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kube_metadata: Use cache when get namespace metadata #27171

Open keisku opened 4 days ago

keisku commented 4 days ago

What does this PR do?

Use cache when workloadmeta-kube_metadata gets namespace metadata.

Motivation

CONS-6403

Improving the kubemetadata.(*collector).parsePods() performance.

Currently, when workloadmeta-kube_metadata parses 100 Pods, the implementation results in 100 communications with the DCA to retrieve namespace metadata. Since the namespace metadata is the same for multiple Pods, there is no need to communicate with the DCA for each Pod.

https://github.com/DataDog/datadog-agent/blob/1248ec4f0bece8d46a33f20ff1447c7de7be6706/comp/core/workloadmeta/collectors/internal/kubemetadata/kubemetadata.go#L210-L293

Additional Notes

https://github.com/DataDog/datadog-agent/blob/1cf1cce4b5711e0929b4eca8d3fd1c51cf3b8a4c/pkg/config/setup/config.go#L1499

https://github.com/DataDog/datadog-agent/blob/a9f51824ae913c875c8c39f187aa91b124b99de2/pkg/util/clusteragent/clusteragent.go#L405-L417

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

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=37916077 --os-family=ubuntu

Note: This applies to commit 0638897b

pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Run ID: 4a11ee24-34cb-4cc5-b4f6-39008a1504ac Metrics dashboard Target profiles

Baseline: 1248ec4f0bece8d46a33f20ff1447c7de7be6706 Comparison: 0638897b4cada159e588c26120ac6dba7c20a2a4

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 | +3.20 | [+3.06, +3.33] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.96 | [-2.87, +6.79] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | +0.83 | [+0.02, +1.65] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&live=false) | | ➖ | basic_py_check | % cpu utilization | +0.76 | [-1.95, +3.47] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.75 | [-11.91, +13.41] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&live=false) | | ➖ | idle | memory utilization | +0.48 | [+0.43, +0.53] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&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%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&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%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -1.01 | [-1.89, -0.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A4a11ee24-34cb-4cc5-b4f6-39008a1504ac&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=1719613490000&to_ts=1719624890000&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".