[APR-208] chore: integrate health registry into topology

[tobz]

Context

This PR addresses #192 by wiring up the topology building code, and all current components, into the health registry.

Most code changes not in saluki-health were purely boilerplate wiring. The glut of changes are, however, in saluki-health: I spent a good amount of time trying to clean up and simplify the code, including making it easier to pass around HealthRegistry in the same way that memory_accounting::ComponentRegistry can be passed around. I didn't quite go as far as ComponentRegistry goes, but it's good enough for now.

Practically speaking, this means you can do curl http://localhost:5400/health/live after building/running ADP and get back this:

{"topology.destinations.dd_metrics_out":{"ready":true,"live":true},"topology.transforms.dsd_agg":{"ready":true,"live":true},"topology.transforms.internal_metrics_agg":{"ready":true,"live":true},"topology.sources.internal_metrics_in":{"ready":true,"live":true},"topology.destinations.dd_events_service_checks_out":{"ready":true,"live":true},"topology.sources.dsd_in":{"ready":true,"live":true},"topology.transforms.enrich":{"ready":true,"live":true}}

[pr-commenter[bot]]

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: ed5fa282-9209-4956-aef9-0990887a8503

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 | links | |------|----------------------------------------------|--------------------|----------|----------------|-------| | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | +1.22 | [+1.01, +1.43] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | +0.04 | [-0.03, +0.11] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.03 | [-0.01, +0.06] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.02 | [+0.01, +0.03] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | +0.00 | [-0.07, +0.07] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.00 | [-0.05, +0.05] | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.00 | [-0.01, +0.01] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | -0.03 | [-0.07, -0.00] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | -0.04 | [-0.08, +0.01] | |

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]]

Regression Detector (Saluki)

Regression Detector Results

Run ID: 5ed1d381-e170-480f-819b-0bacf97679f1

Baseline: d2adfc84dc22babfdc1d6dede30f68e2b292c599 Comparison: 7e8dc08221772857b1a598f0c8d3933987005e5e

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 | links | |------|-------------------------------------------------|--------------------|----------|----------------|-------| | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | +3.59 | [+3.48, +3.70] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | +0.50 | [-2.81, +3.80] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.06 | [-0.11, +0.23] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.03 | [-0.15, +0.21] | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining | ingress throughput | -0.00 | [-0.04, +0.04] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.00 | [-0.21, +0.21] | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining_no_allocs | ingress throughput | -0.00 | [-0.06, +0.06] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | -0.00 | [-0.02, +0.02] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.03 | [-0.11, +0.04] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.07 | [-0.34, +0.20] | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.96 | [-1.06, -0.86] | |

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]]

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]