How to Write Scalar Functions

What's scalar functions

Scalar functions (sometimes referred to as User-Defined Functions / UDFs) return a single value as a return value for each row, not as a result set, and can be used in most places within a query or SET statement, except for the FROM clause.

One to One Mapping execution

┌─────┐                    ┌──────┐
│  a  │                    │   x  │
├─────┤                    ├──────┤
│  b  │                    │   y  │
├─────┤    ScalarFunction  ├──────┤
│  c  │                    │   z  │
├─────┼────────────────────►──────┤
│  d  │     Exec           │   u  │
├─────┤                    ├──────┤
│  e  │                    │   v  │
├─────┤                    ├──────┤
│  f  │                    │   w  │
└─────┘                    └──────┘

Knowledge before writing the eval function

Logical datatypes and physical datatypes.

Logical datatypes are the datatypes that we use in Databend, and physical datatypes are the datatypes that we use in the execution/compute engine. Such as Date32, it's a logical data type, but its physical is Int32, so its column is represented by Int32Column.

We can get logical datatype by data_type function of DataField , and the physical datatype by data_type function in ColumnRef. ColumnsWithField has data_type function which returns the logical datatype.

Arrow's memory layout

Databend's memory layout is based on the Arrow system, you can find Arrow's memory layout [here] (https://arrow.apache.org/docs/format/Columnar.html#format-columnar).

For example a primitive array of int32s:

[1, null, 2, 4, 8] Would look like this:

* Length: 5, Null count: 1
* Validity bitmap buffer:

  |Byte 0 (validity bitmap) | Bytes 1-63            |
  |-------------------------|-----------------------|
  | 00011101                | 0 (padding)           |

* Value Buffer:

  |Bytes 0-3   | Bytes 4-7   | Bytes 8-11  | Bytes 12-15 | Bytes 16-19 | Bytes 20-63 |
  |------------|-------------|-------------|-------------|-------------|-------------|
  | 1          | unspecified | 2           | 4           | 8           | unspecified |

In most cases, we can ignore null for simd operation, and add the null mask to the result after the operation. This is very common optimization and widely used in arrow's compute system.

Special column

Constant column
Sometimes column is constant in the block, such as: SELECT 3 from table, the column 3 is always 3, so we can use a constant column to represent it. This is useful to save the memory space during computation.
So databend's DataColumn is represented by:
```
#[derive(Clone)]
pub struct ConstColumn {
    length: usize,
    column: ColumnRef,
}
```
Nullable column
By default, columns are not nullable. If we want a nullable column, we can use this to represent it.
```
#[derive(Clone)]
pub struct NullableColumn {
    validity: Bitmap,
    column: ColumnRef,
}
```

Writing function guidelines

ScalarFunction trait introduction

All scalar functions implement Function trait, and we register them into a global static FunctionFactory, the factory is just an index map and the key is the name of the scalar function.

tip

Function name in Databend is case-insensitive.

pub trait Function: fmt::Display + Sync + Send + DynClone {
    ...
}

Let's take function sqrt as an example

Declar the function named SqrtFunction

#[derive(Clone)]
pub struct SqrtFunction {
    display_name: String,
}

Implement SqrtFunction to have a constructor and description.

impl SqrtFunction {
    pub fn try_create(display_name: &str, args: &[&DataTypeImpl]) -> Result<Box<dyn Function>> {
        assert_numeric(args[0])?;
        Ok(Box::new(SqrtFunction {
            display_name: display_name.to_string(),
        }))
    }

    pub fn desc() -> FunctionDescription {
        FunctionDescription::creator(Box::new(Self::try_create))
            .features(FunctionFeatures::default().deterministic().num_arguments(1))
    }
}

The try_create is useful to create this function, at last we can register it into the factory.

The desc is used to describe the function. Inside it, we can set the features, such as the number of arguments, the deterministic or not, etc.

Implement the simple function for sqrt

fn sqrt<S>(value: S, _ctx: &mut EvalContext) -> f64
where S: AsPrimitive<f64> {
    value.as_().sqrt()
}

It's really simple, S: AsPrimitive<f64> means we can accept a primitive value as the argument.

Implement Function trait

impl Function for SqrtFunction {
    fn name(&self) -> &str {
        &self.display_name
    }

    fn return_type(&self) -> DataTypeImpl {
        Float64Type::new_impl()
    }

    fn eval(&self, _func_ctx: FunctionContext, columns: &ColumnsWithField, _input_rows: usize) -> Result<ColumnRef> {
        let mut ctx = EvalContext::default();
        with_match_primitive_type_id!(columns[0].data_type().data_type_id(), |$S| {
             let col = scalar_unary_op::<$S, f64, _>(columns[0].column(), sqrt::<$S>, &mut ctx)?;
             Ok(col.arc())
        },{
            unreachable!()
        })
    }
}

By defaults, we enable passthrough_constant, that means: sqrt(constant_column) will be converted into Consntat(sqrt(column), rows) in FunctionAdaptor.

And we have enabled passthrough_nullable, that means: sqrt(nullable_column) will be converted into Nullable(sqrt(no_nullable_column), null_bitmaps) in FunctionAdaptor.

So inside the eval function, we really don't need to care about constant or nullable cases. It's pretty simple and efficient.

The macro with_match_primitive_type_id will match the primitive type id, and cast the column into corresponding type, so we allowed sqrt(i8), sqrt(i16) ... types.

The scalar_unary_op is a helper function to implement the scalar function for unary operator. This is very commonly used and there is scalar_binary_op too. See more in binary, unary

Register the function into the factory

factory.register("sqrt", SqrtFunction::desc());

Testing

To be a good engineer, don't forget to test your codes, please add unit tests and stateless tests after you finish the new scalar functions.

Unit tests
Stateless tests:

SELECT sqrt(-3), sqrt(3), sqrt(0), sqrt(3.0), sqrt( to_uint64(3) ), sqrt(null) ;
+----------+--------------------+---------+--------------------+--------------------+------------+
| sqrt(-3) | sqrt(3)            | sqrt(0) | sqrt(3)            | sqrt(to_uint64(3)) | sqrt(NULL) |
+----------+--------------------+---------+--------------------+--------------------+------------+
|      NaN | 1.7320508075688772 |       0 | 1.7320508075688772 | 1.7320508075688772 |       NULL |
+----------+--------------------+---------+--------------------+--------------------+------------+

SELECT sqrt('-3');
ERROR 1105 (HY000): Code: 1007, displayText = Expected a numeric type, but got String (while in SELECT before projection).

All is done!

Refer to other examples

As you see, adding a new scalar function in Databend is not as hard as you think. Before you start to add one, please refer to other scalar function examples, such as sign, expr, tan, atan

Summary

We welcome all community users to contribute more powerful functions to Databend. If you find any problems, feel free to open an issue in GitHub, we will use our best efforts to help you.

What's scalar functions​

Knowledge before writing the eval function​

Logical datatypes and physical datatypes.​

Arrow's memory layout​

Special column​

Writing function guidelines​

ScalarFunction trait introduction​

Register the function into the factory​

Testing​

Refer to other examples​

Summary​