[CWS] optimize security agent usage of remote workload meta

[paulcacheux]

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What does this PR do?

This PR optimizes the stream operation of the remote workload meta collector, by letting the Recv do its job and removing the DoWithTimeout call.

Each DoWithTimeout call creates a new goroutime, a new timer, and then select on it incurring a huge cost for each Recv Call.

DoWithTimeout is good for unfrequent calls, for example in the tagger where there is a real request/response model. In the case of a stream where we expect a lot of events to go through, this starts to be a bottleneck. Moreover we actually don't really want to have a 10 min timeout, if the connection is still up and the stream flowing we can continue receiving events.

Notebook showing the improvement https://ddstaging.datadoghq.com/notebook/7655823/paul-mar-12-2024-09-52

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter[bot]]

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Regression Detector Results

Run ID: 28f66d3d-532f-43c0-a3ef-6e1fb598beb9 Baseline: c6b52ff2d46479df800861fe1d2991affc430d1a Comparison: 7254e3046d751341ce39f6132e8b5f53051b70b8

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.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	-0.74	[-7.30, +5.82]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	pycheck_1000_100byte_tags	% cpu utilization	+3.48	[-1.87, +8.83]
➖	basic_py_check	% cpu utilization	+0.74	[-1.61, +3.09]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.52	[-2.43, +3.47]
➖	file_tree	memory utilization	+0.36	[+0.26, +0.45]
➖	otel_to_otel_logs	ingress throughput	+0.10	[-0.33, +0.52]
➖	trace_agent_msgpack	ingress throughput	+0.02	[+0.01, +0.03]
➖	trace_agent_json	ingress throughput	+0.01	[-0.02, +0.04]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.00, +0.00]
➖	idle	memory utilization	-0.01	[-0.05, +0.03]
➖	process_agent_standard_check	memory utilization	-0.09	[-0.13, -0.05]
➖	process_agent_real_time_mode	memory utilization	-0.09	[-0.13, -0.05]
➖	process_agent_standard_check_with_stats	memory utilization	-0.11	[-0.14, -0.09]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.12	[-0.22, -0.03]
➖	file_to_blackhole	% cpu utilization	-0.74	[-7.30, +5.82]

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

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