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omnibus: fix python build when using a non default compiler #29473

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@chouquette chouquette commented Sep 20, 2024

What does this PR do?

Remove python's setup for multiarch which causes build failures when using a non default toolchain

Motivation

Python's add_multiarch_paths will systematically add /usr/include and similar paths to CFLAGS, causing mismatches when using a non default compiler which does not rely on /usr/include

We're hitting this problem when we try to build the agent using a custom toolchain that target an arbitrary version of glibc

Describe how to test/QA your changes

The build should be green, and the regular tests passing

Possible Drawbacks / Trade-offs

Additional Notes

Python's add_multiarch_paths will systematically add /usr/include and
similar paths to CFLAGS, causing mismatches when using a non default
compiler which does not rely on /usr/include
@chouquette chouquette added changelog/no-changelog qa/no-code-change Skip QA week as there is no code change in Agent code team/agent-delivery labels Sep 20, 2024
@chouquette chouquette requested a review from a team as a code owner September 20, 2024 20:12
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agent-platform-auto-pr bot commented Sep 20, 2024

[Fast Unit Tests Report]

On pipeline 44836988 (CI Visibility). The following jobs did not run any unit tests:

Jobs:
  • tests_deb-arm64-py3
  • tests_deb-x64-py3
  • tests_flavor_dogstatsd_deb-x64
  • tests_flavor_heroku_deb-x64
  • tests_flavor_iot_deb-x64
  • tests_rpm-arm64-py3
  • tests_rpm-x64-py3
  • tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

@chouquette
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for reference, here's the function that we don't want to call:

    def add_multiarch_paths(self):
        # Debian/Ubuntu multiarch support.
        # https://wiki.ubuntu.com/MultiarchSpec
        tmpfile = os.path.join(self.build_temp, 'multiarch')
        if not os.path.exists(self.build_temp):
            os.makedirs(self.build_temp)
        ret = run_command(
            '%s -print-multiarch > %s 2> /dev/null' % (CC, tmpfile))
        multiarch_path_component = ''
        try:
            if ret == 0:
                with open(tmpfile) as fp:
                    multiarch_path_component = fp.readline().strip()
        finally:
            os.unlink(tmpfile)

        if multiarch_path_component != '':
            add_dir_to_list(self.compiler.library_dirs,
                            '/usr/lib/' + multiarch_path_component)
            add_dir_to_list(self.compiler.include_dirs,
                            '/usr/include/' + multiarch_path_component)
            return

        if not find_executable('dpkg-architecture'):
            return
        opt = ''
        if CROSS_COMPILING:
            opt = '-t' + sysconfig.get_config_var('HOST_GNU_TYPE')
        tmpfile = os.path.join(self.build_temp, 'multiarch')
        if not os.path.exists(self.build_temp):
            os.makedirs(self.build_temp)
        ret = run_command(
            'dpkg-architecture %s -qDEB_HOST_MULTIARCH > %s 2> /dev/null' %
            (opt, tmpfile))
        try:
            if ret == 0:
                with open(tmpfile) as fp:
                    multiarch_path_component = fp.readline().strip()
                add_dir_to_list(self.compiler.library_dirs,
                                '/usr/lib/' + multiarch_path_component)
                add_dir_to_list(self.compiler.include_dirs,
                                '/usr/include/' + multiarch_path_component)
        finally:
            os.unlink(tmpfile)

the part I'm interested in is this one (and the other similar one):

        if multiarch_path_component != '':
            add_dir_to_list(self.compiler.library_dirs,
                            '/usr/lib/' + multiarch_path_component)
            add_dir_to_list(self.compiler.include_dirs,
                            '/usr/include/' + multiarch_path_component)

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pr-commenter bot commented Sep 20, 2024

Regression Detector

Regression Detector Results

Run ID: 5932b915-4a0d-4f89-8537-e0c316f5f81e Metrics dashboard Target profiles

Baseline: 4673873
Comparison: 0e0dcde

Performance changes are noted in the perf column of each table:

  • ✅ = significantly better comparison variant performance
  • ❌ = significantly worse comparison variant performance
  • ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
pycheck_lots_of_tags % cpu utilization +1.56 [-1.01, +4.13] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization +0.96 [+0.20, +1.72] 1 Logs
idle memory utilization +0.51 [+0.46, +0.56] 1 Logs
tcp_syslog_to_blackhole ingress throughput +0.27 [+0.21, +0.33] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.01] 1 Logs
uds_dogstatsd_to_api ingress throughput -0.01 [-0.12, +0.09] 1 Logs
file_tree memory utilization -0.06 [-0.17, +0.05] 1 Logs
basic_py_check % cpu utilization -0.17 [-2.85, +2.51] 1 Logs
otel_to_otel_logs ingress throughput -0.25 [-1.05, +0.56] 1 Logs

Bounds Checks

perf experiment bounds_check_name replicates_passed
idle memory_usage 10/10

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

  1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

  2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

  3. Its configuration does not mark it "erratic".

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