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Allow config leaf nodes to have complex types, remove ArrayNode #30746

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merged 4 commits into from
Nov 6, 2024

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dustmop
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@dustmop dustmop commented Nov 4, 2024

What does this PR do?

In NodeTreeModel, allow leaf nodes to have complex types like map or slice. This breaks an earlier assumption in the design: that leaf values must be scalar types. UnmarshalKey handles these leafs by constructing new nodes on demand, but not inserting those nodes into the config tree.

Motivation

Simplify the design, fix the bug where the config thinks leaf with map values contains more leafs within it.

Describe how to test/QA your changes

Tests cover the change in functionality.

Possible Drawbacks / Trade-offs

Additional Notes

@dustmop dustmop added changelog/no-changelog team/agent-shared-components qa/done QA done before merge and regressions are covered by tests labels Nov 4, 2024
@dustmop dustmop added this to the 7.61.0 milestone Nov 4, 2024
@dustmop dustmop requested a review from a team as a code owner November 4, 2024 21:45
@dustmop dustmop requested a review from hush-hush November 4, 2024 21:45
@github-actions github-actions bot added the long review PR is complex, plan time to review it label Nov 4, 2024
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cit-pr-commenter bot commented Nov 4, 2024

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: d95fc418-77fc-4136-8a05-dcdfeaae0aa4

Baseline: 88a940f
Comparison: f52239f
Diff

Optimization Goals: ❌ Significant changes detected

perf experiment goal Δ mean % Δ mean % CI trials links
pycheck_lots_of_tags % cpu utilization -5.88 [-9.32, -2.43] 1 Logs

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
uds_dogstatsd_to_api_cpu % cpu utilization +2.58 [+1.84, +3.32] 1 Logs
quality_gate_idle memory utilization +1.14 [+1.08, +1.21] 1 Logs bounds checks dashboard
idle_all_features memory utilization +1.03 [+0.89, +1.16] 1 Logs bounds checks dashboard
idle memory utilization +0.08 [+0.02, +0.14] 1 Logs bounds checks dashboard
file_to_blackhole_300ms_latency egress throughput +0.04 [-0.14, +0.22] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.01] 1 Logs
file_to_blackhole_100ms_latency egress throughput -0.00 [-0.22, +0.22] 1 Logs
uds_dogstatsd_to_api ingress throughput -0.01 [-0.11, +0.10] 1 Logs
file_to_blackhole_0ms_latency egress throughput -0.03 [-0.36, +0.31] 1 Logs
file_to_blackhole_500ms_latency egress throughput -0.07 [-0.31, +0.18] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.20 [-0.68, +0.27] 1 Logs
otel_to_otel_logs ingress throughput -0.32 [-1.13, +0.49] 1 Logs
file_tree memory utilization -0.37 [-0.52, -0.22] 1 Logs
tcp_syslog_to_blackhole ingress throughput -0.59 [-0.63, -0.54] 1 Logs
basic_py_check % cpu utilization -1.49 [-5.36, +2.38] 1 Logs
quality_gate_idle_all_features memory utilization -3.59 [-3.70, -3.47] 1 Logs bounds checks dashboard
pycheck_lots_of_tags % cpu utilization -5.88 [-9.32, -2.43] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
quality_gate_idle memory_usage 4/10 bounds checks dashboard
idle memory_usage 9/10 bounds checks dashboard
file_to_blackhole_0ms_latency memory_usage 10/10
file_to_blackhole_1000ms_latency memory_usage 10/10
file_to_blackhole_100ms_latency memory_usage 10/10
file_to_blackhole_300ms_latency memory_usage 10/10
file_to_blackhole_500ms_latency memory_usage 10/10
idle_all_features memory_usage 10/10 bounds checks dashboard
quality_gate_idle_all_features memory_usage 10/10 bounds checks dashboard

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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

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".

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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=48258524 --os-family=ubuntu

Note: This applies to commit f52239f

@dustmop
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dustmop commented Nov 6, 2024

/merge

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dd-devflow bot commented Nov 6, 2024

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 23m.

Use /merge -c to cancel this operation!

@dd-mergequeue dd-mergequeue bot merged commit a007661 into main Nov 6, 2024
213 checks passed
@dd-mergequeue dd-mergequeue bot deleted the dustin.long/leaf-map-or-slice branch November 6, 2024 15:19
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