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// Copyright 2020 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package runtime
// Metrics implementation exported to runtime/metrics.
import (
"runtime/internal/atomic"
"unsafe"
)
var (
// metrics is a map of runtime/metrics keys to
// data used by the runtime to sample each metric's
// value.
metricsSema uint32 = 1
metricsInit bool
metrics map[string]metricData
sizeClassBuckets []float64
timeHistBuckets []float64
)
type metricData struct {
// deps is the set of runtime statistics that this metric
// depends on. Before compute is called, the statAggregate
// which will be passed must ensure() these dependencies.
deps statDepSet
// compute is a function that populates a metricValue
// given a populated statAggregate structure.
compute func(in *statAggregate, out *metricValue)
}
// initMetrics initializes the metrics map if it hasn't been yet.
//
// metricsSema must be held.
func initMetrics() {
if metricsInit {
return
}
sizeClassBuckets = make([]float64, _NumSizeClasses, _NumSizeClasses+1)
// Skip size class 0 which is a stand-in for large objects, but large
// objects are tracked separately (and they actually get placed in
// the last bucket, not the first).
sizeClassBuckets[0] = 1 // The smallest allocation is 1 byte in size.
for i := 1; i < _NumSizeClasses; i++ {
// Size classes have an inclusive upper-bound
// and exclusive lower bound (e.g. 48-byte size class is
// (32, 48]) whereas we want and inclusive lower-bound
// and exclusive upper-bound (e.g. 48-byte size class is
// [33, 49). We can achieve this by shifting all bucket
// boundaries up by 1.
//
// Also, a float64 can precisely represent integers with
// value up to 2^53 and size classes are relatively small
// (nowhere near 2^48 even) so this will give us exact
// boundaries.
sizeClassBuckets[i] = float64(class_to_size[i] + 1)
}
sizeClassBuckets = append(sizeClassBuckets, float64Inf())
timeHistBuckets = timeHistogramMetricsBuckets()
metrics = map[string]metricData{
"/gc/cycles/automatic:gc-cycles": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.gcCyclesDone - in.sysStats.gcCyclesForced
},
},
"/gc/cycles/forced:gc-cycles": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.gcCyclesForced
},
},
"/gc/cycles/total:gc-cycles": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.gcCyclesDone
},
},
"/gc/heap/allocs-by-size:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
hist := out.float64HistOrInit(sizeClassBuckets)
hist.counts[len(hist.counts)-1] = uint64(in.heapStats.largeAllocCount)
// Cut off the first index which is ostensibly for size class 0,
// but large objects are tracked separately so it's actually unused.
for i, count := range in.heapStats.smallAllocCount[1:] {
hist.counts[i] = uint64(count)
}
},
},
"/gc/heap/frees-by-size:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
hist := out.float64HistOrInit(sizeClassBuckets)
hist.counts[len(hist.counts)-1] = uint64(in.heapStats.largeFreeCount)
// Cut off the first index which is ostensibly for size class 0,
// but large objects are tracked separately so it's actually unused.
for i, count := range in.heapStats.smallFreeCount[1:] {
hist.counts[i] = uint64(count)
}
},
},
"/gc/heap/goal:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.heapGoal
},
},
"/gc/heap/objects:objects": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.heapStats.numObjects
},
},
"/gc/pauses:seconds": {
compute: func(_ *statAggregate, out *metricValue) {
hist := out.float64HistOrInit(timeHistBuckets)
// The bottom-most bucket, containing negative values, is tracked
// as a separately as underflow, so fill that in manually and then
// iterate over the rest.
hist.counts[0] = atomic.Load64(&memstats.gcPauseDist.underflow)
for i := range memstats.gcPauseDist.counts {
hist.counts[i+1] = atomic.Load64(&memstats.gcPauseDist.counts[i])
}
},
},
"/memory/classes/heap/free:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(in.heapStats.committed - in.heapStats.inHeap -
in.heapStats.inStacks - in.heapStats.inWorkBufs -
in.heapStats.inPtrScalarBits)
},
},
"/memory/classes/heap/objects:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.heapStats.inObjects
},
},
"/memory/classes/heap/released:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(in.heapStats.released)
},
},
"/memory/classes/heap/stacks:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(in.heapStats.inStacks)
},
},
"/memory/classes/heap/unused:bytes": {
deps: makeStatDepSet(heapStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(in.heapStats.inHeap) - in.heapStats.inObjects
},
},
"/memory/classes/metadata/mcache/free:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.mCacheSys - in.sysStats.mCacheInUse
},
},
"/memory/classes/metadata/mcache/inuse:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.mCacheInUse
},
},
"/memory/classes/metadata/mspan/free:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.mSpanSys - in.sysStats.mSpanInUse
},
},
"/memory/classes/metadata/mspan/inuse:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.mSpanInUse
},
},
"/memory/classes/metadata/other:bytes": {
deps: makeStatDepSet(heapStatsDep, sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(in.heapStats.inWorkBufs+in.heapStats.inPtrScalarBits) + in.sysStats.gcMiscSys
},
},
"/memory/classes/os-stacks:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.stacksSys
},
},
"/memory/classes/other:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.otherSys
},
},
"/memory/classes/profiling/buckets:bytes": {
deps: makeStatDepSet(sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = in.sysStats.buckHashSys
},
},
"/memory/classes/total:bytes": {
deps: makeStatDepSet(heapStatsDep, sysStatsDep),
compute: func(in *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(in.heapStats.committed+in.heapStats.released) +
in.sysStats.stacksSys + in.sysStats.mSpanSys +
in.sysStats.mCacheSys + in.sysStats.buckHashSys +
in.sysStats.gcMiscSys + in.sysStats.otherSys
},
},
"/sched/goroutines:goroutines": {
compute: func(_ *statAggregate, out *metricValue) {
out.kind = metricKindUint64
out.scalar = uint64(gcount())
},
},
}
metricsInit = true
}
// statDep is a dependency on a group of statistics
// that a metric might have.
type statDep uint
const (
heapStatsDep statDep = iota // corresponds to heapStatsAggregate
sysStatsDep // corresponds to sysStatsAggregate
numStatsDeps
)
// statDepSet represents a set of statDeps.
//
// Under the hood, it's a bitmap.
type statDepSet [1]uint64
// makeStatDepSet creates a new statDepSet from a list of statDeps.
func makeStatDepSet(deps ...statDep) statDepSet {
var s statDepSet
for _, d := range deps {
s[d/64] |= 1 << (d % 64)
}
return s
}
// differennce returns set difference of s from b as a new set.
func (s statDepSet) difference(b statDepSet) statDepSet {
var c statDepSet
for i := range s {
c[i] = s[i] &^ b[i]
}
return c
}
// union returns the union of the two sets as a new set.
func (s statDepSet) union(b statDepSet) statDepSet {
var c statDepSet
for i := range s {
c[i] = s[i] | b[i]
}
return c
}
// empty returns true if there are no dependencies in the set.
func (s *statDepSet) empty() bool {
for _, c := range s {
if c != 0 {
return false
}
}
return true
}
// has returns true if the set contains a given statDep.
func (s *statDepSet) has(d statDep) bool {
return s[d/64]&(1<<(d%64)) != 0
}
// heapStatsAggregate represents memory stats obtained from the
// runtime. This set of stats is grouped together because they
// depend on each other in some way to make sense of the runtime's
// current heap memory use. They're also sharded across Ps, so it
// makes sense to grab them all at once.
type heapStatsAggregate struct {
heapStatsDelta
// Derived from values in heapStatsDelta.
// inObjects is the bytes of memory occupied by objects,
inObjects uint64
// numObjects is the number of live objects in the heap.
numObjects uint64
}
// compute populates the heapStatsAggregate with values from the runtime.
func (a *heapStatsAggregate) compute() {
memstats.heapStats.read(&a.heapStatsDelta)
// Calculate derived stats.
a.inObjects = uint64(a.largeAlloc - a.largeFree)
a.numObjects = uint64(a.largeAllocCount - a.largeFreeCount)
for i := range a.smallAllocCount {
n := uint64(a.smallAllocCount[i] - a.smallFreeCount[i])
a.inObjects += n * uint64(class_to_size[i])
a.numObjects += n
}
}
// sysStatsAggregate represents system memory stats obtained
// from the runtime. This set of stats is grouped together because
// they're all relatively cheap to acquire and generally independent
// of one another and other runtime memory stats. The fact that they
// may be acquired at different times, especially with respect to
// heapStatsAggregate, means there could be some skew, but because of
// these stats are independent, there's no real consistency issue here.
type sysStatsAggregate struct {
stacksSys uint64
mSpanSys uint64
mSpanInUse uint64
mCacheSys uint64
mCacheInUse uint64
buckHashSys uint64
gcMiscSys uint64
otherSys uint64
heapGoal uint64
gcCyclesDone uint64
gcCyclesForced uint64
}
// compute populates the sysStatsAggregate with values from the runtime.
func (a *sysStatsAggregate) compute() {
a.stacksSys = memstats.stacks_sys.load()
a.buckHashSys = memstats.buckhash_sys.load()
a.gcMiscSys = memstats.gcMiscSys.load()
a.otherSys = memstats.other_sys.load()
a.heapGoal = atomic.Load64(&memstats.next_gc)
a.gcCyclesDone = uint64(memstats.numgc)
a.gcCyclesForced = uint64(memstats.numforcedgc)
systemstack(func() {
lock(&mheap_.lock)
a.mSpanSys = memstats.mspan_sys.load()
a.mSpanInUse = uint64(mheap_.spanalloc.inuse)
a.mCacheSys = memstats.mcache_sys.load()
a.mCacheInUse = uint64(mheap_.cachealloc.inuse)
unlock(&mheap_.lock)
})
}
// statAggregate is the main driver of the metrics implementation.
//
// It contains multiple aggregates of runtime statistics, as well
// as a set of these aggregates that it has populated. The aggergates
// are populated lazily by its ensure method.
type statAggregate struct {
ensured statDepSet
heapStats heapStatsAggregate
sysStats sysStatsAggregate
}
// ensure populates statistics aggregates determined by deps if they
// haven't yet been populated.
func (a *statAggregate) ensure(deps *statDepSet) {
missing := deps.difference(a.ensured)
if missing.empty() {
return
}
for i := statDep(0); i < numStatsDeps; i++ {
if !missing.has(i) {
continue
}
switch i {
case heapStatsDep:
a.heapStats.compute()
case sysStatsDep:
a.sysStats.compute()
}
}
a.ensured = a.ensured.union(missing)
}
// metricValidKind is a runtime copy of runtime/metrics.ValueKind and
// must be kept structurally identical to that type.
type metricKind int
const (
// These values must be kept identical to their corresponding Kind* values
// in the runtime/metrics package.
metricKindBad metricKind = iota
metricKindUint64
metricKindFloat64
metricKindFloat64Histogram
)
// metricSample is a runtime copy of runtime/metrics.Sample and
// must be kept structurally identical to that type.
type metricSample struct {
name string
value metricValue
}
// metricValue is a runtime copy of runtime/metrics.Sample and
// must be kept structurally identical to that type.
type metricValue struct {
kind metricKind
scalar uint64 // contains scalar values for scalar Kinds.
pointer unsafe.Pointer // contains non-scalar values.
}
// float64HistOrInit tries to pull out an existing float64Histogram
// from the value, but if none exists, then it allocates one with
// the given buckets.
func (v *metricValue) float64HistOrInit(buckets []float64) *metricFloat64Histogram {
var hist *metricFloat64Histogram
if v.kind == metricKindFloat64Histogram && v.pointer != nil {
hist = (*metricFloat64Histogram)(v.pointer)
} else {
v.kind = metricKindFloat64Histogram
hist = new(metricFloat64Histogram)
v.pointer = unsafe.Pointer(hist)
}
hist.buckets = buckets
if len(hist.counts) != len(hist.buckets)-1 {
hist.counts = make([]uint64, len(buckets)-1)
}
return hist
}
// metricFloat64Histogram is a runtime copy of runtime/metrics.Float64Histogram
// and must be kept structurally identical to that type.
type metricFloat64Histogram struct {
counts []uint64
buckets []float64
}
// agg is used by readMetrics, and is protected by metricsSema.
//
// Managed as a global variable because its pointer will be
// an argument to a dynamically-defined function, and we'd
// like to avoid it escaping to the heap.
var agg statAggregate
// readMetrics is the implementation of runtime/metrics.Read.
//
//go:linkname readMetrics runtime/metrics.runtime_readMetrics
func readMetrics(samplesp unsafe.Pointer, len int, cap int) {
// Construct a slice from the args.
sl := slice{samplesp, len, cap}
samples := *(*[]metricSample)(unsafe.Pointer(&sl))
// Acquire the metricsSema but with handoff. This operation
// is expensive enough that queueing up goroutines and handing
// off between them will be noticeably better-behaved.
semacquire1(&metricsSema, true, 0, 0)
// Ensure the map is initialized.
initMetrics()
// Clear agg defensively.
agg = statAggregate{}
// Sample.
for i := range samples {
sample := &samples[i]
data, ok := metrics[sample.name]
if !ok {
sample.value.kind = metricKindBad
continue
}
// Ensure we have all the stats we need.
// agg is populated lazily.
agg.ensure(&data.deps)
// Compute the value based on the stats we have.
data.compute(&agg, &sample.value)
}
semrelease(&metricsSema)
}