
# Functions

## Plugin Implementation

The function framework is used to implement SQL functions. openLooKeng includes a number of built-in functions. In order to implement new functions, you can write a plugin that returns one more more functions from
`getFunctions()`:

``` java
public class ExampleFunctionsPlugin
        implements Plugin
{
    @Override
    public Set<Class<?>> getFunctions()
    {
        return ImmutableSet.<Class<?>>builder()
                .add(ExampleNullFunction.class)
                .add(IsNullFunction.class)
                .add(IsEqualOrNullFunction.class)
                .add(ExampleStringFunction.class)
                .add(ExampleAverageFunction.class)
                .build();
    }
}
```

Note that the `ImmutableSet` class is a utility class from Guava. The `getFunctions()` method contains all of the classes for the functions that we will implement below in this tutorial.

For a full example in the codebase, see either the `presto-ml` module for machine learning functions or the `presto-teradata-functions` module for Teradata-compatible functions, both in the root of the openLooKeng source.

## Scalar Function Implementation

The function framework uses annotations to indicate relevant information about functions, including name, description, return type and parameter types. Below is a sample function which implements `is_null`:

``` java
public class ExampleNullFunction
{
    @ScalarFunction("is_null")
    @Description("Returns TRUE if the argument is NULL")
    @SqlType(StandardTypes.BOOLEAN)
    public static boolean isNull(@SqlNullable @SqlType(StandardTypes.VARCHAR) Slice string)
    {
        return (string == null);
    }
}
```

The function `is_null` takes a single `VARCHAR` argument and returns a `BOOLEAN` indicating if the argument was `NULL`. Note that the argument to the function is of type `Slice`. `VARCHAR` uses `Slice`, which is
essentially a wrapper around `byte[]`, rather than `String` for its native container type.

-   `@SqlType`:

    The `@SqlType` annotation is used to declare the return type and the argument types. Note that the return type and arguments of the Java code must match the native container types of the corresponding annotations.
    
-   `@SqlNullable`:

    The `@SqlNullable` annotation indicates that the argument may be `NULL`. Without this annotation the framework assumes that all functions return `NULL` if any of their arguments are `NULL`. When
    working with a `Type` that has a primitive native container type, such as `BigintType`, use the object wrapper for the native container type when using `@SqlNullable`. The method must be annotated with `@SqlNullable` if it can return `NULL` when the arguments are non-null.

## Parametric Scalar Functions

Scalar functions that have type parameters have some additional complexity. To make our previous example work with any type we need the following:

``` java
@ScalarFunction(name = "is_null")
@Description("Returns TRUE if the argument is NULL")
public final class IsNullFunction
{
    @TypeParameter("T")
    @SqlType(StandardTypes.BOOLEAN)
    public static boolean isNullSlice(@SqlNullable @SqlType("T") Slice value)
    {
        return (value == null);
    }

    @TypeParameter("T")
    @SqlType(StandardTypes.BOOLEAN)
    public static boolean isNullLong(@SqlNullable @SqlType("T") Long value)
    {
        return (value == null);
    }

    @TypeParameter("T")
    @SqlType(StandardTypes.BOOLEAN)
    public static boolean isNullDouble(@SqlNullable @SqlType("T") Double value)
    {
        return (value == null);
    }

    // ...and so on for each native container type
}
```

-   `@TypeParameter`:

    The `@TypeParameter` annotation is used to declare a type parameter which can be used in the argument types `@SqlType` annotation, or return type of the function. It can also be used to annotate a
    parameter of type `Type`. At runtime, the engine will bind the concrete type to this parameter. `@OperatorDependency` may be used to declare that an additional function for operating on the given
    type parameter is needed. For example, the following function will only bind to types which have an equals function defined:

``` java
@ScalarFunction(name = "is_equal_or_null")
@Description("Returns TRUE if arguments are equal or both NULL")
public final class IsEqualOrNullFunction
{
    @TypeParameter("T")
    @SqlType(StandardTypes.BOOLEAN)
    public static boolean isEqualOrNullSlice(
            @OperatorDependency(operator = OperatorType.EQUAL, returnType = StandardTypes.BOOLEAN, argumentTypes = {"T", "T"}) MethodHandle equals,
            @SqlNullable @SqlType("T") Slice value1,
            @SqlNullable @SqlType("T") Slice value2)
    {
        if (value1 == null && value2 == null) {
            return true;
        }
        if (value1 == null || value2 == null) {
            return false;
        }
        return (boolean) equals.invokeExact(value1, value2);
    }

    // ...and so on for each native container type
}
```

## Another Scalar Function Example

The `lowercaser` function takes a single `VARCHAR` argument and returns a `VARCHAR`, which is the argument converted to lower case:

``` java
public class ExampleStringFunction
{
    @ScalarFunction("lowercaser")
    @Description("converts the string to alternating case")
    @SqlType(StandardTypes.VARCHAR)
    public static Slice lowercaser(@SqlType(StandardTypes.VARCHAR) Slice slice)
    {
        String argument = slice.toStringUtf8();
        return Slices.utf8Slice(argument.toLowerCase());
    }
}
```

Note that for most common string functions, including converting a string to lower case, the Slice library also provides implementations that work directly on the underlying `byte[]`, which have much better
performance. This function has no `@SqlNullable` annotations, meaning that if the argument is `NULL`, the result will automatically be `NULL` (the function will not be called).

## Aggregation Function Implementation

Aggregation functions use a similar framework to scalar functions, but are a bit more complex.

-   `AccumulatorState`:

    All aggregation functions accumulate input rows into a state object; this object must implement `AccumulatorState`. For simple aggregations, just extend `AccumulatorState` into a new interface
    with the getters and setters you want, and the framework will generate all the implementations and serializers for you. If you need a more complex state object, you will need to implement `AccumulatorStateFactory` and `AccumulatorStateSerializer` and provide these via the `AccumulatorStateMetadata` annotation.

The following code implements the aggregation function `avg_double` which computes the average of a `DOUBLE` column:

``` java
@AggregationFunction("avg_double")
public class AverageAggregation
{
    @InputFunction
    public static void input(LongAndDoubleState state, @SqlType(StandardTypes.DOUBLE) double value)
    {
        state.setLong(state.getLong() + 1);
        state.setDouble(state.getDouble() + value);
    }

    @CombineFunction
    public static void combine(LongAndDoubleState state, LongAndDoubleState otherState)
    {
        state.setLong(state.getLong() + otherState.getLong());
        state.setDouble(state.getDouble() + otherState.getDouble());
    }

    @OutputFunction(StandardTypes.DOUBLE)
    public static void output(LongAndDoubleState state, BlockBuilder out)
    {
        long count = state.getLong();
        if (count == 0) {
            out.appendNull();
        }
        else {
            double value = state.getDouble();
            DOUBLE.writeDouble(out, value / count);
        }
    }
}
```

The average has two parts: the sum of the `DOUBLE` in each row of the column and the `LONG` count of the number of rows seen. 
`LongAndDoubleState` is an interface which extends `AccumulatorState`:

``` java
public interface LongAndDoubleState
        extends AccumulatorState
{
    long getLong();

    void setLong(long value);

    double getDouble();

    void setDouble(double value);
}
```

As stated above, for simple `AccumulatorState` objects, it is sufficient to just to define the interface with the getters and setters, and the framework will generate the implementation for you.

An in-depth look at the various annotations relevant to writing an aggregation function follows:

-   `@InputFunction`:

    The `@InputFunction` annotation declares the function which accepts input rows and stores them in the `AccumulatorState`. Similar to scalar functions you must annotate the arguments with `@SqlType`.
    Note that, unlike in the above scalar example where `Slice` is used to hold `VARCHAR`, the primitive `double` type is used for the argument to input. In this example, the input function simply keeps
    track of the running count of rows (via `setLong()`) and the running sum (via `setDouble()`).
    
-   `@CombineFunction`:

    The `@CombineFunction` annotation declares the function used to ombine two state objects. This function is used to merge all the partial aggregation states. It takes two state objects, and merges
    the results into the first one (in the above example, just by adding them together).
    
-   `@OutputFunction`:

    The `@OutputFunction` is the last function called when computing an aggregation. It takes the final state object (the result of merging all partial states) and writes the result to a `BlockBuilder`.
    
-   Where does serialization happen, and what is `GroupedAccumulatorState`?
    
The `@InputFunction` is usually run on a different worker from the`@CombineFunction`, so the state objects are serialized and transported between these workers by the aggregation framework. `GroupedAccumulatorState` is used when performing a `GROUP BY` aggregation, and an implementation will be automatically generated for you, if you don\'t specify a `AccumulatorStateFactory`
