Prometheus makes experimental functions and operators available behind a feature flag. The Prometheus project might change the name, syntax, or semantics of experimental functions or remove them entirely. Chronosphere Observability Platform accepts a subset of PromQL experimental functions determined to be relatively stable, and which provide significant value while also being at a low risk of being removed by the Prometheus project. When the upstream Prometheus project makes breaking changes, Observability Platform will either preserve backward compatibility if possible or notify users and provide a deprecation timeline. The following experimental functions are available in Observability Platform:Documentation Index
Fetch the complete documentation index at: https://docs.chronosphere.io/llms.txt
Use this file to discover all available pages before exploring further.
double_exponential_smoothing()
Use the double_exponential_smoothing() experimental function to smooth a time series
based on the importance you assign to older data and possible trends. See the
PromQL documentation.
fill() | fill_left() | fill_right()
The fill() modifiers control how binary operations (arithmetic and comparison
operators) handle series that don’t have matching label sets. By default, PromQL only
produces results for label combinations that exist on both sides of an operation.
Fill modifiers let you specify default values for missing series, allowing operations
to produce results even when label sets don’t perfectly overlap.
Observability Platform supports the following fill() modifier variants:
fill(value): Fills both sides with the specified value when a matching series is missing on either side.fill_left(value): Only fills the left side with the value when left series are missing, but right series exist.fill_right(value): Only fills the right side with the value when right series are missing, but left series exist.
value can be any scalar expression, including special values like NaN, Inf, or
-Inf.
These modifiers can help to compute arithmetic with delta metrics, compare metrics
from different sources without complete label overlap, and let missing series behave
like there’s a specific default value (like zero) rather than being absent.
For example, to add two metrics where you want to treat missing series as zero:
requests_by_endpoint has series for endpoints A, B, and C, but
errors_by_endpoint has series for only A and B, this query produces results for all
three endpoints, treating the missing error count for endpoint C as zero.
To fill only one side, use the directional variants, which helps when you want
asymmetric behavior:
Combine fill variants
You can specify different fill values for each side:5, and missing right-side
series with a value of 10.
Create comparisons
Fill modifiers work with comparison operators (==, !=, >, <, >=, <=),
including the bool modifier:
0.8.
When used with bool, the result is 1 (true) or 0 (false):
Match labels
Fill modifiers can includeon() and ignoring() for label matching:
0.
Match one to many
Fill modifiers work withgroup_left and group_right for many-to-one and
one-to-many joins:
group_left, the fill_right modifier fills the side designated by 1
(right side), and fill_left fills the “many” side (left side). This creates results
for all request series, using 1 as the limit when no matching limit exists.
Edge cases
When using thefill() modifier, consider the following edge cases:
- When one vector is completely empty, the
fill()modifier and the directional variants that would fill the empty side will produce results using only the non-empty vector’s series. - When both sides have the same label set, the
fill()modifier has no effect (all series match). - When label sets have no overlap at all, the
fill()modifier creates results for all series from both sides.
histogram_quantiles()
The histogram_quantiles(v instant-vector, label_name string, quantile1 scalar,[quantile2 scalar, ...])
function computes multiple quantiles (up to ten) from both native and classic
histogram metrics in a single operation. Results are labeled with the specified label
name. This function is more efficient than repeatedly calling histogram_quantile().
Use the following query to compute the fiftieth, ninetieth, and ninety-ninth
percentiles of request duration with results labeled by percentile:
0.5, 0.9, and 0.99. Use the label_name
parameter to choose the label that distinguishes the different quantile series.
ts_of_*(range-vector)
Use the ts_of_... (timestamp of) experimental functions to aggregate timestamps
of samples within a specified interval. For details, see
<aggregation>_over_time()
in the Prometheus documentation.
These experimental functions were introduced by specific Prometheus versions:
| Function name | Description | Version added |
|---|---|---|
| ts_of_first_over_time | Returns the timestamp of the earliest sample in the range vector for each series. | 3.7.0 |
| ts_of_last_over_time | Returns the timestamp of the last sample in the specified interval, per series. | 3.5.0 |
| ts_of_min_over_time | Returns the timestamp of the last float sample that has the minimum value of all float samples in the specified interval, per series. | 3.5.0 |
| ts_of_max_over_time | Returns the timestamp of the last float sample that has the maximum value of all float samples in the specified interval, per series. | 3.5.0 |
holt_winters() (deprecated)
As of Prometheus 3.0, this function is named double_exponential_smoothing().
Although Observability Platform aliases holt_winters() to provide backward
compatibility, you should replace it with the new name to avoid issues when
Observability Platform removes the holt_winters() alias.