fix(datadog_agent source): bugs in internal component metric reporting

[neuronull]

child of: #20043

This change partially implements the ValidatableComponent trait for the datadog_agent source. It only covers the logs endpoint.

In this implementation, two bugs were found and are also fixed in this PR:

1. component_received_event_bytes_total metric was being computed after event enrichment with vector metadata.
2. component_errors_total was not emitte when the request body was successfully decompressed, but failed in JSON parsing.

make test-component-validation
cargo nextest run --no-fail-fast --no-default-features --features component-validation-tests --status-level pass --test-threads 4 components::validation::tests
    Finished test [unoptimized + debuginfo] target(s) in 22.22s
    Starting 7 tests across 3 binaries (461 skipped)
        PASS [   6.060s] vector components::validation::tests::validate_component_tests_validation_components_sources_datadog_agent_yaml
        PASS [   9.931s] vector components::validation::tests::validate_component_tests_validation_components_sources_http_client_yaml
        PASS [  21.398s] vector components::validation::tests::validate_component_tests_validation_components_sinks_http_yaml
        PASS [   5.469s] vector components::validation::tests::validate_component_tests_validation_components_sources_http_server_yaml
        PASS [  21.652s] vector components::validation::tests::validate_component_tests_validation_components_sinks_splunk_hec_logs_yaml
        PASS [  26.212s] vector components::validation::tests::validate_component_tests_validation_components_sinks_datadog_logs_yaml
        PASS [   5.293s] vector components::validation::tests::validate_component_tests_validation_components_sources_splunk_hec_yaml
------------
     Summary [  26.692s] 7 tests run: 7 passed, 461 skipped

2024-03-08T22:34:29.258616Z  INFO vector::components::validation: Successfully validated component 'datadog_agent':
  test case 'happy path': passed
    - sent 2 inputs and received 2 outputs
    - received 1429 telemetry events
    - component_received_events_total: 2
    - component_received_event_bytes_total: 419
    - component_received_bytes_total: 397
    - component_sent_events_total: 2
    - component_sent_event_bytes_total: 483
    - component_sent_bytes_total: 0
    - component_errors_total: 0
    - component_discarded_events_total: 0
  test case 'sad path': passed
    - sent 3 inputs and received 2 outputs
    - received 1376 telemetry events
    - component_received_events_total: 2
    - component_received_event_bytes_total: 419
    - component_received_bytes_total: 568
    - component_sent_events_total: 2
    - component_sent_event_bytes_total: 483
    - component_sent_bytes_total: 0
    - component_errors_total: 1
    - component_discarded_events_total: 0

[datadog-vectordotdev]

Datadog Report

Branch report: neuronull/OPA-1186_component_validation_datadog_agent_source
Commit report: 2980f45
Test service: vector

✅ 0 Failed, 44 Passed, 0 Skipped, 2m 43.63s Wall Time

[github-actions]

Regression Detector Results

Run ID: 15cf8ada-654e-450f-858d-9c99578e895c
Baseline: 80f63bb
Comparison: ad6a48e
Total CPUs: 7

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.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	syslog_humio_logs	ingress throughput	+0.91	[+0.81, +1.02]
➖	fluent_elasticsearch	ingress throughput	+0.83	[+0.36, +1.30]
➖	file_to_blackhole	egress throughput	+0.67	[-1.74, +3.08]
➖	http_to_s3	ingress throughput	+0.63	[+0.35, +0.91]
➖	socket_to_socket_blackhole	ingress throughput	+0.49	[+0.43, +0.55]
➖	splunk_hec_route_s3	ingress throughput	+0.38	[-0.10, +0.86]
➖	otlp_grpc_to_blackhole	ingress throughput	+0.10	[+0.02, +0.19]
➖	http_to_http_noack	ingress throughput	+0.07	[-0.03, +0.17]
➖	http_to_http_json	ingress throughput	+0.06	[-0.03, +0.14]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	-0.00	[-0.15, +0.15]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	-0.01	[-0.14, +0.13]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.05	[-0.16, +0.07]
➖	enterprise_http_to_http	ingress throughput	-0.11	[-0.19, -0.03]
➖	syslog_splunk_hec_logs	ingress throughput	-0.31	[-0.40, -0.22]
➖	http_to_http_acks	ingress throughput	-0.32	[-1.63, +0.99]
➖	http_elasticsearch	ingress throughput	-0.65	[-0.72, -0.57]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	-0.73	[-0.88, -0.59]
➖	datadog_agent_remap_datadog_logs	ingress throughput	-0.75	[-0.87, -0.64]
➖	syslog_loki	ingress throughput	-0.86	[-0.95, -0.77]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	-1.08	[-1.19, -0.97]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	-1.09	[-1.18, -0.99]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	-1.42	[-1.61, -1.23]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	-1.63	[-1.78, -1.48]
➖	datadog_agent_remap_blackhole	ingress throughput	-2.41	[-2.54, -2.28]
➖	otlp_http_to_blackhole	ingress throughput	-2.55	[-2.70, -2.40]
➖	syslog_log2metric_humio_metrics	ingress throughput	-2.56	[-2.70, -2.43]
➖	http_text_to_http_json	ingress throughput	-2.59	[-2.72, -2.46]

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".