chore: make Tagged relevant to materialized tags, not raw tags
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Tagged relevant to materialized tags, not raw tags
Regression Detector (DogStatsD)Regression Detector ResultsRun ID: a450ed6f-612a-4acc-b1f2-412d9f44c853 Baseline: 7.62.0-rc.8 Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.01 | [-0.03, +0.06] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts_dualship | ingress throughput | +0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | +0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.00 | [-0.01, +0.01] | 1 | |
| ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | +0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_40mb_12k_contexts_40_senders | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.00 | [-0.01, +0.01] | 1 | |
| ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -0.20 | [-0.40, -0.01] | 1 | |
| ➖ | quality_gates_idle_rss | memory utilization | -0.60 | [-0.66, -0.53] | 1 |
Bounds Checks: ❌ Failed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ❌ | quality_gates_idle_rss | memory_usage | 0/10 |
Explanation
Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%
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
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:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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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.
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Its configuration does not mark it "erratic".
Regression Detector (Saluki)Regression Detector ResultsRun ID: a51e93de-5d26-45ac-af4a-516f05cff10a Baseline: 507fe24 ❌ Experiments with missing or malformed dataThis is a critical error. No usable optimization goal data was produced by the listed experiments. This may be a result of misconfiguration. Ping #single-machine-performance and we can help out.
Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gates_idle_rss | memory utilization | +0.55 | [+0.52, +0.58] | 1 | |
| ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.02 | [-0.01, +0.05] | 1 | |
| ➖ | dsd_uds_50mb_10k_contexts_no_inlining | ingress throughput | +0.01 | [-0.06, +0.07] | 1 | |
| ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.00 | [-0.01, +0.02] | 1 | |
| ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.00 | [-0.05, +0.05] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts_dualship | ingress throughput | +0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_50mb_10k_contexts_no_inlining_no_allocs | ingress throughput | -0.00 | [-0.06, +0.06] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.00 | [-0.02, +0.01] | 1 | |
| ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | -0.00 | [-0.02, +0.01] | 1 | |
| ➖ | dsd_uds_40mb_12k_contexts_40_senders | ingress throughput | -0.03 | [-0.06, +0.00] | 1 | |
| ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -0.16 | [-0.29, -0.03] | 1 | |
| ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.27 | [-0.41, -0.14] | 1 | |
| ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | -1.03 | [-1.50, -0.57] | 1 |
Bounds Checks: ✅ Passed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ✅ | quality_gates_idle_rss | memory_usage | 10/10 |
Explanation
Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%
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
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:
-
Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
-
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.
-
Its configuration does not mark it "erratic".
Regression Detector LinksExperiment Result Links
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Context
Prior to this PR, the
Taggedtrait was something we added to abstract over the numerous possible ways to supply the borrowed tags that get fed in when resolving a context.This ends up being overkill and unnecessary without the
Resolvablemachinery, and would be better served as a way to abstract getting the materialized tags from different implementations of tag storage.Solution
This PR reworks the
Taggedtrait slightly by moving it undersaluki_context::tags, having it give back&Taginstead of&str, and adding implementations for the major tag storage types,TagSet,SharedTagSet, andContext. This gives us a unified approach to visiting tags, regardless of where they're stored.We've also reverted back, essentially, to the previous method signatures for hashing a context and creating context keys, by simply taking a value that can be turned into an iterator with items that dereference to a string. This more than satisfies the needs of the aforementioned methods, and lets us avoid a second trait related to tags, which would be otherwise confusing.