feat(aggregate transform): add more aggregations to aggregate transform

[esensar]

Adds more functions to aggregate transform.

Closes: #3668

[esensar]

I have made this just a draft, to confirm if it makes sense to add these to the existing aggregate transform. Right now I have just added an enum and match on it, but I think it would probably be better to have different implementations for different aggregates, since this will get complicated quickly this way.

This draft just implements count aggregate for absolute metrics. I will implement other ones once I confirm the right implementation direction.

[esensar]

I still need to clean some things up here, since it is a bit messy, but I wanted to get initial review on this.

[github-actions[bot]]

Regression Detector Results

Run ID: e9949651-5947-4aeb-9c17-86e6c279fc3e Metrics dashboard

Baseline: 9a78ea30fc10de6b41e24dcddcb2aeb86f54e96e Comparison: f346a318535dbeffb97e5cbebec8c40945e36cdb

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

No significant changes in experiment optimization goals

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

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

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

perf	experiment	goal	Δ mean %	Δ mean % CI	links
✅	file_to_blackhole	egress throughput	+26.98	[+19.19, +34.78]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	links
✅	file_to_blackhole	egress throughput	+26.98	[+19.19, +34.78]
➖	http_elasticsearch	ingress throughput	+3.16	[+2.98, +3.34]
➖	socket_to_socket_blackhole	ingress throughput	+2.27	[+2.17, +2.37]
➖	syslog_humio_logs	ingress throughput	+1.83	[+1.69, +1.98]
➖	splunk_hec_route_s3	ingress throughput	+1.06	[+0.76, +1.36]
➖	otlp_grpc_to_blackhole	ingress throughput	+1.06	[+0.95, +1.17]
➖	datadog_agent_remap_datadog_logs	ingress throughput	+1.01	[+0.81, +1.20]
➖	datadog_agent_remap_blackhole	ingress throughput	+0.93	[+0.82, +1.05]
➖	syslog_log2metric_humio_metrics	ingress throughput	+0.79	[+0.64, +0.94]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	+0.67	[+0.60, +0.74]
➖	http_to_http_acks	ingress throughput	+0.61	[-0.66, +1.88]
➖	http_text_to_http_json	ingress throughput	+0.56	[+0.44, +0.69]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.23	[+0.11, +0.35]
➖	http_to_http_noack	ingress throughput	+0.22	[+0.12, +0.32]
➖	http_to_http_json	ingress throughput	+0.04	[-0.00, +0.09]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	+0.02	[-0.06, +0.10]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.01	[-0.10, +0.12]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	+0.01	[-0.09, +0.10]
➖	http_to_s3	ingress throughput	-0.05	[-0.32, +0.22]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	-0.06	[-0.20, +0.07]
➖	otlp_http_to_blackhole	ingress throughput	-0.21	[-0.34, -0.09]
➖	syslog_loki	ingress throughput	-0.30	[-0.38, -0.22]
➖	fluent_elasticsearch	ingress throughput	-0.51	[-1.00, -0.02]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	-0.76	[-0.86, -0.66]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	-0.97	[-1.07, -0.87]
➖	syslog_splunk_hec_logs	ingress throughput	-2.24	[-2.34, -2.15]

Explanation

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".