use Content Length hint to pre-allocate buffer when reading body from sysprobe

[paulcacheux]

What does this PR do?

Now that the core process code is running in the core agent, most of the process agent work is spent fetching connections from the system-probe and this is reflected in the amount of allocations done by the process agent. Example profile.

This PR improves the situation a bit by taking advantage of the Content Length header of system probe responses to pre-allocate the response buffer.

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=48102422 --os-family=ubuntu

Note: This applies to commit a1c4c93

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 72914266-4759-4b4d-b013-b5b08dd7a1e9 Metrics dashboard Target profiles

Baseline: b302cea
Comparison: a1c4c93

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
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.70	[+0.97, +2.44]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+1.32	[-1.14, +3.79]	1	Logs
➖	idle_all_features	memory utilization	+0.90	[+0.80, +1.00]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	+0.45	[+0.40, +0.50]	1	Logs
➖	file_tree	memory utilization	+0.28	[+0.15, +0.41]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.23	[+0.13, +0.33]	1	Logs bounds checks dashboard
➖	file_to_blackhole_300ms_latency	egress throughput	+0.03	[-0.15, +0.21]	1	Logs
➖	idle	memory utilization	+0.01	[-0.04, +0.05]	1	Logs bounds checks dashboard
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.00	[-0.23, +0.22]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.11, +0.10]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.01	[-0.34, +0.32]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.24	[-1.04, +0.57]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.25	[-0.74, +0.24]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.34	[-0.59, -0.10]	1	Logs
➖	quality_gate_idle	memory utilization	-0.40	[-0.45, -0.35]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-0.57	[-3.21, +2.06]	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
✅	quality_gate_idle	memory_usage	10/10
✅	quality_gate_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".

[paulcacheux]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 22m.

Use /merge -c to cancel this operation!