Data Types¶
See also
Data types govern how physical data is interpreted. Their specification allows binary interoperability between different Arrow
implementations, including from different programming languages and runtimes
(for example it is possible to access the same data, without copying, from
both Python and Java using the pyarrow.jvm
bridge module).
Information about a data type in C++ can be represented in three ways:
Using a
arrow::DataType
instance (e.g. as a function argument)Using a
arrow::DataType
concrete subclass (e.g. as a template parameter)Using a
arrow::Type::type
enum value (e.g. as the condition of a switch statement)
The first form (using a arrow::DataType
instance) is the most idiomatic
and flexible. Runtime-parametric types can only be fully represented with
a DataType instance. For example, a arrow::TimestampType
needs to be
constructed at runtime with a arrow::TimeUnit::type
parameter; a
arrow::Decimal128Type
with scale and precision parameters;
a arrow::ListType
with a full child type (itself a
arrow::DataType
instance).
The two other forms can be used where performance is critical, in order to avoid paying the price of dynamic typing and polymorphism. However, some amount of runtime switching can still be required for parametric types. It is not possible to reify all possible types at compile time, since Arrow data types allows arbitrary nesting.
Creating data types¶
To instantiate data types, it is recommended to call the provided factory functions:
std::shared_ptr<arrow::DataType> type;
// A 16-bit integer type
type = arrow::int16();
// A 64-bit timestamp type (with microsecond granularity)
type = arrow::timestamp(arrow::TimeUnit::MICRO);
// A list type of single-precision floating-point values
type = arrow::list(arrow::float32());
Type Traits¶
Writing code that can handle concrete arrow::DataType
subclasses would
be verbose, if it weren’t for type traits. Arrow’s type traits map the Arrow
data types to the specialized array, scalar, builder, and other associated types.
For example, the Boolean type has traits:
template <>
struct TypeTraits<BooleanType> {
using ArrayType = BooleanArray;
using BuilderType = BooleanBuilder;
using ScalarType = BooleanScalar;
using CType = bool;
static constexpr int64_t bytes_required(int64_t elements) {
return bit_util::BytesForBits(elements);
}
constexpr static bool is_parameter_free = true;
static inline std::shared_ptr<DataType> type_singleton() { return boolean(); }
};
See the Type Traits for an explanation of each of these fields.
Using type traits, one can write template functions that can handle a variety of Arrow types. For example, to write a function that creates an array of Fibonacci values for any Arrow numeric type:
template <typename DataType,
typename BuilderType = typename arrow::TypeTraits<DataType>::BuilderType,
typename ArrayType = typename arrow::TypeTraits<DataType>::ArrayType,
typename CType = typename arrow::TypeTraits<DataType>::CType>
arrow::Result<std::shared_ptr<ArrayType>> MakeFibonacci(int32_t n) {
BuilderType builder;
CType val = 0;
CType next_val = 1;
for (int32_t i = 0; i < n; ++i) {
builder.Append(val);
CType temp = val + next_val;
val = next_val;
next_val = temp;
}
std::shared_ptr<ArrayType> out;
ARROW_RETURN_NOT_OK(builder.Finish(&out));
return out;
}
For some common cases, there are type associations on the classes themselves. Use:
Scalar::TypeClass
to get data type class of a scalarArray::TypeClass
to get data type class of an arrayDataType::c_type
to get associated C type of an Arrow data type
Similar to the type traits provided in
std::type_traits,
Arrow provides type predicates such as is_number_type
as well as
corresponding templates that wrap std::enable_if_t
such as enable_if_number
.
These can constrain template functions to only compile for relevant types, which
is useful if other overloads need to be implemented. For example, to write a sum
function for any numeric (integer or float) array:
template <typename ArrayType, typename DataType = typename ArrayType::TypeClass,
typename CType = typename DataType::c_type>
arrow::enable_if_number<DataType, CType> SumArray(const ArrayType& array) {
CType sum = 0;
for (arrow::util::optional<CType> value : array) {
if (value.has_value()) {
sum += value.value();
}
}
return sum;
}
See Type Predicates for a list of these.
Visitor Pattern¶
In order to process arrow::DataType
, arrow::Scalar
, or
arrow::Array
, you may need to write write logic that specializes based
on the particular Arrow type. In these cases, use the
visitor pattern. Arrow provides
the template functions:
To use these, implement Status Visit()
methods for each specialized type, then
pass the class instance to the inline visit function. To avoid repetitive code,
use type traits as documented in the previous section. As a brief example,
here is how one might sum across columns of arbitrary numeric types:
class TableSummation {
double partial = 0.0;
public:
arrow::Result<double> Compute(std::shared_ptr<arrow::RecordBatch> batch) {
for (std::shared_ptr<arrow::Array> array : batch->columns()) {
ARROW_RETURN_NOT_OK(arrow::VisitArrayInline(*array, this));
}
return partial;
}
// Default implementation
arrow::Status Visit(const arrow::Array& array) {
return arrow::Status::NotImplemented("Can not compute sum for array of type ",
array.type()->ToString());
}
template <typename ArrayType, typename T = typename ArrayType::TypeClass>
arrow::enable_if_number<T, arrow::Status> Visit(const ArrayType& array) {
for (arrow::util::optional<typename T::c_type> value : array) {
if (value.has_value()) {
partial += static_cast<double>(value.value());
}
}
return arrow::Status::OK();
}
};
Arrow also provides abstract visitor classes (arrow::TypeVisitor
,
arrow::ScalarVisitor
, arrow::ArrayVisitor
) and an Accept()
method on each of the corresponding base types (e.g. arrow::Array::Accept()
).
However, these are not able to be implemented using template functions, so you
will typically prefer using the inline type visitors.