[WIP] trim_left investigation

[GeorgeHahn]

There's something wrong with the 'trim left' function in the agent's sketch implementation, but I was never quite able to pin it down. The commentary in this codebase got me thinking about it again.

Here it is. This shouldn't happen:

insert: Value::NFloats(65535 * 5, 0.0),
expected: "0:65535 0:65535 0:65535 0:65535 0:65535",
max_bins: 3,

Overflowing while at the bin limit causes the bin limit to be ignored.

[pr-commenter]

Regression Detector (Saluki)

Regression Detector Results

Run ID: 65964b84-ff45-4b5b-9413-54fce30909cf
Baseline: e44858f
Comparison: 76fedac

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

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	distribution_metrics	memory utilization	-1.19	[-1.44, -0.93]

Explanation

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:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. 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.

3. Its configuration does not mark it "erratic".

[pr-commenter]

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: f1c70947-f053-4736-ae75-38d08fb37b0f
Baseline: 7.52.0
Comparison: 7.52.1

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

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	distribution_metrics	memory utilization	-0.06	[-0.20, +0.08]

Explanation

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:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. 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.

3. Its configuration does not mark it "erratic".