dineshg13 commented 1 month ago

What does this PR do?

The PR does the following

Converts serializerexporter to be OCB compatible. Serializerexporter can now be used in OSS collector.
Makes pkg/serializer uses log.Component , to ensure we use consistent logging framework.

After adding serializerexporter to the ocb build, we can use it in the collector configuration.

exporters:
  serializer:
    api:
      key: ${env:DD_API_KEY}
      site: ${env:DD_SITE}

Motivation

We want to use serializerexporter for sending metrics in OSS DD exporter.

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

cit-pr-commenter[bot] commented 1 month ago

Go Package Import Differences

Baseline: 7465d64353f8b6f23eeb23c068e48d3a1d6f446a Comparison: 480715c1247cb18c1fe4c78c060a552ca67c12f8

binary	os	arch	change
agent	linux	amd64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
agent	linux	arm64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
agent	windows	amd64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
agent	darwin	amd64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
agent	darwin	arm64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
iot-agent	linux	amd64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
iot-agent	linux	arm64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
heroku-agent	linux	amd64	+1, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel
serverless	linux	amd64	+3, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/impl +github.com/DataDog/datadog-agent/pkg/util/compression/impl-zlib
serverless	linux	arm64	+3, -0 +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/fx-otel +github.com/DataDog/datadog-agent/comp/serializer/metricscompression/impl +github.com/DataDog/datadog-agent/pkg/util/compression/impl-zlib

agent-platform-auto-pr[bot] commented 1 month ago

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=56413251 --os-family=ubuntu

Note: This applies to commit 480715c1

agent-platform-auto-pr[bot] commented 1 month ago

Uncompressed package size comparison

Comparison with ancestor 7465d64353f8b6f23eeb23c068e48d3a1d6f446a

Diff per package

|package|diff|status|size|ancestor|threshold| |--|--|--|--|--|--| |datadog-heroku-agent-amd64-deb|0.12MB|⚠️|446.00MB|445.88MB|0.50MB| |datadog-agent-x86_64-rpm|0.06MB|⚠️|878.45MB|878.39MB|0.50MB| |datadog-agent-x86_64-suse|0.06MB|⚠️|878.45MB|878.39MB|0.50MB| |datadog-iot-agent-x86_64-rpm|0.06MB|⚠️|61.97MB|61.91MB|0.50MB| |datadog-iot-agent-x86_64-suse|0.06MB|⚠️|61.97MB|61.91MB|0.50MB| |datadog-agent-amd64-deb|0.06MB|⚠️|868.68MB|868.62MB|0.50MB| |datadog-iot-agent-amd64-deb|0.06MB|⚠️|61.90MB|61.84MB|0.50MB| |datadog-agent-aarch64-rpm|0.05MB|⚠️|867.76MB|867.71MB|0.50MB| |datadog-iot-agent-aarch64-rpm|0.05MB|⚠️|59.21MB|59.16MB|0.50MB| |datadog-agent-arm64-deb|0.05MB|⚠️|858.01MB|857.96MB|0.50MB| |datadog-iot-agent-arm64-deb|0.05MB|⚠️|59.14MB|59.09MB|0.50MB| |datadog-dogstatsd-amd64-deb|0.00MB|✅|41.39MB|41.39MB|0.50MB| |datadog-dogstatsd-x86_64-rpm|0.00MB|✅|41.47MB|41.47MB|0.50MB| |datadog-dogstatsd-x86_64-suse|0.00MB|✅|41.47MB|41.47MB|0.50MB| |datadog-dogstatsd-arm64-deb|0.00MB|✅|39.65MB|39.65MB|0.50MB|

Decision

⚠️ Warning

agent-platform-auto-pr[bot] commented 1 month ago

Static quality checks ✅

Please find below the results from static quality gates

Successful checks

### Info |Result|Quality gate|On disk size|On disk size limit|On wire size|On wire size limit| |----|----|----|----|----|----| |✅|static_quality_gate_agent_deb_amd64|840.23MiB|847.49MiB|203.03MiB|212.33MiB| |✅|static_quality_gate_agent_deb_arm64|830.11MiB|836.66MiB|183.21MiB|192.5MiB| |✅|static_quality_gate_agent_rpm_amd64|840.2MiB|847.82MiB|206.16MiB|215.76MiB| |✅|static_quality_gate_agent_rpm_arm64|829.95MiB|836.66MiB|185.32MiB|194.24MiB| |✅|static_quality_gate_agent_suse_amd64|840.16MiB|847.82MiB|206.16MiB|215.76MiB| |✅|static_quality_gate_agent_suse_arm64|829.97MiB|836.66MiB|185.32MiB|194.24MiB| |✅|static_quality_gate_dogstatsd_deb_amd64|39.55MiB|49.7MiB|10.55MiB|20.6MiB| |✅|static_quality_gate_dogstatsd_deb_arm64|37.89MiB|48.1MiB|9.13MiB|19.1MiB| |✅|static_quality_gate_dogstatsd_rpm_amd64|39.55MiB|49.7MiB|10.56MiB|20.6MiB| |✅|static_quality_gate_dogstatsd_suse_amd64|39.55MiB|49.7MiB|10.56MiB|20.6MiB| |✅|static_quality_gate_iot_agent_deb_amd64|59.1MiB|69.0MiB|14.86MiB|24.8MiB| |✅|static_quality_gate_iot_agent_deb_arm64|56.47MiB|66.4MiB|12.83MiB|22.8MiB| |✅|static_quality_gate_iot_agent_rpm_amd64|59.1MiB|69.0MiB|14.87MiB|24.8MiB| |✅|static_quality_gate_iot_agent_rpm_arm64|56.47MiB|66.4MiB|12.83MiB|22.8MiB| |✅|static_quality_gate_iot_agent_suse_amd64|59.11MiB|69.0MiB|14.87MiB|24.8MiB| |✅|static_quality_gate_docker_agent_amd64|924.6MiB|931.7MiB|308.98MiB|318.67MiB| |✅|static_quality_gate_docker_agent_arm64|937.75MiB|944.08MiB|293.97MiB|303.0MiB| |✅|static_quality_gate_docker_agent_jmx_amd64|1.1GiB|1.1GiB|384.08MiB|393.75MiB| |✅|static_quality_gate_docker_agent_jmx_arm64|1.1GiB|1.1GiB|365.03MiB|373.71MiB| |✅|static_quality_gate_docker_dogstatsd_amd64|47.69MiB|57.88MiB|18.25MiB|28.29MiB| |✅|static_quality_gate_docker_dogstatsd_arm64|46.08MiB|56.27MiB|17.02MiB|27.06MiB| |✅|static_quality_gate_docker_cluster_agent_amd64|264.85MiB|274.78MiB|106.31MiB|116.28MiB| |✅|static_quality_gate_docker_cluster_agent_arm64|280.89MiB|290.82MiB|101.15MiB|111.12MiB|

cit-pr-commenter[bot] commented 1 month ago

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: ebc31806-5215-4757-a2c0-5acd12d8c89f

Baseline: 7465d64353f8b6f23eeb23c068e48d3a1d6f446a Comparison: 480715c1247cb18c1fe4c78c060a552ca67c12f8 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 | +0.94 | [+0.02, +1.86] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | +0.24 | [-0.23, +0.71] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency_linear_load%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | quality_gate_idle | memory utilization | +0.16 | [+0.10, +0.21] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&tpl_var_run-id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&view=spans&from_ts=1740053907000&to_ts=1740054507000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.13 | [+0.07, +0.19] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.09 | [-0.68, +0.87] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | quality_gate_idle_all_features | memory utilization | +0.03 | [-0.02, +0.07] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle_all_features%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&tpl_var_run-id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&view=spans&from_ts=1740053907000&to_ts=1740054507000&live=false) | | ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | +0.00 | [-0.78, +0.79] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency_http1%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_300ms_latency | egress throughput | +0.00 | [-0.64, +0.64] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_300ms_latency%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.00 | [-0.62, +0.63] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_100ms_latency%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.27, +0.27] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.02 | [-0.87, +0.82] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.03 | [-0.82, +0.77] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency_http2%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_tree | memory utilization | -0.10 | [-0.17, -0.04] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | file_to_blackhole_500ms_latency | egress throughput | -0.12 | [-0.90, +0.66] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_500ms_latency%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) | | ➖ | quality_gate_logs | % cpu utilization | -0.38 | [-3.37, +2.60] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_logs%20run_id%3Aebc31806-5215-4757-a2c0-5acd12d8c89f&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1740046707000&to_ts=1740058107000&live=false) |

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](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&tpl_var_run-id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&view=spans&from_ts=1740053907000&to_ts=1740054507000&live=false) | | ✅ | quality_gate_idle | memory_usage | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&tpl_var_run-id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&view=spans&from_ts=1740053907000&to_ts=1740054507000&live=false) | | ✅ | quality_gate_idle_all_features | intake_connections | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&tpl_var_run-id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&view=spans&from_ts=1740053907000&to_ts=1740054507000&live=false) | | ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&tpl_var_run-id%5B0%5D=ebc31806-5215-4757-a2c0-5acd12d8c89f&view=spans&from_ts=1740053907000&to_ts=1740054507000&live=false) | | ✅ | 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_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
quality_gate_idle, 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.
quality_gate_idle_all_features, bounds check memory_usage: 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_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.

songy23 commented 4 weeks ago

Need inv tidy and fix linters

dineshg13 commented 3 weeks ago

/merge

dd-devflow[bot] commented 3 weeks ago

View all feedbacks in Devflow UI. 2025-02-20 14:11:34 UTC :information_source: Start processing command /merge

2025-02-20 14:11:38 UTC :information_source: MergeQueue: pull request added to the queue

The median merge time in main is 34m.

2025-02-20 14:40:33 UTC :information_source: MergeQueue: This merge request was merged

DataDog / datadog-agent

Convert serilizerexporter as independent OCB collector component #34038

What does this PR do?

Motivation

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

Go Package Import Differences

Test changes on VM

Uncompressed package size comparison

Decision

Static quality checks ✅

Regression Detector

Regression Detector Results

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

Bounds Checks: ✅ Passed

Explanation

CI Pass/Fail Decision