chore(stringtheory): add Interner trait to generalize interners#846
chore(stringtheory): add Interner trait to generalize interners#846
Interner trait to generalize interners#846Conversation
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Regression Detector (DogStatsD)Regression Detector ResultsRun ID: a582079d-dab8-4760-ac2d-afcfc2a57c9f Baseline: 7.67.1 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_distributions_only | memory utilization | +0.73 | [+0.52, +0.94] | 1 | |
| ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | +0.57 | [+0.47, +0.67] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts | 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_1mb_3k_contexts_dualship | 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_50k_contexts_memlimit | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.00 | [-0.10, +0.09] | 1 | |
| ➖ | dsd_uds_40mb_12k_contexts_40_senders | ingress throughput | -0.00 | [-0.00, +0.00] | 1 | |
| ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | -0.00 | [-0.10, +0.09] | 1 | |
| ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.01 | [-0.07, +0.06] | 1 | |
| ➖ | quality_gates_idle_rss | memory utilization | -0.32 | [-0.36, -0.27] | 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:
-
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 (ADP && Checks)Regression Detector ResultsRun ID: 6295aa03-bade-48ec-b49e-f8291de4caf2 Baseline: 80faf9b Optimization Goals: ✅ No significant changes detected
|
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gates_idle_rss | memory utilization | +0.02 | [-0.00, +0.04] | 1 | |
| ➖ | quality_gates_rss | memory utilization | -0.03 | [-0.05, -0.01] | 1 |
Bounds Checks: ❌ Failed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ✅ | quality_gates_idle_rss | memory_usage | 10/10 | |
| ❌ | quality_gates_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:
-
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 (Saluki)Regression Detector ResultsRun ID: 7780e4d1-63a7-4046-8a95-aff786fbe5d0 Baseline: 80faf9b Optimization Goals: ✅ No significant changes detected
|
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gates_idle_rss | memory utilization | +0.40 | [+0.36, +0.43] | 1 | |
| ➖ | dsd_uds_50mb_10k_contexts_no_inlining_no_allocs | ingress throughput | +0.02 | [-0.08, +0.11] | 1 | |
| ➖ | dsd_uds_40mb_12k_contexts_40_senders | ingress throughput | +0.02 | [-0.01, +0.05] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts_dualship | ingress throughput | +0.01 | [-0.00, +0.02] | 1 | |
| ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | +0.01 | [-0.00, +0.01] | 1 | |
| ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.00 | [-0.01, +0.01] | 1 | |
| ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.00 | [-0.01, +0.02] | 1 | |
| ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.00 | [-0.06, +0.06] | 1 | |
| ➖ | dsd_uds_50mb_10k_contexts_no_inlining | ingress throughput | -0.00 | [-0.09, +0.09] | 1 | |
| ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | -0.00 | [-0.01, +0.00] | 1 | |
| ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.01 | [-0.04, +0.02] | 1 | |
| ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.02 | [-0.06, +0.03] | 1 | |
| ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.29 | [-0.36, -0.21] | 1 | |
| ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -0.39 | [-0.63, -0.14] | 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 LinksADP Experiment Result Links
ADP && Checks Experiment Result Links
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Summary
This PR adds a new
Internertrait tostringtheoryto abstract over varying interner implementations. We've taken the common core methods of bothGenericMapInternerandFixedSizeInterneras the basis for the trait methods.Change Type
How did you test this PR?
Existing unit tests.
References
AGTMETRICS-233