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Document the native histogram feature flag and PromQL (#11446)
Signed-off-by: beorn7 <beorn@grafana.com> Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com> Co-authored-by: Ganesh Vernekar <ganeshvern@gmail.com>
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Björn Rabenstein
GitHub
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41035469d3
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@ -103,3 +103,26 @@ When enabled, the default ports for HTTP (`:80`) or HTTPS (`:443`) will _not_ be
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the address used to scrape a target (the value of the `__address_` label), contrary to the default behavior.
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In addition, if a default HTTP or HTTPS port has already been added either in a static configuration or
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by a service discovery mechanism and the respective scheme is specified (`http` or `https`), that port will be removed.
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## Native Histograms
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`--enable-feature=native-histograms`
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When enabled, Prometheus will ingest native histograms (formerly also known as
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sparse histograms or high-res histograms). Native histograms are still highly
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experimental. Expect breaking changes to happen (including those rendering the
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TSDB unreadable).
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Native histograms are currently only supported in the traditional Prometheus
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protobuf exposition format. This feature flag therefore also enables a new (and
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also experimental) protobuf parser, through which _all_ metrics are ingested
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(i.e. not only native histograms). Prometheus will try to negotiate the
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protobuf format first. The instrumented target needs to support the protobuf
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format, too, _and_ it needs to expose native histograms. The protobuf format
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allows to expose conventional and native histograms side by side. With this
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feature flag disabled, Prometheus will continue to parse the conventional
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histogram (albeit via the text format). With this flag enabled, Prometheus will
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still ingest those conventional histograms that do not come with a
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corresponding native histogram. However, if a native histogram is present,
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Prometheus will ignore the corresponding conventional histogram, with the
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notable exception of exemplars, which are always ingested.
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@ -32,6 +32,16 @@ expression), only some of these types are legal as the result from a
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user-specified expression. For example, an expression that returns an instant
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vector is the only type that can be directly graphed.
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_Notes about the experimental native histograms:_
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* Ingesting native histograms has to be enabled via a [feature
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flag](../feature_flags/#native-histograms).
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* Once native histograms have been ingested into the TSDB (and even after
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disabling the feature flag again), both instant vectors and range vectors may
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now contain samples that aren't simple floating point numbers (float samples)
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but complete histograms (histogram samples). A vector may contain a mix of
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float samples and histogram samples.
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## Literals
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### String literals
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@ -11,6 +11,22 @@ instant-vector)`. This means that there is one argument `v` which is an instant
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vector, which if not provided it will default to the value of the expression
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`vector(time())`.
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_Notes about the experimental native histograms:_
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* Ingesting native histograms has to be enabled via a [feature
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flag](../feature_flags/#native-histograms). As long as no native histograms
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have been ingested into the TSDB, all functions will behave as usual.
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* Functions that do not explicitly mention native histograms in their
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documentation (see below) effectively treat a native histogram as a float
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sample of value 0. (This is confusing and will change before native
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histograms become a stable feature.)
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* Functions that do already act on native histograms might still change their
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behavior in the future.
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* If a function requires the same bucket layout between multiple native
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histograms it acts on, it will automatically convert them
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appropriately. (With the currently supported bucket schemas, that's always
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possible.)
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## `abs()`
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`abs(v instant-vector)` returns the input vector with all sample values converted to
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@ -19,8 +35,8 @@ their absolute value.
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## `absent()`
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`absent(v instant-vector)` returns an empty vector if the vector passed to it
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has any elements and a 1-element vector with the value 1 if the vector passed to
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it has no elements.
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has any elements (floats or native histograms) and a 1-element vector with the
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value 1 if the vector passed to it has no elements.
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This is useful for alerting on when no time series exist for a given metric name
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and label combination.
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@ -42,8 +58,8 @@ of the 1-element output vector from the input vector.
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## `absent_over_time()`
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`absent_over_time(v range-vector)` returns an empty vector if the range vector
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passed to it has any elements and a 1-element vector with the value 1 if the
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range vector passed to it has no elements.
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passed to it has any elements (floats or native histograms) and a 1-element
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vector with the value 1 if the range vector passed to it has no elements.
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This is useful for alerting on when no time series exist for a given metric name
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and label combination for a certain amount of time.
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@ -130,7 +146,14 @@ between now and 2 hours ago:
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delta(cpu_temp_celsius{host="zeus"}[2h])
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```
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`delta` should only be used with gauges.
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`delta` acts on native histograms by calculating a new histogram where each
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compononent (sum and count of observations, buckets) is the difference between
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the respective component in the first and last native histogram in
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`v`. However, each element in `v` that contains a mix of float and native
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histogram samples within the range, will be missing from the result vector.
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`delta` should only be used with gauges and native histograms where the
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components behave like gauges (so-called gauge histograms).
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## `deriv()`
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@ -156,15 +179,19 @@ to the nearest integer.
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## `histogram_count()` and `histogram_sum()`
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_Both functions only act on native histograms, which are an experimental
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feature. The behavior of these functions may change in future versions of
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Prometheus, including their removal from PromQL._
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`histogram_count(v instant-vector)` returns the count of observations stored in
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a native Histogram. Samples that are not native Histograms are ignored and do
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a native histogram. Samples that are not native histograms are ignored and do
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not show up in the returned vector.
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Similarly, `histogram_sum(v instant-vector)` returns the sum of observations
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stored in a native Histogram.
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stored in a native histogram.
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Use `histogram_count` in the following way to calculate a rate of observations
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(in this case corresponding to “requests per second”) from a native Histogram:
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(in this case corresponding to “requests per second”) from a native histogram:
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histogram_count(rate(http_request_duration_seconds[10m]))
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@ -177,57 +204,121 @@ observed values (in this case corresponding to “average request duration”):
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## `histogram_fraction()`
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TODO(beorn7): Add documentation.
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_This function only acts on native histograms, which are an experimental
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feature. The behavior of this function may change in future versions of
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Prometheus, including its removal from PromQL._
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For a native histogram, `histogram_fraction(lower scalar, upper scalar, v
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instant-vector)` returns the estimated fraction of observations between the
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provided lower and upper values. Samples that are not native histograms are
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ignored and do not show up in the returned vector.
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For example, the following expression calculates the fraction of HTTP requests
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over the last hour that took 200ms or less:
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histogram_fraction(0, 0.2, rate(http_request_duration_seconds[1h]))
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The error of the estimation depends on the resolution of the underlying native
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histogram and how closely the provided boundaries are aligned with the bucket
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boundaries in the histogram.
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`+Inf` and `-Inf` are valid boundary values. For example, if the histogram in
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the expression above included negative observations (which shouldn't be the
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case for request durations), the appropriate lower boundary to include all
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observations less than or equal 0.2 would be `-Inf` rather than `0`.
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Whether the provided boundaries are inclusive or exclusive is only relevant if
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the provided boundaries are precisely aligned with bucket boundaries in the
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underlying native histogram. In this case, the behavior depends on the schema
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definition of the histogram. The currently supported schemas all feature
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inclusive upper boundaries and exclusive lower boundaries for positive values
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(and vice versa for negative values). Without a precise alignment of
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boundaries, the function uses linear interpolation to estimate the
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fraction. With the resulting uncertainty, it becomes irrelevant if the
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boundaries are inclusive or exclusive.
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## `histogram_quantile()`
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TODO(beorn7): This needs a lot of updates for Histograms as sample value types.
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`histogram_quantile(φ scalar, b instant-vector)` calculates the φ-quantile (0 ≤
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φ ≤ 1) from a [conventional
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histogram](https://prometheus.io/docs/concepts/metric_types/#histogram) or from
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a native histogram. (See [histograms and
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summaries](https://prometheus.io/docs/practices/histograms) for a detailed
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explanation of φ-quantiles and the usage of the (conventional) histogram metric
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type in general.)
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`histogram_quantile(φ scalar, b instant-vector)` calculates the φ-quantile (0 ≤ φ
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≤ 1) from the buckets `b` of a
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[histogram](https://prometheus.io/docs/concepts/metric_types/#histogram). (See
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[histograms and summaries](https://prometheus.io/docs/practices/histograms) for
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a detailed explanation of φ-quantiles and the usage of the histogram metric type
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in general.) The samples in `b` are the counts of observations in each bucket.
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Each sample must have a label `le` where the label value denotes the inclusive
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upper bound of the bucket. (Samples without such a label are silently ignored.)
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The [histogram metric type](https://prometheus.io/docs/concepts/metric_types/#histogram)
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automatically provides time series with the `_bucket` suffix and the appropriate
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labels.
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_Note that native histograms are an experimental feature. The behavior of this
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function when dealing with native histograms may change in future versions of
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Prometheus._
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The conventional float samples in `b` are considered the counts of observations
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in each bucket of one or more conventional histograms. Each float sample must
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have a label `le` where the label value denotes the inclusive upper bound of
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the bucket. (Float samples without such a label are silently ignored.) The
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other labels and the metric name are used to identify the buckets belonging to
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each conventional histogram. The [histogram metric
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type](https://prometheus.io/docs/concepts/metric_types/#histogram)
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automatically provides time series with the `_bucket` suffix and the
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appropriate labels.
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The native histogram samples in `b` are treated each individually as a separate
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histogram to calculate the quantile from.
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As long as no naming collisions arise, `b` may contain a mix of conventional
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and native histograms.
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Use the `rate()` function to specify the time window for the quantile
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calculation.
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Example: A histogram metric is called `http_request_duration_seconds`. To
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calculate the 90th percentile of request durations over the last 10m, use the
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following expression:
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Example: A histogram metric is called `http_request_duration_seconds` (and
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therefore the metric name for the buckets of a conventional histogram is
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`http_request_duration_seconds_bucket`). To calculate the 90th percentile of request
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durations over the last 10m, use the following expression in case
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`http_request_duration_seconds` is a conventional histogram:
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histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[10m]))
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For a native histogram, use the following expression instead:
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histogram_quantile(0.9, rate(http_request_duration_seconds[10m]))
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The quantile is calculated for each label combination in
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`http_request_duration_seconds`. To aggregate, use the `sum()` aggregator
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around the `rate()` function. Since the `le` label is required by
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`histogram_quantile()`, it has to be included in the `by` clause. The following
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expression aggregates the 90th percentile by `job`:
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`histogram_quantile()` to deal with conventional histograms, it has to be
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included in the `by` clause. The following expression aggregates the 90th
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percentile by `job` for conventional histograms:
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histogram_quantile(0.9, sum by (job, le) (rate(http_request_duration_seconds_bucket[10m])))
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To aggregate everything, specify only the `le` label:
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When aggregating native histograms, the expression simplifies to:
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histogram_quantile(0.9, sum by (job) (rate(http_request_duration_seconds[10m])))
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To aggregate all conventional histograms, specify only the `le` label:
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histogram_quantile(0.9, sum by (le) (rate(http_request_duration_seconds_bucket[10m])))
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The `histogram_quantile()` function interpolates quantile values by
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assuming a linear distribution within a bucket. The highest bucket
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must have an upper bound of `+Inf`. (Otherwise, `NaN` is returned.) If
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a quantile is located in the highest bucket, the upper bound of the
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second highest bucket is returned. A lower limit of the lowest bucket
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is assumed to be 0 if the upper bound of that bucket is greater than
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0. In that case, the usual linear interpolation is applied within that
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bucket. Otherwise, the upper bound of the lowest bucket is returned
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for quantiles located in the lowest bucket.
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With native histograms, aggregating everything works as usual without any `by` clause:
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histogram_quantile(0.9, sum(rate(http_request_duration_seconds[10m])))
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The `histogram_quantile()` function interpolates quantile values by
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assuming a linear distribution within a bucket.
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If `b` has 0 observations, `NaN` is returned. For φ < 0, `-Inf` is
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returned. For φ > 1, `+Inf` is returned. For φ = `NaN`, `NaN` is returned.
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The following is only relevant for conventional histograms: If `b` contains
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fewer than two buckets, `NaN` is returned. The highest bucket must have an
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upper bound of `+Inf`. (Otherwise, `NaN` is returned.) If a quantile is located
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in the highest bucket, the upper bound of the second highest bucket is
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returned. A lower limit of the lowest bucket is assumed to be 0 if the upper
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bound of that bucket is greater than
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0. In that case, the usual linear interpolation is applied within that
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bucket. Otherwise, the upper bound of the lowest bucket is returned for
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quantiles located in the lowest bucket.
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If `b` has 0 observations, `NaN` is returned. If `b` contains fewer than two buckets,
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`NaN` is returned. For φ < 0, `-Inf` is returned. For φ > 1, `+Inf` is returned. For φ = `NaN`, `NaN` is returned.
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## `holt_winters()`
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@ -269,11 +360,17 @@ over the last 5 minutes, per time series in the range vector:
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increase(http_requests_total{job="api-server"}[5m])
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```
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`increase` should only be used with counters. It is syntactic sugar
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for `rate(v)` multiplied by the number of seconds under the specified
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time range window, and should be used primarily for human readability.
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Use `rate` in recording rules so that increases are tracked consistently
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on a per-second basis.
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`increase` acts on native histograms by calculating a new histogram where each
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compononent (sum and count of observations, buckets) is the increase between
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the respective component in the first and last native histogram in
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`v`. However, each element in `v` that contains a mix of float and native
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histogram samples within the range, will be missing from the result vector.
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`increase` should only be used with counters and native histograms where the
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components behave like counters. It is syntactic sugar for `rate(v)` multiplied
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by the number of seconds under the specified time range window, and should be
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used primarily for human readability. Use `rate` in recording rules so that
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increases are tracked consistently on a per-second basis.
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## `irate()`
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@ -385,8 +482,15 @@ over the last 5 minutes, per time series in the range vector:
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rate(http_requests_total{job="api-server"}[5m])
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```
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`rate` should only be used with counters. It is best suited for alerting,
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and for graphing of slow-moving counters.
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`rate` acts on native histograms by calculating a new histogram where each
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compononent (sum and count of observations, buckets) is the rate of increase
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between the respective component in the first and last native histogram in
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`v`. However, each element in `v` that contains a mix of float and native
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histogram samples within the range, will be missing from the result vector.
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`rate` should only be used with counters and native histograms where the
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components behave like counters. It is best suited for alerting, and for
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graphing of slow-moving counters.
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Note that when combining `rate()` with an aggregation operator (e.g. `sum()`)
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or a function aggregating over time (any function ending in `_over_time`),
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@ -306,3 +306,31 @@ highest to lowest.
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Operators on the same precedence level are left-associative. For example,
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`2 * 3 % 2` is equivalent to `(2 * 3) % 2`. However `^` is right associative,
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so `2 ^ 3 ^ 2` is equivalent to `2 ^ (3 ^ 2)`.
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## Operators for native histograms
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Native histograms are an experimental feature. Ingesting native histograms has
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to be enabled via a [feature flag](../feature_flags/#native-histograms). Once
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native histograms have been ingested, they can be queried (even after the
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feature flag has been disabled again). However, the operator support for native
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histograms is still very limited.
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Logical/set binary operators work as expected even if histogram samples are
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involved. They only check for the existence of a vector element and don't
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change their behavior depending on the sample type of an element (float or
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histogram).
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The binary `+` operator between two native histograms and the `sum` aggregation
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operator to aggregate native histograms are fully supported. Even if the
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histograms involved have different bucket layouts, the buckets are
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automatically converted appropriately so that the operation can be
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performed. (With the currently supported bucket schemas, that's always
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possible.) If either operator has to sum up a mix of histogram samples and
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float samples, the corresponding vector element is removed from the output
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vector entirely.
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All other operators do not behave in a meaningful way. They either treat the
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histogram sample as if it were a float sample of value 0, or (in case of
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arithmetic operations between a scalar and a vector) they leave the histogram
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sample unchanged. This behavior will change to a meaningful one before native
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histograms are a stable feature.
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