Try using jemalloc as the global allocator.

[tobz]

Work in progress.

[pr-commenter]

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: 4f05936b-6feb-4ca3-b330-9def66330fa3
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_1mb	ingress throughput	+0.07	[-0.00, +0.13]
➖	uds_dogstatsd_to_api_100mb_antagonistic	ingress throughput	+0.00	[-0.01, +0.01]
➖	uds_dogstatsd_to_api_100mb	ingress throughput	-0.00	[-0.01, +0.01]
➖	uds_dogstatsd_to_api_10mb	ingress throughput	-0.02	[-0.05, +0.01]
➖	distribution_metrics	memory utilization	-1.69	[-1.90, -1.47]

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 (Saluki)

Regression Detector Results

Run ID: 165b4356-9eae-4f17-80d5-af359ef9865a
Baseline: db8ea48
Comparison: d0ab536

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

Significant changes in experiment optimization goals

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

perf	experiment	goal	Δ mean %	Δ mean % CI
❌	distribution_metrics	memory utilization	+133.42	[+133.19, +133.66]
❌	uds_dogstatsd_to_api_100mb_antagonistic	ingress throughput	-64.22	[-69.62, -58.82]
❌	uds_dogstatsd_to_api_1mb	ingress throughput	-98.46	[-98.55, -98.37]
❌	uds_dogstatsd_to_api_10mb	ingress throughput	-99.85	[-99.99, -99.71]
❌	uds_dogstatsd_to_api_100mb	ingress throughput	-99.99	[-99.99, -99.98]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
❌	distribution_metrics	memory utilization	+133.42	[+133.19, +133.66]
❌	uds_dogstatsd_to_api_100mb_antagonistic	ingress throughput	-64.22	[-69.62, -58.82]
❌	uds_dogstatsd_to_api_1mb	ingress throughput	-98.46	[-98.55, -98.37]
❌	uds_dogstatsd_to_api_10mb	ingress throughput	-99.85	[-99.99, -99.71]
❌	uds_dogstatsd_to_api_100mb	ingress throughput	-99.99	[-99.99, -99.98]

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".

[tobz]

Going to close this for now as there wasn't enough evidence early on that we could reasonably overcome the large jumps in memory growth when switching to something other than the system allocator.

We may revisit this in the future.