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
This should improve maintainability of the code related to rollup functions,
since it is located in rollup.go
While at it, properly return empty results from holt_winters(), rate_over_sum(),
sum2_over_time(), geomean_over_time() and distinct_over_time() when there are no real samples
on the selected lookbehind window. Previously the previous sample value was mistakenly
returned from these functions.
* app/vmselect: set proper timestamp for cached instant responses
The change updates `getSumInstantValues` to prefer timestamp
from the most recent results. Before, timestamp from cached series
was used.
The old behavior had negative impact on recording rules as they
were getting responses with shifted timestamps in past.
Subsequent recording or alerting rules fetching results of these
recording rules could get no result due to staleness interval.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5659
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* wip
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
app/vmselect/promql/eval.go:evalAggrFunc shunts evaluation
of AggrFuncExpr over rollupFunc over MetricsExpr to an optimized
path. tryGetArgRollupFuncWithMetricExpr() checks whether expression
can be shunted, but it mangles the AggrFuncExpr when the aggregation
function has more than one argument. This results in queries like
`sum(aggr_over_time("avg_over_time",m))` failing with error message
'expecting at least 2 args to "aggr_over_time"; got 1 args' while
the analogous query `sum(avg_over_time(m))` executes successfully.
This fix removes the unnecessary mangling.
Signed-off-by: Anton Tykhyy <atykhyy@gmail.com>
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
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>
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
- 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>
- Allocate and initialize seriesByWorkerID slice in a single go instead
of initializing every item in the list separately.
This should reduce CPU usage a bit.
- Properly set anti-false sharing padding at timeseriesWithPadding structure
- Document the change at docs/CHANGELOG.md
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
* vmselect/promql: refactor `evalRollupNoIncrementalAggregate` to use lock-less approach for parallel workers computation
Locking there is causing issues when running on highly multi-core system as it introduces lock contention during results merge.
New implementation uses lock less approach to store results per workerID and merges final result in the end, this is expected to significantly reduce lock contention and CPU usage for systems with high number of cores.
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* vmselect/promql: add pooling for `timeseriesWithPadding` to reduce allocations
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* vmselect/promql: refactor `evalRollupFuncWithSubquery` to avoid using locks
Uses same approach as `evalRollupNoIncrementalAggregate` to remove locking between workers and reduce lock contention.
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
This opens the possibility to remove tssLock from evalRollupFuncWithSubquery()
in the follow-up commit from @zekker6 in order to speed up the code
for systems with many CPU cores.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
Note that the parallel execution of `union()` args may take more memory and CPU time
than the sequential execution if args contain heavy queries, which may load all the available CPU,
disk and memory resources and vmselect and vmstorage levels.
- 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
