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DataDog / saluki

An experimental toolkit for building telemetry data planes in Rust.
Apache License 2.0
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[APR-207] chore: push converged image to existing image repo but with ADP-specific tag + other updates #221

Closed tobz closed 2 weeks ago

tobz commented 2 weeks ago

Context

This PR updates the image we push for the converged Datadog Agent image, by using the existing internal image repository with an ADP-specific tag, to avoid having a separate image name which makes it more difficult to ensure our image is replicated to all the necessary downstream repositories. The tag itself also now includes the base Datadog Agent image tag to make things more obvious.

We've also updated the versions of the the datadog/dogstatsd image used in SMP as it was lagging behind quite a bit.

pr-commenter[bot] commented 2 weeks ago

Regression Detector (Saluki)

Regression Detector Results

Run ID: 639f1593-f887-4325-b620-b57b8ff32494

Baseline: 78b7902cb425ac83a7818359c8c53848c061a7d9 Comparison: 1704ed8123f53c7080a3427b220c414e6ac6ed8f

Performance changes are noted in the perf column of each table:

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 | links | |------|-------------------------------------------------|--------------------|----------|----------------|-------| | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | +1.27 | [+1.18, +1.36] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | +1.15 | [-2.06, +4.36] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.04 | [-0.01, +0.09] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.02 | [-0.02, +0.05] | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining | ingress throughput | -0.00 | [-0.00, +0.00] | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining_no_allocs | ingress throughput | -0.01 | [-0.04, +0.02] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | -0.01 | [-0.02, +0.00] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.01 | [-0.27, +0.25] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.02 | [-0.07, +0.03] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.05 | [-0.08, -0.01] | | | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -2.81 | [-2.97, -2.66] | |

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[bot] commented 2 weeks ago

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: fd695b4b-41b7-4e60-ae73-b67634a8c873

Baseline: 7.55.2 Comparison: 7.55.3

Performance changes are noted in the perf column of each table:

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 | links | |------|----------------------------------------------|--------------------|----------|----------------|-------| | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | +0.32 | [+0.11, +0.54] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.04 | [-0.00, +0.08] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.02 | [+0.00, +0.03] | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | +0.00 | [+0.00, +0.01] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | +0.00 | [-0.04, +0.04] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | -0.00 | [-0.08, +0.08] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.02 | [-0.06, +0.02] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | -0.03 | [-0.08, +0.01] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.03 | [-0.10, +0.03] | |

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[bot] commented 2 weeks ago

Regression Detector Links

Experiment Result Links

experiment link(s)
dsd_uds_100mb_250k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_100mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_100mb_3k_contexts_distributions_only [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_10mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_1mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_1mb_50k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_1mb_50k_contexts_memlimit [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_500mb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_512kb_3k_contexts [Profiling (ADP)] [Profiling (DSD)] [SMP Dashboard]
dsd_uds_50mb_10k_contexts_no_inlining (ADP only) [Profiling (ADP)] [SMP Dashboard]
dsd_uds_50mb_10k_contexts_no_inlining_no_allocs (ADP only) [Profiling (ADP)] [SMP Dashboard]