datadog-agent
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[CONTINT-3750] Create a dedicate group allowed to run the secret backends
What does this PR do?
Create a dedicated secret-manager
group allowed to execute the secret-backends shipped in the docker image.
Motivation
The agent and the cluster-agent are checking the permissions of a secret backend before invoking it.
By default, it enforces that the agent user is also the owner of the file and that the file is executable only by this user.
If secret_backend_command_allow_group_exec_perm
is set, which is the case in the agent and cluster agent docker images, then, it allows group to have execution rights on the file.
The secret backends used to belong to root:root
, meaning that paranoid users that want to run the agent with a non-root users have to be in the root
group in order to use the secret backends.
With this change, it’s not needed anymore.
Additional Notes
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes
Run the cluster-agent with a non-root user that has 101
as a supplementary group.
Validate that it can decode secrets.
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=30110386 --os-family=ubuntu
Regression Detector
Regression Detector Results
Run ID: 96fd32e6-996d-4452-9fe6-d4ed0f39a7ac Baseline: fb31d6a6ee6d369f35a16aa9748486d538d1469d Comparison: 7cfc0ca5e804b2e7d9f29efd2ded725f6b8bfe02
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.
Experiments ignored for regressions
Regressions in experiments with settings containing erratic: true
are ignored.
perf | experiment | goal | Δ mean % | Δ mean % CI |
---|---|---|---|---|
➖ | file_to_blackhole | % cpu utilization | -0.00 | [-6.55, +6.54] |
Fine details of change detection per experiment
perf | experiment | goal | Δ mean % | Δ mean % CI |
---|---|---|---|---|
➖ | file_tree | memory utilization | +3.58 | [+3.48, +3.68] |
➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.37 | [-3.87, +6.62] |
➖ | process_agent_standard_check_with_stats | memory utilization | +0.23 | [+0.20, +0.27] |
➖ | idle | memory utilization | +0.22 | [+0.18, +0.26] |
➖ | process_agent_standard_check | memory utilization | +0.04 | [-0.01, +0.08] |
➖ | trace_agent_json | ingress throughput | +0.02 | [-0.00, +0.03] |
➖ | trace_agent_msgpack | ingress throughput | -0.00 | [-0.00, +0.00] |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.00, +0.00] |
➖ | file_to_blackhole | % cpu utilization | -0.00 | [-6.55, +6.54] |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.02 | [-0.22, +0.19] |
➖ | otel_to_otel_logs | ingress throughput | -0.13 | [-0.58, +0.31] |
➖ | tcp_syslog_to_blackhole | ingress throughput | -0.84 | [-0.94, -0.74] |
➖ | process_agent_real_time_mode | memory utilization | -0.88 | [-0.93, -0.83] |
➖ | basic_py_check | % cpu utilization | -3.74 | [-5.98, -1.49] |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -4.47 | [-7.32, -1.62] |
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:
-
Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
-
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.
-
Its configuration does not mark it "erratic".
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