[ASCII-2671] Add check loader name tag on checks.execution_time metric

[pgimalac]

What does this PR do?

Add the check loader name as a tag on the checks.execution_time metrics.

Motivation

In theory each check should be either on the Go loader or the Python loader.
However some python checks are being rewritten in Go, and with custom checks customers could use the same name as a Go check.
Knowing the loader used to run checks would help a lot to know if we risk breaking users with some checks-related changes.

Describe how you validated your changes

Ran the agent locally, with the following configs:

telemetry.enabled: true
telemetry.checks: "*"

Generated a flare, and checked that telemetry.log contained the metric with the tag, with the correct loader name, eg.

checks__execution_time{check_loader="Go",check_name="cpu"} 3

Also updated pkg/collector/check/stats/stats_test.go to have a unit test ensuring the correct check loader name was used.

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor d254442a9588916be0009c2076f431cdb9e96fc2

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-amd64-deb	0.02MB	⚠️	927.72MB	927.70MB	0.50MB
datadog-agent-x86_64-rpm	0.02MB	⚠️	937.38MB	937.37MB	0.50MB
datadog-agent-x86_64-suse	0.02MB	⚠️	937.38MB	937.37MB	0.50MB
datadog-agent-arm64-deb	0.01MB	⚠️	914.87MB	914.86MB	0.50MB
datadog-agent-aarch64-rpm	0.01MB	⚠️	924.52MB	924.50MB	0.50MB
datadog-heroku-agent-amd64-deb	0.01MB	⚠️	478.22MB	478.21MB	0.50MB
datadog-iot-agent-arm64-deb	0.01MB	⚠️	89.98MB	89.96MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.01MB	⚠️	90.05MB	90.03MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.01MB	⚠️	94.00MB	93.99MB	0.50MB
datadog-iot-agent-x86_64-suse	0.01MB	⚠️	94.00MB	93.98MB	0.50MB
datadog-iot-agent-amd64-deb	0.01MB	⚠️	93.93MB	93.92MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	58.95MB	58.95MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	58.95MB	58.95MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	58.87MB	58.87MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.39MB	56.39MB	0.50MB

Decision

⚠️ Warning

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=53323986 --os-family=ubuntu

Note: This applies to commit 6dd3acc

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 3b26ffa2-11d3-401c-9769-0f370a3e676c

Baseline: d254442
Comparison: 6dd3acc
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+0.96	[-2.25, +4.17]	1	Logs
➖	quality_gate_idle	memory utilization	+0.37	[+0.34, +0.41]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+0.32	[+0.17, +0.47]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.32	[-0.46, +1.10]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.24	[-0.23, +0.70]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.18	[-0.54, +0.89]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.07	[-0.80, +0.93]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.03	[-0.86, +0.92]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.01	[-0.71, +0.73]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.10, +0.12]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.02]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.01	[-0.65, +0.62]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.02	[-0.95, +0.91]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.07	[-0.85, +0.72]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.09	[-0.17, -0.01]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.49	[-0.56, -0.41]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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

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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.

[sgnn7]

@pgimalac Drive-by note: Should we make sure that these are all lowercase to avoid issues with capitalization/filtering on the backend?