Previously the lower bound could be too small, which could result in missing values at the beginning of the graph
for default_rollup() function. This function is automatically applied to all the series selectors if they aren't
explicitly wrapped into a rollup function - see https://docs.victoriametrics.com/MetricsQL.html#implicit-query-conversions
While at it, properly take into account `-search.minStalenessInterval` command-line flag when adjusting
the lower bound for the selected time range.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5388
Previously the number of memory allocations inside copyTimeseriesShallow() was equal to 1+len(tss)
Reduce this number to 2 by pre-allocating a slice of timeseries structs with len(tss) length.
evalRollupFuncNoCache() may return time series with identical labels (aka duplicate series)
when performing queries satisfying all the following conditions:
- It must select time series with multiple metric names. For example, {__name__=~"foo|bar"}
- The series selector must be wrapped into rollup function, which drops metric names. For example, rate({__name__=~"foo|bar"})
- The rollup function must be wrapped into aggregate function, which has no streaming optimization.
For example, quantile(0.9, rate({__name__=~"foo|bar"})
In this case VictoriaMetrics shouldn't return `cannot merge series: duplicate series found` error.
Instead, it should fall back to query execution with disabled cache.
Also properly store the merged results. Previously they were incorrectly stored because of a typo
introduced in the commit 41a0fdaf39
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5332
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5337
- If min_over_time(m[offset] @ timestamp) <= min_over_time(m[offset] @ (timestamp-window)),
then the optimization can be applied.
- If max_over_time(m[offset] @ timestamp) >= max_over_time(m[offset] @ (timestamp-window)),
then the optimization can be applied.
This reduction is based on production testing.
Also expose -search.minWindowForInstantRollupOptimization command-line flag, so users could fine-tune this arg for their needs
Repeated instant queries with long lookbehind windows, which contain one of the following rollup functions,
are optimized via partial result caching:
- sum_over_time()
- count_over_time()
- avg_over_time()
- increase()
- rate()
The basic idea of optimization is to calculate
rf(m[d] @ t)
as
rf(m[offset] @ t) + rf(m[d] @ (t-offset)) - rf(m[offset] @ (t-d))
where rf(m[d] @ (t-offset)) is cached query result, which was calculated previously
The offset may be in the range of up to 1 hour.
The new metric gets increased each time `-search.logQueryMemoryUsage` memory limit
is exceeded by a query. This metric should help to identify expensive and heavy queries
without inspecting the logs.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
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
reduce lock contention for heavy aggregation requests
previously lock contetion may happen on machine with big number of CPU due to enabled string interning. sync.Map was a choke point for all aggregation requests.
Now instead of interning, new string is created. It may increase CPU and memory usage for some cases.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5087
`median_over_time` is handled by predefined WITH template in MetricsQL library which translates it to `quantile_over_time(0.5)`
This makes it impossble to use `median_over_time` as a usual rollup function for `aggr_over_time`.
See: https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5034
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* feat: add page to display a list of active queries (#4598)
* app/vmagent: code formatting
* fix: remove console
---------
Co-authored-by: dmitryk-dk <kozlovdmitriyy@gmail.com>
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
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Callers of this function log the returned error and exit.
So let's just log the error with the given filepath and the call stack
inside the function itself and then exit. This simplifies the code
at callers' place while leaves the same level of debuggability in case of errors.
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.
- Expose stats.seriesFetched at `/api/v1/query_range` responses too
for the sake of consistency.
- Initialize QueryStats when it is needed and pass it to EvalConfig then.
This guarantees that the QueryStats is properly collected when the query
contains some subqueries.
The change adds a new field `seriesFetched` to EvalConfig object.
Since EvalConfig object can be copied inside `Exec`,
`seriesFetched` is a pointer which can be updated by all copied
objects.
The reason for having stats is that other components, like vmalert,
could benefit from this information.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
