It is expected that the `deriv(m[d])` returns non-empty value if the lookbehind window `d`
contains less than 2 samples in the same way as `rate()` does.
This is a follow-up after 3e084be06b .
Previously, `predict_linear` returned slightly different results comparing
to Prometheus. The change makes linear regression algorithm compatible
with Prometheus.
`deriv` was excluded from the list of functions which can adjust the time
window for the same reasons.
Adjustment results into discrepancy between Prometheus and VM on time windows
smaller than scrape interval.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
The fix will always return zero if received set of items consists of one
element only, which also means no deviation.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
The change affects `count/stddev/stdvar_over_time` funcs and makes
them to return NaN instead of zero when there is no datapoints
in a time window.
This is needed for improving compatibility with Prometheus.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
The removed fast path optimisations weren't consistent with
`quantile` function behavior and results into discrepancy.
Specifically, results didn't match in cases when:
* 0 < phi > 1;
* values contain only one element.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* app/vmselect: `quantile` func compatiblity with Prometheus
The `quantile` func was previously calculated by https://github.com/valyala/histogram
package. The result of such calculation was always the closest real value to
requested quantile. While in Prometheus implementation interpolation is used.
Such difference may result into discrepancy in output between Prometheus and
VictoriaMetrics.
This commit adds a Prometheus-like `quantile` function. It also used by other
functions which depend on it, such as `quantiles`, `quantile_over_time`, `median` etc.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1625
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* app/vmselect: `quantile` review fixes
* quantile functions were split into multiple to provide
different API for already sorted data;
* float64sPool is used for reducing allocations. Items in pool may have
different sizes, but defining a new pool was complicates due to name collisions;
Signed-off-by: hagen1778 <roman@victoriametrics.com>
This reverts commit 94dfcb6747a3b29a11d14e71bea21a2312bb6346.
It is better to remove staleness marks (decimal.StaleNaN) before calling rollupConfig.Do, e.g. in preFunc
Prometheus stalenss marks shouldn't be changed in removeCounterResets. Otherwise they will be converted to an ordinary NaN values,
which couldn't be removed in dropStaleNaNs() function later. This may result in incorrect calculations for rollup functions.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1526
Due to staleness handling, increase_pure were using incorrect previous value
during calculation in cases where series disappears for period longer
than staleness period and then returns back. The fix suppose to account
for a real datapoint value before staleness takes place. The fix should
remove unexpected spikes while using `increase_pure` for staled series.
This should prevent from double counting for time series at the time when it changes label.
The most common case is in K8S, which changes pod uid label with each new deployment.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/748
This reverts commit e5202a4eae.
Reason for revert: the previous behavior for VictoriaMetrics is easier to understand and use by users -
functions, which don't change the meaning of the time series shouldn't drop metric name.
Now the following functions do not drop metric name:
* max_over_time
* min_over_time
* avg_over_time
* quantile_over_time
* geomean_over_time
* mode_over_time
* holt_winters
* predict_linear
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/674
These functions returns the number of raw samples that don't exceed `le` or are bigger than `gt`.
These functions are complement to already existing `share_le_over_time(m[d], le)` and `share_gt_over_time(m[d], gt)`.
