diff --git a/docs/stream-aggregation.md b/docs/stream-aggregation.md
index b791aaeb2..a61c9a202 100644
--- a/docs/stream-aggregation.md
+++ b/docs/stream-aggregation.md
@@ -562,14 +562,14 @@ Below are aggregation functions that can be put in the `outputs` list at [stream
* [avg](#avg)
* [count_samples](#count_samples)
* [count_series](#count_series)
+* [histogram_bucket](#histogram_bucket)
* [increase](#increase)
* [increase_prometheus](#increase_prometheus)
-* [rate_sum](#rate_sum)
-* [rate_avg](#rate_avg)
-* [histogram_bucket](#histogram_bucket)
* [last](#last)
* [max](#max)
* [min](#min)
+* [rate_avg](#rate_avg)
+* [rate_sum](#rate_sum)
* [stddev](#stddev)
* [stdvar](#stdvar)
* [sum_samples](#sum_samples)
@@ -593,7 +593,13 @@ For example, see below time series produced by config with aggregation interval
-See also [min](#min), [max](#max), [sum_samples](#sum_samples) and [count_samples](#count_samples).
+See also:
+
+- [max](#max)
+- [min](#min)
+- [quantiles](#quantiles)
+- [sum_samples](#sum_samples)
+- [count_samples](#count_samples)
### count_samples
@@ -605,7 +611,10 @@ The results of `count_samples` is equal to the following [MetricsQL](https://doc
sum(count_over_time(some_metric[interval]))
```
-See also [count_series](#count_series) and [sum_samples](#sum_samples).
+See also:
+
+- [count_series](#count_series)
+- [sum_samples](#sum_samples)
### count_series
@@ -617,7 +626,33 @@ The results of `count_series` is equal to the following [MetricsQL](https://docs
count(last_over_time(some_metric[interval]))
```
-See also [count_samples](#count_samples) and [unique_samples](#unique_samples).
+See also:
+
+- [count_samples](#count_samples)
+- [unique_samples](#unique_samples)
+
+### histogram_bucket
+
+`histogram_bucket` returns [VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
+ for the input [sample values](https://docs.victoriametrics.com/keyconcepts/#raw-samples) over the given `interval`.
+`histogram_bucket` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
+See how to aggregate regular histograms [here](#aggregating-histograms).
+
+The results of `histogram_bucket` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
+
+Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
+or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
+
+```metricsql
+sum(histogram_over_time(some_histogram_bucket[interval])) by (vmrange)
+```
+
+See also:
+
+- [quantiles](#quantiles)
+- [avg](#avg)
+- [max](#max)
+- [min](#min)
### increase
@@ -641,33 +676,12 @@ For example, see below time series produced by config with aggregation interval
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
-See also [increase_prometheus](#increase_prometheus) and [total](#total).
+See also:
-### rate_sum
-
-`rate_sum` returns the sum of average per-second change of input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
-`rate_sum` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
-
-The results of `rate_sum` are equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
-
-```metricsql
-sum(rate(some_counter[interval]))
-```
-
-See also [rate_avg](#rate_avg) and [total](#total) outputs.
-
-### rate_avg
-
-`rate_avg` returns the average of average per-second of input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
-`rate_avg` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
-
-The results of `rate_avg` are equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
-
-```metricsql
-avg(rate(some_counter[interval]))
-```
-
-See also [rate_sum](#rate_avg) and [total](#total) outputs.
+- [increase_prometheus](#increase_prometheus)
+- [total](#total)
+- [rate_avg](#rate_avg)
+- [rate_sum](#rate_sum)
### increase_prometheus
@@ -686,25 +700,13 @@ If you need taking into account the first sample per time series, then take a lo
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
-See also [increase](#increase), [total](#total) and [total_prometheus](#total_prometheus).
+See also:
-### histogram_bucket
-
-`histogram_bucket` returns [VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
- for the input [sample values](https://docs.victoriametrics.com/keyconcepts/#raw-samples) over the given `interval`.
-`histogram_bucket` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
-See how to aggregate regular histograms [here](#aggregating-histograms).
-
-The results of `histogram_bucket` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
-
-Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
-or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
-
-```metricsql
-sum(histogram_over_time(some_histogram_bucket[interval])) by (vmrange)
-```
-
-See also [quantiles](#quantiles), [min](#min), [max](#max) and [avg](#avg).
+- [increase](#increase)
+- [rate_avg](#rate_avg)
+- [rate_sum](#rate_sum)
+- [total](#total)
+- [total_prometheus](#total_prometheus)
### last
@@ -716,7 +718,12 @@ The results of `last` is roughly equal to the following [MetricsQL](https://docs
last_over_time(some_metric[interval])
```
-See also [min](#min), [max](#max) and [avg](#avg).
+See also:
+
+- [avg](#avg)
+- [max](#max)
+- [min](#min)
+- [quantiles](#quantiles)
### max
@@ -732,7 +739,12 @@ For example, see below time series produced by config with aggregation interval
-See also [min](#min) and [avg](#avg).
+See also:
+
+- [min](#min)
+- [avg](#avg)
+- [last](#last)
+- [quantiles](#quantiles)
### min
@@ -748,7 +760,46 @@ For example, see below time series produced by config with aggregation interval
-See also [max](#max) and [avg](#avg).
+See also:
+
+- [max](#max)
+- [avg](#avg)
+- [last](#last)
+- [quantiles](#quantiles)
+
+### rate_avg
+
+`rate_avg` returns the average of average per-second increase rates across input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
+`rate_avg` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
+
+The results of `rate_avg` are equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
+
+```metricsql
+avg(rate(some_counter[interval]))
+```
+
+See also:
+
+- [rate_sum](#rate_sum)
+- [increase](#increase)
+- [total](#total)
+
+### rate_sum
+
+`rate_sum` returns the sum of average per-second increase rates across input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
+`rate_sum` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
+
+The results of `rate_sum` are equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
+
+```metricsql
+sum(rate(some_counter[interval]))
+```
+
+See also:
+
+- [rate_avg](#rate_avg)
+- [increase](#increase)
+- [total](#total)
### stddev
@@ -762,7 +813,11 @@ The results of `stddev` is roughly equal to the following [MetricsQL](https://do
histogram_stddev(sum(histogram_over_time(some_metric[interval])) by (vmrange))
```
-See also [stdvar](#stdvar) and [avg](#avg).
+See also:
+
+- [stdvar](#stdvar)
+- [avg](#avg)
+- [quantiles](#quantiles)
### stdvar
@@ -780,7 +835,11 @@ For example, see below time series produced by config with aggregation interval
-See also [stddev](#stddev) and [avg](#avg).
+See also:
+
+- [stddev](#stddev)
+- [avg](#avg)
+- [quantiles](#quantiles)
### sum_samples
@@ -797,7 +856,10 @@ For example, see below time series produced by config with aggregation interval
-See also [count_samples](#count_samples) and [count_series](#count_series).
+See also:
+
+- [count_samples](#count_samples)
+- [count_series](#count_series)
### total
@@ -834,7 +896,13 @@ This changes pod name label, but the `total` accounts for such a scenario and do
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
-See also [total_prometheus](#total_prometheus), [increase](#increase) and [increase_prometheus](#increase_prometheus).
+See also:
+
+- [total_prometheus](#total_prometheus)
+- [increase](#increase)
+- [increase_prometheus](#increase_prometheus)
+- [rate_sum](#rate_sum)
+- [rate_avg](#rate_avg)
### total_prometheus
@@ -857,7 +925,13 @@ The counters are most often reset when the application is restarted.
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
-See also [total](#total), [increase](#increase) and [increase_prometheus](#increase_prometheus).
+See also:
+
+- [total](#total)
+- [increase](#increase)
+- [increase_prometheus](#increase_prometheus)
+- [rate_sum](#rate_sum)
+- [rate_avg](#rate_avg)
### unique_samples
@@ -870,7 +944,10 @@ The results of `unique_samples` is equal to the following [MetricsQL](https://do
count(count_values_over_time(some_metric[interval]))
```
-See also [sum_samples](#sum_samples) and [count_series](#count_series).
+See also:
+
+- [sum_samples](#sum_samples)
+- [count_series](#count_series)
### quantiles
@@ -885,7 +962,12 @@ The results of `quantiles(phi1, ..., phiN)` is equal to the following [MetricsQL
histogram_quantiles("quantile", phi1, ..., phiN, sum(histogram_over_time(some_metric[interval])) by (vmrange))
```
-See also [histogram_bucket](#histogram_bucket), [min](#min), [max](#max) and [avg](#avg).
+See also:
+
+- [histogram_bucket](#histogram_bucket)
+- [avg](#avg)
+- [max](#max)
+- [min](#min)
## Aggregating by labels
@@ -962,11 +1044,13 @@ specified individually per each `-remoteWrite.url`:
# staleness_interval is an optional interval for resetting the per-series state if no new samples
# are received during this interval for the following outputs:
- # - total
- # - total_prometheus
+ # - histogram_bucket
# - increase
# - increase_prometheus
- # - histogram_bucket
+ # - rate_avg
+ # - rate_sum
+ # - total
+ # - total_prometheus
# See https://docs.victoriametrics.com/stream-aggregation/#staleness for more details.
#
# staleness_interval: 2m
@@ -1071,13 +1155,13 @@ support the following approaches for hot reloading stream aggregation configs fr
The following outputs track the last seen per-series values in order to properly calculate output values:
-- [rate_sum](#rate_sum)
-- [rate_avg](#rate_avg)
-- [total](#total)
-- [total_prometheus](#total_prometheus)
+- [histogram_bucket](#histogram_bucket)
- [increase](#increase)
- [increase_prometheus](#increase_prometheus)
-- [histogram_bucket](#histogram_bucket)
+- [rate_avg](#rate_avg)
+- [rate_sum](#rate_sum)
+- [total](#total)
+- [total_prometheus](#total_prometheus)
The last seen per-series value is dropped if no new samples are received for the given time series during two consecutive aggregation
intervals specified in [stream aggregation config](#stream-aggregation-config) via `interval` option.
diff --git a/lib/streamaggr/rate.go b/lib/streamaggr/rate.go
index 6fe401d37..24101e4fe 100644
--- a/lib/streamaggr/rate.go
+++ b/lib/streamaggr/rate.go
@@ -8,11 +8,12 @@ import (
"github.com/VictoriaMetrics/VictoriaMetrics/lib/fasttime"
)
-// rateAggrState calculates output=rate, e.g. the counter per-second change.
+// rateAggrState calculates output=rate_avg and rate_sum, e.g. the average per-second increase rate for counter metrics.
type rateAggrState struct {
m sync.Map
- suffix string
+ // isAvg is set to true if rate_avg() must be calculated instead of rate_sum().
+ isAvg bool
// Time series state is dropped if no new samples are received during stalenessSecs.
stalenessSecs uint64
@@ -30,18 +31,17 @@ type rateLastValueState struct {
timestamp int64
deleteDeadline uint64
- // total stores cumulative difference between registered values
- // in the aggregation interval
- total float64
- // prevTimestamp stores timestamp of the last registered value
- // in the previous aggregation interval
+ // increase stores cumulative increase for the current time series on the current aggregation interval
+ increase float64
+
+ // prevTimestamp is the timestamp of the last registered sample in the previous aggregation interval
prevTimestamp int64
}
-func newRateAggrState(stalenessInterval time.Duration, suffix string) *rateAggrState {
+func newRateAggrState(stalenessInterval time.Duration, isAvg bool) *rateAggrState {
stalenessSecs := roundDurationToSecs(stalenessInterval)
return &rateAggrState{
- suffix: suffix,
+ isAvg: isAvg,
stalenessSecs: stalenessSecs,
}
}
@@ -78,15 +78,15 @@ func (as *rateAggrState) pushSamples(samples []pushSample) {
sv.mu.Unlock()
continue
}
- if lv.prevTimestamp == 0 {
- lv.prevTimestamp = lv.timestamp
- }
+
if s.value >= lv.value {
- lv.total += s.value - lv.value
+ lv.increase += s.value - lv.value
} else {
// counter reset
- lv.total += s.value
+ lv.increase += s.value
}
+ } else {
+ lv.prevTimestamp = s.timestamp
}
lv.value = s.value
lv.timestamp = s.timestamp
@@ -108,54 +108,77 @@ func (as *rateAggrState) pushSamples(samples []pushSample) {
func (as *rateAggrState) flushState(ctx *flushCtx, _ bool) {
currentTime := fasttime.UnixTimestamp()
currentTimeMsec := int64(currentTime) * 1000
- var staleOutputSamples, staleInputSamples int
+
+ suffix := "rate_sum"
+ if as.isAvg {
+ suffix = "rate_avg"
+ }
+
+ as.removeOldEntries(ctx, suffix, currentTime)
m := &as.m
m.Range(func(k, v any) bool {
sv := v.(*rateStateValue)
- sv.mu.Lock()
- // check for stale entries
- deleted := currentTime > sv.deleteDeadline
- if deleted {
+ sv.mu.Lock()
+ lvs := sv.lastValues
+ sumRate := 0.0
+ countSeries := 0
+ for k1, lv := range lvs {
+ d := float64(lv.timestamp-lv.prevTimestamp) / 1000
+ if d > 0 {
+ sumRate += lv.increase / d
+ countSeries++
+ }
+ lv.prevTimestamp = lv.timestamp
+ lv.increase = 0
+ lvs[k1] = lv
+ }
+ sv.mu.Unlock()
+
+ if countSeries == 0 {
+ // Nothing to update
+ return true
+ }
+
+ result := sumRate
+ if as.isAvg {
+ result /= float64(countSeries)
+ }
+
+ key := k.(string)
+ ctx.appendSeries(key, suffix, currentTimeMsec, result)
+ return true
+ })
+}
+
+func (as *rateAggrState) removeOldEntries(ctx *flushCtx, suffix string, currentTime uint64) {
+ m := &as.m
+ var staleOutputSamples, staleInputSamples int
+ m.Range(func(k, v any) bool {
+ sv := v.(*rateStateValue)
+
+ sv.mu.Lock()
+ if currentTime > sv.deleteDeadline {
// Mark the current entry as deleted
- sv.deleted = deleted
- sv.mu.Unlock()
+ sv.deleted = true
staleOutputSamples++
+ sv.mu.Unlock()
m.Delete(k)
return true
}
// Delete outdated entries in sv.lastValues
- var rate float64
lvs := sv.lastValues
- for k1, v1 := range lvs {
- if currentTime > v1.deleteDeadline {
+ for k1, lv := range lvs {
+ if currentTime > lv.deleteDeadline {
delete(lvs, k1)
staleInputSamples++
- continue
- }
- rateInterval := v1.timestamp - v1.prevTimestamp
- if v1.prevTimestamp > 0 && rateInterval > 0 {
- // calculate rate only if value was seen at least twice with different timestamps
- rate += v1.total * 1000 / float64(rateInterval)
- v1.prevTimestamp = v1.timestamp
- v1.total = 0
- lvs[k1] = v1
}
}
- // capture m length after deleted items were removed
- totalItems := len(lvs)
sv.mu.Unlock()
-
- if as.suffix == "rate_avg" && totalItems > 0 {
- rate /= float64(totalItems)
- }
-
- key := k.(string)
- ctx.appendSeries(key, as.suffix, currentTimeMsec, rate)
return true
})
- ctx.a.staleOutputSamples[as.suffix].Add(staleOutputSamples)
- ctx.a.staleInputSamples[as.suffix].Add(staleInputSamples)
+ ctx.a.staleInputSamples[suffix].Add(staleInputSamples)
+ ctx.a.staleOutputSamples[suffix].Add(staleOutputSamples)
}
diff --git a/lib/streamaggr/streamaggr.go b/lib/streamaggr/streamaggr.go
index 7691b9b4a..efdcb5baf 100644
--- a/lib/streamaggr/streamaggr.go
+++ b/lib/streamaggr/streamaggr.go
@@ -27,24 +27,24 @@ import (
)
var supportedOutputs = []string{
- "rate_sum",
- "rate_avg",
- "total",
- "total_prometheus",
+ "avg",
+ "count_samples",
+ "count_series",
+ "histogram_bucket",
"increase",
"increase_prometheus",
- "count_series",
- "count_samples",
- "unique_samples",
- "sum_samples",
"last",
- "min",
"max",
- "avg",
+ "min",
+ "quantiles(phi1, ..., phiN)",
+ "rate_avg",
+ "rate_sum",
"stddev",
"stdvar",
- "histogram_bucket",
- "quantiles(phi1, ..., phiN)",
+ "sum_samples",
+ "total",
+ "total_prometheus",
+ "unique_samples",
}
// maxLabelValueLen is maximum match expression label value length in stream aggregation metrics
@@ -175,24 +175,24 @@ type Config struct {
//
// The following names are allowed:
//
- // - rate_sum - calculates sum of rate for input counters
- // - rate_avg - calculates average of rate for input counters
- // - total - aggregates input counters
- // - total_prometheus - aggregates input counters, ignoring the first sample in new time series
+ // - avg - the average value across all the samples
+ // - count_samples - counts the input samples
+ // - count_series - counts the number of unique input series
+ // - histogram_bucket - creates VictoriaMetrics histogram for input samples
// - increase - calculates the increase over input series
// - increase_prometheus - calculates the increase over input series, ignoring the first sample in new time series
- // - count_series - counts the number of unique input series
- // - count_samples - counts the input samples
- // - unique_samples - counts the number of unique sample values
- // - sum_samples - sums the input sample values
// - last - the last biggest sample value
- // - min - the minimum sample value
// - max - the maximum sample value
- // - avg - the average value across all the samples
+ // - min - the minimum sample value
+ // - quantiles(phi1, ..., phiN) - quantiles' estimation for phi in the range [0..1]
+ // - rate_avg - calculates average of rate for input counters
+ // - rate_sum - calculates sum of rate for input counters
// - stddev - standard deviation across all the samples
// - stdvar - standard variance across all the samples
- // - histogram_bucket - creates VictoriaMetrics histogram for input samples
- // - quantiles(phi1, ..., phiN) - quantiles' estimation for phi in the range [0..1]
+ // - sum_samples - sums the input sample values
+ // - total - aggregates input counters
+ // - total_prometheus - aggregates input counters, ignoring the first sample in new time series
+ // - unique_samples - counts the number of unique sample values
//
// The output time series will have the following names by default:
//
@@ -562,40 +562,40 @@ func newAggregator(cfg *Config, pushFunc PushFunc, ms *metrics.Set, opts Options
continue
}
switch output {
- case "total":
- aggrStates[output] = newTotalAggrState(stalenessInterval, false, true)
- case "total_prometheus":
- aggrStates[output] = newTotalAggrState(stalenessInterval, false, false)
+ case "avg":
+ aggrStates[output] = newAvgAggrState()
+ case "count_samples":
+ aggrStates[output] = newCountSamplesAggrState()
+ case "count_series":
+ aggrStates[output] = newCountSeriesAggrState()
+ case "histogram_bucket":
+ aggrStates[output] = newHistogramBucketAggrState(stalenessInterval)
case "increase":
aggrStates[output] = newTotalAggrState(stalenessInterval, true, true)
case "increase_prometheus":
aggrStates[output] = newTotalAggrState(stalenessInterval, true, false)
- case "rate_sum":
- aggrStates[output] = newRateAggrState(stalenessInterval, "rate_sum")
- case "rate_avg":
- aggrStates[output] = newRateAggrState(stalenessInterval, "rate_avg")
- case "count_series":
- aggrStates[output] = newCountSeriesAggrState()
- case "count_samples":
- aggrStates[output] = newCountSamplesAggrState()
- case "unique_samples":
- aggrStates[output] = newUniqueSamplesAggrState()
- case "sum_samples":
- aggrStates[output] = newSumSamplesAggrState()
case "last":
aggrStates[output] = newLastAggrState()
- case "min":
- aggrStates[output] = newMinAggrState()
case "max":
aggrStates[output] = newMaxAggrState()
- case "avg":
- aggrStates[output] = newAvgAggrState()
+ case "min":
+ aggrStates[output] = newMinAggrState()
+ case "rate_avg":
+ aggrStates[output] = newRateAggrState(stalenessInterval, true)
+ case "rate_sum":
+ aggrStates[output] = newRateAggrState(stalenessInterval, false)
case "stddev":
aggrStates[output] = newStddevAggrState()
case "stdvar":
aggrStates[output] = newStdvarAggrState()
- case "histogram_bucket":
- aggrStates[output] = newHistogramBucketAggrState(stalenessInterval)
+ case "sum_samples":
+ aggrStates[output] = newSumSamplesAggrState()
+ case "total":
+ aggrStates[output] = newTotalAggrState(stalenessInterval, false, true)
+ case "total_prometheus":
+ aggrStates[output] = newTotalAggrState(stalenessInterval, false, false)
+ case "unique_samples":
+ aggrStates[output] = newUniqueSamplesAggrState()
default:
return nil, fmt.Errorf("unsupported output=%q; supported values: %s;", output, supportedOutputs)
}
diff --git a/lib/streamaggr/streamaggr_test.go b/lib/streamaggr/streamaggr_test.go
index 28170cb96..868b7bdbb 100644
--- a/lib/streamaggr/streamaggr_test.go
+++ b/lib/streamaggr/streamaggr_test.go
@@ -891,21 +891,28 @@ foo{abc="123", cde="1"} 4
foo{abc="123", cde="1"} 8.5 10
foo{abc="456", cde="1"} 8
foo{abc="456", cde="1"} 10 10
+foo 12 34
`, `foo:1m_by_cde_rate_avg{cde="1"} 0.325
foo:1m_by_cde_rate_sum{cde="1"} 0.65
-`, "1111")
+`, "11111")
- // rate with duplicated events
+ // rate_sum and rate_avg with duplicated events
f(`
- interval: 1m
- by: [cde]
outputs: [rate_sum, rate_avg]
`, `
foo{abc="123", cde="1"} 4 10
foo{abc="123", cde="1"} 4 10
-`, `foo:1m_by_cde_rate_avg{cde="1"} 0
-foo:1m_by_cde_rate_sum{cde="1"} 0
-`, "11")
+`, ``, "11")
+
+ // rate_sum and rate_avg for a single sample
+ f(`
+- interval: 1m
+ outputs: [rate_sum, rate_avg]
+`, `
+foo 4 10
+bar 5 10
+`, ``, "11")
// unique_samples output
f(`
diff --git a/lib/streamaggr/total.go b/lib/streamaggr/total.go
index e0c26a0fe..c0df106f5 100644
--- a/lib/streamaggr/total.go
+++ b/lib/streamaggr/total.go
@@ -9,7 +9,7 @@ import (
"github.com/VictoriaMetrics/VictoriaMetrics/lib/fasttime"
)
-// totalAggrState calculates output=total, e.g. the summary counter over input counters.
+// totalAggrState calculates output=total, total_prometheus, increase and increase_prometheus.
type totalAggrState struct {
m sync.Map
@@ -124,39 +124,6 @@ func (as *totalAggrState) pushSamples(samples []pushSample) {
}
}
-func (as *totalAggrState) removeOldEntries(ctx *flushCtx, currentTime uint64) {
- m := &as.m
- var staleInputSamples, staleOutputSamples int
- m.Range(func(k, v any) bool {
- sv := v.(*totalStateValue)
-
- sv.mu.Lock()
- deleted := currentTime > sv.deleteDeadline
- if deleted {
- // Mark the current entry as deleted
- sv.deleted = deleted
- staleOutputSamples++
- } else {
- // Delete outdated entries in sv.lastValues
- m := sv.lastValues
- for k1, v1 := range m {
- if currentTime > v1.deleteDeadline {
- delete(m, k1)
- staleInputSamples++
- }
- }
- }
- sv.mu.Unlock()
-
- if deleted {
- m.Delete(k)
- }
- return true
- })
- ctx.a.staleInputSamples[as.suffix].Add(staleInputSamples)
- ctx.a.staleOutputSamples[as.suffix].Add(staleOutputSamples)
-}
-
func (as *totalAggrState) flushState(ctx *flushCtx, resetState bool) {
currentTime := fasttime.UnixTimestamp()
currentTimeMsec := int64(currentTime) * 1000
@@ -185,3 +152,34 @@ func (as *totalAggrState) flushState(ctx *flushCtx, resetState bool) {
return true
})
}
+
+func (as *totalAggrState) removeOldEntries(ctx *flushCtx, currentTime uint64) {
+ m := &as.m
+ var staleInputSamples, staleOutputSamples int
+ m.Range(func(k, v any) bool {
+ sv := v.(*totalStateValue)
+
+ sv.mu.Lock()
+ if currentTime > sv.deleteDeadline {
+ // Mark the current entry as deleted
+ sv.deleted = true
+ staleOutputSamples++
+ sv.mu.Unlock()
+ m.Delete(k)
+ return true
+ }
+
+ // Delete outdated entries in sv.lastValues
+ lvs := sv.lastValues
+ for k1, lv := range lvs {
+ if currentTime > lv.deleteDeadline {
+ delete(lvs, k1)
+ staleInputSamples++
+ }
+ }
+ sv.mu.Unlock()
+ return true
+ })
+ ctx.a.staleInputSamples[as.suffix].Add(staleInputSamples)
+ ctx.a.staleOutputSamples[as.suffix].Add(staleOutputSamples)
+}