From cd1145e5f469dc756e4a7d222935d0b0a875e6c7 Mon Sep 17 00:00:00 2001 From: Aliaksandr Valialkin Date: Tue, 28 Apr 2020 12:51:36 +0300 Subject: [PATCH] app/vmselect: add `-search.estimatedSeriesCountAfterAggregation` command-line flag for tuning the probability of OOMs or false-positive `not enough memory` errors --- app/vmselect/promql/eval.go | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/app/vmselect/promql/eval.go b/app/vmselect/promql/eval.go index 7b9df7f7c..a1e33a7fd 100644 --- a/app/vmselect/promql/eval.go +++ b/app/vmselect/promql/eval.go @@ -18,7 +18,10 @@ import ( ) var ( - maxPointsPerTimeseries = flag.Int("search.maxPointsPerTimeseries", 30e3, "The maximum points per a single timeseries returned from the search") + maxPointsPerTimeseries = flag.Int("search.maxPointsPerTimeseries", 30e3, "The maximum points per a single timeseries returned from the search") + estimatedSeriesCountAfterAggregation = flag.Int("search.estimatedSeriesCountAfterAggregation", 1000, "Estimated number of series returned by aggregation with grouping "+ + "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 "+ + "for queries containing aggregation with grouping") ) // The minimum number of points per timeseries for enabling time rounding. @@ -679,8 +682,7 @@ func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc, if iafc.ae.Modifier.Op != "" { // Increase the number of timeseries for non-empty group list: `aggr() by (something)`, // since each group can have own set of time series in memory. - // Estimate the number of such groups is lower than 1000 :) - timeseriesLen *= 1000 + timeseriesLen *= *estimatedSeriesCountAfterAggregation } } rollupPoints := mulNoOverflow(pointsPerTimeseries, int64(timeseriesLen*len(rcs))) @@ -690,8 +692,8 @@ func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc, rss.Cancel() return nil, fmt.Errorf("not enough memory for processing %d data points across %d time series with %d points in each time series; "+ "possible solutions are: reducing the number of matching time series; switching to node with more RAM; "+ - "increasing -memory.allowedPercent; increasing `step` query arg (%gs)", - rollupPoints, rssLen*len(rcs), pointsPerTimeseries, float64(ec.Step)/1e3) + "increasing -memory.allowedPercent; increasing `step` query arg (%gs); reducing -search.estimatedSeriesCountAfterAggregation", + rollupPoints, timeseriesLen*len(rcs), pointsPerTimeseries, float64(ec.Step)/1e3) } defer rml.Put(uint64(rollupMemorySize))