[workloadmeta/collectors/kubeapiserver] Use minimal pod representation

[davidor]

What does this PR do?

This PR optimizes the memory usage of the kubeapiserver workloadmeta collector.

The collector now uses a minimal pod struct with only the fields we actually need. This avoids unmarshalling the full pod. The saved memory is noticeable in clusters with many pods. It's specially important when the Cluster Agent starts, since it needs to fetch all pods from the API server. These changes help lower the memory spike at startup. This is useful when that peak is used to set Kubernetes memory requests.

I attach a screenshot from one of the clusters where I tested this.
The first chart shows container.memory.usage.peak and the second one container.memory.usage. In both of them, the second half are pods built from this PR.

Describe how you validated your changes

CI + deploying custom image on some clusters to compare memory usage.

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor ffb33d9
📊 Static Quality Gates Dashboard

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${+0.03}$$	$${706.24}$$ < $${708.41}$$	$${-0}$$	$${173.54}$$ < $${174.49}$$
✅	agent_deb_amd64_fips	$${+0.03}$$	$${701.53}$$ < $${704.0}$$	$${+0.02}$$	$${172.49}$$ < $${173.75}$$
✅	agent_heroku_amd64	$${0}$$	$${326.99}$$ < $${329.53}$$	$${-0}$$	$${87.1}$$ < $${88.45}$$
✅	agent_msi	$${+0.04}$$	$${570.81}$$ < $${982.08}$$	$${-0.02}$$	$${142.7}$$ < $${143.02}$$
✅	agent_rpm_amd64	$${+0.03}$$	$${706.22}$$ < $${708.38}$$	$${-0.03}$$	$${176.16}$$ < $${177.66}$$
✅	agent_rpm_amd64_fips	$${+0.03}$$	$${701.52}$$ < $${703.99}$$	$${-0.01}$$	$${175.38}$$ < $${176.6}$$
✅	agent_rpm_arm64	$${+0.03}$$	$${687.72}$$ < $${693.52}$$	$${+0.07}$$	$${159.68}$$ < $${161.26}$$
✅	agent_rpm_arm64_fips	$${+0.03}$$	$${683.87}$$ < $${688.48}$$	$${-0.01}$$	$${158.98}$$ < $${160.55}$$
✅	agent_suse_amd64	$${+0.03}$$	$${706.22}$$ < $${708.38}$$	$${-0.03}$$	$${176.16}$$ < $${177.66}$$
✅	agent_suse_amd64_fips	$${+0.03}$$	$${701.52}$$ < $${703.99}$$	$${-0.01}$$	$${175.38}$$ < $${176.6}$$
✅	agent_suse_arm64	$${+0.03}$$	$${687.72}$$ < $${693.52}$$	$${+0.07}$$	$${159.68}$$ < $${161.26}$$
✅	agent_suse_arm64_fips	$${+0.03}$$	$${683.87}$$ < $${688.48}$$	$${-0.01}$$	$${158.98}$$ < $${160.55}$$
✅	docker_agent_amd64	$${+0.03}$$	$${768.07}$$ < $${770.72}$$	$${+0.01}$$	$${261.28}$$ < $${262.45}$$
✅	docker_agent_arm64	$${+0.03}$$	$${774.15}$$ < $${780.2}$$	$${-0}$$	$${250.34}$$ < $${252.63}$$
✅	docker_agent_jmx_amd64	$${+0.03}$$	$${958.94}$$ < $${961.6}$$	$${+0.01}$$	$${329.91}$$ < $${331.08}$$
✅	docker_agent_jmx_arm64	$${+0.03}$$	$${953.75}$$ < $${959.8}$$	$${-0.01}$$	$${314.96}$$ < $${317.27}$$
✅	docker_cluster_agent_amd64	$${+0.04}$$	$${180.75}$$ < $${181.08}$$	$${+0.01}$$	$${63.86}$$ < $${64.49}$$
✅	docker_cluster_agent_arm64	$${+0.06}$$	$${196.62}$$ < $${198.49}$$	$${+0.01}$$	$${60.13}$$ < $${61.17}$$
✅	docker_cws_instrumentation_amd64	$${0}$$	$${7.13}$$ < $${7.18}$$	$${-0}$$	$${2.99}$$ < $${3.33}$$
✅	docker_cws_instrumentation_arm64	$${0}$$	$${6.69}$$ < $${6.92}$$	$${0}$$	$${2.73}$$ < $${3.09}$$
✅	docker_dogstatsd_amd64	$${-0}$$	$${38.78}$$ < $${39.38}$$	$${-0}$$	$${15.01}$$ < $${15.82}$$
✅	docker_dogstatsd_arm64	$${+0}$$	$${37.13}$$ < $${37.94}$$	$${-0}$$	$${14.34}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${30.0}$$ < $${30.61}$$	$${+0}$$	$${7.94}$$ < $${8.79}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${28.15}$$ < $${29.11}$$	$${-0}$$	$${6.81}$$ < $${7.71}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${30.0}$$ < $${30.61}$$	$${-0}$$	$${7.95}$$ < $${8.8}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${30.0}$$ < $${30.61}$$	$${-0}$$	$${7.95}$$ < $${8.8}$$
✅	iot_agent_deb_amd64	$${0}$$	$${42.99}$$ < $${43.29}$$	$${-0}$$	$${11.26}$$ < $${12.04}$$
✅	iot_agent_deb_arm64	$${0}$$	$${40.12}$$ < $${40.92}$$	$${-0}$$	$${9.63}$$ < $${10.45}$$
✅	iot_agent_deb_armhf	$${0}$$	$${40.7}$$ < $${41.03}$$	$${-0}$$	$${9.82}$$ < $${10.62}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${42.99}$$ < $${43.29}$$	$${+0}$$	$${11.28}$$ < $${12.06}$$
✅	iot_agent_suse_amd64	$${0}$$	$${42.99}$$ < $${43.29}$$	$${+0}$$	$${11.28}$$ < $${12.06}$$

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 16c5c27a-aa8a-43a2-b4ba-526d8a42f0fe

Baseline: ffb33d9
Comparison: 205641c
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	-1.49	[-4.47, +1.49]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	tcp_syslog_to_blackhole	ingress throughput	+0.97	[+0.91, +1.04]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	+0.23	[+0.18, +0.28]	1	Logs
➖	file_tree	memory utilization	+0.08	[+0.03, +0.14]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.12, +0.14]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.01	[-0.09, +0.11]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.08, +0.09]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	-0.02	[-0.15, +0.12]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	-0.02	[-0.25, +0.21]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.02	[-0.41, +0.37]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.05	[-0.42, +0.32]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.05	[-0.46, +0.36]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	-0.05	[-0.21, +0.11]	1	Logs
➖	quality_gate_idle	memory utilization	-0.10	[-0.14, -0.05]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	-0.13	[-0.17, -0.09]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	-0.17	[-0.22, -0.13]	1	Logs
➖	ddot_metrics	memory utilization	-0.19	[-0.43, +0.05]	1	Logs
➖	otlp_ingest_metrics	memory utilization	-0.23	[-0.39, -0.08]	1	Logs
➖	docker_containers_memory	memory utilization	-0.25	[-0.32, -0.18]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	-0.32	[-0.52, -0.11]	1	Logs bounds checks dashboard
➖	ddot_logs	memory utilization	-0.46	[-0.53, -0.38]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	-0.85	[-1.06, -0.64]	1	Logs
➖	quality_gate_logs	% cpu utilization	-1.03	[-2.48, +0.42]	1	Logs bounds checks dashboard
➖	docker_containers_cpu	% cpu utilization	-1.49	[-4.47, +1.49]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	docker_containers_cpu	simple_check_run	10/10
✅	docker_containers_memory	memory_usage	10/10
✅	docker_containers_memory	simple_check_run	10/10
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	lost_bytes	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	cpu_usage	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.

[zhuminyi]

Since protobuf is not supported, if kubernetes_apiserver_use_protobuf is enabled, it will fail to deserialize.

[davidor]

You're right. I think the option is not very widely used, but still, I updated the code so that it fallbacks to the previous behavior when kubernetes_apiserver_use_protobuf is enabled.

[zhuminyi]

resource.Quantity has unexported fields, deepcopy will fail.

  package main

  import (
  	"fmt"
  	"github.com/mohae/deepcopy"
  	"k8s.io/apimachinery/pkg/api/resource"
  )

  func main() {
  	original := resource.MustParse("100m")
  	copied := deepcopy.Copy(original).(resource.Quantity)

  	fmt.Printf("Original: %s (MilliValue: %d)\n", original.String(), original.MilliValue())
  	fmt.Printf("Copied:   %s (MilliValue: %d)\n", copied.String(), copied.MilliValue())
  	fmt.Printf("Equal:    %v\n", original.Equal(copied))
  }

Output:
Original: 100m (MilliValue: 100)
Copied: 0 (MilliValue: 0)
Equal: false

[davidor]

You're right. I've updated the code so that (p *MinimalPod) DeepCopyObject() doesn't rely on github.com/mohae/deepcopy. I've also updated the tests to check for this. This makes the code a bit more complex, but it's needed to fix the issue you mentioned 👍

[davidor]

@zhuminyi I've addressed the 2 issues that you mentioned

[davidor]

/merge

[dd-devflow-routing-codex]

View all feedbacks in Devflow UI.

2026-01-08 13:36:29 UTC ℹ️ Start processing command /merge

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2026-01-08 13:36:34 UTC ℹ️ MergeQueue: pull request added to the queue

The expected merge time in main is approximately 1h (p90).

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2026-01-08 14:28:58 UTC ℹ️ MergeQueue: This merge request was merged