network-path: memory and CPU optimization to parseTCP

[paulcacheux]

What does this PR do?

In some profiles we can see that the network path traceroute feature is, in some cases, allocating a lot of memory: example profile.

The goal of this PR is to improve the performance of the parseTCP function from an allocation standpoint but also from a CPU usage point of view.

Benchmarks:

$ go test -benchmem -run=^$ -tags test -bench ^BenchmarkParseTCP$ github.com/DataDog/datadog-agent/pkg/networkpath/traceroute/tcp
# before
BenchmarkParseTCP-10    	 3401749	       338.8 ns/op	     816 B/op	      13 allocs/op
# after
BenchmarkParseTCP-10    	10998318	       103.4 ns/op	     384 B/op	       1 allocs/op

# benchstat
goos: darwin
goarch: arm64
pkg: github.com/DataDog/datadog-agent/pkg/networkpath/traceroute/tcp
cpu: Apple M1 Max
            │   old.txt    │               new.txt               │
            │    sec/op    │   sec/op     vs base                │
ParseTCP-10   334.90n ± 1%   95.70n ± 2%  -71.43% (p=0.000 n=10)

            │  old.txt   │              new.txt               │
            │    B/op    │    B/op     vs base                │
ParseTCP-10   816.0 ± 0%   384.0 ± 0%  -52.94% (p=0.000 n=10)

            │   old.txt   │              new.txt               │
            │  allocs/op  │ allocs/op   vs base                │
ParseTCP-10   13.000 ± 0%   1.000 ± 0%  -92.31% (p=0.000 n=10)

The main things this PR does, is move the things that are not moving, that are re-usable from one call to parseTCP to the next to a structure, owned by handlePackets. This allows parseTCP to reduce drastically the amount of allocation required.

Motivation

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

[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=46789831 --os-family=ubuntu

Note: This applies to commit 806dbaf

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 01b84eb1-1f2b-46b1-ac54-3a89dc1fefef Metrics dashboard Target profiles

Baseline: f481626
Comparison: 806dbaf

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

No significant changes in experiment optimization goals

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

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	basic_py_check	% cpu utilization	+1.92	[-0.84, +4.67]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+1.37	[+0.56, +2.19]	1	Logs
➖	idle_all_features	memory utilization	+0.82	[+0.72, +0.92]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.21, +0.24]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.33, +0.35]	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	[-0.09, +0.09]	1	Logs
➖	idle	memory utilization	-0.01	[-0.06, +0.05]	1	Logs bounds checks dashboard
➖	file_to_blackhole_500ms_latency	egress throughput	-0.04	[-0.28, +0.21]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.08	[-0.26, +0.10]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.73	[-1.22, -0.24]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.82	[-3.35, +1.71]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-1.00	[-1.05, -0.95]	1	Logs
➖	file_tree	memory utilization	-1.08	[-1.22, -0.95]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.13	[-1.85, -0.42]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10
✅	idle_all_features	memory_usage	10/10

Explanation

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".

[ken-schneider]

This looks great, thank you for helping out!

[paulcacheux]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 25m.

Use /merge -c to cancel this operation!