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Main repository for Datadog Agent
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Add rdnsquerier config, rate limiter, resolver and internal telemetry. #27170

Open jmw51798 opened 4 days ago

jmw51798 commented 4 days ago

What does this PR do?

Continues implementing rdnsquerier functionality for providing reverse DNS enrichment of private IP addresses to NDM NetFlow events. This change includes agent config for the rdnsquerier component, a rate limiter, separated out resolver, and internal telemetry.

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

No specific QA is needed for these changes because the component is disabled by default. QA has already been done to confirm that it is disabled by default and that the changes cause no difference in the agent or NDM NetFlow functionality. These changes can be tested/QAed with subsequent related changes once full reverse DNS enrichment and related configuration is supported.

pr-commenter[bot] commented 4 days ago

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=38135946 --os-family=ubuntu

Note: This applies to commit 73616e1b

pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Run ID: b9da06c4-aebc-4db4-8a9c-f0d86b69965e Metrics dashboard Target profiles

Baseline: 1248ec4f0bece8d46a33f20ff1447c7de7be6706 Comparison: 73616e1bf32b9bf08263aaac9c040adb909f06b7

Performance changes are noted in the perf column of each table:

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 | |------|----------------------------|--------------------|----------|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | file_tree | memory utilization | +1.48 | [+1.36, +1.61] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&live=false) | | ➖ | basic_py_check | % cpu utilization | +0.51 | [-2.24, +3.25] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.46 | [-0.44, +1.36] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +0.26 | [-4.48, +5.00] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&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%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&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%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.11 | [-0.92, +0.71] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&live=false) | | ➖ | idle | memory utilization | -0.63 | [-0.67, -0.58] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -7.79 | [-20.10, +4.53] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Ab9da06c4-aebc-4db4-8a9c-f0d86b69965e&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=1719923324000&to_ts=1719934724000&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".