chore(observability): remove deprecated internal metrics + massive cleanup to vector top and graphql API

[tobz]

Context

This PR is primarily, and simply, focused on one thing: removing now deprecated internal metrics. Specifically:

• events_in_total
• events_out_total
• processed_bytes_total
• processed_events_total

These metrics have been deprecated for a while, replaced by the metrics defined in the Component Specification. For example, component_sent_events_total replaced events_out_total, and processed_bytes_total is replaced by component_sent_bytes_total/component_received_bytes_total: no more having to remember what direction the metric refers to based on what type of component is emitting it.

Solution

The change to simply stop emitting these deprecated metrics is straightforward, but most of the work in this PR centered around updating the GraphQL API, and subsequently vector top, to switch from the processed_* metrics to their component_* counterparts.

While in the area, I adjusted some of the vector top output to display separated bytes in/bytes out, similar to the existing events in/events out split. This also involved a small amount of work to adjust the layout constraints, and so on, to accommodate for that.

Additionally, many updates were made to the component Cue files to remove references to these now-removed metrics.

Reviewer Notes

Dichotomy between processed_bytes_total and the component-prefixed replacements

Prior to this PR, we were backfilling processed_bytes_total in the following way:

• for sources, alias component_received_bytes_total to processed_bytes_total
• for sinks, alias component_sent_bytes_total to processed_bytes_total

As such, we never emitted this metric for transforms, and processed_bytes_total was an analogue for network bytes. A lot of the work has tried to reflect that, such as in the GraphQL API, where spots that had processed_bytes are now replaced with the component type-relevant value, being either received_bytes or sent_bytes.

Closes #9314.
Closes #7346.

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[datadog-vectordotdev]

Datadog Report

Branch report: tobz/bye-bye-events-inout-processed-events
Commit report: 49df177

✅ vector: 0 Failed, 0 New Flaky, 1987 Passed, 0 Skipped, 2m 22.55s Wall Time

[tobz]

This avoids the default behavior where one UI update has a throughput rate, and the next UI update just shows --, back and forth.

Seemingly, the chosen interval should flow through all the way to the GraphQL subscriptions and emit them on that interval, but somewhere along the line, the logic breaksdown and it ends up spitting out zeroes for the throughput values, and in turn, the -- value.

We could/should fix that, I suppose, but also: it's not clear at all why we'd bother allowing lower than a 1000ms interval since it just makes the math around calculating those per-second throughputs a bit wonky.

[tobz]

This just makes sense, I think.

The throughput rates already indicate change so you really don't need to see that you've gone from 7,267,133 events to 7,267,134 to understand events are flowing, etc.

[tobz]

This boils down to showing the time the connection was established, for if users want to take the total events/bytes values and divide them by how long top has been watching, etc.

[jszwedko]

🎉 Thanks! This has been a long time coming. I think it could be worth calling this out in the upgrade guide given the scope of the changes (specifically the internal metrics being dropped; the GraphQL API is still pretty experimental / undocumented). Otherwise this looks great. I pulled it down and played around with a config to ensure everything looked as expected.

[jszwedko]

This is out-of-scope of this PR, but in #17322 we are standardizing on the component estimated JSON byte size for received/sent event bytes. We can make that update later since this preserves the current behavior.

[tobz]

That's fair.

IMO, showing the event bytes in, or event bytes out, for a source or sink, respectively, wouldn't make sense: the numbers can be vastly different in many cases, and, well, people generally expect the network bytes, or bytes that relate to the data actually coming into/going out of Vector. Obviously, for the bytes out of a source, bytes into a sink, or bytes into/out of a transform, it has to be the event bytes: that's all we have.

The top UI is slightly cluttered as-is, but it might be nice/useful to think about somehow demarcating which bytes value we're using in the respective column, so that all components can show a value, but it's clear that, oh, OK, yeah, I'm getting 10MB/s in over the wire to my source, and sending out 5MB/s, and events bytes for the remainder just to fill things out.

[github-actions]

Regression Detector Results

Run ID: 94141464-759a-460b-a51c-c639c89b6bde
Baseline: a551f33
Comparison: 98c54ad
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
datadog_agent_remap_datadog_logs_acks	ingress throughput	+1.15	[+1.04, +1.26]	100.00%
datadog_agent_remap_blackhole	ingress throughput	+1.06	[+0.94, +1.18]	100.00%
syslog_log2metric_humio_metrics	ingress throughput	+0.91	[+0.79, +1.03]	100.00%
syslog_log2metric_splunk_hec_metrics	ingress throughput	+0.85	[+0.76, +0.93]	100.00%
datadog_agent_remap_datadog_logs	ingress throughput	+0.57	[+0.46, +0.67]	100.00%
syslog_loki	ingress throughput	+0.13	[+0.06, +0.20]	98.61%
datadog_agent_remap_blackhole_acks	ingress throughput	+0.08	[-0.01, +0.17]	75.92%
syslog_regex_logs2metric_ddmetrics	ingress throughput	+0.05	[-0.21, +0.32]	19.98%
http_to_http_noack	ingress throughput	+0.04	[-0.02, +0.10]	65.70%
file_to_blackhole	ingress throughput	+0.02	[-0.02, +0.07]	44.79%
enterprise_http_to_http	ingress throughput	+0.01	[-0.02, +0.05]	38.06%
splunk_hec_indexer_ack_blackhole	ingress throughput	+0.01	[-0.03, +0.05]	25.92%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.06, +0.07]	1.91%
fluent_elasticsearch	ingress throughput	-0.00	[-0.00, +0.00]	41.80%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.02	[-0.06, +0.03]	36.16%
syslog_splunk_hec_logs	ingress throughput	-0.41	[-0.47, -0.34]	100.00%
http_text_to_http_json	ingress throughput	-0.73	[-0.81, -0.66]	100.00%
http_to_http_json	ingress throughput	-0.74	[-0.80, -0.69]	100.00%
splunk_hec_route_s3	ingress throughput	-1.00	[-1.14, -0.86]	100.00%
http_to_http_acks	ingress throughput	-1.37	[-2.60, -0.15]	84.95%
otlp_grpc_to_blackhole	ingress throughput	-1.70	[-1.82, -1.59]	100.00%
socket_to_socket_blackhole	ingress throughput	-2.34	[-2.39, -2.29]	100.00%
syslog_humio_logs	ingress throughput	-3.28	[-3.35, -3.20]	100.00%
otlp_http_to_blackhole	ingress throughput	-3.75	[-3.91, -3.58]	100.00%