Switch to ddebpf.Manager in consumer_test.go and related files

[DanielLavie]

What does this PR do?

Replaces manager.Manager with ddebpf.Manager in consumer_test.go and related files.

Motivation

This PR is failing due to the missing usage of the ddebpf.Manager object in consumer_test.go.

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 51560584 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[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=51552634 --os-family=ubuntu

Note: This applies to commit 3030851

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 22022a5b4c2b4f27c34c3a897b3151902435624c

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-amd64-deb	0.00MB	✅	1187.70MB	1187.70MB	140.00MB
datadog-agent-x86_64-rpm	0.00MB	✅	1196.96MB	1196.96MB	140.00MB
datadog-agent-x86_64-suse	0.00MB	✅	1196.96MB	1196.96MB	140.00MB
datadog-agent-arm64-deb	0.00MB	✅	933.68MB	933.68MB	140.00MB
datadog-agent-aarch64-rpm	0.00MB	✅	942.92MB	942.92MB	140.00MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	78.57MB	78.57MB	10.00MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	78.64MB	78.64MB	10.00MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	78.64MB	78.64MB	10.00MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	55.77MB	55.77MB	10.00MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	504.86MB	504.86MB	70.00MB
datadog-iot-agent-amd64-deb	0.00MB	✅	113.31MB	113.31MB	10.00MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	113.38MB	113.38MB	10.00MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	113.38MB	113.38MB	10.00MB
datadog-iot-agent-arm64-deb	0.00MB	✅	108.78MB	108.78MB	10.00MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	108.85MB	108.85MB	10.00MB

Decision

✅ Passed

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: a8cdcd9a-e307-497d-9d6e-977f263c5eae

Baseline: 22022a5
Comparison: 3030851
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	otel_to_otel_logs	ingress throughput	+1.23	[+0.58, +1.88]	1	Logs
➖	quality_gate_logs	% cpu utilization	+1.21	[-2.02, +4.45]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.04	[+0.96, +1.12]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.88	[+0.19, +1.57]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.54	[-0.24, +1.33]	1	Logs
➖	quality_gate_idle	memory utilization	+0.19	[+0.16, +0.23]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.15	[-0.31, +0.62]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.13	[-0.73, +0.99]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.08	[-0.01, +0.16]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	+0.04	[-0.72, +0.80]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.85, +0.89]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.02	[-0.10, +0.14]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.01	[-0.62, +0.65]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.12	[-1.02, +0.79]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.40	[-1.17, +0.37]	1	Logs
➖	file_tree	memory utilization	-0.50	[-0.64, -0.37]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	quality_gate_idle_all_features	memory_usage	9/10	bounds checks dashboard
✅	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_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

❌ Failed. Some Quality Gates were violated.

• 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, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 9/10 replicas passed. Failed 1 which is > 0. Gate FAILED.

[usamasaqib]

Can you change the various functions like NewConsumer and Configure to work with ddebpf.Manager instead of passing manager.Manager?
ddebpf.Manager embeds the eBPF manager so you can directly use its methods. Moreover, for correct functioning it overloads some methods like InitWithOptions. Using manager.Manager may lead to bugs in the future where the incorrect methods get called.

In short, I would suggest to use ddebpf.Manager everywhere.