Histogram¶

A histogram, also known as a mass distribution chart, is used to represent the distribution of data. It is a common statistical chart, generally using the horizontal axis to represent data intervals and the vertical axis to represent the distribution. The chart shape is similar to a bar chart, where the higher the bar, the greater the number of data points falling within that interval.

Chart Query¶

By default, simple queries are supported. After the filter conditions are configured (without configuring the by field at this time), the system performs global total statistics, aggregates the full data without grouping, and calculates the total distribution of the target Metrics. At this time:

• Horizontal axis: The interval division of the selected "attribute value/Metrics value".

• Vertical axis: The total number of records that meet the interval conditions within each interval.

For example: T::RE(.*):(duration)

Represents the distribution of the number of spans in different duration intervals.

For more detailed descriptions of chart query conditions, refer to Chart Query.

When the query data source is "Metrics", and the by field is added to the filter conditions, the system will group the data by the by field (e.g., by host), then calculate the distribution of the target Metrics for each group, and finally count the number of groups falling into the intervals. At this time:

• Horizontal axis: The aggregated values of the by field.

• Vertical axis: The number of groups of data falling into the corresponding intervals (i.e., the number of groups that meet the interval conditions).

For example: M::cpu:(avg(usage_user))[:::avg] BY host

Represents the distribution of the number of hosts in different intervals of cpu usage rate, grouped by host.

Chart Configuration¶

For more details, refer to Chart Configuration.

Y Axis¶

You can switch between Linear and Log scaling modes and define the corresponding maximum and minimum values.

X Axis¶

1. Customize the maximum and minimum values of the X axis to determine the data display boundaries.

2. Set the number of buckets to change the granularity of data grouping.