[EBPF] telemetry: clean maps before stopping the manager

[gjulianm]

What does this PR do?

This PR changes how the telemetry cleanup is done. Instead of relying on an extra call, we use the manager modifier BeforeStop method introduced in #32453 to automatically clean up without explicit calls. Cleanup for other structures the telemetry modifier created has also been added to that hook.

Motivation

Fix an error where GPU and USM modules could not start at the same time, as the shared-libraries program would not re-start due to some leftover data in the telemetry maps.

Describe how you validated your changes

Unit tests included.

Possible Drawbacks / Trade-offs

Additional Notes

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: b21f3dea-88c4-4236-a0df-5d685dd70ce3

Baseline: 409fc48
Comparison: a6d04c3
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	+2.17	[-1.08, +5.42]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.54	[-0.14, +1.22]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.50	[+0.44, +0.57]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.32	[-0.47, +1.10]	1	Logs
➖	quality_gate_idle	memory utilization	+0.17	[+0.14, +0.21]	1	Logs bounds checks dashboard
➖	file_to_blackhole_300ms_latency	egress throughput	+0.15	[-0.49, +0.79]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.13	[-0.35, +0.60]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.10	[-0.77, +0.97]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.09	[-0.69, +0.87]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.77, +0.84]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.02	[-0.86, +0.89]	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	[-0.12, +0.11]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.01	[-0.73, +0.72]	1	Logs
➖	file_tree	memory utilization	-0.36	[-0.48, -0.24]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.66	[-0.74, -0.58]	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	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	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_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_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[val06]

reviewed

[val06]

why not just create a new empty map instead of deleting each element?
e.mapKeys = make(map[telemetryKey]uint64)

[val06]

why this only present for the helper keys?

[val06]

do we really want to do it in the modifer BeforeStop? that feels very wrong design wise.
This should be handled by the ebpfErrorsTelemetry itself and not by the modifier

[usamasaqib]

perfCollector is not even part of errors telemetry. This should definitely not be done here.

[guyarb]

nit - it will be useful to document the functions and the parameters

[guyarb]

nit

Suggested change

	// Copyright 2016-present Datadog, Inc.
	// Copyright 2024-present Datadog, Inc.

[guyarb]

LGTM for USM owned files