Data Types#

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:

  1. Using a arrow::DataType instance (e.g. as a function argument)

  2. Using a arrow::DataType concrete subclass (e.g. as a template parameter)

  3. 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) {
    CType temp = val + next_val;
    val = next_val;
    next_val = temp;
  std::shared_ptr<ArrayType> 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 scalar

  • Array::TypeClass to get data type class of an array

  • DataType::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 (std::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 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;

  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("Cannot compute sum for array of type ",

  template <typename ArrayType, typename T = typename ArrayType::TypeClass>
  arrow::enable_if_number<T, arrow::Status> Visit(const ArrayType& array) {
    for (std::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.