1
0
mirror of https://github.com/open-telemetry/opentelemetry-go.git synced 2025-01-26 03:52:03 +02:00
David Ashpole 81b2a33e1b
Add selector of exemplar reservoir providers to metric.Stream configuration (#5861)
Resolve https://github.com/open-telemetry/opentelemetry-go/issues/5249

### Spec

> exemplar_reservoir: A functional type that generates an exemplar
reservoir a MeterProvider will use when storing exemplars. This
functional type needs to be a factory or callback similar to aggregation
selection functionality which allows different reservoirs to be chosen
by the aggregation.

> Users can provide an exemplar_reservoir, but it is up to their
discretion. Therefore, the stream configuration parameter needs to be
structured to accept an exemplar_reservoir, but MUST NOT obligate a user
to provide one. If the user does not provide an exemplar_reservoir
value, the MeterProvider MUST apply a [default exemplar
reservoir](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exemplar-defaults).

Also,

> the reservoir MUST be given the Attributes associated with its
timeseries point either at construction so that additional sampling
performed by the reservoir has access to all attributes from a
measurement in the "offer" method.

### Changes

In sdk/metric/exemplar, add:
* `exemplar.ReservoirProvider`
* `exemplar.HistogramReservoirProvider`
* `exemplar.FixedSizeReservoirProvider`

In sdk/metric, add:
* `metric.ExemplarReservoirProviderSelector` (func Aggregation ->
ReservoirProvider)
* `metric.DefaultExemplarReservoirProviderSelector` (our default
implementation)
* `ExemplarReservoirProviderSelector` added to `metric.Stream`

Note: the only required public types are
`metric.ExemplarReservoirProviderSelector` and
`ExemplarReservoirProviderSelector` in `metric.Stream`. Others are for
convenience and readability.

### Alternatives considered

#### Add ExemplarReservoirProvider directly to metric.Stream, instead of
ExemplarReservoirProviderSelector

This would mean users can configure a `func() exemplar.Reservoir`
instead of a `func(Aggregation) func() exemplar.Reservoir`.

I don't think this complies with the statement: `This functional type
needs to be a factory or callback similar to aggregation selection
functionality which allows different reservoirs to be chosen by the
aggregation.`. I'm interpreting "allows different reservoirs to be
chosen by the aggregation" as meaning "allows different reservoirs to be
chosen **based on the** aggregation", rather than meaning that the
aggregation is somehow choosing the reservoir.

### Future work

There is some refactoring I plan to do after this to simplify the
interaction between the internal/aggregate and exemplar package. I've
omitted that from this PR to keep the diff smaller.

---------

Co-authored-by: Tyler Yahn <MrAlias@users.noreply.github.com>
Co-authored-by: Robert Pająk <pellared@hotmail.com>
2024-10-18 09:05:10 -04:00

162 lines
4.2 KiB
Go

// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
import (
"context"
"sync"
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
// datapoint is timestamped measurement data.
type datapoint[N int64 | float64] struct {
attrs attribute.Set
value N
res FilteredExemplarReservoir[N]
}
func newLastValue[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *lastValue[N] {
return &lastValue[N]{
newRes: r,
limit: newLimiter[datapoint[N]](limit),
values: make(map[attribute.Distinct]datapoint[N]),
start: now(),
}
}
// lastValue summarizes a set of measurements as the last one made.
type lastValue[N int64 | float64] struct {
sync.Mutex
newRes func(attribute.Set) FilteredExemplarReservoir[N]
limit limiter[datapoint[N]]
values map[attribute.Distinct]datapoint[N]
start time.Time
}
func (s *lastValue[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
s.Lock()
defer s.Unlock()
attr := s.limit.Attributes(fltrAttr, s.values)
d, ok := s.values[attr.Equivalent()]
if !ok {
d.res = s.newRes(attr)
}
d.attrs = attr
d.value = value
d.res.Offer(ctx, value, droppedAttr)
s.values[attr.Equivalent()] = d
}
func (s *lastValue[N]) delta(dest *metricdata.Aggregation) int {
t := now()
// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
// the DataPoints is missed (better luck next time).
gData, _ := (*dest).(metricdata.Gauge[N])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// Do not report stale values.
clear(s.values)
// Update start time for delta temporality.
s.start = t
*dest = gData
return n
}
func (s *lastValue[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
// the DataPoints is missed (better luck next time).
gData, _ := (*dest).(metricdata.Gauge[N])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// TODO (#3006): This will use an unbounded amount of memory if there
// are unbounded number of attribute sets being aggregated. Attribute
// sets that become "stale" need to be forgotten so this will not
// overload the system.
*dest = gData
return n
}
// copyDpts copies the datapoints held by s into dest. The number of datapoints
// copied is returned.
func (s *lastValue[N]) copyDpts(dest *[]metricdata.DataPoint[N], t time.Time) int {
n := len(s.values)
*dest = reset(*dest, n, n)
var i int
for _, v := range s.values {
(*dest)[i].Attributes = v.attrs
(*dest)[i].StartTime = s.start
(*dest)[i].Time = t
(*dest)[i].Value = v.value
collectExemplars(&(*dest)[i].Exemplars, v.res.Collect)
i++
}
return n
}
// newPrecomputedLastValue returns an aggregator that summarizes a set of
// observations as the last one made.
func newPrecomputedLastValue[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *precomputedLastValue[N] {
return &precomputedLastValue[N]{lastValue: newLastValue[N](limit, r)}
}
// precomputedLastValue summarizes a set of observations as the last one made.
type precomputedLastValue[N int64 | float64] struct {
*lastValue[N]
}
func (s *precomputedLastValue[N]) delta(dest *metricdata.Aggregation) int {
t := now()
// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
// the DataPoints is missed (better luck next time).
gData, _ := (*dest).(metricdata.Gauge[N])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// Do not report stale values.
clear(s.values)
// Update start time for delta temporality.
s.start = t
*dest = gData
return n
}
func (s *precomputedLastValue[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
// the DataPoints is missed (better luck next time).
gData, _ := (*dest).(metricdata.Gauge[N])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// Do not report stale values.
clear(s.values)
*dest = gData
return n
}