A tag cloud visualization is a visual representation of text data, typically used to visualize free form text. Tags are usually single words. The font size of word corresponds with its importance.
123 lines
4.8 KiB
Plaintext
123 lines
4.8 KiB
Plaintext
[[visualize]]
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= Visualize
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[partintro]
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--
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_Visualize_ enables you to create visualizations of the data in your
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Elasticsearch indices. You can then build <<dashboard, dashboards>> that
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display related visualizations.
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Kibana visualizations are based on Elasticsearch queries. By using a
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series of Elasticsearch {es-ref}search-aggregations.html[aggregations]
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to extract and process your data, you can create charts that show
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you the trends, spikes, and dips you need to know about.
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You can create visualizations from a search saved from <<discover, Discover>>
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or start with a new search query.
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--
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[[createvis]]
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== Creating a Visualization
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To create a visualization:
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. Click on *Visualize* in the side navigation.
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. Choose the visualization type:
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+
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[horizontal]
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<<area-chart,Area chart>>:: Visualize the total contribution of several
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different series.
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<<data-table,Data table>>:: Display the raw data of a composed aggregation.
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<<line-chart,Line chart>>:: Compare different series.
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<<markdown-widget,Markdown widget>>:: Display free-form information or
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instructions.
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<<metric-chart,Metric>>:: Display a single number.
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<<pie-chart,Pie chart>>:: Display each source's contribution to a total.
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<<tilemap,Tile map>>:: Associate the results of an aggregation with geographic
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locations.
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Timeseries:: Compute and combine data from multiple time series
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data sets.
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<<vertical-bar-chart,Vertical bar chart>>:: Graph values in a bar chart.
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. Specify a search query to retrieve the data for your visualization:
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** To enter new search criteria, select the index pattern for the indices that
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contain the data you want to visualize. This opens the visualization builder
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with a wildcard query that matches all of the documents in the selected
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indices.
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** To build a visualization from a saved search, click the name of the saved
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search you want to use. This opens the visualization builder and loads the
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selected query.
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+
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NOTE: When you build a visualization from a saved search, any subsequent
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modifications to the saved search are automatically reflected in the
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visualization. To disable automatic updates, you can disconnect a visualization
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from the saved search.
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. In the visualization builder, choose the metric aggregation for the
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visualization's Y axis:
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+
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* {es-ref}search-aggregations-metrics-valuecount-aggregation.html[count]
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* {es-ref}search-aggregations-metrics-avg-aggregation.html[average]
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* {es-ref}search-aggregations-metrics-sum-aggregation.html[sum]
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* {es-ref}search-aggregations-metrics-min-aggregation.html[min]
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* {es-ref}search-aggregations-metrics-max-aggregation.html[max]
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* {es-ref}search-aggregations-metrics-cardinality-aggregation.html[unique count]
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* {es-ref}search-aggregations-metrics-percentile-aggregation.html[median] (50th percentile)
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* {es-ref}search-aggregations-metrics-percentile-aggregation.html[percentiles]
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* {es-ref}search-aggregations-metrics-percentile-rank-aggregation.html[percentile ranks]
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. For the visualizations X axis, select a bucket aggregation:
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+
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* {es-ref}search-aggregations-bucket-datehistogram-aggregation.html[date histogram]
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* {es-ref}search-aggregations-bucket-range-aggregation.html[range]
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* {es-ref}search-aggregations-bucket-terms-aggregation.html[terms]
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* {es-ref}search-aggregations-bucket-filters-aggregation.html[filters]
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* {es-ref}search-aggregations-bucket-significantterms-aggregation.html[significant terms]
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For example, if you're indexing Apache server logs, you could build bar chart
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that shows the distribution of incoming requests by geographic location by
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specifying a terms aggregation on the `geo.src` field:
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image::images/bar-terms-agg.jpg[]
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The y-axis shows the number of requests received from each country, and the
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countries are displayed across the x-axis.
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Bar, line, or area chart visualizations use _metrics_ for the y-axis and
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_buckets_ for the x-axis. Buckets are analogous to SQL `GROUP BY`
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statements. Pie charts, use the metric for the slice size and the bucket
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for the number of slices.
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You can futher break down the data by specifying sub aggregations. The first
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aggregation determines the data set for any subsequent aggregations. Sub
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aggregations are applied in order--you can drag the aggregations to change the
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order in which they're applied.
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For example, you could add a terms sub aggregation on the `geo.dest` field to
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the Country of Origin bar chart to see the locations those requests were
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targeting.
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image::images/bar-terms-subagg.jpg[]
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For more information about working with sub aggregations, see
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https://www.elastic.co/blog/kibana-aggregation-execution-order-and-you[Kibana,
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Aggregation Execution Order, and You].
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include::visualize/area.asciidoc[]
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include::visualize/datatable.asciidoc[]
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include::visualize/line.asciidoc[]
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include::visualize/markdown.asciidoc[]
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include::visualize/metric.asciidoc[]
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include::visualize/pie.asciidoc[]
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include::visualize/tilemap.asciidoc[]
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include::visualize/vertbar.asciidoc[]
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include::visualize/tagcloud.asciidoc[]
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