chore(deps): dep updates

[tobz]

Summary

This PR includes a general deps update. We updated containerd-client as well, which required a small code change.

Change Type

• Bug fix
• New feature
• Non-functional (chore, refactoring, docs)
• Performance

How did you test this PR?

Ran unit tests locally.

References

AGTMETRICS-233

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Regression Detector (DogStatsD)

Regression Detector Results

Run ID: 2c6d9a90-a9ec-4594-9553-7adacb42c500

Baseline: 7.65.0-rc.9
Comparison: 7.65.0-rc.9

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	dsd_uds_100mb_3k_contexts_distributions_only	memory utilization	+1.46	[+1.25, +1.67]	1
➖	dsd_uds_1mb_3k_contexts_dualship	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_512kb_3k_contexts	ingress throughput	+0.00	[-0.01, +0.01]	1
➖	dsd_uds_1mb_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_50k_contexts_memlimit	ingress throughput	-0.00	[-0.00, +0.00]	1
➖	dsd_uds_100mb_250k_contexts	ingress throughput	-0.00	[-0.11, +0.11]	1
➖	dsd_uds_10mb_3k_contexts	ingress throughput	-0.00	[-0.08, +0.08]	1
➖	quality_gates_idle_rss	memory utilization	-0.01	[-0.13, +0.11]	1
➖	dsd_uds_100mb_3k_contexts	ingress throughput	-0.01	[-0.12, +0.09]	1
➖	dsd_uds_500mb_3k_contexts	ingress throughput	-1.13	[-1.24, -1.01]	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:

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".

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Regression Detector (Checks Agent Go)

Regression Detector Results

Run ID: fee8599a-746b-45c1-896d-a92fa03b2d25

Baseline: f61d1f4e054b884cb1894254ab2714b84b4684cb
Comparison: f61d1f4e054b884cb1894254ab2714b84b4684cb
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gates_idle_rss	memory utilization	+0.10	[+0.05, +0.14]	1

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:

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 (Checks Agent)

Regression Detector Results

Run ID: 35d0f584-aa74-4667-b20d-833496fd82f9

Baseline: b50e0dd
Comparison: 37f7ded
Diff

Optimization Goals: ✅ Improvement(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	quality_gates_idle_rss	memory utilization	-12.12	[-12.14, -12.10]	1

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	quality_gates_idle_rss	memory utilization	-12.12	[-12.14, -12.10]	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:

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".