PackageJson: Prettify markdown/mdx on commit with lint-staged (#37616)
* Format md,mdx files with prettier on lint-staged * Manually run prettier on docs/sources
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Connor Lindsey
@@ -17,15 +17,14 @@ To examine the details of an exemplar trace:
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1. Place your cursor over an exemplar (highlighted star). Depending on your backend trace data source, you will see a blue button with the label `Query with <data source name>`. In the following example, the tracing data source is Tempo.
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{{< figure src="/static/img/docs/basics/exemplar-details.png" class="docs-image--no-shadow" max-width= "275px" caption="Screenshot showing Exemplar details" >}}
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{{< figure src="/static/img/docs/basics/exemplar-details.png" class="docs-image--no-shadow" max-width= "275px" caption="Screenshot showing Exemplar details" >}}
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1. Click the **Query with Tempo** option next to the `traceID` property. The trace details, including the spans within the trace are listed in a separate panel on the right.
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{{< figure src="/static/img/docs/basics/exemplar-explore-view.png" class="docs-image--no-shadow" max-width= "750px" caption="Explorer view with panel showing trace details" >}}
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{{< figure src="/static/img/docs/basics/exemplar-explore-view.png" class="docs-image--no-shadow" max-width= "750px" caption="Explorer view with panel showing trace details" >}}
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For more information on how to drill down and analyze the trace and span details, refer to the [Analyze trace and span details](#analyze-trace-and-spans) section.
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## In logs
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You can also view exemplar trace details from the Loki logs in Explore. Use regex within the Derived fields links for Loki to extract the `traceID` information. Now when you expand Loki logs, you can see a `traceID` property under the **Detected fields** section. To learn more about how to extract a part of a log message into an internal or external link, refer to [using derived fields in Loki]({{< relref "../../explore/logs-integration.md" >}}).
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@@ -40,24 +39,24 @@ To view the details of an exemplar trace:
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For more information on how to drill down and analyze the trace and span details, refer to the [Analyze trace and span details](#analyze-trace-and-spans) section.
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## Analyze trace and spans
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## Analyze trace and spans
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This panel shows the details of the trace in different segments.
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This panel shows the details of the trace in different segments.
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- The top segment shows the Trace ID to indicate that the query results correspond to the specific trace.
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- The top segment shows the Trace ID to indicate that the query results correspond to the specific trace.
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You can add more traces to the results using the `Add query` button.
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You can add more traces to the results using the `Add query` button.
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- The next segment shows the entire span for the specific trace as a narrow strip. All levels of the trace from the client all the way down to database query is displayed, which provides a bird's eye view of the time distribution across all layers over which the HTTP request was processed.
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1. You can click within this strip view to display a magnified view of a smaller time segment within the span. This magnified view shows up in the bottom segment of the panel.
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1. You can click within this strip view to display a magnified view of a smaller time segment within the span. This magnified view shows up in the bottom segment of the panel.
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1. In the magnified view, you can expand or collapse the various levels of the trace to drill down to the specific span of interest.
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1. In the magnified view, you can expand or collapse the various levels of the trace to drill down to the specific span of interest.
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For example, if the strip view shows that most of the latency was within the app layer, you can expand the trace down the app layer to investigate the problem further. To expand a particular layer of span, click the icon on the left. The same button can collapse an expanded span.
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For example, if the strip view shows that most of the latency was within the app layer, you can expand the trace down the app layer to investigate the problem further. To expand a particular layer of span, click the icon on the left. The same button can collapse an expanded span.
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- To see the details of the span at any level, click the span itself.
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This displays additional metadata associated with the span. The metadata itself is initially shown in a narrow strip but you can see more details by clicking the metadata strip.
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This displays additional metadata associated with the span. The metadata itself is initially shown in a narrow strip but you can see more details by clicking the metadata strip.
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{{< figure src="/static/img/docs/basics/exemplar-span-details.png" class="docs-image--no-shadow" max-width= "750px" caption="Span details" >}}
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{{< figure src="/static/img/docs/basics/exemplar-span-details.png" class="docs-image--no-shadow" max-width= "750px" caption="Span details" >}}
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@@ -43,7 +43,7 @@ For more information about heatmap visualization options, refer to [Heatmap]({{<
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There are a number of data sources supporting histogram over time like Elasticsearch (by using a Histogram bucket
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aggregation) or Prometheus (with [histogram](https://prometheus.io/docs/concepts/metric_types/#histogram) metric type
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and *Format as* option set to Heatmap). But generally, any data source could be used if it meets the requirements:
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and _Format as_ option set to Heatmap). But generally, any data source could be used if it meets the requirements:
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returns series with names representing bucket bound or returns series sorted by the bound in ascending order.
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## Raw data vs aggregated
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@@ -15,7 +15,7 @@ Imagine you wanted to know how the temperature outside changes throughout the da
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| 10:00 | 26°C |
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| 11:00 | 27°C |
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Temperature data like this is one example of what we call a *time series*—a sequence of measurements, ordered in time. Every row in the table represents one individual measurement at a specific time.
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Temperature data like this is one example of what we call a _time series_—a sequence of measurements, ordered in time. Every row in the table represents one individual measurement at a specific time.
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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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@@ -91,15 +91,15 @@ Here are some of the TSDBs supported by Grafana:
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- [InfluxDB](https://www.influxdata.com/products/influxdb-overview/)
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- [Prometheus](https://prometheus.io/)
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```
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weather,location=us-midwest temperature=82 1465839830100400200
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| -------------------- -------------- |
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| | | |
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+-----------+--------+-+---------+-+---------+
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|measurement|,tag_set| |field_set| |timestamp|
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+-----------+--------+-+---------+-+---------+
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```
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```
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weather,location=us-midwest temperature=82 1465839830100400200
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| -------------------- -------------- |
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+-----------+--------+-+---------+-+---------+
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|measurement|,tag_set| |field_set| |timestamp|
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+-----------+--------+-+---------+-+---------+
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```
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### Collecting time series data
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@@ -114,10 +114,9 @@ Here are some examples of collectors:
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A collector either _pushes_ data to a database or lets the database _pull_ the data from it. Both methods come with their own set of pros and cons:
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| | Pros | Cons |
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| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| Push | Easier to replicate data to multiple destinations. | The TSDB has no control over how much data gets sent. |
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| | Pros | Cons |
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| ---- | ------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
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| Push | Easier to replicate data to multiple destinations. | The TSDB has no control over how much data gets sent. |
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| Pull | Better control of how much data that gets ingested, and its authenticity. | Firewalls, VPNs or load balancers can make it hard to access the agents. |
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Since it would be inefficient to write every measurement to the database, collectors pre-aggregate the data and write to the time series database at regular intervals.
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