Lazy load rtloader when scheduling a python check

[pgimalac]

What does this PR do?

Initialize Python and rtloader when a first Python check is scheduled.

Motivation

Should reduce idle RSS.

Describe how you validated your changes

TBD.

Possible Drawbacks / Trade-offs

This only makes sense if the Go check loader has precedence over the Python check loader (which is not the case right now, the PR changes it).

Additional Notes

This is just an experiment to compare with #31571 which adds actual process instead.
This is not the preferred path forward, so no need to take this too seriously.

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 1c505b754d84cefcea4eb49e269e4bd619a0743a

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-amd64-deb	0.00MB	✅	928.91MB	928.91MB	0.50MB
datadog-agent-x86_64-rpm	0.00MB	✅	938.65MB	938.65MB	0.50MB
datadog-agent-x86_64-suse	0.00MB	✅	938.65MB	938.65MB	0.50MB
datadog-agent-arm64-deb	0.00MB	✅	916.09MB	916.09MB	0.50MB
datadog-agent-aarch64-rpm	0.00MB	✅	925.81MB	925.81MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	59.01MB	59.01MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.50MB	56.50MB	0.50MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	477.88MB	477.88MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	93.87MB	93.87MB	0.50MB
datadog-iot-agent-arm64-deb	0.00MB	✅	89.93MB	89.93MB	0.50MB
datadog-dogstatsd-x86_64-rpm	-0.00MB	✅	59.09MB	59.09MB	0.50MB
datadog-dogstatsd-x86_64-suse	-0.00MB	✅	59.09MB	59.09MB	0.50MB
datadog-iot-agent-x86_64-rpm	-0.00MB	✅	93.94MB	93.94MB	0.50MB
datadog-iot-agent-x86_64-suse	-0.00MB	✅	93.94MB	93.94MB	0.50MB
datadog-iot-agent-aarch64-rpm	-0.00MB	✅	90.00MB	90.00MB	0.50MB

Decision

✅ Passed

[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=54165371 --os-family=ubuntu

Note: This applies to commit 0d64a6f

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 38334b2c-e9a1-444d-b381-9d8134cc49e4

Baseline: 1c505b7
Comparison: 0d64a6f
Diff

Optimization Goals: ✅ Improvement(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	quality_gate_logs	% cpu utilization	-6.52	[-9.55, -3.49]	1	Logs
✅	quality_gate_idle_all_features	memory utilization	-9.26	[-9.32, -9.20]	1	Logs bounds checks dashboard
✅	file_tree	memory utilization	-10.77	[-10.83, -10.70]	1	Logs
✅	quality_gate_idle	memory utilization	-19.03	[-19.08, -18.99]	1	Logs bounds checks dashboard

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.84	[+0.03, +1.65]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.11	[-0.68, +0.89]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.03	[-0.72, +0.79]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.00	[-0.87, +0.87]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.00	[-0.64, +0.64]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.28, +0.27]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.04	[-0.79, +0.71]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.06	[-0.52, +0.41]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.06	[-0.72, +0.60]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.07	[-0.97, +0.84]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.23	[-0.30, -0.17]	1	Logs
✅	quality_gate_logs	% cpu utilization	-6.52	[-9.55, -3.49]	1	Logs
✅	quality_gate_idle_all_features	memory utilization	-9.26	[-9.32, -9.20]	1	Logs bounds checks dashboard
✅	file_tree	memory utilization	-10.77	[-10.83, -10.70]	1	Logs
✅	quality_gate_idle	memory utilization	-19.03	[-19.08, -18.99]	1	Logs bounds checks dashboard

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