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.
The fix makes the binary comparison func to check for NaNs
before executing the actual comparison. This prevents VM
to return values for non-existing samples for expressions
which contain bool comparisons. Please see added test
for example.
It appeared, that `testRowsEqual` NaN comparison was incorrect.
The fix caused some tests to fail. Please see the change and
tests updated.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
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>
* app/vmselect: make sorting for query result similar to Prometheus
Updated sorting allows to get the order of series in result similar or equal
to what Prometheus returns.
The change is needed for compatibility reasons.
* Update app/vmselect/promql/exec_test.go
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
This option allows reducing CPU usage a bit when VictoriaMetrics is used
for collecting and processing non-Prometheus data. For example, InfluxDB line protocol, Graphite, OpenTSDB, CSV, etc.
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
- Support durations anywhere in MetricsQL queries. E.g. sum_over_time(m[1h])/1h is equivalent to sum_over_time(m[1h])/3600
- Support durations without suffix. E.g. rate(m[300]) is equivalent to rate(m[5m])
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.
