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Docs: updates for file-based menu (#28500)

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* initial

* cleanup

* remove bad aliases

* cleanup, fix links

* add docs-file-based-command

* update docs

* update readme

* fix broken links

* fix spelling

Co-authored-by: Diana Payton <52059945+oddlittlebird@users.noreply.github.com>
This commit is contained in:
Robby Milo
2020-11-09 12:26:49 -08:00
committed by GitHub
co-authored by Diana Payton
parent a7e092f243
commit abee02dbcd
271 changed files with 260 additions and 1266 deletions
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docs/sources/getting-started/timeseries.md
+3 -8
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@@ -2,12 +2,7 @@
title = "Time series"
description = "Introduction to time series"
keywords = ["grafana", "intro", "guide", "concepts", "timeseries"]
type = "docs"
aliases = ["/docs/grafana/latest/guides/timeseries"]
[menu.docs]
name = "Time series"
identifier = "time_series"
parent = "guides"
weight = 300
+++
@@ -38,7 +33,7 @@ While each of these examples are sequences of chronologically ordered measuremen
- New data is appended at the end, at regular intervals—for example, hourly at 09:00, 10:00, 11:00, and so on.
- Measurements are seldom updated after they were added—for example, yesterday's temperature doesn't change.
Time series are powerful. They help you understand the past by letting you analyze the state of the system at any point in time. Time series could tell you that the server crashed moments after the free disk space went down to zero.
Time series are powerful. They help you understand the past by letting you analyze the state of the system at any point in time. Time series could tell you that the server crashed moments after the free disk space went down to zero.
Time series can also help you predict the future, by uncovering trends in your data. If the number of registered users has been increasing monthly by 4% for the past few months, you can predict how big your user base is going to be at the end of the year.
@@ -65,9 +60,9 @@ In the IT industry, time series data is often collected to monitor things like i
### Time series databases
A time series database (TSDB) is a database explicitly designed for time series data. While it's possible to use any regular database to store measurements, a TSDB comes with some useful optimizations.
A time series database (TSDB) is a database explicitly designed for time series data. While it's possible to use any regular database to store measurements, a TSDB comes with some useful optimizations.
Modern time series databases take advantage of the fact that measurements are only ever appended, and rarely updated or removed. For example, the timestamps for each measurement change very little over time, which results in redundant data being stored.
Modern time series databases take advantage of the fact that measurements are only ever appended, and rarely updated or removed. For example, the timestamps for each measurement change very little over time, which results in redundant data being stored.
Look at this sequence of Unix timestamps: