Revert "[v9.5.x] [feat] docs; update admonition syntax" (#68989)
Revert "[v9.5.x] [feat] docs; update admonition syntax (#68856)"
This reverts commit 08ea5c94c6.
I believe that because of automatic conflict resolution, this backport PR contained a number of extra changes that were not part of the update to use admonition syntax.
I'm first going to revert this PR to prevent the confusing behavior in PRs like #68832.
If we still want admonition changes backported, we can open a new PR which I would like to be a reviewer on to double check.
Signed-off-by: Jack Baldry <jack.baldry@grafana.com>
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Jack Baldry
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@@ -21,15 +21,11 @@ Server-side expressions allow you to manipulate data returned from queries with
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Expressions are most commonly used for [Grafana Alerting]({{< relref "../../../alerting/" >}}). The processing is done server-side, so expressions can operate without a browser session. However, expressions can also be used with backend data sources and visualization.
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{{% admonition type="note" %}}
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Expressions do not work with legacy dashboard alerts.
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{{% /admonition %}}
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> **Note:** Expressions do not work with legacy dashboard alerts.
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Expressions are meant to augment data sources by enabling queries from different data sources to be combined or by providing operations unavailable in a data source.
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{{% admonition type="note" %}}
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When possible, you should do data processing inside the data source. Copying data from storage to the Grafana server for processing is inefficient, so expressions are targeted at lightweight data processing.
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{{% /admonition %}}
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> **Note:** When possible, you should do data processing inside the data source. Copying data from storage to the Grafana server for processing is inefficient, so expressions are targeted at lightweight data processing.
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Expressions work with data source queries that return time series or number data. They also operate on [multiple-dimensional data]({{< relref "../../../fundamentals/timeseries-dimensions/" >}}). For example, a query that returns multiple series, where each series is identified by labels or tags.
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@@ -115,9 +111,7 @@ abs returns the absolute value of its argument which can be a number or a series
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is_inf takes a number or a series and returns `1` for `Inf` values (negative or positive) and `0` for other values. For example `is_inf($A)`.
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{{% admonition type="note" %}}
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If you need to specifically check for negative infinity for example, you can do a comparison like `$A == infn()`.
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{{% /admonition %}}
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> **Note:** If you need to specifically check for negative infinity for example, you can do a comparison like `$A == infn()`.
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###### is_nan
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@@ -200,9 +200,7 @@ In the example below, the panel has three queries (A, B, C). I removed the B que
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{{< figure src="/static/img/docs/transformations/filter-by-query-stat-example-7-0.png" class="docs-image--no-shadow" max-width= "1100px" >}}
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{{% admonition type="note" %}}
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This transformation is not available for Graphite because this data source does not support correlating returned data with queries.
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{{% /admonition %}}
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> **Note:** This transformation is not available for Graphite because this data source does not support correlating returned data with queries.
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### Filter data by value
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@@ -492,9 +490,7 @@ Here is the result after applying the Merge transformation.
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Use this transformation to rename, reorder, or hide fields returned by the query.
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{{% admonition type="note" %}}
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This transformation only works in panels with a single query. If your panel has multiple queries, then you must either apply an Outer join transformation or remove the extra queries.
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{{% /admonition %}}
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> **Note:** This transformation only works in panels with a single query. If your panel has multiple queries, then you must either apply an Outer join transformation or remove the extra queries.
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Grafana displays a list of fields returned by the query. You can:
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@@ -665,9 +661,7 @@ As you can see each row in the source data becomes a separate field. Each field
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### Prepare time series
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{{% admonition type="note" %}}
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This transformation is available in Grafana 7.5.10+ and Grafana 8.0.6+.
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{{% /admonition %}}
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> **Note:** This transformation is available in Grafana 7.5.10+ and Grafana 8.0.6+.
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Prepare time series transformation is useful when a data source returns time series data in a format that isn't supported by the panel you want to use. For more information about data frame formats, refer to [Data frames]({{< relref "../../../developers/plugins/data-frames/" >}}).
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@@ -679,9 +673,7 @@ Select the `Wide time series` option to transform the time series data frame fro
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### Series to rows
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{{% admonition type="note" %}}
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This transformation is available in Grafana 7.1+.
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{{% /admonition %}}
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> **Note:** This transformation is available in Grafana 7.1+.
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Use this transformation to combine the result from multiple time series data queries into one single result. This is helpful when using the table panel visualization.
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@@ -745,8 +737,6 @@ Here is the result after adding a Limit transformation with a value of '3':
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### Time series to table transform
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{{% admonition type="note" %}}
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This transformation is available in Grafana 9.5+ as an opt-in beta feature. Modify Grafana [configuration file]({{< relref "../../../setup-grafana/configure-grafana/#configuration-file-location" >}}) to enable the `timeSeriesTable` [feature toggle]({{< relref "../../../setup-grafana/configure-grafana/#feature_toggles" >}}) to use it.
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{{% /admonition %}}
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> **Note:** This transformation is available in Grafana 9.5+ as an opt-in beta feature. Modify Grafana [configuration file]({{< relref "../../../setup-grafana/configure-grafana/#configuration-file-location" >}}) to enable the `timeSeriesTable` [feature toggle]({{< relref "../../../setup-grafana/configure-grafana/#feature_toggles" >}}) to use it.
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Use this transformation to convert time series result into a table, converting time series data frame into a "Trend" field. "Trend" field can then be rendered using [sparkline cell type]({{< relref "../../visualizations/table/#sparkline" >}}), producing an inline sparkline for each table row. If there are multiple time series queries, each will result in a separate table data frame. These can be joined using join or merge transforms to produce a single table with multiple sparklines per row.
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