# `HAMMING_SIMILARITY`

**Applies to:** ✅ Data 360 SQL ✅ Tableau Hyper API

Calculates the similarity between two quantized (binary) embeddings using the normalized Hamming distance.

## Syntax

```sql
hamming_similarity(<bytea1>, <bytea2>)
```

## Arguments

### Required

- `<bytea1>`: The first binary-quantized vector.
- `<bytea2>`: The second binary-quantized vector.

## Returns

Returns a double-precision value between 0 and 1, where 1 indicates identical binary vectors.

## Considerations

- Both inputs must be bytea values of the same length.
- This function provides fast, approximate similarity. Use exact methods when precision is critical.

## Examples

### Calculate Hamming Similarity

Compare two binary-quantized vectors.

```sql
SELECT hamming_similarity('\x0a'::bytea, '\x03'::bytea) AS result;
```

Returns `0.75`, indicating 3 out of 4 bits match between the two vectors.

## Related Documentation

- [Array Data Type](/docs/data/data-cloud-query-guide/references/dc-sql-reference/array.md)