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>
This reverts commit 252643d100.
Reason for revert: the commit incorrectly fixes the the issue.
The `remoteAddr` must be properly quoted inside lib/httpserver.GetQuotedRemoteAddr().
It isn't quoted properly if the request contains X-Forwarded-For header.
The proper fix will be included in the follow-up commit.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/4676
This eliminates the need in .(*T) casting for results obtained from Load()
Leave atomic.Value for map, since atomic.Pointer[map[...]...] makes double pointer to map,
because map is already a pointer type.
- Add `Active queries` chapter to VMUI docs
- Set `Content-Type: json` header inside promql.WriteActiveQueries() handler,
in order to be consistent with other request handlers called at app/vmselect/main.go
- Pass the request to promql.WriteActiveQueries() handler, so it can change its output
depending on the provided request params. This also improves consistency of
promql.WriteActiveQueries() args with other request hanlers at app/vmselect/main.go
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/4653
* 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.
