datamodel.md

Metrics Data Model

Status: Experimental

Overview

The OpenTelemetry data model for metrics consists of a protocol specification and semantic conventions for delivery of pre-aggregated metric timeseries data. The data model is designed for importing data from existing systems and exporting data into existing systems, as well as to support internal OpenTelemetry use-cases for generating Metrics from streams of Spans or Logs.

Popular existing metrics data formats can be unambiguously translated into the OpenTelemetry data model for metrics, without loss of semantics or fidelity. Translation from the Prometheus and Statsd exposition formats is explicitly specified.

The data model specifies a number of semantics-preserving data transformations for use on the collection path, supporting flexible system configuration. The model supports reliability and statelessness controls, through the choice of cumulative and delta transport. The model supports cost controls, through spatial and temporal reaggregation.

The OpenTelemetry collector is designed to accept metrics data in a number of formats, transport data using the OpenTelemetry data model, and then export into existing systems. The data model can be unambiguously translated into the Prometheus Remote Write protocol without loss of features or semantics, through well-defined translations of the data, including the ability to automatically remove attributes and lower histogram resolution.

Events → Data Stream → Timeseries

The OTLP Metrics protocol is designed as a standard for transporting metric data. To describe the intended use of this data and the associated semantic meaning, OpenTelemetry metric data stream types will be linked into a framework containing a higher-level model, about Metrics APIs and discrete input values, and a lower-level model, defining the Timeseries and discrete output values. The relationship between models is displayed in the diagram below.

This protocol was designed to meet the requirements of the OpenCensus Metrics system, particularly to meet its concept of Metrics Views. Views are accomplished in the OpenTelemetry Metrics data model through support for data transformation on the collection path.

OpenTelemetry has identified three kinds of semantics-preserving Metric data transformation that are useful in building metrics collection systems as ways of controlling cost, reliability, and resource allocation. The OpenTelemetry Metrics data model is designed to support these transformations both inside an SDK as the data originates, or as a reprocessing stage inside the OpenTelemetry collector. These transformations are:

1. Temporal reaggregation: Metrics that are collected at a high-frequency can be re-aggregated into longer intervals, allowing low-resolution timeseries to be pre-calculated or used in place of the original metric data.
2. Spatial reaggregation: Metrics that are produced with unwanted dimensions can be re-aggregated into metrics having fewer dimensions.
3. Delta-to-Cumulative: Metrics that are input and output with Delta temporality unburden the client from keeping high-cardinality state. The use of deltas allows downstream services to bear the cost of conversion into cumulative timeseries, or to forego the cost and calculate rates directly.

OpenTelemetry Metrics data streams are designed so that these transformations can be applied automatically to streams of the same type, subject to conditions outlined below. Every OTLP data stream has an intrinsic decomposable aggregate function making it semantically well-defined to merge data points across both temporal and spatial dimensions. Every OTLP data point also has two meaningful timestamps which, combined with intrinsic aggregation, make it possible to carry out the standard metric data transformations for each of the model’s basic points while ensuring that the result carries the intended meaning.

As in OpenCensus Metrics, metrics data can be transformed into one or more Views, just by selecting the aggregation interval and the desired dimensions. One stream of OTLP data can be transformed into multiple timeseries outputs by configuring different Views, and the required Views processing may be applied inside the SDK or by an external collector.

Example Use-cases

The metric data model is designed around a series of "core" use cases. While this list is not exhaustive, it is meant to be representative of the scope and breadth of OTel metrics usage.

1. OTel SDK exports 10 second resolution to a single OTel collector, using cumulative temporality for a stateful client, stateless server:
   • Collector passes-through original data to an OTLP destination
   • Collector re-aggregates into longer intervals without changing dimensions
   • Collector re-aggregates into several distinct views, each with a subset of the available dimensions, outputs to the same destination
2. OTel SDK exports 10 second resolution to a single OTel collector, using delta temporality for a stateless client, stateful server:
   • Collector re-aggregates into 60 second resolution
   • Collector converts delta to cumulative temporality
3. OTel SDK exports both 10 seconds resolution (e.g. CPU, request latency) and 15 minutes resolution (e.g. room temperature) to a single OTel Collector. The collector exports streams upstream with or without aggregation.
4. A number of OTel SDKs running locally each exports 10 second resolution, each reports to a single (local) OTel collector.
   • Collector re-aggregates into 60 second resolution
   • Collector re-aggregates to eliminate the identity of individual SDKs (e.g., distinct service.instance.id values)
   • Collector outputs to an OTLP destination
5. Pool of OTel collectors receive OTLP and export Prometheus Remote Write
   • Collector joins service discovery with metric resources
   • Collector computes “up”, staleness marker
   • Collector applies a distinct external label
6. OTel collector receives Statsd and exports OTLP
   • With delta temporality: stateless collector
   • With cumulative temporality: stateful collector
7. OTel SDK exports directly to 3P backend

These are considered the "core" use-cases used to analyze tradeoffs and design decisions within the metrics data model.

Out of Scope Use-cases

The metrics data model is NOT designed to be a perfect rosetta stone of metrics. Here are a set of use cases that, while won't be outright unsupported, are not in scope for key design decisions:

• Using OTLP as an intermediary format between two non-compatible formats
  • Importing statsd => Prometheus PRW
  • Importing collectd => Prometheus PRW
  • Importing Prometheus endpoint scrape => [statsd push | collectd | opencensus]
  • Importing OpenCensus "oca" => any non OC or OTel format
• TODO: define others.

Model Details

OpenTelemetry fragments metrics into three interacting models:

• An Event model, representing how instrumentation reports metric data.
• A Timeseries model, representing how backends store metric data.
• A Metric Stream model, defining the OpenTeLemetry Protocol (OTLP) representing how metric data streams are manipulated and transmitted between the Event model and the Timeseries storage.

Event Model

The event model is where recording of data happens. Its foundation is made of Instruments, which are used to record data observations via events. These raw events are then transformed in some fashion before being sent to some other system. OpenTelemetry metrics are designed such that the same instrument and events can be used in different ways to generate metric streams.

Even though observation events could be reported directly to a backend, in practice this would be infeasible due to the sheer volume of data used in observability systems, and the limited amount of network/CPU resources available for telemetry collection purposes. The best example of this is the Histogram metric where raw events are recorded in a compressed format rather than individual timeseries.

Note: The above picture shows how one instrument can transform events into more than one type of metric stream. There are caveats and nuances for when and how to do this. Instrument and metric configuration are outlined in the metrics API specification.

While OpenTelemetry provides flexibility in how instruments can be transformed into metric streams, the instruments are defined such that a reasonable default mapping can be provided. The exact OpenTelemetry instruments are more fully detailed in the API specification.

In the Event model, the primary data are (instrument, number) points, originally observed in real time or on demand (for the synchronous and asynchronous cases, respectively).

Timeseries Model

In this low-level metrics data model, a Timeseries is defined by an entity consisting of several metadata properties:

• Metric name
• Label set
• Kind of point (integer, floating point, etc)
• Unit of measurement

The primary data of each timeseries are ordered (timestamp, value) points, for three value types:

1. Counter (Monotonic, Cumulative)
2. Gauge
3. Histogram

This model may be viewed as an idealization of Prometheus Remote Write. Like that protocol, we are additionally concerned with knowing when a point value is defined, as compared with being implicitly or explicitly absent. A metric stream of delta data points defines time-interval values, not point-in-time values. To precisely define presence and absence of data requires further development of the correspondence between these models.

OpenTelemetry Protocol data model

The OpenTelmetry protocol data model is composed of Metric data streams. These streams are in turn composed of metric data points. Metric data streams can be converted directly into Timeseries, and share the same identity characteristics for a Timeseries. A metric stream is identified by:

• The originating Resource
• The metric stream's name.
• The attached Attributes
• The metric stream's point kind.

It is possible (and likely) that more than one metric stream is created per Instrument in the event model.

Note: The same Resource, name and Attributes but differing point kind coming out of an OpenTelemetry SDK is considered an "error state" that should be handled by an SDK.

A metric stream can use one of three basic point kinds, all of which satisfy the requirements above, meaning they define a decomposable aggregate function (also known as a “natural merge” function) for points of the same kind. ¹

The basic point kinds are:

1. Sum
2. Gauge
3. Histogram

Comparing the OTLP Metric Data Stream and Timeseries data models, OTLP does not map 1:1 from its point types into timeseries points. In OTLP, a Sum point can represent a monotonic count or a non-monotonic count. This means an OTLP Sum is either translated into a Timeseries Counter, when the sum is monotonic, or a Gauge when the sum is not monotonic.

Specifically, in OpenTelemetry Sums always have an aggregate function where you can combine via addition. So, for non-monotonic sums in OpenTelemetry we can aggregate (naturally) via addition. In the timeseries model, you cannot assume that any particular Gauge is a sum, so the default aggregation would not be addition.

In addition to the core point kinds used in OTLP, there are also data types designed for compatibility with existing metric formats.

• Summary

Metric points

Sums

Sums in OTLP consist of the following:

• An Aggregation Temporality of delta or cumulative.
• A flag denoting whether the Sum is monotonic. In this case of metrics, this means the sum is nominally increasing, which we assume without loss of generality.
  • For delta monotonic sums, this means the reader should expect non-negative values.
  • For cumulative monotonic sums, this means the reader should expect values that are not less than the previous value.
• A set of data points, each containing:
  • An independent set of Attribute name-value pairs.
  • A time window (of (start, end]) time for which the Sum was calculated.
    • The time interval is inclusive of the end time.
    • Times are specified in Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January 1970
    • (optional) a set of examplars (see Exemplars).

The aggregation temporality is used to understand the context in which the sum was calculated. When the aggregation temporality is "delta", we expect to have no overlap in time windows for metric streams, e.g.

Contrast with cumulative aggregation temporality where we expect to report the full sum since 'start' (where usually start means a process/application start):

There are various tradeoffs between using Delta vs. Cumulative aggregation, in various use cases, e.g.:

• Detecting process restarts
• Calculating rates
• Push vs. Pull based metric reporting

OTLP supports both models, and allows APIs, SDKs and users to determine the best tradeoff for their use case.

Gauge

A Gauge in OTLP represents a sampled value at a given time. A Gauge stream consists of:

• A set of data points, each containing:
  • An independent set of Attribute name-value pairs.
  • A sampled value (e.g. current cpu temperature)
  • A timestamp when the value was sampled (time_unix_nano)
  • (optional) A timestamp (start_time_unix_nano) which has TBD semantics.
  • (optional) a set of examplars (see Exemplars).

In OTLP, a point within a Gauge stream represents the last-sampled event for a given time window.

In this example, we can see an underlying timeseries we are sampling with our Gauge. While the event model can sample more than once for a given metric reporting interval, only the last value is reported in the metric stream via OTLP.

Gauges do not provide an aggregation semantic, instead "last sample value" is used when performing operations like temporal alignment or adjusting resolution.

Gauges can be aggregated through transformation into histograms, or other metric types. These operations are not done by default, and require direct user configuration.

Histogram

Histogram metric data points convey a population of recorded measurements in a compressed format. A histogram bundles a set of events into divided populations with an overall event count and aggregate sum for all events.

Histograms consist of the following:

• An Aggregation Temporality of delta or cumulative.
• A set of data points, each containing:
  • An independent set of Attribute name-value pairs.
  • A time window (of (start, end]) time for which the Histogram was bundled.
    • The time interval is inclusive of the end time.
    • Time values are specified as nanoseconds since the UNIX Epoch (00:00:00 UTC on 1 January 1970).
  • A count (count) of the total population of points in the histogram.
  • A sum (sum) of all the values in the histogram.
  • (optional) A series of buckets with:
    • Explicit boundary values. These values denote the lower and upper bounds for buckets and whether not a given observation would be recorded in this bucket.
    • A count of the number of observations that fell within this bucket.
  • (optional) a set of examplars (see Exemplars).

Like Sums, Histograms also define an aggregation temporality. The picture above denotes Delta temporality where accumulated event counts are reset to zero after reporting and a new aggregation occurs. Cumulative, on the other hand, continues to aggregate events, resetting with the use of a new start time.

Summary (Legacy)

Summary metric data points convey quantile summaries, e.g. What is the 99-th percentile latency of my HTTP server. Unlike other point types in OpenTelemetry, Summary points cannot always be merged in a meaningful way. This point type is not recommended for new applications and exists for compatibility with other formats.

Exemplars

An exemplar is a recorded value that associates OpenTelemetry context to a metric event within a Metric. One use case is to allow users to link Trace signals w/ Metrics.

Exemplars consist of:

• (optional) The trace associated with a recording (trace_id, span_id)
• The time of the observation (time_unix_nano)
• The recorded value (value)
• A set of filtered attributes (filtered_attributes) which provide additional insight into the Context when the observation was made.

For Histograms, when an exemplar exists, its value already participates in bucket_counts, count and sum reported by the histogram point.

For Sums, when an exemplar exists, its value is already included in the overall sum.

For Gauges, when an exemplar exists, its value was seen at some point within the gauge interval for the same source.

Single-Writer

All metric data streams within OTLP must have one logical writer. This means, conceptually, that any Timeseries created from the Protocol must have one originating source of truth. In practical terms, this implies the following:

• All metric data streams produced by OTel SDKs must be globally uniquely produced and free from duplicates. All metric data streams can be uniquely identified in some way.
• Aggregations of metric streams must only be written from a single logical source. Note: This implies aggregated metric streams must reach one destination.

In systems, there is the possibility of multiple writers sending data for the same metric stream (duplication). For example, if an SDK implementation fails to find uniquely identifying Resource attributes for a component, then all instances of that component could be reporting metrics as if they are from the same resource. In this case, metrics will be reported at inconsistent time intervals. For metrics like cumulative sums, this could cause issues where pairs of points appear to reset the cumulative sum leading to unusable metrics.

Multiple writers for a metric stream is considered an error state, or misbehaving system. Receivers SHOULD presume a single writer was intended and eliminate overlap / deduplicate.

Note: Identity is an important concept in most metrics systems. For example, Prometheus directly calls out uniqueness:

Care must be taken with labeldrop and labelkeep to ensure that metrics are still uniquely labeled once the labels are removed.

For OTLP, the Single-Writer principle grants a way to reason over error scenarios and take corrective actions. Additionally, it ensures that well-behaved systems can perform metric stream manipulation without undesired degradation or loss of visibility.

Temporality

The notion of temporality refers to the way additive quantities are expressed, in relation to time, indicating whether reported values incorporate previous measurements or not. Sum and Histogram data points, in particular, support a choice of aggregation temporality.

Every OTLP metric data point has two associated timestamps. The first, mandatory timestamp is the one associated with the observation, the moment when the measurement became current or took effect, and is referred to as TimeUnixNano. The second, optional timestamp is used to indicate when a sequence of points is unbroken, and is referred to as StartTimeUnixNano.

The second timestamp is strongly recommended for Sum, Histogram, and Summary points, as it is necessary to correctly interpret the rate from an OTLP stream, in a manner that is aware of restarts. The use of StartTimeUnixNano to indicate the start of an unbroken sequence of points means it can also be used to encode implicit gaps in the stream.

• Cumulative temporality means that successive data points repeat the starting timestamp. For example, from start time T0, cumulative data points cover time ranges (T₀, T₁), (T₀, T₂), (T₀, T₃), and so on.
• Delta temporality means that successive data points advance the starting timestamp. For example, from start time T0, delta data points cover time ranges (T₀, T₁), (T₁, T₂), (T₂, T₃), and so on.

The use of cumulative temporality for monotonic sums is common, exemplified by Prometheus. Systems based in cumulative monotonic sums are naturally simpler, in terms of the cost of adding reliability. When collection fails intermittently, gaps in the data are naturally averaged from cumulative measurements. Cumulative data requires the sender to remember all previous measurements, an “up-front” memory cost proportional to cardinality.

The use of delta temporality for metric sums is also common, exemplified by Statsd. There is a connection between OpenTelemetry tracing, in which a Span event commonly is translated into two metric events (a 1-count and a timing measurement). Delta temporality enables sampling and supports shifting the cost of cardinality outside of the process.

Resets and Gaps

When the StartTimeUnixNano field is present, it allows the consumer to observe when there are gaps and overlapping writers in a stream. Correctly used, the consumer can observe both transient and ongoing violations of the single-writer principle as well as reset events. In an unbroken sequence of observations, the StartTimeUnixNano always matches either the TimeUnixNano or the StartTimeUnixNano of other points in the same sequence. For the initial points in an unbroken sequence:

• When StartTimeUnixNano is less than TimeUnixNano, a new unbroken sequence of observations begins with a "true" reset at a known start time. The zero value is implicit, it is not necessary to record the starting point.
• When StartTimeUnixNano equals TimeUnixNano, a new unbroken sequence of observations begins with a reset at an unknown start time. The initial observed value is recorded to indicate that an unbroken sequence of observations resumes. These points have zero duration, and indicate that nothing is known about previously-reported points and that data may have been lost.

For subsequent points in an unbroken sequence:

• For points with delta aggregation temporality, the StartTimeUnixNano of each point matches the TimeUnixNano of the preceding point
• Otherwise, the StartTimeUnixNano of each point matches the StartTimeUnixNano of the initial observation.

A metric stream has a gap, where it is implicitly undefined, anywhere there is a range of time such that no point covers that range range with its StartTimeUnixNano and TimeUnixNano fields.

Cumulative streams: handling unknown start time

An unbroken stream of observations is resumed with a zero-duration point and non-zero value, as described above. For points with cumulative aggregation temporality, the rate contributed to the timeseries by each point depends on the prior point value in the stream.

To correctly compute the rate contribution of the first point in a unbroken sequence requires knowing whether it is the first point. Unknown start-time reset points appear with TimeUnixNano equal to the StartTimeUnixNano of a stream of points, in which case the rate contribution of the first point is considered zero. An earlier sequence of observations is expected to have reported the same cumulative state prior to a gap in observations.

The presence or absence of a point with TimeUnixNano equal to the StartTimeUnixNano indicates how to count rate contribution from the first point in a sequence. If the first point in an unknown start-time reset sequence is lost, the consumer of this data might overcount the rate contribution of the second point, as it then appears like a "true" reset.

Various approaches can be taken to avoid overcounting. A system could use state from earlier in the stream to resolve start-time ambiguity, for example.

Cumulative streams: inserting true reset points

The absolute value of the cumulative counter is often considered meaningful, but when the cumulative value is only used to calculate a rate function, it is possible to drop the initial unknown start-time reset point, but remember the initially observed value in order to modify subsequent observations. Later in the cumulative sequence are output relative to the initial value, thus appears as a true reset offset by an unknown constant.

This process is known as inserting true reset points, a special case of reaggregation for cumulative series.

Overlap

Overlap occurs when more than one metric data point is defined for a metric stream within a time window. Overlap is usually caused through mis-configuration, and it can lead to serious mis-interpretation of the data. StartTimeUnixNano is recommended so that consumers can recognize and response to overlapping points.

We define three principles for handling overlap:

• Resolution (correction via dropping points)
• Observability (allowing the data to flow to backends)
• Interpolation (correction via data manipulation)

Overlap resolution

When more than one process writes the same metric data stream, OTLP data points may appear to overlap. This condition typically results from misconfiguration, but can also result from running identical processes (indicative of operating system or SDK bugs, like missing process attributes). When there are overlapping points, receivers SHOULD eliminate points so that there are no overlaps. Which data to select in overlapping cases is not specified.

Overlap observability

OpenTelemetry collectors SHOULD export telemetry when they observe overlapping points in data streams, so that the user can monitor for erroneous configurations.

Overlap interpolation

When one process starts just as another exits, the appearance of overlapping points may be expected. In this case, OpenTelemetry collectors SHOULD modify points at the change-over using interpolation for Sum data points, to reduce gaps to zero width in these cases, without any overlap.

Resources

Pending

Temporal Alignment

Pending

External Labels

Pending

Stream Manipulations

Pending introduction.

Sums: Delta-to-Cumulative

While OpenTelemetry (and some metric backends) allows both Delta and Cumulative sums to be reported, the timeseries model we target does not support delta counters. To this end, converting from delta to cumulative needs to be defined so that backends can use this mechanism.

Note: This is not the only possible Delta to Cumulative algorithm. It is just one possible implementation that fits the OTel Data Model.

Converting from delta points to cumulative point is inherently a stateful operation. To successfully translate, we need all incoming delta points to reach one destination which can keep the current counter state and generate a new cumulative stream of data (see single writer principle).

The algorithm is scheduled out as follows:

• Upon receiving the first Delta point for a given counter we set up the following:
  • A new counter which stores the cumulative sum, set to the initial counter.
  • A start time that aligns with the start time of the first point.
  • A "last seen" time that aligns with the time of the first point.
• Upon receiving future Delta points, we do the following:
  • If the next point aligns with the expected next-time window (see detecting delta restarts)
    • Update the "last seen" time to align with the time of the current point.
    • Add the current value to the cumulative counter
    • Output a new cumulative point with the original start time and current last seen time and count.
  • if the current point precedes the start time, then drop this point. Note: there are algorithms which can deal with late arriving points.
  • if the next point does NOT align with the expected next-time window, then reset the counter following the same steps performed as if the current point was the first point seen.

Sums: detecting alignment issues

When the next delta sum reported for a given metric stream does not align with where we expect it, one of several things could have occurred:

• the process reporting metrics was rebooted, leading to a new reporting interval for the metric.
• A Single-Writer principle violation where multiple processes are reporting the same metric stream.
• There was a lost data point, or dropped information.

In all of these scenarios we do our best to give any cumulative metric knowledge that some data was lost, and reset the counter.

We detect alignment via two mechanisms:

• If the incoming delta time interval has significant overlap with the previous time interval, we must assume a violation of the single-writer principle.
• If the incoming delta time interval has a significant gap from the last seen time, we assume some kind of reboot/restart and reset the cumulative counter.

Sums: Missing Timestamps

One degenerate case for the delta-to-cumulative algorithm is when timestamps are missing from metric data points. While this shouldn't be the case when using OpenTelemetry generated metrics, it can occur when adapting other metric formats, e.g. StatsD counts.

In this scenario, the algorithm listed above would reset the cumulative sum on every data point due to not being able to determine alignment or point overlap. For comparison, see the simple logic used in statsd sums where all points are added, and lost points are ignored.

Footnotes

[1]: OTLP supports data point kinds that do not satisfy these conditions; they are well-defined but do not support standard metric data transformations.