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8207879fa3
Also tried to make it catch "Authorisation" in the future, fixed a lot of other misspells along the way, but didn't make it catch "Authorisation" anyway. - Fix misspelled "Authorization" header name - Fix misspelled "organization" - Fix more misspells
1204 lines
56 KiB
Markdown
1204 lines
56 KiB
Markdown
---
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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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[vmagent](https://docs.victoriametrics.com/vmagent/) and [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/)
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can aggregate incoming [samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) in streaming mode by time and by labels before data is written to remote storage
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(or local storage for single-node VictoriaMetrics).
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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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after applying all the configured [relabeling stages](https://docs.victoriametrics.com/vmagent/#relabeling).
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**By default, stream aggregation ignores timestamps associated with the input [samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples).
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It expects that the ingested samples have timestamps close to the current time. See [how to ignore old samples](#ignoring-old-samples).**
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## Configuration
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Stream aggregation can be configured via the following command-line flags:
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- `-streamAggr.config` at [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/)
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and at [vmagent](https://docs.victoriametrics.com/vmagent/).
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- `-remoteWrite.streamAggr.config` at [vmagent](https://docs.victoriametrics.com/vmagent/) only. This flag can be specified individually
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per each `-remoteWrite.url`, so the aggregation happens independently per each remote storage destination.
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This allows writing different aggregates to different remote storage systems.
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These flags must point to a file containing [stream aggregation config](#stream-aggregation-config).
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The file may contain `%{ENV_VAR}` placeholders which are substituted by the corresponding `ENV_VAR` environment variable values.
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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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- `-streamAggr.keepInput` at [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/)
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and [vmagent](https://docs.victoriametrics.com/vmagent/). At [vmagent](https://docs.victoriametrics.com/vmagent/)
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`-remoteWrite.streamAggr.keepInput` flag can be specified individually per each `-remoteWrite.url`.
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If one of these flags is set, then all the input samples are written to the storage alongside the aggregated samples.
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- `-streamAggr.dropInput` at [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/)
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and [vmagent](https://docs.victoriametrics.com/vmagent/). At [vmagent](https://docs.victoriametrics.com/vmagent/)
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`-remoteWrite.streamAggr.dropInput` flag can be specified individually per each `-remoteWrite.url`.
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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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## Routing
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[Single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) supports relabeling,
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deduplication and stream aggregation for all the received data, scraped or pushed.
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The processed data is then stored in local storage and **can't be forwarded further**.
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[vmagent](https://docs.victoriametrics.com/vmagent/) supports relabeling, deduplication and stream aggregation for all
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the received data, scraped or pushed. Then, the collected data will be forwarded to specified `-remoteWrite.url` destinations.
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The data processing order is the following:
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1. all the received data is relabeled according to the specified [`-remoteWrite.relabelConfig`](https://docs.victoriametrics.com/vmagent/#relabeling) (if it is set)
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1. all the received data is deduplicated according to specified [`-streamAggr.dedupInterval`](https://docs.victoriametrics.com/stream-aggregation/#deduplication)
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(if it is set to duration bigger than 0)
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1. all the received data is aggregated according to specified [`-streamAggr.config`](https://docs.victoriametrics.com/stream-aggregation/#configuration) (if it is set)
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1. the resulting data is then replicated to each `-remoteWrite.url`
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1. data sent to each `-remoteWrite.url` can be additionally relabeled according to the corresponding `-remoteWrite.urlRelabelConfig` (set individually per URL)
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1. data sent to each `-remoteWrite.url` can be additionally deduplicated according to the corresponding `-remoteWrite.streamAggr.dedupInterval` (set individually per URL)
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1. data sent to each `-remoteWrite.url` can be additionally aggregated according to the corresponding `-remoteWrite.streamAggr.config` (set individually per URL)
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It isn't recommended using `-streamAggr.config` and `-remoteWrite.streamAggr.config` simultaneously, unless you understand the complications.
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Typical scenarios for data routing with `vmagent`:
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1. **Aggregate incoming data and replicate to N destinations**. Specify [`-streamAggr.config`](https://docs.victoriametrics.com/stream-aggregation/#configuration) command-line flag
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to aggregate the incoming data before replicating it to all the configured `-remoteWrite.url` destinations.
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2. **Individually aggregate incoming data for each destination**. Specify [`-remoteWrite.streamAggr.config`](https://docs.victoriametrics.com/stream-aggregation/#configuration)
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command-line flag for each `-remoteWrite.url` destination. [Relabeling](https://docs.victoriametrics.com/vmagent/#relabeling) via `-remoteWrite.urlRelabelConfig`
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can be used for routing only the selected metrics to each `-remoteWrite.url` destination.
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## Deduplication
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[vmagent](https://docs.victoriametrics.com/vmagent/) supports online [de-duplication](https://docs.victoriametrics.com/#deduplication) of samples
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before sending them to the configured `-remoteWrite.url`. The de-duplication can be enabled via the following options:
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- By specifying the desired de-duplication interval via `-streamAggr.dedupInterval` command-line flag for all received data
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or via `-remoteWrite.streamAggr.dedupInterval` command-line flag for the particular `-remoteWrite.url` destination.
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For example, `./vmagent -remoteWrite.url=http://remote-storage/api/v1/write -remoteWrite.streamAggr.dedupInterval=30s` instructs `vmagent` to leave
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only the last sample per each seen [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) per every 30 seconds.
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The de-deduplication is performed after applying [relabeling](https://docs.victoriametrics.com/vmagent/#relabeling) and
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before performing the aggregation.
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- By specifying `dedup_interval` option individually per each [stream aggregation config](#stream-aggregation-config)
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in `-remoteWrite.streamAggr.config` or `-streamAggr.config` configs.
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[Single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) supports two types of de-duplication:
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- After storing the duplicate samples to local storage. See [`-dedup.minScrapeInterval`](https://docs.victoriametrics.com/#deduplication) command-line option.
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- Before storing the duplicate samples to local storage. This type of de-duplication can be enabled via the following options:
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- By specifying the desired de-duplication interval via `-streamAggr.dedupInterval` command-line flag.
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For example, `./victoria-metrics -streamAggr.dedupInterval=30s` instructs VictoriaMetrics to leave only the last sample per each
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seen [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) per every 30 seconds.
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The de-duplication is performed after applying `-relabelConfig` [relabeling](https://docs.victoriametrics.com/#relabeling).
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- By specifying `dedup_interval` option individually per each [stream aggregation config](#stream-aggregation-config) at `-streamAggr.config`.
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It is possible to drop the given labels before applying the de-duplication. See [these docs](#dropping-unneeded-labels).
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The online de-duplication uses the same logic as [`-dedup.minScrapeInterval` command-line flag](https://docs.victoriametrics.com/#deduplication) at VictoriaMetrics.
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## Ignoring old samples
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By default, all the input samples are taken into account during stream aggregation. If samples with old timestamps
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outside the current [aggregation interval](#stream-aggregation-config) must be ignored, then the following options can be used:
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- To pass `-streamAggr.ignoreOldSamples` command-line flag to [single-node VictoriaMetrics](https://docs.victoriametrics.com/)
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or to [vmagent](https://docs.victoriametrics.com/vmagent/). At [vmagent](https://docs.victoriametrics.com/vmagent/)
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`-remoteWrite.streamAggr.ignoreOldSamples` flag can be specified individually per each `-remoteWrite.url`.
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This enables ignoring old samples for all the [aggregation configs](#stream-aggregation-config).
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- To set `ignore_old_samples: true` option at the particular [aggregation config](#stream-aggregation-config).
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This enables ignoring old samples for that particular aggregation config.
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## Ignore aggregation intervals on start
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Streaming aggregation results may be incorrect for some time after the restart of [vmagent](https://docs.victoriametrics.com/vmagent/)
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or [single-node VictoriaMetrics](https://docs.victoriametrics.com/) until all the buffered [samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
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are sent from remote sources to the `vmagent` or single-node VictoriaMetrics via [supported data ingestion protocols](https://docs.victoriametrics.com/vmagent/#how-to-push-data-to-vmagent).
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In this case it may be a good idea to drop the aggregated data during the first `N` [aggregation intervals](#stream-aggregation-config)
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just after the restart of `vmagent` or single-node VictoriaMetrics. This can be done via the following options:
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- The `-streamAggr.ignoreFirstIntervals=N` command-line flag at `vmagent` and single-node VictoriaMetrics. This flag instructs skipping the first `N`
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[aggregation intervals](#stream-aggregation-config) just after the restart across all the [configured stream aggregation configs](#configuration).
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The `-remoteWrite.streamAggr.ignoreFirstIntervals` command-line flag can be specified individually per each `-remoteWrite.url` at [vmagent](https://docs.victoriametrics.com/vmagent/).
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- The `ignore_first_intervals: N` option at the particular [aggregation config](#stream-aggregation-config).
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See also:
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- [Flush time alignment](#flush-time-alignment)
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- [Ignoring old samples](#ignoring-old-samples)
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## Flush time alignment
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By default, the time for aggregated data flush is aligned by the `interval` option specified in [aggregate config](#stream-aggregation-config).
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For example:
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- if `interval: 1m` is set, then the aggregated data is flushed to the storage at the end of every minute
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- if `interval: 1h` is set, then the aggregated data is flushed to the storage at the end of every hour
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If you do not need such an alignment, then set `no_align_flush_to_interval: true` option in the [aggregate config](#stream-aggregation-config).
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In this case aggregated data flushes will be aligned to the `vmagent` start time or to [config reload](#configuration-update) time.
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The aggregated data on the first and the last interval is dropped during `vmagent` start, restart or [config reload](#configuration-update),
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since the first and the last aggregation intervals are incomplete, so they usually contain incomplete confusing data.
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If you need preserving the aggregated data on these intervals, then set `flush_on_shutdown: true` option in the [aggregate config](#stream-aggregation-config).
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See also:
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- [Ignore aggregation intervals on start](#ignore-aggregation-intervals-on-start)
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- [Ignoring old samples](#ignoring-old-samples)
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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/#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/#time-series)
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with `http_request_duration_seconds_bucket` name. This alerting query can be accelerated 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/#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/#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/#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/#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/#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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```text
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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/#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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```text
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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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```yaml
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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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```text
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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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```yaml
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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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```text
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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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```text
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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/#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
|
|
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/) 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/#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/#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/#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/#histogram) is a set of [counter](https://docs.victoriametrics.com/keyconcepts/#counter)
|
|
metrics with different `vmrange` or `le` labels. As they're counters, the applicable aggregation output is
|
|
[total](https://docs.victoriametrics.com/stream-aggregation/#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/#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 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/#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 before optional [deduplication](#deduplication).
|
|
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
|
|
```
|
|
|
|
See also [dropping unneeded labels](#dropping-unneeded-labels).
|
|
|
|
|
|
## Dropping unneeded labels
|
|
|
|
If you need dropping some labels from input samples before [input relabeling](#relabeling), [de-duplication](#deduplication)
|
|
and [stream aggregation](#aggregation-outputs), then the following options exist:
|
|
|
|
- To specify comma-separated list of label names to drop in `-streamAggr.dropInputLabels` command-line flag
|
|
or via `-remoteWrite.streamAggr.dropInputLabels` individually per each `-remoteWrite.url`.
|
|
For example, `-streamAggr.dropInputLabels=replica,az` instructs to drop `replica` and `az` labels from input samples
|
|
before applying de-duplication and stream aggregation.
|
|
|
|
- To specify `drop_input_labels` list with the labels to drop in [stream aggregation config](#stream-aggregation-config).
|
|
For example, the following config drops `replica` label from input samples with the name `process_resident_memory_bytes`
|
|
before calculating the average over one minute:
|
|
|
|
```yaml
|
|
- match: process_resident_memory_bytes
|
|
interval: 1m
|
|
drop_input_labels: [replica]
|
|
outputs: [avg]
|
|
keep_metric_names: true
|
|
```
|
|
|
|
Typical use case is to drop `replica` label from samples, which are received from high availability replicas.
|
|
|
|
## 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`.
|
|
|
|
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)
|
|
* [histogram_bucket](#histogram_bucket)
|
|
* [increase](#increase)
|
|
* [increase_prometheus](#increase_prometheus)
|
|
* [last](#last)
|
|
* [max](#max)
|
|
* [min](#min)
|
|
* [rate_avg](#rate_avg)
|
|
* [rate_sum](#rate_sum)
|
|
* [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/#raw-samples).
|
|
`avg` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#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:
|
|
|
|
|
|
|
|
See also:
|
|
|
|
- [max](#max)
|
|
- [min](#min)
|
|
- [quantiles](#quantiles)
|
|
- [sum_samples](#sum_samples)
|
|
- [count_samples](#count_samples)
|
|
|
|
### count_samples
|
|
|
|
`count_samples` counts the number of input [samples](https://docs.victoriametrics.com/keyconcepts/#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)
|
|
- [sum_samples](#sum_samples)
|
|
|
|
### count_series
|
|
|
|
`count_series` counts the number of unique [time series](https://docs.victoriametrics.com/keyconcepts/#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)
|
|
- [unique_samples](#unique_samples)
|
|
|
|
### histogram_bucket
|
|
|
|
`histogram_bucket` returns [VictoriaMetrics histogram buckets](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
|
|
for the input [sample values](https://docs.victoriametrics.com/keyconcepts/#raw-samples) over the given `interval`.
|
|
`histogram_bucket` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge).
|
|
See how to aggregate regular histograms [here](#aggregating-histograms).
|
|
|
|
The results of `histogram_bucket` is equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
|
|
|
|
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
|
|
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
|
|
|
|
```metricsql
|
|
sum(histogram_over_time(some_histogram_bucket[interval])) by (vmrange)
|
|
```
|
|
|
|
See also:
|
|
|
|
- [quantiles](#quantiles)
|
|
- [avg](#avg)
|
|
- [max](#max)
|
|
- [min](#min)
|
|
|
|
### increase
|
|
|
|
`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/#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 first 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:
|
|
|
|
|
|
|
|
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
|
|
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
|
|
|
|
See also:
|
|
|
|
- [increase_prometheus](#increase_prometheus)
|
|
- [total](#total)
|
|
- [rate_avg](#rate_avg)
|
|
- [rate_sum](#rate_sum)
|
|
|
|
### 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/#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).
|
|
|
|
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
|
|
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
|
|
|
|
See also:
|
|
|
|
- [increase](#increase)
|
|
- [rate_avg](#rate_avg)
|
|
- [rate_sum](#rate_sum)
|
|
- [total](#total)
|
|
- [total_prometheus](#total_prometheus)
|
|
|
|
### last
|
|
|
|
`last` returns the last input [sample value](https://docs.victoriametrics.com/keyconcepts/#raw-samples) over the given `interval`.
|
|
|
|
The results of `last` is roughly equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
|
|
|
|
```metricsql
|
|
last_over_time(some_metric[interval])
|
|
```
|
|
|
|
See also:
|
|
|
|
- [avg](#avg)
|
|
- [max](#max)
|
|
- [min](#min)
|
|
- [quantiles](#quantiles)
|
|
|
|
### max
|
|
|
|
`max` returns the maximum input [sample value](https://docs.victoriametrics.com/keyconcepts/#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:
|
|
|
|
|
|
|
|
See also:
|
|
|
|
- [min](#min)
|
|
- [avg](#avg)
|
|
- [last](#last)
|
|
- [quantiles](#quantiles)
|
|
|
|
### min
|
|
|
|
`min` returns the minimum input [sample value](https://docs.victoriametrics.com/keyconcepts/#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:
|
|
|
|
|
|
|
|
See also:
|
|
|
|
- [max](#max)
|
|
- [avg](#avg)
|
|
- [last](#last)
|
|
- [quantiles](#quantiles)
|
|
|
|
### rate_avg
|
|
|
|
`rate_avg` returns the average of average per-second increase rates across input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
|
|
`rate_avg` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
|
|
|
|
The results of `rate_avg` are equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
|
|
|
|
```metricsql
|
|
avg(rate(some_counter[interval]))
|
|
```
|
|
|
|
See also:
|
|
|
|
- [rate_sum](#rate_sum)
|
|
- [increase](#increase)
|
|
- [total](#total)
|
|
|
|
### rate_sum
|
|
|
|
`rate_sum` returns the sum of average per-second increase rates across input [time series](https://docs.victoriametrics.com/keyconcepts/#time-series) over the given `interval`.
|
|
`rate_sum` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
|
|
|
|
The results of `rate_sum` are equal to the following [MetricsQL](https://docs.victoriametrics.com/metricsql/) query:
|
|
|
|
```metricsql
|
|
sum(rate(some_counter[interval]))
|
|
```
|
|
|
|
See also:
|
|
|
|
- [rate_avg](#rate_avg)
|
|
- [increase](#increase)
|
|
- [total](#total)
|
|
|
|
### stddev
|
|
|
|
`stddev` returns [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) for the input [sample values](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
|
|
over the given `interval`.
|
|
`stddev` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#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)
|
|
- [avg](#avg)
|
|
- [quantiles](#quantiles)
|
|
|
|
### stdvar
|
|
|
|
`stdvar` returns [standard variance](https://en.wikipedia.org/wiki/Variance) for the input [sample values](https://docs.victoriametrics.com/keyconcepts/#raw-samples)
|
|
over the given `interval`.
|
|
`stdvar` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#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:
|
|
|
|
|
|
|
|
See also:
|
|
|
|
- [stddev](#stddev)
|
|
- [avg](#avg)
|
|
- [quantiles](#quantiles)
|
|
|
|
### sum_samples
|
|
|
|
`sum_samples` sums input [sample values](https://docs.victoriametrics.com/keyconcepts/#raw-samples) over the given `interval`.
|
|
`sum_samples` makes sense only for aggregating [gauges](https://docs.victoriametrics.com/keyconcepts/#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:
|
|
|
|
|
|
|
|
See also:
|
|
|
|
- [count_samples](#count_samples)
|
|
- [count_series](#count_series)
|
|
|
|
### total
|
|
|
|
`total` generates output [counter](https://docs.victoriametrics.com/keyconcepts/#counter) by summing the input counters over the given `interval`.
|
|
`total` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
|
|
|
|
The results of `total` is roughly equal to 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 first 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:
|
|
|
|
|
|
|
|
`total` is not affected by [counter resets](https://docs.victoriametrics.com/keyconcepts/#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:
|
|
|
|
|
|
|
|
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_interval](#staleness) option.
|
|
|
|
See also:
|
|
|
|
- [total_prometheus](#total_prometheus)
|
|
- [increase](#increase)
|
|
- [increase_prometheus](#increase_prometheus)
|
|
- [rate_sum](#rate_sum)
|
|
- [rate_avg](#rate_avg)
|
|
|
|
### total_prometheus
|
|
|
|
`total_prometheus` generates output [counter](https://docs.victoriametrics.com/keyconcepts/#counter) by summing the input counters over the given `interval`.
|
|
`total_prometheus` makes sense only for aggregating [counters](https://docs.victoriametrics.com/keyconcepts/#counter).
|
|
|
|
The results of `total_prometheus` is roughly equal to 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/#counter) -
|
|
it continues to increase monotonically with respect to the previous value.
|
|
The counters are most often reset when the application is restarted.
|
|
|
|
Aggregating irregular and sporadic metrics (received from [Lambdas](https://aws.amazon.com/lambda/)
|
|
or [Cloud Functions](https://cloud.google.com/functions)) can be controlled via [staleness_interval](#staleness) option.
|
|
|
|
See also:
|
|
|
|
- [total](#total)
|
|
- [increase](#increase)
|
|
- [increase_prometheus](#increase_prometheus)
|
|
- [rate_sum](#rate_sum)
|
|
- [rate_avg](#rate_avg)
|
|
|
|
### 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/#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)
|
|
- [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/#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/#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)
|
|
- [avg](#avg)
|
|
- [max](#max)
|
|
- [min](#min)
|
|
|
|
|
|
## 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 `-streamAggr.config` command-line flag at
|
|
[single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/) and [vmagent](https://docs.victoriametrics.com/vmagent/).
|
|
At [vmagent](https://docs.victoriametrics.com/vmagent/) `-remoteWrite.streamAggr.config` command-line flag can be
|
|
specified individually per each `-remoteWrite.url`:
|
|
|
|
```yaml
|
|
|
|
# name is an optional name of the given streaming aggregation config.
|
|
#
|
|
# If it is set, then it is used as `name` label in the exposed metrics
|
|
# for the given aggregation config at /metrics page.
|
|
# See https://docs.victoriametrics.com/vmagent/#monitoring and https://docs.victoriametrics.com/#monitoring
|
|
- name: 'foobar'
|
|
|
|
# match is an optional filter for incoming samples to aggregate.
|
|
# It can contain arbitrary Prometheus series selector
|
|
# according to https://docs.victoriametrics.com/keyconcepts/#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 unless -remoteWrite.streamAggr.dedupInterval or -streamAggr.dedupInterval
|
|
# command-line flags are set.
|
|
#
|
|
# dedup_interval: 30s
|
|
|
|
# staleness_interval is an optional interval for resetting the per-series state if no new samples
|
|
# are received during this interval for the following outputs:
|
|
# - histogram_bucket
|
|
# - increase
|
|
# - increase_prometheus
|
|
# - rate_avg
|
|
# - rate_sum
|
|
# - total
|
|
# - total_prometheus
|
|
# See https://docs.victoriametrics.com/stream-aggregation/#staleness for more details.
|
|
#
|
|
# staleness_interval: 2m
|
|
|
|
# no_align_flush_to_interval disables aligning of flush times for the aggregated data to multiples of interval.
|
|
# By default, flush times for the aggregated data is aligned to multiples of interval.
|
|
# For example:
|
|
# - if `interval: 1m` is set, then flushes happen at the end of every minute,
|
|
# - if `interval: 1h` is set, then flushes happen at the end of every hour
|
|
#
|
|
# no_align_flush_to_interval: false
|
|
|
|
# flush_on_shutdown instructs to flush aggregated data to the storage on the first and the last intervals
|
|
# during vmagent starts, restarts or configuration reloads.
|
|
# Incomplete aggregated data isn't flushed to the storage by default, since it is usually confusing.
|
|
#
|
|
# 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/#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/#aggregating-by-labels
|
|
#
|
|
# by: [job, vmrange]
|
|
|
|
# outputs is the list of unique aggregations to perform on the input data.
|
|
# See https://docs.victoriametrics.com/stream-aggregation/#aggregation-outputs
|
|
#
|
|
outputs: [total]
|
|
|
|
# keep_metric_names instructs keeping the original metric names for the aggregated samples.
|
|
# This option can't be enabled together with `-streamAggr.keepInput` or `-remoteWrite.streamAggr.keepInput`.
|
|
# This option can be set only if outputs list contains 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
|
|
|
|
# ignore_old_samples instructs ignoring input samples with old timestamps outside the current aggregation interval.
|
|
# See https://docs.victoriametrics.com/stream-aggregation/#ignoring-old-samples
|
|
# See also -remoteWrite.streamAggr.ignoreOldSamples and -streamAggr.ignoreOldSamples command-line flag.
|
|
#
|
|
# ignore_old_samples: false
|
|
|
|
# ignore_first_intervals instructs ignoring the first N aggregation intervals after process start.
|
|
# See https://docs.victoriametrics.com/stream-aggregation/#ignore-aggregation-intervals-on-start
|
|
# See also -remoteWrite.streamAggr.ignoreFirstIntervals and -streamAggr.ignoreFirstIntervals command-line flags.
|
|
#
|
|
# ignore_first_intervals: N
|
|
|
|
# drop_input_labels instructs dropping the given labels from input samples.
|
|
# The labels' dropping is performed before input_relabel_configs are applied.
|
|
# This also means that the labels are dropped before de-duplication ( https://docs.victoriametrics.com/stream-aggregation/#deduplication )
|
|
# and stream aggregation.
|
|
#
|
|
# drop_input_labels: [replica, availability_zone]
|
|
|
|
# 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/#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/) and [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/)
|
|
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).
|
|
|
|
|
|
## Troubleshooting
|
|
|
|
- [Unexpected spikes for `total` or `increase` outputs](#staleness).
|
|
- [Lower than expected values for `total_prometheus` and `increase_prometheus` outputs](#staleness).
|
|
- [High memory usage and CPU usage](#high-resource-usage).
|
|
- [Unexpected results in vmagent cluster mode](#cluster-mode).
|
|
|
|
### Staleness
|
|
|
|
The following outputs track the last seen per-series values in order to properly calculate output values:
|
|
|
|
- [histogram_bucket](#histogram_bucket)
|
|
- [increase](#increase)
|
|
- [increase_prometheus](#increase_prometheus)
|
|
- [rate_avg](#rate_avg)
|
|
- [rate_sum](#rate_sum)
|
|
- [total](#total)
|
|
- [total_prometheus](#total_prometheus)
|
|
|
|
The last seen per-series value is dropped if no new samples are received for the given time series during two consecutive aggregation
|
|
intervals specified in [stream aggregation config](#stream-aggregation-config) via `interval` option.
|
|
If a new sample for the existing time series is received after that, then it is treated as the first sample for a new time series.
|
|
This may lead to the following issues:
|
|
|
|
- Lower than expected results for [total_prometheus](#total_prometheus) and [increase_prometheus](#increase_prometheus) outputs,
|
|
since they ignore the first sample in a new time series.
|
|
- Unexpected spikes for [total](#total) and [increase](#increase) outputs, since they assume that new time series start from 0.
|
|
|
|
These issues can be fixed in the following ways:
|
|
|
|
- By increasing the `interval` option at [stream aggregation config](#stream-aggregation-config), so it covers the expected
|
|
delays in data ingestion pipelines.
|
|
- By specifying the `staleness_interval` option at [stream aggregation config](#stream-aggregation-config), so it covers the expected
|
|
delays in data ingestion pipelines. By default, the `staleness_interval` equals to `2 x interval`.
|
|
|
|
### High resource usage
|
|
|
|
The following solutions can help reducing memory usage and CPU usage during streaming aggregation:
|
|
|
|
- To use more specific `match` filters at [streaming aggregation config](#stream-aggregation-config), so only the really needed
|
|
[raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) are aggregated.
|
|
- To increase aggregation interval by specifying bigger duration for the `interval` option at [streaming aggregation config](#stream-aggregation-config).
|
|
- To generate lower number of output time series by using less specific [`by` list](#aggregating-by-labels) or more specific [`without` list](#aggregating-by-labels).
|
|
- To drop unneeded long labels in input samples via [input_relabel_configs](#relabeling).
|
|
|
|
### Cluster mode
|
|
|
|
If you use [vmagent in cluster mode](https://docs.victoriametrics.com/vmagent/#scraping-big-number-of-targets) for streaming aggregation
|
|
then be careful when using [`by` or `without` options](#aggregating-by-labels) or when modifying sample labels
|
|
via [relabeling](#relabeling), since incorrect usage may result in duplicates and data collision.
|
|
|
|
For example, if more than one `vmagent` instance calculates [increase](#increase) for `http_requests_total` metric
|
|
with `by: [path]` option, then all the `vmagent` instances will aggregate samples to the same set of time series with different `path` labels.
|
|
The proper fix would be [adding an unique label](https://docs.victoriametrics.com/vmagent/#adding-labels-to-metrics) for all the output samples
|
|
produced by each `vmagent`, so they are aggregated into distinct sets of [time series](https://docs.victoriametrics.com/keyconcepts/#time-series).
|
|
These time series then can be aggregated later as needed during querying.
|
|
|
|
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` via `-remoteWrite.label=vmagent=%{HOSTNAME}` command-line flag.
|
|
See [these docs](https://docs.victoriametrics.com/#environment-variables) on how to refer environment variables in VictoriaMetrics components.
|