[workloadmeta/collectors] Fix linter issues

[davidor]

What does this PR do?

Fixes a variety of linter issues in the workloadmeta collectors directory that I found while working on the code.

All the fixes are minor. The list includes:

• rename some vars to follow Go's guidelines (avoid using "_").
• sort imports.
• fix some typos.
• delete redundant aliases in imports.
• delete redundant "//+build".
• improve error handling in some tests.

Describe how to test/QA your changes

Skip.

[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=38098719 --os-family=ubuntu

Note: This applies to commit 7b15558e

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: d7e9818e-b3db-41e6-b1bc-68c4b8564ad8 Metrics dashboard Target profiles

Baseline: ff3269e00c93678d2a3dd7df167bd272a6071fcf Comparison: 7b15558e8d77d9583fae497be0ffe78f5271c9ba

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 | links | |------|----------------------------|--------------------|----------|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | tcp_syslog_to_blackhole | ingress throughput | +6.73 | [-6.57, +20.03] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | +0.36 | [-0.45, +1.17] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | idle | memory utilization | +0.13 | [+0.09, +0.17] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | file_tree | memory utilization | +0.01 | [-0.04, +0.06] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -0.41 | [-5.28, +4.46] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.73 | [-1.62, +0.15] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) | | ➖ | basic_py_check | % cpu utilization | -1.29 | [-3.81, +1.23] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3Ad7e9818e-b3db-41e6-b1bc-68c4b8564ad8&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719906820000&to_ts=1719918220000&live=false) |

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".

[davidor]

/merge

[dd-devflow[bot]]

:steam_locomotive: MergeQueue: pull request added to the queue

The median merge time in main is 25m.

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