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# Configure time interval for time-axis widgets

## Balance detail and performance in time-based visualizations[​](#balance-detail-and-performance-in-time-based-visualizations "Direct link to Balance detail and performance in time-based visualizations")

Custom Dashboards visualize data from logs, metrics, spans, and DataPrime queries. To render time-based data efficiently, time-axis widgets group results into [time buckets](#optimize-chart-granularity-and-aggregation) (time intervals).

The time interval defines the size of those buckets and how many data points the chart renders.

Smaller intervals create more buckets and show more detail.

Larger intervals create fewer buckets and show fewer data points.

Time interval settings also affect how queries are executed and how [interval variables](#use-interval-variables-in-queries) are calculated.

## Optimize chart granularity and aggregation[​](#optimize-chart-granularity-and-aggregation "Direct link to Optimize chart granularity and aggregation")

Time-axis widgets group data into fixed interval time buckets, such as 10 seconds, 1 minute, or 1 hour. Each bucket aggregates the data points within that window.

The size of the bucket determines the chart’s granularity:

* Smaller interval = More detail, more data points, higher precision
* Larger interval = Fewer data points, reduced visual density

Granularity depends on:

* The selected time range
* Max data points (system or user-defined)
* [Min interval](#set-a-safe-lower-bound-with-min-interval) (system or user-defined)
* The minimum interval supported by the data source

For example, viewing 1 hour of data may allow 1-minute buckets, while viewing 30 days requires larger buckets to avoid rendering thousands of points.

Understanding this relationship helps you choose whether to let the system determine the interval [automatically](#choose-auto-mode-for-adaptive-resolution) or set [it explicitly](#set-interval-with-manual-mode-for-controlled-granularity).

## What you need[​](#what-you-need "Direct link to What you need")

* Permission to edit dashboards
* A time-axis widget, such as line chart, area chart, time-based bar chart, or heat map

## Configure the time interval[​](#configure-the-time-interval "Direct link to Configure the time interval")

Configure the interval settings in the Time Bucket panel in the widget settings.

[![image](data:image/webp;base64,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)](data:image/webp;base64,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)

To configure the interval for a time-axis widget:

1. Open the widget in edit mode.
2. In **Time Bucket**, choose **Auto** or **Manual**.
3. Adjust **Max data points** or **Min interval** if needed.
4. Review [**Effective interval**](#confirm-the-applied-interval-with-effective-interval) to confirm the effective value used in the query.

## Choose Auto mode for adaptive resolution[​](#choose-auto-mode-for-adaptive-resolution "Direct link to Choose Auto mode for adaptive resolution")

In **Auto** mode, the system determines the interval automatically based on the selected time range and available chart width.

[![auto-mode](data:image/webp;base64,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)](data:image/webp;base64,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)

Use **Auto** when you want the system to select an appropriate interval automatically.

Auto mode calculates the interval based on the selected time range and available widget width.

**Max data** **points** reflects how many points the widget can render at the current size. Because of this, the effective interval can change when the time range or widget width changes.

As you change the time range or resize the widget, the interval adjusts automatically to maintain a readable and performant chart.

Use **Auto** mode when:

* Building general-purpose dashboards
* Sharing dashboards with different screen sizes
* You do not need strict control over bucket size

## Set interval with Manual mode for controlled granularity[​](#set-interval-with-manual-mode-for-controlled-granularity "Direct link to Set interval with Manual mode for controlled granularity")

In **Manual mode**, you select a specific interval value from the available options.

[![manual-mode](data:image/webp;base64,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)](data:image/webp;base64,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)

Use [**Manual**](#refine-chart-density-with-max-data-points) when you want to request a specific interval, such as 10 seconds, 1 minute, or 1 hour.

**Manual** mode allows you to request a specific interval from supported values.

**Manual** mode is **not affected by widget width**, and it does **not bypass Coralogix system limits**.

2 limits apply:

* **Max data points** prevents rendering too many points.
* **Min interval** prevents grouping data below supported limits.

If the selected time range would exceed the **Max data points** or fall below the **Min interval**, the system **increases the interval automatically** (it never decreases it).

When this happens, **Effective interval** shows the effective value used in the query.

Use Manual mode when:

* Investigating short spikes
* Aligning interval with external systems
* Comparing dashboards using consistent bucket sizes

## Refine chart density with Max data points[​](#refine-chart-density-with-max-data-points "Direct link to Refine chart density with Max data points")

Expand **Limits (advanced)** to configure Max data points and Min interval.

[![limits-advanced](data:image/webp;base64,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)](data:image/webp;base64,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)

**Max data points** defines the maximum number of points the widget will fetch and render.

In **Auto** mode, the default value adapts to widget width. Wider widgets allow more points.

Reducing Max data points:

* Increases interval size
* Reduces visual density
* Improves performance for large time ranges

Increasing Max data points allows finer granularity but may impact performance.

## Set a safe lower bound with Min interval[​](#set-a-safe-lower-bound-with-min-interval "Direct link to Set a safe lower bound with Min interval")

**Min interval** defines the smallest allowed bucket size.

If the **effective interval** would be smaller than the **Min interval**, the system uses the **Min interval** instead.

**Min interval** protects against:

* Excessive backend load
* Unsupported aggregation sizes
* Misleading or duplicated data points

For metrics, very small intervals can produce unreliable results. Min interval ensures queries align with data availability and system capabilities.

### Minimum interval for Prometheus metrics[​](#minimum-interval-for-prometheus-metrics "Direct link to Minimum interval for Prometheus metrics")

For Prometheus-based metrics queries, the minimum supported interval is 15 seconds.

Using a smaller interval can produce duplicated series, so Coralogix enforces 15 seconds as the practical minimum.

## Confirm the applied interval with Effective interval[​](#confirm-the-applied-interval-with-effective-interval "Direct link to Confirm the applied interval with Effective interval")

**Effective interval** shows the interval actually used by the query after applying system limits.

[![effective-interval](data:image/webp;base64,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)

Effective interval shows the interval actually used to query and render the widget after:

* Applying Max data points
* Enforcing Min interval
* Rounding to supported interval values

Effective interval updates when you:

* Change the time range
* Resize the widget
* Modify Max data points or Min interval

If Manual mode requests a value that cannot be applied, Effective interval reflects the adjusted value.

## How interval calculation works[​](#how-interval-calculation-works "Direct link to How interval calculation works")

In **Auto** mode, the interval is calculated from the selected time range and **Max data points**, and it is affected by widget width. The effective interval is constrained by the **Min interval**.

In **Manual** mode, the selected interval is used unless it would exceed **Max data points** or fall below **Min interval**. In such cases, the system increases the interval to comply with limits.

## Use interval variables in queries[​](#use-interval-variables-in-queries "Direct link to Use interval variables in queries")

Time interval settings directly affect global interval variables used in queries.

### `${__interval}`[​](#__interval "Direct link to __interval")

Returns the effective interval used by the widget. This value reflects **Auto** or **Manual** mode after constraints are applied.

### `${__rate_interval}`[​](#__rate_interval "Direct link to __rate_interval")

Calculated based on `${__interval}` and scrape interval rules. Used primarily in PromQL rate calculations.

### `$p.timeRange.suggestedInterval`[​](#ptimerangesuggestedinterval "Direct link to ptimerangesuggestedinterval")

Returns the effective interval used by DataPrime queries.

This variable uses Auto interval behavior, targeting:

* Up to 1000 data points
* A minimum interval of 15 seconds
* Interval adjustment based on widget width

For more information, see:

* [Use variables in queries](https://docs-docusaurus.kinsta.page/user-guides/custom-dashboards/tutorials/create-and-manage-variables/.md#use-variables-in-queries)
* [DataPrime query reference](https://docs-docusaurus.kinsta.page/dataprime/language-reference/.md)
* [PromQL support in custom dashboards](https://docs-docusaurus.kinsta.page/user-guides/custom-dashboards/tutorials/create-and-manage-variables/.md#promql-predefined-variables)

To learn more about global dashboard variables, see the Global variables section in [Create and Manage Variables](https://docs-docusaurus.kinsta.page/user-guides/custom-dashboards/tutorials/create-and-manage-variables/.md#global-variables).

### Dynamic widget: groupby timestamp uses Auto interval[​](#dynamic-widget-groupby-timestamp-uses-auto-interval "Direct link to Dynamic widget: groupby timestamp uses Auto interval")

When you use the **groupby timestamp** Lucene function in a Dynamic widget, Coralogix uses Auto interval behavior under the hood.

This includes:

* Maximum 1000 data points
* A minimum interval of 15 seconds
* Interval calculation based on widget width

## Use cases[​](#use-cases "Direct link to Use cases")

### Investigate a short spike[​](#investigate-a-short-spike "Direct link to Investigate a short spike")

Select **Manual** mode and choose a smaller interval. If you expand the time range, verify **Effective interval** to confirm the effective value.

### Analyze long-term trends[​](#analyze-long-term-trends "Direct link to Analyze long-term trends")

Increase interval size manually or use **Auto** mode. Larger intervals smooth short-term noise and improve clarity.

### Troubleshooting[​](#troubleshooting "Direct link to Troubleshooting")

If the interval appears larger than expected, check:

* Time range
* [Max data points](#refine-chart-density-with-max-data-points)
* [Min interval](#set-a-safe-lower-bound-with-min-interval)

Effective interval always shows the effective value.

## Known behaviors and limits[​](#known-behaviors-and-limits "Direct link to Known behaviors and limits")

### Frequent Search queries may reach limits[​](#frequent-search-queries-may-reach-limits "Direct link to Frequent Search queries may reach limits")

Time-axis widgets that render very large numbers of points may return an error for Frequent Search queries.

If this happens, [reduce the query size](#optimize-chart-granularity-and-aggregation) by:

* Shortening the time range
* Reducing series cardinality
* Increasing the interval

### Hover highlight may be delayed for large datasets[​](#hover-highlight-may-be-delayed-for-large-datasets "Direct link to Hover highlight may be delayed for large datasets")

For [large datasets](#refine-chart-density-with-max-data-points), Coralogix may delay series hover highlighting to reduce UI load and prevent performance issues.

### Dynamic widget series rendering limit[​](#dynamic-widget-series-rendering-limit "Direct link to Dynamic widget series rendering limit")

Dynamic widgets currently render **up to 2000 series**.

If the query returns more than 2000 series, the widget may display **partial results** (missing lines).

## Limitations[​](#limitations "Direct link to Limitations")

* Interval values may be adjusted to supported values.
* Manual mode does not override Coralogix’s guardrails.
* Very long time ranges result in larger intervals.
* Non–time-axis widgets use fixed defaults for Max data points.

## Next steps[​](#next-steps "Direct link to Next steps")

Learn how to [set auto refresh for Custom Dashboards](https://docs-docusaurus.kinsta.page/user-guides/custom-dashboards/tutorials/auto-refresh/.md).