[OTEL-2304] export obfuscateStatsGroup function

[jackgopack4]

What does this PR do?

removes *Agent type receiver from obfuscateStatsGroup function and exports for use in other Datadog components

Motivation

enable obfuscation for APM trace metrics for OTel Collector customers inside datadogconnector: open-telemetry/opentelemetry-collector-contrib#35401

We don't want to have to duplicate this function or duplicate instantiating an entire Agent inside datadogconnector component; if this is accepted and merged I can just import this function and pass in the obfuscator we instantiate there.

Describe how you validated your changes

made change to existing unit test

Possible Drawbacks / Trade-offs

need to pass obfuscator pointer to the function instead of just calling on Agent struct pointer; any downstream dependencies that import this function

Additional Notes

If we know that any other datadog or downstream dependencies are using this function currently; I'd be happy to break out a helper function that takes obfuscator as a pointer and keep the existing signature as a shell for compatibility. Let me know if this is the case

[jackgopack4]

if we have other dependencies not in Agent using this function; I could break out this change to a helper function instead of changing the signature

[agent-platform-auto-pr]

Package size comparison

Comparison with ancestor 3c882e71c8b1830d40be8d4cfae580e40414d207

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-amd64-deb	0.01MB	⚠️	1265.88MB	1265.86MB	140.00MB
datadog-iot-agent-amd64-deb	0.00MB	✅	113.28MB	113.28MB	10.00MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	78.41MB	78.41MB	10.00MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	526.65MB	526.65MB	70.00MB
datadog-agent-x86_64-rpm	0.01MB	⚠️	1275.11MB	1275.10MB	140.00MB
datadog-agent-x86_64-suse	0.01MB	⚠️	1275.11MB	1275.10MB	140.00MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	113.35MB	113.35MB	10.00MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	113.35MB	113.35MB	10.00MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	78.49MB	78.49MB	10.00MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	78.49MB	78.49MB	10.00MB
datadog-agent-arm64-deb	0.02MB	⚠️	1000.88MB	1000.87MB	140.00MB
datadog-iot-agent-arm64-deb	0.00MB	✅	108.77MB	108.77MB	10.00MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	55.64MB	55.64MB	10.00MB
datadog-agent-aarch64-rpm	0.02MB	⚠️	1010.10MB	1010.08MB	140.00MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	108.84MB	108.84MB	10.00MB

Decision

⚠️ Warning

[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=50965208 --os-family=ubuntu

Note: This applies to commit 8f860a0

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 9b2d9a97-0a50-4204-8b74-a93564fe96ff

Baseline: 3c882e7
Comparison: 8f860a0
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_idle_all_features	memory utilization	+1.77	[+1.64, +1.89]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.57	[-0.15, +1.29]	1	Logs
➖	file_tree	memory utilization	+0.42	[+0.29, +0.54]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.29	[-0.50, +1.09]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.22	[-0.54, +0.99]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.06	[-0.80, +0.92]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.05	[-0.41, +0.51]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.02	[-0.88, +0.92]	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
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.00	[-0.83, +0.83]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.01	[-0.64, +0.63]	1	Logs
➖	quality_gate_idle	memory utilization	-0.03	[-0.07, +0.02]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	-0.03	[-0.75, +0.69]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.14	[-0.79, +0.52]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-1.92	[-1.98, -1.86]	1	Logs
➖	quality_gate_logs	% cpu utilization	-4.03	[-6.94, -1.13]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	file_to_blackhole_100ms_latency	lost_bytes	9/10
❌	file_to_blackhole_300ms_latency	lost_bytes	9/10
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	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	memory_usage	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_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.
• 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.

[ichinaski]

LGTM! Just a small comment around the lazy initialization of the Obfuscator, I wonder if we can further simplify this part

[ichinaski]

@ajgajg1134 is there any reason why this initialization happens outside the constructor? I think it could simplify the code a bit if we move this to the Agent constructor, especially now that the obfuscation function will be exported?

[ajgajg1134]

Huh, yeah it looks like it used to be initialized in the newAgent but very recently was moved here: #31336 @sethsamuel do you happen to recall why you moved the obfuscator initialization to be lazy init outside the NewAgent func?

I'm not entirely sure why it was moved but it seems like it should probably be moved back to the agent so that a "NewAgent" can know that it safely can use the obfuscator field and doesn't have to always remember to lazy init it for every use :P

[jackgopack4]

seems reasonable to me; we are going to be calling obfuscate.NewObfuscator in the datadog connector

[jackgopack4]

looks like @sethsamuel is OOO until Monday

[jackgopack4]

this may not be the exact function we need to use for datadogconnector; I think it may be missing out on more data types. We are calculating more than just sql, cassandra, and redis metrics in connector so we will either need to enhance this function, or write our own. Converting this PR to draft for now, especially since I'm out for two weeks and will be calking with APM Astra after the holidays @jdgumz