> ## Documentation Index
> Fetch the complete documentation index at: https://docs.narrative.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Configuring dataset statistics
> Choose which column-level metrics to compute, set refresh schedules, and add per-field overrides from the dataset details page
Dataset statistics configuration lets you control which column-level metrics are computed for a dataset, how often they refresh, and whether specific fields get different treatment than the defaults. You configure all of this from a side panel on the dataset details page.
This guide covers the UI-based configuration. You can also manage statistics configuration programmatically through the [API](/reference/architecture/dataset-statistics#api-endpoints) or the [TypeScript SDK](/guides/sdk/managing-datasets#getting-dataset-statistics).
## Prerequisites
* A dataset you own or have write access to
* The dataset must be on an active [data plane](/concepts/primitives/data-planes)
## Opening the configuration panel
Go to **My Data > Datasets** and select the dataset you want to configure.
Click the **Actions** button on the dataset details page to open the actions dropdown.
Under the **Stats** group, click **Configure statistics**. A side panel opens with the configuration form.
If a configuration already exists, the panel loads it in edit mode. Otherwise, you see the create form with sensible defaults pre-selected.
## Setting default statistics
The **Defaults** section controls which statistics are computed for all eligible fields across the dataset. Select or deselect individual statistics, or use the category-level and global toggles to select groups at once.
Available statistics are organized into five categories:
| Category | Statistics |
| ---------------- | ---------------------------------------------- |
| **Counts** | Value count, Null value count, NaN value count |
| **Bounds** | Lower bound, Upper bound |
| **Distinctness** | Approx count distinct, Count distinct |
| **Distribution** | Histogram, Mean, Standard deviation |
| **Quality** | Completeness |
**Approx count distinct** and **Count distinct** are mutually exclusive — you can enable one or the other, but not both. Approximate counting uses HyperLogLog and is significantly faster on large datasets.
Not all statistics apply to all data types. For example, Mean and Standard deviation only apply to numeric columns (`double` and `long`). The UI automatically filters out incompatible statistics when you configure per-field overrides. See the [type compatibility matrix](/reference/architecture/dataset-statistics#type-compatibility-matrix) for the full mapping.
### Histogram options
When you enable **Histogram**, additional options appear:
| Option | Description | Default |
| ------------ | ------------------------------------------------------------------- | ------- |
| **Max bins** | Maximum number of histogram buckets (2–100,000) | 50 |
| **Overflow** | How to handle values exceeding the bin count — `none` or `truncate` | none |
## Setting a refresh schedule
The **Refresh Trigger** section controls when statistics are recomputed. Choose one of three options:
| Trigger | Behavior |
| ------------- | ------------------------------------------------------------------------------------------ |
| **Manual** | Statistics are only recomputed when you manually trigger an update from the actions menu |
| **On update** | Statistics are automatically recomputed whenever new data is ingested into the dataset |
| **Cron** | Statistics are recomputed on a UTC cron schedule (e.g., `0 0 * * *` for daily at midnight) |
For most datasets, a daily cron schedule (`0 0 * * *`) provides a good balance between freshness and compute cost. Use **On update** only for datasets where you need statistics to reflect every data change immediately.
## Adding field overrides
By default, all eligible fields inherit the statistics you set in the Defaults section. You can override this at two levels:
### Namespace scope overrides
Expand the **Dataset Field Overrides** or **Rosetta Stone Overrides** section and enable the scope override toggle. This lets you set different default statistics for all fields within that namespace, overriding the global defaults.
### Per-field overrides
Within each namespace section, click **Add Field Override** to configure statistics for a specific column or attribute. Each override row lets you:
1. Select a field from the dropdown
2. Choose which statistics to compute for that field
3. Optionally configure histogram options specific to that field
For non-primitive fields (objects and arrays), only **Null value count** is available as a self-statistic. Statistics on nested child fields within objects and arrays are not yet configurable from the UI.
The inheritance model means a per-field override completely replaces the stat set from the parent level. If you want a field to compute only `value_count` and `histogram`, select just those two — it won't also inherit the defaults. For details on how inheritance works, see the [inheritance model](/reference/architecture/dataset-statistics#inheritance-model) in the reference.
## Saving and deleting
* Click **Save** to create or update the configuration. A confirmation toast appears on success.
* In edit mode, click **Delete Configuration** to remove the configuration entirely. This stops automatic statistics computation for the dataset.
## Manually triggering a statistics update
Separately from the configuration, you can trigger an immediate one-off recomputation. From the dataset actions menu under the **Stats** group, click **Update statistics**. This runs the `datasets_calculate_column_stats` [job](/reference/architecture/job-types) regardless of the refresh trigger setting.
## Related content
Why statistics matter and where they appear in the platform
Full API reference, configuration schema, and validation errors
Manage datasets programmatically including reading statistics
Job types including statistics computation