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Add detail about StartTimeUnixNano, gaps and resets (open-telemetry#1646
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* Add detail about StartTimeUnixNano, gaps and resets

* Changelog

* Edit

* Call out special case of reaggregation

* Apply feedback from reviewers.
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jmacd authored May 17, 2021
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -13,6 +13,7 @@ release.

### Metrics

- Adds detail about when to use `StartTimeUnixNano` and handling of unknown start-time resets. ([#1646](https://github.com/open-telemetry/opentelemetry-specification/pull/1646))
- Expand `Gauge` metric description in the data model ([#1661](https://github.com/open-telemetry/opentelemetry-specification/pull/1661))
- Expand `Histogram` metric description in the data model ([#1664](https://github.com/open-telemetry/opentelemetry-specification/pull/1664))

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99 changes: 86 additions & 13 deletions specification/metrics/datamodel.md
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Expand Up @@ -426,14 +426,24 @@ degradation or loss of visibility.

## Temporality

Every OTLP point has two associated timestamps. For OTLP Sum and Histogram
points, the two timestamps indicate when the point was reset and when the sum
was captured. For OTLP Gauge points, the two timestamps indicate when the
measurement was taken and when it was reported as being still the last value.

The notion of temporality refers to a configuration choice made in the system
as a whole, indicating whether reported values incorporate previous
measurements, or not.
The notion of temporality refers to the way additive quanties 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
Expand All @@ -457,13 +467,76 @@ 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 occurs for a data stream
within a time window. This is particularly problematic for data points meant
to represent an entire time window, e.g. a Histogram reporting population
density of collected metric data points for a time window. If two of these show
up with overlapping time windows, how do backends handle this situation?
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:

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