﻿---
title: Custom visualizations with Vega
description: Vega and Vega-Lite are both grammars for creating custom visualizations. They are recommended for advanced users who are comfortable writing Elasticsearch...
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/visualize/custom-visualizations-with-vega
products:
  - Kibana
---

# Custom visualizations with Vega
**Vega** and **Vega-Lite** are both grammars for creating custom visualizations. They are recommended for advanced users who are comfortable writing Elasticsearch queries manually. **Vega-Lite** is a good starting point for users who are new to both grammars, but they are not compatible.
**Vega** and **Vega-Lite** panels can display one or more data sources, including Elasticsearch, Elastic Map Service, URL, or static data, and support [Kibana extensions](#reference-for-kibana-extensions) that allow you to embed the panels on your dashboard and add interactive tools.
Use **Vega** or **Vega-Lite** when you want to create visualizations with:
- Aggregations that use `nested` or `parent/child` mapping
- Aggregations without a data view
- Queries that use custom time filters
- Complex calculations
- Extracted data from _source instead of aggregations
- Scatter charts, sankey charts, and custom maps
- An unsupported visual theme

These grammars have some limitations: they do not support tables, and can’t run queries conditionally.
![Vega UI](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega.png)

Both **Vega** and **Vega-Lite** use JSON, but Kibana has made this simpler to type by integrating [HJSON](https://hjson.github.io/). HJSON supports the following:
- Optional quotes
- Double quotes or single quotes
- Optional commas
- Comments using // or /* syntax
- Multiline strings


### Tutorials: Create custom panels

Learn how to connect **Vega-Lite** with Kibana filters and Elasticsearch data, then learn how to create more Kibana interaction using **Vega**.
As you edit the specs, work in small steps, and frequently save your work. Small changes can cause unexpected results. To save, click **Save** in the toolbar.
Before starting, add the eCommerce sample data that you’ll use in your spec, then create the dashboard.
1. [Install the eCommerce sample data set](/elastic/docs-content/pull/4438/explore-analyze#gs-get-data-into-kibana).
2. Go to **Dashboards**.
3. On the **Dashboards** page, click **Create dashboard**.


#### Open and set up Vega-Lite

Open **Vega-Lite** and change the time range.
1. On the dashboard, click **Select type**, then select **Custom visualization**.
   A pre-populated line chart displays the total number of documents.
2. Make sure the [time filter](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/query-filter/filtering) is **Last 7 days**.


## Tutorial: Create a stacked area chart from an Elasticsearch search query

Learn how to query Elasticsearch from **Vega-Lite**, displaying the results in a stacked area chart.
1. In the **Vega-Lite** spec, replace `index: _all` with the following, then click **Update**:

```yaml
index: kibana_sample_data_ecommerce
```

A flat line appears with zero results.
To add the data fields from the **kibana_sample_data_ecommerce** data view, replace the following, then click **Update**:
- `%timefield%: @timestamp` with `%timefield%: order_date`
- `field: @timestamp` with `field: order_date`


#### Add the aggregations

To create the stacked area chart, add the aggregations.
To check your work, open and use the [**Console**](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/query-filter/tools/console) on a separate browser tab.
1. Open Kibana on a new tab.
2. Go to the **Developer tools** page using the navigation menu or the [global search field](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/find-and-organize/find-apps-and-objects).
3. On the **Console** editor, enter the aggregation, then click **Click to send request**:

```js
POST kibana_sample_data_ecommerce/_search
{
  "query": {
    "range": {
      "order_date": {
        "gte": "now-7d"
      }
    }
  },
  "aggs": {
    "time_buckets": {
      "date_histogram": {
        "field": "order_date",
        "fixed_interval": "1d",
        "extended_bounds": {
          "min": "now-7d"
        },
        "min_doc_count": 0
      }
    }
  },
  "size": 0
}
```

Add the [terms aggregation](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/tree/main/reference/aggregations/search-aggregations-bucket-terms-aggregation), then click **Click to send request**:
```js
POST kibana_sample_data_ecommerce/_search
{
  "query": {
    "range": {
      "order_date": {
        "gte": "now-7d"
      }
    }
  },
  "aggs": {
    "categories": {
      "terms": { "field": "category.keyword" },
      "aggs": {
        "time_buckets": {
          "date_histogram": {
            "field": "order_date",
            "fixed_interval": "1d",
            "extended_bounds": {
              "min": "now-7d"
            },
            "min_doc_count": 0
          }
        }
      }
    }
  },
  "size": 0
}
```

The response format is different from the first aggregation query:
```json
{
  "aggregations" : {
    "categories" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [{
        "key" : "Men's Clothing",
        "doc_count" : 1661,
        "time_buckets" : {
          "buckets" : [{
            "key_as_string" : "2020-06-30T00:00:00.000Z",
            "key" : 1593475200000,
            "doc_count" : 19
          }, {
            "key_as_string" : "2020-07-01T00:00:00.000Z",
            "key" : 1593561600000,
            "doc_count" : 71
          }]
        }
      }]
    }
  }
}
```

In the **Vega-Lite** spec, enter the aggregations, then click **Update**:
```yaml
  data: {
    url: {
      %context%: true
      %timefield%: order_date
      index: kibana_sample_data_ecommerce
      body: {
        aggs: {
          categories: {
            terms: { field: "category.keyword" }
            aggs: {
              time_buckets: {
                date_histogram: {
                  field: order_date
                  interval: {%autointerval%: true}
                  extended_bounds: {
                    min: {%timefilter%: "min"}
                    max: {%timefilter%: "max"}
                  }
                  min_doc_count: 0
                }
              }
            }
          }
        }
        size: 0
      }
    }
    format: {property: "aggregations.categories.buckets" }
  }
```

For information about the queries, refer to [reference for writing Elasticsearch queries in Vega](#vega-queries).

#### Debug the warning

To generate the data, **Vega-Lite** uses the `source_0` and `data_0`. `source_0` contains the results from the Elasticsearch query, and `data_0` contains the visually encoded results that are shown on the chart. To debug the warning, compare `source_0` and `data_0`.
1. In the toolbar, click **Inspect**.
2. From the **View** dropdown, select **Vega debug**.
3. From the dropdown, select **source_0**.
   ![Table for data_0 with columns key](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_lite_tutorial_4.png)
4. To compare to the visually encoded data, select **data_0** from the dropdown.
   ![Table for data_0 where the key is NaN instead of a string](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_lite_tutorial_5.png)
   **key** is unable to convert because the property is category (`Men's Clothing`, `Women's Clothing`, etc.) instead of a timestamp.


#### Add and debug the encoding block

In the **Vega-Lite** spec, add the `encoding` block:
```yaml
  encoding: {
    x: {
      field: time_buckets.buckets.key
      type: temporal
      axis: { title: null }
    }
    y: {
      field: time_buckets.buckets.doc_count
      type: quantitative
      axis: { title: "Document count" }
    }
  }
```

1. Click **Inspect**, then select **Vega Debug** from the **View** dropdown.
2. From the dropdown, select **data_0**.
   ![Table for data_0 showing that the column time_buckets.buckets.key is undefined](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_lite_tutorial_6.png)

**Vega-Lite** is unable to extract the `time_buckets.buckets` inner array.

#### Extract the `time_buckets.buckets` inner array

In Kibana 7.9 and later, use the **Vega-Lite** [flatten transformation](https://vega.github.io/vega-lite/docs/flatten.html) to extract the `time_buckets.buckets` inner array.
If you are using Kibana 7.8 and earlier, the flatten transformation is available only in **Vega**.
In the **Vega-Lite** spec, add a `transform` block, then click **Update**:
```yaml
  transform: [{
    flatten: ["time_buckets.buckets"]
  }]
```

1. Click **Inspect**, then select **Vega Debug** from the **View** dropdown.
2. From the dropdown, select **data_0**.
   ![Table showing data_0 with multiple pages of results](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_lite_tutorial_7.png)
   Vega-Lite displays **undefined** values because there are duplicate names.
3. To resolve the duplicate names, add the `transform` and `encoding` blocks, then click **Update**:

```yaml
  transform: [{
    flatten: ["time_buckets.buckets"],
    as: ["buckets"]
  }]

  mark: area

  encoding: {
    x: {
      field: buckets.key
      type: temporal
      axis: { title: null }
    }
    y: {
      field: buckets.doc_count
      type: quantitative
      axis: { title: "Document count" }
    }
    color: {
      field: key
      type: nominal
    }
  }
```


#### Add hover states and tooltips

With the **Vega-Lite** spec, you can add hover states and tooltips to the stacked area chart with the `selection` block.
In the **Vega-Lite** spec, add the `encoding` block, then click **Update**:
```yaml
  encoding: {
    tooltip: [{
      field: buckets.key
      type: temporal
      title: "Date"
    }, {
      field: key
      type: nominal
      title: "Category"
    }, {
      field: buckets.doc_count
      type: quantitative
      title: "Count"
    }]
  }
```

When you hover over the area series on the stacked area chart, a multi-line tooltip appears, but is unable to indicate the nearest point. To indicate the nearest point, add a second layer.
Add composite marks, then click **Update**:
```yaml
  layer: [{
    mark: area
  }, {
    mark: point
  }]
```

The points are unable to stack and align with the stacked area chart.
Change the y `encoding`:
```yaml
    y: {
      field: buckets.doc_count
      type: quantitative
      axis: { title: "Document count" }
      stack: true
    }
```

Add a `selection` block inside `mark: point`:
```yaml
  layer: [{
    mark: area
  }, {
    mark: point

    selection: {
      pointhover: {
        type: single
        on: mouseover
        clear: mouseout
        empty: none
        fields: ["buckets.key", "key"]
        nearest: true
      }
    }

    encoding: {
      size: {
        condition: {
          selection: pointhover
          value: 100
        }
        value: 5
      }
      fill: {
        condition: {
          selection: pointhover
          value: white
        }
      }
    }
  }]
```

Move your cursor around the stacked area chart. The points are able to indicate the nearest point.
![Vega-Lite tutorial selection enabled](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_lite_tutorial_2.png)

The selection is controlled by a signal. To view the signal, click **Inspect** in the toolbar.
<dropdown title="Expand final Vega-Lite spec">
  ```yaml
  {
    $schema: https://vega.github.io/schema/vega-lite/v4.json
    title: Event counts from ecommerce
    data: {
      url: {
        %context%: true
        %timefield%: order_date
        index: kibana_sample_data_ecommerce
        body: {
          aggs: {
            categories: {
              terms: { field: "category.keyword" }
              aggs: {
                time_buckets: {
                  date_histogram: {
                    field: order_date
                    interval: {%autointerval%: true}
                    extended_bounds: {
                      min: {%timefilter%: "min"}
                      max: {%timefilter%: "max"}
                    }
                    min_doc_count: 0
                  }
                }
              }
            }
          }
          size: 0
        }
      }
      format: {property: "aggregations.categories.buckets" }
    }

    transform: [{
      flatten: ["time_buckets.buckets"]
      as: ["buckets"]
    }]

    encoding: {
      x: {
        field: buckets.key
        type: temporal
        axis: { title: null }
      }
      y: {
        field: buckets.doc_count
        type: quantitative
        axis: { title: "Document count" }
        stack: true
      }
      color: {
        field: key
        type: nominal
        title: "Category"
      }
      tooltip: [{
        field: buckets.key
        type: temporal
        title: "Date"
      }, {
        field: key
        type: nominal
        title: "Category"
      }, {
        field: buckets.doc_count
        type: quantitative
        title: "Count"
      }]
    }

    layer: [{
      mark: area
    }, {
      mark: point

      selection: {
        pointhover: {
          type: single
          on: mouseover
          clear: mouseout
          empty: none
          fields: ["buckets.key", "key"]
          nearest: true
        }
      }

      encoding: {
        size: {
          condition: {
            selection: pointhover
            value: 100
          }
          value: 5
        }
        fill: {
          condition: {
            selection: pointhover
            value: white
          }
        }
      }
    }]
  }
  ```
</dropdown>


## Tutorial: Update Kibana filters from Vega

To build an area chart using an Elasticsearch search query, edit the **Vega** spec, then add click and drag handlers to update the Kibana filters.
In the **Vega** spec, enter the following, then click **Update**:
```yaml
{
  $schema: "https://vega.github.io/schema/vega/v5.json"
  data: [{
    name: source_0
  }]

  scales: [{
    name: x
    type: time
    range: width
  }, {
    name: y
    type: linear
    range: height
  }]

  axes: [{
    orient: bottom
    scale: x
  }, {
    orient: left
    scale: y
  }]

  marks: [
    {
      type: area
      from: {
        data: source_0
      }
      encode: {
        update: {
        }
      }
    }
  ]
}
```

Add the Elasticsearch search query with the `data` block, then click **Update**:
```yaml
  data: [
    {
      name: source_0
      url: {
        %context%: true
        %timefield%: order_date
        index: kibana_sample_data_ecommerce
        body: {
          aggs: {
            time_buckets: {
              date_histogram: {
                field: order_date
                fixed_interval: "3h"
                extended_bounds: {
                  min: {%timefilter%: "min"}
                  max: {%timefilter%: "max"}
                }
                min_doc_count: 0
              }
            }
          }
          size: 0
        }
      }
      format: { property: "aggregations.time_buckets.buckets" }
    }
  ]
```


#### Change the x- and y-axes

Display labels for the x- and y-axes.
In the **Vega** spec, add the `scales` block, then click **Update**:
```yaml
  scales: [{
    name: x
    type: time
    range: width
    domain: {
      data: source_0
      field: key
    }
  }, {
    name: y
    type: linear
    range: height
    domain: {
      data: source_0
      field: doc_count
    }
  }]
```

Add the `key` and `doc_count` fields as the X- and Y-axis values, then click **Update**:
```yaml
  marks: [
    {
      type: area
      from: {
        data: source_0
      }
      encode: {
        update: {
          x: {
            scale: x
            field: key
          }
          y: {
            scale: y
            value: 0
          }
          y2: {
            scale: y
            field: doc_count
          }
        }
      }
    }
  ]
```

![vega tutorial 3](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_tutorial_3.png)


#### Add a block to the `marks` section

Show the clickable points on the area chart to filter for a specific date.
In the **Vega** spec, add to the `marks` block, then click **Update**:
```yaml
  {
    name: point
    type: symbol
    style: ["point"]
    from: {
      data: source_0
    }
    encode: {
      update: {
        x: {
          scale: x
          field: key
        }
        y: {
          scale: y
          field: doc_count
        }
        size: {
          value: 100
        }
        fill: {
          value: black
        }
      }
    }
  }
```


#### Create a signal

To make the points clickable, create a **Vega** signal. You can access the clicked `datum` in the expression used to update.
In the **Vega** spec, add a `signals` block to specify that the cursor clicks add a time filter with the three hour interval, then click **Update**:
```yaml
  signals: [
    {
      name: point_click
      on: [{
        events: {
          source: scope
          type: click
          markname: point
        }
        update: '''kibanaSetTimeFilter(datum.key, datum.key + 3 * 60 * 60 * 1000)'''
      }]
    }
  ]
```

The event uses the `kibanaSetTimeFilter` custom function to generate a filter that applies to the entire dashboard on a click.
To make the area chart interactive, locate the `marks` block, then update the `point` and add `cursor: { value: "pointer" }` to `encoding`:
```yaml
  {
    name: point
    type: symbol
    style: ["point"]
    from: {
      data: source_0
    }
    encode: {
      update: {
        ...
        cursor: { value: "pointer" }
      }
    }
  }
```


#### Add a drag interaction

To allow users to filter based on a time range, add a drag interaction, which requires additional signals and a rectangle overlay.
![vega tutorial 4](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_tutorial_4.png)

In the **Vega** spec, add a `signal` to track the X position of the cursor:
```yaml
    {
      name: currentX
      value: -1
      on: [{
        events: {
          type: mousemove
          source: view
        },
        update: "clamp(x(), 0, width)"
      }, {
        events: {
          type: mouseout
          source: view
        }
        update: "-1"
      }]
    }
```

To indicate the current cursor position, add a `mark` block:
```yaml
    {
      type: rule
      interactive: false
      encode: {
        update: {
          y: {value: 0}
          y2: {signal: "height"}
          stroke: {value: "gray"}
          strokeDash: {
            value: [2, 1]
          }
          x: {signal: "max(currentX,0)"}
          defined: {signal: "currentX > 0"}
        }
      }
    }
```

To track the selected time range, add a signal that updates until the user releases their cursor or presses Return:
```yaml
    {
      name: selected
      value: [0, 0]
      on: [{
        events: {
          type: mousedown
          source: view
        }
        update: "[clamp(x(), 0, width), clamp(x(), 0, width)]"
      }, {
        events: {
          type: mousemove
          source: window
          consume: true
          between: [{
            type: mousedown
            source: view
          }, {
            merge: [{
              type: mouseup
              source: window
            }, {
              type: keydown
              source: window
              filter: "event.key === 'Escape'"
            }]
          }]
        }
        update: "[selected[0], clamp(x(), 0, width)]"
      }, {
        events: {
          type: keydown
          source: window
          filter: "event.key === 'Escape'"
        }
        update: "[0, 0]"
      }]
    }
```

There is a signal that tracks the time range from the user.
To indicate the range visually, add a mark that only appears conditionally:
```yaml
    {
      type: rect
      name: selectedRect
      encode: {
        update: {
          height: {signal: "height"}
          fill: {value: "#333"}
          fillOpacity: {value: 0.2}
          x: {signal: "selected[0]"}
          x2: {signal: "selected[1]"}
          defined: {signal: "selected[0] !== selected[1]"}
        }
      }
    }
```

Add a signal that updates the Kibana time filter when the cursor is released while dragging:
```yaml
    {
      name: applyTimeFilter
      value: null
      on: [{
        events: {
          type: mouseup
          source: view
        }
        update: '''selected[0] !== selected[1] ? kibanaSetTimeFilter(
               invert('x',selected[0]),
               invert('x',selected[1])) : null'''
      }]
    }
```

<dropdown title="Expand final Vega spec">
  ```yaml
  {
    $schema: "https://vega.github.io/schema/vega/v5.json"
    data: [
      {
        name: source_0
        url: {
          %context%: true
          %timefield%: order_date
          index: kibana_sample_data_ecommerce
          body: {
            aggs: {
              time_buckets: {
                date_histogram: {
                  field: order_date
                  fixed_interval: "3h"
                  extended_bounds: {
                    min: {%timefilter%: "min"}
                    max: {%timefilter%: "max"}
                  }
                  min_doc_count: 0
                }
              }
            }
            size: 0
          }
        }
        format: { property: "aggregations.time_buckets.buckets" }
      }
    ]

    scales: [{
      name: x
      type: time
      range: width
      domain: {
        data: source_0
        field: key
      }
    }, {
      name: y
      type: linear
      range: height
      domain: {
        data: source_0
        field: doc_count
      }
    }]

    axes: [{
      orient: bottom
      scale: x
    }, {
      orient: left
      scale: y
    }]

    marks: [
      {
        type: area
        from: {
          data: source_0
        }
        encode: {
          update: {
            x: {
              scale: x
              field: key
            }
            y: {
              scale: y
              value: 0
            }
            y2: {
              scale: y
              field: doc_count
            }
          }
        }
      },
      {
        name: point
        type: symbol
        style: ["point"]
        from: {
          data: source_0
        }
        encode: {
          update: {
            x: {
              scale: x
              field: key
            }
            y: {
              scale: y
              field: doc_count
            }
            size: {
              value: 100
            }
            fill: {
              value: black
            }
            cursor: { value: "pointer" }
          }
        }
      },
      {
        type: rule
        interactive: false
        encode: {
          update: {
            y: {value: 0}
            y2: {signal: "height"}
            stroke: {value: "gray"}
            strokeDash: {
              value: [2, 1]
            }
            x: {signal: "max(currentX,0)"}
            defined: {signal: "currentX > 0"}
          }
        }
      },
      {
        type: rect
        name: selectedRect
        encode: {
          update: {
            height: {signal: "height"}
            fill: {value: "#333"}
            fillOpacity: {value: 0.2}
            x: {signal: "selected[0]"}
            x2: {signal: "selected[1]"}
            defined: {signal: "selected[0] !== selected[1]"}
          }
        }
      }
    ]

    signals: [
      {
        name: point_click
        on: [{
          events: {
            source: scope
            type: click
            markname: point
          }
          update: '''kibanaSetTimeFilter(datum.key, datum.key + 3 * 60 * 60 * 1000)'''
        }]
      }
      {
        name: currentX
        value: -1
        on: [{
          events: {
            type: mousemove
            source: view
          },
          update: "clamp(x(), 0, width)"
        }, {
          events: {
            type: mouseout
            source: view
          }
          update: "-1"
        }]
      }
      {
        name: selected
        value: [0, 0]
        on: [{
          events: {
            type: mousedown
            source: view
          }
          update: "[clamp(x(), 0, width), clamp(x(), 0, width)]"
        }, {
          events: {
            type: mousemove
            source: window
            consume: true
            between: [{
              type: mousedown
              source: view
            }, {
              merge: [{
                type: mouseup
                source: window
              }, {
                type: keydown
                source: window
                filter: "event.key === 'Escape'"
              }]
            }]
          }
          update: "[selected[0], clamp(x(), 0, width)]"
        }, {
          events: {
            type: keydown
            source: window
            filter: "event.key === 'Escape'"
          }
          update: "[0, 0]"
        }]
      }
      {
        name: applyTimeFilter
        value: null
        on: [{
          events: {
            type: mouseup
            source: view
          }
          update: '''selected[0] !== selected[1] ? kibanaSetTimeFilter(
                 invert('x',selected[0]),
                 invert('x',selected[1])) : null'''
        }]
      }
    ]
  }
  ```
</dropdown>


## Vega reference

Learn more about Kibana extension, additional **Vega** resources, and examples.

#### Reference for Kibana extensions

Kibana has extended Vega and Vega-Lite with extensions that support:
- Automatic sizing
- Default theme to match Kibana
- Writing Elasticsearch queries using the time range and filters from dashboards
- `stack: preview` `serverless: preview` Using the Elastic Map Service in Vega maps
- Additional tooltip styling
- Advanced setting to enable URL loading from any domain
- Debugging support using the Kibana inspector
- (Vega only) Expression functions which can update the time range and dashboard filters


##### Automatic sizing

Most users will want their Vega visualizations to take the full available space, so unlike Vega examples, `width` and `height` are not required parameters in Kibana because your spec will be merged with the default Kibana settings in most cases:
```
autosize: {
  type: fit
  contains: padding
}
width: container
height: container
```

These default settings are **not** applied if:
- [Your spec uses `type=map`](#vega-with-a-map)
- Your spec is Vega-Lite and contains a facet, row, column, repeat, or concat operator. In these cases, providing `width` and `height` will affect the child size.

To set the width or height manually, set `autosize: none` and provide the exact pixel sizes, including padding for the title, legend and axes.
```
autosize: none
width: 600
height: 200
padding: {
  top: 20
  bottom: 20
  left: 55
  right: 150
}
```

To learn more, read about [Vega autosize](https://vega.github.io/vega/docs/specification/#autosize) and [Vega-Lite autosize](https://vega.github.io/vega-lite/docs/size.html).
<note>
  Autosize in Vega-Lite has [several limitations](https://vega.github.io/vega-lite/docs/size.html#limitations) which can affect the height and width of your visualization, but these limitations do not exist in Vega.
</note>


##### Default theme to match Kibana

Kibana registers a default [Vega color scheme](https://vega.github.io/vega/docs/schemes/) 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.

##### Writing Elasticsearch queries in Vega

Kibana extends the Vega [data](https://vega.github.io/vega/docs/data/) elements with support for direct Elasticsearch queries specified as `url`.
Kibana 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 Elasticsearch query in Vega, set the `url` to an object. Kibana parses the object looking for special tokens that allow your query to integrate with Kibana.
Tokens include the following:
- `%context%: true`: Set at the top level, and replaces the `query` section with filters from dashboard
- `%timefield%: <name>`: Set at the top level, integrates the query with the dashboard time filter
- `{%timefilter%: true}`: Replaced by an Elasticsearch 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 Kibana 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

<note>
  Vega supports the `interval` parameter, which is unsupported Elasticsearch 8.0.0 and later. To use intervals, use `fixed_interval` or `calendar_interval` instead.
</note>

For example, the following query counts the number of documents in a specific index:
```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 
          // 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
  }
}
```

The full result includes the following structure:
```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, 
          "doc_count": 330
        }, ...
```

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:
```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.
</note>

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).

#### Access Elastic Map Service files

```
stack: preview
serverless: preview
```

Access the Elastic Map Service files using the same mechanism:
```yaml
url: {
  // "type" defaults to "elasticsearch" otherwise
  %type%: emsfile
  // Name of the file, exactly as in https://maps.elastic.co
  name: World Countries
}
// The result is either a topojson file or a geojson file.
// Refer to the Default format for the file at https://maps.elastic.co
// Get its features to use this data source with the "shape" marks
// https://vega.github.io/vega/docs/marks/shape/
// For a topojson file use
format: {type: "topojson", feature: "data"}

// For a geojson file use
format: {property: "features"}
```


### Vega with a Map

```
stack: preview
serverless: preview
```

To enable **Maps**, the graph must specify `type=map` in the host configuration:
```yaml
{
  "config": {
    "kibana": {
      "type": "map",

      // Initial map position
      "latitude": 40.7,  
      "longitude": -74,  
      "zoom": 7,         

      // Defaults to 'true', disables the base map layer.
      "mapStyle": false,

      // When 'mapStyle' is 'undefined' or 'true', sets the EMS-layer for the map.
      // May either be: "road_map", "road_map_desaturated", "dark_map", "road_map_desaturated_v9", "dark_map_v9".
      // If 'emsTileServiceId' is 'undefined', it falls back to the auto-switch-dark-light behavior.
      "emsTileServiceId": "road_map",

      // 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,
    }
  },
  /* 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.
`stack: preview` `serverless: preview` You can use the **Vega** [data](https://vega.github.io/vega/docs/data/) element to access [Elastic Maps Service (EMS)](https://www.elastic.co/elastic-maps-service) vector shapes of administrative boundaries in your Vega map by setting `url.data` to `emsFile`:
```yaml
  "data": [
    {
      "name": "countries",
      "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 topojson file, get its features to use
      // this data source with the "shape" marks
      // https://vega.github.io/vega/docs/marks/shape/
      "format": {"type": "topojson", "feature": "data"},
    }
  ],
  "marks": [
    {
      "type": "shape",
      "from": {"data": "countries"},
      "transform": [{"type": "geoshape", "projection": "projection"}]
    }
  ]
```


##### Additional tooltip styling

Kibana has installed the [Vega tooltip plugin](https://vega.github.io/vega-lite/docs/tooltip.html), so tooltips can be defined in the ways documented there. Beyond that, Kibana also supports a configuration option for changing the tooltip position and padding:
```js
{
  config: {
    kibana: {
      tooltips: {
        position: 'top',
        padding: 15,
        textTruncate: true,
      }
    }
  }
}
```


##### Enable URL loading from any domain

**Vega** can load data from any URL. To enable, set `vis_type_vega.enableExternalUrls: true` in [`kibana.yml`](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/deploy-manage/stack-settings), then restart Kibana.
The files that the external URLs load must allow [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS). The remote URL must include `Access-Control-Allow-Origin`, which allows requests from the Kibana URL.
You can make the current time range part of the external as a millisecond timestamp by using the placeholders `%timefilter_min%` and `%timefilter_max%`, e.g. `http://example.com?min=%timefilter_min%`.

##### Vega Inspector

Use the contextual **Inspect** tool to gain insights into different elements.

##### Inspect Elasticsearch requests

**Vega** uses the [Elasticsearch search API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search) to get documents and aggregation results from Elasticsearch. 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.
![vega tutorial inspect requests](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-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 [runtime scope](https://vega.github.io/vega/docs/api/debugging/#scope).
![vega tutorial inspect data sets](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_tutorial_inspect_data_sets.png)

To debug more complex specs, copy the Vega spec from the **Spec** tab and use the [online Vega Editor](https://vega.github.io/editor/) to debug it.

##### Asking for help with a Vega spec

Because of the dynamic nature of the data in Elasticsearch, 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 **Spec**.
![vega tutorial getting help](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4438/explore-analyze/images/kibana-vega_tutorial_getting_help.png)

To copy the response, click **Copy to clipboard**. Paste the copied data to [gist.github.com](https://gist.github.com/), possibly with a .json extension. Use the [raw] button, and share that when asking for help.

#### (Vega only) Expression functions which can update the time range and dashboard filters

Kibana has extended the Vega expression language with these functions. These functions will trigger new data to be fetched, which by default will reset Vega signals. To keep signal values set `restoreSignalValuesOnRefresh: true` in the Vega config.
```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
  * @param {string} Custom label of the filter shown in the filter bar
  */
kibanaAddFilter(query, index, alias)

/**
  * @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)
```


#### Additional configuration options

```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
      // Defaults to 'false', restores Vega signal values on refresh
      restoreSignalValuesOnRefresh: false
    }
  }
}
```


### Resources and examples

To learn more about Vega and Vega-Lite, refer to the resources and examples.

#### Vega editor

The [Vega Editor](https://vega.github.io/editor/) includes examples for Vega & Vega-Lite, but does not support any Kibana-specific features like Elasticsearch requests and interactive base maps.

#### Vega-Lite resources

- [Tutorials](https://vega.github.io/vega-lite/tutorials/getting_started.html)
- [Docs](https://vega.github.io/vega-lite/docs/)
- [Examples](https://vega.github.io/vega-lite/examples/)


#### Vega resources

- [Tutorials](https://vega.github.io/vega/tutorials/)
- [Docs](https://vega.github.io/vega/docs/)
- [Examples](https://vega.github.io/vega/examples/)

<tip>
  When you use the examples in Kibana, 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"`.
</tip>