Build pie charts with Kibana

Pie charts display parts of a whole as slices, where each slice represents a data category and its size is proportional to the quantity it represents. Pie charts are ideal for showing comparisons between categories, illustrating the dominance of one category over others, and displaying percentage or proportional data.

You can create pie charts in Kibana using Lens.

Pie charts work best when:

• You have a maximum of 6 slices. More slices make the chart difficult to read.
• Values are around 25%, 50%, or 75%. These proportions are easy to perceive accurately.
• One category is significantly larger than the others.
• You want to show part-to-whole relationships at a glance.

Consider using bar charts instead when:

• You need to compare the exact size of slices.
• You have more than 6 categories.
• You need to compare multiple data sets. Use part-to-whole bar charts with percentages instead.
• Your data includes negative values.

To build a pie chart:

Donut charts are pie charts with a hollow center. The empty space can provide a visual focal point and space for displaying related information.

1. Create a Pie chart visualization.
2. Configure your Slice by and Metric dimensions.
3. Select Style.
4. In Appearance, set Donut hole to Small, Medium, or Large depending on how much center space you want.

By default, pie charts use a single metric with Slice by to split that metric by values in a categorical field. The Multiple metrics option lets you take a different approach: each slice represents a distinct metric you define, rather than values from your data.

This is useful when:

• You want to compare metrics that apply different filters to the same aggregation
• You need to show custom-defined categories that don't exist as values in a field
• You want to compare different calculations or formulas side by side

Imagine your web logs data includes multiple numeric fields representing different resource types, such as response_bytes, processing_time_ms, and memory_used_kb. You want to visualize how each resource contributes to overall server load.

1. Create a Pie chart and remove any existing Slice by dimension.

2. Open Layer settings:

   • Select Layer settings.
   • Select , then select Layer settings.

3. Select Multiple metrics, then close the settings.

4. Add metrics for each resource type:

   Slice	Metric configuration
   Bandwidth	Sum(response_bytes)
   Processing time	Sum(processing_time_ms)
   Memory usage	Sum(memory_used_kb)

   For each metric, select the Metric dimension and configure the field. Customize the Name to display meaningful labels in the legend.

5. Optionally, assign custom colors to each metric by selecting the metric and choosing a Series color.

This example demonstrates the core value of multiple metrics: comparing different numeric fields that represent distinct concepts. Unlike Slice by, which splits a single metric by category values, multiple metrics lets you place fundamentally different measurements side by side.

When you have many categories with small values, you can group them into an "Other" category to simplify the visualization.

1. In the Slice by configuration, select the field.
2. Use Top values to limit the number of slices displayed.
3. Expand Advanced.
4. Enable Group other values as "Other" to combine remaining values into a single slice.

This example uses the Kibana Sample Data Logs data set. If you haven't installed it yet, refer to Sample data for instructions.

1. Create a Pie chart using the Kibana Sample Data Logs data view.
2. In the Slice by dimension, select host.keyword with Top values set to 3.
3. Expand Advanced and make sure that the Group other values as "Other" option is active.
4. Set the Metric to the Count function.

The resulting chart shows the 3 most common hosts, with all remaining hosts combined into a single "Other" slice. This keeps the chart readable while still accounting for the full data set.

Customize your pie chart to display exactly the information you need, formatted the way you want.

The Slice by dimension defines how your pie is divided into segments. You can add up to 3 levels of slicing to create hierarchical visualizations.

Data

The Slice by dimension supports the following functions:

• Top values: Create slices for the most common values in a field. Configure the number of values to display, ranking criteria, and sort direction.

  • Number of values: How many top values to display.
  • Rank by: Specifies the dimension the top values are ranked by. Available options:
    • Count of records: Rank by the number of documents containing each value. This is the default when a metric is defined.
    • Alphabetical: Rank by the term key alphabetically. This is the default when no metric is defined.
    • Rarity: Find terms that appear in very few documents, using a rare terms aggregation. You can configure the Max doc count to set the maximum number of documents a term can appear in to be considered rare (default: 1, max: 100). Only available for non-numeric fields and single-field terms.
    • Significance: Find statistically unusual terms compared to the overall data set, using a significant terms aggregation. Only available for keyword fields and single-field terms.
    • Custom: Define a custom metric aggregation to rank by (for example, rank by the sum of a numeric field rather than by count).
  • Rank direction: Ascending or descending order. Disabled when Rank by is set to Rarity or Significance.
  • Collapse by: Aggregate values into a single number using Sum, Average, Min, or Max.
  Advanced settings

  Several advanced options allow you to refine the behavior of the breakdown:

  • Include documents without the selected field: Off by default.
  • Group remaining values as "Other": On by default.
  • Enable accuracy mode: This option improves results for high-cardinality data, but increases the load on the Elasticsearch cluster.
  • Include values: Values from the breakdown dimension to always show a tile for.
  • Exclude values: Values from the breakdown dimension to always exclude from the displayed tiles.

• Date histogram: Group data into time-based buckets (useful for showing time-based composition). Configure the time interval and how to handle date formatting.

  • Include empty rows: This option is on by default. Turn it off to exclude empty rows from the data.

  • Bind to global time picker: Associate the selected field to the Lens or dashboard main time selector.

  • Minimum interval: Define the time interval for aggregating the data. For example, 30s, 20m, 24h, 2d, 1w, 1M

  • Drop partial intervals: Exclude incomplete intervals from the data. This option is off by default.

  • Collapse by: Aggregate values into a single number using Sum, Average, Min, or Max.

• Intervals: Create numeric ranges for continuous data. Useful for grouping numeric fields into buckets. You can define the interval granularity or specify custom ranges.

  • Include empty rows: Include intervals with no matching documents. On by default.
  • Collapse by: Aggregate values into a single number using Sum, Average, Min, or Max.
  How does interval granularity work?

  Interval granularity divides the field into evenly spaced intervals based on the minimum and maximum values for the field.

  The size of the interval is a "nice" value. When the granularity of the slider changes, the interval stays the same when the "nice" interval is the same. The minimum granularity is 1, and the maximum value is histogram:maxBars. To change the maximum granularity, go to Advanced settings.

  Intervals are incremented by 10, 5 or 2. For example, an interval can be 100 or 0.2.

• Filters: Define custom KQL filters to create specific slices. Each filter creates one slice in the chart.

  • Collapse by: Aggregate values into a single number using Sum, Average, Min, or Max.

Appearance

Configure slice-level options:

• Name: Customize the legend label.
• Color mapping: Select a color palette or assign specific colors to categories. Refer to Assign colors to terms for details.

The Metric dimension defines the size of each slice.

Data

The main value that appears prominently in your chart. When you drag a field onto the chart, Kibana suggests a function based on the field type. You can change it and use aggregation functions like Sum, Average, Count, Median, and more, or create custom calculations with formulas. Refer to Lens > Use formulas to perform math for examples, or to the Formula reference available from Lens.

Advanced settings

Depending on the data you defined, several options allow you to apply additional filtering to the data taken into account to compute the final value to show.

Based on the type of visualization you're creating, only some of the following options can be available:

• Normalize by unit: Normalize the metric values to show per unit of time.
• Filter by: Specify a query.
• Reduced time range: Reduce the time range specified on the dashboard's time filter by the specified duration.
• Time shift: Shift the time range by the specified duration. This is useful if the value should use a different time range than the one selected on the dashboard.
• Hide zero values: Don't show values equal to zero. This option is on by default.

Appearance

• Name: Customize the metric label displayed in tooltips and legends.
• Value format: Control how numeric values are displayed (number, percent, bytes, and more).
• Series color: When using multiple metrics without a Slice by dimension, assign a specific color to each metric.

When creating or editing a visualization, you can customize several appearance options from the Style or Legend menus.

Appearance

Donut hole

Transform your pie chart into a donut by adding a center hole:

• None: Standard pie chart with no hole (default).
• Small: Small center hole.
• Medium: Medium center hole.
• Large: Large center hole for emphasis or to display additional information.

Titles and text

Slice labels

Control how labels appear on the slices:

• Hide: Do not display labels on slices.
• Inside: Display labels inside the slices.
• Auto: Automatically position labels for optimal readability (default).

Slice values

Control what values appear on the slices (when labels are visible):

• Hide: Do not display values.
• Integer: Display the raw numeric value.
• Percentage: Display the percentage of the total.

When displaying percentages, you can also configure the Decimal places (0-10) for precision.

Configure elements of your pie chart's legend:

Visibility

Specify whether to automatically show the legend or hide it:

• Auto: Show the legend when there are multiple slices (default).
• Show: Always show the legend.
• Hide: Never show the legend.

Nested

When using multiple Slice by dimensions, enable this option to show the legend in a hierarchical format that reflects the slice hierarchy.

Statistics

Show the Value statistic in the legend to display the numeric value alongside each legend entry.

Label truncation

Choose whether to truncate long legend labels, and set a limit for how many lines to display.

Width

Set the width of the legend.

The following examples show various configuration options for building impactful pie charts.

Website traffic by source

Visualize the distribution of traffic sources to your website:

• Example based on: Kibana Sample Data Logs
• Configuration: Multiple metrics
• Metrics: 4 metrics based on formulas
  • count(kql='referer : *elastic*'), named "Elastic website"
  • count(kql='referer : *twitter*'), named "Twitter/X"
  • count(kql='referer : *facebook*'), named "Facebook"
  • count(kql='referer : *nytimes*'), named "NY Times"
• Style: Donut with medium hole
• Legend: Show

Revenue distribution by product category

Show how revenue is distributed across product categories:

• Example based on: Kibana Sample Data eCommerce
• Slice by: category.keyword (Top values, 6)
• Metric: Sum of taxful_total_price
• Style: Pie (no donut hole)

Error distribution by type

Display the proportion of different error types in your application:

• Example based on: Kibana Sample Data Logs
• Slice by: error.type using Filters:
  • "Client Error": response.keyword >= "400" AND response.keyword < "500"
  • "Server Error": response.keyword >= "500"
  • "Success": response.keyword >= "200" AND response.keyword < "400"
• Metric: Count of records
• Style: Donut with large hole
• Color mapping: Red for client errors, yellow for server errors, green for success