cleanup shortcodes, image paths (#34827)
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Robby Milo
@@ -65,7 +65,7 @@ This step varies depending on the query language that you selected when you set
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In the query editor, click **select measurement**.
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Grafana displays a list of possible series. Click one to select it, and Grafana graphs any available data. If there is no data to display, then try another selection or check your data source.
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@@ -80,19 +80,19 @@ Create a simple Flux query.
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This generic query returns a list of buckets.
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You can also create Flux queries in the InfluxDB Explore view.
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1. In your browser, log in to the InfluxDB native UI (OSS is typically something like http://localhost:8086 or for InfluxDB Cloud use: https://cloud2.influxdata.com).
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1. Click **Explore** to open the Data Explorer.
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1. The InfluxDB Data Explorer provides two mechanisms for creating Flux queries: a graphical query editor and a script editor. Using the graphical query editor, [create a query](https://docs.influxdata.com/influxdb/cloud/query-data/execute-queries/data-explorer/). It will look something like this:
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1. Click **Script Editor** to view the text of the query, and then copy all the lines of your Flux code, which will look something like this:
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1. In Grafana, [add a panel](../panels/add-a-panel.md) and then paste your Flux code into the query editor.
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1. Click **Apply**. Your new panel should be visible with data from your Flux query.
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@@ -61,4 +61,4 @@ Now that you have a curated list of queries, create [dashboards]({{< relref "../
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The following image shows a dashboard with three panels showing some system metrics.
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<img width="850px" src="/img/docs/getting-started/simple_grafana_prom_dashboard.png" caption="Prometheus dashboards" >
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<img width="850px" src="/static/img/docs/getting-started/simple_grafana_prom_dashboard.png" caption="Prometheus dashboards" >
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@@ -30,18 +30,18 @@ To add MS SQL Server data source:
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1. Filter by `mssql` and select the **Microsoft SQL Server** option.
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1. Click **Add data source** in the top right header to open the configuration page.
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1. Enter the information specified in the table below, then click **Save & Test**.
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Name | Description
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------------ | -------------
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`Name` | The data source name. This is how you refer to the data source in panels and queries.
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`Host` | The IP address/hostname and optional port of your MS SQL instance. If port is omitted, the default 1433 will be used.
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`Database` | Name of your MS SQL database.
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`User` | Database user's login/username.
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`Password` | Database user's password.
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`Password` | Database user's password.
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For installations from the [grafana/grafana](https://github.com/grafana/grafana/tree/master) repository, `gdev-mssql` data source is available. Once you add this data source, you can use the `Datasource tests - MSSQL` dashboard with three panels showing metrics generated from a test database.
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<img src="/img/docs/getting-started/gdev-sql-dashboard.png" class="no-shadow" width="700px">
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<img src="/static/img/docs/getting-started/gdev-sql-dashboard.png" class="no-shadow" width="700px">
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Optionally, play around this dashboard and customize it to:
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- Create different panels.
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@@ -55,5 +55,5 @@ Optionally, play around this dashboard and customize it to:
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Now that you have gained some idea of using the pre-packaged MS SQL data source and some test data, the next step is to setup your own instance of MS SQL Server database and data your development or sandbox area. In the previous steps, if you followed along the path of deploying your own instance of MS SQL Server, you are already on your way.
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To fetch data from your own instance of MS SQL Server, add the data source using instructions in Step 4 of this topic. In Grafana [Explore]({{< relref "../explore/_index.md" >}}) build queries to experiment with the metrics you want to monitor.
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Once you have a curated list of queries, create [dashboards]({{< relref "../dashboards/_index.md" >}}) to render metrics from the SQL Server database. For troubleshooting, user permissions, known issues, and query examples, refer to [Using Microsoft SQL Server in Grafana]({{< relref "../datasources/mssql.md" >}}).
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@@ -18,7 +18,7 @@ and the bar height represents the frequency (such as count) of values that fell
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This histogram shows the value distribution of a couple of time series. You can easily see that
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most values land between 240-300 with a peak between 260-280.
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Histograms only look at _value distributions_ over a specific time range. The problem with histograms is you cannot see any trends or changes in the distribution over time.
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This is where heatmaps become useful.
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@@ -29,7 +29,7 @@ A _heatmap_ is like a histogram, but over time where each time slice represents
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In this example, you can clearly see what values are more common and how they trend over time.
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## Pre-bucketed data
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@@ -14,7 +14,7 @@ In [Introduction to time series]({{< relref "timeseries.md#time-series-databases
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With time series data, the data often contain more than a single series, and is a set of multiple time series. Many Grafana data sources support this type of data.
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{{< docs-imagebox img="/img/docs/example_graph_multi_dim.png" class="docs-image--no-shadow" max-width="850px" >}}
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{{< figure src="/static/img/docs/example_graph_multi_dim.png" class="docs-image--no-shadow" max-width="850px" >}}
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The common case is issuing a single query for a measurement with one or more additional properties as dimensions. For example, querying a temperature measurement along with a location property. In this case, multiple series are returned back from that single query and each series has unique location as a dimension.
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@@ -20,7 +20,7 @@ Temperature data like this is one example of what we call a *time series*—a se
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Tables are useful when you want to identify individual measurements but make it difficult to see the big picture. A more common visualization for time series is the _graph_, which instead places each measurement along a time axis. Visual representations like the graph make it easier to discover patterns and features of the data that otherwise would be difficult to see.
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{{< docs-imagebox img="/img/docs/example_graph.png" class="docs-image--no-shadow" max-width="850px" >}}
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{{< figure src="/static/img/docs/example_graph.png" class="docs-image--no-shadow" max-width="850px" >}}
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Temperature data like the one in the example, is far from the only example of a time series. Other examples of time series are:
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