[CWS] add kernel bpf filter for raw packet

[safchain]

What does this PR do?

This PR introduces bpf filter to eBPF filters. It converts bpf filters in to a chain of tail calls that is called from the first raw packet classifier and that eventually call the prog that send the raw packet event.

Motivation

Apply a first filtering pass at the eBPF/kernel level to limit the performance impact.

Describe how to test/QA your changes

There are already functional tests for the raw packets feature, they should still pass.

Possible Drawbacks / Trade-offs

Additional Notes

[cit-pr-commenter]

Go Package Import Differences

Baseline: bd961d3
Comparison: 41b7614

binary	os	arch	change
system-probe	linux	amd64	+2, -0 +github.com/DataDog/datadog-agent/pkg/security/ebpf/probes/rawpacket +github.com/cloudflare/cbpfc
system-probe	linux	arm64	+2, -0 +github.com/DataDog/datadog-agent/pkg/security/ebpf/probes/rawpacket +github.com/cloudflare/cbpfc

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 0ea8150b-7a93-4756-81f3-cdcef72fcb1f

Baseline: bd961d3
Comparison: 41b7614
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+2.60	[-0.93, +6.13]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.80	[+0.15, +1.45]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.45	[-0.28, +1.18]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.28	[-0.50, +1.05]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.11	[+0.04, +0.18]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.06	[-0.72, +0.84]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.75, +0.82]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.78, +0.82]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.00	[-0.62, +0.63]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.11, +0.10]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.18	[-0.64, +0.28]	1	Logs
➖	quality_gate_idle	memory utilization	-0.51	[-0.57, -0.46]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-0.57	[-4.36, +3.22]	1	Logs
➖	file_tree	memory utilization	-1.32	[-1.44, -1.20]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-2.54	[-2.68, -2.39]	1	Logs bounds checks dashboard

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_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

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_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.

[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 create-vm --pipeline-id=49841243 --os-family=ubuntu

Note: This applies to commit 41b7614

[YoannGh]

Should we have a check here against RawPacketFilterMaxTailCall to make sure we don't end up overwriting the classifier_raw_packet_sender program entry?

[safchain]

/merge

[dd-devflow]

Devflow running: /merge

View all feedbacks in Devflow UI.

---

2024-11-25 12:54:55 UTC ℹ️ MergeQueue: pull request added to the queue

The median merge time in main is 24m.