New DataValues system

Summary

Short comes of current DataType

• DataType is an enum type, we must use specific type after matching. For example, if we want to create deserializer/serializer by DataType, we should always do matching. It does not mean that match is not necessary. If we want to add more and more functions to DataType, matching may be very annoyment.

• DataType represented as enum type, we can't use it as generic argument.

• DataType may involve some nested datatypes, such as DataType::Struct, but we put DataField inside DataType, it's logically unreasonable。

• Hard to put attributes into enum based DataType, such as nullable attribute #3726 #3769

Too many concepts about column (Series/Column/Array)

• DataColumn is an enum, including Constant(value) and Array(Series)

pub enum DataColumn {
    // Array of values.
    Array(Series),
    // A Single value.
    Constant(DataValue, usize),
}

• Series is a wrap of SeriesTrait

pub struct Series(pub Arc<dyn SeriesTrait>);

• SeriesTrait can implement various array，using many macros.

pub struct SeriesWrap<T>(pub T);
   impl SeriesTrait for SeriesWrap<$da> {
            fn data_type(&self) -> &DataType {
                self.0.data_type()
            }

            fn len(&self) -> usize {
                self.0.len()
            }
            ...
  }

• For functions, we must consider about Constant case for Column, so there are many branch matching.

match (
            columns[0].column().cast_with_type(&DataType::String)?,
            columns[1].column().cast_with_type(&DataType::UInt64)?,
        ) {
            (
                DataColumn::Constant(DataValue::String(input_string), _),
                DataColumn::Constant(DataValue::UInt64(times), _),
            ) => Ok(DataColumn::Constant(
                DataValue::String(repeat(input_string, times)?),
                input_rows,
            )),
            (
                DataColumn::Constant(DataValue::String(input_string), _),
                DataColumn::Array(times),
            )
            ...

New DataValues system design

Introduce DataType as a trait

#[typetag::serde(tag = "type")]
pub trait DataType: std::fmt::Debug + Sync + Send + DynClone {
    fn data_type_id(&self) -> TypeID;

    fn is_nullable(&self) -> bool {
        false
    }
    ..
 }

Nullable is a special case of DataType, it's a wrapper of DataType.

pub struct DataTypeNull {inner: DataTypeImpl}

Simplify DataValue

pub enum DataValue {
    /// Base type.
    Null,
    Boolean(bool),
    Int64(i64),
    UInt64(u64),
    Float64(f64),
    String(Vec<u8>),
    // Container struct.
    Array(Vec<DataValue>),
    Struct(Vec<DataValue>),
}

DataValue can convert into proper DataType by it's value.

// convert to minialized data type
    pub fn data_type(&self) -> DataTypeImpl {
        match self {
            DataValue::Null => Arc::new(NullType {}),
            DataValue::Boolean(_) => BooleanType::new_impl(),
            DataValue::Int64(n) => {
                if *n >= i8::MIN as i64 && *n <= i8::MAX as i64 {
                    return Int8Type::new_impl();
                }
            ...
   }

Also, DataValue can convert into rust primitive values and vice versa.

Uniform Series/Array/Column into Column

• Column as a trait

pub type ColumnRef = Arc<dyn Column>;
pub trait Column: Send + Sync {
    fn as_any(&self) -> &dyn Any;
    /// Type of data that column contains. It's an underlying physical type:
    /// UInt16 for Date, UInt32 for DateTime, so on.
    fn data_type_id(&self) -> TypeID {
        self.data_type().data_type_id()
    }
    fn data_type(&self) -> DataTypeImpl;

    fn is_nullable(&self) -> bool {
        false
    }

    fn is_const(&self) -> bool {
        false
    }
   ..
 }

• Introduce Constant column

Constant column is a wrapper of a Column with a single value(size = 1)

#[derive(Clone)]
pub struct ConstColumn {
    length: usize,
    column: ColumnRef,
}
impl Column for ConstColumn {..}

• Introduce nullable column

nullable column is a wrapper of a Column and keep an extra bitmap to indicate null values.

pub struct NullableColumn {
    validity: Bitmap,
    column: ColumnRef,
}
impl Column for NullableColumn {..}

• No extra cost convert from or into Arrow's column format.

 fn as_arrow_array(&self) -> common_arrow::arrow::array::ArrayRef {
    let data_type = self.data_type().arrow_type();
    Arc::new(PrimitiveArray::<T>::from_data(
        data_type,
        self.values.clone(),
        None,
    ))
 }

• Keep Series as a tool struct, this may help to fast generate a column.

// nullable column from options
let column = Series::from_data(vec![Some(1i8), None, Some(3), Some(4), Some(5)]);

// no nullable column
let column = Series::from_data(vec![1，2，3，4);

• Downcast into the specific Column

impl Series {
    /// Get a pointer to the underlying data of this Series.
    /// Can be useful for fast comparisons.
    /// # Safety
    /// Assumes that the `column` is  T.
    pub unsafe fn static_cast<T>(column: &ColumnRef) -> &T {
        let object = column.as_ref();
        &*(object as *const dyn Column as *const T)
    }

    pub fn check_get<T: 'static + Column>(column: &ColumnRef) -> Result<&T> {
        let arr = column.as_any().downcast_ref::<T>().ok_or_else(|| {
            ErrorCode::UnknownColumn(format!(
                "downcast column error, column type: {:?}",
                column.data_type()
            ))
        });
        arr
    }
}

• Convinient way to view a column by ColumnViewer

No need to care about Constants and Nullable.

let wrapper = ColumnViewer::<i8>::try_create(&column)?;

assert_eq!(wrapper.len(), 10);
assert!(!wrapper.null_at(0));
for i in 0..wrapper.len() {
    assert_eq!(*wrapper.value(i), (i + 1) as i8);
}
Ok(())


let wrapper = ColumnViewer::<bool>::try_create(&column)?;
let c = wrapper.value(0);

let wrapper =  ColumnViewer::<&str>::try_create(&column)?;
let c = wrapper.value(1);
Ok(())

TODO

• Make datavalues2 more mature.
• Merge datavalues2 into Databend.