--- title: Scatter visualization description: The Scatter view uses a scatter plot to display time series data. url: https://docs.influxdata.com/influxdb/v2/visualize-data/visualization-types/scatter/ estimated_tokens: 1609 product: InfluxDB OSS v2 version: v2 publisher: InfluxData canonical: https://docs.influxdata.com/influxdb/v2/visualize-data/visualization-types/scatter/ date: '2025-04-02T15:54:32-06:00' lastmod: '2025-04-02T15:54:32-06:00' --- This page documents an earlier version of InfluxDB OSS.[InfluxDB 3 Core](/influxdb3/core/) is the latest stable version. #### API token hashing is enabled by default in InfluxDB OSS 2.9.0 Stronger token security: tokens are stored as hashes on disk, so a copy of the database file doesn’t expose usable tokens. Existing tokens are hashed on first startup and the original strings can’t be recovered afterward — **capture any plaintext tokens you still need before you upgrade**. For more information, see [Token hashing](/influxdb/v2/admin/tokens/#token-hashing). The **Scatter** view uses a scatter plot to display time series data. Scatter plot example Select the **Scatter** option from the visualization dropdown in the upper left. ## Scatter behavior The scatter visualization maps each data point to X and Y coordinates. X and Y axes are specified with the [X Column](#data) and [Y Column](#data) visualization options. Each unique series is differentiated using fill colors and symbols. Use the [Symbol Column](#data) and [Fill Column](#data) options to select columns used to differentiate points in the visualization. ## Scatter controls To view **Scatter** controls, click **Customize** next to the visualization dropdown. ###### Data * **Symbol Column**: Define a column containing values that should be differentiated with symbols. * **Fill Column**: Define a column containing values that should be differentiated with fill color. * **X Column**: Select a column to display on the x-axis. * **Y Column**: Select a column to display on the y-axis. * **Time Format**: Select the time format. Options include: * YYYY-MM-DD HH:mm:ss ZZ * YYYY-MM-DD hh:mm:ss a ZZ * DD/MM/YYYY HH:mm:ss.sss * DD/MM/YYYY hh:mm:ss.sss a * MM/DD/YYYY HH:mm:ss.sss * MM/DD/YYYY hh:mm:ss.sss a * YYYY/MM/DD HH:mm:ss * YYYY/MM/DD hh:mm:ss a * HH:mm * hh:mm a * HH:mm:ss * hh:mm:ss a * HH:mm:ss ZZ * hh:mm:ss a ZZ * HH:mm:ss.sss * hh:mm:ss.sss a * MMMM D, YYYY HH:mm:ss * MMMM D, YYYY hh:mm:ss a * dddd, MMMM D, YYYY HH:mm:ss * dddd, MMMM D, YYYY hh:mm:ss a ###### Options * **Color Scheme**: Select a color scheme to use for your scatter plot. ###### X Axis * **X Axis Label**: Label for the x-axis. * **Generate X-Axis Tick Marks**: Select the method to generate x-axis tick marks: * **Auto**: Select to automatically generate tick marks. * **Custom**: To customize the number of x-axis tick marks, select this option, and then enter the following: * **Total Tick Marks**: Enter the total number of ticks to display. * **Start Tick Marks At**: Enter the value to start ticks at. * **Tick Mark Interval**: Enter the interval in between each tick. * **X Axis Domain**: The x-axis value range. * **Auto**: Automatically determine the value range based on values in the data set. * **Custom**: Manually specify the minimum x-axis value, maximum x-axis value, or range by including both. * **Min**: Minimum x-axis value. * **Max**: Maximum x-axis value. ###### Y Axis * **Y Axis Label**: Label for the y-axis. * **Y Tick Prefix**: Prefix to be added to y-value. * **Y Tick Suffix**: Suffix to be added to y-value. * **Generate Y-Axis Tick Marks**: Select the method to generate y-axis tick marks: * **Auto**: Select to automatically generate tick marks. * **Custom**: To customize the number of y-axis tick marks, select this option, and then enter the following: * **Total Tick Marks**: Enter the total number of ticks to display. * **Start Tick Marks At**: Enter the value to start ticks at. * **Tick Mark Interval**: Enter the interval in between each tick. * **Y Axis Domain**: The y-axis value range. * **Auto**: Automatically determine the value range based on values in the data set. * **Custom**: Manually specify the minimum y-axis value, maximum y-axis value, or range by including both. * **Min**: Minimum y-axis value. * **Max**: Maximum y-axis value. ###### Hover Legend * **Orientation**: Select the orientation of the legend that appears upon hover: * **Horizontal**: Select to display the legend horizontally. * **Vertical**: Select to display the legend vertically. * **Opacity**: Adjust the legend opacity using the slider. * **Colorize Rows**: Select to display legend rows in colors. ## Scatter examples ### Cross-measurement correlation The following example explores possible correlation between CPU and Memory usage. It uses data collected with the Telegraf [Mem](/telegraf/v1/plugins//#mem)and [CPU](/telegraf/v1/plugins//#cpu) input plugins. ###### Query CPU and memory usage The following query creates a union of CPU and memory usage. It scales the CPU usage metric to better align with baseline memory usage. ```js cpu = from(bucket: "example-bucket") |> range(start: v.timeRangeStart, stop: v.timeRangeStop) |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system" and r.cpu == "cpu-total") // Scale CPU usage |> map(fn: (r) => ({r with _value: r._value + 60.0, _time: r._time})) mem = from(bucket: "example-bucket") |> range(start: v.timeRangeStart, stop: v.timeRangeStop) |> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent") union(tables: [cpu, mem]) ``` ###### Use a scatter plot to visualize correlation In the Scatter visualization controls, points are differentiated based on their group keys. Heatmap correlation example ## Important notes ### Differences between a scatter plot and a heatmap Scatter plots and [Heatmaps](/influxdb/v2/visualize-data/visualization-types/heatmap/)both visualize the distribution of data points on X and Y axes. However, in certain cases, scatterplots can “hide” points if they share the same X and Y coordinates. For example, the dashboard cells below visualize the same query results: Heatmap vs Scatter plot The heatmap indicates isolated high point density, which isn’t visible in the scatter plot. In the scatter plot visualization, points that share the same X and Y coordinates appear as a single point. #### Related * [Heatmap visualization](/influxdb/v2/visualize-data/visualization-types/heatmap/)