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Move v0.4 and v1 models into a new crate so native trace normalization can reuse them without creating a trace-utils cycle.
Motivation
Normalizing traces before they enter the stats computation pipeline for CSS correctness.
Additional Notes
The original approach was to re-export moved types from libdd-trace-utils to maintain backward compatibility on the consumers, and have less renaming to do in the repo too.
But I find it cleaner to have no re-export, only one way to import it. And this way we might realize we don't need libdd-trace-utils but only its types for some crates.
How to test the change?
No logic change, if CI pass it should be enough
[Generated using codex]
Eldolfin
changed the title
refactor(trace): extract shared span types
refactor(trace)!: extract shared span types
Sep 10, 2026
Found 13 performance improvements and 10 performance regressions! Performance is the same for 133 metrics, 0 unstable metrics.
Explanation
This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:
🟩 = significantly better candidate vs. baseline
🟥 = significantly worse candidate vs. baseline
We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.
If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.
Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.
More details about the CI and significant changes
You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.
CIs of the difference of means are often centered around 0%, because often changes are not that big:
---------------------------------(------|---^--------)-------------------------------->
-0.6% 0% 0.3% +1.2%
| | |
lower bound of the CI --' | |
sample mean (center of the CI) -------------' |
upper bound of the CI ----------------------'
As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).
For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:
----------------------------------------|---------|---(---------^---------)---------->
0% 1% 1.3% 2.2% 3.1%
| | | |
significant impact threshold --------------' | | |
lower bound of CI --------------' | |
sample mean (center of the CI) --------------------------' |
upper bound of CI ----------------------------------'
Found 12 performance improvements and 19 performance regressions! Performance is the same for 135 metrics, 10 unstable metrics.
Explanation
This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:
🟩 = significantly better candidate vs. baseline
🟥 = significantly worse candidate vs. baseline
We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.
If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.
Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.
More details about the CI and significant changes
You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.
CIs of the difference of means are often centered around 0%, because often changes are not that big:
---------------------------------(------|---^--------)-------------------------------->
-0.6% 0% 0.3% +1.2%
| | |
lower bound of the CI --' | |
sample mean (center of the CI) -------------' |
upper bound of the CI ----------------------'
As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).
For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:
----------------------------------------|---------|---(---------^---------)---------->
0% 1% 1.3% 2.2% 3.1%
| | | |
significant impact threshold --------------' | | |
lower bound of CI --------------' | |
sample mean (center of the CI) --------------------------' |
upper bound of CI ----------------------------------'
scenario:alloc_free/system/4096
🟥 execution_time [+8.772ns; +8.912ns] or [+10.460%; +10.626%]
Comment "@codex review" or "@codex security review".
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Eldolfin
changed the title
What does this PR do?
Move v0.4 and v1 models into a new crate so native trace normalization can reuse them without creating a trace-utils cycle.
Motivation
Normalizing traces before they enter the stats computation pipeline for CSS correctness.
Additional Notes
The original approach was to re-export moved types from
libdd-trace-utilsto maintain backward compatibility on the consumers, and have less renaming to do in the repo too.But I find it cleaner to have no re-export, only one way to import it. And this way we might realize we don't need
libdd-trace-utilsbut only its types for some crates.How to test the change?
[Generated using codex]