> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Distribution analysis overview

> Functions for analyzing data distribution with histograms and approximate row counts

export const HYPERTABLE = 'hypertable';

Distribution analysis functions help you understand how data is distributed across your datasets and perform fast
approximate row counting on large tables.

## Samples

### Histogram distribution

Create a histogram showing the distribution of battery levels across devices:

```sql theme={"dark"}
SELECT device_id, histogram(battery_level, 20, 60, 5)
FROM readings
GROUP BY device_id
LIMIT 10;
```

The histogram partitions values into buckets between 20 and 60, with 5 equal-width buckets. The result includes an
underflow bucket (values \< 20) and an overflow bucket (values >= 60).

### Approximate row count

Get a fast approximate count of rows in a {HYPERTABLE} without a full table scan:

```sql theme={"dark"}
ANALYZE conditions;

SELECT * FROM approximate_row_count('conditions');
```

This uses database statistics to provide a quick estimate, which is particularly useful for very large tables where
exact counts would be expensive.

## Available functions

### Distribution analysis

* [`histogram()`][histogram]: partition a dataset into buckets and get the number of counts in each bucket

### Approximate aggregation

* [`approximate_row_count()`][approximate_row_count]: estimate the number of rows in a table using catalog statistics

[approximate_row_count]: /api-reference/timescaledb/hyperfunctions/distribution-analysis/approximate_row_count

[histogram]: /api-reference/timescaledb/hyperfunctions/distribution-analysis/histogram