---
title: "Scalar functions"
sidebarTitle: "Scalar Functions"
description: "The per-row function set: math, string, regex, date/time, array, struct, and what Lance adds."
icon: calculator
keywords: ["scalar functions", "string functions", "math functions", "regex", "array functions", "json"]
---
Enterprise [SQL runs on a deployment.](/sql)
Functions that take a value and return a value, one row at a time. The set is
based on DataFusion's registration plus Lance and deployment-specific
functions. The names below are selected examples, not an exhaustive catalog
for every deployment. Availability depends on the deployed version and build.
For per-group functions see [Aggregation](/sql/dql/aggregation); for per-window,
[Window functions](/sql/dql/window).
## Math {#math}
```text
abs ceil floor round trunc signum power sqrt cbrt exp
ln log log2 log10 factorial gcd lcm pi random nanvl
isnan iszero cot degrees radians to_degrees to_radians
sin cos tan asin acos atan atan2
sinh cosh tanh asinh acosh atanh
```
## String {#string}
```text
ascii bit_length btrim chr concat concat_ws contains ends_with
starts_with levenshtein lower upper ltrim rtrim octet_length
repeat replace split_part to_hex uuid
```
Unicode-aware variants:
```text
character_length find_in_set initcap left right lpad rpad
reverse strpos substr substr_index substring translate
```
For keyword matching on a text column, [`contains_tokens`](#contains-tokens) is
index-accelerated where `contains` and `LIKE` are not.
## Regular expressions {#regex}
```text
regexp_count regexp_instr regexp_like regexp_match regexp_replace
```
## Date and time {#datetime}
```text
current_date current_time date_bin date_part date_trunc
from_unixtime to_unixtime make_date make_time to_char
to_date to_time to_local_time
```
`date_bin` is the one worth knowing for analytics: it buckets timestamps into
fixed-width intervals, which is how you build a time series without a
self-join.
```sql
SELECT date_bin(INTERVAL '1 hour', created_at) AS hour, count(*)
FROM events GROUP BY hour ORDER BY hour
```
## Arrays and maps {#arrays}
```text
make_array array_has array_filter array_transform array_any_match
array_compact array_normalize array_slice arrays_zip cardinality
flatten range generate_series
```
```text
map map_keys map_values map_entries map_extract
```
Distance functions over arrays:
```text
cosine_distance distance exact_distance inner_product
```
Distance functions can evaluate arrays per row. Some deployment versions also
recognize `ORDER BY distance(vector, query_vector) LIMIT k` over a table scan
and plan it as vector search, with indexed or flat search as appropriate.
Where available, `exact_distance` evaluates distance without that rewrite.
Confirm the
functions supported by your deployment and inspect SQL
[`EXPLAIN`](/sql/dql/explain); the function name alone does not guarantee an
index-backed plan. See [SQL vector search](/basics/sql#no-vector-sql).
## Structs and fields {#structs}
```text
named_struct get_field coalesce nullif nvl nvl2 greatest least
union_extract union_tag
```
## Crypto and encoding {#crypto}
```text
digest md5 sha224 sha256 sha384 sha512 encode decode
```
## Type functions {#type}
```text
arrow_cast arrow_try_cast arrow_typeof arrow_field arrow_metadata
with_metadata version
```
`arrow_typeof` is the fastest way to answer "what is this column, really" when a
result does not have the shape you expected:
```sql
SELECT arrow_typeof(vector) FROM documents LIMIT 1
-- FixedSizeList(Field { name: "item", data_type: Float32, .. }, 1536)
```
`arrow_cast` casts to an Arrow type by name, reaching types SQL has no spelling
for. See [Data types](/sql/data-types).
## What Lance adds {#lance}
These are registered by Lance on top of DataFusion's set.
### `contains_tokens(column, tokens)` {#contains-tokens}
```sql
SELECT * FROM my_docs WHERE contains_tokens(text, 'fox jumps dog')
```
True when the column contains **all** the tokens, split on whitespace and
punctuation. Equivalent to an `fts()` match query with fuzziness off, `AND`
semantics and the simple tokenizer.
**Index-accelerated.** When the column has a full-text index that suits the
query, the planner uses it instead of scanning — which is the reason to prefer
it over `LIKE` for keyword tests.
### JSON accessors {#json}
| Function | Returns |
| --- | --- |
| `json_get(col, key)` | the value at `key`, still JSON |
| `json_get_string(col, key)` | `Utf8` |
| `json_get_int(col, key)` | `Int64` |
| `json_get_float(col, key)` | `Float64` |
| `json_get_bool(col, key)` | `Boolean` |
| `json_extract(col, path)` | `Utf8`, by path |
| `json_extract_with_type(col, path, type)` | the requested type |
| `json_exists(col, path)` | `Boolean` |
| `json_array_contains(col, value)` | `Boolean` |
| `json_array_length(col)` | length |
`json_get` returns JSON, so accessors nest:
```sql
SELECT * FROM events
WHERE json_get_string(json_get(json_get(data, 'user'), 'profile'), 'name') = 'Alice'
```
**`json_get`, its typed variants, and `json_extract` are index-accelerated** by a
JSON scalar index on the column. The rest evaluate per row.
### Geospatial {#geo}
Lance defines `st_*` functions on GeoArrow, but they sit behind a build feature
**LanceDB does not ship**. Calling one returns an error naming the missing
feature rather than an unknown-function error, and the spatial index is gated
the same way.
## Reaching a function not listed here {#escape-hatch}
Nothing wraps or restricts the set — a query reaches any registered function by
writing it. When a name is not recognised, check it against
[DataFusion's scalar function reference](https://datafusion.apache.org/user-guide/sql/scalar_functions.html)
for version 54 before assuming it is unsupported; functions are added in most
DataFusion releases, and a function documented on `main` may not exist here yet.