[WIP] add microbenchmarks for native span events

[quinna-h]

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:

releasenotes/notes/add-native-span-events-b04983442822d1b0.yaml         @DataDog/apm-python
benchmarks/encoder/config.yaml                                          @DataDog/apm-core-python
benchmarks/encoder/scenario.py                                          @DataDog/apm-core-python
benchmarks/encoder/utils.py                                             @DataDog/apm-core-python
ddtrace/_trace/span.py                                                  @DataDog/apm-sdk-api-python
ddtrace/internal/_encoding.pyx                                          @DataDog/apm-core-python
ddtrace/internal/encoding.py                                            @DataDog/apm-core-python
ddtrace/internal/writer/writer.py                                       @DataDog/apm-core-python
ddtrace/settings/_agent.py                                              @DataDog/apm-core-python
tests/telemetry/test_writer.py                                          @DataDog/apm-python
tests/tracer/test_encoders.py                                           @DataDog/apm-sdk-api-python

[github-actions]

Bootstrap import analysis

Comparison of import times between this PR and base.

Summary

The average import time from this PR is: 228 ± 2 ms.

The average import time from base is: 230 ± 2 ms.

The import time difference between this PR and base is: -1.21 ± 0.09 ms.

Import time breakdown

The following import paths have grown:

ddtrace.auto 0.071 ms (0.03%)

ddtrace 0.071 ms (0.03%)

ddtrace.trace 0.071 ms (0.03%)

ddtrace._trace.filters 0.071 ms (0.03%)

ddtrace._trace.processor 0.071 ms (0.03%)

ddtrace._trace.sampler 0.071 ms (0.03%)

ddtrace._trace.span 0.071 ms (0.03%)

The following import paths have shrunk:

ddtrace.auto 1.902 ms (0.83%)

ddtrace.bootstrap.sitecustomize 1.244 ms (0.54%)

ddtrace.bootstrap.preload 1.244 ms (0.54%)

ddtrace.internal.products 1.244 ms (0.54%)

ddtrace.internal.remoteconfig.client 0.611 ms (0.27%)

ddtrace 0.658 ms (0.29%)

[pr-commenter]

Benchmarks

Benchmark execution time: 2025-03-31 20:35:46

Comparing candidate commit 3d3423a in PR branch quinna.halim/top-level-span-events-microbenchmarks with baseline commit 3c7821e in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 498 metrics, 2 unstable metrics.