ci: native extension hash cache

[P403n1x87]

We make the native extension cache more fine-grained by generating hashes for each single native integration that we build. We then store each generated artifact in the cache according to this FS structure

.ext_cache/           # Example:
└─ <module_name>/     #   "ddtrace.profiling.collector"
   └─ <source_hash>/  #   <sha256 hex digest>
      └─ <binary>     #   "collector.cpython-310-darwin.so"

We implement a custom command to allow retrieving the source to target mapping from setup.py directly, instead of coding the same information elsewhere, which would increase the maintenance cost of the whole build process.

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:

scripts/gen_ext_cache_scripts.py                                        @DataDog/apm-core-python
.gitlab-ci.yml                                                          @DataDog/apm-core-python
scripts/gen_gitlab_config.py                                            @DataDog/apm-core-python
setup.py                                                                @DataDog/python-guild

[github-actions]

Bootstrap import analysis

Comparison of import times between this PR and base.

Summary

The average import time from this PR is: 273 ± 2 ms.

The average import time from base is: 276 ± 2 ms.

The import time difference between this PR and base is: -2.1 ± 0.1 ms.

Import time breakdown

The following import paths have shrunk:

ddtrace.auto 1.980 ms (0.72%)

ddtrace.bootstrap.sitecustomize 1.307 ms (0.48%)

ddtrace.bootstrap.preload 1.307 ms (0.48%)

ddtrace.internal.remoteconfig.client 0.629 ms (0.23%)

ddtrace 0.673 ms (0.25%)

ddtrace.internal._unpatched 0.032 ms (0.01%)

json 0.032 ms (0.01%)

json.decoder 0.032 ms (0.01%)

re 0.032 ms (0.01%)

enum 0.032 ms (0.01%)

types 0.032 ms (0.01%)

[pr-commenter]

Benchmarks

Benchmark execution time: 2025-06-17 19:21:57

Comparing candidate commit 7ea29ec in PR branch ci/ext-hash-cache with baseline commit 75b029a in branch main.

Found 0 performance improvements and 4 performance regressions! Performance is the same for 557 metrics, 3 unstable metrics.

scenario:iastaspectsospath-ospathbasename_aspect

• 🟥 execution_time [+614.163ns; +775.184ns] or [+14.492%; +18.292%]

scenario:iastaspectsospath-ospathsplit_aspect

• 🟥 execution_time [+756.817ns; +870.258ns] or [+15.648%; +17.994%]

scenario:iastaspectsospath-ospathsplitdrive_aspect

• 🟥 execution_time [+388.873ns; +439.888ns] or [+10.712%; +12.117%]

scenario:telemetryaddmetric-1-distribution-metric-1-times

• 🟥 execution_time [+312.824ns; +376.171ns] or [+10.758%; +12.937%]

[brettlangdon]

high level looks good

[P403n1x87]

Perhaps we can include riot in the cached venv?