AMLII-2249 - fortify log tailer pipeline for journald and windows events

[ddrthall]

What does this PR do?

Explicitly prevent the journald and windows event tailers from being routed through a line handler

Motivation

The two tailers being modified receive no functional benefit from routing through a line handler, and may be errantly marked as Truncated if they ever are. Making an explicit no-op handler both increases the clarity of the code and reduces the chance for unfortunate and unforeseen side effects.

Describe how you validated your changes

Existing automated tests already verify that a message can be cleanly passed from the tailer all the way through the decoder. Additional sanity checking was performed to guarantee that over-large messages were not subject to (invalid) truncation labels and were successfully able to pass through the pipe.

Possible Drawbacks / Trade-offs

Additional Notes

[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=54124939 --os-family=ubuntu

Note: This applies to commit bb1f113

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 45ceeac7fa1a182b4d25abaf01fcaccba27e9a48

Diff per package

package	diff	status	size	ancestor	threshold
datadog-iot-agent-arm64-deb	0.01MB	⚠️	89.93MB	89.93MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.01MB	⚠️	90.00MB	90.00MB	0.50MB
datadog-agent-aarch64-rpm	0.00MB	✅	925.81MB	925.81MB	0.50MB
datadog-agent-arm64-deb	0.00MB	✅	916.09MB	916.09MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	93.95MB	93.94MB	0.50MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	93.95MB	93.94MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	93.88MB	93.87MB	0.50MB
datadog-agent-x86_64-rpm	0.00MB	✅	938.65MB	938.65MB	0.50MB
datadog-agent-x86_64-suse	0.00MB	✅	938.65MB	938.65MB	0.50MB
datadog-agent-amd64-deb	0.00MB	✅	928.91MB	928.91MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	59.01MB	59.01MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	59.09MB	59.09MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	59.09MB	59.09MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.50MB	56.50MB	0.50MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	477.88MB	477.88MB	0.50MB

Decision

⚠️ Warning

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 3f01ff05-8034-49e7-9601-d69a13818949

Baseline: 45ceeac
Comparison: bb1f113
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_logs	% cpu utilization	+2.23	[-0.85, +5.32]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.57	[+1.51, +1.64]	1	Logs
➖	file_tree	memory utilization	+0.63	[+0.56, +0.69]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.36	[+0.30, +0.41]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.09	[-0.37, +0.54]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.08	[-0.71, +0.87]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.07	[-0.77, +0.92]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.03	[-0.90, +0.95]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.76, +0.80]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.02	[-0.78, +0.81]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.28, +0.29]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.03, +0.02]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.65, +0.62]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.06	[-0.84, +0.72]	1	Logs
➖	quality_gate_idle	memory utilization	-0.99	[-1.02, -0.95]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-2.13	[-3.01, -1.25]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	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	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	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	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10
✅	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_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_logs, 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_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.