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1. split package rule under /app/vmalert, expose needed objects 2. add vmalert-tool with unittest subcmd https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2945
698 lines
34 KiB
Markdown
698 lines
34 KiB
Markdown
---
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sort: 98
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weight: 98
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title: Streaming aggregation
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menu:
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docs:
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parent: 'victoriametrics'
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weight: 98
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aliases:
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- /stream-aggregation.html
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---
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# Streaming aggregation
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[vmagent](https://docs.victoriametrics.com/vmagent.html) and [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html)
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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.
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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)
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and/or scraped from [Prometheus-compatible targets](https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter).
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Stream aggregation ignores timestamps associated with the input [samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
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It expects that the ingested samples have timestamps close to the current time.
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Stream aggregation is configured via the following command-line flags:
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- `-remoteWrite.streamAggr.config` at [vmagent](https://docs.victoriametrics.com/vmagent.html).
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This flag can be specified individually per each `-remoteWrite.url`.
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This allows writing different aggregates to different remote storage destinations.
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- `-streamAggr.config` at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
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These flags must point to a file containing [stream aggregation config](#stream-aggregation-config).
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By default, the following data is written to the storage when stream aggregation is enabled:
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- the aggregated samples;
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- the raw input samples, which didn't match any `match` option in the provided [config](#stream-aggregation-config).
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This behaviour can be changed via the following command-line flags:
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- `-remoteWrite.streamAggr.keepInput` at [vmagent](https://docs.victoriametrics.com/vmagent.html) and `-streamAggr.keepInput`
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at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
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If one of these flags are set, then all the input samples are written to the storage alongside the aggregated samples.
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The `-remoteWrite.streamAggr.keepInput` flag can be specified individually per each `-remoteWrite.url`.
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- `-remoteWrite.streamAggr.dropInput` at [vmagent](https://docs.victoriametrics.com/vmagent.html) and `-streamAggr.dropInput`
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at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
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If one of these flags are set, then all the input samples are dropped, while only the aggregated samples are written to the storage.
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The `-remoteWrite.streamAggr.dropInput` flag can be specified individually per each `-remoteWrite.url`.
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By default, all the input samples are aggregated. Sometimes it is needed to de-duplicate samples before the aggregation.
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For example, if the samples are received from replicated sources.
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The following command-line flag can be used for enabling the [de-duplication](https://docs.victoriametrics.com/#deduplication)
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before aggregation in this case:
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- `-remoteWrite.streamAggr.dedupInterval` at [vmagent](https://docs.victoriametrics.com/vmagent.html).
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This flag can be specified individually per each `-remoteWrite.url`.
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This allows setting different de-duplication intervals per each configured remote storage.
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- `-streamAggr.dedupInterval` at [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html).
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## Use cases
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Stream aggregation can be used in the following cases:
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* [Statsd alternative](#statsd-alternative)
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* [Recording rules alternative](#recording-rules-alternative)
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* [Reducing the number of stored samples](#reducing-the-number-of-stored-samples)
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* [Reducing the number of stored series](#reducing-the-number-of-stored-series)
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### Statsd alternative
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Stream aggregation can be used as [statsd](https://github.com/statsd/statsd) alternative in the following cases:
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* [Counting input samples](#counting-input-samples)
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* [Summing input metrics](#summing-input-metrics)
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* [Quantiles over input metrics](#quantiles-over-input-metrics)
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* [Histograms over input metrics](#histograms-over-input-metrics)
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* [Aggregating histograms](#aggregating-histograms)
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Currently, streaming aggregation is available only for [supported data ingestion protocols](https://docs.victoriametrics.com/#how-to-import-time-series-data)
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and not available for [Statsd metrics format](https://github.com/statsd/statsd/blob/master/docs/metric_types.md).
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### Recording rules alternative
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Sometimes [alerting queries](https://docs.victoriametrics.com/vmalert.html#alerting-rules) may require non-trivial amounts of CPU, RAM,
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disk IO and network bandwidth at metrics storage side. For example, if `http_request_duration_seconds` histogram is generated by thousands
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of application instances, then the alerting query `histogram_quantile(0.99, sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)) > 0.5`
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can become slow, since it needs to scan too big number of unique [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series)
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with `http_request_duration_seconds_bucket` name. This alerting query can be speed up by pre-calculating
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the `sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)` via [recording rule](https://docs.victoriametrics.com/vmalert.html#recording-rules).
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But this recording rule may take too much time to execute too. In this case the slow recording rule can be substituted
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with the following [stream aggregation config](#stream-aggregation-config):
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```yaml
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- match: 'http_request_duration_seconds_bucket'
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interval: 5m
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without: [instance]
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outputs: [total]
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```
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This stream aggregation generates `http_request_duration_seconds_bucket:5m_without_instance_total` output series according to [output metric naming](#output-metric-names).
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Then these series can be used in [alerting rules](https://docs.victoriametrics.com/vmalert.html#alerting-rules):
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```metricsql
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histogram_quantile(0.99, last_over_time(http_request_duration_seconds_bucket:5m_without_instance_total[5m])) > 0.5
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```
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This query is executed much faster than the original query, because it needs to scan much lower number of time series.
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [aggregating by labels](#aggregating-by-labels).
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Field `interval` is recommended to be set to a value at least several times higher than your metrics collect interval.
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### Reducing the number of stored samples
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If per-[series](https://docs.victoriametrics.com/keyConcepts.html#time-series) samples are ingested at high frequency,
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then this may result in high disk space usage, since too much data must be stored to disk. This also may result
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in slow queries, since too much data must be processed during queries.
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This can be fixed with the stream aggregation by increasing the interval between per-series samples stored in the database.
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For example, the following [stream aggregation config](#stream-aggregation-config) reduces the frequency of input samples
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to one sample per 5 minutes per each input time series (this operation is also known as downsampling):
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```yaml
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# Aggregate metrics ending with _total with `total` output.
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# See https://docs.victoriametrics.com/stream-aggregation.html#aggregation-outputs
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- match: '{__name__=~".+_total"}'
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interval: 5m
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outputs: [total]
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# Downsample other metrics with `count_samples`, `sum_samples`, `min` and `max` outputs
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# See https://docs.victoriametrics.com/stream-aggregation.html#aggregation-outputs
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- match: '{__name__!~".+_total"}'
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interval: 5m
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outputs: [count_samples, sum_samples, min, max]
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```
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The aggregated output metrics have the following names according to [output metric naming](#output-metric-names):
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```
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# For input metrics ending with _total
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some_metric_total:5m_total
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# For input metrics not ending with _total
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some_metric:5m_count_samples
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some_metric:5m_sum_samples
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some_metric:5m_min
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some_metric:5m_max
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```
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [aggregating histograms](#aggregating-histograms) and [aggregating by labels](#aggregating-by-labels).
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### Reducing the number of stored series
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Sometimes applications may generate too many [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series).
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For example, the `http_requests_total` metric may have `path` or `user` label with too big number of unique values.
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In this case the following stream aggregation can be used for reducing the number metrics stored in VictoriaMetrics:
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```yaml
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- match: 'http_requests_total'
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interval: 30s
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without: [path, user]
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outputs: [total]
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```
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This config specifies labels, which must be removed from the aggregate output, in the `without` list.
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See [these docs](#aggregating-by-labels) for more details.
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The aggregated output metric has the following name according to [output metric naming](#output-metric-names):
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```
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http_requests_total:30s_without_path_user_total
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```
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [aggregating histograms](#aggregating-histograms).
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### Counting input samples
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If the monitored application generates event-based metrics, then it may be useful to count the number of such metrics
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at stream aggregation level.
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For example, if an advertising server generates `hits{some="labels"} 1` and `clicks{some="labels"} 1` metrics
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per each incoming hit and click, then the following [stream aggregation config](#stream-aggregation-config)
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can be used for counting these metrics per every 30 second interval:
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```yml
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- match: '{__name__=~"hits|clicks"}'
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interval: 30s
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outputs: [count_samples]
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```
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This config generates the following output metrics for `hits` and `clicks` input metrics
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according to [output metric naming](#output-metric-names):
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```
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hits:30s_count_samples count1
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clicks:30s_count_samples count2
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```
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [aggregating by labels](#aggregating-by-labels).
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### Summing input metrics
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If the monitored application calculates some events and then sends the calculated number of events to VictoriaMetrics
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at irregular intervals or at too high frequency, then stream aggregation can be used for summing such events
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and writing the aggregate sums to the storage at regular intervals.
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For example, if an advertising server generates `hits{some="labels} N` and `clicks{some="labels"} M` metrics
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at irregular intervals, then the following [stream aggregation config](#stream-aggregation-config)
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can be used for summing these metrics per every minute:
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```yml
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- match: '{__name__=~"hits|clicks"}'
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interval: 1m
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outputs: [sum_samples]
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```
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This config generates the following output metrics according to [output metric naming](#output-metric-names):
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```
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hits:1m_sum_samples sum1
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clicks:1m_sum_samples sum2
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```
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [aggregating by labels](#aggregating-by-labels).
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### Quantiles over input metrics
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If the monitored application generates measurement metrics per each request, then it may be useful to calculate
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the pre-defined set of [percentiles](https://en.wikipedia.org/wiki/Percentile) over these measurements.
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For example, if the monitored application generates `request_duration_seconds N` and `response_size_bytes M` metrics
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per each incoming request, then the following [stream aggregation config](#stream-aggregation-config)
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can be used for calculating 50th and 99th percentiles for these metrics every 30 seconds:
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```yaml
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- match:
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- request_duration_seconds
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- response_size_bytes
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interval: 30s
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outputs: ["quantiles(0.50, 0.99)"]
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```
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This config generates the following output metrics according to [output metric naming](#output-metric-names):
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```
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request_duration_seconds:30s_quantiles{quantile="0.50"} value1
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request_duration_seconds:30s_quantiles{quantile="0.99"} value2
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response_size_bytes:30s_quantiles{quantile="0.50"} value1
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response_size_bytes:30s_quantiles{quantile="0.99"} value2
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```
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [histograms over input metrics](#histograms-over-input-metrics) and [aggregating by labels](#aggregating-by-labels).
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### Histograms over input metrics
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If the monitored application generates measurement metrics per each request, then it may be useful to calculate
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a [histogram](https://docs.victoriametrics.com/keyConcepts.html#histogram) over these metrics.
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For example, if the monitored application generates `request_duration_seconds N` and `response_size_bytes M` metrics
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per each incoming request, then the following [stream aggregation config](#stream-aggregation-config)
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can be used for calculating [VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
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for these metrics every 60 seconds:
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```yaml
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- match:
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- request_duration_seconds
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- response_size_bytes
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interval: 60s
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outputs: [histogram_bucket]
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```
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This config generates the following output metrics according to [output metric naming](#output-metric-names).
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```
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request_duration_seconds:60s_histogram_bucket{vmrange="start1...end1"} count1
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request_duration_seconds:60s_histogram_bucket{vmrange="start2...end2"} count2
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...
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request_duration_seconds:60s_histogram_bucket{vmrange="startN...endN"} countN
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response_size_bytes:60s_histogram_bucket{vmrange="start1...end1"} count1
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response_size_bytes:60s_histogram_bucket{vmrange="start2...end2"} count2
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...
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response_size_bytes:60s_histogram_bucket{vmrange="startN...endN"} countN
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```
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The resulting histogram buckets can be queried with [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) in the following ways:
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1. An estimated 50th and 99th [percentiles](https://en.wikipedia.org/wiki/Percentile) of the request duration over the last hour:
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```metricsql
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histogram_quantiles("quantile", 0.50, 0.99, sum(increase(request_duration_seconds:60s_histogram_bucket[1h])) by (vmrange))
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```
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This query uses [histogram_quantiles](https://docs.victoriametrics.com/MetricsQL.html#histogram_quantiles) function.
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1. An estimated [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of the request duration over the last hour:
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```metricsql
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histogram_stddev(sum(increase(request_duration_seconds:60s_histogram_bucket[1h])) by (vmrange))
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```
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This query uses [histogram_stddev](https://docs.victoriametrics.com/MetricsQL.html#histogram_stddev) function.
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1. An estimated share of requests with the duration smaller than `0.5s` over the last hour:
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```metricsql
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histogram_share(0.5, sum(increase(request_duration_seconds:60s_histogram_bucket[1h])) by (vmrange))
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```
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This query uses [histogram_share](https://docs.victoriametrics.com/MetricsQL.html#histogram_share) function.
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [quantiles over input metrics](#quantiles-over-input-metrics) and [aggregating by labels](#aggregating-by-labels).
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### Aggregating histograms
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[Histogram](https://docs.victoriametrics.com/keyConcepts.html#histogram) is a set of [counter](https://docs.victoriametrics.com/keyConcepts.html#counter)
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metrics with different `vmrange` or `le` labels. As they're counters, the applicable aggregation output is
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[total](https://docs.victoriametrics.com/stream-aggregation.html#total):
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```yaml
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- match: 'http_request_duration_seconds_bucket'
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interval: 1m
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without: [instance]
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outputs: [total]
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output_relabel_configs:
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- source_labels: [__name__]
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target_label: __name__
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```
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This config generates the following output metrics according to [output metric naming](#output-metric-names):
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```
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http_request_duration_seconds_bucket:1m_without_instance_total{le="0.1"} value1
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http_request_duration_seconds_bucket:1m_without_instance_total{le="0.2"} value2
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http_request_duration_seconds_bucket:1m_without_instance_total{le="0.4"} value3
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http_request_duration_seconds_bucket:1m_without_instance_total{le="1"} value4
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http_request_duration_seconds_bucket:1m_without_instance_total{le="3"} value5
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http_request_duration_seconds_bucket:1m_without_instance_total{le="8"} value6
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http_request_duration_seconds_bucket:1m_without_instance_total{le="20"} value7
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http_request_duration_seconds_bucket:1m_without_instance_total{le="60"} value8
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http_request_duration_seconds_bucket:1m_without_instance_total{le="120"} value9
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http_request_duration_seconds_bucket:1m_without_instance_total{le="+Inf" value10
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```
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The resulting metrics can be used in [histogram_quantile](https://docs.victoriametrics.com/MetricsQL.html#histogram_quantile)
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function:
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```metricsql
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histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket:1m_without_instance_total[5m])) by(le))
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```
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Please note, histograms can be aggregated if their `le` labels are configured identically.
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[VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
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have no such requirement.
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See [the list of aggregate output](#aggregation-outputs), which can be specified at `output` field.
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See also [histograms over input metrics](#histograms-over-input-metrics) and [quantiles over input metrics](#quantiles-over-input-metrics).
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## Output metric names
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Output metric names for stream aggregation are constructed according to the following pattern:
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```
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<metric_name>:<interval>[_by_<by_labels>][_without_<without_labels>]_<output>
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```
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- `<metric_name>` is the original metric name.
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- `<interval>` is the interval specified in the [stream aggregation config](#stream-aggregation-config).
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- `<by_labels>` is `_`-delimited sorted list of `by` labels specified in the [stream aggregation config](#stream-aggregation-config).
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If the `by` list is missing in the config, then the `_by_<by_labels>` part isn't included in the output metric name.
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- `<without_labels>` is an optional `_`-delimited sorted list of `without` labels specified in the [stream aggregation config](#stream-aggregation-config).
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If the `without` list is missing in the config, then the `_without_<without_labels>` part isn't included in the output metric name.
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- `<output>` is the aggregate used for constructing the output metric. The aggregate name is taken from the `outputs` list
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at the corresponding [stream aggregation config](#stream-aggregation-config).
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Both input and output metric names can be modified if needed via relabeling according to [these docs](#relabeling).
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## Relabeling
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It is possible to apply [arbitrary relabeling](https://docs.victoriametrics.com/vmagent.html#relabeling) to input and output metrics
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during stream aggregation via `input_relabel_configs` and `output_relabel_config` options in [stream aggregation config](#stream-aggregation-config).
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For example, the following config removes the `:1m_sum_samples` suffix added [to the output metric name](#output-metric-names):
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```yml
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- interval: 1m
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outputs: [sum_samples]
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output_relabel_configs:
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- source_labels: [__name__]
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target_label: __name__
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regex: "(.+):.+"
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```
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## Aggregation outputs
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The aggregations are calculated during the `interval` specified in the [config](#stream-aggregation-config)
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and then sent to the storage.
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If `by` and `without` lists are specified in the [config](#stream-aggregation-config),
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then the [aggregation by labels](#aggregating-by-labels) is performed additionally to aggregation by `interval`.
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Below are aggregation functions that can be put in the `outputs` list at [stream aggregation config](#stream-aggregation-config).
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### total
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`total` generates output [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) by summing the input counters.
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`total` only makes sense for aggregating [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) metrics.
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The results of `total` is equal to the `sum(some_counter)` query.
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For example, see below time series produced by config with aggregation interval `1m` and `by: ["instance"]` and the regular query:
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<img alt="total aggregation" src="stream-aggregation-check-total.png">
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`total` is not affected by [counter resets](https://docs.victoriametrics.com/keyConcepts.html#counter) -
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it continues to increase monotonically with respect to the previous value.
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The counters are most often reset when the application is restarted.
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For example:
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<img alt="total aggregation counter reset" src="stream-aggregation-check-total-reset.png">
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The same behavior will occur when creating or deleting new series in an aggregation group -
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`total` will increase monotonically considering the values of the series set.
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An example of changing a set of series can be restarting a pod in the Kubernetes.
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This changes a label with pod's name in the series, but `total` account for such a scenario and do not reset the state of aggregated metric.
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Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
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or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_inteval](#stream-aggregation-config).
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### increase
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`increase` returns the increase of input [counters](https://docs.victoriametrics.com/keyConcepts.html#counter).
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`increase` only makes sense for aggregating [counter](https://docs.victoriametrics.com/keyConcepts.html#counter) metrics.
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The results of `increase` with aggregation interval of `1m` is equal to the `increase(some_counter[1m])` query.
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For example, see below time series produced by config with aggregation interval `1m` and `by: ["instance"]` and the regular query:
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<img alt="increase aggregation" src="stream-aggregation-check-increase.png">
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`increase` can be used as an alternative for [rate](https://docs.victoriametrics.com/MetricsQL.html#rate) function.
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For example, if we have `increase` with `interval` of `5m` for a counter `some_counter`, then to get `rate` we should divide
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the resulting aggregation by the `interval` in seconds: `some_counter:5m_increase / 5m` is similar to `rate(some_counter[5m])`.
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Please note, opposite to [rate](https://docs.victoriametrics.com/MetricsQL.html#rate), `increase` aggregations can be
|
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combined safely afterwards. This is helpful when the aggregation is calculated by more than one vmagent.
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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).
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### count_series
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`count_series` counts the number of unique [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series).
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The results of `count_series` is equal to the `count(some_metric)` query.
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### count_samples
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`count_samples` counts the number of input [samples](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
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The results of `count_samples` with aggregation interval of `1m` is equal to the `count_over_time(some_metric[1m])` query.
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### sum_samples
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`sum_samples` sums input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
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`sum_samples` makes sense only for aggregating [gauge](https://docs.victoriametrics.com/keyConcepts.html#gauge) metrics.
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The results of `sum_samples` with aggregation interval of `1m` is equal to the `sum_over_time(some_metric[1m])` query.
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For example, see below time series produced by config with aggregation interval `1m` and the regular query:
|
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|
|
<img alt="sum_samples aggregation" src="stream-aggregation-check-sum-samples.png">
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|
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### last
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`last` returns the last input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
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The results of `last` with aggregation interval of `1m` is equal to the `last_over_time(some_metric[1m])` query.
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|
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This aggregation output doesn't make much sense with `by` lists specified in the [config](#stream-aggregation-config).
|
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The result of aggregation by labels in this case will be undetermined, because it depends on the order of processing the time series.
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### min
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`min` returns the minimum input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
|
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The results of `min` with aggregation interval of `1m` is equal to the `min_over_time(some_metric[1m])` query.
|
|
|
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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.png">
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### max
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`max` returns the maximum input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
|
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The results of `max` with aggregation interval of `1m` is equal to the `max_over_time(some_metric[1m])` query.
|
|
|
|
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.png">
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|
|
### avg
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|
|
`avg` returns the average input [sample value](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
|
|
|
|
The results of `avg` with aggregation interval of `1m` is equal to the `avg_over_time(some_metric[1m])` query.
|
|
|
|
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.png">
|
|
|
|
### 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).
|
|
`stddev` makes sense only for aggregating [gauge](https://docs.victoriametrics.com/keyConcepts.html#gauge) metrics.
|
|
|
|
The results of `stddev` with aggregation interval of `1m` is equal to the `stddev_over_time(some_metric[1m])` query.
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|
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### stdvar
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|
|
`stdvar` returns [standard variance](https://en.wikipedia.org/wiki/Variance) for the input [sample values](https://docs.victoriametrics.com/keyConcepts.html#raw-samples).
|
|
`stdvar` makes sense only for aggregating [gauge](https://docs.victoriametrics.com/keyConcepts.html#gauge) metrics.
|
|
|
|
The results of `stdvar` with aggregation interval of `1m` is equal to the `stdvar_over_time(some_metric[1m])` query.
|
|
|
|
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.png">
|
|
|
|
### 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).
|
|
`histogram_bucket` makes sense only for aggregating [gauge](https://docs.victoriametrics.com/keyConcepts.html#gauge) metrics.
|
|
See how to aggregate regular histograms [here](#aggregating-histograms).
|
|
|
|
The results of `histogram_bucket` with aggregation interval of `1m` is equal to the `histogram_over_time(some_histogram_bucket[1m])` 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_inteval](#stream-aggregation-config).
|
|
|
|
### 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).
|
|
The `phi` must be in the range `[0..1]`, where `0` means `0th` percentile, while `1` means `100th` percentile.
|
|
`quantiles(...)` makes sense only for aggregating [gauge](https://docs.victoriametrics.com/keyConcepts.html#gauge) metrics.
|
|
|
|
The results of `quantiles(phi1, ..., phiN)` with aggregation interval of `1m`
|
|
is equal to the `quantiles_over_time("quantile", phi1, ..., phiN, some_histogram_bucket[1m])` query.
|
|
|
|
Please note, `quantiles` aggregation won't produce correct results when vmagent is in [cluster mode](#cluster-mode)
|
|
since percentiles should be calculated only on the whole matched data set.
|
|
|
|
## 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
|
|
|
|
# staleness_interval defines an interval after which the series state will be reset if no samples have been sent during it.
|
|
# It 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, is equal to x2 of the `interval` field.
|
|
# The parameter is only relevant for outputs: total, increase and histogram_bucket.
|
|
#
|
|
# staleness_interval: 2m
|
|
|
|
# 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]
|
|
|
|
# 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:
|
|
|
|
```console
|
|
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.
|