Improve auto multiline v2 truncation behavior to be consistent with single line logs

[gh123man]

What does this PR do?

Use the same truncation logic as the single line handler (introduced previously in this PR #30434). While the current approach correctly truncated joined multiline logs, it did not consistently handle single line logs that were too long.

This PR also removes the possibility for duplicate truncated tags to occur in single line mode.

Motivation

Describe how you validated your changes

There are a few permutations to test:

Base settings for all tests:

logs_enabled: true
logs_config:
    experimental_auto_multi_line_detection: true
    tag_multi_line_logs: true
    tag_truncated_logs: true

Test 1 - happy path. Send multiline logs and confirm the are correctly aggregated ex:

2024-08-14 18:06:05 INFO Main main Some log line 150
2024-08-14 18:06:05 SEVERE Main main Exception occur
java.lang.Exception: boom
	at Main.funcd(Main.java:50)
	at Main.funcc(Main.java:49)
	at Main.funcb(Main.java:48)
	at Main.funca(Main.java:47)
	at Main.main(Main.java:29)

2024-08-14 18:06:05 INFO Main main Some log line 151
2024-08-14 18:06:05 INFO Main main Some log line 152
2024-08-14 18:06:05 WARNING Main main something happened!!

Test 2 - limit the max line length:

logs_config:
    max_message_size_bytes: 50

Send the same logs and they should be truncated like this:

Note that each multiline log segment has the start/end truncation.
Single line logs are also correctly truncated.

Test 3 - in the logs explorer search for

host:<HOST_NAME> multiline:* truncated:* and ensure you can find all the multiline and truncated logs.

Test 4 - Test single line logs only have 1 tag

with the same settings as test 3, disable experimental_auto_multi_line_detection. tail the logs again and search for them with this query:

``host:<HOST_NAME> truncated:single_line`

confirm that the tag only exists once.

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 3f997d247e11871e8395be4aa8cae1250c6f7c14

Diff per package

package	diff	status	size	ancestor	threshold
datadog-heroku-agent-amd64-deb	0.01MB	⚠️	506.57MB	506.56MB	0.50MB
datadog-iot-agent-arm64-deb	0.01MB	⚠️	109.44MB	109.43MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.01MB	⚠️	109.51MB	109.50MB	0.50MB
datadog-agent-x86_64-rpm	0.00MB	✅	1022.09MB	1022.08MB	0.50MB
datadog-agent-x86_64-suse	0.00MB	✅	1022.09MB	1022.08MB	0.50MB
datadog-agent-amd64-deb	0.00MB	✅	1012.77MB	1012.77MB	0.50MB
datadog-agent-arm64-deb	0.00MB	✅	941.95MB	941.94MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	114.01MB	114.01MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	114.08MB	114.08MB	0.50MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	114.08MB	114.08MB	0.50MB
datadog-agent-aarch64-rpm	0.00MB	✅	951.24MB	951.24MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	58.82MB	58.82MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	58.90MB	58.90MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	58.90MB	58.90MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.33MB	56.33MB	0.50MB

Decision

⚠️ Warning

[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=52601934 --os-family=ubuntu

Note: This applies to commit a4342a7

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: ccdf3d46-4372-40e2-a1a4-2cdf3dc5a82d

Baseline: 3f997d2
Comparison: a4342a7
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+3.78	[+3.09, +4.48]	1	Logs
➖	quality_gate_logs	% cpu utilization	+2.21	[-1.03, +5.44]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.10	[+0.01, +0.19]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+0.09	[-0.04, +0.22]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.08	[+0.02, +0.14]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.08	[-0.83, +1.00]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.03	[-0.83, +0.89]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.65, +0.69]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.00	[-0.77, +0.77]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.01	[-0.83, +0.81]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.66, +0.62]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.02	[-0.14, +0.10]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.06	[-0.52, +0.39]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.15	[-0.92, +0.63]	1	Logs
➖	quality_gate_idle	memory utilization	-0.60	[-0.64, -0.56]	1	Logs bounds checks dashboard

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	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	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_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.