[AMLII-2110] Fix Path Errors in Integrations Launcher

[soberpeach]

What does this PR do?

Fixes a path error where the integrations launcher would append a path to an already complete file path because of confusion in joining the file name and file path.

Motivation

Bug found in QA

Describe how to test/QA your changes

1. Create a check in checks.d like so:

custom_checkvalue.py:

from datadog_checks.base import AgentCheck
from datadog_checks.base.utils.time import get_timestamp

class HelloCheck(AgentCheck):
    def check(self, instance):
        data = dict()
        data['timestamp'] = get_timestamp()
        data['message'] = "Wow, this is a custom log message!"
        data['ddtags'] = "env:dev,bar:foo"
        self.gauge('qa.check.logs', 1)
        self.send_log(data)

and a configuration for it in conf.d like so

custom_checkvalue.yaml:

init_config:
instances:
  [{}]

logs:
  - type: integration
    source: my_source
    service: my_service

2. Set the logs_config.integrations_logs_files_max_size to a size of your choice (default is 10 MB, but you can set it to something like 1 MB for this case)
3. Make sure the agent creates an appropriate log file based off this configuration, it should be located in /opt/datadog-agent/run/integrations/{integration_id}.log
4. Write logs to this file until the file size reaches limit set by logs_config.integrations_logs_files_max_size, keep writing data to the file, the agent should delete it and recreate it again.

Note: if you're not getting logs, ensure there is more than 20% space available on your machine or otherwise set the logs_config.integrations_logs_disk_ratio to a higher value (default 0.80).

Possible Drawbacks / Trade-offs

Additional Notes

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: e32d828c-6bdb-4a97-80e1-7aac543db240 Metrics dashboard Target profiles

Baseline: b86caed
Comparison: a23b203

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	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+1.09	[-1.31, +3.49]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.92	[+0.20, +1.63]	1	Logs
➖	idle_all_features	memory utilization	+0.83	[+0.74, +0.92]	1	Logs
➖	file_tree	memory utilization	+0.66	[+0.55, +0.77]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.04	[-0.19, +0.26]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.04	[-0.30, +0.37]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.03	[-0.15, +0.21]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.08, +0.08]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.02	[-0.27, +0.23]	1	Logs
➖	idle	memory utilization	-0.28	[-0.33, -0.24]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.30	[-0.79, +0.19]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.99	[-1.04, -0.94]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-1.24	[-2.03, -0.44]	1	Logs
➖	basic_py_check	% cpu utilization	-1.67	[-4.35, +1.02]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10

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".

[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 create-vm --pipeline-id=46331375 --os-family=ubuntu

Note: This applies to commit a23b203