[APR-239] Add support for configuring the maximum idle connection pool size for HTTP clients.

[rayz]

Context

When building an HTTP client, we should be able to configure the maximum idle connection age before the connection is terminated. Open connections consume resources on the downstream target systems, and we should prefer to be sparing with how long we try to hold on to reusable resources before we declare the efficiency gains we stand to make not worth the imposition on the downstream target systems themselves.

Solution

Use Builder::pool_max_idle_per_host

The value is hardcoded in the datadog agent.

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Regression Detector (Saluki)

Regression Detector Results

Run ID: b6725fce-5072-4082-b010-0a78e14c3067

Baseline: 6a70c394a927a215f047c470d57b5d3f50c64ad6 Comparison: e09a1921859aa584784bec388cd88b72be2f8e3c

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_500mb_3k_contexts | ingress throughput | +1.09 | [+1.00, +1.18] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | +0.06 | [-0.08, +0.20] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.03 | [-0.04, +0.11] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | +0.03 | [-0.01, +0.07] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | +0.03 | [-0.24, +0.30] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | +0.02 | [-0.04, +0.08] | | | ➖ | 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 | [-0.04, +0.04] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | -0.00 | [-0.02, +0.01] | | | ➖ | dsd_uds_100mb_3k_contexts_distributions_only | memory utilization | -0.97 | [-1.11, -0.83] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | -2.92 | [-6.04, +0.20] | |

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: 5f8079c2-993f-440a-aaad-d5a479fece7e

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 | +0.77 | [+0.55, +0.99] | | | ➖ | dsd_uds_512kb_3k_contexts | ingress throughput | +0.02 | [-0.06, +0.09] | | | ➖ | dsd_uds_100mb_3k_contexts | ingress throughput | +0.01 | [-0.00, +0.02] | | | ➖ | dsd_uds_100mb_250k_contexts | ingress throughput | +0.00 | [-0.06, +0.07] | | | ➖ | dsd_uds_1mb_50k_contexts_memlimit | ingress throughput | +0.00 | [-0.03, +0.03] | | | ➖ | dsd_uds_1mb_3k_contexts | ingress throughput | -0.00 | [-0.02, +0.02] | | | ➖ | dsd_uds_500mb_3k_contexts | ingress throughput | -0.00 | [-0.01, +0.01] | | | ➖ | dsd_uds_10mb_3k_contexts | ingress throughput | -0.01 | [-0.05, +0.02] | | | ➖ | dsd_uds_1mb_50k_contexts | ingress throughput | -0.02 | [-0.05, +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".

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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]