optimise tagger server chunking function using iter package

[adel121]

What does this PR do?

This PR leverages the iter golang package introduced in golang 1.23.0 in order to optimise the chunk function used in the tagger server.

Motivation

Optimise memory consumption of the chunking function used to split the tagger stream events into a sequence of chunks, each having a bounded size (in bytes).

Describe how you validated your changes

Unit tests are implemented.
E2E should be enough since they validated the remote tagger is still working.

Possible Drawbacks / Trade-offs

None

Additional Notes

None

[clamoriniere]

The change looks good.
Could you try to put in place a benchmark to compare the 2 implementations?

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=50335573 --os-family=ubuntu

Note: This applies to commit c59e510

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: e6bebf07-eccf-412a-ba2d-754927d91cef

Baseline: d4ed527
Comparison: c59e510
Diff

Optimization Goals: ❌ Regression(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+5.94	[+2.07, +9.82]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+5.94	[+2.07, +9.82]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+2.78	[-0.81, +6.36]	1	Logs
➖	quality_gate_logs	% cpu utilization	+2.76	[-0.26, +5.77]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+1.09	[+0.99, +1.20]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	+0.55	[-0.16, +1.25]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.32	[+0.23, +0.40]	1	Logs
➖	quality_gate_idle	memory utilization	+0.07	[+0.02, +0.11]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.84, +0.92]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.64, +0.69]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.08, +0.10]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.04	[-0.68, +0.59]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.10	[-0.56, +0.36]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.25	[-1.02, +0.52]	1	Logs
➖	file_tree	memory utilization	-0.30	[-0.44, -0.16]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.42	[-1.19, +0.34]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.01	[-1.75, -0.28]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	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_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_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	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

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