Use container-scoped outputBase for Windows GitLab jobs

[rdesgroppes]

What does this PR do?

tools/bazel.bat now also passes --output_base=%SYSTEMDRIVE%\bob whenever it detects we're running inside a Windows container, so each container's per-workspace Bazel state (server\, lock, action_cache\, execroot\, external\, bazel-out\) lives on its own writable outputBase rather than any shared outputUserRoot (under a bind mount).

install_base, disk_cache and repository_cache continue to live under the shared outputUserRoot.

Motivation

GitLab Windows runners might bind-mount the same host c:\bzl into several concurrent containers, or more likely a filesystem handle to a file under that directory may leak when a job is interrupted.
Inside each container, Bazel indeed resolves the same outputUserRoot and hashes the same in-container workspace path to the same outputBase directory:

outputUserRoot\<same workspace hash>\server\jvm.out
               ^^^^^^^^^^^^^^^^^^^^^ outputBase

... so concurrent bazel server (JVM) spawns race:

FATAL: ExecuteDaemon(c:\bzl\bazel\install\5ea2e927a67192a1c29b93353d8199e3\embedded_tools\jdk\bin\java.exe): CreateJvmOutputFile(c:\bzl\bazel\bv4fleb2\server\jvm.out) failed: (error: 32): The process cannot access the file because it is being used by another process.

[github-actions]

@codex review

[datadog-prod-us1-3]

✨ Fix all issues with BitsAI

⚠️ Warnings

🚦 1 Pipeline job failed

DataDog/datadog-agent | oracle: [21.3.0-xe]     

🛟 This job is unlikely to succeed on retry. Please review your pipeline configuration.

Failed to ping oracle instance: ORA-12514: TNS:listener does not currently know of service requested in connect descriptor.

ℹ️ Info

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 50.48% (+0.14%)

Useful? React with 👍 / 👎

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 9f561f0 | Docs | Datadog PR Page | Give us feedback!}

[chatgpt-codex-connector]

Codex Review: Didn't find any major issues. Chef's kiss.

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[github-actions]

@codex review

[Copilot]

Pull request overview

This PR updates the Windows Bazel wrapper (tools/bazel.bat) to avoid Bazel server/state collisions in GitLab CI by forcing a custom --output_base when running in CI (but not on GitHub Actions), so per-workspace Bazel state does not live under a shared/bind-mounted --output_user_root.

Changes:

• In GitLab CI (non-GitHub Actions), append --output_base=%SYSTEMDRIVE%\bob as a Bazel startup option.
• Keep using --config=ci for CI runs while leaving other caches under the shared --output_user_root.

---

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[chatgpt-codex-connector]

Codex Review: Didn't find any major issues. Breezy!

ℹ️ About Codex in GitHub

Codex has been enabled to automatically review pull requests in this repo. Reviews are triggered when you

• Open a pull request for review
• Mark a draft as ready
• Comment "@codex review".

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When you sign up for Codex through ChatGPT, Codex can also answer questions or update the PR, like "@codex address that feedback".

[Copilot]

Pull request overview

Copilot reviewed 1 out of 2 changed files in this pull request and generated no new comments.

[dd-octo-sts]

Files inventory check summary

File checks results against ancestor 7e031281:

Results for datadog-agent_7.81.0~devel.git.139.9f561f0.pipeline.114443009-1_amd64.deb:

No change detected

[dd-octo-sts]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 7e03128
📊 Static Quality Gates Dashboard
🔗 SQG Job

33 successful checks with minimal change (< 2 KiB)

	Quality gate	Current Size
✅	agent_deb_amd64	745.209 MiB
✅	agent_deb_amd64_fips	702.967 MiB
✅	agent_heroku_amd64	310.165 MiB
✅	agent_msi	609.161 MiB
✅	agent_rpm_amd64	745.192 MiB
✅	agent_rpm_amd64_fips	702.951 MiB
✅	agent_rpm_arm64	722.752 MiB
✅	agent_rpm_arm64_fips	683.680 MiB
✅	agent_suse_amd64	745.192 MiB
✅	agent_suse_amd64_fips	702.951 MiB
✅	agent_suse_arm64	722.752 MiB
✅	agent_suse_arm64_fips	683.680 MiB
✅	docker_agent_amd64	805.350 MiB
✅	docker_agent_arm64	807.730 MiB
✅	docker_agent_jmx_amd64	996.269 MiB
✅	docker_agent_jmx_arm64	987.428 MiB
✅	docker_cluster_agent_amd64	206.995 MiB
✅	docker_cluster_agent_arm64	220.952 MiB
✅	docker_cws_instrumentation_amd64	7.154 MiB
✅	docker_cws_instrumentation_arm64	6.689 MiB
✅	docker_dogstatsd_amd64	39.487 MiB
✅	docker_dogstatsd_arm64	37.690 MiB
✅	docker_host_profiler_amd64	302.187 MiB
✅	docker_host_profiler_arm64	313.675 MiB
✅	dogstatsd_deb_amd64	30.143 MiB
✅	dogstatsd_deb_arm64	28.268 MiB
✅	dogstatsd_rpm_amd64	30.143 MiB
✅	dogstatsd_suse_amd64	30.143 MiB
✅	iot_agent_deb_amd64	44.578 MiB
✅	iot_agent_deb_arm64	41.535 MiB
✅	iot_agent_deb_armhf	42.243 MiB
✅	iot_agent_rpm_amd64	44.578 MiB
✅	iot_agent_suse_amd64	44.578 MiB

[cit-pr-commenter-54b7da]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: e903cc95-418f-4a20-8a24-27ac3e373905

Baseline: 832f42e
Comparison: d07574c
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	docker_containers_cpu	% cpu utilization	-5.29	[-8.15, -2.43]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+1.56	[+0.53, +2.59]	1	Logs bounds checks dashboard
➖	quality_gate_metrics_logs	memory utilization	+0.53	[+0.28, +0.78]	1	Logs bounds checks dashboard
➖	otlp_ingest_metrics	memory utilization	+0.43	[+0.27, +0.59]	1	Logs
➖	ddot_metrics	memory utilization	+0.36	[+0.16, +0.57]	1	Logs
➖	docker_containers_memory	memory utilization	+0.32	[+0.22, +0.42]	1	Logs
➖	ddot_logs	memory utilization	+0.24	[+0.19, +0.30]	1	Logs
➖	file_tree	memory utilization	+0.17	[+0.13, +0.22]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	+0.03	[-0.18, +0.24]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.22, +0.19]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.02	[-0.11, +0.07]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.02	[-0.15, +0.11]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.03	[-0.48, +0.41]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.07	[-0.46, +0.33]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.07	[-0.60, +0.46]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.09	[-0.14, -0.05]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	-0.19	[-0.35, -0.03]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	-0.20	[-0.44, +0.03]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.23	[-0.29, -0.18]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	-0.24	[-0.29, -0.19]	1	Logs bounds checks dashboard
➖	ddot_metrics_sum_delta	memory utilization	-0.43	[-0.62, -0.24]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.75	[-0.93, -0.57]	1	Logs
➖	otlp_ingest_logs	memory utilization	-0.87	[-0.99, -0.76]	1	Logs
✅	docker_containers_cpu	% cpu utilization	-5.29	[-8.15, -2.43]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	observed_value	links
✅	docker_containers_cpu	simple_check_run	10/10	713 ≥ 26
✅	docker_containers_memory	memory_usage	10/10	247.70MiB ≤ 370MiB
✅	docker_containers_memory	simple_check_run	10/10	718 ≥ 26
✅	file_to_blackhole_0ms_latency	memory_usage	10/10	0.16GiB ≤ 1.20GiB
✅	file_to_blackhole_0ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10	0.21GiB ≤ 1.20GiB
✅	file_to_blackhole_1000ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_100ms_latency	memory_usage	10/10	0.17GiB ≤ 1.20GiB
✅	file_to_blackhole_100ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_500ms_latency	memory_usage	10/10	0.18GiB ≤ 1.20GiB
✅	file_to_blackhole_500ms_latency	missed_bytes	10/10	0B = 0B
✅	quality_gate_idle	intake_connections	10/10	3 ≤ 4	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	143.48MiB ≤ 147MiB	bounds checks dashboard
✅	quality_gate_idle	total_bytes_received	10/10	739.52KiB ≤ 819.20KiB	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	2 ≤ 4	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	427.74MiB ≤ 495MiB	bounds checks dashboard
✅	quality_gate_idle_all_features	total_bytes_received	10/10	1.12MiB ≤ 1.25MiB	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	4 ≤ 6	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	173.58MiB ≤ 195MiB	bounds checks dashboard
✅	quality_gate_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard
✅	quality_gate_logs	total_bytes_received	10/10	264.38MiB ≤ 292MiB	bounds checks dashboard
✅	quality_gate_metrics_logs	cpu_usage	10/10	357.46 ≤ 2000	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	4 ≤ 6	bounds checks dashboard
✅	quality_gate_metrics_logs	memory_usage	10/10	374.12MiB ≤ 430MiB	bounds checks dashboard
✅	quality_gate_metrics_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard
✅	quality_gate_metrics_logs	total_bytes_received	10/10	0.94GiB ≤ 1.04GiB	bounds checks dashboard

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_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check total_bytes_received: 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.

[alopezz]

This is fine, but the reason looks wrong.

GitLab Windows runners bind-mount the same host c:\bzl into several concurrent containers.

This should not happen in the current setup where runners are set up to run only one job at a time. I think a more likely hypothesis is unfinished (leaked) processes after a job. This PR would still probably mitigate this.

[rdesgroppes]

This should not happen in the current setup where runners are set up to run only one job at a time. I think a more likely hypothesis is unfinished (leaked) processes after a job.

@alopezz seems like it, indeed.