- Overview
- Meter provider
- Instrument properties
- The instruments
- Sets of labels
- Synchronous instrument details
- Asynchronous instrument details
- Concurrency
- Related OpenTelemetry work
The OpenTelemetry Metrics API supports capturing measurements about the execution of a computer program at run time. The Metrics API is designed explicitly for processing raw measurements, generally with the intent to produce continuous summaries of those measurements, efficiently and simultaneously. Hereafter, "the API" refers to the OpenTelemetry Metrics API.
The API provides functions for capturing raw measurements, through several calling conventions that offer different levels of performance. Regardless of calling convention, we define a metric event as the logical thing that happens when a new measurement is captured. This moment of capture (at "run time") defines an implicit timestamp, which is the wall time an SDK would read from a clock at that moment.
The word "semantic" or "semantics" as used here refers to how we give meaning to metric events, as they take place under the API. The term is used extensively in this document to define and explain these API functions and how we should interpret them. As far as possible, the terminology used here tries to convey the intended semantics, and a standard implementation will be described below to help us understand their meaning. Standard implementations perform aggregation corresponding to the default interpretation for each kind of metric event.
Monitoring and alerting systems commonly use the data provided through metric events, after applying various aggregations and converting into various exposition formats. However, we find that there are many other uses for metric events, such as to record aggregated or raw measurements in tracing and logging systems. For this reason, OpenTelemetry requires a separation of the API from the SDK, so that different SDKs can be configured at run time.
The term capture is used in this document to describe the action performed when the user passes a measurement to the API. The result of a capture depends on the configured SDK, and if there is no SDK installed, the default action is to do nothing in response to captured events. This usage is intended to convey that anything can happen with the measurement, depending on the SDK, but implying that the user has put effort into taking some kind of measurement. For both performance and semantic reasons, the API let users choose between two kinds of measurement.
The term additive is used to specify a characteristic of some measurements, meant to indicate that only the sum is considered useful information. These are measurements that you would naturally combine using arithmetic addition, usually real quantities of something (e.g., number of bytes).
Non-additive measurements are used when the set of values, also known as the population, is presumed to have useful information. A non-additive measurement is either one that you would not naturally combine using arithmetic addition (e.g., request latency), or it is a measurement you would naturally add where the intention is to monitor the distribution of values (e.g., queue size). The median value is considered useful information for non-additive measurements.
Non-additive instruments semantically capture more information than additive instruments. Non-additive measurements are more expensive than additive measurements, by this definition. Users will choose additive instruments except when they expect to get value from the additional cost of information about individual values. None of this is to prevent an SDK from re-interpreting measurements based on configuration. Anything can happen with any kind of measurement.
A metric instrument is a device for capturing raw measurements in the API. The standard instruments, listed in the table below, each have a dedicated purpose. The API purposefully avoids optional features that change the semantic interpretation of an instrument; the API instead prefers instruments that support a single method each with fixed interpretation.
All measurements captured by the API are associated with the
instrument used to make the measurement, thus giving the measurement its semantic properties.
Instruments are created and defined through calls to a Meter API,
which is the user-facing entry point to the SDK.
Instruments are classified in several ways that distinguish them from one another.
- Synchronicity: A synchronous instrument is called by the user in a distributed Context (i.e., Span context, Correlation context). An asynchronous instrument is called by the SDK once per collection interval, lacking a Context.
- Additivity: An additive instrument is one that records additive measurements, as described above.
- Monotonicity: A monotonic instrument is an additive instrument, where the progression of each sum is non-decreasing. Monotonic instruments are useful for monitoring rate information.
The metric instruments names are shown below along with whether they are synchronous, additive, and/or monotonic.
| Name | Synchronous | Additive | Monotonic |
|---|---|---|---|
| Counter | Yes | Yes | Yes |
| UpDownCounter | Yes | Yes | No |
| ValueRecorder | Yes | No | No |
| SumObserver | No | Yes | Yes |
| UpDownSumObserver | No | Yes | No |
| ValueObserver | No | No | No |
The synchronous instruments are useful for measurements that are gathered in a distributed Context (i.e., Span context, Correlation context). The asynchronous instruments are useful when measurements are expensive, therefore should be gathered periodically. Read more characteristics of synchronous and asynchronous instruments below.
The synchronous and asynchronous additive instruments have a significant difference: synchronous instruments are used to capture changes in a sum, whereas asynchronous instruments are used to capture sums directly. Read more characteristics of additive instruments below.
The monotonic additive instruments are significant because they support rate calculations. Read more information about choosing metric instruments below.
An instrument definition describes several properties of the instrument, including its name and its kind. The other properties of a metric instrument are optional, including a description and the unit of measurement. An instrument definition is associated with the data that it produces.
Label is the term used to refer to a key-value attribute associated with a metric event, similar to a Span attribute in the tracing API. Each label categorizes the metric event, allowing events to be filtered and grouped for analysis.
Each of the instrument calling conventions (detailed below) accepts a set of labels as an argument. The set of labels is defined as a unique mapping from key to value. Typically, labels are passed to the API in the form of a list of key:values, in which case the specification dictates that duplicate entries for a key are resolved by taking the last value to appear in the list.
Measurements by a synchronous instrument are commonly combined with other measurements having exactly the same label set, which enables significant optimizations. Read more about combining measurements through aggregation below.
The API defines a Meter interface. This interface consists of a set
of instrument constructors, and a facility for capturing batches of
measurements in a semantically atomic way.
There is a global Meter instance available for use that facilitates
automatic instrumentation for third-party code. Use of this instance
allows code to statically initialize its metric instruments, without
explicit dependency injection. The global Meter instance acts as a
no-op implementation until the application initializes a global
Meter by installing an SDK either explicitly, through a service
provider interface, or other language-specific support. Note that it
is not necessary to use the global instance: multiple instances of the
OpenTelemetry SDK may run simultaneously.
As an obligatory step, the API requires the caller to provide the name of the
instrumenting library (optionally, the version) when obtaining a Meter
implementation. The library name is meant to be used for identifying
instrumentation produced from that library, for such purposes as disabling
instrumentation, configuring aggregation, and applying sampling policies. See
the specification on TracerProvider for more
details.
Aggregation refers to the process of combining multiple measurements into exact or estimated statistics about the measurements that took place during an interval of time, during program execution.
Each instrument specifies a default aggregation that is suited to the semantics of the instrument, that serves to explain its properties and give users an understanding of how it is meant to be used. Instruments, in the absence of any configuration override, can be expected to deliver a useful, economical aggregation out of the box.
The additive instruments (Counter, UpDownCounter, SumObserver,
UpDownSumObserver) use a Sum aggregation by default. Details about
computing a Sum aggregation vary, but from the user's perspective this
means they will be able to monitor the sum of values captured. The
distinction between synchronous and asynchronous instruments is
crucial to specifying how exporters work, a topic that is covered in
the SDK specification (WIP).
The non-additive instruments (ValueRecorder, ValueObserver) use
a MinMaxSumCount aggregation, by default. This aggregation keeps track
of the minimum value, the maximum value, the sum of values, and the
count of values. These four values support monitoring the range of
values, the rate of events, and the average event value.
Other standard aggregations are available, especially for non-additive instruments, where we are generally interested in a variety of different summaries, such as histograms, quantile summaries, cardinality estimates, and other kinds of sketch data structure.
The default OpenTelemetry SDK implements a Views API (WIP), which supports configuring non-default aggregation behavior(s) on the level of an individual instrument. Even though OpenTelemetry SDKs can be configured to treat instruments in non-standard ways, users are expected to select instruments based on their semantic meaning, which is explained using the default aggregation.
Time is a fundamental property of metric events, but not an explicit one. Users do not provide explicit timestamps for metric events. SDKs are discouraged from capturing the current timestamp for each event (by reading from a clock) unless there is a definite need for high-precision timestamps calculated on every event.
This non-requirement stems from a common optimization in metrics reporting, which is to configure metric data collection with a relatively small period (e.g., 1 second) and use a single timestamp to describe a batch of exported data, since the loss of precision is insignificant when aggregating data across minutes or hours of data.
Aggregations are commonly computed over a series of events that fall into a contiguous region of time, known as the collection interval. Since the SDK controls the decision to start collection, it is possible to collect aggregated metric data while only reading the clock once per collection interval. The default SDK takes this approach.
Metric events produced with synchronous instruments happen at an instant in time, thus fall into a collection interval where they are aggregated together with other events from the same instrument and label set. Because events may happen simultaneously with one another, the most recent event is technically not well defined.
Asynchronous instruments allow the SDK to evaluate metric instruments through observations made once per collection interval. Because of this coupling with collection (unlike synchronous instruments), these instruments unambiguously define the most recent event. We define the Last Value of an instrument and label set, with repect to a moment in time, as the value that was measured during the most recent collection interval.
Because metric events are implicitly timestamped, we could refer to a series of metric events as a time series. However, we reserve the use of this term for the SDK specification, to refer to parts of a data format that express explicitly timestamped values, in a sequence, resulting from an aggregation of raw measurements over time.
Metric events have the same logical representation, regardless of instrument kind. Metric events captured through any instrument consist of:
- timestamp (implicit)
- instrument definition (name, kind, description, unit of measure)
- label set (keys and values)
- value (signed integer or floating point number)
- resources associated with the SDK at startup.
Synchronous events have one additional property, the distributed Context (i.e., Span context, Correlation context) that was active at the time.
A concrete MeterProvider implementation can be obtained by initializing and
configuring an OpenTelemetry Metrics SDK. This document does not
specify how to construct an SDK, only that they must implement the
MeterProvider. Once configured, the application or library chooses
whether it will use a global instance of the MeterProvider
interface, or whether it will use dependency injection for greater
control over configuring the provider.
New Meter instances can be created via a MeterProvider and its
GetMeter(name, version) 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 (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.version(optional): Specifies the version of the instrumentation library (e.g.semver:1.0.0).
Each distinctly named Meter establishes a separate namespace for its
metric instruments, making it possible for multiple instrumentation
libraries to report the metrics with the same instrument name used by
other libraries. The name of the Meter is explicitly not intended
to be used as part of the instrument name, as that would prevent
instrumentation libraries from capturing metrics by the same name.
Use of a global instance may be seen as an anti-pattern in many
situations, but in most cases it is the correct pattern for telemetry
data, in order to combine telemetry data from inter-dependent
libraries without use of dependency injection. OpenTelemetry
language APIs SHOULD offer a global instance for this reason.
Languges that offer a global instance MUST ensure that Meter
instances allocated through the global MeterProvider and instruments
allocated through those Meter instances have their initialization
deferred until the a global SDK is first initialized.
Since the global MeterProvider is a singleton and supports a single
method, callers can obtain a global Meter using a global GetMeter
call. For example, global.GetMeter(name, version) calls GetMeter
on the global MeterProvider and returns a named Meter instance.
A global function installs a MeterProvider as the global SDK. For
example, use global.SetMeterProvider(MeterProvider) to install the
SDK after it is initialized.
Because the API is separated from the SDK, the implementation ultimately determines how metric events are handled. Therefore, the choice of instrument should be guided by semantics and the intended interpretation. The semantics of the individual instruments is defined by several properties, detailed here, to assist with instrument selection.
Metric instruments are primarily defined by their name, which is how we refer to them in external systems. Metric instrument 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 belong to the alphanumeric characters, '_', '.', and '-'.
Metric instrument names belong to a namespace, established by the the
associated Meter instance. Meter implementations MUST return an
error when multiple instruments are registered by the same name.
TODO: The following paragraph is a placeholder for a more-detailed document that is needed.
Metric instrument names SHOULD be semantically meaningful, independent of the originating Meter name. For example, when instrumenting an http server library, "latency" is not an appropriate instrument name, as it is too generic. Instead, as an example, we should favor a name like "http_request_latency", as it would inform the viewer of the semantic meaning of the latency measurement. Multiple instrumentation libraries may be written to generate this metric.
Synchronous instruments are called inside a request, meaning they have an associated distributed Context (i.e., Span context, Correlation context). Multiple metric events may occur for a synchronous instrument within a given collection interval.
Asynchronous instruments are reported by a callback, once per collection interval, and lack Context. They are permitted to report only one value per distinct label set per period. If the application observes multiple values for the same label set, in a single callback, the last value is the only value kept.
To ensure that the definition of last value is consistent across asynchronous instruments, the timestamp associated with asynchronous events is fixed to the timestamp at the end of the interval in which it was computed. All asynchronous events are timestamped with the end of the interval, which is the moment they become the last value corresponding to the instrument and label set. (For this reasons, SDKs SHOULD run asynchronous instrument callbacks near the end of the collection interval.)
Additive instruments are used to capture information about a sum,
where, by definition, only the sum is of interest. Individual events
are considered not meaningful for these instruments, the event count
is not computed. This means, for example, that two Counter events
Add(N) and Add(M) are equivalent to one Counter event Add(N + M). This is the case because Counter is synchronous, and
synchronous additive instruments are used to capture changes to a sum.
Asynchronous, additive instruments (e.g., SumObserver) are used to
capture sums directly. This means, for example, that in any sequence
of SumObserver observations for a given instrument and label set,
the Last Value defines the sum of the instrument.
In both synchronous and asynchronous cases, the additive instruments are inexpensively aggregated into a single number per collection interval without loss of information. This property makes additive instruments higher performance, in general, than non-additive instruments.
Non-additive instruments use a relatively inexpensive aggregation method default (MinMaxSumCount), but still more expensive than the default for additive instruments (Sum). Unlike additive instruments, where only the sum is of interest by definition, non-additive instruments can be configured with even more expensive aggregators.
Monotonicity applies only to additive instruments. Counter and
SumObserver instruments are defined as monotonic because the sum
captured by either instrument is non-decreasing. The UpDown-
variations of these two instruments are non-monotonic, meaning the sum
can increase, decrease, or remain constant without any guarantees.
Monotonic instruments are commonly used to capture information about a sum that is meant to be monitored as a rate. The Monotonic property is defined by this API to refer to a non-decreasing sum. Non-increasing sums are not considered a feature in the Metric API.
Each instrument supports a single function, named to help convey the instrument's semantics.
Synchronous additive instruments support an Add() function,
signifying that they add to a sum and do not directly capture a sum.
Synchronous non-additive instruments support a Record() function,
signifying that they capture individual events, not only a sum.
Asynchronous instruments all support an Observe() function,
signifying that they capture only one value per measurement interval.
Counter is the most common synchronous instrument. This instrument
supports an Add(increment) function for reporting a sum, and is
restricted to non-negative increments. The default aggregation is
Sum, as for any additive instrument.
Example uses for Counter:
- count the number of bytes received
- count the number of requests completed
- count the number of accounts created
- count the number of checkpoints run
- count the number of 5xx errors.
These example instruments would be useful for monitoring the rate of any of these quantities. In these situations, it is usually more convenient to report by how much a sum changes, as it happens, than to calculate and report the sum on every measurement.
UpDownCounter is similar to Counter except that Add(increment)
supports negative increments. This makes UpDownCounter not useful
for computing a rate aggregation. It aggregates a Sum, only the sum
is non-monotonic. It is generally useful for capturing changes in an
amount of resources used, or any quantity that rises and falls during a
request.
Example uses for UpDownCounter:
- count the number of active requests
- count memory in use by instrumenting
newanddelete - count queue size by instrumenting
enqueueanddequeue - count semaphore
upanddownoperations.
These example instruments would be useful for monitoring resource levels across a group of processes.
ValueRecorder is a non-additive synchronous instrument useful for
recording any non-additive number, positive or negative. Values
captured by a Record(value) are treated as individual events
belonging to a distribution that is being summarized. ValueRecorder
should be chosen either when capturing measurements that do not
contribute meaningfully to a sum, or when capturing numbers that are
additive in nature, but where the distribution of individual
increments is considered interesting.
One of the most common uses for ValueRecorder is to capture latency
measurements. Latency measurements are not additive in the sense that
there is little need to know the latency-sum of all processed
requests. We use a ValueRecorder instrument to capture latency
measurements typically because we are interested in knowing mean,
median, and other summary statistics about individual events.
The default aggregation for ValueRecorder computes the minimum and
maximum values, the sum of event values, and the count of events,
allowing the rate, the mean, and range of input values to be
monitored.
Example uses for ValueRecorder that are non-additive:
- capture any kind of timing information
- capture the acceleration experienced by a pilot
- capture nozzle pressure of a fuel injector
- capture the velocity of a MIDI key-press.
Example additive uses of ValueRecorder capture measurements that
are additive, but where we may have an interest in the distribution of
values and not only the sum:
- capture a request size
- capture an account balance
- capture a queue length
- capture a number of board feet of lumber.
These examples show that although they are additive in nature,
choosing ValueRecorder as opposed to Counter or UpDownCounter
implies an interest in more than the sum. If you did not care to
collect information about the distribution, you would have chosen one
of the additive instruments instead. Using ValueRecorder makes
sense for capturing distributions that are likely to be important in
an observability setting.
Use these with caution because they naturally cost more than the use of additive measurements.
SumObserver is the asynchronous instrument corresponding to
Counter, used to capture a monotonic sum with Observe(sum). "Sum"
appears in the name to remind users that it is used to capture sums
directly. Use a SumObserver to capture any value that starts at
zero and rises throughout the process lifetime and never falls.
Example uses for SumObserver.
- capture process user/system CPU seconds
- capture the number of cache misses.
A SumObserver is a good choice in situations where a measurement is
expensive to compute, such that it would be wasteful to compute on
every request. For example, a system call is needed to capture
process CPU usage, therefore it should be done periodically, not on
each request. A SumObserver is also a good choice in situations
where it would be impractical or wasteful to instrument individual
changes that comprise a sum. For example, even though the number of
cache misses is a sum of individual cache-miss events, it would be too
expensive to synchronously capture each event using a Counter.
UpDownSumObserver is the asynchronous instrument corresponding to
UpDownCounter, used to capture a non-monotonic count with
Observe(sum). "Sum" appears in the name to remind users that it is
used to capture sums directly. Use a UpDownSumObserver to capture
any value that starts at zero and rises or falls throughout the
process lifetime.
Example uses for UpDownSumObserver.
- capture process heap size
- capture number of active shards
- capture number of requests started/completed
- capture current queue size.
The same considerations mentioned for choosing SumObserver over the
synchronous Counter apply for choosing UpDownSumObserver over the
synchronous UpDownCounter. If a measurement is expensive to
compute, or if the corresponding changes happen so frequently that it
would be impractical to instrument them, use a UpDownSumObserver.
ValueObserver is the asynchronous instrument corresponding to
ValueRecorder, used to capture non-additive measurements with
Observe(value). These instruments are especially useful for
capturing measurements that are expensive to compute, since it gives
the SDK control over how often they are evaluated.
Example uses for ValueObserver:
- capture CPU fan speed
- capture CPU temperature.
Note that these examples use non-additive measurements. In the
ValueRecorder case above, example uses were given for capturing
synchronous additive measurements during a request (e.g.,
current queue size seen by a request). In the asynchronous case,
however, how should users decide whether to use ValueObserver as
opposed to UpDownSumObserver?
Consider how to report the size of a queue asynchronously. Both
ValueObserver and UpDownSumObserver logically apply in this case.
Asynchronous instruments capture only one measurement per interval, so
in this example the UpDownSumObserver reports a current sum, while the
ValueObserver reports a current sum (equal to the max and the min)
and a count equal to 1. When there is no aggregation, these results
are equivalent.
It may seem pointless to define a default aggregation when there is
exactly one data point. The default aggregation is specified to apply
when performing spatial aggregation, meaning to combine measurements
across label sets or in a distributed setting. Although a
ValueObserver observes one value per collection interval, the
default aggregation specifies how it will be aggregated with other
values, absent any other configuration.
Therefore, considering the choice between ValueObserver and
UpDownSumObserver, the recommendation is to choose the instrument
with the more-appropriate default aggregation. If you are observing a
queue size across a group of machines and the only thing you want to
know is the aggregate queue size, use SumObserver because it
produces a sum, not a distribution. If you are observing a queue size
across a group of machines and you are interested in knowing the
distribution of queue sizes across those machines, use
ValueObserver.
How are the instruments fundamentally different, and why are there only three? Why not one instrument? Why not ten?
As we have seen, the instruments are categorized as to whether they are synchronous, additive, and/or and monotonic. This approach gives each of the instruments unique semantics, in ways that meaningfully improve the performance and interpretation of metric events.
Establishing different kinds of instrument is important because in
most cases it allows the SDK to provide good default functionality
"out of the box", without requiring alternative behaviors to be
configured. The choice of instrument determines not only the meaning
of the events but also the name of the function called by the user.
The function names--Add() for additive instruments, Record() for
non-additive instruments, and Observe() for asynchronous
instruments--help convey the meaning of these actions.
The properties and standard implementation described for the individual instruments is summarized in the table below.
| Name | Instrument kind | Function(argument) | Default aggregation | Notes |
|---|---|---|---|---|
| Counter | Synchronous additive monotonic | Add(increment) | Sum | Per-request, part of a monotonic sum |
| UpDownCounter | Synchronous additive | Add(increment) | Sum | Per-request, part of a non-monotonic sum |
| ValueRecorder | Synchronous | Record(value) | MinMaxSumCount | Per-request, any non-additive measurement |
| SumObserver | Asynchronous additive monotonic | Observe(sum) | Sum | Per-interval, reporting a monotonic sum |
| UpDownSumObserver | Asynchronous additive | Observe(sum) | Sum | Per-interval, reporting a non-monotonic sum |
| ValueObserver | Asynchronous | Observe(value) | MinMaxSumCount | Per-interval, any non-additive measurement |
The Meter interface supports functions to create new, registered
metric instruments. Instrument constructors are named by adding a
New- prefix to the kind of instrument it constructs, with a
builder pattern, or some other idiomatic approach in the language.
There is at least one constructor representing each kind of instrument in this specification (see above), and possibly more as dictated by the language. For example, if specializations are provided for integer and floating pointer numbers, the OpenTelemetry API would support 2 constructors per instrument kind.
Binding instruments to a single Meter instance has two benefits:
- Instruments can be exported from the zero state, prior to first use, without an explicit registration call
- The library-name and version are implicitly associated with the metric event.
Some existing metric systems support allocating metric instruments
statically and providing the equivalent of a Meter interface at the
time of use. In one 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 MeterProvider to construct a static Meter, and to
construct and use globally-scoped metric instruments.
The situation is similar for users of existing Prometheus clients, where
instruments can be allocated to the global Registerer.
Such code may not have access to an appropriate MeterProvider or Meter
instance at the location 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.
Semantically, a set of labels is a unique mapping from string key to value. Across the API, a set of labels MUST be passed in the same, idiomatic form. Common representations include an ordered list of key:values, or a map of key:values.
When labels are passed as an ordered list of key:values, and there are duplicate keys found, the last value in the list for any given key is taken in order to form a unique mapping.
The type of the label value is generally presumed to be a string by exporters, although as a language-level decision, the label value type could be any idiomatic type in that language that has a string representation.
Users are not required to pre-declare the set of label keys that will be used with metric instruments in the API. Users can freely use any set of labels for any metric event when calling the API.
Label handling can be a significant cost in the production of metric data overall.
SDK support for in-process aggregation depends on the ability to find
an active record for an instrument, label set combination pair. This
allows measurements to be combined. Label handling costs can be
lowered through the use of bound synchronous instruments and
batch-reporting functions (RecordBatch, BatchObserver).
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.
The following details are specified for synchronous instruments.
The metrics API provides three semantically equivalent ways to capture measurements using synchronous instruments:
- calling bound instruments, which have a pre-associated set of labels
- directly calling instruments, passing the associated set of labels
- batch recording measurements for multiple instruments using a single set of labels.
All three methods generate equivalent metric events, but offer varying degrees of performance and convenience.
The performance of the metric API depends on the work done to enter a
new measurement, which is typically dominated by the cost of handling
labels. Bound instruments are the highest-performance calling
convention, because they can amortize the cost of handling labels
across many uses. Recording multiple measurements via
RecordBatch(), another calling convention, is a good option for
improving performance, since the cost of handling labels is spread
across multiple measurements. The direct calling convention is the
most convenient, but least performant calling convention for entering
measurements through the API.
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 corresponding synchronous API (i.e.,
Add() or Record()). Bound instruments are invoked without labels;
the corresponding metric event is associated with the labels that were
bound to the instrument. Bound instruments may consume SDK resources
indefinitely until the user calls Unbind() to release the bound
instrument.
For example, to repeatedly update a counter with the same labels:
func (s *server) processStream(ctx context.Context) {
// The result of Bind() is a bound instrument
// (e.g., a BoundInt64Counter).
counter2 := s.instruments.counter2.Bind(
kv.String("labelA", "..."),
kv.String("labelB", "..."),
)
defer counter2.Unbind()
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 when values are not known ahead of time, users may elect to operate directly on metric instruments, meaning to supply 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,
kv.String("labelA", "..."),
kv.String("labelB", "..."),
)
}Direct calls are convenient because they do not require allocating and storing a bound instrument. They are appropriate for use in cases where an instrument will be used rarely, or rarely used with the same set of labels. Unlike bound instruments, there is not a long-term consumption of SDK resources when using the direct calling convention.
There is one final API for entering measurements, which is like the
direct access calling convention but supports multiple simultaneous
measurements. The use of the RecordBatch API supports entering
multiple measurements, implying a semantically atomic update to
several instruments. Calls to RecordBatch amortize the cost of
label handling across multiple measurements.
For example:
func (s *server) method(ctx context.Context) {
// ... other work
s.meter.RecordBatch(ctx, labels,
s.instruments.counter.Measurement(1),
s.instruments.updowncounter.Measurement(10),
s.instruments.valuerecorder.Measurement(123.45),
)
}Another valid interface for recording batches uses a builder pattern:
meter.RecordBatch(labels).
put(s.instruments.counter, 1).
put(s.instruments.updowncounter, 10).
put(s.instruments.valuerecorder, 123.45).
record();Using the record batch 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, because the SDK can enqueue a single bulk update, or take a lock only once, for example. Like the direct calling convention, there is not a long-term consumption of SDK resources when using the batch calling convention.
Synchronous measurements are implicitly associated with the distributed Context at runtime, which may include a Span context and Correlation values. The Metric SDK may use this information in many ways, but one feature is of particular interest in OpenTelemetry.
Correlation context is supported in OpenTelemetry as a means for labels to propagate from one process to another in a distributed computation. Sometimes it is useful to aggregate metric data using distributed correlation values as metric labels.
The use of correlation context must be explicitly configured, using the Views API (WIP) to select specific key correlation values that should be applied as labels. The default SDK will not automatically use correlation context labels in the export pipeline, since using correlation labels can be a significant expense.
Configuring views for applying Correlation context labels is a work in progress.
The following details are specified for asynchronous instruments.
The metrics API provides two semantically equivalent ways to capture measurements using asynchronous instruments, either through single-instrument callbacks or through multi-instrument batch callbacks.
Whether single or batch, asynchronous instruments must be observed
through only one callback. The constructors return no-op instruments
for null observer callbacks. It is considered an error when more
than one callback is specified for any asynchronous instrument.
Instruments may not observe more than one value per distinct label set per instrument. When more than one value is observed for a single instrument and label set, the last observed value is taken and earlier values are discarded without error.
A single instrument callback is bound to one instrument. Its
callback receives an ObserverResult with an Observe(value, labels...) function.
func (s *server) registerObservers(.Context) {
s.observer1 = s.meter.NewInt64SumObserver(
"service_load_factor",
metric.WithCallback(func(result metric.Float64ObserverResult) {
for _, listener := range s.listeners {
result.Observe(
s.loadFactor(),
kv.String("name", server.name),
kv.String("port", listener.port),
)
}
}),
metric.WithDescription("The load factor use for load balancing purposes"),
)
}A BatchObserver callback supports observing multiple instruments in
one callback. Its callback receives an BatchObserverResult with an
Observe(labels, observations...) function.
An observation is returned by calling Observation(value), on an
asynchronous instrument.
func (s *server) registerObservers(.Context) {
batch := s.meter.NewBatchObserver(func (result BatchObserverResult) {
result.Observe(
[]kv.KeyValue{
kv.String("name", server.name),
kv.String("port", listener.port),
},
s.observer1.Observation(value1),
s.observer2.Observation(value2),
s.observer3.Observation(value3),
},
)
s.observer1 = batch.NewSumObserver(...)
s.observer2 = batch.NewUpDownSumObserver(...)
s.observer3 = batch.NewValueObserver(...)
}Asynchronous instrument callbacks are permitted to observe one value per instrument, per distinct label set, per callback invocation. The set of values recorded by one callback invocation represent a current snapshot of the instrument; it is this set of values that defines the Last Value for the instrument until the next collection interval.
Asynchronous instruments are expected to record an observation for every label set that it considers "current". This means that asynchronous callbacks are expected to observe a value, even when the value has not changed since the last callback invocation. To not observe a label set implies that a value is no longer current. The Last Value becomes undefined, as it is no longer current, when it is not observed during a collection interval.
The definition of Last Value is possible for asynchronous instruments, because their collection is coordinated by the SDK and because they are expected to report all current values. Another expression of this property is that an SDK can keep just one collection interval worth of observations in memory to lookup the current Last Value of any instrument and label set. In this way, asynchronous instruments support querying current values, independent of the duration of a collection interval, using data collected at a single point in time.
Recall that Last Value is not defined for synchronous instruments, and it is precisely because there is not a well-defined notion of what is "current". To determine the "last-recorded" value for a synchronous instrument could require inspecting multiple collection windows of data, because there is no mechanism to ensure that a current value is recorded during each interval.
The notion of a current set developed for asynchronous instruments above can be useful for monitoring ratios. When the set of observed values for an instrument add up to a whole, then each observation may be divided by the sum of observed values from the same interval to calculate its current relative contribution. Current relative contribution is defined in this way, independent of the collection interval duration, thanks to the properties of asynchronous instruments.
For languages which support concurrent execution the Metrics APIs provide specific guarantees and safeties. Not all of API functions are safe to be called concurrently.
MeterProvider - all methods are safe to be called concurrently.
Meter - all methods are safe to be called concurrently.
Instrument - All methods of any Instrument are safe to be called concurrently.
Bound Instrument - All methods of any Bound Instrument are safe to be called concurrently.
Several ongoing efforts are underway as this specification is being written.
The API does not support configurable aggregations for metric instruments.
A View API is defined as an interface to an SDK mechanism that supports configuring aggregations, including which operator is applied (sum, p99, last-value, etc.) and which dimensions are used.
See the current issue discussion on this topic and the current OTEP draft.
The OTLP protocol is designed to export metric data in a memoryless way, as documented above. Several details of the protocol are being worked out. See the current protocol.
The OpenTelemetry SDK includes default support for the metric API. The specification for the default SDK is underway, see the current draft.