There is a bug here where if you have a single bucket like:
foo{vmrange="4.084e+02...4.642e+02"} 2 123
The expected output is three le encoded buckets like:
foo{le="4.084e+02"} 0 123
foo{le="4.642e+02"} 2 123
foo{le="+Inf"} 2 123
This correctly encodes the start and end of the vmrange.
If however, the input contains the previous bucket, and that bucket is
empty then you only get the end le and +Inf out currently, i.e:
foo{vmrange="7.743e+05...8.799e+05"} 5 123
foo{vmrange="6.813e+05...7.743e+05"} 0 123
results in:
foo{le="8.799e+05"} 5 123
foo{le="+Inf"} 5 123
This causes issues when you go to compute a quantile because this means
that the assumed lower bound of the buckets is 0 and this we interpolate
between 0->end rather than the vmrange start->end as expected.
* app/vmselect: ignore empty series for `limit_offset`
VictoriaMetrics doesn't return empty series (with all NaN values) to
the user. But such series are filtered after transform functions.
It means `limit_offset` will account for empty series as well.
For example, let's consider following data set:
```
time series:
foo{label="1"} NaN, NaN, NaN, NaN // empty series
foo{label="2"} 1, 2, 3, 4
foo{label="3"} 4, 3, 2, 1
```
When user requests all series for metric `foo` the empty series
will be filtered out:
```
/query=foo:
foo{label="v2"} 1, 2, 3, 4
foo{label="v3"} 4, 3, 2, 1
```
But `limit_offset(1, 1, foo)` is applied to original series, not filtered yet.
So it will return `foo{label="v2"}` (skips the first in list)
```
/query=limit_offset(1, 1, foo):
foo{label="v2"} 1, 2, 3, 4
```
Expected result would be to apply `limit_offset` to already filtered list,
so in result we receive `foo{label="v3"}`:
```
/query=limit_offset(1, 1, foo):
foo{label="v3"} 4, 3, 2, 1
```
The change does exactly that - filters empty series before applying `limit_offset`.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* app/vmselect: ignore empty series for `limit_offset`
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* vmselect/promql: add alphanumeric sort by label (sort_by_label_numeric)
* vmselect/promql: fix tests, add documentation
* vmselect/promql: update test
* vmselect/promql: update for alphanumeric sorting, fix tests
* vmselect/promql: remove comments
* vmselect/promql: cleanup
* vmselect/promql: avoid memory allocations, update functions descriptions
* vmselect/promql: make linter happy (remove ineffectual assigment)
* vmselect/promql: add test case, fix behavior when strings are equal
* vendor: update github.com/VictoriaMetrics/metricsql from v0.44.1 to v0.45.0
this adds support for sort_by_label_numeric and sort_by_label_numeric_desc functions
* wip
* lib/promscrape: read response body into memory in stream parsing mode before parsing it
This reduces scrape duration for targets returning big responses.
The response body was already read into memory in stream parsing mode before this change,
so this commit shouldn't increase memory usage.
* wip
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
- Use getScalar() function for obtaining the expected scalar from phi arg
- Reduce the error message returned to the user when incorrect phi is passed to histogram_quantiles
- Improve the description of this bugfix in the docs/CHANGELOG.md
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3026
* 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>
