What if we used zstd in our trace writers

[ajgajg1134]

What does this PR do?

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[cit-pr-commenter[bot]]

Go Package Import Differences

Baseline: 20cde8458a938987c5a681edb89fb9ce639c0e41 Comparison: a6508c496ae6db5c01c930855f89ce79a64fd4bc

binary	os	arch	change
trace-agent	linux	arm64	+1, -0 +github.com/DataDog/zstd
trace-agent	windows	amd64	+1, -0 +github.com/DataDog/zstd
trace-agent	windows	386	+1, -0 +github.com/DataDog/zstd
trace-agent	darwin	amd64	+1, -0 +github.com/DataDog/zstd
trace-agent	darwin	arm64	+1, -0 +github.com/DataDog/zstd

[pr-commenter[bot]]

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=32596725 --os-family=ubuntu

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: d2bd9f41-5efc-4234-8a09-3d48d83103c3 Baseline: 1941f35b9b4c146b13cdf7422e9ad157f0fa7b31 Comparison: 146e2a710e3d37b3f7eeb9611824d2ca14965395

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	-0.46	[-6.83, +5.91]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	process_agent_standard_check_with_stats	memory utilization	+0.47	[+0.43, +0.51]
➖	process_agent_standard_check	memory utilization	+0.38	[+0.34, +0.43]
➖	process_agent_real_time_mode	memory utilization	+0.23	[+0.18, +0.28]
➖	uds_dogstatsd_to_api	ingress throughput	+0.02	[-0.19, +0.22]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.02	[-0.01, +0.04]
➖	trace_agent_json	ingress throughput	+0.01	[-0.03, +0.05]
➖	trace_agent_msgpack	ingress throughput	+0.01	[+0.00, +0.01]
➖	file_tree	memory utilization	-0.03	[-0.15, +0.10]
➖	idle	memory utilization	-0.03	[-0.07, +0.02]
➖	otel_to_otel_logs	ingress throughput	-0.10	[-0.54, +0.35]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.16	[-0.25, -0.08]
➖	basic_py_check	% cpu utilization	-0.41	[-2.95, +2.13]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.41	[-3.15, +2.32]
➖	file_to_blackhole	% cpu utilization	-0.46	[-6.83, +5.91]
➖	pycheck_1000_100byte_tags	% cpu utilization	-1.47	[-6.31, +3.37]

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".

[ahmed-mez]

/merge

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