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docs/Single-server-VictoriaMetrics.md: imrpove docs about metrics deletion

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Aliaksandr Valialkin 2020-04-16 17:32:02 +03:00
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docs/Single-server-VictoriaMetrics.md
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@ -602,7 +602,7 @@ Steps for restoring from a snapshot:
Send a request to `http://<victoriametrics-addr>:8428/api/v1/admin/tsdb/delete_series?match[]=<timeseries_selector_for_delete>`,
where `<timeseries_selector_for_delete>` may contain any [time series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors)
for metrics to delete. After that all the time series matching the given selector are deleted. Storage space for
the deleted time series isn't freed instantly - it is freed during subsequent merges of data files.
the deleted time series isn't freed instantly - it is freed during subsequent [background merges of data files](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
It is recommended verifying which metrics will be deleted with the call to `http://<victoria-metrics-addr>:8428/api/v1/series?match[]=<timeseries_selector_for_delete>`
before actually deleting the metrics.
@ -617,9 +617,10 @@ The delete API is intended mainly for the following cases:
It isn't recommended using delete API for the following cases, since it brings non-zero overhead:
* Regular cleanups for unneded data. Just prevent writing unneeded data into VictoriaMetrics.
This can be done with relabeling in [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md).
See [this article](https://www.robustperception.io/relabelling-can-discard-targets-timeseries-and-alerts) for details.
* Reducing disk space usage by deleting unneded time series. This doesn't work as expected, since the deleted
time series occupy disk space until the next merge operation, which can never occur.
time series occupy disk space until the next merge operation, which can never occur when deleting too old data.
It is better using `-retentionPeriod` command-line flag for efficient pruning of old data.