diff --git a/docs/apm/apm-app-users.asciidoc b/docs/apm/apm-app-users.asciidoc index d766c866f87e..3f0a42251304 100644 --- a/docs/apm/apm-app-users.asciidoc +++ b/docs/apm/apm-app-users.asciidoc @@ -84,7 +84,7 @@ Here are two examples: | Allow the use of the the {beat_kib_app} | Spaces -| `Read` or `All` on Dashboards, Visualize, and Discover +| `Read` or `All` on Dashboards and Discover | Allow the user to view, edit, and create dashboards, as well as browse data. |==== diff --git a/docs/canvas/canvas-elements.asciidoc b/docs/canvas/canvas-elements.asciidoc new file mode 100644 index 000000000000..782bae061b8c --- /dev/null +++ b/docs/canvas/canvas-elements.asciidoc @@ -0,0 +1,167 @@ +[role="xpack"] +[[add-canvas-elements]] +=== Add elements + +Create a story about your data by adding elements to your workpad that include images, text, charts, and more. You can create your own elements and connect them to your data sources, add saved objects, and add your own images. + +[float] +[[create-canvas-element]] +==== Create an element + +Choose the type of element you want to use, then connect it to your own data. + +. Click *Add element*, then select the element you want to use. ++ +[role="screenshot"] +image::images/canvas-element-select.gif[Canvas elements] + +. To familiarize yourself with the element, use the preconfigured data demo data. ++ +By default, most of the elements you create use demo data until you change the data source. The demo data includes a small data set that you can use to experiment with your element. + +. To connect the element to your data, select *Data*, then select one of the following data sources: + +* *{es} SQL* — Access your data in {es} using SQL syntax. For information about SQL syntax, refer to {ref}/sql-spec.html[SQL language]. + +* *{es} documents* — Access your data in {es} without using aggregations. To use, select an index and fields, and optionally enter a query using the <>. Use the *{es} documents* data source when you have low volume datasets, to view raw documents, or to plot exact, non-aggregated values on a chart. + +* *Timelion* — Access your time series data using <> queries. To use Timelion queries, you can enter a query using the <>. + +Each element can display a different data source. Pages and workpads often contain multiple data sources. + +[float] +[[canvas-add-object]] +==== Add a saved object + +Add <> to your workpad, such as maps and visualizations. + +. Click *Add element > Add from Visualize Library*. + +. Select the saved object you want to add. ++ +[role="screenshot"] +image::images/canvas-map-embed.gif[] + +. To use the customization options, click the panel menu, then select one of the following options: + +* *Edit map* — Opens <> or <> so that you can edit the original saved object. + +* *Edit panel title* — Adds a title to the saved object. + +* *Customize time range* — Exposes a time filter dedicated to the saved object. + +* *Inspect* — Allows you to drill down into the element data. + +[float] +[[canvas-add-image]] +==== Add your own image + +To personalize your workpad, add your own logos and graphics. + +. Click *Add element > Manage assets*. + +. On the *Manage workpad assets* window, drag and drop your images. + +. To add the image to the workpad, click the *Create image element* icon. ++ +[role="screenshot"] +image::images/canvas-add-image.gif[] + +[float] +[[move-canvas-elements]] +==== Organize elements + +Move and resize your elements to meet your design needs. + +* To move, click and hold the element, then drag to the new location. + +* To move by 1 pixel, select the element, press and hold Shift, then use your arrow keys. + +* To move by 10 pixels, select the element, then use your arrow keys. + +* To resize, click and drag the resize handles to the new dimensions. + +[float] +[[format-canvas-elements]] +==== Format elements + +For consistency and readability across your workpad pages, align, distribute, and reorder elements. + +To align two or more elements: + +. Press and hold Shift, then select the elements you want to align. + +. Click *Edit > Alignment*, then select the alignment option. + +To distribute three or more elements: + +. Press and hold Shift, then select the elements you want to distribute. + +. Click *Edit > Distribution*, then select the distribution option. + +To reorder elements: + +. Select the element you want to reorder. + +. Click *Edit > Order*, then select the order option. + +[float] +[[data-display]] +==== Change the element display options + +Each element has its own display options to fit your design needs. + +To choose the display options, click *Display*, then make your changes. + +To define the appearance of the container and border: + +. Next to *Element style*, click *+*, then select *Container style*. + +. Expand *Container style*. + +. Change the *Appearance* and *Border* options. + +To apply CSS overrides: + +. Next to *Element style*, click *+*, then select *CSS*. + +. Enter the *CSS*. ++ +For example, to center the Markdown element, enter: ++ +[source,text] +-------------------------------------------------- +.canvasRenderEl h1 { +text.align: center; +} +-------------------------------------------------- + +. Click *Apply stylesheet*. + +[float] +[[save-elements]] +==== Save elements + +To use the elements across all workpads, save the elements. + +When you're ready to save your element, select the element, then click *Edit > Save as new element*. + +[role="screenshot"] +image::images/canvas_save_element.png[] + +To save a group of elements, press and hold Shift, select the elements you want to save, then click *Edit > Save as new element*. + +To access your saved elements, click *Add element > My elements*. + +[float] +[[delete-elements]] +==== Delete elements + +When you no longer need an element, delete it from your workpad. + +. Select the element you want to delete. + +. Click *Edit > Delete*. ++ +[role="screenshot"] +image::images/canvas_element_options.png[] diff --git a/docs/discover/search.asciidoc b/docs/discover/search.asciidoc index eef2a12a964b..da58382deb89 100644 --- a/docs/discover/search.asciidoc +++ b/docs/discover/search.asciidoc @@ -1,7 +1,7 @@ [[search]] == Search data Many Kibana apps embed a query bar for real-time search, including -*Discover*, *Visualize*, and *Dashboard*. +*Discover* and *Dashboard*. [float] === Search your data @@ -84,7 +84,7 @@ query language you can also submit queries using the {ref}/query-dsl.html[Elasti [[save-open-search]] === Save a search -A saved search persists your current view of Discover for later retrieval and reuse. You can reload a saved search into Discover, add it to a dashboard, and use it as the basis for a <>. +A saved search persists your current view of Discover for later retrieval and reuse. You can reload a saved search into Discover, add it to a dashboard, and use it as the basis for a visualization. A saved search includes the query text, filters, and optionally, the time filter. A saved search also includes the selected columns in the document table, the sort order, and the current index pattern. @@ -120,7 +120,7 @@ used for the saved search will also be automatically selected. [[save-load-delete-query]] === Save a query -A saved query is a portable collection of query text and filters that you can reuse in <>, <>, and <>. Save a query when you want to: +A saved query is a portable collection of query text and filters that you can reuse in <> and <>. Save a query when you want to: * Retrieve results from the same query at a later time without having to reenter the query text, add the filters or set the time filter * View the results of the same query in multiple apps @@ -148,7 +148,7 @@ image::discover/images/saved-query-save-form-default-filters.png["Example of the . Click *Save*. ==== Load a query -To load a saved query into Discover, Dashboard, or Visualize: +To load a saved query into Discover or Dashboard: . Click *#* in the search bar, next to the query text input. . Select the query you want to load. You might need to scroll down to find the query you are looking for. diff --git a/docs/drilldowns/explore-underlying-data.asciidoc b/docs/drilldowns/explore-underlying-data.asciidoc deleted file mode 100644 index c2bba599730d..000000000000 --- a/docs/drilldowns/explore-underlying-data.asciidoc +++ /dev/null @@ -1,41 +0,0 @@ -[[explore-underlying-data]] -== Explore the underlying data for a visualization - -++++ -Explore the underlying data -++++ - -Dashboard panels have an *Explore underlying data* action that navigates you to *Discover*, -where you can narrow your documents to the ones you'll most likely use in a visualization. -This action is available for visualizations backed by a single index pattern. - -You can access *Explore underlying data* in two ways: from the panel context -menu or from the menu that appears when you interact with the chart. - -[float] -[[explore-data-from-panel-context-menu]] -=== Explore data from panel context menu - -The *Explore underlying data* action in the panel menu navigates you to Discover, -carrying over the index pattern, filters, query, and time range for the visualization. - -[role="screenshot"] -image::images/explore_data_context_menu.png[Explore underlying data from panel context menu] - -[float] -[[explore-data-from-chart]] -=== Explore data from chart action - -Initiating *Explore underlying data* from the chart also navigates to Discover, -carrying over the current context for the visualization. In addition, this action -applies the filters and time range created by the events that triggered the action. - -[role="screenshot"] -image::images/explore_data_in_chart.png[Explore underlying data from chart] - -To enable this action add the following line to your `kibana.yml` config. - -["source","yml"] ------------ -xpack.discoverEnhanced.actions.exploreDataInChart.enabled: true ------------ diff --git a/docs/getting-started/add-sample-data.asciidoc b/docs/getting-started/add-sample-data.asciidoc deleted file mode 100644 index ab4343160188..000000000000 --- a/docs/getting-started/add-sample-data.asciidoc +++ /dev/null @@ -1,28 +0,0 @@ -[[add-sample-data]] -== Add sample data - -{kib} has several sample data sets that you can use to explore {kib} before loading your own data. -These sample data sets showcase a variety of use cases: - -* *eCommerce orders* includes visualizations for product-related information, -such as cost, revenue, and price. -* *Flight data* enables you to view and interact with flight routes. -* *Web logs* lets you analyze website traffic. - -To get started, go to the {kib} home page and click the link underneath *Add sample data*. - -Once you've loaded a data set, click *View data* to view prepackaged -visualizations, dashboards, Canvas workpads, Maps, and Machine Learning jobs. - -[role="screenshot"] -image::images/add-sample-data.png[] - -NOTE: The timestamps in the sample data sets are relative to when they are installed. -If you uninstall and reinstall a data set, the timestamps will change to reflect the most recent installation. - -[float] -=== Next steps - -* Explore {kib} by following the <>. - -* Learn how to load data, define index patterns, and build visualizations by <>. diff --git a/docs/getting-started/images/gs_maps_time_filter.png b/docs/getting-started/images/gs_maps_time_filter.png new file mode 100644 index 000000000000..83e20c279906 Binary files /dev/null and b/docs/getting-started/images/gs_maps_time_filter.png differ diff --git a/docs/getting-started/images/tutorial-discover-2.png b/docs/getting-started/images/tutorial-discover-2.png index 7190c90d8e5b..681e4834de83 100644 Binary files a/docs/getting-started/images/tutorial-discover-2.png and b/docs/getting-started/images/tutorial-discover-2.png differ diff --git a/docs/getting-started/images/tutorial-pattern-1.png b/docs/getting-started/images/tutorial-pattern-1.png index 8a289f93fc66..0026b1877551 100644 Binary files a/docs/getting-started/images/tutorial-pattern-1.png and b/docs/getting-started/images/tutorial-pattern-1.png differ diff --git a/docs/getting-started/images/tutorial-visualize-bar-1.5.png b/docs/getting-started/images/tutorial-visualize-bar-1.5.png index c02b9ca59dff..009152f9407e 100644 Binary files a/docs/getting-started/images/tutorial-visualize-bar-1.5.png and b/docs/getting-started/images/tutorial-visualize-bar-1.5.png differ diff --git a/docs/getting-started/images/tutorial-visualize-map-2.png b/docs/getting-started/images/tutorial-visualize-map-2.png index f4d1d0e47fe6..ed2fd47cb27d 100644 Binary files a/docs/getting-started/images/tutorial-visualize-map-2.png and b/docs/getting-started/images/tutorial-visualize-map-2.png differ diff --git a/docs/getting-started/images/tutorial-visualize-md-2.png b/docs/getting-started/images/tutorial-visualize-md-2.png index 9e9a670ba196..af56faa3b051 100644 Binary files a/docs/getting-started/images/tutorial-visualize-md-2.png and b/docs/getting-started/images/tutorial-visualize-md-2.png differ diff --git a/docs/getting-started/images/tutorial-visualize-pie-2.png b/docs/getting-started/images/tutorial-visualize-pie-2.png index ef5d62b4ceee..ca8f5e92146b 100644 Binary files a/docs/getting-started/images/tutorial-visualize-pie-2.png and b/docs/getting-started/images/tutorial-visualize-pie-2.png differ diff --git a/docs/getting-started/images/tutorial-visualize-pie-3.png b/docs/getting-started/images/tutorial-visualize-pie-3.png index 6974c8d34b0d..59fce360096c 100644 Binary files a/docs/getting-started/images/tutorial-visualize-pie-3.png and b/docs/getting-started/images/tutorial-visualize-pie-3.png differ diff --git a/docs/getting-started/images/tutorial_index_patterns.png b/docs/getting-started/images/tutorial_index_patterns.png new file mode 100644 index 000000000000..430baf898b61 Binary files /dev/null and b/docs/getting-started/images/tutorial_index_patterns.png differ diff --git a/docs/getting-started/tutorial-dashboard.asciidoc b/docs/getting-started/tutorial-dashboard.asciidoc deleted file mode 100644 index 2ee2d76024ae..000000000000 --- a/docs/getting-started/tutorial-dashboard.asciidoc +++ /dev/null @@ -1,53 +0,0 @@ -[[tutorial-dashboard]] -=== Add the visualizations to a dashboard - -Build a dashboard that contains the visualizations and map that you saved during -this tutorial. - -. Open the menu, go to *Dashboard*, then click *Create dashboard*. -. Set the time filter to May 18, 2015 to May 20, 2015. -. Click *Add*, then select the following: - * *Bar Example* - * *Map Example* - * *Markdown Example* - * *Pie Example* -+ -Your sample dashboard looks like this: -+ -[role="screenshot"] -image::images/tutorial-dashboard.png[] - -. Try out the editing controls. -+ -You can rearrange the visualizations by clicking a the header of a -visualization and dragging. The gear icon in the top right of a visualization -displays controls for editing and deleting the visualization. A resize control -is on the lower right. - -. *Save* your dashboard. - -==== Inspect the data - -Seeing visualizations of your data is great, -but sometimes you need to look at the actual data to -understand what's really going on. You can inspect the data behind any visualization -and view the {es} query used to retrieve it. - -. Click the pie chart *Options* menu, then select *Inspect*. -+ -[role="screenshot"] -image::images/tutorial-full-inspect1.png[] - -. To look at the query used to fetch the data for the visualization, select *View > Requests*. - -[float] -=== Next steps - -Now that you have the basics, you're ready to start exploring -your own data with {kib}. - -* To learn about searching and filtering your data, refer to {kibana-ref}/discover.html[Discover]. -* To learn about the visualization types {kib} has to offer, refer to {kibana-ref}/visualize.html[Visualize]. -* To learn about configuring {kib} and managing your saved objects, refer to {kibana-ref}/management.html[Management]. -* To learn about the interactive console you can use to submit REST requests to {es}, refer to {kibana-ref}/console-kibana.html[Console]. - diff --git a/docs/getting-started/tutorial-define-index.asciidoc b/docs/getting-started/tutorial-define-index.asciidoc index 254befa55fae..fbe7450683db 100644 --- a/docs/getting-started/tutorial-define-index.asciidoc +++ b/docs/getting-started/tutorial-define-index.asciidoc @@ -1,7 +1,7 @@ [[tutorial-define-index]] === Define your index patterns -Index patterns tell Kibana which Elasticsearch indices you want to explore. +Index patterns tell {kib} which {es} indices you want to explore. An index pattern can match the name of a single index, or include a wildcard (*) to match multiple indices. @@ -10,28 +10,29 @@ series of indices in the format `logstash-YYYY.MMM.DD`. To explore all of the log data from May 2018, you could specify the index pattern `logstash-2018.05*`. - [float] -==== Create your first index pattern +==== Create the index patterns First you'll create index patterns for the Shakespeare data set, which has an index named `shakespeare,` and the accounts data set, which has an index named `bank`. These data sets don't contain time series data. . Open the menu, then go to *Stack Management > {kib} > Index Patterns*. + . If this is your first index pattern, the *Create index pattern* page opens. -Otherwise, click *Create index pattern*. -. In the *Index pattern field*, enter `shakes*`. + +. In the *Index pattern name* field, enter `shakes*`. + [role="screenshot"] -image::images/tutorial-pattern-1.png[] +image::images/tutorial-pattern-1.png[shakes* index patterns] . Click *Next step*. -. Select the *Time Filter field name*, then click *Create index pattern*. + +. On the *Configure settings* page, *Create index pattern*. + You’re presented a table of all fields and associated data types in the index. -. Return to the *Index patterns* page and create a second index pattern named `ba*`. +. Create a second index pattern named `ba*`. [float] ==== Create an index pattern for the time series data @@ -39,15 +40,12 @@ You’re presented a table of all fields and associated data types in the index. Create an index pattern for the Logstash index, which contains the time series data. -. Define an index pattern named `logstash*`. -. Click *Next step*. -. From the *Time Filter field name* dropdown, select *@timestamp*. -. Click *Create index pattern*. +. Create an index pattern named `logstash*`, then click *Next step*. -NOTE: When you define an index pattern, the indices that match that pattern must -exist in Elasticsearch and they must contain data. To check which indices are -available, open the menu, then go to *Dev Tools > Console* and enter `GET _cat/indices`. Alternately, use -`curl -XGET "http://localhost:9200/_cat/indices"`. +. From the *Time field* dropdown, select *@timestamp, then click *Create index pattern*. ++ +[role="screenshot"] +image::images/tutorial_index_patterns.png[All tutorial index patterns] diff --git a/docs/getting-started/tutorial-discovering.asciidoc b/docs/getting-started/tutorial-discovering.asciidoc index 31d77be1275e..ec07a74b8ac0 100644 --- a/docs/getting-started/tutorial-discovering.asciidoc +++ b/docs/getting-started/tutorial-discovering.asciidoc @@ -1,9 +1,8 @@ -[[tutorial-discovering]] -=== Discover your data +[[explore-your-data]] +=== Explore your data -Using *Discover*, enter -an {ref}/query-dsl-query-string-query.html#query-string-syntax[Elasticsearch -query] to search your data and filter the results. +With *Discover*, you use {ref}/query-dsl-query-string-query.html#query-string-syntax[Elasticsearch +queries] to explore your data and narrow the results with filters. . Open the menu, then go to *Discover*. + @@ -13,7 +12,7 @@ The `shakes*` index pattern appears. + By default, all fields are shown for each matching document. -. In the *Search* field, enter the following: +. In the *Search* field, enter the following, then click *Update*: + [source,text] account_number<100 AND balance>47500 @@ -32,3 +31,5 @@ account numbers. + [role="screenshot"] image::images/tutorial-discover-3.png[] + +Now that you know what your documents contain, it's time to gain insight into your data with visualizations. diff --git a/docs/getting-started/tutorial-full-experience.asciidoc b/docs/getting-started/tutorial-full-experience.asciidoc index e6f2de87905b..1e6fe39dbd01 100644 --- a/docs/getting-started/tutorial-full-experience.asciidoc +++ b/docs/getting-started/tutorial-full-experience.asciidoc @@ -1,32 +1,23 @@ -[[tutorial-build-dashboard]] -== Build your own dashboard +[[create-your-own-dashboard]] +== Create your own dashboard -Want to load some data into Kibana and build a dashboard? This tutorial shows you how to: +Ready to add data to {kib} and create your own dashboard? In this tutorial, you'll use three types of data sets that'll help you learn to: -* <> -* <> -* <> -* <> -* <> - -When you complete this tutorial, you'll have a dashboard that looks like this. - -[role="screenshot"] -image::images/tutorial-dashboard.png[] +* <> +* <> +* <> +* <> [float] -[[tutorial-load-dataset]] -=== Load sample data +[[download-the-data]] +=== Download the data -This tutorial requires you to download three data sets: +To complete the tutorial, you'll download and use the following data sets: * The complete works of William Shakespeare, suitably parsed into fields -* A set of fictitious accounts with randomly generated data +* A set of fictitious bank accounts with randomly generated data * A set of randomly generated log files -[float] -==== Download the data sets - Create a new working directory where you want to download the files. From that directory, run the following commands: [source,shell] @@ -34,7 +25,7 @@ curl -O https://download.elastic.co/demos/kibana/gettingstarted/8.x/shakespeare. curl -O https://download.elastic.co/demos/kibana/gettingstarted/8.x/accounts.zip curl -O https://download.elastic.co/demos/kibana/gettingstarted/8.x/logs.jsonl.gz -Two of the data sets are compressed. To extract the files, use these commands: +Two of the data sets are compressed. To extract the files, use the following commands: [source,shell] unzip accounts.zip @@ -43,7 +34,7 @@ gunzip logs.jsonl.gz [float] ==== Structure of the data sets -The Shakespeare data set has this structure: +The Shakespeare data set has the following structure: [source,json] { @@ -55,7 +46,7 @@ The Shakespeare data set has this structure: "text_entry": "String", } -The accounts data set is structured as follows: +The accounts data set has the following structure: [source,json] { @@ -72,7 +63,7 @@ The accounts data set is structured as follows: "state": "String" } -The logs data set has dozens of different fields. Here are the notable fields for this tutorial: +The logs data set has dozens of different fields. The notable fields include the following: [source,json] { @@ -94,7 +85,7 @@ You must also have the `create`, `manage` `read`, `write,` and `delete` index privileges. See {ref}/security-privileges.html[Security privileges] for more information. -Open *Dev Tools*. On the *Console* page, set up a mapping for the Shakespeare data set: +Open the menu, then go to *Dev Tools*. On the *Console* page, set up a mapping for the Shakespeare data set: [source,js] PUT /shakespeare @@ -111,10 +102,11 @@ PUT /shakespeare //CONSOLE -This mapping specifies field characteristics for the data set: +The mapping specifies field characteristics for the data set: * The `speaker` and `play_name` fields are keyword fields. These fields are not analyzed. The strings are treated as a single unit even if they contain multiple words. + * The `line_id` and `speech_number` fields are integers. The logs data set requires a mapping to label the latitude and longitude pairs @@ -177,6 +169,7 @@ PUT /logstash-2015.05.20 The accounts data set doesn't require any mappings. [float] +[[load-the-data-sets]] ==== Load the data sets At this point, you're ready to use the Elasticsearch {ref}/docs-bulk.html[bulk] @@ -195,14 +188,20 @@ Invoke-RestMethod "http://:/_bulk?pretty" -Method Post -ContentType These commands might take some time to execute, depending on the available computing resources. -Verify successful loading: +When you define an index pattern, the indices that match the pattern must +exist in {es} and contain data. + +To verify the availability of the indices, open the menu, go to *Dev Tools > Console*, then enter: [source,js] GET /_cat/indices?v -//CONSOLE +Alternately, use: + +[source,shell] +`curl -XGET "http://localhost:9200/_cat/indices"`. -Your output should look similar to this: +The output should look similar to: [source,shell] health status index pri rep docs.count docs.deleted store.size pri.store.size diff --git a/docs/getting-started/tutorial-sample-data.asciidoc b/docs/getting-started/tutorial-sample-data.asciidoc index 2460a55e1329..18ef862272f8 100644 --- a/docs/getting-started/tutorial-sample-data.asciidoc +++ b/docs/getting-started/tutorial-sample-data.asciidoc @@ -1,207 +1,159 @@ -[[tutorial-sample-data]] +[[explore-kibana-using-sample-data]] == Explore {kib} using sample data -Ready to get some hands-on experience with Kibana? -In this tutorial, you’ll work -with Kibana sample data and learn to: +Ready to get some hands-on experience with {kib}? +In this tutorial, you’ll work with {kib} sample data and learn to: -* <> -* <> -* <> -* <> +* <> +* <> -NOTE: If security is enabled, you must have `read`, `write`, and `manage` privileges -on the `kibana_sample_data_*` indices. See -{ref}/security-privileges.html[Security privileges] for more information. +* <> +NOTE: If security is enabled, you must have `read`, `write`, and `manage` privileges +on the `kibana_sample_data_*` indices. For more information, refer to +{ref}/security-privileges.html[Security privileges]. [float] -=== Add sample data +[[add-the-sample-data]] +=== Add the sample data -Install the Flights sample data set, if you haven't already. +Add the *Sample flight data*. . On the home page, click *Load a data set and a {kib} dashboard*. + . On the *Sample flight data* card, click *Add data*. -. Once the data is added, click *View data > Dashboard*. -+ -You’re taken to the *Global Flight* dashboard, a collection of charts, graphs, -maps, and other visualizations of the the data in the `kibana_sample_data_flights` index. -+ -[role="screenshot"] -image::getting-started/images/tutorial-sample-dashboard.png[] [float] -[[tutorial-sample-filter]] -=== Filter and query the data +[[explore-the-data]] +=== Explore the data -You can use filters and queries to -narrow the view of the data. -For more detailed information on these actions, see -{ref}/query-filter-context.html[Query and filter context]. +Explore the documents in the index that +match the selected index pattern. The index pattern tells {kib} which {es} index you want to +explore. -[float] -==== Filter the data +. Open the menu, then go to *Discover*. -. In the *Controls* visualization, select an *Origin City* and a *Destination City*. -. Click *Apply changes*. +. Make sure `kibana_sample_data_flights` is the current index pattern. +You might need to click *New* in the {kib} toolbar to refresh the data. + -The `OriginCityName` and the `DestCityName` fields filter the data on the dasbhoard to match -the data you specified. +You'll see a histogram that shows the distribution of +documents over time. A table lists the fields for +each document that matches the index. By default, all fields are shown. + -For example, the following dashboard shows the data for flights from London to Milan. +[role="screenshot"] +image::getting-started/images/tutorial-sample-discover1.png[] + +. Hover over the list of *Available fields*, then click *Add* next +to each field you want explore in the table. + [role="screenshot"] -image::getting-started/images/tutorial-sample-filter.png[] +image::getting-started/images/tutorial-sample-discover2.png[] -. To add a filter manually, click *Add filter*, -then specify the data you want to view. +[float] +[[view-and-analyze-the-data]] +=== View and analyze the data -. When you are finished experimenting, remove all filters. +A _dashboard_ is a collection of panels that provide you with an overview of your data that you can +use to analyze your data. Panels contain everything you need, including visualizations, +interactive controls, Markdown, and more. + +To open the *Global Flight* dashboard, open the menu, then go to *Dashboard*. +[role="screenshot"] +image::getting-started/images/tutorial-sample-dashboard.png[] [float] -[[tutorial-sample-query]] -==== Query the data +[[change-the-panel-data]] +==== Change the panel data -. To find all flights out of Rome, enter this query in the query bar and click *Update*: -+ -[source,text] -OriginCityName:Rome +To gain insights into your data, change the appearance and behavior of the panels. +For example, edit the metric panel to find the airline that has the lowest average fares. -. For a more complex query with AND and OR, try this: -+ -[source,text] -OriginCityName:Rome AND (Carrier:JetBeats OR "Kibana Airlines") -+ -The dashboard updates to show data for the flights out of Rome on JetBeats and -{kib} Airlines. -+ -[role="screenshot"] -image::getting-started/images/tutorial-sample-query.png[] +. In the {kib} toolbar, click *Edit*. -. When you are finished exploring the dashboard, remove the query by -clearing the contents in the query bar and clicking *Update*. +. In the *Average Ticket Price* metric panel, open the panel menu, then select *Edit visualization*. -[float] -[[tutorial-sample-discover]] -=== Discover the data +. To change the data on the panel, use an {es} {ref}/search-aggregations.html[bucket aggregation], +which sorts the documents that match your search criteria into different categories or buckets. -In Discover, you have access to every document in every index that -matches the selected index pattern. The index pattern tells {kib} which {es} index you are currently -exploring. You can submit search queries, filter the -search results, and view document data. +.. In the *Buckets* pane, select *Add > Split group*. -. From the menu, click *Discover*. +.. From the *Aggregation* dropdown, select *Terms*. -. Ensure `kibana_sample_data_flights` is the current index pattern. -You might need to click *New* in the menu bar to refresh the data. +.. From the *Field* dropdown, select *Carrier*. + +.. Set *Descending* to *4*, then click *Update*. + -You'll see a histogram that shows the distribution of -documents over time. A table lists the fields for -each matching document. By default, all fields are shown. +The average ticket price for all four airlines appear in the visualization builder. + [role="screenshot"] -image::getting-started/images/tutorial-sample-discover1.png[] +image::getting-started/images/tutorial-sample-edit1.png[] -. To choose which fields to display, -hover the pointer over the list of *Available fields*, and then click *add* next -to each field you want include as a column in the table. -+ -For example, if you add the `DestAirportID` and `DestWeather` fields, -the display includes columns for those two fields. +. To save your changes, click *Save and return* in the {kib} toolbar. + +. To save the dashboard, click *Save* in the {kib} toolbar. + [role="screenshot"] -image::getting-started/images/tutorial-sample-discover2.png[] +image::getting-started/images/tutorial-sample-edit2.png[] [float] -[[tutorial-sample-edit]] -=== Edit a visualization - -You have edit permissions for the *Global Flight* dashboard, so you can change -the appearance and behavior of the visualizations. For example, you might want -to see which airline has the lowest average fares. - -. In the side navigation, click *Recently viewed* and open the *Global Flight Dashboard*. -. In the menu bar, click *Edit*. -. In the *Average Ticket Price* visualization, click the gear icon in -the upper right. -. From the *Options* menu, select *Edit visualization*. -+ -*Average Ticket Price* is a metric visualization. -To specify which groups to display -in this visualization, you use an {es} {ref}/search-aggregations.html[bucket aggregation]. -This aggregation sorts the documents that match your search criteria into different -categories, or buckets. +[[filter-and-query-the-data]] +==== Filter and query the data -[float] -==== Create a bucket aggregation +To focus in on the data you want to explore, use filters and queries. +For more information, refer to +{ref}/query-filter-context.html[Query and filter context]. + +To filter the data: -. In the *Buckets* pane, select *Add > Split group*. -. In the *Aggregation* dropdown, select *Terms*. -. In the *Field* dropdown, select *Carrier*. -. Set *Descending* to *4*. -. Click *Apply changes* image:images/apply-changes-button.png[]. +. In the *Controls* visualization, select an *Origin City* and *Destination City*, then click *Apply changes*. + -You now see the average ticket price for all four airlines. +The `OriginCityName` and the `DestCityName` fields filter the data in the panels. + -[role="screenshot"] -image::getting-started/images/tutorial-sample-edit1.png[] - -[float] -==== Save the visualization - -. In the menu bar, click *Save*. -. Leave the visualization name as is and confirm the save. -. Go to the *Global Flight* dashboard and scroll the *Average Ticket Price* visualization to see the four prices. -. Optionally, edit the dashboard. Resize the panel -for the *Average Ticket Price* visualization by dragging the -handle in the lower right. You can also rearrange the visualizations by clicking -the header and dragging. Be sure to save the dashboard. +For example, the following dashboard shows the data for flights from London to Milan. + [role="screenshot"] -image::getting-started/images/tutorial-sample-edit2.png[] +image::getting-started/images/tutorial-sample-filter.png[] -[float] -[[tutorial-sample-inspect]] -=== Inspect the data +. To manually add a filter, click *Add filter*, +then specify the data you want to view. -Seeing visualizations of your data is great, -but sometimes you need to look at the actual data to -understand what's really going on. You can inspect the data behind any visualization -and view the {es} query used to retrieve it. +. When you are finished experimenting, remove all filters. -. In the dashboard, hover the pointer over the pie chart, and then click the icon in the upper right. -. From the *Options* menu, select *Inspect*. +[[query-the-data]] +To query the data: + +. To view all flights out of Rome, enter the following in the *KQL* query bar, then click *Update*: + -The initial view shows the document count. +[source,text] +OriginCityName: Rome + +. For a more complex query with AND and OR, enter: ++ +[source,text] +OriginCityName:Rome AND (Carrier:JetBeats OR Carrier:"Kibana Airlines") ++ +The dashboard panels update to display the flights out of Rome on JetBeats and +{kib} Airlines. + [role="screenshot"] -image::getting-started/images/tutorial-sample-inspect1.png[] - -. To look at the query used to fetch the data for the visualization, select *View > Requests* -in the upper right of the Inspect pane. - -[float] -[[tutorial-sample-remove]] -=== Remove the sample data set -When you’re done experimenting with the sample data set, you can remove it. +image::getting-started/images/tutorial-sample-query.png[] -. Go to the *Sample data* page. -. On the *Sample flight data* card, click *Remove*. +. When you are finished exploring, remove the query by +clearing the contents in the *KQL* query bar, then click *Update*. [float] === Next steps -Now that you have a handle on the {kib} basics, you might be interested in the -tutorial <>, where you'll learn to: +Now that you know the {kib} basics, try out the <> tutorial, where you'll learn to: + +* Add a data set to {kib} -* Load data * Define an index pattern -* Discover and explore data -* Create visualizations -* Add visualizations to a dashboard +* Discover and explore data +* Create and add panels to a dashboard diff --git a/docs/getting-started/tutorial-visualizing.asciidoc b/docs/getting-started/tutorial-visualizing.asciidoc index 20b4e3358307..33a703516024 100644 --- a/docs/getting-started/tutorial-visualizing.asciidoc +++ b/docs/getting-started/tutorial-visualizing.asciidoc @@ -1,47 +1,76 @@ [[tutorial-visualizing]] === Visualize your data -In *Visualize*, you can shape your data using a variety -of charts, tables, and maps, and more. In this tutorial, you'll create four -visualizations: +Shape your data using a variety +of {kib} supported visualizations, tables, and more. In this tutorial, you'll create four +visualizations that you'll use to create a dashboard. -* <> -* <> -* <> -* <> +To begin, open the menu, go to *Dashboard*, then click *Create new dashboard*. [float] -[[tutorial-visualize-pie]] -=== Pie chart +[[compare-the-number-of-speaking-parts-in-the-play]] +=== Compare the number of speaking parts in the plays -Use the pie chart to -gain insight into the account balances in the bank account data. +To visualize the Shakespeare data and compare the number of speaking parts in the plays, create a bar chart using *Lens*. -. Open then menu, then go to *Visualize*. -. Click *Create visualization*. +. Click *Create new*, then click *Lens* on the *New Visualization* window. + [role="screenshot"] -image::images/tutorial-visualize-wizard-step-1.png[] -. Click *Pie*. +image::images/tutorial-visualize-wizard-step-1.png[Bar chart] -. On the *Choose a source* window, select `ba*`. +. Make sure the index pattern is *shakes*. + +. Display the play data along the x-axis. + +.. From the *Available fields* list, drag and drop *play_name* to the *X-axis* field. + +.. Click *Top values of play_name*. + +.. From the *Order direction* dropdown, select *Ascending*. + +.. In the *Label* field, enter `Play Name`. + +. Display the number of speaking parts per play along the y-axis. + +.. From the *Available fields* list, drag and drop *speaker* to the *Y-axis* field. + +.. Click *Unique count of speaker*. + +.. In the *Label* field, enter `Speaking Parts`. ++ +[role="screenshot"] +image::images/tutorial-visualize-bar-1.5.png[Bar chart] + +. *Save* the chart with the name `Bar Example`. + -Initially, the pie contains a single "slice." -That's because the default search matches all documents. +To show a tooltip with the number of speaking parts for that play, hover over a bar. + -To specify which slices to display in the pie, you use an Elasticsearch -{ref}/search-aggregations.html[bucket aggregation]. This aggregation -sorts the documents that match your search criteria into different -categories. You'll use a bucket aggregation to establish -multiple ranges of account balances and find out how many accounts fall into -each range. +Notice how the individual play names show up as whole phrases, instead of +broken up into individual words. This is the result of the mapping +you did at the beginning of the tutorial, when you marked the `play_name` field +as `not analyzed`. -. In the *Buckets* pane, click *Add > Split slices.* +[float] +[[view-the-average-account-balance-by-age]] +=== View the average account balance by age + +To gain insight into the account balances in the bank account data, create a pie chart. In this tutorial, you'll use the {es} +{ref}/search-aggregations.html[bucket aggregation] to specify the pie slices to display. The bucket aggregation sorts the documents that match your search criteria into different +categories and establishes multiple ranges of account balances so that you can find how many accounts fall into each range. + +. Click *Create new*, then click *Pie* on the *New Visualization* window. + +. On the *Choose a source* window, select `ba*`. + +Since the default search matches all documents, the pie contains a single slice. + +. In the *Buckets* pane, click *Add > Split slices.* + .. From the *Aggregation* dropdown, select *Range*. + .. From the *Field* dropdown, select *balance*. -.. Click *Add range* four times to bring the total number of ranges to six. -.. Define the following ranges: + +.. Click *Add range* until there are six rows of fields, then define the following ranges: + [source,text] 0 999 @@ -53,80 +82,83 @@ each range. . Click *Update*. + -Now you can see what proportion of the 1000 accounts fall into each balance -range. +The pie chart displays the proportion of the 1,000 accounts that fall into each of the ranges. + [role="screenshot"] -image::images/tutorial-visualize-pie-2.png[] +image::images/tutorial-visualize-pie-2.png[Pie chart] -. Add another bucket aggregation that looks at the ages of the account -holders. +. Add another bucket aggregation that displays the ages of the account holders. .. In the *Buckets* pane, click *Add*, then click *Split slices*. + .. From the *Sub aggregation* dropdown, select *Terms*. -.. From the *Field* dropdown, select *age*. -. Click *Update*. +.. From the *Field* dropdown, select *age*, then click *Update*. + The break down of the ages of the account holders are displayed in a ring around the balance ranges. + [role="screenshot"] -image::images/tutorial-visualize-pie-3.png[] +image::images/tutorial-visualize-pie-3.png[Final pie chart] . Click *Save*, then enter `Pie Example` in the *Title* field. [float] -[[tutorial-visualize-bar]] -=== Bar chart +[role="xpack"] +[[visualize-geographic-information]] +=== Visualize geographic information -Use a bar chart to look at the Shakespeare data set and compare -the number of speaking parts in the plays. +To visualize geographic information in the log file data, use <>. -. Click *Create visualization > Vertical Bar*, then set the source to `shakes*`. +. Click *Create new*, then click *Maps* on the *New Visualization* window. + +. To change the time, use the time filter. + +.. Set the *Start date* to `May 18, 2015 @ 12:00:00.000`. + +.. Set the *End date* to `May 20, 2015 @ 12:00:00.000`. + -Initially, the chart is a single bar that shows the total count -of documents that match the default wildcard query. +[role="screenshot"] +image::images/gs_maps_time_filter.png[Time filter for Maps tutorial] -. Show the number of speaking parts per play along the y-axis. +.. Click *Update* + +. Map the geo coordinates from the log files. -.. In the *Metrics* pane, expand *Y-axis*. -.. From the *Aggregation* dropdown, select *Unique Count*. -.. From the *Field* dropdown, select *speaker*. -.. In the *Custom label* field, enter `Speaking Parts`. +.. Click *Add layer > Clusters and grids*. -. Click *Update*. +.. From the *Index pattern* dropdown, select *logstash*. -. Show the plays along the x-axis. +.. Click *Add layer*. -.. In the *Buckets* pane, click *Add > X-axis*. -.. From the *Aggregation* dropdown, select *Terms*. -.. From the *Field* dropdown, select *play_name*. -.. To list the plays alphabetically, select *Ascending* from the *Order* dropdown. -.. In the *Custom label* field, enter `Play Name`. +. Specify the *Layer Style*. -. Click *Update*. +.. From the *Fill color* dropdown, select the yellow to red color ramp. + +.. In the *Border width* field, enter `3`. + +.. From the *Border color* dropdown, select *#FFF*, then click *Save & close*. + [role="screenshot"] -image::images/tutorial-visualize-bar-1.5.png[] -. *Save* the chart with the name `Bar Example`. -+ -Hovering over a bar shows a tooltip with the number of speaking parts for -that play. -+ -Notice how the individual play names show up as whole phrases, instead of -broken into individual words. This is the result of the mapping -you did at the beginning of the tutorial, when you marked the `play_name` field -as `not analyzed`. +image::images/tutorial-visualize-map-2.png[Map] + +. Click *Save*, then enter `Map Example` in the *Title* field. + +. Add the map to your dashboard. + +.. Open the menu, go to *Dashboard*, then click *Add*. + +.. On the *Add panels* flyout, click *Map Example*. [float] [[tutorial-visualize-markdown]] -=== Markdown +=== Add context to your visualizations with Markdown -Add formatted text to your dashboard with a markdown tool. +Add context to your new visualizations with Markdown text. -. Click *Create visualization > Markdown*. -. In the text field, enter the following: +. Click *Create new*, then click *Markdown* on the *New Visualization* window. + +. In the *Markdown* text field, enter: + [source,markdown] # This is a tutorial dashboard! @@ -140,40 +172,22 @@ The Markdown renders in the preview pane. [role="screenshot"] image::images/tutorial-visualize-md-2.png[] -. *Save* the tool with the name `Markdown Example`. +. Click *Save*, then enter `Markdown Example` in the *Title* field. -[float] -[[tutorial-visualize-map]] -=== Map +[role="screenshot"] +image::images/tutorial-dashboard.png[] -Using <>, you can visualize geographic information in the log file sample data. +[float] +=== Next steps -. Click *Create visualization > Maps*. +Now that you have the basics, you're ready to start exploring your own system data with {kib}. -. Set the time. -.. In the time filter, click *Show dates*. -.. Click the start date, then *Absolute*. -.. Set the *Start date* to May 18, 2015. -.. Click *now*, then *Absolute*. -.. Set the *End date* to May 20, 2015. -.. Click *Update* +* To add your own data to {kib}, refer to <>. -. Map the geo coordinates from the log files. +* To search and filter your data, refer to {kibana-ref}/discover.html[Discover]. -.. Click *Add layer > Clusters and Grids*. -.. From the *Index pattern* dropdown, select *logstash*. -.. Click *Add layer*. +* To create a dashboard with your own data, refer to <>. -. Set the *Layer Style*. -.. From the *Fill color* dropdown, select the yellow to red color ramp. -.. From the *Border color* dropdown, select white. -.. Click *Save & close*. -+ -The map looks like this: -+ -[role="screenshot"] -image::images/tutorial-visualize-map-2.png[] +* To create maps that you can add to your dashboards, refer to <>. -. Navigate the map by clicking and dragging. Use the controls -to zoom the map and set filters. -. *Save* the map with the name `Map Example`. +* To create presentations of your live data, refer to <>. diff --git a/docs/glossary.asciidoc b/docs/glossary.asciidoc index 1edb33032418..be24402170bb 100644 --- a/docs/glossary.asciidoc +++ b/docs/glossary.asciidoc @@ -151,7 +151,7 @@ that you are interested in. A navigation path that retains context (time range and filters) from the source to the destination, so you can view the data from a new perspective. A dashboard that shows the overall status of multiple data centers -might have a drilldown to a dashboard for a single data center. See {kibana-ref}/drilldowns.html[Drilldowns]. +might have a drilldown to a dashboard for a single data center. See {kibana-ref}/dashboard.html[Drilldowns]. // end::drilldown-def[] @@ -238,7 +238,7 @@ support for scripted fields. See Enables you to build visualizations by dragging and dropping data fields. Lens makes makes smart visualization suggestions for your data, allowing you to switch between visualization types. -See {kibana-ref}/lens.html[Lens]. +See {kibana-ref}/dashboard.html[Lens]. // end::lens-def[] @@ -350,7 +350,7 @@ A {kib} control that constrains the search results to a particular time period. [[glossary-timelion]] Timelion :: // tag::timelion-def[] A tool for building a time series visualization that analyzes data in time order. -See {kibana-ref}/timelion.html[Timelion]. +See {kibana-ref}/dashboard.html[Timelion]. // end::timelion-def[] @@ -364,7 +364,7 @@ Timestamped data such as logs, metrics, and events that is indexed on an ongoing // tag::tsvb-def[] A time series data visualizer that allows you to combine an infinite number of aggregations to display complex data. -See {kibana-ref}/TSVB.html[TSVB]. +See {kibana-ref}/dashboard.html[TSVB]. // end::tsvb-def[] @@ -388,7 +388,7 @@ indices and guides you through resolving issues, including reindexing. See [[glossary-vega]] Vega :: // tag::vega-def[] A declarative language used to create interactive visualizations. -See {kibana-ref}/vega-graph.html[Vega]. +See {kibana-ref}/dashboard.html[Vega]. // end::vega-def[] [[glossary-vector]] vector data:: diff --git a/docs/management/index-patterns.asciidoc b/docs/management/index-patterns.asciidoc index 05036311c094..7de2a042160e 100644 --- a/docs/management/index-patterns.asciidoc +++ b/docs/management/index-patterns.asciidoc @@ -7,7 +7,7 @@ you want to work with. Once you create an index pattern, you're ready to: * Interactively explore your data in <>. -* Analyze your data in charts, tables, gauges, tag clouds, and more in <>. +* Analyze your data in charts, tables, gauges, tag clouds, and more in <>. * Show off your data in a <> workpad. * If your data includes geo data, visualize it with <>. diff --git a/docs/management/managing-saved-objects.asciidoc b/docs/management/managing-saved-objects.asciidoc index 51de5ad620b4..8c885ddca52e 100644 --- a/docs/management/managing-saved-objects.asciidoc +++ b/docs/management/managing-saved-objects.asciidoc @@ -92,5 +92,5 @@ index pattern. This is useful if the index you were working with has been rename WARNING: Validation is not performed for object properties. Submitting an invalid change will render the object unusable. A more failsafe approach is to use -*Discover*, *Visualize*, or *Dashboard* to create new objects instead of +*Discover* or *Dashboard* to create new objects instead of directly editing an existing one. diff --git a/docs/management/numeral.asciidoc b/docs/management/numeral.asciidoc index 5d4d48ca785e..a8834a3278a9 100644 --- a/docs/management/numeral.asciidoc +++ b/docs/management/numeral.asciidoc @@ -10,7 +10,7 @@ Numeral formatting patterns are used in multiple places in {kib}, including: * <> * <> -* <> +* <> * <> The simplest pattern format is `0`, and the default {kib} pattern is `0,0.[000]`. diff --git a/docs/management/rollups/create_and_manage_rollups.asciidoc b/docs/management/rollups/create_and_manage_rollups.asciidoc index 831b536f8c1c..8aa57f50fe94 100644 --- a/docs/management/rollups/create_and_manage_rollups.asciidoc +++ b/docs/management/rollups/create_and_manage_rollups.asciidoc @@ -60,7 +60,7 @@ You can read more at {ref}/rollup-job-config.html[rollup job configuration]. === Try it: Create and visualize rolled up data This example creates a rollup job to capture log data from sample web logs. -To follow along, add the <>. +To follow along, add the <>. In this example, you want data that is older than 7 days in the target index pattern `kibana_sample_data_logs` to roll up once a day into the index `rollup_logstash`. You’ll bucket the @@ -145,7 +145,7 @@ is `rollup_logstash,kibana_sample_data_logs`. In this index pattern, `rollup_log matches the rolled up index pattern and `kibana_sample_data_logs` matches the index pattern for raw data. -. Go to *Visualize* and create a vertical bar chart. +. Go to *Dashboard* and create a vertical bar chart. . Choose `rollup_logstash,kibana_sample_data_logs` as your source to see both the raw and rolled up data. diff --git a/docs/redirects.asciidoc b/docs/redirects.asciidoc index 1a20c1df582e..42d1d89145d7 100644 --- a/docs/redirects.asciidoc +++ b/docs/redirects.asciidoc @@ -59,7 +59,7 @@ This page has moved. Please see <>. [role="exclude",id="add-sample-data"] == Add sample data -This page has moved. Please see <>. +This page has moved. Please see <>. [role="exclude",id="tilemap"] == Coordinate map @@ -95,3 +95,8 @@ More information on this new feature is available in <>. == Role-based access control This content has moved to the <> page. + +[role="exclude",id="TSVB"] +== TSVB + +This page was deleted. See <>. diff --git a/docs/setup/connect-to-elasticsearch.asciidoc b/docs/setup/connect-to-elasticsearch.asciidoc index f750784c4704..ea02afb8a9fd 100644 --- a/docs/setup/connect-to-elasticsearch.asciidoc +++ b/docs/setup/connect-to-elasticsearch.asciidoc @@ -11,7 +11,7 @@ To start working with your data in {kib}, you can: * Connect {kib} with existing {es} indices. -If you're not ready to use your own data, you can add a <> +If you're not ready to use your own data, you can add a <> to see all that you can do in {kib}. [float] diff --git a/docs/user/canvas.asciidoc b/docs/user/canvas.asciidoc index 317ec67dd7c0..0b0eb7a31849 100644 --- a/docs/user/canvas.asciidoc +++ b/docs/user/canvas.asciidoc @@ -137,7 +137,7 @@ image::images/canvas-map-embed.gif[] . To use the customization options, click the panel menu, then select one of the following options: -* *Edit map* — Opens <> or <> so that you can edit the original saved object. +* *Edit map* — Opens <> or a visualization builder so that you can edit the original saved object. * *Edit panel title* — Adds a title to the saved object. diff --git a/docs/user/dashboard.asciidoc b/docs/user/dashboard.asciidoc deleted file mode 100644 index b812af7e981b..000000000000 --- a/docs/user/dashboard.asciidoc +++ /dev/null @@ -1,191 +0,0 @@ -[[dashboard]] -= Dashboard - -[partintro] --- - -A _dashboard_ is a collection of visualizations, searches, and -maps, typically in real-time. Dashboards provide -at-a-glance insights into your data and enable you to drill down into details. - -With *Dashboard*, you can: - -* Add visualizations, saved searches, and maps for side-by-side analysis - -* Arrange dashboard elements to display exactly how you want - -* Customize time ranges to display only the data you want - -* Inspect and edit dashboard elements to find out exactly what kind of data is displayed - -[role="screenshot"] -image:images/Dashboard_example.png[Example dashboard] - -[float] -[[dashboard-read-only-access]] -=== [xpack]#Read only access# -If you see -the read-only icon in the application header, -then you don't have sufficient privileges to create and save dashboards. The buttons to create and edit -dashboards are not visible. For more information, see <>. - -[role="screenshot"] -image::images/dashboard-read-only-badge.png[Example of Dashboard read only access indicator in Kibana header] - --- - -[[dashboard-create-new-dashboard]] -== Create a dashboard - -To create a dashboard, you must have data indexed into {es}, an index pattern -to retrieve the data from {es}, and -visualizations, saved searches, or maps. If these don't exist, you're prompted to -add them as you create the dashboard, or you can add -<>, -which include pre-built dashboards. - -To begin, open the menu, go to *Dashboard*, then click *Create dashboard.* - -[float] -[[dashboard-add-elements]] -=== Add elements - -The visualizations, saved searches, and maps are stored as elements in panels -that you can move and resize. - -You can add elements from multiple indices, and the same element can appear in -multiple dashboards. - -To create an element: - -. Click *Create new*. -. On the *New Visualization* window, click the visualization type. -+ -[role="screenshot"] -image:images/Dashboard_add_new_visualization.png[Example add new visualization to dashboard] -+ -For information on how to create visualizations, see <>. -+ -For information on how to create maps, see <>. - -To add an existing element: - -. Click *Add*. - -. On the *Add panels* flyout, select the panel. -+ -When a dashboard element has a stored query, -both queries are applied. -+ -[role="screenshot"] -image:images/Dashboard_add_visualization.png[Example add visualization to dashboard] - -[float] -[[customizing-your-dashboard]] -=== Arrange dashboard elements - -In *Edit* mode, you can move, resize, customize, and delete panels to suit your needs. - -[[moving-containers]] -* To move a panel, click and hold the panel header and drag to the new location. - -[[resizing-containers]] -* To resize a panel, click the resize control and drag -to the new dimensions. - -* To toggle the use of margins and panel titles, use the *Options* menu. - -* To delete a panel, open the panel menu and select *Delete from dashboard.* Deleting a panel from a -dashboard does *not* delete the saved visualization or search. - -[float] -[[cloning-a-panel]] -=== Clone dashboard elements - -In *Edit* mode, you can clone any panel on a dashboard. - -To clone an existing panel, open the panel menu of the element you wish to clone, then select *Clone panel*. - -* Cloned panels appear beside the original, and will move other panels down to make room if necessary. - -* Clones support all of the original panel's functionality, including renaming, editing, and cloning. - -* All cloned visualizations will appear in the visualization list. - -[role="screenshot"] -image:images/clone_panel.gif[clone panel] - - -[float] -[[viewing-detailed-information]] -=== Inspect and edit elements - -Many dashboard elements allow you to drill down into the data and requests -behind the element. - -From the panel menu, select *Inspect*. -The data that displays depends on the element that you inspect. - -[role="screenshot"] -image:images/Dashboard_inspect.png[Inspect in dashboard] - -To open an element for editing, put the dashboard in *Edit* mode, -and then select *Edit visualization* from the panel menu. The changes you make appear in -every dashboard that uses the element. - -[float] -[[dashboard-customize-filter]] -=== Customize time ranges - -You can configure each visualization, saved search, and map on your dashboard -for a specific time range. For example, you might want one visualization to show -the monthly trend for CPU usage and another to show the current CPU usage. - -From the panel menu, select *Customize time range* to expose a time filter -dedicated to that panel. Panels that are not restricted by a specific -time range are controlled by the -global time filter. - -[role="screenshot"] -image:images/time_range_per_panel.gif[Time range per dashboard panel] - -[float] -[[save-dashboards]] -=== Save the dashboard - -When you're finished adding and arranging the panels, save the dashboard. - -. In the {kib} toolbar, click *Save*. - -. Enter the dashboard *Title* and optional *Description*, then *Save* the dashboard. - -include::{kib-repo-dir}/drilldowns/drilldowns.asciidoc[] -include::{kib-repo-dir}/drilldowns/explore-underlying-data.asciidoc[] - -[[sharing-dashboards]] -== Share the dashboard - -[[embedding-dashboards]] -Share your dashboard outside of {kib}. - -From the *Share* menu, you can: - -* Embed the code in a web page. Users must have {kib} access -to view an embedded dashboard. -* Share a direct link to a {kib} dashboard -* Generate a PDF report -* Generate a PNG report - -TIP: To create a link to a dashboard by title, use: + -`${domain}/${basepath?}/app/dashboards#/list?title=${yourdashboardtitle}` - -TIP: When sharing a link to a dashboard snapshot, use the *Short URL*. Snapshot -URLs are long and can be problematic for Internet Explorer and other -tools. To create a short URL, you must have write access to {kib}. - -[float] -[[import-dashboards]] -=== Export the dashboard - -To export the dashboard, open the menu, then click *Stack Management > Saved Objects*. For more information, -refer to <>. diff --git a/docs/user/dashboard/aggregation-reference.asciidoc b/docs/user/dashboard/aggregation-reference.asciidoc new file mode 100644 index 000000000000..1bcea3bb36ae --- /dev/null +++ b/docs/user/dashboard/aggregation-reference.asciidoc @@ -0,0 +1,242 @@ +[[aggregation-reference]] +== Aggregation reference + +{kib} supports many types of {ref}/search-aggregations.html[{es} aggregations] that you can use to build complex summaries of your data. + +By using a series of {es} aggregations to extract and process your data, you can create panels that tell a +story about the trends, patterns, and outliers in your data. + +[float] +[[bucket-aggregations]] +=== Bucket aggregations + +For information about Elasticsearch bucket aggregations, refer to {ref}/search-aggregations-bucket.html[Bucket aggregations]. + +[options="header"] +|=== + +| Type | Visualizations | Data table | Markdown | Lens | TSVB + +| Histogram +^| X +^| X +^| X +| +| + +| Date histogram +^| X +^| X +^| X +^| X +^| X + +| Date range +^| X +^| X +^| X +| +| + +| Filter +^| X +^| X +^| X +| +^| X + +| Filters +^| X +^| X +^| X +| +^| X + +| GeoHash grid +^| X +^| X +^| X +| +| + +| IP range +^| X +^| X +^| X +| +| + +| Range +^| X +^| X +^| X +| +| + +| Terms +^| X +^| X +^| X +^| X +^| X + +| Significant terms +^| X +^| X +^| X +| +^| X + +|=== + +[float] +[[metrics-aggregations]] +=== Metrics aggregations + +For information about Elasticsearch metrics aggregations, refer to {ref}/search-aggregations-metrics.html[Metrics aggregations]. + +[options="header"] +|=== + +| Type | Visualizations | Data table | Markdown | Lens | TSVB + +| Average +^| X +^| X +^| X +^| X +^| X + +| Sum +^| X +^| X +^| X +^| X +^| X + +| Unique count (Cardinality) +^| X +^| X +^| X +^| X +^| X + +| Max +^| X +^| X +^| X +^| X +^| X + +| Min +^| X +^| X +^| X +^| X +^| X + +| Percentiles +^| X +^| X +^| X +| +^| X + +| Percentiles Rank +^| X +^| X +^| X +| +^| X + +| Top hit +^| X +^| X +^| X +| +^| X + +| Value count +| +| +| +| +^| X + +|=== + +[float] +[[pipeline-aggregations]] +=== Pipeline aggregations + +For information about Elasticsearch pipeline aggregations, refer to {ref}/search-aggregations-pipeline.html[Pipeline aggregations]. + +[options="header"] +|=== + +| Type | Visualizations | Data table | Markdown | Lens | TSVB + +| Avg bucket +^| X +^| X +^| X +| +^| X + +| Derivative +^| X +^| X +^| X +| +^| X + +| Max bucket +^| X +^| X +^| X +| +^| X + +| Min bucket +^| X +^| X +^| X +| +^| X + +| Sum bucket +^| X +^| X +^| X +^| +^| X + +| Moving average +^| X +^| X +^| X +^| +^| X + +| Cumulative sum +^| X +^| X +^| X +^| +^| X + +| Bucket script +| +| +| +| +^| X + +| Serial differencing +^| X +^| X +^| X +| +^| X + +|=== diff --git a/docs/user/dashboard/dashboard.asciidoc b/docs/user/dashboard/dashboard.asciidoc new file mode 100644 index 000000000000..0c0151cc3ace --- /dev/null +++ b/docs/user/dashboard/dashboard.asciidoc @@ -0,0 +1,472 @@ +[[dashboard]] += Dashboard + +[partintro] +-- + +A _dashboard_ is a collection of panels that you use to analyze your data. On a dashboard, you can add a variety of panels that +you can rearrange and tell a story about your data. Panels contain everything you need, including visualizations, +interactive controls, markdown, and more. + +With *Dashboard*s, you can: + +* Add multiple panels to see many aspects and views of your data in one place. + +* Arrange panels for analysis and comparison. + +* Add text and images to provide context to the panels and make them easy to consume. + +* Create and apply filters to focus on the data you want to display. + +* Control who can use your data, and share the dashboard with a small or large audience. + +* Generate reports based on your findings. + +To begin, open the menu, go to *Dashboard*, then click *Create dashboard*. + +[role="screenshot"] +image:images/Dashboard_example.png[Example dashboard] + +[float] +[[dashboard-read-only-access]] +=== [xpack]#Read only access# +If you see +the read-only icon in the application header, +then you don't have sufficient privileges to create and save dashboards. The buttons to create and edit +dashboards are not visible. For more information, see <>. + +[role="screenshot"] +image::images/dashboard-read-only-badge.png[Example of Dashboard read only access indicator in Kibana header] + +[float] +[[types-of-panels]] +== Types of panels + +Panels contain everything you need to tell a story about you data, including visualizations, +interactive controls, Markdown, and more. + +[cols="50, 50"] +|=== + +a| *Area* + +Displays data points, connected by a line, where the area between the line and axes are shaded. +Use area charts to compare two or more categories over time, and display the magnitude of trends. + +| image:images/area.png[Area chart] + +a| *Stacked area* + +Displays the evolution of the value of several data groups. The values of each group are displayed +on top of each other. Use stacked area charts to visualize part-to-whole relationships, and to show +how each category contributes to the cumulative total. + +| image:images/stacked_area.png[Stacked area chart] + +a| *Bar* + +Displays bars side-by-side where each bar represents a category. Use bar charts to compare data across a +large number of categories, display data that includes categories with negative values, and easily identify +the categories that represent the highest and lowest values. Kibana also supports horizontal bar charts. + +| image:images/bar.png[Bar chart] + +a| *Stacked bar* + +Displays numeric values across two or more categories. Use stacked bar charts to compare numeric values between +levels of a categorical value. Kibana also supports stacked horizontal bar charts. + +| image:images/stacked_bar.png[Stacked area chart] + + +a| *Line* + +Displays data points that are connected by a line. Use line charts to visualize a sequence of values, discover +trends over time, and forecast future values. + +| image:images/line.png[Line chart] + +a| *Pie* + +Displays slices that represent a data category, where the slice size is proportional to the quantity it represents. +Use pie charts to show comparisons between multiple categories, illustrate the dominance of one category over others, +and show percentage or proportional data. + +| image:images/pie.png[Pie chart] + +a| *Donut* + +Similar to the pie chart, but the central circle is removed. Use donut charts when you’d like to display multiple statistics at once. + +| image:images/donut.png[Donut chart] + + +a| *Tree map* + +Relates different segments of your data to the whole. Each rectangle is subdivided into smaller rectangles, or sub branches, based on +its proportion to the whole. Use treemaps to make efficient use of space to show percent total for each category. + +| image:images/treemap.png[Tree map] + + +a| *Heat map* + +Displays graphical representations of data where the individual values are represented by colors. Use heat maps when your data set includes +categorical data. For example, use a heat map to see the flights of origin countries compared to destination countries using the sample flight data. + +| image:images/heat_map.png[Heat map] + +a| *Goal* + +Displays how your metric progresses toward a fixed goal. Use the goal to display an easy to read visual of the status of your goal progression. + +| image:images/goal.png[Goal] + + +a| *Gauge* + +Displays your data along a scale that changes color according to where your data falls on the expected scale. Use the gauge to show how metric +values relate to reference threshold values, or determine how a specified field is performing versus how it is expected to perform. + +| image:images/gauge.png[Gauge] + + +a| *Metric* + +Displays a single numeric value for an aggregation. Use the metric visualization when you have a numeric value that is powerful enough to tell +a story about your data. + +| image:images/metric.png[Metric] + + +a| *Data table* + +Displays your raw data or aggregation results in a tabular format. Use data tables to display server configuration details, track counts, min, +or max values for a specific field, and monitor the status of key services. + +| image:images/data_table.png[Data table] + + +a| *Tag cloud* + +Graphical representations of how frequently a word appears in the source text. Use tag clouds to easily produce a summary of large documents and +create visual art for a specific topic. + +| image:images/tag_cloud.png[Tag cloud] + + +a| *Maps* + +For all your mapping needs, use <>. + +| image:images/maps.png[Maps] + + +|=== + +[float] +[[create-panels]] +== Create panels + +To create a panel, make sure you have {ref}/getting-started-index.html[data indexed into {es}] and an <> +to retrieve the data from {es}. If you aren’t ready to use your own data, {kib} comes with several pre-built dashboards that you can test out. For more information, +refer to <>. + +To begin, click *Create new*, then choose one of the following options on the +*New Visualization* window: + +* Click on the type of panel you want to create, then configure the options. + +* Select an editor to help you create the panel. + +[role="screenshot"] +image:images/Dashboard_add_new_visualization.png[Example add new visualization to dashboard] + +{kib} provides you with several editors that help you create panels. + +[float] +[[lens]] +=== Create panels with Lens + +*Lens* is the simplest and fastest way to create powerful visualizations of your data. To use *Lens*, you drag and drop as many data fields +as you want onto the visualization builder pane, and *Lens* uses heuristics to decide how to apply each field to the visualization. + +With *Lens*, you can: + +* Use the automatically generated suggestions to change the visualization type. +* Create visualizations with multiple layers and indices. +* Change the aggregation and labels to customize the data. + +[role="screenshot"] +image::images/lens_drag_drop.gif[Drag and drop] + +TIP: Drag-and-drop capabilities are available only when *Lens* knows how to use the data. If *Lens* is unable to automatically generate a +visualization, configure the customization options for your visualization. + +[float] +[[fiter-the-data-fields]] +==== Filter the data fields + +The data fields that are displayed are based on the selected <> and the <>. + +To view the data fields in a different index pattern, click the index pattern, then select a new one. The data fields automatically update. + +To filter the data fields: + +* Enter the name in the *Search field names*. +* Click *Field by type*, then select the filter. To show all fields in the index pattern, deselect *Only show fields with data*. + +[float] +[[view-data-summaries]] +==== View data summaries + +To help you decide exactly the data you want to display, get a quick summary of each field. The summary shows the distribution of +values within the specified time range. + +To view the data field summary information, navigate to the field, then click *i*. + +[role="screenshot"] +image::images/lens_data_info.png[Data summary window] + +[float] +[[change-the-visualization-type]] +==== Change the visualization type + +Use the automatically generated suggestions to change the visualization type, or manually select the type of visualization you want to view. + +*Suggestions* are shortcuts to alternative visualizations that *Lens* generates for you. + +[role="screenshot"] +image::images/lens_suggestions.gif[Visualization suggestions] + +If you’d like to use a visualization type outside of the suggestions, click the visualization type, then select a new one. + +[role="screenshot"] +image::images/lens_viz_types.png[] + +When there is an exclamation point (!) next to a visualization type, *Lens* is unable to transfer your data, but still allows you to make the change. + +[float] +[[customize-the-data]] +==== Customize the data + +For each visualization type, you can customize the aggregation and labels. The options available depend on the selected visualization type. + +. Click a data field name in the editor, or click *Drop a field here*. +. Change the options that appear. ++ +[role="screenshot"] +image::images/lens_aggregation_labels.png[Quick function options] + +[float] +[[add-layers-and-indices]] +==== Add layers and indices + +To compare and analyze data from different sources, you can visualize multiple data layers and indices. Multiple layers and indices are +supported in area, line, and bar charts. + +To add a layer, click *+*, then drag and drop the data fields for the new layer. + +[role="screenshot"] +image::images/lens_layers.png[Add layers] + +To view a different index, click the index name in the editor, then select a new one. + +[role="screenshot"] +image::images/lens_index_pattern.png[Add index pattern] + +Ready to try out *Lens*? Refer to the <>. + +[float] +[[tsvb]] +=== Create panels with TSVB + +*TSVB* is a time series data visualizer that allows you to use the full power of the Elasticsearch aggregation framework. To use *TSVB*, +you can combine an infinite number of <> to display your data. + +With *TSVB*, you can: + +* Create visualizations, data tables, and markdown panels. +* Create visualizations with multiple indices. +* Change the aggregation and labels to customize the data. ++ +[role="screenshot"] +image::images/tsvb.png[TSVB UI] + +[float] +[[configure-the-data]] +==== Configure the data + +With *TSVB*, you can add and display multiple data sets to compare and analyze. {kib} uses many types of <> that you can use to build +complex summaries of that data. + +. Select *Data*. If you are using *Table*, select *Columns*. +. From the *Aggregation* drop down, select the aggregation you want to visualize. ++ +If you don’t see any data, change the <>. ++ +To add multiple aggregations, click *+*. +. From the *Group by* drop down, select how you want to group or split the data. +. To add another data set, click *+*. ++ +When you have more than one aggregation, the last value is displayed, which is indicated by the eye icon. + +[float] +[[change-the-data-display]] +==== Change the data display + +To find the best way to display your data, *TSVB* supports several types of panels and charts. + +To change the *Time Series* chart type: + +. Click *Data > Options*. +. Select the *Chart type*. + +To change the panel type, click on the panel options: + +[role="screenshot"] +image::images/tsvb_change_display.gif[TSVB change the panel type] + +[float] +[[custommize-the-data]] +==== Customize the data + +View data in a different <>, and change the data label name and colors. The options available depend on the panel type. + +To change the index pattern, click *Panel options*, then enter the new *Index Pattern*. + +To override the index pattern for a data set, click *Data > Options*. Select *Yes* to override, then enter the new *Index pattern*. + +To change the data labels and colors: + +. Click *Data*. +. Enter the *Label* name, which *TSVB* uses on the legends and data labels. +. Click the color picker, then select the color for the data. ++ +[role="screenshot"] +image::images/tsvb_color_picker.png[TSVB color picker] + +[float] +[[add-annotations]] +==== Add annotations + +You can overlay annotation events on top of your *Time Series* charts. The options available depend on the data source. + +To begin, click *Annotations*, click *Add data source*, then configure the options. + +[role="screenshot"] +image::images/tsvb_annotations.png[TSVB annotations] + +[float] +[[filter-the-panel]] +==== Filter the panel + +The data that displays on the panel is based on the <> and <>. +You can filter the data on the panels using the <>. + +Click *Panel options*, then enter the syntax in the *Panel Filter* field. + +If you want to ignore filters from all of {kib}, select *Yes* for *Ignore global filter*. + +[float] +[[vega]] +=== Create custom panels with Vega + +Build custom visualizations using *Vega* and *Vega-Lite*, backed by one or more data sources including {es}, Elastic Map Service, +URL, or static data. Use the {kib} extensions to embed *Vega* in your dashboard, and add interactive tools. + +Use *Vega* and *Vega-Lite* when you want to create a visualization for: + +* Aggregations that use `nested` or `parent/child` mapping +* Aggregations without an index pattern +* Queries that use custom time filters +* Complex calculations +* Extracting data from _source instead of aggregations +* Scatter charts, sankey charts, and custom maps +* Using an unsupported visual theme + +[role="screenshot"] +image::images/vega.png[Vega UI] + +*Vega* and *Vega-Lite* are declarative formats that: + +* Create complex visualizations +* Use JSON and a different syntax for declaring visualizations +* Are not fully interchangeable + +For more information about *Vega* and *Vega-Lite*, refer to: + +* <> +* <> +* <> +* <> + +[float] +[[timelion]] +=== Create panels with Timelion + +*Timelion* is a time series data visualizer that enables you to combine independent data sources within a single visualization. + +*Timelion* is driven by a simple expression language that you use to: + +* Retrieve time series data +* Perform calculation to tease out the answers to complex questions +* Visualize the results + +[role="screenshot"] +image::images/timelion.png[Timelion UI] + +Ready to try out Timelion? For step-by-step tutorials, refer to: + +* <> +* <> +* <> + +[float] +[[save-panels]] +=== Save panels + +When you’ve finished making changes, save the panels. + +. Click *Save*. +. Add the *Title* and optional *Description*. +. Click *Save and return*. + +[float] +[[add-existing-panels]] +== Add existing panels + +Add panels that you’ve already created to your dashboard. + +On the dashboard, click *Add an existing*, then select the panel you want to add. + +When a panel contains a stored query, both queries are applied. + +[role="screenshot"] +image:images/Dashboard_add_visualization.png[Example add visualization to dashboard] + +To make changes to the panel, put the dashboard in *Edit* mode, then select the edit option from the panel menu. +The changes you make appear in every dashboard that uses the panel, except if you edit the panel title. Changes to the panel title appear only on the dashboard where you made the change. + +[float] +[[save-dashboards]] +== Save dashboards + +When you’ve finished adding the panels, save the dashboard. + +. In the toolbar, click *Save*. + +. Enter the dashboard *Title* and optional *Description*, then *Save* the dashboard. + +-- +include::edit-dashboards.asciidoc[] + +include::explore-dashboard-data.asciidoc[] + +include::share-dashboards.asciidoc[] + +include::tutorials.asciidoc[] + +include::aggregation-reference.asciidoc[] + +include::vega-reference.asciidoc[] diff --git a/docs/drilldowns/drilldowns.asciidoc b/docs/user/dashboard/drilldowns.asciidoc similarity index 93% rename from docs/drilldowns/drilldowns.asciidoc rename to docs/user/dashboard/drilldowns.asciidoc index e2dfaa5af39c..5fca974d5813 100644 --- a/docs/drilldowns/drilldowns.asciidoc +++ b/docs/user/dashboard/drilldowns.asciidoc @@ -1,5 +1,6 @@ +[float] [[drilldowns]] -== Use drilldowns for dashboard actions +=== Use drilldowns for dashboard actions Drilldowns, also known as custom actions, allow you to configure a workflow for analyzing and troubleshooting your data. @@ -13,7 +14,7 @@ that shows a single data center or server. [float] [[how-drilldowns-work]] -=== How drilldowns work +==== How drilldowns work Drilldowns are user-configurable {kib} actions that are stored with the dashboard metadata. Drilldowns are specific to the dashboard panel @@ -35,7 +36,7 @@ to learn how to code drilldowns. [float] [[create-manage-drilldowns]] -=== Create and manage drilldowns +==== Create and manage drilldowns Your dashboard must be in *Edit* mode to create a drilldown. Once a panel has at least one drilldown, the menu also includes a *Manage drilldowns* action @@ -46,14 +47,13 @@ image::images/drilldown_menu.png[Panel menu with Create drilldown and Manage dri [float] [[drilldowns-example]] -=== Try it: Create a drilldown +==== Try it: Create a drilldown This example shows how to create the *Host Overview* drilldown shown earlier in this doc. -[float] -==== Set up the dashboards +*Set up the dashboards* -. Add the <> data set. +. Add the <> data set. . Create a new dashboard, called `Host Overview`, and include these visualizations from the sample data set: @@ -74,9 +74,7 @@ TIP: If you don’t see data for a panel, try changing the time range. Search: `extension.keyword:( “gz” or “css” or “deb”)` Filter: `geo.src : CN` -[float] -==== Create the drilldown - +*Create the drilldown* . In the dashboard menu bar, click *Edit*. diff --git a/docs/user/dashboard/edit-dashboards.asciidoc b/docs/user/dashboard/edit-dashboards.asciidoc new file mode 100644 index 000000000000..7534ea1e9e9f --- /dev/null +++ b/docs/user/dashboard/edit-dashboards.asciidoc @@ -0,0 +1,115 @@ +[[edit-dashboards]] +== Edit dashboards + +Now that you have added panels to your dashboard, you can add filter panels to interact with the data, and Markdown panels to add context to the dashboard. +To make your dashboard look the way you want, use the editing options. + +[float] +[[add-controls]] +=== Add controls + +To filter the data on your dashboard in real-time, add a *Controls* panel. + +You can add two types of *Controls*: + +* Options list — Filters content based on one or more specified options. The dropdown menu is dynamically populated with the results of a terms aggregation. +For example, use the options list on the sample flight dashboard when you want to filter the data by origin city and destination city. + +* Range slider — Filters data within a specified range of numbers. The minimum and maximum values are dynamically populated with the results of a +min and max aggregation. For example, use the range slider when you want to filter the sample flight dashboard by a specific average ticket price. + +[role="screenshot"] +image::images/dashboard-controls.png[] + +To configure *Controls* for your dashboard: + +. Click *Options*, then configure the following: + +* *Update Kibana filters on each change* — When selected, all interactive inputs create filters that refresh the dashboard. When unselected, + {kib} filters are created only when you click *Apply changes*. + +* *Use time filter* — When selected, the aggregations that generate the options list and time range are connected to the <>. + +* *Pin filters to global state* — When selected, all filters created by interacting with the inputs are automatically pinned. + +. Click *Update*. + +[float] +[[add-markdown]] +=== Add Markdown + +*Markdown* is a text entry field that accepts GitHub-flavored Markdown text. When you enter the text, the tool populates the results on the dashboard. + +Use Markdown when you want to add context to the other panels on your dashboard, such as important information, instructions and images. + +For information about GitHub-flavored Markdown text, click *Help*. + +For example, when you enter: + +[role="screenshot"] +image::images/markdown_example_1.png[] + +The following instructions are displayed: + +[role="screenshot"] +image::images/markdown_example_2.png[] + +Or when you enter: + +[role="screenshot"] +image::images/markdown_example_3.png[] + +The following image is displayed: + +[role="screenshot"] +image::images/markdown_example_4.png[] + +[float] +[[arrange-panels]] +[[moving-containers]] +[[resizing-containers]] +=== Arrange panels + +To make your dashboard panels look exactly how you want, you can move, resize, customize, and delete them. + +Put the dashboard in *Edit* mode, then use the following options: + +* To move, click and hold the panel header, then drag to the new location. + +* To resize, click the resize control, then drag to the new dimensions. + +* To delete, open the panel menu, then select Delete from dashboard. When you delete a panel from the dashboard, the +visualization or saved search from the panel is still available in Kibana. + +[float] +[[clone-panels]] +=== Clone panels + +To duplicate a panel and its configured functionality, clone the panel. Cloned panels support all of the original functionality, +including renaming, editing, and cloning. + +. Put the dashboard in *Edit* mode. + +. For the panel you want to clone, open the panel menu, then select *Clone panel*. + +Cloned panels appear beside the original, and move other panels down to make room when necessary. +All cloned visualization panels appear in the visualization list. + +[role="screenshot"] +image:images/clone_panel.gif[clone panel] + +[float] +[[dashboard-customize-filter]] +=== Customize time ranges + +You can configure each visualization, saved search, and map on your dashboard +for a specific time range. For example, you might want one visualization to show +the monthly trend for CPU usage and another to show the current CPU usage. + +From the panel menu, select *Customize time range* to expose a time filter +dedicated to that panel. Panels that are not restricted by a specific +time range are controlled by the +<>. + +[role="screenshot"] +image:images/time_range_per_panel.gif[Time range per dashboard panel] diff --git a/docs/user/dashboard/explore-dashboard-data.asciidoc b/docs/user/dashboard/explore-dashboard-data.asciidoc new file mode 100644 index 000000000000..a0564f5bceb3 --- /dev/null +++ b/docs/user/dashboard/explore-dashboard-data.asciidoc @@ -0,0 +1,20 @@ +[[explore-dashboard-data]] +== Explore dashboard data + +Get a closer look at your data by inspecting elements and using drilldown actions. + +[float] +[[viewing-detailed-information]] +=== Inspect elements + +To view the data and requests behind the visualizations and saved searches, you can drill down into the elements. + +From the panel menu, select *Inspect*. +The data that displays depends on the element that you inspect. + +[role="screenshot"] +image:images/Dashboard_inspect.png[Inspect in dashboard] + +include::explore-underlying-data.asciidoc[] + +include::drilldowns.asciidoc[] diff --git a/docs/user/dashboard/explore-underlying-data.asciidoc b/docs/user/dashboard/explore-underlying-data.asciidoc new file mode 100644 index 000000000000..9b7be21dc45d --- /dev/null +++ b/docs/user/dashboard/explore-underlying-data.asciidoc @@ -0,0 +1,27 @@ +[float] +[[explore-the-underlying-data]] +=== Explore the underlying data for panels + +To explore the underlying data of the panels on your dashboard, {kib} opens *Discover*, +where you can view and filter the data in the visualization panel. When {kib} opens *Discover*, the index pattern, filters, query, and time range for the visualization continue to apply. + +TIP: The *Explore underlying data* option is available only for visualization panels with a single index pattern. + +To use the *Explore underlying data* option: + +* Click the from the panel menu, then click *Explore underlying data*. ++ +[role="screenshot"] +image::images/explore_data_context_menu.png[Explore underlying data from panel context menu] + +* Interact with the chart, then click *Explore underlying data* on the menu that appears. ++ +[role="screenshot"] +image::images/explore_data_in_chart.png[Explore underlying data from chart] ++ +To enable, open `kibana.yml`, then add the following: + +["source","yml"] +----------- +xpack.discoverEnhanced.actions.exploreDataInChart.enabled: true +----------- diff --git a/docs/user/dashboard/images/area.png b/docs/user/dashboard/images/area.png new file mode 100644 index 000000000000..85d21a9e178c Binary files 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a/docs/user/dashboard/share-dashboards.asciidoc b/docs/user/dashboard/share-dashboards.asciidoc new file mode 100644 index 000000000000..cfa146d60fda --- /dev/null +++ b/docs/user/dashboard/share-dashboards.asciidoc @@ -0,0 +1,27 @@ +[[share-dashboards]] +== Share dashboards + +[[embedding-dashboards]] +Share your dashboard outside of {kib}. + +From the *Share* menu, you can: + +* Embed the code in a web page. Users must have {kib} access +to view an embedded dashboard. +* Share a direct link to a {kib} dashboard +* Generate a PDF report +* Generate a PNG report + +TIP: To create a link to a dashboard by title, use: + +`${domain}/${basepath?}/app/dashboards#/list?title=${yourdashboardtitle}` + +TIP: When sharing a link to a dashboard snapshot, use the *Short URL*. Snapshot +URLs are long and can be problematic for Internet Explorer and other +tools. To create a short URL, you must have write access to {kib}. + +[float] +[[import-dashboards]] +=== Export the dashboard + +To export the dashboard, open the menu, then click *Stack Management > Saved Objects*. For more information, +refer to <>. \ No newline at end of file diff --git a/docs/visualize/vega.asciidoc b/docs/user/dashboard/tutorials.asciidoc similarity index 60% rename from docs/visualize/vega.asciidoc rename to docs/user/dashboard/tutorials.asciidoc index b231159e86bd..931720ccbe25 100644 --- a/docs/visualize/vega.asciidoc +++ b/docs/user/dashboard/tutorials.asciidoc @@ -1,36 +1,79 @@ -[[vega-graph]] -== Vega +[[tutorials]] +== Tutorials -Build custom visualizations using Vega and Vega-Lite, backed by one or more -data sources including {es}, Elastic Map Service, URL, -or static data. Use the {kib} extensions to Vega to embed Vega into -your dashboard, and to add interactivity to the visualizations. +Learn how to use *Lens*, *Vega*, and *Timelion* by going through one of the step-by-step tutorials. -Vega and Vega-Lite are both declarative formats to create visualizations -using JSON. Both use a different syntax for declaring visualizations, -and are not fully interchangeable. +[[lens-tutorial]] +=== Compare sales over time with Lens + +Ready to create your own visualization with Lens? Use the following tutorial to create a visualization that lets you compare sales over time. + +[float] +[[lens-before-begin]] +==== Before you begin + +To start, you'll need to add the <>. + +[float] +==== Build the visualization + +Drag and drop your data onto the visualization builder pane. + +. Select the *kibana_sample_data_ecommerce* index pattern. + +. Click image:images/time-filter-calendar.png[], then click *Last 7 days*. ++ +The fields in the data panel update. + +. Drag and drop the *taxful_total_price* data field to the visualization builder pane. ++ +[role="screenshot"] +image::images/lens_tutorial_1.png[Lens tutorial] + +To display the average order prices over time, *Lens* automatically added in *order_date* field. + +To break down your data, drag the *category.keyword* field to the visualization builder pane. Lens +knows that you want to show the top categories and compare them across the dates, +and creates a chart that compares the sales for each of the top three categories: + +[role="screenshot"] +image::images/lens_tutorial_2.png[Lens tutorial] [float] -[[when-to-vega]] -=== When to use Vega - -Vega and Vega-Lite are capable of building most of the visualizations -that {kib} provides, but with higher complexity. The most common reason -to use Vega in {kib} is that {kib} is missing support for the query or -visualization, for example: - -* Aggregations using the `nested` or `parent/child` mapping -* Aggregations without a {kib} index pattern -* Queries using custom time filters -* Complex calculations -* Extracting data from _source instead of aggregation -* Scatter charts -* Sankey charts -* Custom maps -* Using a visual theme that {kib} does not provide - -[[vega-lite-tutorial]] -=== Tutorial: First visualization in Vega-Lite +[[customize-lens-visualization]] +==== Customize your visualization + +Make your visualization look exactly how you want with the customization options. + +. Click *Average of taxful_total_price*, then change the *Label* to `Sales`. ++ +[role="screenshot"] +image::images/lens_tutorial_3.1.png[Lens tutorial] + +. Click *Top values of category.keyword*, then change *Number of values* to `10`. ++ +[role="screenshot"] +image::images/lens_tutorial_3.2.png[Lens tutorial] ++ +The visualization updates to show there are only six available categories. ++ +Look at the *Suggestions*. An area chart is not an option, but for the sales data, a stacked area chart might be the best option. + +. To switch the chart type, click *Stacked bar chart* in the column, then click *Stacked area* from the *Select a visualizations* window. ++ +[role="screenshot"] +image::images/lens_tutorial_3.png[Lens tutorial] + +[float] +[[lens-tutorial-next-steps]] +==== Next steps + +Now that you've created your visualization, you can add it to a <> or <>. + +[[vega-lite-tutorial-create-your-first-visualizations]] +=== Create your first visualization with Vega-Lite + +experimental[] In this tutorial, you will learn about how to edit Vega-Lite in {kib} to create a stacked area chart from an {es} search query. It will give you a starting point @@ -65,6 +108,7 @@ which is similar to JSON but optimized for human editing. HJSON supports: * Multiline strings [float] +[[small-steps]] ==== Small steps Always work on Vega in the smallest steps possible, and save your work frequently. @@ -633,8 +677,10 @@ The final result of this tutorial is this spec: ==== -[[vega-tutorial]] -=== Tutorial: Updating {kib} filters from Vega +[[vega-tutorial-update-kibana-filters-from-vega]] +=== Update {kib} filters from Vega + +experimental[] In this tutorial you will build an area chart in Vega using an {es} search query, and add a click handler and drag handler to update {kib} filters. @@ -1225,415 +1271,486 @@ The final Vega spec for this tutorial is here: ---- ==== -[[vega-reference]] -=== Reference for {kib} extensions - -{kib} has extended Vega and Vega-Lite with extensions that support: - -* Default height and width -* Default theme to match {kib} -* Writing {es} queries using the time range and filters from dashboards -* Using the Elastic Map Service in Vega maps -* Additional tooltip styling -* Advanced setting to enable URL loading from any domain -* Limited debugging support using the browser dev tools -* (Vega only) Expression functions which can update the time range and dashboard filters - -[[vega-sizing-and-positioning]] -==== Default height and width +[[timelion-tutorial-create-time-series-visualizations]] +=== Create time series visualizations with Timelion -By default, Vega visualizations use the `autosize = { type: 'fit', contains: 'padding' }` layout. -`fit` uses all available space, ignores `width` and `height` values, -and respects the padding values. To override this behavior, change the -`autosize` value. +To compare the real-time percentage of CPU time spent in user space to the results offset by one hour, create a time series visualization. -[[vega-theme]] -==== Default theme to match {kib} - -{kib} registers a default https://vega.github.io/vega/docs/schemes/[Vega color scheme] -with the id `elastic`, and sets a default color for each `mark` type. -Override it by providing a different `stroke`, `fill`, or `color` (Vega-Lite) value. - -[[vega-queries]] -==== Writing {es} queries in Vega - -{kib} extends the Vega https://vega.github.io/vega/docs/data/[data] elements -with support for direct {es} queries specified as a `url`. - -Because of this, {kib} is **unable to support dynamically loaded data**, -which would otherwise work in Vega. All data is fetched before it's passed to -the Vega renderer. +[float] +[[define-the-functions]] +==== Define the functions -To define an {es} query in Vega, set the `url` to an object. {kib} will parse -the object looking for special tokens that allow your query to integrate with {kib}. -These tokens are: +To start tracking the real-time percentage of CPU, enter the following in the *Timelion Expression* field: -* `%context%: true`: Set at the top level, and replaces the `query` section with filters from dashboard -* `%timefield%: `: Set at the top level, integrates the query with the dashboard time filter -* `{%timefilter%: true}`: Replaced by an {es} range query with upper and lower bounds -* `{%timefilter%: "min" | "max"}`: Replaced only by the upper or lower bounds -* `{%timefilter: true, shift: -1, unit: 'hour'}`: Generates a time range query one hour in the past -* `{%autointerval%: true}`: Replaced by the string which contains the automatic {kib} time interval, such as `1h` -* `{%autointerval%: 10}`: Replaced by a string which is approximately dividing the time into 10 ranges, allowing - you to influence the automatic interval -* `"%dashboard_context-must_clause%"`: String replaced by object containing filters -* `"%dashboard_context-filter_clause%"`: String replaced by an object containing filters -* `"%dashboard_context-must_not_clause%"`: String replaced by an object containing filters +[source,text] +---------------------------------- +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') +---------------------------------- -Putting this together, an example query that counts the number of documents in -a specific index: +[role="screenshot"] +image::images/timelion-create01.png[] +{nbsp} -[source,yaml] ----- -// An object instead of a string for the URL value -// is treated as a context-aware Elasticsearch query. -url: { - // Specify the time filter. - %timefield%: @timestamp - // Apply dashboard context filters when set - %context%: true - - // Which indexes to search - index: kibana_sample_data_logs - // The body element may contain "aggs" and "query" keys - body: { - aggs: { - time_buckets: { - date_histogram: { - // Use date histogram aggregation on @timestamp field - field: @timestamp <1> - // interval value will depend on the time filter - // Use an integer to set approximate bucket count - interval: { %autointerval%: true } - // Make sure we get an entire range, even if it has no data - extended_bounds: { - min: { %timefilter%: "min" } - max: { %timefilter%: "max" } - } - // Use this for linear (e.g. line, area) graphs - // Without it, empty buckets will not show up - min_doc_count: 0 - } - } - } - // Speed up the response by only including aggregation results - size: 0 - } -} ----- +[float] +[[compare-the-data]] +==== Compare the data -<1> `@timestamp` — Filters the time range and breaks it into histogram -buckets. +To compare the two data sets, add another series with data from the previous hour, separated by a comma: -The full result includes the following structure: +[source,text] +---------------------------------- +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct'), +.es(offset=-1h, <1> + index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') +---------------------------------- -[source,yaml] ----- -{ - "aggregations": { - "time_buckets": { - "buckets": [{ - "key_as_string": "2015-11-30T22:00:00.000Z", - "key": 1448920800000,<1> - "doc_count": 28 - }, { - "key_as_string": "2015-11-30T23:00:00.000Z", - "key": 1448924400000, <1> - "doc_count": 330 - }, ... ----- +<1> `offset` offsets the data retrieval by a date expression. In this example, `-1h` offsets the data back by one hour. -<1> `"key"` — The unix timestamp you can use without conversions by the -Vega date expressions. +[role="screenshot"] +image::images/timelion-create02.png[] +{nbsp} -For most visualizations, you only need the list of bucket values. To focus on -only the data you need, use `format: {property: "aggregations.time_buckets.buckets"}`. +[float] +[[add-label-names]] +==== Add label names -Specify a query with individual range and dashboard context. The query is -equivalent to `"%context%": true, "%timefield%": "@timestamp"`, -except that the time range is shifted back by 10 minutes: +To easily distinguish between the two data sets, add the label names: -[source,yaml] ----- -{ - body: { - query: { - bool: { - must: [ - // This string will be replaced - // with the auto-generated "MUST" clause - "%dashboard_context-must_clause%" - { - range: { - // apply timefilter (upper right corner) - // to the @timestamp variable - @timestamp: { - // "%timefilter%" will be replaced with - // the current values of the time filter - // (from the upper right corner) - "%timefilter%": true - // Only work with %timefilter% - // Shift current timefilter by 10 units back - shift: 10 - // week, day (default), hour, minute, second - unit: minute - } - } - } - ] - must_not: [ - // This string will be replaced with - // the auto-generated "MUST-NOT" clause - "%dashboard_context-must_not_clause%" - ] - filter: [ - // This string will be replaced - // with the auto-generated "FILTER" clause - "%dashboard_context-filter_clause%" - ] - } - } - } -} ----- +[source,text] +---------------------------------- +.es(offset=-1h,index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct').label('last hour'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct').label('current hour') <1> +---------------------------------- -NOTE: When using `"%context%": true` or defining a value for `"%timefield%"` the body cannot contain a query. To customize the query within the VEGA specification (e.g. add an additional filter, or shift the timefilter), define your query and use the placeholders as in the example above. The placeholders will be replaced by the actual context of the dashboard or visualization once parsed. +<1> `.label()` adds custom labels to the visualization. -The `"%timefilter%"` can also be used to specify a single min or max -value. The date_histogram's `extended_bounds` can be set -with two values - min and max. Instead of hardcoding a value, you may -use `"min": {"%timefilter%": "min"}`, which will be replaced with the -beginning of the current time range. The `shift` and `unit` values are -also supported. The `"interval"` can also be set dynamically, depending -on the currently picked range: `"interval": {"%autointerval%": 10}` will -try to get about 10-15 data points (buckets). +[role="screenshot"] +image::images/timelion-create03.png[] +{nbsp} [float] -[[vega-esmfiles]] -=== Access Elastic Map Service files - -Access the Elastic Map Service files via the same mechanism: +[[add-a-title]] +==== Add a title + +Add a meaningful title: + +[source,text] +---------------------------------- +.es(offset=-1h, + index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('last hour'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('current hour') + .title('CPU usage over time') <1> +---------------------------------- + +<1> `.title()` adds a title with a meaningful name. Titles make is easier for unfamiliar users to understand the purpose of the visualization. -[source,yaml] ----- -url: { - // "type" defaults to "elasticsearch" otherwise - type: emsfile - // Name of the file, exactly as in the Region map visualization - name: World Countries -} -// The result is a geojson file, get its features to use -// this data source with the "shape" marks -// https://vega.github.io/vega/docs/marks/shape/ -format: {property: "features"} ----- - -To enable Maps, the graph must specify `type=map` in the host -configuration: - -[source,yaml] ----- -{ - "config": { - "kibana": { - "type": "map", +[role="screenshot"] +image::images/timelion-customize01.png[] +{nbsp} - // Initial map position - "latitude": 40.7, // default 0 - "longitude": -74, // default 0 - "zoom": 7, // default 2 +[float] +[[change-the-chart-type]] +==== Change the chart type + +To differentiate between the current hour data and the last hour data, change the chart type: + +[source,text] +---------------------------------- +.es(offset=-1h, + index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('last hour') + .lines(fill=1,width=0.5), <1> +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('current hour') + .title('CPU usage over time') +---------------------------------- + +<1> `.lines()` changes the appearance of the chart lines. In this example, `.lines(fill=1,width=0.5)` sets the fill level to `1`, and the border width to `0.5`. - // defaults to "default". Use false to disable base layer. - "mapStyle": false, +[role="screenshot"] +image::images/timelion-customize02.png[] +{nbsp} - // default 0 - "minZoom": 5, +[float] +[[change-the-line-colors]] +==== Change the line colors + +To make the current hour data stand out, change the line colors: + +[source,text] +---------------------------------- +.es(offset=-1h, + index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('last hour') + .lines(fill=1,width=0.5) + .color(gray), <1> +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('current hour') + .title('CPU usage over time') + .color(#1E90FF) +---------------------------------- + +<1> `.color()` changes the color of the data. Supported color types include standard color names, hexadecimal values, or a color schema for grouped data. In this example, `.color(gray)` represents the last hour, and `.color(#1E90FF)` represents the current hour. - // defaults to the maximum for the given style, - // or 25 when base is disabled - "maxZoom": 13, +[role="screenshot"] +image::images/timelion-customize03.png[] +{nbsp} - // defaults to true, shows +/- buttons to zoom in/out - "zoomControl": false, +[float] +[[make-adjustments-to-the-legend]] +==== Make adjustments to the legend + +Change the position and style of the legend: + +[source,text] +---------------------------------- +.es(offset=-1h, + index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('last hour') + .lines(fill=1,width=0.5) + .color(gray), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='avg:system.cpu.user.pct') + .label('current hour') + .title('CPU usage over time') + .color(#1E90FF) + .legend(columns=2, position=nw) <1> +---------------------------------- + +<1> `.legend()` sets the position and style of the legend. In this example, `.legend(columns=2, position=nw)` places the legend in the north west position of the visualization with two columns. - // Defaults to 'false', disables mouse wheel zoom. If set to - // 'true', map may zoom unexpectedly while scrolling dashboard - "scrollWheelZoom": false, +[role="screenshot"] +image::images/timelion-customize04.png[] +{nbsp} - // When false, repaints on each move frame. - // Makes the graph slower when moving the map - "delayRepaint": true, // default true - } - }, - /* the rest of Vega JSON */ -} ----- +[[timelion-tutorial-create-visualizations-with-mathematical-functions]] +=== Timelion tutorial: Create visualizations with mathematical functions -The visualization automatically injects a `"projection"`, which you can use to -calculate the position of all geo-aware marks. -Additionally, you can use `latitude`, `longitude`, and `zoom` signals. -These signals can be used in the graph, or can be updated to modify the -position of the map. +To create a visualization for inbound and outbound network traffic, use mathematical functions. [float] -[[vega-tooltip]] -==== Additional tooltip styling +[[mathematical-functions-define-functions]] +==== Define the functions -{kib} has installed the https://vega.github.io/vega-lite/docs/tooltip.html[Vega tooltip plugin], -so tooltips can be defined in the ways documented there. Beyond that, {kib} also supports -a configuration option for changing the tooltip position and padding: +To start tracking the inbound and outbound network traffic, enter the following in the *Timelion Expression* field: -```js -{ - config: { - kibana: { - tooltips: { - position: 'top', - padding: 15 - } - } - } -} -``` +[source,text] +---------------------------------- +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.in.bytes) +---------------------------------- -[[vega-url-loading]] -==== Advanced setting to enable URL loading from any domain +[role="screenshot"] +image::images/timelion-math01.png[] +{nbsp} -Vega can load data from any URL, but this is disabled by default in {kib}. -To change this, set `vis_type_vega.enableExternalUrls: true` in `kibana.yml`, -then restart {kib}. +[float] +[[mathematical-functions-plot-change]] +==== Plot the rate of change -[[vega-inspector]] -==== Vega Inspector -Use the contextual *Inspect* tool to gain insights into different elements. -For Vega visualizations, there are two different views: *Request* and *Vega debug*. +Change how the data is displayed so that you can easily monitor the inbound traffic: -===== Inspect Elasticsearch requests +[source,text] +---------------------------------- +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.in.bytes) + .derivative() <1> +---------------------------------- -Vega uses the {ref}/search-search.html[{es} search API] to get documents and aggregation -results from {es}. To troubleshoot these requests, click *Inspect*, which shows the most recent requests. -In case your specification has more than one request, you can switch between the views using the *View* dropdown. +<1> `.derivative` plots the change in values over time. [role="screenshot"] -image::visualize/images/vega_tutorial_inspect_requests.png[] - -===== Vega debugging - -With the *Vega debug* view, you can inspect the *Data sets* and *Signal Values* runtime data. - -The runtime data is read from the -https://vega.github.io/vega/docs/api/debugging/#scope[runtime scope]. +image::images/timelion-math02.png[] +{nbsp} + +Add a similar calculation for outbound traffic: + +[source,text] +---------------------------------- +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.in.bytes) + .derivative(), +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.out.bytes) + .derivative() + .multiply(-1) <1> +---------------------------------- + +<1> `.multiply()` multiplies the data series by a number, the result of a data series, or a list of data series. For this example, `.multiply(-1)` converts the outbound network traffic to a negative value since the outbound network traffic is leaving your machine. [role="screenshot"] -image::visualize/images/vega_tutorial_inspect_data_sets.png[] - -To debug more complex specs, access to the `view` variable. For more information, refer to -the <>. +image::images/timelion-math03.png[] +{nbsp} -===== Asking for help with a Vega spec - -Because of the dynamic nature of the data in {es}, it is hard to help you with -Vega specs unless you can share a dataset. To do this, click *Inspect*, select the *Vega debug* view, -then select the *Spec* tab: +[float] +[[mathematical-functions-convert-data]] +==== Change the data metric + +To make the visualization easier to analyze, change the data metric from bytes to megabytes: + +[source,text] +---------------------------------- +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.in.bytes) + .derivative() + .divide(1048576), +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.out.bytes) + .derivative() + .multiply(-1) + .divide(1048576) <1> +---------------------------------- + +<1> `.divide()` accepts the same input as `.multiply()`, then divides the data series by the defined divisor. [role="screenshot"] -image::visualize/images/vega_tutorial_getting_help.png[] +image::images/timelion-math04.png[] +{nbsp} -To copy the response, click *Copy to clipboard*. Paste the copied data to -https://gist.github.com/[gist.github.com], possibly with a .json extension. Use the [raw] button, -and share that when asking for help. +[float] +[[mathematical-functions-add-labels]] +==== Customize and format the visualization + +Customize and format the visualization using functions: + +[source,text] +---------------------------------- +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.in.bytes) + .derivative() + .divide(1048576) + .lines(fill=2, width=1) + .color(green) + .label("Inbound traffic") <1> + .title("Network traffic (MB/s)"), <2> +.es(index=metricbeat*, + timefield=@timestamp, + metric=max:system.network.out.bytes) + .derivative() + .multiply(-1) + .divide(1048576) + .lines(fill=2, width=1) <3> + .color(blue) <4> + .label("Outbound traffic") + .legend(columns=2, position=nw) <5> +---------------------------------- + +<1> `.label()` adds custom labels to the visualization. +<2> `.title()` adds a title with a meaningful name. +<3> `.lines()` changes the appearance of the chart lines. In this example, `.lines(fill=2, width=1)` sets the fill level to `2`, and the border width to `1`. +<4> `.color()` changes the color of the data. Supported color types include standard color names, hexadecimal values, or a color schema for grouped data. In this example, `.color(green)` represents the inbound network traffic, and `.color(blue)` represents the outbound network traffic. +<5> `.legend()` sets the position and style of the legend. For this example, `legend(columns=2, position=nw)` places the legend in the north west position of the visualization with two columns. -[[vega-browser-debugging-console]] -==== Browser debugging console +[role="screenshot"] +image::images/timelion-math05.png[] +{nbsp} -experimental[] Use browser debugging tools (for example, F12 or Ctrl+Shift+J in Chrome) to -inspect the `VEGA_DEBUG` variable: +[[timelion-tutorial-create-visualizations-withconditional-logic-and-tracking-trends]] +=== Create visualizations with conditional logic and tracking trends using Timelion -* `view` — Access to the Vega View object. See https://vega.github.io/vega/docs/api/debugging/[Vega Debugging Guide] -on how to inspect data and signals at runtime. For Vega-Lite, -`VEGA_DEBUG.view.data('source_0')` gets the pre-transformed data, and `VEGA_DEBUG.view.data('data_0')` -gets the encoded data. For Vega, it uses the data name as defined in your Vega spec. +To easily detect outliers and discover patterns over time, modify time series data with conditional logic and create a trend with a moving average. -* `vega_spec` — Vega JSON graph specification after some modifications by {kib}. In case -of Vega-Lite, this is the output of the Vega-Lite compiler. +With Timelion conditional logic, you can use the following operator values to compare your data: -* `vegalite_spec` — If this is a Vega-Lite graph, JSON specification of the graph before -Vega-Lite compilation. +[horizontal] +`eq`:: equal +`ne`:: not equal +`lt`:: less than +`lte`:: less than or equal to +`gt`:: greater than +`gte`:: greater than or equal to [float] -[[vega-expression-functions]] -==== (Vega only) Expression functions which can update the time range and dashboard filters +[[conditional-define-functions]] +==== Define the functions -{kib} has extended the Vega expression language with these functions: - -```js -/** - * @param {object} query Elastic Query DSL snippet, as used in the query DSL editor - * @param {string} [index] as defined in Kibana, or default if missing - */ -kibanaAddFilter(query, index) - -/** - * @param {object} query Elastic Query DSL snippet, as used in the query DSL editor - * @param {string} [index] as defined in Kibana, or default if missing - */ -kibanaRemoveFilter(query, index) - -kibanaRemoveAllFilters() - -/** - * Update dashboard time filter to the new values - * @param {number|string|Date} start - * @param {number|string|Date} end - */ -kibanaSetTimeFilter(start, end) -``` +To chart the maximum value of `system.memory.actual.used.bytes`, enter the following in the *Timelion Expression* field: -[float] -[[vega-additional-configuration-options]] -==== Additional configuration options +[source,text] +---------------------------------- +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') +---------------------------------- -[source,yaml] ----- -{ - config: { - kibana: { - // Placement of the Vega-defined signal bindings. - // Can be `left`, `right`, `top`, or `bottom` (default). - controlsLocation: top - // Can be `vertical` or `horizontal` (default). - controlsDirection: vertical - // If true, hides most of Vega and Vega-Lite warnings - hideWarnings: true - // Vega renderer to use: `svg` or `canvas` (default) - renderer: canvas - } - } -} ----- +[role="screenshot"] +image::images/timelion-conditional01.png[] +{nbsp} +[float] +[[conditional-track-memory]] +==== Track used memory + +To track the amount of memory used, create two thresholds: + +[source,text] +---------------------------------- +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .if(gt, <1> + 11300000000, <2> + .es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), + null) + .label('warning') + .color('#FFCC11'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .if(gt, + 11375000000, + .es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), + null) + .label('severe') + .color('red') +---------------------------------- + +<1> Timelion conditional logic for the _greater than_ operator. In this example, the warning threshold is 11.3GB (`11300000000`), and the severe threshold is 11.375GB (`11375000000`). If the threshold values are too high or low for your machine, adjust the values accordingly. +<2> `if()` compares each point to a number. If the condition evaluates to `true`, adjust the styling. If the condition evaluates to `false`, use the default styling. -[[vega-notes]] -[[vega-useful-links]] -=== Resources and examples +[role="screenshot"] +image::images/timelion-conditional02.png[] +{nbsp} -To learn more about Vega and Vega-Lite, refer to the resources and examples. +[float] +[[conditional-determine-trend]] +==== Determine the trend + +To determine the trend, create a new data series: + +[source,text] +---------------------------------- +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .if(gt,11300000000, + .es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), + null) + .label('warning') + .color('#FFCC11'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .if(gt,11375000000, + .es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), + null). + label('severe') + .color('red'), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .mvavg(10) <1> +---------------------------------- + +<1> `mvavg()` calculates the moving average over a specified period of time. In this example, `.mvavg(10)` creates a moving average with a window of 10 data points. -==== Vega editor -The https://vega.github.io/editor/[Vega Editor] includes examples for Vega & Vega-Lite, but does not support any -{kib}-specific features like {es} requests and interactive base maps. +[role="screenshot"] +image::images/timelion-conditional03.png[] +{nbsp} -==== Vega-Lite resources -* https://vega.github.io/vega-lite/tutorials/getting_started.html[Tutorials] -* https://vega.github.io/vega-lite/docs/[Docs] -* https://vega.github.io/vega-lite/examples/[Examples] +[float] +[[conditional-format-visualization]] +==== Customize and format the visualization + +Customize and format the visualization using functions: + +[source,text] +---------------------------------- +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .label('max memory') <1> + .title('Memory consumption over time'), <2> +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .if(gt, + 11300000000, + .es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), + null) + .label('warning') + .color('#FFCC11') <3> + .lines(width=5), <4> +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .if(gt, + 11375000000, + .es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes'), + null) + .label('severe') + .color('red') + .lines(width=5), +.es(index=metricbeat-*, + timefield='@timestamp', + metric='max:system.memory.actual.used.bytes') + .mvavg(10) + .label('mvavg') + .lines(width=2) + .color(#5E5E5E) + .legend(columns=4, position=nw) <5> +---------------------------------- + +<1> `.label()` adds custom labels to the visualization. +<2> `.title()` adds a title with a meaningful name. +<3> `.color()` changes the color of the data. Supported color types include standard color names, hexadecimal values, or a color schema for grouped data. +<4> `.lines()` changes the appearance of the chart lines. In this example, .lines(width=5) sets border width to `5`. +<5> `.legend()` sets the position and style of the legend. For this example, `(columns=4, position=nw)` places the legend in the north west position of the visualization with four columns. -==== Vega resources -* https://vega.github.io/vega/tutorials/[Tutorials] -* https://vega.github.io/vega/docs/[Docs] -* https://vega.github.io/vega/examples/[Examples] +[role="screenshot"] +image::images/timelion-conditional04.png[] +{nbsp} -TIP: When you use the examples in {kib}, you may -need to modify the "data" section to use absolute URL. For example, -replace `"url": "data/world-110m.json"` with -`"url": "https://vega.github.io/editor/data/world-110m.json"`. +For additional information on Timelion conditional capabilities, go to https://www.elastic.co/blog/timeseries-if-then-else-with-timelion[I have but one .condition()]. \ No newline at end of file diff --git a/docs/user/dashboard/vega-reference.asciidoc b/docs/user/dashboard/vega-reference.asciidoc new file mode 100644 index 000000000000..eed8d9a35b87 --- /dev/null +++ b/docs/user/dashboard/vega-reference.asciidoc @@ -0,0 +1,437 @@ +[[vega-reference]] +== Vega reference + +experimental[] + +For additional *Vega* and *Vega-Lite* information, refer to the reference sections. + +[float] +[[reference-for-kibana-extensions]] +=== Reference for {kib} extensions + +{kib} has extended Vega and Vega-Lite with extensions that support: + +* Default height and width +* Default theme to match {kib} +* Writing {es} queries using the time range and filters from dashboards +* Using the Elastic Map Service in Vega maps +* Additional tooltip styling +* Advanced setting to enable URL loading from any domain +* Limited debugging support using the browser dev tools +* (Vega only) Expression functions which can update the time range and dashboard filters + +[float] +[[vega-sizing-and-positioning]] +==== Default height and width + +By default, Vega visualizations use the `autosize = { type: 'fit', contains: 'padding' }` layout. +`fit` uses all available space, ignores `width` and `height` values, +and respects the padding values. To override this behavior, change the +`autosize` value. + +[float] +[[vega-theme]] +==== Default theme to match {kib} + +{kib} registers a default https://vega.github.io/vega/docs/schemes/[Vega color scheme] +with the id `elastic`, and sets a default color for each `mark` type. +Override it by providing a different `stroke`, `fill`, or `color` (Vega-Lite) value. + +[float] +[[vega-queries]] +==== Writing {es} queries in Vega + +experimental[] {kib} extends the Vega https://vega.github.io/vega/docs/data/[data] elements +with support for direct {es} queries specified as a `url`. + +Because of this, {kib} is **unable to support dynamically loaded data**, +which would otherwise work in Vega. All data is fetched before it's passed to +the Vega renderer. + +To define an {es} query in Vega, set the `url` to an object. {kib} will parse +the object looking for special tokens that allow your query to integrate with {kib}. +These tokens are: + +* `%context%: true`: Set at the top level, and replaces the `query` section with filters from dashboard +* `%timefield%: `: Set at the top level, integrates the query with the dashboard time filter +* `{%timefilter%: true}`: Replaced by an {es} range query with upper and lower bounds +* `{%timefilter%: "min" | "max"}`: Replaced only by the upper or lower bounds +* `{%timefilter: true, shift: -1, unit: 'hour'}`: Generates a time range query one hour in the past +* `{%autointerval%: true}`: Replaced by the string which contains the automatic {kib} time interval, such as `1h` +* `{%autointerval%: 10}`: Replaced by a string which is approximately dividing the time into 10 ranges, allowing + you to influence the automatic interval +* `"%dashboard_context-must_clause%"`: String replaced by object containing filters +* `"%dashboard_context-filter_clause%"`: String replaced by an object containing filters +* `"%dashboard_context-must_not_clause%"`: String replaced by an object containing filters + +Putting this together, an example query that counts the number of documents in +a specific index: + +[source,yaml] +---- +// An object instead of a string for the URL value +// is treated as a context-aware Elasticsearch query. +url: { + // Specify the time filter. + %timefield%: @timestamp + // Apply dashboard context filters when set + %context%: true + + // Which indexes to search + index: kibana_sample_data_logs + // The body element may contain "aggs" and "query" keys + body: { + aggs: { + time_buckets: { + date_histogram: { + // Use date histogram aggregation on @timestamp field + field: @timestamp <1> + // interval value will depend on the time filter + // Use an integer to set approximate bucket count + interval: { %autointerval%: true } + // Make sure we get an entire range, even if it has no data + extended_bounds: { + min: { %timefilter%: "min" } + max: { %timefilter%: "max" } + } + // Use this for linear (e.g. line, area) graphs + // Without it, empty buckets will not show up + min_doc_count: 0 + } + } + } + // Speed up the response by only including aggregation results + size: 0 + } +} +---- + +<1> `@timestamp` — Filters the time range and breaks it into histogram +buckets. + +The full result includes the following structure: + +[source,yaml] +---- +{ + "aggregations": { + "time_buckets": { + "buckets": [{ + "key_as_string": "2015-11-30T22:00:00.000Z", + "key": 1448920800000,<1> + "doc_count": 28 + }, { + "key_as_string": "2015-11-30T23:00:00.000Z", + "key": 1448924400000, <1> + "doc_count": 330 + }, ... +---- + +<1> `"key"` — The unix timestamp you can use without conversions by the +Vega date expressions. + +For most visualizations, you only need the list of bucket values. To focus on +only the data you need, use `format: {property: "aggregations.time_buckets.buckets"}`. + +Specify a query with individual range and dashboard context. The query is +equivalent to `"%context%": true, "%timefield%": "@timestamp"`, +except that the time range is shifted back by 10 minutes: + +[source,yaml] +---- +{ + body: { + query: { + bool: { + must: [ + // This string will be replaced + // with the auto-generated "MUST" clause + "%dashboard_context-must_clause%" + { + range: { + // apply timefilter (upper right corner) + // to the @timestamp variable + @timestamp: { + // "%timefilter%" will be replaced with + // the current values of the time filter + // (from the upper right corner) + "%timefilter%": true + // Only work with %timefilter% + // Shift current timefilter by 10 units back + shift: 10 + // week, day (default), hour, minute, second + unit: minute + } + } + } + ] + must_not: [ + // This string will be replaced with + // the auto-generated "MUST-NOT" clause + "%dashboard_context-must_not_clause%" + ] + filter: [ + // This string will be replaced + // with the auto-generated "FILTER" clause + "%dashboard_context-filter_clause%" + ] + } + } + } +} +---- + +NOTE: When using `"%context%": true` or defining a value for `"%timefield%"` the body cannot contain a query. To customize the query within the VEGA specification (e.g. add an additional filter, or shift the timefilter), define your query and use the placeholders as in the example above. The placeholders will be replaced by the actual context of the dashboard or visualization once parsed. + +The `"%timefilter%"` can also be used to specify a single min or max +value. The date_histogram's `extended_bounds` can be set +with two values - min and max. Instead of hardcoding a value, you may +use `"min": {"%timefilter%": "min"}`, which will be replaced with the +beginning of the current time range. The `shift` and `unit` values are +also supported. The `"interval"` can also be set dynamically, depending +on the currently picked range: `"interval": {"%autointerval%": 10}` will +try to get about 10-15 data points (buckets). + +[float] +[[vega-esmfiles]] +=== Access Elastic Map Service files + +experimental[] Access the Elastic Map Service files via the same mechanism: + +[source,yaml] +---- +url: { + // "type" defaults to "elasticsearch" otherwise + type: emsfile + // Name of the file, exactly as in the Region map visualization + name: World Countries +} +// The result is a geojson file, get its features to use +// this data source with the "shape" marks +// https://vega.github.io/vega/docs/marks/shape/ +format: {property: "features"} +---- + +To enable Maps, the graph must specify `type=map` in the host +configuration: + +[source,yaml] +---- +{ + "config": { + "kibana": { + "type": "map", + + // Initial map position + "latitude": 40.7, // default 0 + "longitude": -74, // default 0 + "zoom": 7, // default 2 + + // defaults to "default". Use false to disable base layer. + "mapStyle": false, + + // default 0 + "minZoom": 5, + + // defaults to the maximum for the given style, + // or 25 when base is disabled + "maxZoom": 13, + + // defaults to true, shows +/- buttons to zoom in/out + "zoomControl": false, + + // Defaults to 'false', disables mouse wheel zoom. If set to + // 'true', map may zoom unexpectedly while scrolling dashboard + "scrollWheelZoom": false, + + // When false, repaints on each move frame. + // Makes the graph slower when moving the map + "delayRepaint": true, // default true + } + }, + /* the rest of Vega JSON */ +} +---- + +The visualization automatically injects a `"projection"`, which you can use to +calculate the position of all geo-aware marks. +Additionally, you can use `latitude`, `longitude`, and `zoom` signals. +These signals can be used in the graph, or can be updated to modify the +position of the map. + +[float] +[[vega-tooltip]] +==== Additional tooltip styling + +{kib} has installed the https://vega.github.io/vega-lite/docs/tooltip.html[Vega tooltip plugin], +so tooltips can be defined in the ways documented there. Beyond that, {kib} also supports +a configuration option for changing the tooltip position and padding: + +```js +{ + config: { + kibana: { + tooltips: { + position: 'top', + padding: 15 + } + } + } +} +``` + +[float] +[[vega-url-loading]] +==== Advanced setting to enable URL loading from any domain + +Vega can load data from any URL, but this is disabled by default in {kib}. +To change this, set `vis_type_vega.enableExternalUrls: true` in `kibana.yml`, +then restart {kib}. + +[float] +[[vega-inspector]] +==== Vega Inspector +Use the contextual *Inspect* tool to gain insights into different elements. +For Vega visualizations, there are two different views: *Request* and *Vega debug*. + +[float] +[[inspect-elasticsearch-requests]] +===== Inspect {es} requests + +Vega uses the {ref}/search-search.html[{es} search API] to get documents and aggregation +results from {es}. To troubleshoot these requests, click *Inspect*, which shows the most recent requests. +In case your specification has more than one request, you can switch between the views using the *View* dropdown. + +[role="screenshot"] +image::visualize/images/vega_tutorial_inspect_requests.png[] + +[float] +[[vega-debugging]] +===== Vega debugging + +With the *Vega debug* view, you can inspect the *Data sets* and *Signal Values* runtime data. + +The runtime data is read from the +https://vega.github.io/vega/docs/api/debugging/#scope[runtime scope]. + +[role="screenshot"] +image::visualize/images/vega_tutorial_inspect_data_sets.png[] + +To debug more complex specs, access to the `view` variable. For more information, refer to +the <>. + +[float] +[[asking-for-help-with-a-vega-spec]] +===== Asking for help with a Vega spec + +Because of the dynamic nature of the data in {es}, it is hard to help you with +Vega specs unless you can share a dataset. To do this, click *Inspect*, select the *Vega debug* view, +then select the *Spec* tab: + +[role="screenshot"] +image::visualize/images/vega_tutorial_getting_help.png[] + +To copy the response, click *Copy to clipboard*. Paste the copied data to +https://gist.github.com/[gist.github.com], possibly with a .json extension. Use the [raw] button, +and share that when asking for help. + +[float] +[[vega-browser-debugging-console]] +==== Browser debugging console + +experimental[] Use browser debugging tools (for example, F12 or Ctrl+Shift+J in Chrome) to +inspect the `VEGA_DEBUG` variable: + +* `view` — Access to the Vega View object. See https://vega.github.io/vega/docs/api/debugging/[Vega Debugging Guide] +on how to inspect data and signals at runtime. For Vega-Lite, +`VEGA_DEBUG.view.data('source_0')` gets the pre-transformed data, and `VEGA_DEBUG.view.data('data_0')` +gets the encoded data. For Vega, it uses the data name as defined in your Vega spec. + +* `vega_spec` — Vega JSON graph specification after some modifications by {kib}. In case +of Vega-Lite, this is the output of the Vega-Lite compiler. + +* `vegalite_spec` — If this is a Vega-Lite graph, JSON specification of the graph before +Vega-Lite compilation. + +[float] +[[vega-expression-functions]] +==== (Vega only) Expression functions which can update the time range and dashboard filters + +{kib} has extended the Vega expression language with these functions: + +```js +/** + * @param {object} query Elastic Query DSL snippet, as used in the query DSL editor + * @param {string} [index] as defined in Kibana, or default if missing + */ +kibanaAddFilter(query, index) + +/** + * @param {object} query Elastic Query DSL snippet, as used in the query DSL editor + * @param {string} [index] as defined in Kibana, or default if missing + */ +kibanaRemoveFilter(query, index) + +kibanaRemoveAllFilters() + +/** + * Update dashboard time filter to the new values + * @param {number|string|Date} start + * @param {number|string|Date} end + */ +kibanaSetTimeFilter(start, end) +``` + +[float] +[[vega-additional-configuration-options]] +==== Additional configuration options + +[source,yaml] +---- +{ + config: { + kibana: { + // Placement of the Vega-defined signal bindings. + // Can be `left`, `right`, `top`, or `bottom` (default). + controlsLocation: top + // Can be `vertical` or `horizontal` (default). + controlsDirection: vertical + // If true, hides most of Vega and Vega-Lite warnings + hideWarnings: true + // Vega renderer to use: `svg` or `canvas` (default) + renderer: canvas + } + } +} +---- + +[[vega-notes]] +[[resources-and-examples]] +=== Resources and examples + +experimental[] To learn more about Vega and Vega-Lite, refer to the resources and examples. + +[float] +[[vega-editor]] +==== Vega editor +The https://vega.github.io/editor/[Vega Editor] includes examples for Vega & Vega-Lite, but does not support any +{kib}-specific features like {es} requests and interactive base maps. + +[float] +[[vega-lite-resources]] +==== Vega-Lite resources +* https://vega.github.io/vega-lite/tutorials/getting_started.html[Tutorials] +* https://vega.github.io/vega-lite/docs/[Docs] +* https://vega.github.io/vega-lite/examples/[Examples] + +[float] +[[vega-resources]] +==== Vega resources +* https://vega.github.io/vega/tutorials/[Tutorials] +* https://vega.github.io/vega/docs/[Docs] +* https://vega.github.io/vega/examples/[Examples] + +TIP: When you use the examples in {kib}, you may +need to modify the "data" section to use absolute URL. For example, +replace `"url": "data/world-110m.json"` with +`"url": "https://vega.github.io/editor/data/world-110m.json"`. diff --git a/docs/user/getting-started.asciidoc b/docs/user/getting-started.asciidoc index 2ff3a09152df..a877f6a66a79 100644 --- a/docs/user/getting-started.asciidoc +++ b/docs/user/getting-started.asciidoc @@ -1,19 +1,19 @@ -[[getting-started]] +[[get-started]] = Get started [partintro] -- -Ready to try out {kib} and see what it can do? To quickest way to get started with {kib} is to set up on Cloud, then add a sample data set that helps you get a handle on the full range of {kib} features. +Ready to try out {kib} and see what it can do? The quickest way to get started with {kib} is to set up on Cloud, then add a sample data set to explore the full range of {kib} features. [float] -[[cloud-set-up]] +[[set-up-on-cloud]] == Set up on cloud include::{docs-root}/shared/cloud/ess-getting-started.asciidoc[] [float] -[[get-data-in]] +[[gs-get-data-into-kibana]] == Get data into {kib} The easiest way to get data into {kib} is to add a sample data set. @@ -42,12 +42,11 @@ NOTE: The timestamps in the sample data sets are relative to when they are insta If you uninstall and reinstall a data set, the timestamps change to reflect the most recent installation. [float] -[[getting-started-next-steps]] == Next steps -* To get a hands-on experience creating visualizations, follow the <> tutorial. +* To get a hands-on experience creating visualizations, follow the <> tutorial. -* If you're ready to load an actual data set and build a dashboard, follow the <> tutorial. +* If you're ready to load an actual data set and build a dashboard, follow the <> tutorial. -- @@ -60,5 +59,3 @@ include::{kib-repo-dir}/getting-started/tutorial-define-index.asciidoc[] include::{kib-repo-dir}/getting-started/tutorial-discovering.asciidoc[] include::{kib-repo-dir}/getting-started/tutorial-visualizing.asciidoc[] - -include::{kib-repo-dir}/getting-started/tutorial-dashboard.asciidoc[] diff --git a/docs/user/index.asciidoc b/docs/user/index.asciidoc index abbdbeb68d9c..e909626c5779 100644 --- a/docs/user/index.asciidoc +++ b/docs/user/index.asciidoc @@ -2,8 +2,6 @@ include::introduction.asciidoc[] include::whats-new.asciidoc[] -include::getting-started.asciidoc[] - include::setup.asciidoc[] include::monitoring/configuring-monitoring.asciidoc[leveloffset=+1] @@ -13,9 +11,11 @@ include::monitoring/monitoring-kibana.asciidoc[leveloffset=+2] include::security/securing-kibana.asciidoc[] +include::getting-started.asciidoc[] + include::discover.asciidoc[] -include::dashboard.asciidoc[] +include::dashboard/dashboard.asciidoc[] include::canvas.asciidoc[] @@ -25,8 +25,6 @@ include::ml/index.asciidoc[] include::graph/index.asciidoc[] -include::visualize.asciidoc[] - include::{kib-repo-dir}/observability/index.asciidoc[] include::{kib-repo-dir}/logs/index.asciidoc[] diff --git a/docs/user/introduction.asciidoc b/docs/user/introduction.asciidoc index ff936fb4d556..079d183dd959 100644 --- a/docs/user/introduction.asciidoc +++ b/docs/user/introduction.asciidoc @@ -83,12 +83,6 @@ image::images/intro-dashboard.png[] {kib} also offers these visualization features: -* <> allows you to display your data in -charts, graphs, and tables -(just to name a few). It's also home to Lens. -Visualize supports the ability to add interactive -controls to your dashboard, filter dashboard content in real time, and add your own images and logos for your brand. - * <> gives you the ability to present your data in a visually compelling, pixel-perfect report. Give your data the “wow” factor needed to impress your CEO or to captivate coworkers with a big-screen display. @@ -98,7 +92,7 @@ questions of your location-based data. Maps supports multiple layers and data sources, mapping of individual geo points and shapes, and dynamic client-side styling. -* <> allows you to combine +* <> allows you to combine an infinite number of aggregations to display complex data. With TSVB, you can analyze multiple index patterns and customize every aspect of your visualization. Choose your own date format and color @@ -161,6 +155,6 @@ and start exploring data in minutes. You can also <> — no code, no additional infrastructure required. -Our <> and in-product guidance can +Our <> and in-product guidance can help you get up and running, faster. Click the help icon image:images/intro-help-icon.png[] in the top navigation bar for help with questions or to provide feedback. diff --git a/docs/user/reporting/automating-report-generation.asciidoc b/docs/user/reporting/automating-report-generation.asciidoc index 3e227229ddcc..371855deb2f3 100644 --- a/docs/user/reporting/automating-report-generation.asciidoc +++ b/docs/user/reporting/automating-report-generation.asciidoc @@ -13,7 +13,7 @@ URL that triggers a report to generate. To create the POST URL for PDF reports: -. Go to *Visualize* or *Dashboard*, then open the visualization or dashboard. +. Go to *Dashboard*, then open the visualization or dashboard. + To specify a relative or absolute time period, use the time filter. diff --git a/docs/user/reporting/index.asciidoc b/docs/user/reporting/index.asciidoc index 4f4d59315faf..50ae92382fb2 100644 --- a/docs/user/reporting/index.asciidoc +++ b/docs/user/reporting/index.asciidoc @@ -11,7 +11,7 @@ saved search, or Canvas workpad. Depending on the object type, you can export th a PDF, PNG, or CSV document, which you can keep for yourself, or share with others. Reporting is available from the *Share* menu -in *Discover*, *Visualize*, *Dashboard*, and *Canvas*. +in *Discover*, *Dashboard*, and *Canvas*. [role="screenshot"] image::user/reporting/images/share-button.png["Share"] diff --git a/docs/user/security/rbac_tutorial.asciidoc b/docs/user/security/rbac_tutorial.asciidoc index 3a4b2202201e..cc4af9041bcd 100644 --- a/docs/user/security/rbac_tutorial.asciidoc +++ b/docs/user/security/rbac_tutorial.asciidoc @@ -28,7 +28,7 @@ To complete this tutorial, you'll need the following: * **A space**: In this tutorial, use `Dev Mortgage` as the space name. See <> for details on creating a space. -* **Data**: You can use <> or +* **Data**: You can use <> or live data. In the following steps, Filebeat and Metricbeat data are used. [float] diff --git a/docs/user/security/reporting.asciidoc b/docs/user/security/reporting.asciidoc index 4e02759ce99c..daf9720a0f1d 100644 --- a/docs/user/security/reporting.asciidoc +++ b/docs/user/security/reporting.asciidoc @@ -47,7 +47,7 @@ image::user/security/images/reporting-privileges-example.png["Reporting privileg Reporting users typically save searches, create visualizations, and build dashboards. They require a space that provides read and write privileges in -*Discover*, *Visualize*, and *Dashboard*. +*Discover* and *Dashboard*. . Save your new role. diff --git a/docs/user/visualize.asciidoc b/docs/user/visualize.asciidoc deleted file mode 100644 index dc116962f9e9..000000000000 --- a/docs/user/visualize.asciidoc +++ /dev/null @@ -1,142 +0,0 @@ -[[visualize]] -= Visualize - -[partintro] --- -_Visualize_ enables you to create visualizations of the data from your {es} indices, which you can then add to dashboards for analysis. - -{kib} visualizations are based on {es} queries. By using a series of {es} {ref}/search-aggregations.html[aggregations] to extract and process your data, you can create charts that show you the trends, spikes, and dips you need to know about. - -To begin, open the menu, go to *Visualize*, then click *Create visualization*. - -[float] -[[visualization-types]] -== Types of visualizations - -{kib} supports several types of visualizations. - -<>:: -Quickly build several types of basic visualizations by simply dragging and dropping the data fields you want to display. - -<>:: - -* *Line, area, and bar charts* — Compares different series in X/Y charts. - -* *Pie chart* — Displays each source contribution to a total. - -* *Data table* — Flattens aggregations into table format. - -* *Metric* — Displays a single number. - -* *Goal and gauge* — Displays a number with progress indicators. - -* *Tag cloud* — Displays words in a cloud, where the size of the word corresponds to its importance. - -<>:: Visualizes time series data using pipeline aggregations. - -<>:: Computes and combine data from multiple time series -data sets. - -<>:: -* *<>* — Displays geospatial data in {kib}. - -* <>:: Display shaded cells within a matrix. - -<>:: - -* *Markdown widget* — Displays free-form information or instructions. - -* *Controls* — Adds interactive inputs to a dashboard. - -<>:: Completes control over query and display. - -[float] -[[choose-your-data]] -== Choose your data - -Specify a search query to retrieve the data for your visualization, or used rolled up data. - -* To enter new search criteria, select the <> for the indices that -contain the data you want to visualize. The visualization builder opens -with a wildcard query that matches all of the documents in the selected -indices. - -* To build a visualization from a saved search, click the name of the saved -search you want to use. The visualization builder opens and loads the -selected query. -+ -NOTE: When you build a visualization from a saved search, any subsequent -modifications to the saved search are reflected in the -visualization. To disable automatic updates, delete the visualization -on the *Saved Object* page. - -* To build a visualization using <>, select -the index pattern that includes the data. Rolled up data is summarized into -time buckets that can be split into sub buckets for numeric field values or -terms. To lower granularity, use a time aggregation that uses and combines -several time buckets. For an example, refer to <>. - -[float] -[[vis-inspector]] -== Inspect visualizations - -Many visualizations allow you to inspect the query and data behind the visualization. - -. In the {kib} toolbar, click *Inspect*. -. To download the data, click *Download CSV*, then choose one of the following options: -* *Formatted CSV* - Downloads the data in table format. -* *Raw CSV* - Downloads the data as provided. -. To view the requests for collecting data, select *Requests* from the *View* -dropdown. - -[float] -[[save-visualize]] -== Save visualizations -To use your visualizations in <>, you must save them. - -. In the {kib} toolbar, click *Save*. -. Enter the visualization *Title* and optional *Description*, then *Save* the visualization. - -To access the saved visualization, go to *Management > {kib} > Saved Objects*. - -[float] -[[save-visualization-read-only-access]] -==== Read only access -When you have insufficient privileges to save visualizations, the following indicator is -displayed and the *Save* button is not visible. - -For more information, refer to <>. - -[role="screenshot"] -image::visualize/images/read-only-badge.png[Example of Visualize's read only access indicator in Kibana's header] - -[float] -[[visualize-share-options]] -== Share visualizations - -When you've finished your visualization, you can share it outside of {kib}. - -From the *Share* menu, you can: - -* Embed the code in a web page. Users must have {kib} access -to view an embedded visualization. -* Share a direct link to a {kib} visualization. -* Generate a PDF report. -* Generate a PNG report. - --- -include::{kib-repo-dir}/visualize/aggregations.asciidoc[] - -include::{kib-repo-dir}/visualize/lens.asciidoc[] - -include::{kib-repo-dir}/visualize/most-frequent.asciidoc[] - -include::{kib-repo-dir}/visualize/tsvb.asciidoc[] - -include::{kib-repo-dir}/visualize/timelion.asciidoc[] - -include::{kib-repo-dir}/visualize/tilemap.asciidoc[] - -include::{kib-repo-dir}/visualize/for-dashboard.asciidoc[] - -include::{kib-repo-dir}/visualize/vega.asciidoc[] diff --git a/docs/visualize/aggregations.asciidoc b/docs/visualize/aggregations.asciidoc deleted file mode 100644 index ef38f716f230..000000000000 --- a/docs/visualize/aggregations.asciidoc +++ /dev/null @@ -1,110 +0,0 @@ -[[supported-aggregations]] -== Supported aggregations - -Use the supported aggregations to build your visualizations. - -[float] -[[visualize-metric-aggregations]] -=== Metric aggregations - -Metric aggregations extract field from documents to generate data values. - -{ref}/search-aggregations-metrics-avg-aggregation.html[Average]:: The mean value. - -{ref}/search-aggregations-metrics-valuecount-aggregation.html[Count]:: The total number of documents that match the query, which allows you to visualize the number of documents in a bucket. Count is the default value. - -{ref}/search-aggregations-metrics-max-aggregation.html[Max]:: The highest value. - -{ref}/search-aggregations-metrics-percentile-aggregation.html[Median]:: The value that is in the 50% percentile. - -{ref}/search-aggregations-metrics-min-aggregation.html[Min]:: The lowest value. - -{ref}/search-aggregations-metrics-percentile-rank-aggregation.html[Percentile ranks]:: Returns the percentile rankings for the values in the specified numeric field. Select a numeric field from the drop-down, then specify one or more percentile rank values in the *Values* fields. - -{ref}/search-aggregations-metrics-percentile-aggregation.html[Percentiles]:: Divides the -values in a numeric field into specified percentile bands. Select a field from the drop-down, then specify one or more ranges in the *Percentiles* fields. - -Standard Deviation:: Requires a numeric field. Uses the {ref}/search-aggregations-metrics-extendedstats-aggregation.html[_extended stats_] aggregation. - -{ref}/search-aggregations-metrics-sum-aggregation.html[Sum]:: The total value. - -{ref}/search-aggregations-metrics-top-hits-aggregation.html[Top hit]:: Returns a sample of individual documents. When the Top Hit aggregation is matched to more than one document, you must choose a technique for combining the values. Techniques include average, minimum, maximum, and sum. - -Unique Count:: The {ref}/search-aggregations-metrics-cardinality-aggregation.html[Cardinality] of the field within the bucket. - -Alternatively, you can override the field values with a script using JSON input. For example: - -[source,shell] -{ "script" : "doc['grade'].value * 1.2" } - -The example implements a {es} {ref}/search-aggregations.html[Script Value Source], which replaces -the value in the metric. The options available depend on the aggregation you choose. - -[float] -[[visualize-parent-pipeline-aggregations]] -=== Parent pipeline aggregations - -Parent pipeline aggregations assume the bucket aggregations are ordered and are especially useful for time series data. For each parent pipeline aggregation, you must define a bucket aggregation and metric aggregation. - -You can also nest these aggregations. For example, if you want to produce a third derivative. - -{ref}/search-aggregations-pipeline-bucket-script-aggregation.html[Bucket script]:: Executes a script that performs computations for each bucket that specifies metrics in the parent multi-bucket aggregation. - -{ref}/search-aggregations-pipeline-cumulative-sum-aggregation.html[Cumulative sum]:: Calculates the cumulative sum of a specified metric in a parent histogram. - -{ref}/search-aggregations-pipeline-derivative-aggregation.html[Derivative]:: Calculates the derivative of specific metrics. - -{ref}/search-aggregations-pipeline-movavg-aggregation.html[Moving avg]:: Slides a window across the data and emits the average value of the window. - -{ref}/search-aggregations-pipeline-serialdiff-aggregation.html[Serial diff]:: Values in a time series are subtracted from itself at different time lags or periods. - -[float] -[[visualize-sibling-pipeline-aggregations]] -=== Sibling pipeline aggregations - -Sibling pipeline aggregations condense many buckets into one. For each sibling pipeline aggregation, you must define a bucket aggregations and metric aggregation. - -{ref}/search-aggregations-pipeline-avg-bucket-aggregation.html[Average bucket]:: Calculates the mean, or average, value of a specified metric in a sibling aggregation. - -{ref}/search-aggregations-pipeline-avg-bucket-aggregation.html[Max Bucket]:: Calculates the maximum value of a specified metric in a sibling aggregation. - -{ref}/search-aggregations-pipeline-avg-bucket-aggregation.html[Min Bucket]:: Calculates the minimum value of a specified metric in a sibling aggregation. - -{ref}/search-aggregations-pipeline-avg-bucket-aggregation.html[Sum Bucket]:: Calculates the sum of the values of a specified metric in a sibling aggregation. - -[float] -[[visualize-bucket-aggregations]] -=== Bucket aggregations - -Bucket aggregations sort documents into buckets, depending on the contents of the document. - -{ref}/search-aggregations-bucket-datehistogram-aggregation.html[Date histogram]:: Splits a date field into buckets by interval. If the date field is the primary time field for the index pattern, it chooses an automatic interval for you. Intervals are labeled at the start of the interval, using the date-key returned by {es}. For example, the tooltip for a monthly interval displays the first day of the month. - -{ref}/search-aggregations-bucket-daterange-aggregation.html[Date range]:: Reports values that are within a range of dates that you specify. You can specify the ranges for the dates using {ref}/common-options.html#date-math[_date math_] expressions. - -{ref}/search-aggregations-bucket-filter-aggregation.html[Filter]:: Each filter creates a bucket of documents. You can specify a filter as a -<> or <> query string. - -{ref}/search-aggregations-bucket-geohashgrid-aggregation.html[Geohash]:: Displays points based on a geohash. Supported by data table visualizations and <>. - -{ref}/search-aggregations-bucket-geotilegrid-aggregation.html[Geotile]:: Groups points based on web map tiling. Supported by data table visualizations and <>. - -{ref}/search-aggregations-bucket-histogram-aggregation.html[Histogram]:: Builds from a numeric field. - -{ref}/search-aggregations-bucket-iprange-aggregation.html[IPv4 range]:: Specify ranges of IPv4 addresses. - -{ref}/search-aggregations-bucket-range-aggregation.html[Range]:: Specify ranges of values for a numeric field. - -{ref}/search-aggregations-bucket-significantterms-aggregation.html[Significant terms]:: Returns interesting or unusual occurrences of terms in a set. Supports {es} {ref}/search-aggregations-bucket-terms-aggregation.html#_filtering_values_4[exclude and include patterns]. - -{ref}/search-aggregations-bucket-terms-aggregation.html[Terms]:: Specify the top or bottom _n_ elements of a given field to display, ordered by count or a custom metric. Supports {es} {ref}/search-aggregations-bucket-terms-aggregation.html#_filtering_values_4[exclude and include patterns]. - -{kib} filters string fields with only regular expression patterns, and does not filter numeric fields or match with arrays. - -For example: - -* You want to exclude the metricbeat process from your visualization of top processes: `metricbeat.*` -* You only want to show processes collecting beats: `.*beat` -* You want to exclude two specific values, the string `"empty"` and `"none"`: `empty|none` - -Patterns are case sensitive. diff --git a/docs/visualize/for-dashboard.asciidoc b/docs/visualize/for-dashboard.asciidoc deleted file mode 100644 index 400179e9ceae..000000000000 --- a/docs/visualize/for-dashboard.asciidoc +++ /dev/null @@ -1,67 +0,0 @@ -[[for-dashboard]] -== Dashboard tools - -Visualize comes with controls and Markdown tools that you can add to dashboards for an interactive experience. - -[float] -[[controls]] -=== Controls -experimental[] - -The controls tool enables you to add interactive inputs -on a dashboard. - -You can add two types of interactive inputs: - -* *Options list* — Filters content based on one or more specified options. The dropdown menu is dynamically populated with the results of a terms aggregation. For example, use the options list on the sample flight dashboard when you want to filter the data by origin city and destination city. - -* *Range slider* — Filters data within a specified range of numbers. The minimum and maximum values are dynamically populated with the results of a min and max aggregation. For example, use the range slider when you want to filter the sample flight dashboard by a specific average ticket price. - -[role="screenshot"] -image::images/dashboard-controls.png[] - -[float] -[[controls-options]] -==== Controls options - -Configure the settings that apply to the interactive inputs on a dashboard. - -. Click *Options*, then configure the following: - -* *Update {kib} filters on each change* — When selected, all interactive inputs create filters that refresh the dashboard. When unselected, {kib} filters are created only when you click *Apply changes*. - -* *Use time filter* — When selected, the aggregations that generate the options list and time range are connected to the <>. - -* *Pin filters to global state* — When selected, all filters created by interacting with the inputs are automatically pinned. - -. Click *Update*. - -[float] -[[markdown-widget]] -=== Markdown - -The Markdown tool is a text entry field that accepts GitHub-flavored Markdown text. When you enter the text, the tool populates the results on the dashboard. - -Markdown is helpful when you want to include important information, instructions, and images on your dashboard. - -For information about GitHub-flavored Markdown text, click *Help*. - -For example, when you enter: - -[role="screenshot"] -image::images/markdown_example_1.png[] - -The following instructions are displayed: - -[role="screenshot"] -image::images/markdown_example_2.png[] - -Or when you enter: - -[role="screenshot"] -image::images/markdown_example_3.png[] - -The following image is displayed: - -[role="screenshot"] -image::images/markdown_example_4.png[] diff --git a/docs/visualize/lens.asciidoc b/docs/visualize/lens.asciidoc deleted file mode 100644 index 6e51433bca3f..000000000000 --- a/docs/visualize/lens.asciidoc +++ /dev/null @@ -1,173 +0,0 @@ -[role="xpack"] -[[lens]] -== Lens - -beta[] - -*Lens* is a simple and fast way to create visualizations of your {es} data. To create visualizations, -you drag and drop your data fields onto the visualization builder pane, and *Lens* automatically generates -a visualization that best displays your data. - -With Lens, you can: - -* Use the automatically generated visualization suggestions to change the visualization type. - -* Create visualizations with multiple layers and indices. - -* Add your visualizations to dashboards and Canvas workpads. - -To get started with *Lens*, select a field in the data panel, then drag and drop the field on a highlighted area. - -[role="screenshot"] -image::images/lens_drag_drop.gif[Drag and drop] - -You can incorporate many fields into your visualization, and Lens uses heuristics to decide how to apply each one to the visualization. - -TIP: Drag-and-drop capabilities are available only when Lens knows how to use the data. If *Lens* is unable to automatically generate a visualization, -you can still configure the customization options for your visualization. - -[float] -[[apply-lens-filters]] -==== Change the data panel fields - -The fields in the data panel are based on the selected <> and <>. - -To change the index pattern, click it, then select a new one. The fields in the data panel automatically update. - -To filter the fields in the data panel: - -* Enter the name in *Search field names*. - -* Click *Filter by type*, then select the filter. To show all of the fields in the index pattern, deselect *Only show fields with data*. - -[float] -[[view-data-summaries]] -==== Data summaries - -To help you decide exactly the data you want to display, get a quick summary of each field. The summary shows the distribution of values within the selected time range. - -To view the field summary information, navigate to the field, then click *i*. - -[role="screenshot"] -image::images/lens_data_info.png[Data summary window] - -[float] -[[change-the-visualization-type]] -==== Change the visualization type - -*Lens* enables you to switch between any supported visualization type at any time. - -*Suggestions* are shortcuts to alternate visualizations that *Lens* generates for you. - -[role="screenshot"] -image::images/lens_suggestions.gif[Visualization suggestions] - -If you'd like to use a visualization type that is not suggested, click the visualization type, -then select a new one. - -[role="screenshot"] -image::images/lens_viz_types.png[] - -When there is an exclamation point (!) -next to a visualization type, Lens is unable to transfer your data, but -still allows you to make the change. - -[float] -[[customize-operation]] -==== Change the aggregation and labels - -For each visualization, Lens allows some customizations of the data. - -. Click *Drop a field here* or the field name in the column. - -. Change the options that appear. Options vary depending on the type of field. -+ -[role="screenshot"] -image::images/lens_aggregation_labels.png[Quick function options] - -[float] -[[layers]] -==== Add layers and indices - -Area, line, and bar charts allow you to visualize multiple data layers and indices so that you can compare and analyze data from multiple sources. - -To add a layer, click *+*, then drag and drop the fields for the new layer. - -[role="screenshot"] -image::images/lens_layers.png[Add layers] - -To view a different index, click it, then select a new one. - -[role="screenshot"] -image::images/lens_index_pattern.png[Add index pattern] - -[float] -[[lens-tutorial]] -=== Lens tutorial - -Ready to create your own visualization with Lens? Use the following tutorial to create a visualization that -lets you compare sales over time. - -[float] -[[lens-before-begin]] -==== Before you begin - -To start, you'll need to add the <>. - -[float] -==== Build the visualization - -Drag and drop your data onto the visualization builder pane. - -. Select the *kibana_sample_data_ecommerce* index pattern. - -. Click image:images/time-filter-calendar.png[], then click *Last 7 days*. -+ -The fields in the data panel update. - -. Drag and drop the *taxful_total_price* data field to the visualization builder pane. -+ -[role="screenshot"] -image::images/lens_tutorial_1.png[Lens tutorial] - -To display the average order prices over time, *Lens* automatically added in *order_date* field. - -To break down your data, drag the *category.keyword* field to the visualization builder pane. Lens -knows that you want to show the top categories and compare them across the dates, -and creates a chart that compares the sales for each of the top three categories: - -[role="screenshot"] -image::images/lens_tutorial_2.png[Lens tutorial] - -[float] -[[customize-lens-visualization]] -==== Customize your visualization - -Make your visualization look exactly how you want with the customization options. - -. Click *Average of taxful_total_price*, then change the *Label* to `Sales`. -+ -[role="screenshot"] -image::images/lens_tutorial_3.1.png[Lens tutorial] - -. Click *Top values of category.keyword*, then change *Number of values* to `10`. -+ -[role="screenshot"] -image::images/lens_tutorial_3.2.png[Lens tutorial] -+ -The visualization updates to show there are only six available categories. -+ -Look at the *Suggestions*. An area chart is not an option, but for the sales data, a stacked area chart might be the best option. - -. To switch the chart type, click *Stacked bar chart* in the column, then click *Stacked area* from the *Select a visualizations* window. -+ -[role="screenshot"] -image::images/lens_tutorial_3.png[Lens tutorial] - -[float] -[[lens-tutorial-next-steps]] -==== Next steps - -Now that you've created your visualization, you can add it to a dashboard or Canvas workpad. - -For more information, refer to <> or <>. diff --git a/docs/visualize/most-frequent.asciidoc b/docs/visualize/most-frequent.asciidoc deleted file mode 100644 index f716930e7e65..000000000000 --- a/docs/visualize/most-frequent.asciidoc +++ /dev/null @@ -1,59 +0,0 @@ -[[most-frequent]] -== Most frequently used visualizations - -The most frequently used visualizations allow you to plot aggregated data from a <> or <>. - -The most frequently used visualizations include: - -* Line, area, and bar charts -* Pie chart -* Data table -* Metric, goal, and gauge -* Tag cloud - -[[metric-chart]] - -[float] -=== Configure your visualization - -You configure visualizations using the default editor. Each visualization supports different configurations of the metrics and buckets. - -For example, a bar chart allows you to add an x-axis: - -[role="screenshot"] -image::images/add-bucket.png["",height=478] - -A common configuration for the x-axis is to use a {es} {ref}/search-aggregations-bucket-datehistogram-aggregation.html[date histogram] aggregation: - -[role="screenshot"] -image::images/visualize-date-histogram.png[] - -To see your changes, click *Apply changes* image:images/apply-changes-button.png[] - -If it's supported by the visualization, you can add more buckets. In this example we have -added a -{es} {ref}/search-aggregations-bucket-terms-aggregation.html[terms] aggregation on the field -`geo.src` to show the top 5 sources of log traffic. - -[role="screenshot"] -image::images/visualize-date-histogram-split-1.png[] - -The new aggregation is added after the first one, so the result shows -the top 5 sources of traffic per 3 hours. If you want to change the aggregation order, you can do -so by dragging: - -[role="screenshot"] -image::images/visualize-drag-reorder.png["",width=366] - -The visualization -now shows the top 5 sources of traffic overall, and compares them in 3 hour increments: - -[role="screenshot"] -image::images/visualize-date-histogram-split-2.png[] - -For more information about how aggregations are used in visualizations, see <>. - -Each visualization also has its own customization options. Most visualizations allow you to customize the color of a specific series: - -[role="screenshot"] -image::images/color-picker.png[An array of color dots that users can select,height=267] diff --git a/docs/visualize/tilemap.asciidoc b/docs/visualize/tilemap.asciidoc deleted file mode 100644 index c889bd0bb6ca..000000000000 --- a/docs/visualize/tilemap.asciidoc +++ /dev/null @@ -1,27 +0,0 @@ -[[heat-map]] -== Heat map - -Display graphical representations of data where the individual values are represented by colors. Use heat maps when your data set includes categorical data. For example, use a heat map to see the flights of origin countries compared to destination countries using the sample flight data. - -[role="screenshot"] -image::images/visualize_heat_map_example.png[] - -[float] -[[navigate-heatmap]] -=== Change the color ranges - -When only one color displays on the heat map, you might need to change the color ranges. - -To specify the number of color ranges: - -. Click *Options*. - -. Enter the *Number of colors* to display. - -To specify custom ranges: - -. Click *Options*. - -. Select *Use custom ranges*. - -. Enter the ranges to display. diff --git a/docs/visualize/timelion.asciidoc b/docs/visualize/timelion.asciidoc deleted file mode 100644 index 4869664fab0a..000000000000 --- a/docs/visualize/timelion.asciidoc +++ /dev/null @@ -1,547 +0,0 @@ -[[timelion]] -== Timelion - -Timelion is a time series data visualizer that enables you to combine totally -independent data sources within a single visualization. It's driven by a simple -expression language you use to retrieve time series data, perform calculations -to tease out the answers to complex questions, and visualize the results. - -For example, Timelion enables you to easily get the answers to questions like: - -* <> -* <> -* <> - -[float] -[[time-series-before-you-begin]] -=== Before you begin - -In this tutorial, you'll use the time series data from https://www.elastic.co/guide/en/beats/metricbeat/current/index.html[Metricbeat]. To ingest the data locally, link:https://www.elastic.co/downloads/beats/metricbeat[download Metricbeat]. - -[float] -[[time-series-intro]] -=== Create time series visualizations - -To compare the real-time percentage of CPU time spent in user space to the results offset by one hour, create a time series visualization. - -[float] -[[time-series-define-functions]] -==== Define the functions - -To start tracking the real-time percentage of CPU, enter the following in the *Timelion Expression* field: - -[source,text] ----------------------------------- -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') ----------------------------------- - -[role="screenshot"] -image::images/timelion-create01.png[] -{nbsp} - -[float] -[[time-series-compare-data]] -==== Compare the data - -To compare the two data sets, add another series with data from the previous hour, separated by a comma: - -[source,text] ----------------------------------- -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct'), -.es(offset=-1h, <1> - index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') ----------------------------------- - -<1> `offset` offsets the data retrieval by a date expression. In this example, `-1h` offsets the data back by one hour. - -[role="screenshot"] -image::images/timelion-create02.png[] -{nbsp} - -[float] -[[time-series-add-labels]] -==== Add label names - -To easily distinguish between the two data sets, add the label names: - -[source,text] ----------------------------------- -.es(offset=-1h,index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct').label('last hour'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct').label('current hour') <1> ----------------------------------- - -<1> `.label()` adds custom labels to the visualization. - -[role="screenshot"] -image::images/timelion-create03.png[] -{nbsp} - -[float] -[[time-series-title]] -==== Add a title - -Add a meaningful title: - -[source,text] ----------------------------------- -.es(offset=-1h, - index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('last hour'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('current hour') - .title('CPU usage over time') <1> ----------------------------------- - -<1> `.title()` adds a title with a meaningful name. Titles make is easier for unfamiliar users to understand the purpose of the visualization. - -[role="screenshot"] -image::images/timelion-customize01.png[] -{nbsp} - -[float] -[[time-series-change-chart-type]] -==== Change the chart type - -To differentiate between the current hour data and the last hour data, change the chart type: - -[source,text] ----------------------------------- -.es(offset=-1h, - index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('last hour') - .lines(fill=1,width=0.5), <1> -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('current hour') - .title('CPU usage over time') ----------------------------------- - -<1> `.lines()` changes the appearance of the chart lines. In this example, `.lines(fill=1,width=0.5)` sets the fill level to `1`, and the border width to `0.5`. - -[role="screenshot"] -image::images/timelion-customize02.png[] -{nbsp} - -[float] -[[time-series-change-color]] -==== Change the line colors - -To make the current hour data stand out, change the line colors: - -[source,text] ----------------------------------- -.es(offset=-1h, - index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('last hour') - .lines(fill=1,width=0.5) - .color(gray), <1> -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('current hour') - .title('CPU usage over time') - .color(#1E90FF) ----------------------------------- - -<1> `.color()` changes the color of the data. Supported color types include standard color names, hexadecimal values, or a color schema for grouped data. In this example, `.color(gray)` represents the last hour, and `.color(#1E90FF)` represents the current hour. - -[role="screenshot"] -image::images/timelion-customize03.png[] -{nbsp} - -[float] -[[time-series-adjust-legend]] -==== Make adjustments to the legend - -Change the position and style of the legend: - -[source,text] ----------------------------------- -.es(offset=-1h, - index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('last hour') - .lines(fill=1,width=0.5) - .color(gray), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='avg:system.cpu.user.pct') - .label('current hour') - .title('CPU usage over time') - .color(#1E90FF) - .legend(columns=2, position=nw) <1> ----------------------------------- - -<1> `.legend()` sets the position and style of the legend. In this example, `.legend(columns=2, position=nw)` places the legend in the north west position of the visualization with two columns. - -[role="screenshot"] -image::images/timelion-customize04.png[] -{nbsp} - -[float] -[[mathematical-functions-intro]] -=== Create visualizations with mathematical functions - -To create a visualization for inbound and outbound network traffic, use mathematical functions. - -[float] -[[mathematical-functions-define-functions]] -==== Define the functions - -To start tracking the inbound and outbound network traffic, enter the following in the *Timelion Expression* field: - -[source,text] ----------------------------------- -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.in.bytes) ----------------------------------- - -[role="screenshot"] -image::images/timelion-math01.png[] -{nbsp} - -[float] -[[mathematical-functions-plot-change]] -==== Plot the rate of change - -Change how the data is displayed so that you can easily monitor the inbound traffic: - -[source,text] ----------------------------------- -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.in.bytes) - .derivative() <1> ----------------------------------- - -<1> `.derivative` plots the change in values over time. - -[role="screenshot"] -image::images/timelion-math02.png[] -{nbsp} - -Add a similar calculation for outbound traffic: - -[source,text] ----------------------------------- -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.in.bytes) - .derivative(), -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.out.bytes) - .derivative() - .multiply(-1) <1> ----------------------------------- - -<1> `.multiply()` multiplies the data series by a number, the result of a data series, or a list of data series. For this example, `.multiply(-1)` converts the outbound network traffic to a negative value since the outbound network traffic is leaving your machine. - -[role="screenshot"] -image::images/timelion-math03.png[] -{nbsp} - -[float] -[[mathematical-functions-convert-data]] -==== Change the data metric - -To make the visualization easier to analyze, change the data metric from bytes to megabytes: - -[source,text] ----------------------------------- -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.in.bytes) - .derivative() - .divide(1048576), -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.out.bytes) - .derivative() - .multiply(-1) - .divide(1048576) <1> ----------------------------------- - -<1> `.divide()` accepts the same input as `.multiply()`, then divides the data series by the defined divisor. - -[role="screenshot"] -image::images/timelion-math04.png[] -{nbsp} - -[float] -[[mathematical-functions-add-labels]] -==== Customize and format the visualization - -Customize and format the visualization using functions: - -[source,text] ----------------------------------- -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.in.bytes) - .derivative() - .divide(1048576) - .lines(fill=2, width=1) - .color(green) - .label("Inbound traffic") <1> - .title("Network traffic (MB/s)"), <2> -.es(index=metricbeat*, - timefield=@timestamp, - metric=max:system.network.out.bytes) - .derivative() - .multiply(-1) - .divide(1048576) - .lines(fill=2, width=1) <3> - .color(blue) <4> - .label("Outbound traffic") - .legend(columns=2, position=nw) <5> ----------------------------------- - -<1> `.label()` adds custom labels to the visualization. -<2> `.title()` adds a title with a meaningful name. -<3> `.lines()` changes the appearance of the chart lines. In this example, `.lines(fill=2, width=1)` sets the fill level to `2`, and the border width to `1`. -<4> `.color()` changes the color of the data. Supported color types include standard color names, hexadecimal values, or a color schema for grouped data. In this example, `.color(green)` represents the inbound network traffic, and `.color(blue)` represents the outbound network traffic. -<5> `.legend()` sets the position and style of the legend. For this example, `legend(columns=2, position=nw)` places the legend in the north west position of the visualization with two columns. - -[role="screenshot"] -image::images/timelion-math05.png[] -{nbsp} - -[float] -[[timelion-conditional-intro]] -=== Create visualizations with conditional logic and tracking trends - -To easily detect outliers and discover patterns over time, modify time series data with conditional logic and create a trend with a moving average. - -With Timelion conditional logic, you can use the following operator values to compare your data: - -[horizontal] -`eq`:: equal -`ne`:: not equal -`lt`:: less than -`lte`:: less than or equal to -`gt`:: greater than -`gte`:: greater than or equal to - -[float] -[[conditional-define-functions]] -==== Define the functions - -To chart the maximum value of `system.memory.actual.used.bytes`, enter the following in the *Timelion Expression* field: - -[source,text] ----------------------------------- -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') ----------------------------------- - -[role="screenshot"] -image::images/timelion-conditional01.png[] -{nbsp} - -[float] -[[conditional-track-memory]] -==== Track used memory - -To track the amount of memory used, create two thresholds: - -[source,text] ----------------------------------- -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .if(gt, <1> - 11300000000, <2> - .es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), - null) - .label('warning') - .color('#FFCC11'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .if(gt, - 11375000000, - .es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), - null) - .label('severe') - .color('red') ----------------------------------- - -<1> Timelion conditional logic for the _greater than_ operator. In this example, the warning threshold is 11.3GB (`11300000000`), and the severe threshold is 11.375GB (`11375000000`). If the threshold values are too high or low for your machine, adjust the values accordingly. -<2> `if()` compares each point to a number. If the condition evaluates to `true`, adjust the styling. If the condition evaluates to `false`, use the default styling. - -[role="screenshot"] -image::images/timelion-conditional02.png[] -{nbsp} - -[float] -[[conditional-determine-trend]] -==== Determine the trend - -To determine the trend, create a new data series: - -[source,text] ----------------------------------- -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .if(gt,11300000000, - .es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), - null) - .label('warning') - .color('#FFCC11'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .if(gt,11375000000, - .es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), - null). - label('severe') - .color('red'), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .mvavg(10) <1> ----------------------------------- - -<1> `mvavg()` calculates the moving average over a specified period of time. In this example, `.mvavg(10)` creates a moving average with a window of 10 data points. - -[role="screenshot"] -image::images/timelion-conditional03.png[] -{nbsp} - -[float] -[[conditional-format-visualization]] -==== Customize and format the visualization - -Customize and format the visualization using functions: - -[source,text] ----------------------------------- -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .label('max memory') <1> - .title('Memory consumption over time'), <2> -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .if(gt, - 11300000000, - .es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), - null) - .label('warning') - .color('#FFCC11') <3> - .lines(width=5), <4> -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .if(gt, - 11375000000, - .es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes'), - null) - .label('severe') - .color('red') - .lines(width=5), -.es(index=metricbeat-*, - timefield='@timestamp', - metric='max:system.memory.actual.used.bytes') - .mvavg(10) - .label('mvavg') - .lines(width=2) - .color(#5E5E5E) - .legend(columns=4, position=nw) <5> ----------------------------------- - -<1> `.label()` adds custom labels to the visualization. -<2> `.title()` adds a title with a meaningful name. -<3> `.color()` changes the color of the data. Supported color types include standard color names, hexadecimal values, or a color schema for grouped data. -<4> `.lines()` changes the appearance of the chart lines. In this example, .lines(width=5) sets border width to `5`. -<5> `.legend()` sets the position and style of the legend. For this example, `(columns=4, position=nw)` places the legend in the north west position of the visualization with four columns. - -[role="screenshot"] -image::images/timelion-conditional04.png[] -{nbsp} - -For additional information on Timelion conditional capabilities, go to https://www.elastic.co/blog/timeseries-if-then-else-with-timelion[I have but one .condition()]. - -[float] -[[timelion-deprecation]] -=== Timelion App deprecation - -Deprecated since 7.0, the Timelion app will be removed in 8.0. If you have any Timelion worksheets, you must migrate them to a dashboard. - -NOTE: Only the Timelion app is deprecated. {kib} continues to support Timelion visualizations on dashboards, in Visualize, and in Canvas. - -[float] -[[timelion-app-to-vis]] -==== Create a dashboard from a Timelion worksheet - -To replace a Timelion worksheet with a dashboard, follow the same process for adding a visualization. -In addition, you must migrate the Timelion graphs to Visualize. - -. Open the menu, click **Dashboard**, then click **Create dashboard**. - -. On the dashboard, click **Create New**, then select the Timelion visualization. -+ -[role="screenshot"] -image::images/timelion-create-new-dashboard.png[] -+ -The only thing you need is the Timelion expression for each graph. - -. Open the Timelion app on a new tab, select the chart you want to copy, and copy its expression. -+ -[role="screenshot"] -image::images/timelion-copy-expression.png[] - -. Return to the other tab and paste the copied expression to the *Timelion Expression* field and click **Update**. -+ -[role="screenshot"] -image::images/timelion-vis-paste-expression.png[] - -. Save the new visualization, give it a name, and click **Save and Return**. -+ -Your Timelion visualization will appear on the dashboard. Repeat this for all your charts on each worksheet. -+ -[role="screenshot"] -image::images/timelion-dashboard.png[] diff --git a/docs/visualize/tsvb.asciidoc b/docs/visualize/tsvb.asciidoc deleted file mode 100644 index 9a1e81670b65..000000000000 --- a/docs/visualize/tsvb.asciidoc +++ /dev/null @@ -1,138 +0,0 @@ -[[TSVB]] -== TSVB - -TSVB is a time series data visualizer that allows you to use the full power of the -Elasticsearch aggregation framework. With TSVB, you can combine an infinite -number of aggregations to display complex data. - -NOTE: In Elasticsearch version 7.3.0 and later, the time series data visualizer is now referred to as TSVB instead of Time Series Visual Builder. - -[float] -[[tsvb-visualization-types]] -=== Types of TSVB visualizations - -TSVB comes with these types of visualizations: - -Time Series:: A histogram visualization that supports area, line, bar, and steps along with multiple y-axis. - -[role="screenshot"] -image:images/tsvb-screenshot.png["Time series visualization"] - -Metric:: A metric that displays the latest number in a data series. - -[role="screenshot"] -image:images/tsvb-metric.png["Metric visualization"] - -Top N:: A horizontal bar chart where the y-axis is based on a series of metrics, and the x-axis is the latest value in the series. - -[role="screenshot"] -image:images/tsvb-top-n.png["Top N visualization"] - -Gauge:: A single value gauge visualization based on the latest value in a series. - -[role="screenshot"] -image:images/tsvb-gauge.png["Gauge visualization"] - -Markdown:: Edit the data using using Markdown text and Mustache template syntax. - -[role="screenshot"] -image:images/tsvb-markdown.png["Markdown visualization"] - -Table:: Display data from multiple time series by defining the field group to show in the rows, and the columns of data to display. - -[role="screenshot"] -image:images/tsvb-table.png["Table visualization"] - -[float] -[[create-tsvb-visualization]] -=== Create TSVB visualizations - -To create a TSVB visualization, choose the data series you want to display, then choose how you want to display the data. The options available are dependent on the visualization. - -[float] -[[tsvb-data-series-options]] -==== Configure the data series - -To create a single metric, add multiple data series with multiple aggregations. - -. Select the visualization type. - -. Specify the data series labels and colors. - -.. Select *Data*. -+ -If you are using the *Table* visualization, select *Columns*. - -.. In the *Label* field, enter a name for the data series, which is used on legends and titles. -+ -For series that are grouped by a term, you can specify a mustache variable of `{{key}}` to substitute the term. - -.. If supported by the visualization, click the swatch and choose a color for the data series. - -.. To add another data series, click *+*, then repeat the steps to specify the labels and colors. - -. Specify the data series metrics. - -.. Select *Metrics*. - -.. From the dropdown lists, choose your options. - -.. To add another metric, click *+*. -+ -When you add more than one metric, the last metric value is displayed, which is indicated by the eye icon. - -. To specify the format and display options, select *Options*. - -. To specify how to group or split the data, choose an option from the *Group by* drop down list. -+ -By default, the data series are grouped by everything. - -[float] -[[tsvb-panel-options]] -==== Configure the panel - -Change the data that you want to display and choose the style options for the panel. - -. Select *Panel options*. - -. Under *Data*, specify how much of the data that you want to display in the visualization. - -. Under *Style*, specify how you want the visualization to look. - -[float] -[[tsvb-add-annotations]] -==== Add annotations - -If you are using the Time Series visualization, add annotation data sources. - -. Select *Annotations*. - -. Click *Add data source*, then specify the options. - -[float] -[[tsvb-enter-markdown]] -==== Enter Markdown text - -Edit the source for the Markdown visualization. - -. Select *Markdown*. - -. In the editor, enter enter your Markdown text, then press Enter. - -. To insert the mustache template variable into the editor, click the variable name. -+ -The http://mustache.github.io/mustache.5.html[mustache syntax] uses the Handlebar.js processor, which is an extended version of the Mustache template language. - -[float] -[[tsvb-style-markdown]] -==== Style Markdown text - -Style your Markdown visualization using http://lesscss.org/features/[less syntax]. - -. Select *Markdown*. - -. Select *Panel options*. - -. Enter styling rules in *Custom CSS* section -+ -Less in TSVB does not support custom plugins or inline JavaScript.