Fix Config type issues

[dineshg13]

What does this PR do?

JIRA https://datadoghq.atlassian.net/browse/OTEL-2192

When Collector enforced strict types see issue , the OTLP ingest was breaking as we are alway do a get .

To overcome the issue we do following

• After the string map is constructed, we decode into serializerexporter.MetricsConfig
• We reverse decode serializerexporter.MetricsConfig into map, that would result in map with strict types.

Motivation

Describe how to test/QA your changes

QA done. We also have a e2e test that uses this configuration.

Possible Drawbacks / Trade-offs

Additional Notes

[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 create-vm --pipeline-id=48549725 --os-family=ubuntu

Note: This applies to commit 9fa4f66

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 21aed789-3be9-4bb6-9ee5-de12ef620488

Baseline: 361d38b
Comparison: 9fa4f66
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	file_tree	memory utilization	+0.52	[+0.39, +0.64]	1	Logs
➖	idle	memory utilization	+0.33	[+0.28, +0.38]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.22	[-0.29, +0.72]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.16	[+0.11, +0.21]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.07	[-0.12, +0.26]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.39, +0.43]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.27, +0.31]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.09, +0.12]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	quality_gate_idle	memory utilization	-0.19	[-0.24, -0.15]	1	Logs bounds checks dashboard
➖	file_to_blackhole_500ms_latency	egress throughput	-0.26	[-0.50, -0.02]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.54	[-1.26, +0.18]	1	Logs
➖	idle_all_features	memory utilization	-0.57	[-0.67, -0.47]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	-0.61	[-3.99, +2.77]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.67	[-0.77, -0.58]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-1.05	[-4.80, +2.70]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	file_to_blackhole_100ms_latency	lost_bytes	6/10
❌	quality_gate_idle	memory_usage	6/10	bounds checks dashboard
❌	idle	memory_usage	7/10	bounds checks dashboard
❌	idle_all_features	memory_usage	9/10	bounds checks dashboard
❌	quality_gate_idle_all_features	memory_usage	9/10	bounds checks dashboard
✅	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_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	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".

[dineshg13]

/merge

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2024-11-08 19:09:40 UTC ℹ️ MergeQueue: pull request added to the queue

The median merge time in main is 24m.