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app/vmselect: add -search.estimatedSeriesCountAfterAggregation
command-line flag for tuning the probability of OOMs or false-positive not enough memory
errors
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1 changed files with 7 additions and 5 deletions
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@ -18,7 +18,10 @@ import (
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)
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)
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var (
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var (
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maxPointsPerTimeseries = flag.Int("search.maxPointsPerTimeseries", 30e3, "The maximum points per a single timeseries returned from the search")
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maxPointsPerTimeseries = flag.Int("search.maxPointsPerTimeseries", 30e3, "The maximum points per a single timeseries returned from the search")
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estimatedSeriesCountAfterAggregation = flag.Int("search.estimatedSeriesCountAfterAggregation", 1000, "Estimated number of series returned by aggregation with grouping "+
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"such as `sum(...) by (...)`. Increase this value in order to reduce the probability of OOMs. Reduce this value in order to reduce 'not enough memory' errors "+
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"for queries containing aggregation with grouping")
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)
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)
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// The minimum number of points per timeseries for enabling time rounding.
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// The minimum number of points per timeseries for enabling time rounding.
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@ -679,8 +682,7 @@ func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc,
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if iafc.ae.Modifier.Op != "" {
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if iafc.ae.Modifier.Op != "" {
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// Increase the number of timeseries for non-empty group list: `aggr() by (something)`,
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// Increase the number of timeseries for non-empty group list: `aggr() by (something)`,
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// since each group can have own set of time series in memory.
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// since each group can have own set of time series in memory.
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// Estimate the number of such groups is lower than 1000 :)
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timeseriesLen *= *estimatedSeriesCountAfterAggregation
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timeseriesLen *= 1000
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}
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}
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}
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}
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rollupPoints := mulNoOverflow(pointsPerTimeseries, int64(timeseriesLen*len(rcs)))
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rollupPoints := mulNoOverflow(pointsPerTimeseries, int64(timeseriesLen*len(rcs)))
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@ -690,8 +692,8 @@ func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc,
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rss.Cancel()
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rss.Cancel()
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return nil, fmt.Errorf("not enough memory for processing %d data points across %d time series with %d points in each time series; "+
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return nil, fmt.Errorf("not enough memory for processing %d data points across %d time series with %d points in each time series; "+
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"possible solutions are: reducing the number of matching time series; switching to node with more RAM; "+
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"possible solutions are: reducing the number of matching time series; switching to node with more RAM; "+
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"increasing -memory.allowedPercent; increasing `step` query arg (%gs)",
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"increasing -memory.allowedPercent; increasing `step` query arg (%gs); reducing -search.estimatedSeriesCountAfterAggregation",
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rollupPoints, rssLen*len(rcs), pointsPerTimeseries, float64(ec.Step)/1e3)
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rollupPoints, timeseriesLen*len(rcs), pointsPerTimeseries, float64(ec.Step)/1e3)
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}
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}
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defer rml.Put(uint64(rollupMemorySize))
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defer rml.Put(uint64(rollupMemorySize))
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