Repository: DataDog/datadog-agent
Pull request #35503: [EBPF] gpu: add colllected test data for benchmarking and test
Author: login: gjulianm
Labels: name: changelog/no-changelog
description: No changelog entry needed
name: team/ebpf-platform
name: qa/done
description: QA done before merge and regressions are covered by tests
name: long review
description: PR is complex, plan time to review it
Created: 2025-03-26T10:47:58Z | Merged: 2025-03-26T16:21:26Z

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### What does this PR do?

This PR adds test data collected from a real GPU monitored host, in order to have end-to-end tests and benchmarks of the consumer routine.

### Motivation

Improve our benchmarking capacities and also have more complete 

### Describe how you validated your changes
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The changes are tests/benchmarks that should pass in CI.

### Possible Drawbacks / Trade-offs

### Additional Notes
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Diff:
diff --git a/pkg/gpu/consumer.go b/pkg/gpu/consumer.go
index 18528220cfa8..231c49d5d97c 100644
--- a/pkg/gpu/consumer.go
+++ b/pkg/gpu/consumer.go
@@ -131,15 +131,7 @@ func (c *cudaEventConsumer) Start() {
 
 				header := (*gpuebpf.CudaEventHeader)(unsafe.Pointer(&batchData.Data[0]))
 				dataPtr := unsafe.Pointer(&batchData.Data[0])
-
-				var err error
-				eventType := gpuebpf.CudaEventType(header.Type)
-				c.telemetry.events.Inc(eventType.String())
-				if isStreamSpecificEvent(eventType) {
-					err = c.handleStreamEvent(header, dataPtr, dataLen)
-				} else {
-					err = c.handleGlobalEvent(header, dataPtr, dataLen)
-				}
+				err := c.handleEvent(header, dataPtr, dataLen)
 
 				if err != nil {
 					log.Errorf("Error processing CUDA event: %v", err)
@@ -161,6 +153,15 @@ func isStreamSpecificEvent(eventType gpuebpf.CudaEventType) bool {
 	return eventType != gpuebpf.CudaEventTypeSetDevice
 }
 
+func (c *cudaEventConsumer) handleEvent(header *gpuebpf.CudaEventHeader, dataPtr unsafe.Pointer, dataLen int) error {
+	eventType := gpuebpf.CudaEventType(header.Type)
+	c.telemetry.events.Inc(eventType.String())
+	if isStreamSpecificEvent(eventType) {
+		return c.handleStreamEvent(header, dataPtr, dataLen)
+	}
+	return c.handleGlobalEvent(header, dataPtr, dataLen)
+}
+
 func handleTypedEvent[K any](c *cudaEventConsumer, handler func(*K), eventType gpuebpf.CudaEventType, data unsafe.Pointer, dataLen int, expectedSize int) error {
 	if dataLen != expectedSize {
 		evStr := eventType.String()
diff --git a/pkg/gpu/consumer_test.go b/pkg/gpu/consumer_test.go
index eac3cc6ce9e3..8998784a610e 100644
--- a/pkg/gpu/consumer_test.go
+++ b/pkg/gpu/consumer_test.go
@@ -108,3 +108,33 @@ func TestGetStreamKeyUpdatesCorrectlyWhenChangingDevice(t *testing.T) {
 	require.Equal(t, globalStreamID, globalStream.metadata.streamID)
 	require.Equal(t, testutil.GPUUUIDs[1], globalStream.metadata.gpuUUID)
 }
+
+// BenchmarkConsumer benchmarks the consumer with a data sample, with and without fatbin parsing enabled
+// Note that the NVML library is mocked here, so if some of the API calls are slow in the real implementation
+// the results will not reflect that. This benchmark is useful to measure the performance of the event intake,
+// such as the event parsing, stream handling, the effect of the fatbin parsing and the related caches, etc
+func BenchmarkConsumer(b *testing.B) {
+	events := testutil.GetGPUTestEvents(b, testutil.DataSamplePytorchBatchedKernels)
+	for _, fatbinParsingEnabled := range []bool{true, false} {
+		name := "fatbinParsingDisabled"
+		if fatbinParsingEnabled {
+			name = "fatbinParsingEnabled"
+		}
+		b.Run(name, func(b *testing.B) {
+			ctx, err := getSystemContext(testutil.GetBasicNvmlMock(), kernel.ProcFSRoot(), testutil.GetWorkloadMetaMock(b), testutil.GetTelemetryMock(b))
+			require.NoError(b, err)
+			handlers := newStreamCollection(ctx, testutil.GetTelemetryMock(b))
+
+			ctx.fatbinParsingEnabled = fatbinParsingEnabled
+
+			cfg := config.New()
+			pid := testutil.DataSampleInfos[testutil.DataSamplePytorchBatchedKernels].ActivePID
+			ctx.visibleDevicesCache[pid] = []nvml.Device{testutil.GetDeviceMock(0), testutil.GetDeviceMock(1)}
+			ctx.pidMaps[pid] = nil
+
+			consumer := newCudaEventConsumer(ctx, handlers, nil, cfg, testutil.GetTelemetryMock(b))
+			b.ResetTimer()
+			injectEventsToConsumer(b, consumer, events, b.N)
+		})
+	}
+}
diff --git a/pkg/gpu/e2e_events_test.go b/pkg/gpu/e2e_events_test.go
new file mode 100644
index 000000000000..c442452a184b
--- /dev/null
+++ b/pkg/gpu/e2e_events_test.go
@@ -0,0 +1,134 @@
+// Unless explicitly stated otherwise all files in this repository are licensed
+// under the Apache License Version 2.0.
+// This product includes software developed at Datadog (https://www.datadoghq.com/).
+// Copyright 2024-present Datadog, Inc.
+
+// This file contains e2e tests for the consumer + stats generator based on events
+// gatherered with the event collector. They are placed in a separate file as they don't match
+// with any specific struct but are rather integration tests for the consumer and stats generator.
+
+//go:build linux_bpf && nvml
+
+package gpu
+
+import (
+	"testing"
+
+	"github.com/NVIDIA/go-nvml/pkg/nvml"
+	"github.com/stretchr/testify/require"
+
+	"github.com/DataDog/datadog-agent/pkg/gpu/config"
+	"github.com/DataDog/datadog-agent/pkg/gpu/testutil"
+	"github.com/DataDog/datadog-agent/pkg/util/kernel"
+)
+
+// injectEventsToConsumer injects events to the consumer. If count is 0, all events will be injected. The count can be higher
+// than the number of events in the collection, in which case the events will be injected multiple times.
+func injectEventsToConsumer(tb testing.TB, consumer *cudaEventConsumer, events *testutil.EventCollection, count int) {
+	if count == 0 {
+		count = len(events.Events)
+	}
+
+	for count > 0 {
+		for _, event := range events.Events {
+			err := consumer.handleEvent(&event.Header, event.Pointer, event.DataLength)
+			require.NoError(tb, err)
+			count--
+			if count == 0 {
+				return
+			}
+		}
+	}
+}
+
+func TestPytorchBatchedKernels(t *testing.T) {
+	cfg := config.New()
+	ctx, err := getSystemContext(testutil.GetBasicNvmlMock(), kernel.ProcFSRoot(), testutil.GetWorkloadMetaMock(t), testutil.GetTelemetryMock(t))
+	require.NoError(t, err)
+
+	handlers := newStreamCollection(ctx, testutil.GetTelemetryMock(t))
+	consumer := newCudaEventConsumer(ctx, handlers, nil, cfg, testutil.GetTelemetryMock(t))
+	require.NotNil(t, consumer)
+
+	events := testutil.GetGPUTestEvents(t, testutil.DataSamplePytorchBatchedKernels)
+
+	// Setup the visibleDevicesCache so that we don't get warnings
+	// about missing devices
+	executingPID := testutil.DataSampleInfos[testutil.DataSamplePytorchBatchedKernels].ActivePID
+	ctx.visibleDevicesCache[executingPID] = []nvml.Device{testutil.GetDeviceMock(0), testutil.GetDeviceMock(1)}
+
+	injectEventsToConsumer(t, consumer, events, 0)
+
+	// Check that the consumer has the expected number of streams
+	require.Equal(t, 1, handlers.streamCount())
+
+	// Check the state of those streams. As there's only one we can just get it
+	// by iterating over the map
+	var stream *StreamHandler
+	for s := range handlers.allStreams() {
+		require.Nil(t, stream) // There should be only one stream, so it should be set to nil at this point
+		stream = s
+	}
+
+	// Let's explain the input data here a bit. It consists of a stream of kernel launches,
+	// interseded with some cudaStreamSynchronize calls. After all that there's a longer cudaStreamSynchronize
+	// and then a batch of synchronizations.
+
+	require.NotNil(t, stream)
+	require.Len(t, stream.kernelSpans, 10)                             // There are 10 uninterrupted sequences of kernel launches
+	require.Len(t, stream.kernelLaunches, 0)                           // And we should have no kernel launches in the stream pending
+	require.Equal(t, stream.metadata.gpuUUID, testutil.DefaultGpuUUID) // Ensure the metadata is set correctly
+	require.Equal(t, stream.metadata.pid, uint32(executingPID))
+
+	totalThreadSeconds := 0.0
+	activeSeconds := 0.0
+
+	for _, span := range stream.kernelSpans {
+		totalThreadSeconds += float64(span.avgThreadCount) * float64(span.endKtime-span.startKtime) / 1e9
+		activeSeconds += float64(span.endKtime-span.startKtime) / 1e9
+	}
+
+	// Manually calculated
+	expectedTotalThreadSeconds := 3588668.5
+	expectedActiveSeconds := 3.379124306
+	require.InDelta(t, totalThreadSeconds, expectedTotalThreadSeconds, 0.1)
+	require.InDelta(t, activeSeconds, expectedActiveSeconds, 0.001)
+
+	// Check that the kernel launches don't span the entire interval of events
+	startTs := events.Events[0].Header.Ktime_ns
+	endTs := events.Events[len(events.Events)-1].Header.Ktime_ns
+	firstSyncAfterLastKernelLaunchTs := events.Events[866].Header.Ktime_ns
+
+	firstSpanStart := stream.kernelSpans[0].startKtime
+	lastSpanEnd := stream.kernelSpans[len(stream.kernelSpans)-1].endKtime
+
+	require.Equal(t, firstSpanStart, startTs)
+	require.Equal(t, lastSpanEnd, firstSyncAfterLastKernelLaunchTs)
+	require.Less(t, lastSpanEnd, endTs-60e6) // The last span should end at least 60 milliseconds before the end of the interval
+
+	// Now let's check the stats generator and see the output
+	statsGen, _, _ := getStatsGeneratorForTest(t)
+	statsGen.streamHandlers = consumer.streamHandlers // Replace the streamHandlers with the ones from the consumer
+
+	// Tell the generator the last generation time is before the start of our first event
+	statsGen.lastGenerationKTime = int64(startTs - 1)
+	statsGen.currGenerationKTime = int64(startTs - 1)
+
+	// And get the stats for the full interval
+	stats := statsGen.getStats(int64(endTs + 1))
+	require.NotNil(t, stats)
+	require.Len(t, stats.Metrics, 1)
+
+	metrics := stats.Metrics[0]
+
+	require.Equal(t, metrics.Key.PID, uint32(executingPID))
+
+	// Because the number of threads reported by the mock devices is very low, lower than the
+	// number of cores used by the sample events, the number of cores is going to get normalized
+	// to the number of cores available. This makes the used cores numbers computable based just
+	// on the number of active seconds
+	sampleDurationSeconds := float64(endTs-startTs) / 1e9
+	activeFraction := activeSeconds / sampleDurationSeconds
+	expectedUsedCores := activeFraction * float64(testutil.DefaultGpuCores)
+	require.InDelta(t, expectedUsedCores, metrics.UtilizationMetrics.UsedCores, 0.001)
+}
diff --git a/pkg/gpu/testdata/pytorch_batched_kernels.ndjson b/pkg/gpu/testdata/pytorch_batched_kernels.ndjson
new file mode 100644
index 000000000000..9476409a1364
--- /dev/null
+++ b/pkg/gpu/testdata/pytorch_batched_kernels.ndjson
@@ -0,0 +1,990 @@
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555415778257,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379996256896,"Shared_mem_size":0,"Grid_size":{"X":1,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555416037105,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379982489904,"Shared_mem_size":0,"Grid_size":{"X":50176,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555416136470,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379966716096,"Shared_mem_size":0,"Grid_size":{"X":25088,"Y":1,"Z":1},"Block_size":{"X":256,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555416480925,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379996256896,"Shared_mem_size":0,"Grid_size":{"X":1,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555416675825,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379982489904,"Shared_mem_size":0,"Grid_size":{"X":12544,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555416875159,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379996256896,"Shared_mem_size":0,"Grid_size":{"X":1,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555417064884,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379996264176,"Shared_mem_size":0,"Grid_size":{"X":12544,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513555417119333,"Type":0,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]},"Kernel_addr":125379982489904,"Shared_mem_size":0,"Grid_size":{"X":12544,"Y":1,"Z":1},"Block_size":{"X":128,"Y":1,"Z":1}}
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+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558814217536,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558814270076,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558814316165,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558814354514,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558814389632,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558816817203,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558816923137,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558816977470,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558817034925,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558817065193,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558817093105,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558817763735,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558817848771,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558865536440,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
+{"Header":{"Pid_tgid":107030585041240,"Stream_id":0,"Ktime_ns":513558865626375,"Type":2,"Pad":0,"Cgroup":[99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,53,102,48,101,97,56,55,57,53,98,56,54,52,50,101,55,102,54,51,48,49,98,50,50,57,54,55,100,100,102,50,99,56,100,56,50,102,102,98,49,101,54,57,99,57,56,101,99,98,51,48,49,101,51,101,102,51,54,55,56,55,101,99,46,115,99,111,112,101,0,0,0,0,0,0,0,0,0,0,0,99,114,105,45,99,111,110,116,97,105,110,101,114,100,45,99,55,49,50,53,50,55,99,97,55,53,99,98,51,100,100,57,52],"Pad_cgo_0":[0,0,0,0,0,0,0]}}
diff --git a/pkg/gpu/testutil/events.go b/pkg/gpu/testutil/events.go
index 3449b473537f..4843539dcf74 100644
--- a/pkg/gpu/testutil/events.go
+++ b/pkg/gpu/testutil/events.go
@@ -13,45 +13,71 @@ import (
 	"fmt"
 	"io"
 	"os"
+	"path/filepath"
 	"strings"
+	"testing"
+	"unsafe"
+
+	"github.com/stretchr/testify/require"
 
 	"github.com/DataDog/datadog-agent/pkg/gpu/ebpf"
+
+	usmtestutil "github.com/DataDog/datadog-agent/pkg/network/protocols/http/testutil"
 )
 
-type partialEvent struct {
-	Header ebpf.CudaEventHeader `json:"header"`
+type dataSample string
+
+const (
+	// DataSamplePytorchBatchedKernels is a data sample that contains a sequence of kernel launches
+	// interspersed with cudaStreamSynchronize calls.
+	DataSamplePytorchBatchedKernels dataSample = "pytorch_batched_kernels.ndjson"
+)
+
+// DataSampleInfo contains information about a data sample
+type DataSampleInfo struct {
+	// ActivePID is the PID of the process that is active during the data sample
+	ActivePID int
+
+	// EventCount is the number of events in the data sample
+	EventCount int
 }
 
-func parseEventWithType[K any](data []byte) (*K, error) {
-	var event K
-	err := json.Unmarshal(data, &event)
-	if err != nil {
-		return nil, err
-	}
+// DataSampleInfos contains information about the data samples available in the testdata directory,
+// for validation and reference
+var DataSampleInfos = map[dataSample]DataSampleInfo{
+	DataSamplePytorchBatchedKernels: {
+		ActivePID:  24920,
+		EventCount: 990,
+	},
+}
+
+// GetGPUTestEvents returns a collection of events from the testdata directory. The datasetName
+// should be the name of the file in the testdata directory (with the extension).
+func GetGPUTestEvents(tb testing.TB, datasetName dataSample) *EventCollection {
+	curDir, err := usmtestutil.CurDir()
+	require.NoError(tb, err)
 
-	return &event, nil
+	eventsFile := filepath.Join(curDir, "..", "testdata", string(datasetName))
+	events, err := NewEventCollection(eventsFile)
+	require.NoError(tb, err)
+	require.Len(tb, events.Events, DataSampleInfos[datasetName].EventCount)
+
+	return events
 }
 
-func parseCompleteEvent(eventType ebpf.CudaEventType, data []byte) (any, error) {
-	switch eventType {
-	case ebpf.CudaEventTypeKernelLaunch:
-		return parseEventWithType[ebpf.CudaKernelLaunch](data)
-	case ebpf.CudaEventTypeMemory:
-		return parseEventWithType[ebpf.CudaMemEvent](data)
-	case ebpf.CudaEventTypeSync:
-		return parseEventWithType[ebpf.CudaSync](data)
-	case ebpf.CudaEventTypeSetDevice:
-		return parseEventWithType[ebpf.CudaSetDeviceEvent](data)
-	default:
-		return nil, fmt.Errorf("unsupported event type %d", eventType)
-	}
+// Event wraps a parsed event and its length, to emulate the behavior of the ring buffer events
+type Event struct {
+	Data       any
+	DataLength int
+	Header     ebpf.CudaEventHeader
+	Pointer    unsafe.Pointer
 }
 
 // EventCollection represents a collection of recorded CUDA events from the system-probe
 // module, as returned by the `gpu/debug/collect-events` endpoint. This struct encapsulates methods to
 // parse, manipulate and display the events.
 type EventCollection struct {
-	Events       []any
+	Events       []Event
 	firstKtimeNs uint64
 	lastKtimeNs  uint64
 }
@@ -82,7 +108,7 @@ func NewEventCollection(path string) (*EventCollection, error) {
 		}
 
 		// parse the rest of the event now that we know the event type:
-		completeEvent, err := parseCompleteEvent(ebpf.CudaEventType(event.Header.Type), []byte(line))
+		completeEvent, err := parseCompleteEvent(event.Header, []byte(line))
 		if err != nil {
 			return nil, fmt.Errorf("cannot parse line %d: %v", lineNumber, err)
 		}
@@ -98,6 +124,40 @@ func NewEventCollection(path string) (*EventCollection, error) {
 	return coll, nil
 }
 
+type partialEvent struct {
+	Header ebpf.CudaEventHeader `json:"header"`
+}
+
+func parseEventWithType[K any](header ebpf.CudaEventHeader, data []byte) (Event, error) {
+	var parsed K
+	err := json.Unmarshal(data, &parsed)
+	if err != nil {
+		return Event{}, err
+	}
+
+	return Event{
+		Data:       &parsed,
+		DataLength: int(unsafe.Sizeof(parsed)),
+		Header:     header,
+		Pointer:    unsafe.Pointer(&parsed),
+	}, nil
+}
+
+func parseCompleteEvent(header ebpf.CudaEventHeader, data []byte) (Event, error) {
+	switch ebpf.CudaEventType(header.Type) {
+	case ebpf.CudaEventTypeKernelLaunch:
+		return parseEventWithType[ebpf.CudaKernelLaunch](header, data)
+	case ebpf.CudaEventTypeMemory:
+		return parseEventWithType[ebpf.CudaMemEvent](header, data)
+	case ebpf.CudaEventTypeSync:
+		return parseEventWithType[ebpf.CudaSync](header, data)
+	case ebpf.CudaEventTypeSetDevice:
+		return parseEventWithType[ebpf.CudaSetDeviceEvent](header, data)
+	default:
+		return Event{}, fmt.Errorf("unsupported event type %d", header.Type)
+	}
+}
+
 // headerToString converts a CUDA event header to a human-readable string, including relative time
 // from the previous event if it's not zero
 func (c *EventCollection) headerToString(header *ebpf.CudaEventHeader, prevKtimeNs uint64) string {
@@ -126,41 +186,36 @@ func (c *EventCollection) OutputEvents(writer io.Writer) error {
 	groupers := make(map[uint64]*eventGrouper)
 
 	for i, ev := range c.Events {
-		var header *ebpf.CudaEventHeader
 		var evStr string
-		switch e := ev.(type) {
+		switch e := ev.Data.(type) {
 		case *ebpf.CudaKernelLaunch:
-			header = &e.Header
 			evStr = fmt.Sprintf("kernel launch addr 0x%X", e.Kernel_addr)
 		case *ebpf.CudaMemEvent:
 			memName := "allocation"
 			if ebpf.CudaMemEventType(e.Type) == ebpf.CudaMemFree {
 				memName = "free"
 			}
-			header = &e.Header
 			evStr = fmt.Sprintf("memory %s addr 0x%X size %d", memName, e.Addr, e.Size)
 		case *ebpf.CudaSync:
-			header = &e.Header
 			evStr = "sync event"
 		case *ebpf.CudaSetDeviceEvent:
-			header = &e.Header
 			evStr = fmt.Sprintf("set device event device %d", e.Device)
 		default:
 			return fmt.Errorf("%d: unsupported event type: %T", i, e)
 		}
 
-		headerStr := c.headerToString(header, prevKtimeNs)
-		prevKtimeNs = header.Ktime_ns
+		headerStr := c.headerToString(&ev.Header, prevKtimeNs)
+		prevKtimeNs = ev.Header.Ktime_ns
 
 		fmt.Fprintf(writer, "%d: [%s] %s\n", i, headerStr, evStr)
 
-		tsMsec := float64(header.Ktime_ns-c.firstKtimeNs) / 1e6
+		tsMsec := float64(ev.Header.Ktime_ns-c.firstKtimeNs) / 1e6
 
-		pid := header.Pid_tgid >> 32
+		pid := ev.Header.Pid_tgid >> 32
 		if _, ok := groupers[pid]; !ok {
 			groupers[pid] = &eventGrouper{}
 		}
-		groupers[pid].addEvent(ebpf.CudaEventType(header.Type), tsMsec, i)
+		groupers[pid].addEvent(ebpf.CudaEventType(ev.Header.Type), tsMsec, i)
 	}
 
 	// flush the last value
diff --git a/pkg/gpu/testutil/mocks.go b/pkg/gpu/testutil/mocks.go
index cfb52ef6e77e..6d3d7dc7b268 100644
--- a/pkg/gpu/testutil/mocks.go
+++ b/pkg/gpu/testutil/mocks.go
@@ -162,7 +162,7 @@ func GetBasicNvmlMock() *nvmlmock.Interface {
 }
 
 // GetWorkloadMetaMock returns a mock of the workloadmeta.Component.
-func GetWorkloadMetaMock(t *testing.T) workloadmetamock.Mock {
+func GetWorkloadMetaMock(t testing.TB) workloadmetamock.Mock {
 	return fxutil.Test[workloadmetamock.Mock](t, fx.Options(
 		core.MockBundle(),
 		workloadmetafxmock.MockModule(workloadmeta.NewParams()),
@@ -170,7 +170,7 @@ func GetWorkloadMetaMock(t *testing.T) workloadmetamock.Mock {
 }
 
 // GetTelemetryMock returns a mock of the telemetry.Component.
-func GetTelemetryMock(t *testing.T) telemetry.Component {
+func GetTelemetryMock(t testing.TB) telemetry.Component {
 	return fxutil.Test[telemetry.Component](t, telemetryimpl.MockModule())
 }
 


Reviews:
user: login: copilot-pull-request-reviewer[bot]
body: ## Pull Request Overview

This PR adds end-to-end tests and benchmarking support for the GPU event consumer by incorporating collected test data from a real GPU monitored host. It also refactors parts of event parsing and adjusts mock interfaces for consistency.
- Adds a new file with integration tests and a benchmark for the CUDA event consumer.
- Refactors event parsing in test utilities to use a typed Event struct.
- Updates mock functions and benchmark tests for improved consistency and clarity.

### Reviewed Changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 1 comment.

<details>
<summary>Show a summary per file</summary>

| File                              | Description                                                                                       |
| --------------------------------- | ------------------------------------------------------------------------------------------------- |
| pkg/gpu/e2e_events_test.go         | New end-to-end tests for the GPU event consumer using injected events and metric assertions.     |
| pkg/gpu/testutil/events.go         | Updates event collection and parsing logic for better type handling and data integrity.           |
| pkg/gpu/consumer_test.go           | Adds benchmark tests for the consumer and ensures consistent usage of data sample constants.      |
| pkg/gpu/consumer.go                | Refactors event handling to reduce code duplication and improve telemetry instrumentation.        |
| pkg/gpu/testutil/mocks.go          | Adjusts mock interface functions to accept testing.TB for consistency across tests.               |
</details>



<details>
<summary>Comments suppressed due to low confidence (2)</summary>

**pkg/gpu/e2e_events_test.go:68**
* [nitpick] Instead of iterating over the streams and checking for a nil value, consider directly asserting that there is exactly one stream (e.g., using require.Len(handlers.allStreams(), 1)) for improved clarity.
```
for s := range handlers.allStreams() {
```
**pkg/gpu/consumer_test.go:113**
* [nitpick] Consider using the defined constant testutil.DataSamplePytorchBatchedKernels instead of a literal string for the dataset name to maintain consistency and reduce the risk of typos.
```
events := testutil.GetGPUTestEvents(b, "pytorch_batched_kernels.ndjson")
```
</details>


state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-26T11:19:59Z
user: login: gjulianm
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-26T11:26:03Z
user: login: val06
state: APPROVED
author_association: CONTRIBUTOR
submitted_at: 2025-03-26T13:35:04Z

Review comments:
diff_hunk: @@ -98,6 +124,40 @@ func NewEventCollection(path string) (*EventCollection, error) {
 	return coll, nil
 }
 
+type partialEvent struct {
+	Header ebpf.CudaEventHeader `json:"header"`
+}
+
+func parseEventWithType[K any](header ebpf.CudaEventHeader, data []byte) (Event, error) {
+	var parsed K
+	err := json.Unmarshal(data, &parsed)
+	if err != nil {
+		return Event{}, err
+	}
+
+	return Event{
+		Data:       &parsed,
+		DataLength: int(unsafe.Sizeof(parsed)),
path: pkg/gpu/testutil/events.go
user: login: Copilot
body: [nitpick] Using unsafe.Sizeof on a parsed value may not accurately represent the serialized event's byte length; consider determining DataLength from the original data to ensure consistency with expected sizes.
```suggestion
		DataLength: len(data),
```
created_at: 2025-03-26T11:19:59Z
author_association: CONTRIBUTOR
diff_hunk: @@ -98,6 +124,40 @@ func NewEventCollection(path string) (*EventCollection, error) {
 	return coll, nil
 }
 
+type partialEvent struct {
+	Header ebpf.CudaEventHeader `json:"header"`
+}
+
+func parseEventWithType[K any](header ebpf.CudaEventHeader, data []byte) (Event, error) {
+	var parsed K
+	err := json.Unmarshal(data, &parsed)
+	if err != nil {
+		return Event{}, err
+	}
+
+	return Event{
+		Data:       &parsed,
+		DataLength: int(unsafe.Sizeof(parsed)),
path: pkg/gpu/testutil/events.go
user: login: gjulianm
body: For reference, DataLength here refers to the length of the Data field of the Event, not of the input, so this comment is wrong.
created_at: 2025-03-26T11:26:03Z
author_association: CONTRIBUTOR

PR comments:
user: login: agent-platform-auto-pr[bot]
created_at: 2025-03-26T11:54:13Z
body: 
[Fast Unit Tests Report]

On pipeline [60039242](https://gitlab.ddbuild.io/DataDog/datadog-agent/-/pipelines/60039242) ([CI Visibility](https://app.datadoghq.com/ci/pipeline-executions?query=ci_level%3Apipeline%20%40ci.pipeline.name%3ADataDog%2Fdatadog-agent%20%40ci.pipeline.id%3A60039242&fromUser=false&index=cipipeline)). The following jobs did not run any unit tests:

<details>
<summary>Jobs:</summary>

  - tests_windows-x64
</details>

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help
author_association: CONTRIBUTOR
user: login: agent-platform-auto-pr[bot]
created_at: 2025-03-26T11:54:32Z
body: ## Uncompressed package size comparison

Comparison with [ancestor](https://github.com/DataDog/datadog-agent/commit/bbe92165dd66d75d15be30419cb5bd4507a0e815) `bbe92165dd66d75d15be30419cb5bd4507a0e815`


<details>
  <summary> Diff per package </summary>

|package|diff|status|size|ancestor|threshold|
|--|--|--|--|--|--|
|datadog-agent-aarch64-rpm|0.00MB|✅|807.97MB|807.97MB|0.50MB|
|datadog-agent-arm64-deb|0.00MB|✅|798.15MB|798.15MB|0.50MB|
|datadog-agent-amd64-deb|0.00MB|✅|807.15MB|807.15MB|0.50MB|
|datadog-agent-x86_64-rpm|0.00MB|✅|816.99MB|816.99MB|0.50MB|
|datadog-agent-x86_64-suse|0.00MB|✅|816.99MB|816.99MB|0.50MB|
|datadog-dogstatsd-amd64-deb|0.00MB|✅|39.48MB|39.48MB|0.50MB|
|datadog-dogstatsd-x86_64-rpm|0.00MB|✅|39.56MB|39.56MB|0.50MB|
|datadog-dogstatsd-x86_64-suse|0.00MB|✅|39.56MB|39.56MB|0.50MB|
|datadog-dogstatsd-arm64-deb|0.00MB|✅|38.00MB|38.00MB|0.50MB|
|datadog-heroku-agent-amd64-deb|0.00MB|✅|442.26MB|442.26MB|0.50MB|
|datadog-iot-agent-amd64-deb|0.00MB|✅|61.12MB|61.12MB|0.50MB|
|datadog-iot-agent-x86_64-rpm|0.00MB|✅|61.19MB|61.19MB|0.50MB|
|datadog-iot-agent-x86_64-suse|0.00MB|✅|61.19MB|61.19MB|0.50MB|
|datadog-iot-agent-arm64-deb|0.00MB|✅|58.41MB|58.41MB|0.50MB|
|datadog-iot-agent-aarch64-rpm|0.00MB|✅|58.49MB|58.49MB|0.50MB|

</details>

## Decision
✅ Passed



author_association: CONTRIBUTOR
user: login: agent-platform-auto-pr[bot]
created_at: 2025-03-26T11:56:21Z
body: ## Test changes on VM

Use this command from [test-infra-definitions](https://github.com/DataDog/test-infra-definitions) to manually test this PR changes on a VM:
```sh
dda inv aws.create-vm --pipeline-id=60039242 --os-family=ubuntu
```
Note: This applies to commit **0d82a7d7**
author_association: CONTRIBUTOR
user: login: agent-platform-auto-pr[bot]
created_at: 2025-03-26T11:58:30Z
body: ## Static quality checks ✅

Please find below the results from static quality gates


<details>
<summary>Successful checks</summary>

### Info

|Result|Quality gate|On disk size|On disk size limit|On wire size|On wire size limit|
|----|----|----|----|----|----|
|✅|static_quality_gate_agent_deb_amd64|780.83 MiB|801.8 MiB|190.75 MiB|202.62 MiB|
|✅|static_quality_gate_agent_deb_arm64|772.22 MiB|793.14 MiB|172.69 MiB|184.51 MiB|
|✅|static_quality_gate_agent_rpm_amd64|780.82 MiB|801.79 MiB|193.18 MiB|205.03 MiB|
|✅|static_quality_gate_agent_rpm_arm64|772.21 MiB|793.09 MiB|174.28 MiB|186.44 MiB|
|✅|static_quality_gate_agent_suse_amd64|780.82 MiB|801.81 MiB|193.18 MiB|205.03 MiB|
|✅|static_quality_gate_agent_suse_arm64|772.21 MiB|793.14 MiB|174.28 MiB|186.44 MiB|
|✅|static_quality_gate_dogstatsd_deb_amd64|37.73 MiB|47.67 MiB|9.78 MiB|19.78 MiB|
|✅|static_quality_gate_dogstatsd_deb_arm64|36.32 MiB|46.27 MiB|8.48 MiB|18.49 MiB|
|✅|static_quality_gate_dogstatsd_rpm_amd64|37.73 MiB|47.67 MiB|9.79 MiB|19.79 MiB|
|✅|static_quality_gate_dogstatsd_suse_amd64|37.73 MiB|47.67 MiB|9.79 MiB|19.79 MiB|
|✅|static_quality_gate_iot_agent_deb_amd64|58.36 MiB|69.0 MiB|14.74 MiB|24.8 MiB|
|✅|static_quality_gate_iot_agent_deb_arm64|55.78 MiB|66.4 MiB|12.73 MiB|22.8 MiB|
|✅|static_quality_gate_iot_agent_rpm_amd64|58.36 MiB|69.0 MiB|14.76 MiB|24.8 MiB|
|✅|static_quality_gate_iot_agent_rpm_arm64|55.78 MiB|66.4 MiB|12.77 MiB|22.8 MiB|
|✅|static_quality_gate_iot_agent_suse_amd64|58.36 MiB|69.0 MiB|14.76 MiB|24.8 MiB|
|✅|static_quality_gate_docker_agent_amd64|865.95 MiB|886.12 MiB|291.35 MiB|304.21 MiB|
|✅|static_quality_gate_docker_agent_arm64|880.63 MiB|900.79 MiB|277.82 MiB|290.47 MiB|
|✅|static_quality_gate_docker_agent_jmx_amd64|1.04 GiB|1.06 GiB|366.46 MiB|379.33 MiB|
|✅|static_quality_gate_docker_agent_jmx_arm64|1.04 GiB|1.06 GiB|348.92 MiB|361.55 MiB|
|✅|static_quality_gate_docker_dogstatsd_amd64|45.87 MiB|55.78 MiB|17.29 MiB|27.28 MiB|
|✅|static_quality_gate_docker_dogstatsd_arm64|44.49 MiB|54.45 MiB|16.16 MiB|26.16 MiB|
|✅|static_quality_gate_docker_cluster_agent_amd64|262.78 MiB|274.78 MiB|105.54 MiB|116.28 MiB|
|✅|static_quality_gate_docker_cluster_agent_arm64|278.74 MiB|290.82 MiB|100.38 MiB|111.12 MiB|

</details>

author_association: CONTRIBUTOR
user: login: cit-pr-commenter[bot]
created_at: 2025-03-26T12:15:13Z
body: ## Regression Detector

# Regression Detector Results

[Metrics dashboard](https://app.datadoghq.com/dashboard/ykh-ua8-vcu/SMP-Regression-Detector-Metrics?fromUser=true&refresh_mode=paused&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&live=false)  
[Target profiles](https://app.datadoghq.com/profiling/explorer?query=env%3Asingle-machine-performance%20service%3Adatadog-agent%20job_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&viz=stream&start=1742993444000&end=1743004844000&paused=true)  
Run ID: a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898

Baseline: bbe92165dd66d75d15be30419cb5bd4507a0e815
Comparison: 0d82a7d7c704a6212dff09cf8f296b2b552aaf1a
[Diff](bbe92165dd66d75d15be30419cb5bd4507a0e815...0d82a7d7c704a6212dff09cf8f296b2b552aaf1a)


## Optimization Goals: ✅ No significant changes detected


<details>
<summary><h2>
Fine details of change detection per experiment
</h2></summary>

| perf | experiment                                   | goal               | Δ mean % | Δ mean % CI    | trials | links                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|------|----------------------------------------------|--------------------|----------|----------------|--------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ➖   | uds_dogstatsd_to_api_cpu                     | % cpu utilization  | +0.92    | [+0.08, +1.77] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                   |
| ➖   | file_tree                                    | memory utilization | +0.71    | [+0.54, +0.88] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                                  |
| ➖   | otlp_ingest_logs                             | memory utilization | +0.52    | [+0.36, +0.68] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotlp_ingest_logs%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                           |
| ➖   | quality_gate_idle                            | memory utilization | +0.47    | [+0.41, +0.52] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&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=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&live=false)                           |
| ➖   | uds_dogstatsd_20mb_12k_contexts_20_senders   | memory utilization | +0.14    | [+0.09, +0.19] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_20mb_12k_contexts_20_senders%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                 |
| ➖   | file_to_blackhole_0ms_latency_http1          | egress throughput  | +0.01    | [-0.77, +0.80] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency_http1%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                        |
| ➖   | file_to_blackhole_100ms_latency              | egress throughput  | +0.01    | [-0.72, +0.74] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_100ms_latency%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                            |
| ➖   | file_to_blackhole_300ms_latency              | egress throughput  | +0.01    | [-0.63, +0.64] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_300ms_latency%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                            |
| ➖   | tcp_dd_logs_filter_exclude                   | ingress throughput | +0.00    | [-0.02, +0.02] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                 |
| ➖   | file_to_blackhole_0ms_latency                | egress throughput  | -0.00    | [-0.84, +0.83] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                              |
| ➖   | uds_dogstatsd_to_api                         | ingress throughput | -0.01    | [-0.30, +0.28] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                       |
| ➖   | file_to_blackhole_0ms_latency_http2          | egress throughput  | -0.01    | [-0.81, +0.79] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency_http2%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                        |
| ➖   | file_to_blackhole_500ms_latency              | egress throughput  | -0.01    | [-0.79, +0.76] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_500ms_latency%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                            |
| ➖   | file_to_blackhole_1000ms_latency_linear_load | egress throughput  | -0.10    | [-0.55, +0.36] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency_linear_load%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                               |
| ➖   | quality_gate_idle_all_features               | memory utilization | -0.27    | [-0.36, -0.18] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle_all_features%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&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=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&live=false) |
| ➖   | otlp_ingest_metrics                          | memory utilization | -0.35    | [-0.49, -0.21] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotlp_ingest_metrics%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                        |
| ➖   | file_to_blackhole_1000ms_latency             | egress throughput  | -0.37    | [-1.15, +0.40] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                           |
| ➖   | tcp_syslog_to_blackhole                      | ingress throughput | -0.85    | [-0.93, -0.77] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                    |
| ➖   | otlp_ingest_traces                           | memory utilization | -2.38    | [-2.84, -1.91] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotlp_ingest_traces%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                         |
| ➖   | quality_gate_logs                            | % cpu utilization  | -2.53    | [-5.34, +0.27] | 1      | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_logs%20run_id%3Aa5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&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=1742993444000&to_ts=1743004844000&live=false)                                                                                                                                                                                                                                                                                                                                                                          |
</details>


<details>
<summary><h2>
Bounds Checks: ✅ Passed
</h2></summary>

| 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=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&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=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&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=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&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=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&tpl_var_run-id%5B0%5D=a5e1d3ac-f300-4bb2-a6f4-9d9ce9b01898&view=spans&from_ts=1743000644000&to_ts=1743001244000&live=false) |
| ✅   | quality_gate_logs                            | intake_connections | 10/10             |                                                                                                                                                                                                                                                                                                                                                             |
| ✅   | quality_gate_logs                            | lost_bytes         | 10/10             |                                                                                                                                                                                                                                                                                                                                                             |
| ✅   | quality_gate_logs                            | memory_usage       | 10/10             |                                                                                                                                                                                                                                                                                                                                                             |
</details>


<details>
<summary><h2>
Explanation
</h2></summary>

**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".
</details>

## 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.
author_association: NONE
user: login: gjulianm
created_at: 2025-03-26T15:02:34Z
body: /merge
author_association: CONTRIBUTOR
user: login: dd-devflow[bot]
created_at: 2025-03-26T15:02:38Z
body: <!-- "FeedbackWorkflowId":"00b60cdf-1f7e-4ece-86c8-84d414d03407_36" -->
[View all feedbacks in Devflow UI.](https://devflow.static-app.us1.prod.dog/logs/00b60cdf-1f7e-4ece-86c8-84d414d03407_36)
`2025-03-26 15:02:38 UTC` :information_source: Start processing command `/merge`

---
`2025-03-26 15:02:49 UTC` :information_source: **MergeQueue:** waiting for PR to be ready

This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals.
**Note:** if you pushed new commits since the last approval, you may need additional approval.
You can remove it from the waiting list with `/remove` command.

---
`2025-03-26 15:54:14 UTC` :information_source: **MergeQueue:** merge request added to the queue

The expected merge time in `main` is approximately 54m (p90).

---
`2025-03-26 16:21:29 UTC` :information_source: **MergeQueue:** This merge request was merged

author_association: NONE