[EBPF-429] Increase the limit of allocations in ebpfcheck test to avoid flakes

[gjulianm]

What does this PR do?

Increase the limit of allocations in the MultipleBatch mode of the entry count function in ebpfcheck.

Motivation

The test pkg/collector/corechecks/ebpf/probe/ebpfcheck.TestHashMapNumberOfEntriesNoExtraAllocations/10000MaxEntries/MultipleBatch was detected as flaky. It fails very sporadically due to an extra allocation over the limit defined as acceptable. Investigating further, it seems that Go might do an extra allocation in the map that maintains the list of keys that were already seen. As we do not have any control over how Go does those allocations, I've just increased the limit to avoid the flakes.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 3be44f31-952c-443a-aaa6-59fc9b73a434 Baseline: f0e5f932ecda5207391ca3770a178d3308f42621 Comparison: 5e684247fa32b5f4b724a10f41642f2564fff65f Total CPUs: 7

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

Experiments with missing or malformed data

• basic_py_check

Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.

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	-1.23	[-7.79, +5.33]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	idle	memory utilization	+0.40	[+0.38, +0.43]
➖	otel_to_otel_logs	ingress throughput	+0.36	[-0.25, +0.97]
➖	process_agent_standard_check_with_stats	memory utilization	+0.16	[+0.13, +0.20]
➖	trace_agent_json	ingress throughput	+0.01	[-0.01, +0.03]
➖	trace_agent_msgpack	ingress throughput	+0.01	[+0.00, +0.01]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.07	[-1.49, +1.35]
➖	file_tree	memory utilization	-0.21	[-0.29, -0.12]
➖	process_agent_standard_check	memory utilization	-0.68	[-0.72, -0.64]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.81	[-0.87, -0.75]
➖	process_agent_real_time_mode	memory utilization	-0.93	[-0.96, -0.89]
➖	file_to_blackhole	% cpu utilization	-1.23	[-7.79, +5.33]

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".

[brycekahle]

/merge

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