[APR-205] Add bounds (and bounding itself) to environment providers.

[tobz]

Contexts

Currently, a large potential source of unbounded allocations exists in the environment provider, specifically with workloads. As the workload provider exists to deal with entity-based tag enrichment, it is ripe for allocating many strings as well as holding on to them.

Solution

We've added bounds calculations to saluki-env, specifically for host and workload providers. These bounds are still best guesses in some cases, as not all allocation sources can be trivially quantified upfront.

Most of the work in this PR is adding bounding (e.g., using a string interner for tags, or bounding the entities allowed in the TagStore) and then the rest is the wiring up of bounds themselves based on that work.

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Regression Detector (Saluki)

Regression Detector Results

Run ID: 68602dcc-56b0-4b4b-bb4f-989cb407e375

Baseline: e9927147281f7a174208151eeb6fd29dc6c3e023 Comparison: 6d679aa1a0a4b6e9b5d2b93e40af50cbe1ac2057

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_1mb_50k_contexts_memlimit | ingress throughput | +1.08 | [-2.20, +4.36] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.07 | [+0.03, +0.10] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.05 | [-0.15, +0.25] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | +0.02 | [-0.26, +0.29] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.00 | [-0.01, +0.01] | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining | ingress throughput | -0.00 | [-0.02, +0.02] | | | ➖ | dsd_uds_50mb_10k_contexts_no_inlining_no_allocs | ingress throughput | -0.00 | [-0.03, +0.03] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | -0.01 | [-0.22, +0.21] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.05 | [-0.26, +0.16] | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.32 | [-0.42, -0.23] | | | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -1.07 | [-1.22, -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".

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Regression Detector (DogStatsD)

Regression Detector Results

Run ID: fa191d65-d06b-4c00-aeba-24867dce5ce8

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_1mb_3k_contexts | ingress throughput | +0.05 | [-0.03, +0.13] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.03 | [-0.00, +0.06] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | +0.03 | [-0.04, +0.09] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.02 | [-0.05, +0.09] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.02 | [-0.04, +0.07] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.00 | [-0.01, +0.02] | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.00 | [-0.01, +0.00] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | -0.01 | [-0.05, +0.04] | | | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -1.15 | [-1.35, -0.94] | |

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".

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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]