fix(throttle transform)!: make events_discarded_total internal metric with key tag opt-in

[dsmith3197]

The key tag has unbounded cardinality, so this tag is now opt-in.

Fixes #17778

[pront]

Deleting TODOs sparks joy.

[jszwedko]

I think we should consider making this tag opt-in since users do seem to find it useful to be able to graph dropped events per partition. key is generally a bounded, though potentially large, set.

[dsmith3197]

I think we should consider making this tag opt-in since users do seem to find it useful to be able to graph dropped events per partition. key is generally a bounded, though potentially large, set.

That is fair. This is very similar to the file tag. It may be worthwhile to come up with a comprehensive solution here. Particularly, I think it would be worthwhile to include cardinality controls for tags with potentially unbounded cardinality. We would set an upper bound on cardinality for the given tag and only include the tag if we're below the limit. Note that the cardinality limit transform is not sufficient here because users have reported performance issues in the internal metrics source itself.

[jszwedko]

We would set an upper bound on cardinality for the given tag and only include the tag if we're below the limit. Note that the cardinality limit transform is not sufficient here because users have reported performance issues in the internal metrics source itself.

Yeah, I agree. It would be useful to come up with a generalized solution here that could be applied across components that could include things like a cardinality limit. In absence of that, I'd suggest just making this one opt-in or opt-out.

[jszwedko]

Outside of the scope of this PR, but we may want to think about renaming this to component_events_discarded_total in the future.

[dsmith3197]

Yeah, I'm going back and forth on that. That would require extending the current component specification for the component_events_discarded_total metric and could also significantly increase the cardinality of that metric. If there are cardinality issues, I would prefer that the impact be isolated to a one-off, single-use metric rather than impacting a metric used by all components. However, that does result in an inferior UX on the other hand in my opinion.

[dsmith3197]

I'll add back the TODO.

[github-actions]

Regression Detector Results

Run ID: 53f15605-a4b1-41fd-81f2-052dca114025
Baseline: c68b2b8
Comparison: 678605c
Total vector CPUs: 7

Explanation

A regression test is an integrated performance test for vector in a repeatable rig, with varying configuration for vector. What follows is a statistical summary of a brief vector run for each configuration across SHAs given above. The goal of these tests are to determine quickly if vector performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
syslog_log2metric_humio_metrics	ingress throughput	+1.17	[+1.06, +1.28]	100.00%
syslog_regex_logs2metric_ddmetrics	ingress throughput	+1.05	[+0.95, +1.15]	100.00%
datadog_agent_remap_blackhole	ingress throughput	+1.03	[+0.95, +1.11]	100.00%
datadog_agent_remap_blackhole_acks	ingress throughput	+0.49	[+0.40, +0.58]	100.00%
fluent_elasticsearch	ingress throughput	+0.36	[-0.09, +0.81]	80.68%
datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.31	[+0.24, +0.39]	100.00%
http_elasticsearch	ingress throughput	+0.28	[+0.22, +0.34]	100.00%
datadog_agent_remap_datadog_logs	ingress throughput	+0.22	[+0.12, +0.31]	99.99%
http_to_http_noack	ingress throughput	+0.16	[+0.06, +0.25]	99.46%
http_to_s3	ingress throughput	+0.07	[-0.20, +0.34]	31.50%
http_to_http_acks	ingress throughput	+0.03	[-1.27, +1.33]	3.24%
http_to_http_json	ingress throughput	+0.01	[-0.06, +0.08]	19.15%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.15]	0.69%
splunk_hec_indexer_ack_blackhole	ingress throughput	-0.00	[-0.14, +0.14]	3.02%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.02	[-0.13, +0.10]	18.03%
http_text_to_http_json	ingress throughput	-0.03	[-0.16, +0.11]	24.56%
splunk_hec_route_s3	ingress throughput	-0.05	[-0.55, +0.45]	12.82%
enterprise_http_to_http	ingress throughput	-0.05	[-0.11, +0.01]	82.29%
otlp_grpc_to_blackhole	ingress throughput	-0.15	[-0.24, -0.05]	98.40%
syslog_humio_logs	ingress throughput	-0.32	[-0.40, -0.25]	100.00%
syslog_loki	ingress throughput	-0.44	[-0.49, -0.40]	100.00%
syslog_splunk_hec_logs	ingress throughput	-0.59	[-0.63, -0.56]	100.00%
file_to_blackhole	egress throughput	-0.90	[-3.36, +1.56]	45.21%
syslog_log2metric_splunk_hec_metrics	ingress throughput	-1.76	[-1.88, -1.63]	100.00%
socket_to_socket_blackhole	ingress throughput	-1.94	[-2.00, -1.88]	100.00%
otlp_http_to_blackhole	ingress throughput	-2.03	[-2.18, -1.87]	100.00%