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docs: replace speed up with more clear accelerate wording

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Aliaksandr Valialkin 2024-03-12 02:54:46 +02:00
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2
docs/VictoriaLogs/LogsQL.md
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@ -246,7 +246,7 @@ The list of LogsQL filters:
### Time filter ### Time filter
VictoriaLogs scans all the logs per each query if it doesn't contain the filter on [`_time` field](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#time-field). VictoriaLogs scans all the logs per each query if it doesn't contain the filter on [`_time` field](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#time-field).
It uses various optimizations in order to speed up full scan queries without the `_time` filter, It uses various optimizations in order to accelerate full scan queries without the `_time` filter,
but such queries can be slow if the storage contains large number of logs over long time range. The easiest way to optimize queries but such queries can be slow if the storage contains large number of logs over long time range. The easiest way to optimize queries
is to narrow down the search with the filter on [`_time` field](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#time-field). is to narrow down the search with the filter on [`_time` field](https://docs.victoriametrics.com/VictoriaLogs/keyConcepts.html#time-field).

2
docs/stream-aggregation.md
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@ -124,7 +124,7 @@ Sometimes [alerting queries](https://docs.victoriametrics.com/vmalert.html#alert
disk IO and network bandwidth at metrics storage side. For example, if `http_request_duration_seconds` histogram is generated by thousands disk IO and network bandwidth at metrics storage side. For example, if `http_request_duration_seconds` histogram is generated by thousands
of application instances, then the alerting query `histogram_quantile(0.99, sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)) > 0.5` of application instances, then the alerting query `histogram_quantile(0.99, sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)) > 0.5`
can become slow, since it needs to scan too big number of unique [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series) can become slow, since it needs to scan too big number of unique [time series](https://docs.victoriametrics.com/keyConcepts.html#time-series)
with `http_request_duration_seconds_bucket` name. This alerting query can be speed up by pre-calculating with `http_request_duration_seconds_bucket` name. This alerting query can be accelerated by pre-calculating
the `sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)` via [recording rule](https://docs.victoriametrics.com/vmalert.html#recording-rules). the `sum(increase(http_request_duration_seconds_bucket[5m])) without (instance)` via [recording rule](https://docs.victoriametrics.com/vmalert.html#recording-rules).
But this recording rule may take too much time to execute too. In this case the slow recording rule can be substituted But this recording rule may take too much time to execute too. In this case the slow recording rule can be substituted
with the following [stream aggregation config](#stream-aggregation-config): with the following [stream aggregation config](#stream-aggregation-config):

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docs/vmbackup.md
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@ -14,7 +14,7 @@ aliases:
`vmbackup` creates VictoriaMetrics data backups from [instant snapshots](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-work-with-snapshots). `vmbackup` creates VictoriaMetrics data backups from [instant snapshots](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-work-with-snapshots).
`vmbackup` supports incremental and full backups. Incremental backups are created automatically if the destination path already contains data from the previous backup. `vmbackup` supports incremental and full backups. Incremental backups are created automatically if the destination path already contains data from the previous backup.
Full backups can be speed up with `-origin` pointing to an already existing backup on the same remote storage. In this case `vmbackup` makes server-side copy for the shared Full backups can be accelerated with `-origin` pointing to an already existing backup on the same remote storage. In this case `vmbackup` makes server-side copy for the shared
data between the existing backup and new backup. It saves time and costs on data transfer. data between the existing backup and new backup. It saves time and costs on data transfer.
Backup process can be interrupted at any time. It is automatically resumed from the interruption point when restarting `vmbackup` with the same args. Backup process can be interrupted at any time. It is automatically resumed from the interruption point when restarting `vmbackup` with the same args.
@ -54,7 +54,7 @@ Regular backup can be performed with the following command:
### Regular backups with server-side copy from existing backup ### Regular backups with server-side copy from existing backup
If the destination GCS bucket already contains the previous backup at `-origin` path, then new backup can be speed up If the destination GCS bucket already contains the previous backup at `-origin` path, then new backup can be accelerated
with the following command: with the following command:
```sh ```sh

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docs/vmbackupmanager.md
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@ -124,7 +124,7 @@ The result on the GCS bucket
<img alt="latest folder" src="vmbackupmanager_latest_folder.webp"> <img alt="latest folder" src="vmbackupmanager_latest_folder.webp">
`vmbackupmanager` uses [smart backups](https://docs.victoriametrics.com/vmbackup.html#smart-backups) technique in order `vmbackupmanager` uses [smart backups](https://docs.victoriametrics.com/vmbackup.html#smart-backups) technique in order
to speed up backups and save both data transfer costs and data copying costs. This includes server-side copy of already existing to accelerate backups and save both data transfer costs and data copying costs. This includes server-side copy of already existing
objects. Typical object storage systems implement server-side copy by creating new names for already existing objects. objects. Typical object storage systems implement server-side copy by creating new names for already existing objects.
This is very fast and efficient. Unfortunately there are systems such as [S3 Glacier](https://aws.amazon.com/s3/storage-classes/glacier/), This is very fast and efficient. Unfortunately there are systems such as [S3 Glacier](https://aws.amazon.com/s3/storage-classes/glacier/),
which perform full object copy during server-side copying. This may be slow and expensive. which perform full object copy during server-side copying. This may be slow and expensive.