}

// scaleHeapSample adjusts the data from a heap Sample to
// account for its probability of appearing in the collected
// data. heap profiles are a sampling of the memory allocations
// requests in a program. We estimate the unsampled value by dividing
// each collected sample by its probability of appearing in the
// profile. heap profiles rely on a poisson process to determine
// which samples to collect, based on the desired average collection

	const expectedLocationNewInliner = "runtime/pprof.TestGenericsInlineLocations;" + expectedLocation
	var s *profile.Sample
	for _, sample := range p.Sample {
		if sampleToString(sample) == expectedSample {
			s = sample
			break
		}
	}
	if s == nil {
		t.Fatalf("expected \n%s\ngot\n%s", expectedSample, strings.Join(profileToStrings(p), "\n"))
	}
	loc := s.Location[0]

	// header and stack of each sample, measured in 64-bit words. Every sample
	// has a required header of two words. With a small additional header (a
	// word or two) and stacks at the profiler's maximum length of 64 frames,
	// that capacity can support 1900 samples or 19 thread-seconds at a 100 Hz
	// sample rate, at a cost of 1 MiB.
	profBufWordCount = 1 << 17

# Grafana Dashboards

This folder contains Istio's official Grafana dashboards.
These get publish to [Grafana](https://grafana.com/orgs/istio/dashboards) during release, and are bundled into our
[Grafana sample](../../../samples/addons/grafana.yaml).

## Jsonnet

Newer dashboards are generated with [Jsonnet](https://jsonnet.org/) with the [Grafonnet](https://grafana.github.io/grafonnet/index.html).

#
# To apply all 4 Bookinfo services, their corresponding service accounts, and deployments:
#
#   kubectl apply -f samples/bookinfo/platform/kube/bookinfo.yaml
#
# Alternatively, you can deploy any resource separately:
#
#   kubectl apply -f samples/bookinfo/platform/kube/bookinfo.yaml -l service=reviews # reviews Service

#
# To apply all 4 Bookinfo services, their corresponding service accounts, and deployments:
#
#   kubectl apply -f samples/bookinfo/platform/kube/bookinfo.yaml
#
# Alternatively, you can deploy any resource separately:
#
#   kubectl apply -f samples/bookinfo/platform/kube/bookinfo.yaml -l service=reviews # reviews Service

	FakeP    = 1000000 + iota
	TimerP   // depicts timer unblocks
	NetpollP // depicts network unblocks
	SyscallP // depicts returns from syscalls
	GCP      // depicts GC state
	ProfileP // depicts recording of CPU profile samples
)

// Event types in the trace.
// Verbatim copy from src/runtime/trace.go with the "trace" prefix removed.
const (
	EvNone              = 0  // unused

	if err != nil {
		return fmt.Errorf("period type: %v", err)
	}

	// Identify common sample types
	numSampleTypes := len(profiles[0].SampleType)
	for _, p := range profiles[1:] {
		if numSampleTypes != len(p.SampleType) {
			return fmt.Errorf("inconsistent samples type count: %d != %d", numSampleTypes, len(p.SampleType))
		}
	}
	sampleType := make([]*profile.ValueType, numSampleTypes)

-- go.work --
go 1.23

use ./a
use ./b
-- a/go.mod --
module example.com/a

go 1.23

require rsc.io/sampler v1.2.1

require golang.org/x/text v0.0.0-20170915032832-14c0d48ead0c // indirect
-- a/a.go --
package a

import "rsc.io/sampler"

func A() string {
    return sampler.Hello()
}
-- b/go.mod --
module example.com/b

go 1.23

// The indrect comment below is inaccurate. Its purpose

			case "runtime._System", "runtime._GC", "runtime._ExternalCode", "runtime._VDSO":
				continue
			}
			stack := strings.Join(fns, "|")
			samples := int(s.Value[0])
			pprofStacks[stack] += samples
		}
	}
	for stack, samples := range pprofStacks {
		fmt.Fprintf(os.Stderr, "%s\t%d\n", stack, samples)
	}
}

func cpuHogger(f func(x int) int, y *int, dur time.Duration) {
	// We only need to get one 100 Hz clock tick, so we've got