Note: This specification for the v0.3 OpenTelemetry milestone does not include specification related to the Observer instrument, as described in the overview. Observer instruments were detailed in OTEP 72-metric-observer and will be added to this document following the v0.3 milestone. Gauge instruments will be removed from this specification folowing the v0.3 milestone too, as discussed in OTEP 80-remove-metric-gauge.
Metric instruments are the entry point for application and framework developers to instrument their code using counters, gauges, and measures.
Metrics are created by calling methods on a Meter which is in turn created by a global MeterProvider.
New Meter instances can be created via a MeterProvider and its getMeter method.
MeterProviders are generally expected to be used as singletons.
Implementations SHOULD provide a single global default MeterProvider. The getMeter method expects two string arguments:
name(required): This name must identify the instrumentation library (also referred to as integration, e.g.io.opentelemetry.contrib.mongodb) and not the instrumented library. In case an invalid name (null or empty string) is specified, a working defaultMeterimplementation is returned as a fallback rather than returning null or throwing an exception. AMeterProvidercould also return a no-opMeterhere if application owners configure the SDK to suppress telemetry produced by this library. This name will be used as thenamespacefor any metrics created using the returnedMeter.version(optional): Specifies the version of the instrumentation library (e.g.semver:1.0.0).
Metric instruments have names, which are how we refer to them in external systems. Metric names conform to the following syntax:
- They are non-empty strings
- They are case-insensitive
- The first character must be non-numeric, non-space, non-punctuation
- Subsequent characters must be belong to the alphanumeric characters, '_', '.', and '-'.
Metric names belong to a namespace, which is the name of the associated Meter,
allowing the same metric name to be used in multiple libraries of code,
unambiguously, within the same application.
Metric instruments are defined using a Meter instance, using a variety
of New methods specific to the kind of metric and type of input(integer
or floating point). The Meter will return an error when a metric name is
already registered with a different kind for the same name. Metric systems
are expected to automatically prefix exported metrics by the namespace in a
manner consistent with the target system. For example, a Prometheus exporter
SHOULD use the namespace followed by _ as the
application prefix.
Regardless of the instrument kind or method of input, metric events include the instrument, a numerical value, and an optional set of labels. The instrument, discussed in detail below, contains the metric name and various optional settings.
Labels are key:value pairs associated with events describing various dimensions or categories that describe the event. A "label key" refers to the key component while "label value" refers to the correlated value component of a label. Label refers to the pair of label key and value. Labels are passed in to the metric event at construction time.
Metric events always have an associated component name, the name
passed when constructing the corresponding Meter. Metric events are
associated with the current (implicit or explicit) OpenTelemetry
context, including distributed correlation context and span context.
The Meter interface allows creating of a registered metric
instrument using methods specific to each kind of metric. There are
six constructors representing the three kinds of instrument taking
either floating point or integer inputs, see the detailed design below.
Binding instruments to a single Meter instance has two benefits:
- Instruments can be exported from the zero state, prior to first use, with no explicit
Registercall - The name provided by the
Metersatisfies a namespace requirement
The recommended practice is to define structures to contain the instruments in use and keep references only to the instruments that are specifically needed.
We recognize that many existing metric systems support allocating
metric instruments statically and providing the Meter interface at
the time of use. In this example, typical of statsd clients, existing
code may not be structured with a convenient place to store new metric
instruments. Where this becomes a burden, it is recommended to use
the global meter provider to construct a static Meter, to
construct metric instruments.
The situation is similar for users of Prometheus clients, where
instruments are allocated statically and there is an implicit global.
Such code may not have access to the appropriate Meter where
instruments are defined. Where this becomes a burden, it is
recommended to use the global meter provider to construct a static
named Meter, to construct metric instruments.
Applications are expected to construct long-lived instruments. Instruments are considered permanent for the lifetime of a SDK, there is no method to delete them.
In this Golang example, a struct holding four instruments is built
using the provided, non-global Meter instance.
type instruments struct {
counter1 metric.Int64Counter
counter2 metric.Float64Counter
gauge3 metric.Int64Gauge
measure4 metric.Float64Measure
}
func newInstruments(metric.Meter meter) *instruments {
return &instruments{
counter1: meter.NewCounter("counter1", ...), // Optional parameters
counter2: meter.NewCounter("counter2", ...), // are discussed below.
gauge3: meter.NewGauge("gauge3", ...),
measure4: meter.NewMeasure("measure4", ...),
}
}Code will be structured to call newInstruments somewhere in a
constructor and keep the instruments reference for use at runtime.
Here's an example of building a server with configured instruments and
a single metric operation.
type server struct {
meter metric.Meter
instruments *instruments
// ... other fields
}
func newServer(meter metric.Meter) *server {
return &server{
meter: meter,
instruments: newInstruments(meter),
// ... other fields
}
}
// ...
func (s *server) operate(ctx context.Context) {
// ... other work
s.instruments.counter1.Add(ctx, 1,
key.String("label1", "..."),
key.String("label2", "..."),
}The metrics API provides three semantically equivalent ways to capture measurements:
- calling bound metric instruments
- calling unbound metric instruments with labels
- batch recording without a metric instrument
All three methods generate equivalent metric events, but offer varying degrees of performance and convenience.
This section applies to calling conventions for counter, gauge, and measure instruments.
As described above, metric events consist of an instrument, a set of labels, and a numerical value, plus associated context. The performance of a metric API depends on the work done to enter a new measurement. One approach to reduce cost is to aggregate intermediate results in the SDK, so that subsequent events happening in the same collection period, for the same set of labels, combine into the same working memory.
In this document, the term "aggregation" is used to describe the process of coalescing metric events for a complete set of labels, whereas "grouping" is used to describe further coalescing aggregate metric data into a reduced number of key dimensions. SDKs may be designed to perform aggregation and/or grouping in the process, with various trade-offs in terms of complexity and performance.
In situations where performance is a requirement and a metric instrument is repeatedly used with the same set of labels, the developer may elect to use the bound instrument calling convention as an optimization. For bound instruments to be a benefit, it requires that a specific instrument will be re-used with specific labels. If an instrument will be used with the same labels more than once, obtaining a bound instrument corresponding to the labels ensures the highest performance available.
To bind an instrument, use the Bind(labels) method to return an interface
that supports the Add(), Set(), or Record() method of the instrument in
question.
Bound instruments may consume SDK resources indefinitely.
func (s *server) processStream(ctx context.Context) {
// The result of Bind() is a bound instrument
// (e.g., a BoundInt64Counter).
counter2 := s.instruments.counter2.Bind(
key.String("labelA", "..."),
key.String("labelB", "..."),
)
for _, item := <-s.channel {
// ... other work
// High-performance metric calling convention: use of bound
// instruments.
counter2.Add(ctx, item.size())
}
}When convenience is more important than performance, or there is no re-use to potentially optimize with bound instruments, users may elect to operate directly on metric instruments, supplying labels at the call site. This method offers the greatest convenience possible
For example, to update a single counter:
func (s *server) method(ctx context.Context) {
// ... other work
s.instruments.counter1.Add(ctx, 1, ...)
}There is one final API for entering measurements, which is like the direct access calling convention but supports multiple simultaneous measurements. The use of a RecordBatch API supports entering multiple measurements, implying a semantically atomic update to several instruments.
For example:
func (s *server) method(ctx context.Context) {
// ... other work
s.meter.RecordBatch(ctx, labels,
s.instruments.counter1.Measurement(1),
s.instruments.gauge1.Measurement(10),
s.instruments.measure2.Measurement(123.45),
)
}Using the RecordBatch calling convention is semantically identical to
a sequence of direct calls, with the addition of atomicity. Because
values are entered in a single call,
the SDK is potentially able to implement an atomic update, from the
exporter's point of view. Calls to RecordBatch may potentially
reduce costs because the SDK can enqueue a single bulk update, or take
a lock only once, for example.
When the SDK interprets labels in the context of grouping aggregated values for an exporter, and where there are keys that are missing, the SDK is required to consider these values explicitly unspecified, a distinct value type of the exported data model.
As a language-level decision, APIs may support label key ordering. In this case, the user may specify an ordered sequence of label keys, which is used to create an unordered set of labels from a sequence of similarly ordered label values. For example:
var rpcLabelKeys = OrderedLabelKeys("a", "b", "c")
for _, input := range stream {
labels := rpcLabelKeys.Values(1, 2, 3) // a=1, b=2, c=3
// ...
}This is specified as a language-optional feature because its safety, and therefore its value as an input for monitoring, depends on the availability of type-checking in the source language. Passing unordered labels (i.e., a mapping from keys to values) is considered the safer alternative.
See the SDK-facing Metrics API specification for an in-depth summary of each method in the Metrics API.
Instruments are constructed using the appropriate New method for the
kind of instrument (Counter, Gauge, Measure) and for the type of input
(integer or floating point).
Meter method |
Kind of instrument |
|---|---|
NewIntCounter(name, options...) |
An integer counter |
NewFloatCounter(name, options...) |
A floating point counter |
NewIntGauge(name, options...) |
An integer gauge |
NewFloatGauge(name, options...) |
A floating point gauge |
NewIntMeasure(name, options...) |
An integer measure |
NewFloatMeasure(name, options...) |
A floating point measure |
As in all OpenTelemetry specifications, these names are examples. Each language committee will decide on the appropriate names based on conventions in that language.
Instruments may be defined with a recommended set of label keys. This setting may be used by SDKs as a good default for grouping exported metrics, where used with pre-aggregation. The recommended label keys are usually selected by the developer for exhibiting low cardinality, importance for monitoring purposes, and an intention to provide these variables locally.
SDKs should consider grouping exported metric data by the recommended label keys of each instrument, unless superceded by another form of configuration. Recommended keys that are missing will be considered explicitly unspecified, as for missing labels in general.
Instruments provide several optional settings, summarized here. The kind of instrument and input value type are implied by the constructor that it used, and the metric name is the only required field.
| Option | Option name | Explanation |
|---|---|---|
| Description | WithDescription(string) | Descriptive text documenting the instrument. |
| Unit | WithUnit(string) | Units specified according to the UCUM. |
| Recommended label keys | WithRecommendedKeys(list) | Recommended grouping keys for this instrument. |
| Monotonic | WithMonotonic(boolean) | Configure a counter or gauge that accepts only monotonic/non-monotonic updates. |
| Absolute | WithAbsolute(boolean) | Configure a measure that does or does not accept negative updates. |
See the Metric API specification overview for more information about the kind-specific monotonic and absolute options.
Counter, gauge, and measure instruments each support allocating bound
instruments for the high-performance calling convention. The
Instrument.Bind(labels) method returns an interface which implements the
Add(), Set() or Record() method, respectively, for counter, gauge, and
measure instruments.
Counter, gauge, and measure instruments support the appropriate
Add(), Set(), and Record() method for submitting individual
metric events.
As described above, labels are strictly "local". I.e., the application explicitly declares these labels, whereas distributed correlation context labels are implicitly associated with the event.
There is a clear intention to pre-aggregate metrics within the SDK, using labels to derive grouping keys. There are two available options for users to apply distributed correlation context to the local grouping function used for metrics pre-aggregation:
- The distributed context, whether implicit or explicit, is associated with every metric event. The SDK could automatically project selected label keys from the distributed correlation into the metric event.
- The user can explicitly perform the same projection of distributed correlation into labels by extracting labels from the correlation context and including them in the call to create the metric or bound instrument.
An example of an explicit projection follows.
import "go.opentelemetry.io/api/distributedcontext"
func (s *server) doThing(ctx context.Context) {
var doLabels []core.KeyValue{
key1.String("..."),
key2.String("..."),
}
correlations := distributedcontext.FromContext()
if val, ok := correlations.Value(key3); ok {
doLabels = append(doLabels, key3.Value(val))
}
labels := s.meter.Labels(doLabels)
// ...
}