### Describe Your Changes
In most cases histograms are exposed in sorted manner with lower buckets
being first. This means that during scraping buckets with lower bounds
have higher chance of being updated earlier than upper ones.
Previously, values were propagated from upper to lower bounds, which
means that in most cases that would produce results higher than expected
once all buckets will become updated.
Propagating from upper bound effectively limits highest value of
histogram to the value of previous scrape. Once the data will become
consistent in the subsequent evaluation this causes spikes in the
result.
Changing propagation to be from lower to higher buckets reduces value
spikes in most cases due to nature of the original inconsistency.
See: https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4580
An example histogram with previous(red) and updated(blue) versions:
This also makes logic of filling nan values with lower buckets values: [1 2 3 nan nan nan] => [1 2 3 3 3 3] obsolete.
Since buckets are now fixed from lower ones to upper this happens in the main loop, so there is no need in a second one.
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Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Andrii Chubatiuk <andrew.chubatiuk@gmail.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
Use metricsql.IsLikelyInvalid() function for determining whether the given query is likely invalid,
e.g. there is high change the query is incorrectly written, so it will return unexpected results.
The query is invalid most of the time if it passes something other than series selector into rollup function.
For example:
- rate(sum(foo))
- rate(foo + bar)
- rate(foo > bar)
Improtant note: the query is considered valid if it misses the lookbehind window in square brackes inside rollup function,
e.g. rate(foo), since this is very convenient MetricsQL extention to PromQL, and this query returns the expected results
most of the time.
Other unsafe query types can be added in the future into metricsql.IsLikelyInvalid().
TODO: probably, the -search.disableImplicitConversion command-line flag must be set by default in the future releases of VictoriaMetrics.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4338
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6180
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6450
Check for ranged vector arguments in aggregate expressions when
`-search.disableImplicitConversion` or `-search.logImplicitConversion`
are enabled.
For example, `sum(up[5m])` will fail to execute if these flags are set.
### Describe Your Changes
Please provide a brief description of the changes you made. Be as
specific as possible to help others understand the purpose and impact of
your modifications.
### Checklist
The following checks are **mandatory**:
- [*] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
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Signed-off-by: hagen1778 <roman@victoriametrics.com>
* metricsql: fix label_join() when `dst_label` is equal to one of the `src_label`
* Update app/vmselect/promql/transform.go
* Update docs/CHANGELOG.md
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Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Prevsiously they were swapped - the first arg should be the label name and the second arg should be label filters
This is a follow-up for e389b7b959e8144fdff5075bf7a5a39b2b0c6dd3
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5847
* app/vmselect/promql: properly handle possible negative results caused by float operations precision error in rollup functions like rate() or increase()
* fix test
This can be useful in the following queries:
drop_empty_series(temperature <= 30) default 40
This query drops temperature series with all the values bigger than 30 on the selected time range,
while replacing gaps in the remaining series with 40.
The query without drop_empty_series:
(temperature <= 30) default 40
would leave all the temperature series with all the values bigger than 30 on the selected time range,
and replace all their values with 40. This is not what could be epxected in some cases
like here - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5071
The `a op b keep_metric_names` is ambigouos to `a op (b keep_metric_names)` when `b` is a transform or rollup function.
For example, `a + rate(b) keep_metric_names`. So it is better to use more clear syntax: `(a op b) keep_metric_names`
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3710
* metricsql: add support of using keep_metric_names for binary operations
This should help to avoid confusion with queries like one in the issue #3710.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* wip
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Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* metricsql: support optional 2nd argument for rollup functions
Support optional 2nd argument `min`, `max` or `avg` for rollup functions:
* rollup
* rollup_delta
* rollup_deriv
* rollup_increase
* rollup_rate
* rollup_scrape_interval
If second argument is passed, then rollup function will return only the selected aggregation type.
This change can be useful for situations where only one type of rollup calculation is needed.
For example, `rollup_rate(requests_total[5m], "max")`.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* wip
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Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Previously too short lookbehind window d for rate(m[d]) could be automatically extended
if it didn't cover at least two raw samples. This was needed in order to guarantee
non-empty results from rate(m[d]) on short time ranges.
Now the lookbehind window isn't extended if it is set explicitly,
since it is expected that the user knows what he is doing.
The lookbehind window continues to be extended when needed if it isn't set explicitly.
For example, in the case of rate(m).
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3483
Previously empty series (e.g. series with all NaN samples) were passed to aggregate functions.
Such series must be ingored by all the aggregate functions.
So it is better from consistency PoV filtering out empty series before applying aggregate functions.
