Support checks

[scottopell]

To run:

DD_LOG_LEVEL=debug RUST_BACKTRACE=1 DD_ADP_USE_NOOP_WORKLOAD_PROVIDER=true  DD_API_KEY=<some api key> cargo run --

I included an example py check in the conf.d folder.

• Find a python check on disk
• Load and run python checks on an interval
• Submit metrics from the python checks
• Support init_config and arbitrary fields in the instance-config
• Test HTTPS requests (they work, requires setting ca_cert via init_config though)
• Try out full default integration suite

[pr-commenter]

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: a6a70f56-99de-4d0e-a634-ecddbb5dfa12
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
➖	distribution_metrics	memory utilization	+0.04	[-0.18, +0.25]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.02, +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".

[scottopell]

continued in #277