VictoriaMetrics/docs/stream-aggregation.md

---
sort: 98
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title: Streaming aggregation
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  docs:
    parent: 'victoriametrics'
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---

# Streaming aggregation

[vmagent](https://docs.victoriametrics.com/vmagent.html) and [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html)
can aggregate incoming [samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) in streaming mode by time and by labels before data is written to remote storage
(or local storage for single-node VictoriaMetrics).
The aggregation is applied to all the metrics received via any [supported data ingestion protocol](https://docs.victoriametrics.com/#how-to-import-time-series-data)
and/or scraped from [Prometheus-compatible targets](https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter)
after applying all the configured [relabeling stages](https://docs.victoriametrics.com/vmagent.html#relabeling).

Stream aggregation ignores timestamps associated with the input [samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
It expects that the ingested samples have timestamps close to the current time.

Stream aggregation is configured via the following command-line flags:

- `-remoteWrite.streamAggr.config` at [vmagent](https://docs.victoriametrics.com/vmagent.html).
  This flag can be specified individually per each `-remoteWrite.url`.
  This allows writing different aggregates to different remote storage destinations.
- `-streamAggr.config` at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).

These flags must point to a file containing [stream aggregation config](#stream-aggregation-config).
The file may contain `%{ENV_VAR}` placeholders which are substituted by the corresponding `ENV_VAR` environment variable values.

By default, the following data is written to the storage when stream aggregation is enabled:

- the aggregated samples;
- the raw input samples, which didn't match any `match` option in the provided [config](#stream-aggregation-config).

This behaviour can be changed via the following command-line flags:

- `-remoteWrite.streamAggr.keepInput` at [vmagent](https://docs.victoriametrics.com/vmagent.html) and `-streamAggr.keepInput`
  at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
  If one of these flags is set, then all the input samples are written to the storage alongside the aggregated samples.
  The `-remoteWrite.streamAggr.keepInput` flag can be specified individually per each `-remoteWrite.url`.
- `-remoteWrite.streamAggr.dropInput` at [vmagent](https://docs.victoriametrics.com/vmagent.html) and `-streamAggr.dropInput`
  at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
  If one of these flags are set, then all the input samples are dropped, while only the aggregated samples are written to the storage.
  The `-remoteWrite.streamAggr.dropInput` flag can be specified individually per each `-remoteWrite.url`.

## Deduplication

By default, all the input samples are aggregated. Sometimes it is needed to de-duplicate samples for the same [time series](https://docs.victoriametrics.com/keyconcepts/#time-series)
before the aggregation. For example, if the samples are received from replicated sources.
The [de-duplication](https://docs.victoriametrics.com/#deduplication) can be enabled via the following options:

- `-remoteWrite.streamAggr.dedupInterval` command-line flag at [vmagent](https://docs.victoriametrics.com/vmagent.html).
  This flag can be specified individually per each `-remoteWrite.url`.
  This allows setting different de-duplication intervals per each configured remote storage.
- `-streamAggr.dedupInterval` command-line flag at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
- `dedup_interval` option per each [aggregate config](#stream-aggregation-config).

De-duplicatation is performed after performing the input relabeling with `input_relabel_configs` - see [these docs](#relabeling).

## Use cases

Stream aggregation can be used in the following cases:

* [Statsd alternative](#statsd-alternative)
* [Recording rules alternative](#recording-rules-alternative)
* [Reducing the number of stored samples](#reducing-the-number-of-stored-samples)
* [Reducing the number of stored series](#reducing-the-number-of-stored-series)

### Statsd alternative

Stream aggregation can be used as [statsd](https://github.com/statsd/statsd) alternative in the following cases:

* [Counting input samples](#counting-input-samples)
* [Summing input metrics](#summing-input-metrics)
* [Quantiles over input metrics](#quantiles-over-input-metrics)
* [Histograms over input metrics](#histograms-over-input-metrics)
* [Aggregating histograms](#aggregating-histograms)

Currently, streaming aggregation is available only for [supported data ingestion protocols](https://docs.victoriametrics.com/#how-to-import-time-series-data)
and not available for [Statsd metrics format](https://github.com/statsd/statsd/blob/master/docs/metric_types.md).

### Recording rules alternative

Sometimes [alerting queries](https://docs.victoriametrics.com/vmalert.html#alerting-rules) may require non-trivial amounts of CPU, RAM,
disk IO and network bandwidth at metrics storage side. For example, if `http_request_duration_seconds` histogram is generated by thousands
of application instances, then the alerting query `histogram_quantile(0.99, sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)) > 0.5`
can become slow, since it needs to scan too big number of unique [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series)
with `http_request_duration_seconds_bucket` name. This alerting query can be speed up by pre-calculating
the `sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)` via [recording rule](https://docs.victoriametrics.com/vmalert.html#recording-rules).
But this recording rule may take too much time to execute too. In this case the slow recording rule can be substituted
with the following [stream aggregation config](#stream-aggregation-config):

```yaml
- match: 'http_request_duration_seconds_bucket'
  interval: 5m
  without: [instance]
  outputs: [total]
```

This stream aggregation generates `http_request_duration_seconds_bucket:5m_without_instance_total` output series according to [output metric naming](#output-metric-names).
Then these series can be used in [alerting rules](https://docs.victoriametrics.com/vmalert.html#alerting-rules):

```metricsql
histogram_quantile(0.99, last_over_time(http_request_duration_seconds_bucket:5m_without_instance_total[5m])) > 0.5
```

This query is executed much faster than the original query, because it needs to scan much lower number of time series.

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [aggregating by labels](#aggregating-by-labels).

Field `interval` is recommended to be set to a value at least several times higher than your metrics collect interval.


### Reducing the number of stored samples

If per-[series](https://docs.victoriametrics.com/keyConcepts.html#time-series) samples are ingested at high frequency,
then this may result in high disk space usage, since too much data must be stored to disk. This also may result
in slow queries, since too much data must be processed during queries.

This can be fixed with the stream aggregation by increasing the interval between per-series samples stored in the database.

For example, the following [stream aggregation config](#stream-aggregation-config) reduces the frequency of input samples
to one sample per 5 minutes per each input time series (this operation is also known as downsampling):

```yaml
  # Aggregate metrics ending with _total with `total` output.
  # See https://docs.victoriametrics.com/stream-aggregation.html#aggregation-outputs
- match: '{__name__=~".+_total"}'
  interval: 5m
  outputs: [total]

  # Downsample other metrics with `count_samples`, `sum_samples`, `min` and `max` outputs
  # See https://docs.victoriametrics.com/stream-aggregation.html#aggregation-outputs
- match: '{__name__!~".+_total"}'
  interval: 5m
  outputs: [count_samples, sum_samples, min, max]
```

The aggregated output metrics have the following names according to [output metric naming](#output-metric-names):

```text
# For input metrics ending with _total
some_metric_total:5m_total

# For input metrics not ending with _total
some_metric:5m_count_samples
some_metric:5m_sum_samples
some_metric:5m_min
some_metric:5m_max
```

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [aggregating histograms](#aggregating-histograms) and [aggregating by labels](#aggregating-by-labels).

### Reducing the number of stored series

Sometimes applications may generate too many [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series).
For example, the `http_requests_total` metric may have `path` or `user` label with too big number of unique values.
In this case the following stream aggregation can be used for reducing the number metrics stored in VictoriaMetrics:

```yaml
- match: 'http_requests_total'
  interval: 30s
  without: [path, user]
  outputs: [total]
```

This config specifies labels, which must be removed from the aggregate output, in the `without` list.
See [these docs](#aggregating-by-labels) for more details.

The aggregated output metric has the following name according to [output metric naming](#output-metric-names):

```text
http_requests_total:30s_without_path_user_total
```

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [aggregating histograms](#aggregating-histograms).

### Counting input samples

If the monitored application generates event-based metrics, then it may be useful to count the number of such metrics
at stream aggregation level.

For example, if an advertising server generates `hits{some="labels"} 1` and `clicks{some="labels"} 1` metrics
per each incoming hit and click, then the following [stream aggregation config](#stream-aggregation-config)
can be used for counting these metrics per every 30 second interval:

```yaml
- match: '{__name__=~"hits|clicks"}'
  interval: 30s
  outputs: [count_samples]
```

This config generates the following output metrics for `hits` and `clicks` input metrics
according to [output metric naming](#output-metric-names):

```text
hits:30s_count_samples count1
clicks:30s_count_samples count2
```

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [aggregating by labels](#aggregating-by-labels).


### Summing input metrics

If the monitored application calculates some events and then sends the calculated number of events to VictoriaMetrics
at irregular intervals or at too high frequency, then stream aggregation can be used for summing such events
and writing the aggregate sums to the storage at regular intervals.

For example, if an advertising server generates `hits{some="labels} N` and `clicks{some="labels"} M` metrics
at irregular intervals, then the following [stream aggregation config](#stream-aggregation-config)
can be used for summing these metrics per every minute:

```yaml
- match: '{__name__=~"hits|clicks"}'
  interval: 1m
  outputs: [sum_samples]
```

This config generates the following output metrics according to [output metric naming](#output-metric-names):

```text
hits:1m_sum_samples sum1
clicks:1m_sum_samples sum2
```

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [aggregating by labels](#aggregating-by-labels).


### Quantiles over input metrics

If the monitored application generates measurement metrics per each request, then it may be useful to calculate
the pre-defined set of [percentiles](https://en.wikipedia.org/wiki/Percentile) over these measurements.

For example, if the monitored application generates `request_duration_seconds N` and `response_size_bytes M` metrics
per each incoming request, then the following [stream aggregation config](#stream-aggregation-config)
can be used for calculating 50th and 99th percentiles for these metrics every 30 seconds:

```yaml
- match:
  - request_duration_seconds
  - response_size_bytes
  interval: 30s
  outputs: ["quantiles(0.50, 0.99)"]
```

This config generates the following output metrics according to [output metric naming](#output-metric-names):

```text
request_duration_seconds:30s_quantiles{quantile="0.50"} value1
request_duration_seconds:30s_quantiles{quantile="0.99"} value2

response_size_bytes:30s_quantiles{quantile="0.50"} value1
response_size_bytes:30s_quantiles{quantile="0.99"} value2
```

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [histograms over input metrics](#histograms-over-input-metrics) and [aggregating by labels](#aggregating-by-labels).

### Histograms over input metrics

If the monitored application generates measurement metrics per each request, then it may be useful to calculate
a [histogram](https://docs.victoriametrics.com/keyConcepts.html#histogram) over these metrics.

For example, if the monitored application generates `request_duration_seconds N` and `response_size_bytes M` metrics
per each incoming request, then the following [stream aggregation config](#stream-aggregation-config)
can be used for calculating [VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
for these metrics every 60 seconds:

```yaml
- match:
  - request_duration_seconds
  - response_size_bytes
  interval: 60s
  outputs: [histogram_bucket]
```

This config generates the following output metrics according to [output metric naming](#output-metric-names).

```text
request_duration_seconds:60s_histogram_bucket{vmrange="start1...end1"} count1
request_duration_seconds:60s_histogram_bucket{vmrange="start2...end2"} count2
...
request_duration_seconds:60s_histogram_bucket{vmrange="startN...endN"} countN

response_size_bytes:60s_histogram_bucket{vmrange="start1...end1"} count1
response_size_bytes:60s_histogram_bucket{vmrange="start2...end2"} count2
...
response_size_bytes:60s_histogram_bucket{vmrange="startN...endN"} countN
```

The resulting histogram buckets can be queried with [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) in the following ways:

1. An estimated 50th and 99th [percentiles](https://en.wikipedia.org/wiki/Percentile) of the request duration over the last hour:

   ```metricsql
   histogram_quantiles("quantile", 0.50, 0.99, sum(increase(request_duration_seconds:60s_histogram_bucket[1h])) by (vmrange))
   ```

   This query uses [histogram_quantiles](https://docs.victoriametrics.com/MetricsQL.html#histogram_quantiles) function.

1. An estimated [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of the request duration over the last hour:

   ```metricsql
   histogram_stddev(sum(increase(request_duration_seconds:60s_histogram_bucket[1h])) by (vmrange))
   ```

   This query uses [histogram_stddev](https://docs.victoriametrics.com/MetricsQL.html#histogram_stddev) function.

1. An estimated share of requests with the duration smaller than `0.5s` over the last hour:

   ```metricsql
   histogram_share(0.5, sum(increase(request_duration_seconds:60s_histogram_bucket[1h])) by (vmrange))
   ```

   This query uses [histogram_share](https://docs.victoriametrics.com/MetricsQL.html#histogram_share) function.

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [quantiles over input metrics](#quantiles-over-input-metrics) and [aggregating by labels](#aggregating-by-labels).

### Aggregating histograms

[Histogram](https://docs.victoriametrics.com/keyConcepts.html#histogram) is a set of [counter](https://docs.victoriametrics.com/keyConcepts.html#counter)
metrics with different `vmrange` or `le` labels. As they're counters, the applicable aggregation output is 
[total](https://docs.victoriametrics.com/stream-aggregation.html#total):

```yaml
- match: 'http_request_duration_seconds_bucket'
  interval: 1m
  without: [instance]
  outputs: [total]
```

This config generates the following output metrics according to [output metric naming](#output-metric-names):

```text
http_request_duration_seconds_bucket:1m_without_instance_total{le="0.1"} value1
http_request_duration_seconds_bucket:1m_without_instance_total{le="0.2"} value2
http_request_duration_seconds_bucket:1m_without_instance_total{le="0.4"} value3
http_request_duration_seconds_bucket:1m_without_instance_total{le="1"}   value4
http_request_duration_seconds_bucket:1m_without_instance_total{le="3"}   value5
http_request_duration_seconds_bucket:1m_without_instance_total{le="+Inf" value6
```

The resulting metrics can be passed to [histogram_quantile](https://docs.victoriametrics.com/MetricsQL.html#histogram_quantile)
function:

```metricsql
histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket:1m_without_instance_total[5m])) by(le))
```

Please note, histograms can be aggregated if their `le` labels are configured identically. 
[VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
have no such requirement.

See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
See also [histograms over input metrics](#histograms-over-input-metrics) and [quantiles over input metrics](#quantiles-over-input-metrics).

## Output metric names

Output metric names for stream aggregation are constructed according to the following pattern:

```text
<metric_name>:<interval>[_by_<by_labels>][_without_<without_labels>]_<output>
```

- `<metric_name>` is the original metric name.
- `<interval>` is the interval specified in the [stream aggregation config](#stream-aggregation-config).
- `<by_labels>` is `_`-delimited sorted list of `by` labels specified in the [stream aggregation config](#stream-aggregation-config).
  If the `by` list is missing in the config, then the `_by_<by_labels>` part isn't included in the output metric name.
- `<without_labels>` is an optional `_`-delimited sorted list of `without` labels specified in the [stream aggregation config](#stream-aggregation-config).
  If the `without` list is missing in the config, then the `_without_<without_labels>` part isn't included in the output metric name.
- `<output>` is the aggregate used for constructing the output metric. The aggregate name is taken from the `outputs` list
  at the corresponding [stream aggregation config](#stream-aggregation-config).

Both input and output metric names can be modified if needed via relabeling according to [these docs](#relabeling).

It is possible to leave the original metric name after the aggregation by specifying `keep_metric_names: true` option at [stream aggregation config](#stream-aggregation-config).
The `keep_metric_names` option can be used only if only a single output is set in [`outputs` list](#aggregation-outputs).

## Relabeling

It is possible to apply [arbitrary relabeling](https://docs.victoriametrics.com/vmagent.html#relabeling) to input and output metrics
during stream aggregation via `input_relabel_configs` and `output_relabel_configs` options in [stream aggregation config](#stream-aggregation-config).

Relabeling rules inside `input_relabel_configs` are applied to samples matching the `match` filters.
Relabeling rules inside `output_relabel_configs` are applied to aggregated samples before sending them to the remote storage.

For example, the following config removes the `:1m_sum_samples` suffix added [to the output metric name](#output-metric-names):

```yaml
- interval: 1m
  outputs: [sum_samples]
  output_relabel_configs:
  - source_labels: [__name__]
    target_label: __name__
    regex: "(.+):.+"
```

Another option to remove the suffix, which is added by stream aggregation, is to add `keep_metric_names: true` to the config:

```yaml
- interval: 1m
  outputs: [sum_samples]
  keep_metric_names: true
```

## Aggregation outputs

The aggregations are calculated during the `interval` specified in the [config](#stream-aggregation-config)
and then sent to the storage once per `interval`. The aggregated samples are named according to [output metric naming](#output-metric-names).

If `by` and `without` lists are specified in the [config](#stream-aggregation-config),
then the [aggregation by labels](#aggregating-by-labels) is performed additionally to aggregation by `interval`.

On vmagent shutdown or [configuration reload](#configuration-update) unfinished aggregated states are discarded,
as they might produce lower values than user expects. It is possible to specify `flush_on_shutdown: true` setting in 
aggregation config to make vmagent to send unfinished states to the remote storage.

Below are aggregation functions that can be put in the `outputs` list at [stream aggregation config](#stream-aggregation-config):

* [avg](#avg)
* [count_samples](#count_samples)
* [count_series](#count_series)
* [increase](#increase)
* [increase_prometheus](#increase_prometheus)
* [histogram_bucket](#histogram_bucket)
* [last](#last)
* [max](#max)
* [min](#min)
* [stddev](#stddev)
* [stdvar](#stdvar)
* [sum_samples](#sum_samples)
* [total](#total)
* [total_prometheus](#total_prometheus)
* [unique_samples](#unique_samples)
* [quantiles](#quantiles)

### avg

`avg` returns the average over input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
`avg` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).

The results of `avg` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(sum_over_time(some_metric[interval])) / sum(count_over_time(some_metric[interval]))
```

For example, see below time series produced by config with aggregation interval `1m` and `by: ["instance"]` and  the regular query:

<img alt="avg aggregation" src="stream-aggregation-check-avg.webp">

See also [min](#min), [max](#max), [sum_samples](#sum_samples) and [count_samples](#count_samples).

### count_samples

`count_samples` counts the number of input [samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) over the given `interval`.

The results of `count_samples` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(count_over_time(some_metric[interval]))
```

See also [count_series](#count_series) and [sum_samples](#sum_samples).

### count_series

`count_series` counts the number of unique [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series) over the given `interval`.

The results of `count_series` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
count(last_over_time(some_metric[interval]))
```

See also [count_samples](#count_samples) and [unique_samples](#unique_samples).

### increase

`increase` returns the increase of input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given 'interval'.
`increase` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyConcepts.html#counter).

The results of `increase` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(increase_pure(some_counter[interval]))
```

`increase` assumes that all the counters start from 0. For example, if the fist seen sample for new [time series](https://docs.victoriametrics.com/keyconcepts/#time-series)
is `10`, then `increase` assumes that the time series has been increased by `10`. If you need ignoring the first sample for new time series,
then take a look at [increase_prometheus](#increase_prometheus).

For example, see below time series produced by config with aggregation interval `1m` and `by: ["instance"]` and the regular query:

<img alt="increase aggregation" src="stream-aggregation-check-increase.webp">

`increase` can be used as an alternative for [rate](https://docs.victoriametrics.com/MetricsQL.html#rate) function.
For example, if `increase` is calculated for `some_counter` with `interval: 5m`, then `rate` can be calculated
by dividing the resulting aggregation by `5m`:

```metricsql
some_counter:5m_increase / 5m
```

This is similar to `rate(some_counter[5m])`.

Please note, opposite to [rate](https://docs.victoriametrics.com/MetricsQL.html#rate), `increase` aggregations can be 
combined safely afterwards. This is helpful when the aggregation is calculated by more than one vmagent.

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_inteval](#stream-aggregation-config).

See also [increase_prometheus](#increase_prometheus) and [total](#total).

### increase_prometheus

`increase_prometheus` returns the increase of input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
`increase_prometheus` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyConcepts.html#counter).

The results of `increase_prometheus` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(increase_prometheus(some_counter[interval]))
```

`increase_prometheus` skips the first seen sample value per each [time series](https://docs.victoriametrics.com/keyconcepts/#time-series).
If you need taking into account the first sample per time series, then take a look at [increase](#increase).

See also [increase](#increase), [total](#total) and [total_prometheus](#total_prometheus).

### 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.html#raw-samples) over the given `interval`.
`histogram_bucket` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#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:

```metricsql
sum(histogram_over_time(some_histogram_bucket[interval])) by (vmrange)
```

See also [quantiles](#quantiles), [min](#min), [max](#max) and [avg](#avg).

### last

`last` returns the last input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) over the given `interval`.

The results of `last` is roughly equal to the the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
last_over_time(some_metric[interval])
```

See also [min](#min), [max](#max) and [avg](#avg).

### max

`max` returns the maximum input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) over the given `interval`.

The results of `max` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
max(max_over_time(some_metric[interval]))
```

For example, see below time series produced by config with aggregation interval `1m` and the regular query:

<img alt="total aggregation" src="stream-aggregation-check-max.webp">

See also [min](#min) and [avg](#avg).

### min

`min` returns the minimum input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) over the given `interval`.

The results of `min` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
min(min_over_time(some_metric[interval]))
```

For example, see below time series produced by config with aggregation interval `1m` and the regular query:

<img alt="min aggregation" src="stream-aggregation-check-min.webp">

See also [max](#max) and [avg](#avg).

### stddev

`stddev` returns [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) for the input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples)
over the given `interval`.
`stddev` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).

The results of `stddev` is roughly equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
histogram_stddev(sum(histogram_over_time(some_metric[interval])) by (vmrange))
```

See also [stdvar](#stdvar) and [avg](#avg).

### stdvar

`stdvar` returns [standard variance](https://en.wikipedia.org/wiki/Variance) for the input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples)
over the given `interval`.
`stdvar` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).

The results of `stdvar` is roughly equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
histogram_stdvar(sum(histogram_over_time(some_metric[interval])) by (vmrange))
```

For example, see below time series produced by config with aggregation interval `1m` and the regular query:

<img alt="stdvar aggregation" src="stream-aggregation-check-stdvar.webp">

See also [stddev](#stddev) and [avg](#avg).

### sum_samples

`sum_samples` sums input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) over the given `interval`.
`sum_samples` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).

The results of `sum_samples` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(sum_over_time(some_metric[interval]))
```

For example, see below time series produced by config with aggregation interval `1m` and the regular query:

<img alt="sum_samples aggregation" src="stream-aggregation-check-sum-samples.webp">

See also [count_samples](#count_samples) and [count_series](#count_series).

### total

`total` generates output [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) by summing the input counters over the given `interval`.
`total` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyConcepts.html#counter).

The results of `total` is roughly equal to the the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(running_sum(increase_pure(some_counter)))
```

`total` assumes that all the counters start from 0. For example, if the fist seen sample for new [time series](https://docs.victoriametrics.com/keyconcepts/#time-series)
is `10`, then `total` assumes that the time series has been increased by `10`. If you need ignoring the first sample for new time series,
then take a look at [total_prometheus](#total_prometheus).

For example, see below time series produced by config with aggregation interval `1m` and `by: ["instance"]` and the regular query:

<img alt="total aggregation" src="stream-aggregation-check-total.webp">

`total` is not affected by [counter resets](https://docs.victoriametrics.com/keyConcepts.html#counter) -
it continues to increase monotonically with respect to the previous value.
The counters are most often reset when the application is restarted.

For example:

<img alt="total aggregation counter reset" src="stream-aggregation-check-total-reset.webp">

The same behavior occurs when creating or deleting new series in an aggregation group -
`total` output increases monotonically considering the values of the series set.
An example of changing a set of series can be restarting a pod in the Kubernetes.
This changes pod name label, but the `total` accounts for such a scenario and doesn't reset the state of aggregated metric.

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_inteval](#stream-aggregation-config).

See also [total_prometheus](#total_prometheus), [increase](#increase) and [increase_prometheus](#increase_prometheus).

### total_prometheus

`total_prometheus` generates output [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) by summing the input counters over the given `interval`.
`total_prometheus` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyConcepts.html#counter).

The results of `total_prometheus` is roughly equal to the the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
sum(running_sum(increase_prometheus(some_counter)))
```

`total_prometheus` skips the first seen sample value per each [time series](https://docs.victoriametrics.com/keyconcepts/#time-series).
If you need taking into account the first sample per time series, then take a look at [total](#total).

`total_prometheus` is not affected by [counter resets](https://docs.victoriametrics.com/keyConcepts.html#counter) -
it continues to increase monotonically with respect to the previous value.
The counters are most often reset when the application is restarted.

See also [total](#total), [increase](#increase) and [increase_prometheus](#increase_prometheus).

### unique_samples

`unique_samples` counts the number of unique sample values over the given `interval`.
`unique_samples` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).

The results of `unique_samples` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```metricsql
count(count_values_over_time(some_metric[interval]))
```

See also [sum_samples](#sum_samples) and [count_series](#count_series).

### quantiles

`quantiles(phi1, ..., phiN)` returns [percentiles](https://en.wikipedia.org/wiki/Percentile) for the given `phi*`
over the input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples) on the given `interval`.
`phi` must be in the range `[0..1]`, where `0` means `0th` percentile, while `1` means `100th` percentile.
`quantiles(...)` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyConcepts.html#gauge).

The results of `quantiles(phi1, ..., phiN)` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:

```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).


## Aggregating by labels

All the labels for the input metrics are preserved by default in the output metrics. For example,
the input metric `foo{app="bar",instance="host1"}` results to the output metric `foo:1m_sum_samples{app="bar",instance="host1"}`
when the following [stream aggregation config](#stream-aggregation-config) is used:

```yaml
- interval: 1m
  outputs: [sum_samples]
```

The input labels can be removed via `without` list specified in the config. For example, the following config
removes the `instance` label from output metrics by summing input samples across all the instances:

```yaml
- interval: 1m
  without: [instance]
  outputs: [sum_samples]
```

In this case the `foo{app="bar",instance="..."}` input metrics are transformed into `foo:1m_without_instance_sum_samples{app="bar"}`
output metric according to [output metric naming](#output-metric-names).

It is possible specifying the exact list of labels in the output metrics via `by` list.
For example, the following config sums input samples by the `app` label:

```yaml
- interval: 1m
  by: [app]
  outputs: [sum_samples]
```

In this case the `foo{app="bar",instance="..."}` input metrics are transformed into `foo:1m_by_app_sum_samples{app="bar"}`
output metric according to [output metric naming](#output-metric-names).

The labels used in `by` and `without` lists can be modified via `input_relabel_configs` section - see [these docs](#relabeling).

See also [aggregation outputs](#aggregation-outputs).


## Stream aggregation config

Below is the format for stream aggregation config file, which may be referred via `-remoteWrite.streamAggr.config` command-line flag
at [vmagent](https://docs.victoriametrics.com/vmagent.html) or via `-streamAggr.config` command-line flag
at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html):

```yaml
  # match is an optional filter for incoming samples to aggregate.
  # It can contain arbitrary Prometheus series selector
  # according to https://docs.victoriametrics.com/keyConcepts.html#filtering .
  # If match isn't set, then all the incoming samples are aggregated.
  #
  # match also can contain a list of series selectors. Then the incoming samples are aggregated
  # if they match at least a single series selector.
  #
- match: 'http_request_duration_seconds_bucket{env=~"prod|staging"}'

  # interval is the interval for the aggregation.
  # The aggregated stats is sent to remote storage once per interval.
  #
  interval: 1m

  # dedup_interval is an optional interval for de-duplication of input samples before the aggregation.
  # Samples are de-duplicated on a per-series basis. See https://docs.victoriametrics.com/keyconcepts/#time-series
  # and https://docs.victoriametrics.com/#deduplication
  # The deduplication is performed after input_relabel_configs relabeling is applied.
  # By default the deduplication is disabled.
  #
  # dedup_interval: 30s

  # staleness_interval is an optional interval after which the series state will be reset if no samples have been sent during it.
  # This means that:
  # - no data point will be written for a resulting time series if it didn't receive any updates during configured interval,
  # - if the series receives updates after the configured interval again, then the time series will be calculated from the initial state
  #   (it's like this series didn't exist until now).
  # Increase this parameter if it is expected for matched metrics to be delayed or collected with irregular intervals exceeding the `interval` value.
  # By default, it equals to x2 of the `interval` field.
  # The parameter is only relevant for outputs: total, increase and histogram_bucket.
  #
  # staleness_interval: 2m
  
  # flush_on_shutdown defines whether to flush the unfinished aggregation states on process restarts
  # or config reloads. It is not recommended changing this setting, unless unfinished aggregations states
  # are preferred to missing data points.
  # Unfinished aggregation states aren't flushed on shutdown by default.
  #
  # flush_on_shutdown: false

  # without is an optional list of labels, which must be removed from the output aggregation.
  # See https://docs.victoriametrics.com/stream-aggregation.html#aggregating-by-labels
  #
  without: [instance]

  # by is an optional list of labels, which must be preserved in the output aggregation.
  # See https://docs.victoriametrics.com/stream-aggregation.html#aggregating-by-labels
  #
  # by: [job, vmrange]

  # outputs is the list of aggregations to perform on the input data.
  # See https://docs.victoriametrics.com/stream-aggregation.html#aggregation-outputs
  #
  outputs: [total]

  # keep_metric_names instructs keeping the original metric names for the aggregated samples.
  # This option can be set only if outputs list contains only a single output.
  # By default a special suffix is added to original metric names in the aggregated samples.
  # See https://docs.victoriametrics.com/stream-aggregation/#output-metric-names
  #
  # keep_metric_names: false

  # input_relabel_configs is an optional relabeling rules,
  # which are applied to the incoming samples after they pass the match filter
  # and before being aggregated.
  # See https://docs.victoriametrics.com/stream-aggregation.html#relabeling
  #
  input_relabel_configs:
  - target_label: vmaggr
    replacement: before

  # output_relabel_configs is an optional relabeling rules,
  # which are applied to the aggregated output metrics.
  #
  output_relabel_configs:
  - target_label: vmaggr
    replacement: after
```

The file can contain multiple aggregation configs. The aggregation is performed independently
per each specified config entry.

### Configuration update

[vmagent](https://docs.victoriametrics.com/vmagent.html) and [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html)
support the following approaches for hot reloading stream aggregation configs from `-remoteWrite.streamAggr.config` and `-streamAggr.config`:

* By sending `SIGHUP` signal to `vmagent` or `victoria-metrics` process:

  ```sh
  kill -SIGHUP `pidof vmagent`
  ```

* By sending HTTP request to `/-/reload` endpoint (e.g. `http://vmagent:8429/-/reload` or `http://victoria-metrics:8428/-/reload).

## Cluster mode

If you use [vmagent in cluster mode](https://docs.victoriametrics.com/vmagent.html#scraping-big-number-of-targets) for streaming aggregation
(with `-promscrape.cluster.*` parameters or with `VMAgent.spec.shardCount > 1` for [vmoperator](https://docs.victoriametrics.com/operator))
then be careful when aggregating metrics via `by`, `without` or modifying via `*_relabel_configs` parameters, since incorrect usage
may result in duplicates and data collision. For example, if more than one `vmagent` instance calculates `increase` for metric `http_requests_total`
with `by: [path]` directive, then all the `vmagent` instances will aggregate samples to the same set of time series with different `path` labels.
The proper fix would be to add an unique [`-remoteWrite.label`](https://docs.victoriametrics.com/vmagent.html#adding-labels-to-metrics) per each `vmagent`,
so every `vmagent` aggregates data into distinct set of time series. These time series then can be aggregated later as needed during querying.

For example, if `vmagent` instances run in Docker or Kubernetes, then you can refer `POD_NAME` or `HOSTNAME` environment variables
as an unique label value per each `vmagent`: `-remoteWrite.label='vmagent=%{HOSTNAME}` . See [these docs](https://docs.victoriametrics.com/#environment-variables)
on how to refer environment variables in VictoriaMetrics components.