cleanup shortcodes, image paths (#34827)

2021-05-28 11:31:48 +02:00
parent c17faa52f0
commit ede2a61809
135 changed files with 547 additions and 547 deletions
@@ -11,12 +11,12 @@ An exemplar is a specific trace representative of a repeated pattern of data in

 Suppose your company website is experiencing a surge in traffic volumes. While more than eighty percent of the users are able to access the website in under two seconds, some users are experiencing a higher than normal response time resulting in bad user experience

-To identify the factors that are contributing to the latency, you must compare a trace for a fast response against a trace for a slow response. Given the vast amount of data in a typical production environment, it will be extremely laborious and time-consuming effort. 
+To identify the factors that are contributing to the latency, you must compare a trace for a fast response against a trace for a slow response. Given the vast amount of data in a typical production environment, it will be extremely laborious and time-consuming effort.

 Use exemplars to help isolate problems within your data distribution by pinpointing query traces exhibiting high latency within a time interval. Once you localize the latency problem to a few exemplar traces, you can combine it with additional system based information or location properties to perform a root cause analysis faster, leading to quick resolutions to performance issues.

 Support for exemplars is available for the Prometheus data source only. Once you enable the functionality, exemplars data is available by default. For more information on exemplar configuration and how to enable exemplars, refer to [configuring exemplars in Prometheus data source]({{< relref "../datasources/prometheus.md#configuring-exemplars" >}}).

-Grafana shows exemplars alongside a metric in the Explore view and in dashboards. Each exemplar displays as a highlighted star. You can hover your cursor over an exemplar to view the unique traceID, which is a combination of a key value pair. To investigate further, click the blue button next to the `traceID` property. 
+Grafana shows exemplars alongside a metric in the Explore view and in dashboards. Each exemplar displays as a highlighted star. You can hover your cursor over an exemplar to view the unique traceID, which is a combination of a key value pair. To investigate further, click the blue button next to the `traceID` property.

-{{< docs-imagebox img="/img/docs/v74/exemplars.png" class="docs-image--no-shadow" max-width= "750px" caption="Screenshot showing the detail window of an Exemplar" >}}
+{{< figure src="/static/img/docs/v74/exemplars.png" class="docs-image--no-shadow" max-width= "750px" caption="Screenshot showing the detail window of an Exemplar" >}}
@@ -19,7 +19,7 @@ and the bar height represents the frequency (such as count) of values that fell
 This histogram shows the value distribution of a couple of time series. You can easily see that
 most values land between 240-300 with a peak between 260-280.

-![](/img/docs/v43/heatmap_histogram.png)
+![](/static/img/docs/v43/heatmap_histogram.png)

 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.
 This is where heatmaps become useful.
@@ -30,7 +30,7 @@ A _heatmap_ is like a histogram, but over time where each time slice represents

 In this example, you can clearly see what values are more common and how they trend over time.

-![](/img/docs/v43/heatmap_histogram_over_time.png)
+![](/static/img/docs/v43/heatmap_histogram_over_time.png)

 ## Pre-bucketed data

@@ -14,7 +14,7 @@ In [Introduction to time series]({{< relref "timeseries.md#time-series-databases

 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.

-{{< docs-imagebox img="/img/docs/example_graph_multi_dim.png" class="docs-image--no-shadow" max-width="850px" >}}
+{{< figure src="/static/img/docs/example_graph_multi_dim.png" class="docs-image--no-shadow" max-width="850px" >}}

 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.

@@ -19,7 +19,7 @@ Temperature data like this is one example of what we call a *time series*—a se

 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.

-{{< docs-imagebox img="/img/docs/example_graph.png" class="docs-image--no-shadow" max-width="850px" >}}
+{{< figure src="/static/img/docs/example_graph.png" class="docs-image--no-shadow" max-width="850px" >}}

 Temperature data like the one in the example, is far from the only example of a time series. Other examples of time series are: