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A working group convened on 8/21/2019 to discuss and debate the two metrics RFCs (0003 and 0004) and several surrounding concerns. This document has been revised with related updates that were agreed upon during this working session. See the meeting notes.
Introduce a Measure kind of metric object that supports a Record API method. Like existing Gauge and Cumulative metrics, the new Measure metric supports pre-defined labels. A new RecordBatch measurement API is introduced for recording multiple metric observations simultaneously.
This RFC changes how "Measure" is used in the OpenTelemetry metrics specification. Before, "Measure" was the name of a series of raw measurements. After, "Measure" is the kind of a metric object used for recording a series raw measurements.
Since this document will be read in the future after the proposal has been written, uses of the word "current" lead to confusion. For this document, the term "preceding" refers to the state that was current prior to these changes.
The preceding specification used the term TimeSeries to describe an instrument bound with a set of pre-defined labels. In this document, the term "Handle" is used to describe an instrument with bound labels. In a future OTEP this will be again changed to "Bound instrument". The term "Handle" is used throughout this document to refer to a bound instrument.
In the preceding Metric.GetOrCreateTimeSeries API for Gauges and Cumulatives, the caller obtains a TimeSeries handle for repeatedly recording metrics with certain pre-defined label values set. This enables an important optimization for exporting pre-aggregated metrics, since the implementation is able to compute the aggregate summary "entry" using a pointer or fast table lookup. The efficiency gain requires that the aggregation keys be a subset of the pre-defined labels.
Application programs with long-lived objects and associated Metrics can take advantage of pre-defined labels by computing label values once per object (e.g., in a constructor), rather than once per call site. In this way, the use of pre-defined labels improves the usability of the API as well as makes an important optimization possible to the implementation.
The preceding raw statistics API did not specify support for pre-defined labels. This RFC replaces the raw statistics API by a new, general-purpose kind of metric, MeasureMetric, generally intended for recording individual measurements like the preceding raw statistics API, with explicit support for pre-defined labels.
The preceding raw statistics API supported all-or-none recording for interdependent measurements using a common label set. This RFC introduces a RecordBatch API to support recording batches of measurements in a single API call, where a Measurement is now defined as a pair of MeasureMetric and Value (integer or floating point).
The common use for MeasureMetric, like the preceding raw statistics API, is for reporting information about rates and distributions over structured, numerical event data. Measure metrics are the most general-purpose of metrics. Informally, the individual metric event has a logical format expressed as one primary key=value (the metric name and a numerical value) and any number of secondary key=values (the labels, resources, and context).
metric_name=_number_
pre_defined1=_any_value_
pre_defined2=_any_value_
...
resource1=_any_value_
resource2=_any_value_
...
context_tag1=_any_value_
context_tag2=_any_value_
...
Here, "pre_defined" keys are those captured in the metrics handle, "resource" keys are those configured when the SDK was initialized, and "context_tag" keys are those propagated via context.
Events of this form can logically capture a single update to a named metric, whether a cumulative, gauge, or measure kind of metric. This logical structure defines a low-level encoding of any metric event, across the three kinds of metric. An SDK could simply encode a stream of these events and the consumer, provided access to the metric definition, should be able to interpret these events according to the semantics prescribed for each kind of metric.
The Meter interface represents the metrics portion of the OpenTelemetry API.
There are three kinds of metric instrument, CumulativeMetric, GaugeMetric, and MeasureMetric.
Metric instruments are constructed through the Meter API. Constructing an instrument automatically registers it with the SDK. The common attributes of a metric instrument are:
| Field | Description |
|---|---|
| Name | A string. |
| Kind | One of Cumulative, Gauge, or Measure. |
| Recommended Keys | Default aggregation keys. |
| Unit | The unit of measurement being recorded. |
| Description | Information about this metric. |
See the specification for more information on these fields, including formatting and uniqueness requirements. To define a new metric, use one of the Meter API methods (e.g., with names like NewCumulativeMetric, NewGaugeMetric, or NewMeasureMetric).
Metric instrument Handles combine a metric instrument with a set of pre-defined labels. Handles are obtained by calling a language-specific API method (e.g., GetHandle) on the metric instrument with certain label values. Handles may be used to Set(), Add(), or Record() metrics according to their kind.
By separation of API and implementation in OpenTelemetry, we know that an implementation is free to do anything in response to a metric API call. By the low-level interpretation defined above, all metric events have the same structural representation, only their logical interpretation varies according to the metric definition. Therefore, we select metric kinds based on two primary concerns:
- What should be the default implementation behavior? Unless configured otherwise, how should the implementation treat this metric variable?
- How will the program source code read? Each metric uses a different verb, which helps convey meaning and describe default behavior. Cumulatives have an
Add()method. Gauges have aSet()method. Measures have aRecord()method.
To guide the user in selecting the right kind of metric for an application, we'll consider the following questions about the primary intent of reporting given data. We use "of primary interest" here to mean information that is almost certainly useful in understanding system behavior. Consider these questions:
- Does the measurement represent a quantity of something? Is it also non-negative?
- Is the sum a matter of primary interest?
- Is the event count a matter of primary interest?
- Is the distribution (p50, p99, etc.) a matter of primary interest?
The specification will be updated with the following guidance.
Likely to be the most common kind of metric, cumulative metric events express the computation of a sum. Choose this kind of metric when the value is a quantity, the sum is of primary interest, and the event count and distribution are not of primary interest. To raise (or lower) a cumulative metric, call the Add() method.
If the quantity in question is always non-negative, it implies that the sum is monotonic. This is the common case, Monotonic(true), where cumulative sums only rise, and these metric instruments serve to compute a rate. For this reason, cumulative metrics have a Monotonic(false) option to be declared as allowing negative inputs, the uncommon case. The SDK should reject negative inputs to monotonic cumulative metrics, but it is not required to.
For cumulative metrics, the default OpenTelemetry implementation exports the sum of event values taken over an interval of time.
Gauge metrics express a pre-calculated value that is either Set() by explicit instrumentation or observed through a callback. Generally, this kind of metric should be used when the metric cannot be expressed as a sum or a rate because the measurement interval is arbitrary. Use this kind of metric when the measurement is a computed value and the sum and event count are not of interest.
Only the gauge kind of metric supports observing the metric via a gauge Observer callback (as an option, see 0008-metric-observer.md). Semantically, there is an important difference between explicitly setting a gauge and observing it through a callback. In case of setting the gauge explicitly, the Set() call happens inside of an implicit or explicit context. The implementation is free to associate the explicit Set() event with a context, for example. When observing gauge metrics via a callback, there is no context associated with the event.
As a special case, to support existing metrics infrastructure and the Observer pattern, a gauge metric may be declared as a precomputed, monotonic sum using the Monotonic(true) option, in which case it is may be used to define a rate. The initial value is presumed to be zero. The SDK should reject descending updates to monotonic gauges, but it is not required to.
For gauge metrics, the default OpenTelemetry implementation exports the last value that was explicitly Set(), or if using a callback, the current value from the Observer.
Measure metrics express a distribution of measured values. This kind of metric should be used when the count or rate of events is meaningful and either:
- The sum is of interest in addition to the count (rate)
- Quantile information is of interest.
The key property of a measure metric event is that computing quantiles and/or summarizing a distribution (e.g., via a histogram) may be expensive. Not only will implementations have various capabilities and algorithms for this task, users may wish to control the quality and cost of aggregating measure metrics.
Like cumulative metrics, non-negative measures are an important case because they support rate calculations. Measure metrics are described as Absolute(true) when the inputs are non-negative. As an option, measure metrics may be declared as Absolute(false) to support positive and negative values. The SDK should reject negative measurements for Absolute measures, but it is not required to.
Metric instruments are enabled by default, meaning that SDKs will export metric data for this instrument without configuration. Metric instruments support a Disabled option, marking them as verbose sources of information that may be configured on an as-needed basis to control cost (e.g., using a "views" API).
The kind-specific optional properties of a metric instrument are:
| Property | Description | Metric kind |
|---|---|---|
| Monotonic(true) | Indicates a cumulative that accepts only non-negative values | Cumulative (default) |
| Indicate a gauge supports ascending value sequences starting at 0 | Gauge | |
| Monotonic(false) | Indicates a cumulative that accepts positive and negative values | Cumulative |
| Indicate a gauge that expresses a monotonic cumulative value | Gauge (default) | |
| Absolute(true) | Indicates a measure that accepts non-negative values | Measure (default) |
| Absolute(false) | Indicates a measure that accepts positive and negative values | Measure |
Applications sometimes want to act upon multiple metric instruments in a single API call, either because the values are inter-related to each other, or because it lowers overhead. RecordBatch logically updates each instrument in the batch using the supplied value. A single label set applies to the batch.
A single measurement is defined as:
- Instrument: the measure instrument (not a Handle)
- Value: the recorded floating point or integer data
The batch measurement API uses a language-specific method name (e.g., RecordBatch). The entire batch of measurements takes place within a (implicit or explicit) context.
Prometheus supports the notion of vector metrics, which are those that support pre-defined labels for a specific set of required keys. The vector-metric API supports a variety of methods like WithLabelValues to associate labels with a metric handle, similar to GetHandle in OpenTelemetry. As in this proposal, Prometheus supports a vector API for all metric types.
Argument ordering has been proposed as the way to pass pre-defined label values in GetHandle. The argument list must match the parameter list exactly, and if it doesn't we generally find out at runtime or not at all. This model has more optimization potential, but is easier to misuse than the alternative. The alternative approach is to always pass label:value pairs to GetOrCreateTimeseries, as opposed to an ordered list of values.
The discussion above can be had for the proposed RecordBatch method. It can be declared with an ordered list of metrics, then the Record API takes only an ordered list of numbers. Alternatively, and less prone to misuse, the RecordBatch API has been declared with a list of metric:number pairs.
Instead of a mechanism to obtain a default handle, some languages may prefer to simply operate on the metric instrument directly in this case. Should OpenTelemetry eliminate GetDefaultHandle and instead specify that cumulative, gauge, and measure metric instruments implement Add(), Set(), and Record() with the same interpretation?
If we eliminate GetDefaultHandle(), the SDK may keep a map of metric instrument to default handle on its own.
In the 8/21 working session, we agreed to limit RecordBatch to recording of simultaneous measure metrics, meaning to exclude cumulatives and gauges from batch recording. There are arguments in favor of supporting batch recording for all metric instruments.
- If atomicity (i.e., the all-or-none property) is the reason for batch reporting, it makes sense to include all the metric instruments in the API
RecordBatchsupport for cumulatives and gauges will be natural for SDKs that act as forwarders for metric events . The natural implementation forAdd()andSet()methods will beRecordBatchwith a single event.- Likewise, it is simple for an SDK that acts as an aggregator (not a forwarder) to redirect
Add()andSet()APIs to the handle-specificAdd()andSet()methods; while the SDK, as the implementation, still may (not must) treat these cumulative and gauge updates as atomic.
Arguments against batch recording for all metric instruments:
- The
RecordinRecordBatchsuggests it is to be applied to measure metrics. This is due to measure metrics being the most general-purpose of metric instruments.
Raw vs. other metrics / measurements are unclear