makes sense to a user of the library
• If necessary, author has acknowledged and discussed the performance implications of this PR as reported in the benchmarks PR comment
• Backport labels are set in a manner that is consistent with the release branch maintenance policy

[github-actions]

CODEOWNERS have been resolved as:

ddtrace/internal/datadog/profiling/test/.gitignore                      @DataDog/profiling-python
tests/profiling_v2/native_tests                                         @DataDog/profiling-python
.gitlab/package.yml                                                     @DataDog/python-guild @DataDog/apm-core-python
ddtrace/internal/datadog/profiling/dd_wrapper/test/CMakeLists.txt       @DataDog/profiling-python
ddtrace/internal/datadog/profiling/stack_v2/test/CMakeLists.txt         @DataDog/profiling-python
riotfile.py                                                             @DataDog/apm-python
setup.py                                                                @DataDog/python-guild
.riot/requirements/118a763.txt                                          @DataDog/apm-python
.riot/requirements/1359ebb.txt                                          @DataDog/apm-python
.riot/requirements/15e90ee.txt                                          @DataDog/apm-python
.riot/requirements/1600ae2.txt                                          @DataDog/apm-python
.riot/requirements/18008a7.txt                                          @DataDog/apm-python
.riot/requirements/1a9ec51.txt                                          @DataDog/apm-python
.riot/requirements/1b284db.txt                                          @DataDog/apm-python
.riot/requirements/1bc8c1c.txt                                          @DataDog/apm-python
.riot/requirements/1d20be2.txt                                          @DataDog/apm-python
.riot/requirements/1d21682.txt                                          @DataDog/apm-python
.riot/requirements/2e36381.txt                                          @DataDog/apm-python
.riot/requirements/4dd0ff3.txt                                          @DataDog/apm-python
.riot/requirements/6fe81be.txt                                          @DataDog/apm-python
.riot/requirements/a541d7e.txt                                          @DataDog/apm-python
.riot/requirements/b83f7ca.txt                                          @DataDog/apm-python
.riot/requirements/d2b6740.txt                                          @DataDog/apm-python
.riot/requirements/d3718d9.txt                                          @DataDog/apm-python
.riot/requirements/d62d369.txt                                          @DataDog/apm-python
.riot/requirements/f59e90e.txt                                          @DataDog/apm-python
ddtrace/internal/datadog/profiling/dd_wrapper/test/test_api.cpp         @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/test/test_code_provenance.cpp  @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/test/test_forking.cpp     @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/test/test_initialization.cpp  @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/test/test_threading.cpp   @DataDog/profiling-python
ddtrace/internal/datadog/profiling/stack_v2/test/test_thread_span_links.cpp  @DataDog/profiling-python

[datadog-dd-trace-py-rkomorn]

Datadog Report

Branch report: taegyunkim/prof-10664-native-ci
Commit report: 95cfd6a
Test service: dd-trace-py

✅ 0 Failed, 592 Passed, 694 Skipped, 19m 15.94s Total duration (17m 34.75s time saved)

[pr-commenter]

Benchmarks

Benchmark execution time: 2024-11-11 22:35:21

Comparing candidate commit 95cfd6a in PR branch taegyunkim/prof-10664-native-ci with baseline commit 498e125 in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 388 metrics, 2 unstable metrics.

[sanchda]

Generally speaking this looks good and I really like how it works within the framework of our existing tests.

I left some comments about the use of RPATH, since this is loader-facing change which will affect how resolution occurs in customer environments. In practice I don't think this actually matters, so I'm not blocking the PR based on this feedback, but the general guidance is to minimize dependency resolution at dynamic load-time.