Splunk® IT Service Intelligence

Service Insights Manual

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Add visualizations to glass tables in ITSI

Visualizations in the glass table editor in IT Service Intelligence (ITSI) comprise KPI and ad hoc search widgets, charts, tables, shapes, icons, and text. You can modify the layout in the UI and in the visualization section of the source editor.

Add a KPI visualization

Add a KPI visualization to display the associated search values. The KPIs continuously update in real-time according to the search schedule that you define when you create the search. By default, KPIs are represented by single value visualizations with a sparkline. For more information about KPIs, see Overview of creating KPIs in ITSI.

  1. Click the data icon Data icon. to display the services available in your IT environment. Only services you have permission to read are listed.
  2. Expand a service to view its KPIs.
  3. Select a KPI to add it as a visualization.
  4. (Optional) Configure the following visualization settings:
    Setting Description
    Show Trending Value Display a trending arrow comparing the current value to the previous one. For Trend Display as, specify whether the trend value is displayed as an absolute count or a percentage of how much the last value changed in comparison with the current one.
    Show Sparkline Show the single value sparkline that displays time-based trends. Choose from the following sparkline locations:
    • Before - Displays the sparkline to the left of the value.
    • After - Displays the sparkline to the right of the value.
    • Below - Displays the sparkline below the value.
    Digit Precision Specify the number of decimal places to display. For example, to display 3 decimal places, use a value of 3. The maximum value is 20.
    Unit Specify a label to show next to the single value. Specify whether the unit label appears before or after the single value.
  5. Click saveSave icon.when you're finished.

When you add a KPI to a glass table, the data powering the KPI is automatically populated in the dataSources section of the source definition. For more information, see Add data sources to glass tables in ITSI.

Add an ad hoc search visualization

Create a custom visualization to display search results based on an ad hoc search.

  1. Click the data icon Data icon. to display the services available in your IT environment. Only services you have permission to read are listed.
  2. Click Create Ad hoc Search.
  3. Provide a data source name and enter a search. For example:

    index=_internal | timechart count

    Note that for a single-value visualization, the first column in the search results must be the number you want to display. For example, the above search produces results in the format count, _time, _span. You need to make sure the results force the column count first. For example:

    index=_internal | timechart count | fields count *

  4. Click Run & Save. The search is added to the list of ad hoc searches.
  5. Click the ad hoc search to add it to the glass table.
  6. (Optional) Configure the following visualization settings:
    Setting Description
    Show Trending Value Display a trending arrow comparing the current value to the previous one. For Trend Display as, specify whether the trend value is displayed as an absolute count or a percentage of how much the last value changed in comparison with the current one.
    Show Sparkline Show the single value sparkline that displays time-based trends.
    Threshold Settings Thresholding is currently not supported for ad hoc search visualizations.
    Digit Precision Specify the number of decimal places to display. For example, to display 3 decimal places, use a value of 3. The maximum value is 20.
    Unit Specify a label to show next to the single value. Specify whether the unit label appears before or after the single value.
  7. Click saveSave icon.when you're finished.

When you add an ad hoc search to a glass table, the data powering the search is automatically populated in the dataSources section of the source definition. For more information, see Add data sources to glass tables in ITSI.

Add a chart, table, or map visualization

A chart visualization shows one or more data dimensions in a results set. A table visualization shows patterns of one or more metrics across a data set.

  1. Click the chart icon Chart icon and select a chart type.
  2. (Optional) Provide a title and description of the visualization. These fields show up in both Edit and View mode.
  3. Provide a data source name and a search query to generate results in the correct format for the visualization you're building. For information and examples, see Data structure requirements for visualizations in the Dashboards and Visualizations manual.
  4. Click saveSave icon.when you're finished.

For information about how chart visualizations are structured in the source editor, see Chart overview. For information about how table visualizations are structured in the source editor, see Table visualization overview.

Set different time ranges for visualizations

By default, the global time range picker sets the time range for all visualizations. Alternatively, you can add additional inputs to set custom time ranges on a per-visualization basis. You might leverage this functionality if you want to view one visualization for the past hour, and compare those results to another visualization over the past day.

An individual KPI can "opt out" from search aggregation by setting "skipAggregation": true within the meta section of the data source definition. When this flag is enabled a KPI data source can use its own custom time range or refresh rate.

  1. Add your visualizations to the glass table.
  2. Click saveSave icon.before entering source mode to make sure you don't lose any work.
  3. Click sourceUDFsource.png to edit the JSON source code directly.
  4. To add a new time picker, copy the globalTime section under inputs and paste it within the inputs section.
  5. Give the new time picker a name other than "globalTime" and change the token value.
  6. Add the name of the new time range picker under globalInputs in the layout section.
  7. Update the query parameters in one of the visualization's data sources to use the token of the time range picker you just added.
  8. Click Back to return to the glass table editor. Verify that updating the time range for one of the time pickers only updates the corresponding visualization and not all visualizations.

Example

The following code sample shows how to add an additional time picker to a glass table. The new time picker is called otherTime and its token is otherT:

{
	"dataSources": {
		"ds_PXKvqo5R": {
			"type": "ds.search",
			"name": "Search_2",
			"options": {
				"refresh": "$RefreshRate$",
				"query": "<Your SPL search>",
				"refreshType": "delay",
				"queryParameters": {
					"earliest": "$TimeRange.earliest$",
					"latest": "$TimeRange.latest$"
				}
			}
		},
		"ds_m5NHtoCZ": {
			"type": "ds.search",
			"name": "Search_1",
			"options": {
				"refresh": "$RefreshRate$",
				"query": "<Your SPL search>",
				"refreshType": "delay",
				"queryParameters": {
					"earliest": "$otherT.earliest$",
					"latest": "$otherT.latest$"
				}
			}
		}
	},
	"visualizations": {
		"viz_oJNIk1y0": {
			"type": "viz.singlevalue",
			"title": "Viz 1",
			"encoding": {
				"trend": "primary[0]"
			},
			"dataSources": {
				"primary": "ds_PXKvqo5R"
			}
		},
		"viz_pYbQzRnM": {
			"type": "viz.singlevalue",
			"title": "Viz 2",
			"encoding": {
				"trend": "primary[0]"
			},
			"dataSources": {
				"primary": "ds_m5NHtoCZ"
			}
		}
	},
	"title": "<Glass Table Title>",
	"layout": {
		"type": "absolute",
		"structure": [
			{
				"type": "block",
				"item": "viz_pYbQzRnM",
				"position": {
					"w": 150,
					"h": 150,
					"y": 160,
					"x": 700
				}
			},
			{
				"type": "block",
				"item": "viz_oJNIk1y0",
				"position": {
					"w": 150,
					"h": 150,
					"y": 160,
					"x": 300
				}
			}
		],
		"globalInputs": [
			"globalTime",
			"otherTime",
			"globalRefreshRate"
		],
		"options": {
			"backgroundColor": "#FFFFFF",
			"height": 1080,
			"width": 1920
		}
	},
	"inputs": {
		"globalTime": {
			"type": "input.timerange",
			"options": {
				"token": "TimeRange",
				"defaultValue": "-60m, now"
			}
		},
		"otherTime": {
			"type": "input.timerange",
			"options": {
				"token": "otherT",
				"defaultValue": "-30m, now"
			}
		},
		"globalRefreshRate": {
			"type": "input.dropdown",
			"title": "Refresh Rate",
			"options": {
				"token": "RefreshRate",
				"defaultValue": "60s",
				"items": [
					{
						"value": "60s",
						"label": "1 Minute"
					},
					{
						"value": "300s",
						"label": "5 Minutes"
					},
					{
						"value": "1800s",
						"label": "30 Minutes"
					},
					{
						"value": "3600s",
						"label": "1 Hour"
					},
					{
						"value": "86400s",
						"label": "24 Hours"
					}
				]
			}
		}
	},
	"description": ""
}


Alternatively, if you don't want to add a second time picker, you can just hardcode a fixed time into the JSON for that visualization. For example:

"options": {
	"queryParameters": {
		"earliest": "-4h@h",
		"latest": "now"
	},

Configure drilldowns

Add drilldown links that redirect to saved ITSI views such as service analyzers, deep dives, dashboards, or to a custom URL. Drilldowns are supported for icons, shapes (except lines), images, text, and all visualization types except Gauge.

  1. Select the object you want to drill down from.
  2. In the Configuration panel, click Add Drilldown.
  3. For On Click, choose from the following drilldown options:
    Option Notes
    Link to a saved ITSI view Drill down to one of the following object types:
    • Saved Glass Table
    • Saved Deep Dive
    • Saved Dashboard
    • Saved Episode Review
    • Saved Service Analyzer
    Link to custom URL Provide a relative or absolute URL. If you don't provide an http:// address, the generated URL is considered internal to ITSI and uses a localhost URL, for example localhost:8000/<app-name>/<user_provided_url>. If you provide an http:// prefix (for example, http://splunk.com), the drilldown directs to that external URL.


    The drilldown URL includes the visualization's time range as specified in the glass table's source JSON. If you configure a drilldown and then change the time range values of the visualization in the source, the updated values aren't added to the drilldown URL. To update the time range values, manually edit the drilldown URL in the source JSON.

    You can pass time range tokens in the drilldown URL. For example:

    /en-US/app/itsi/deep_dive?savedDeepDiveID=5f8780b0bebff6293b20d2ab&earliest=$global_time.earliest$&latest=$global_time.latest$
    
  4. (Optional) Select Open in new tab so the drilldown link opens in a new tab.
  5. Click Save.
  6. Click View to enter view mode and test the link. If drilling down from a KPI visualization, you must click the actual value in the visualization, not just anywhere on the visualization, in order to go to the link.

Add text

You can add custom text to your glass table.

  1. While in edit mode, click the text button Button with a lower case A and an upper case A. in the editing toolbar.
  2. In the Font Settings section of the Configuration panel, choose a font, size, and color.
  3. Click saveSave icon.to save your changes.

Add icons

You can add custom icons to the icon gallery as long as they are in SVG format. Icons uploaded to the icon gallery are available to all users.

  1. In the editing toolbar, click the icon button A button with four shapes. .
  2. Drop your image or click browse... to add it to the icon gallery.
  3. Click the the icon to add it to your glass table.
  4. (Optional) Provide a title and description for the icon. These fields show up in both Edit and View mode.
  5. Change the icon color and opacity on the Configuration panel.

If you upload a multi-colored icon, the original colors are retained in the gallery but you cannot recolor in multi-color. For example, if you upload a red and black icon, you cannot change red to blue and black to green. If you want to change the color, you must choose a single color.

Uploaded icons remain in the KV store so you can use them again later. To delete an icon from the icon gallery, click the X that appears when you hover over it.

Add shapes

Use the glass table toolbar to add shapes to your glass table. You can add ellipses, lines, and rectangles. Use the Styling tab in the editing panel tab to format your shapes.

  1. On the editing toolbar, click the shapes buttonSquare button with a small arrow in the right corner.
  2. Select a shape to add it to the glass table.
  3. (Optional) Provide a title and description for the shape. These fields show up in both Edit and View mode.

Connect a shape or icon to a data source

You can connect a shape or icon to a specific data source, such as a KPI or service health score, so that the object's color changes based on the status of that metric.

You currently cannot connect lines to data sources. Only ellipses and rectangles are supported.

  1. Select a shape or icon and click Setup Primary Data Source.
  2. Select a KPI or service health score to connect the visualization to. When the metric's severity level changes, the color of the shape or icon changes accordingly.

Configure visualizations in the source editor

While you can add visualizations from the glass table editor itself, all configuration options must be set in the source editor. The visualization section of the source editor lists all the visualizations on your glass table, including their type, options, associated data sources, and any inputs and tokens. For a comprehensive list of the source options currently available for each visualization type, see Source options reference for the glass table editor in ITSI.

The following visualization types are available in ITSI glass tables:

Visualization type Syntax
Area chart viz.area
Bar chart viz.bar
Bubble chart viz.bubble
Column chart viz.column
Ellipse viz.ellipse
Image viz.img
Line chart viz.line
Pie chart viz.pie
Punchcard viz.punchcard
Rectangle viz.rectangle
Scatter chart viz.scatter
Single-value widget viz.singlevalue
Single-value radial widget viz.singlevalueradial
Table viz.table
Text box viz.text
USA map viz.geojson.us
World map viz.geojson.world

The following glass table shows some of the visualizations currently supported by the glass tables editor:

The image shows a large glass table with several line charts, bar graphs, and scatter plots.

Sample visualizations glass table definition

The following glass table definition shows how the visualizations above were configured using the source editor.

{
	"visualizations": {
		"viz_OlDu8cpr": {
			"dataSources": {
				"primary": "search3"
			},
			"type": "viz.line",
			"options": {
				"lineDashStyle": "shortDashDotDot",
				"seriesColors": "[#377D5E,#09D0AC,#F6CF47,#CF5656,#EB3844, #DB5566, #3C6DC7, #619FDD]",
				"lineWidth": "2",
				"backgroundColor": "#ffffff",
				"foregroundColor": "transparent",
				"fontColor": "#0000000"
			}
		},
		"viz_oW1DFpN4": {
			"options": {
				"unit": "$",
				"sparklinePosition": "after",
				"showSparklineTooltip": "true",
				"rangeColors": [
					"#FFFFFF",
					"#1E3765",
					"#3C6DC7",
					"#7755F6",
					"#EB3844",
					"#F3A846",
					"#F6CF47",
					"#09D0AC"
				],
				"sparklineHighlightSegments": 6,
				"rangeValues": [
					100,
					200,
					300,
					400,
					500,
					600,
					700,
					1000
				],
				"showTrendIndicator": true,
				"sparklineHighlightDots": 7,
				"sparklineStrokeColor": "#ffffff",
				"colorMode": "block",
				"unitPosition": "before",
				"useThousandSeparators": true,
				"showSparkline": true,
				"trendDisplayMode": "percent"
			},
			"type": "viz.singlevalue",
			"dataSources": {
				"primary": "search1"
			}
		},
		"viz_VadW0za4": {
			"options": {
				"src": "http://www.splunk.com/content/dam/splunk2/images/social/splunk-logo.jpg",
				"preserveAspectRatio": true
			},
			"type": "viz.img"
		},
		"viz_Fx3n0Ryb": {
			"options": {
				"count": 10,
				"unit": "$",
				"dataOverlayMode": "heatmap",
				"unitPosition": "before",
				"rowNumbers": true,
				"useThousandSeparators": true,
				"precision": 2
			},
			"type": "viz.table",
			"dataSources": {
				"primary": "search2"
			}
		},
		"viz_sn0IH59g": {
			"options": {
				"content": "Sample Viz Snippets",
				"fontSize": 65,
				"color": "#ffffff",
				"backgroundColor": "transparent"
			},
			"type": "viz.text"
		},
		"viz_wDKJuhbX": {
			"dataSources": {
				"primary": "search4"
			},
			"type": "viz.pie",
			"options": {
				"seriesColors": "[#377D5E,#09D0AC,#F6CF47,#CF5656,#EB3844, #DB5566, #3C6DC7, #619FDD,#C2D48D]",
				"backgroundColor": "transparent",
				"fontColor": "#ffffff"
			}
		},
		"viz_wGpdheSr": {
			"type": "viz.bubble",
			"options": {
				"seriesColors": "[#9EC9A3,#09D0AC,#F6CF47,#CF5656,#EB3844, #DB5566, #3C6DC7, #619FDD]"
			},
			"dataSources": {
				"primary": "search6"
			}
		},
		"viz_iU0C02xJ": {
			"title": "This is a Scatter Chart",
			"description": "This is a scatter chart description",
			"type": "viz.scatter",
			"options": {
				"seriesColors": "[#377D5E,#09D0AC,#F6CF47,#CF5656,#EB3844, #DB5566, #3C6DC7, #619FDD]",
				"fieldColors": "{foo: #3C6DC7, bar: #619FDD}",
				"backgroundColor": "#ffffff",
				"fontColor": "#616161"
			},
			"dataSources": {
				"primary": "search2"
			}
		}
	},
	"dataSources": {
		"search4": {
			"type": "ds.test",
			"options": {
				"data": {
					"fields": [
						{
							"name": "sourcetype"
						},
						{
							"name": "count",
							"type_special": "count"
						},
						{
							"name": "percent",
							"type_special": "percent"
						}
					],
					"columns": [
						[
							"splunkd",
							"splunkd_ui_access",
							"splunkd_access",
							"splunk_web_access",
							"scheduler",
							"splunk_web_service"
						],
						[
							"600",
							"525",
							"295",
							"213",
							"122",
							"19"
						],
						[
							"87.966380",
							"50.381304",
							"60.023780",
							"121.183272",
							"70.250513",
							"90.194752"
						]
					]
				},
				"meta": {}
			},
			"name": "search4"
		},
		"search6": {
			"options": {
				"data": {
					"columns": [
						[
							"2017-08-20T00:00:00.000-07:00",
							"2017-08-20T00:30:00.000-07:00",
							"2017-08-20T01:00:00.000-07:00",
							"2017-08-20T01:30:00.000-07:00",
							"2017-08-20T02:00:00.000-07:00",
							"2017-08-20T02:30:00.000-07:00",
							"2017-08-20T03:00:00.000-07:00",
							"2017-08-20T03:30:00.000-07:00",
							"2017-08-20T04:00:00.000-07:00",
							"2017-08-20T04:30:00.000-07:00"
						],
						[
							"100",
							"200",
							"170",
							"100",
							"22",
							"301",
							"430",
							"104",
							"221",
							"42"
						],
						[
							"220",
							"302",
							"332",
							"112",
							"460",
							"154",
							"121",
							"36",
							"576",
							"165"
						],
						[
							"320",
							"3202",
							"1332",
							"2112",
							"2460",
							"2614",
							"1121",
							"316",
							"276",
							"465"
						],
						[
							"430",
							"302",
							"332",
							"312",
							"460",
							"354",
							"321",
							"336",
							"376",
							"365"
						],
						[
							"520",
							"502",
							"532",
							"112",
							"860",
							"754",
							"121",
							"36",
							"576",
							"265"
						],
						[
							"620",
							"902",
							"32",
							"512",
							"860",
							"184",
							"181",
							"76",
							"576",
							"465"
						],
						[
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800"
						]
					],
					"fields": [
						{
							"name": "_time"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.000",
							"name": "10.1.1.000"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.002",
							"name": "10.1.1.002"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.003",
							"name": "10.1.1.003"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.004",
							"name": "10.1.1.004"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.005",
							"name": "10.1.1.005"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.006",
							"name": "10.1.1.006"
						},
						{
							"name": "_span"
						}
					]
				},
				"meta": {}
			},
			"type": "ds.test"
		},
		"search3": {
			"options": {
				"data": {
					"columns": [
						[
							"2017-08-20T00:00:00.000-07:00",
							"2017-08-20T00:30:00.000-07:00",
							"2017-08-20T01:00:00.000-07:00",
							"2017-08-20T01:30:00.000-07:00",
							"2017-08-20T02:00:00.000-07:00",
							"2017-08-20T02:30:00.000-07:00",
							"2017-08-20T03:00:00.000-07:00",
							"2017-08-20T03:30:00.000-07:00",
							"2017-08-20T04:00:00.000-07:00",
							"2017-08-20T04:30:00.000-07:00"
						],
						[
							"000",
							"200",
							"170",
							"100",
							"22",
							"301",
							"430",
							"104",
							"221",
							"42"
						],
						[
							"20",
							"302",
							"332",
							"112",
							"460",
							"154",
							"121",
							"36",
							"576",
							"165"
						],
						[
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800",
							"1800"
						]
					],
					"fields": [
						{
							"name": "_time"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.000",
							"name": "10.1.1.000"
						},
						{
							"data_source": "sum(date_hour)",
							"splitby_field": "clientip",
							"splitby_value": "10.1.1.002",
							"name": "10.1.1.002"
						},
						{
							"name": "_span"
						}
					]
				},
				"meta": {}
			},
			"type": "ds.test"
		},
		"search2": {
			"options": {
				"data": {
					"columns": [
						[
							"100000.8765",
							"200000.88998",
							"300000.5675",
							"4300000.8765",
							"340000.8765",
							"60000.4566",
							"70000.333",
							"8500000.8765",
							"900000.22233",
							"70000.333",
							"8500000.8765",
							"900000.22233"
						],
						[
							"1",
							"2",
							"3",
							"4",
							"5",
							"6",
							"7",
							"8",
							"9",
							"10",
							"11",
							"12"
						]
					],
					"fields": [
						{
							"name": "foo"
						},
						{
							"name": "bar"
						}
					]
				},
				"meta": {}
			},
			"type": "ds.test",
			"name": "search2"
		},
		"search1": {
			"options": {
				"data": {
					"columns": [
						[
							"100",
							"200",
							"300",
							"430",
							"340",
							"600",
							"700",
							"850",
							"900"
						],
						[
							"1",
							"2",
							"3",
							"4",
							"5",
							"6",
							"7",
							"8"
						]
					],
					"fields": [
						{
							"name": "foo"
						},
						{
							"name": "bar"
						}
					]
				},
				"meta": {}
			},
			"type": "ds.test",
			"name": "search1"
		}
	},
	"inputs": {
		"globalTime": {
			"type": "input.timerange",
			"options": {
				"token": "TimeRange",
				"defaultValue": "-60m, now"
			}
		},
		"globalRefreshRate": {
			"title": "Refresh Rate",
			"type": "input.dropdown",
			"options": {
				"token": "RefreshRate",
				"items": [
					{
						"value": "60s",
						"label": "1 Minute"
					},
					{
						"value": "300s",
						"label": "5 Minutes"
					},
					{
						"value": "1800s",
						"label": "30 Minutes"
					},
					{
						"value": "3600s",
						"label": "1 Hour"
					},
					{
						"value": "86400s",
						"label": "24 Hours"
					}
				],
				"defaultValue": "60s"
			}
		}
	},
	"layout": {
		"globalInputs": [
			"globalTime",
			"globalRefreshRate"
		],
		"type": "absolute",
		"options": {
			"display": "auto-scale",
			"width": 2000,
			"backgroundColor": "#708794",
			"height": 1600
		},
		"structure": [
			{
				"type": "block",
				"item": "viz_oW1DFpN4",
				"position": {
					"w": 610,
					"y": 170,
					"x": 40,
					"h": 380
				}
			},
			{
				"type": "block",
				"item": "viz_Fx3n0Ryb",
				"position": {
					"w": 610,
					"y": 570,
					"x": 40,
					"h": 480
				}
			},
			{
				"type": "block",
				"item": "viz_VadW0za4",
				"position": {
					"w": 610,
					"y": 170,
					"x": 1340,
					"h": 380
				}
			},
			{
				"type": "block",
				"item": "viz_sn0IH59g",
				"position": {
					"w": 1970,
					"y": 40,
					"x": 14,
					"h": 100
				}
			},
			{
				"type": "block",
				"item": "viz_OlDu8cpr",
				"position": {
					"w": 640,
					"y": 170,
					"x": 680,
					"h": 380
				}
			},
			{
				"item": "viz_wDKJuhbX",
				"type": "block",
				"position": {
					"x": 510,
					"y": 440,
					"w": 1020,
					"h": 790
				}
			},
			{
				"item": "viz_wGpdheSr",
				"type": "block",
				"position": {
					"x": 1350,
					"y": 570,
					"w": 600,
					"h": 470
				}
			},
			{
				"item": "viz_iU0C02xJ",
				"type": "block",
				"position": {
					"x": 40,
					"y": 1060,
					"w": 1910,
					"h": 510
				}
			}
		]
	},
	"description": "",
	"title": "Sample Viz"
}
Last modified on 22 March, 2021
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This documentation applies to the following versions of Splunk® IT Service Intelligence: 4.7.0, 4.7.1, 4.7.2, 4.8.0 Cloud only, 4.8.1 Cloud only


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