# Canonical Extension Types#

## Introduction#

The Arrow columnar format allows defining extension types so as to extend standard Arrow data types with custom semantics. Often these semantics will be specific to a system or application. However, it is beneficial to share the definitions of well-known extension types so as to improve interoperability between different systems integrating Arrow columnar data.

### Standardization#

These rules must be followed for the standardization of canonical extension types:

Canonical extension types are described and maintained below in this document.

Each canonical extension type requires a distinct discussion and vote on the Arrow development mailing-list.

The specification text to be added

*must*follow these requirements:It

*must*define a well-defined extension name starting with “`arrow.`

”.Its parameters, if any,

*must*be described in the proposal.Its serialization

*must*be described in the proposal and should not require unduly implementation work or unusual software dependencies (for example, a trivial custom text format or JSON would be acceptable).Its expected semantics

*should*be described as well and any potential ambiguities or pain points addressed or at least mentioned.

The extension type

*should*have one implementation submitted; preferably two if non-trivial (for example if parameterized).

### Making Modifications#

Like standard Arrow data types, canonical extension types should be considered stable once standardized. Modifying a canonical extension type (for example to expand the set of parameters) should be an exceptional event, follow the same rules as laid out above, and provide backwards compatibility guarantees.

## Official List#

### Fixed shape tensor#

Extension name: arrow.fixed_shape_tensor.

The storage type of the extension:

`FixedSizeList`

where:**value_type**is the data type of individual tensor elements.**list_size**is the product of all the elements in tensor shape.

Extension type parameters:

**value_type**= the Arrow data type of individual tensor elements.**shape**= the physical shape of the contained tensors as an array.

Optional parameters describing the logical layout:

**dim_names**= explicit names to tensor dimensions as an array. The length of it should be equal to the shape length and equal to the number of dimensions.`dim_names`

can be used if the dimensions have well-known names and they map to the physical layout (row-major).**permutation**= indices of the desired ordering of the original dimensions, defined as an array.The indices contain a permutation of the values [0, 1, .., N-1] where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor (the i-th dimension of the logical view corresponds to the dimension with number

`permutations[i]`

of the physical tensor).Permutation can be useful in case the logical order of the tensor is a permutation of the physical order (row-major).

When logical and physical layout are equal, the permutation will always be ([0, 1, .., N-1]) and can therefore be left out.

Description of the serialization:

The metadata must be a valid JSON object including shape of the contained tensors as an array with key

**“shape”**plus optional dimension names with keys**“dim_names”**and ordering of the dimensions with key**“permutation”**.Example:

`{ "shape": [2, 5]}`

Example with

`dim_names`

metadata for NCHW ordered data:`{ "shape": [100, 200, 500], "dim_names": ["C", "H", "W"]}`

Example of permuted 3-dimensional tensor:

`{ "shape": [100, 200, 500], "permutation": [2, 0, 1]}`

This is the physical layout shape and the shape of the logical layout would in this case be

`[500, 100, 200]`

.

Note

Elements in a fixed shape tensor extension array are stored in row-major/C-contiguous order.

Note

Other Data Structures in Arrow include a Tensor (Multi-dimensional Array) to be used as a message in the interprocess communication machinery (IPC).

This structure has no relationship with the Fixed shape tensor extension type defined by this specification. Instead, this extension type lets one use fixed shape tensors as elements in a field of a RecordBatch or a Table.

### Variable shape tensor#

Extension name: arrow.variable_shape_tensor.

The storage type of the extension is:

`StructArray`

where struct is composed of**data**and**shape**fields describing a single tensor per row:**data**is a`List`

holding tensor elements (each list element is a single tensor). The List’s value type is the value type of the tensor, such as an integer or floating-point type.**shape**is a`FixedSizeList<int32>[ndim]`

of the tensor shape where the size of the list`ndim`

is equal to the number of dimensions of the tensor.

Extension type parameters:

**value_type**= the Arrow data type of individual tensor elements.

Optional parameters describing the logical layout:

**dim_names**= explicit names to tensor dimensions as an array. The length of it should be equal to the shape length and equal to the number of dimensions.`dim_names`

can be used if the dimensions have well-known names and they map to the physical layout (row-major).**permutation**= indices of the desired ordering of the original dimensions, defined as an array.The indices contain a permutation of the values [0, 1, .., N-1] where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor (the i-th dimension of the logical view corresponds to the dimension with number

`permutations[i]`

of the physical tensor).Permutation can be useful in case the logical order of the tensor is a permutation of the physical order (row-major).

When logical and physical layout are equal, the permutation will always be ([0, 1, .., N-1]) and can therefore be left out.

**uniform_shape**= sizes of individual tensor’s dimensions which are guaranteed to stay constant in uniform dimensions and can vary in non-uniform dimensions. This holds over all tensors in the array. Sizes in uniform dimensions are represented with int32 values, while sizes of the non-uniform dimensions are not known in advance and are represented with null. If`uniform_shape`

is not provided it is assumed that all dimensions are non-uniform. An array containing a tensor with shape (2, 3, 4) and whose first and last dimensions are uniform would have`uniform_shape`

(2, null, 4). This allows for interpreting the tensor correctly without accounting for uniform dimensions while still permitting optional optimizations that take advantage of the uniformity.

Description of the serialization:

The metadata must be a valid JSON object that optionally includes dimension names with keys

**“dim_names”**and ordering of dimensions with key**“permutation”**. Shapes of tensors can be defined in a subset of dimensions by providing key**“uniform_shape”**. Minimal metadata is an empty string.Example with

`dim_names`

metadata for NCHW ordered data (note that the first logical dimension,`N`

, is mapped to the**data**List array: each element in the List is a CHW tensor and the List of tensors implicitly constitutes a single NCHW tensor):`{ "dim_names": ["C", "H", "W"] }`

Example with

`uniform_shape`

metadata for a set of color images with fixed height, variable width and three color channels:`{ "dim_names": ["H", "W", "C"], "uniform_shape": [400, null, 3] }`

Example of permuted 3-dimensional tensor:

`{ "permutation": [2, 0, 1] }`

For example, if the physical

**shape**of an individual tensor is`[100, 200, 500]`

, this permutation would denote a logical shape of`[500, 100, 200]`

.

Note

With the exception of `permutation`

, the parameters and storage
of VariableShapeTensor relate to the *physical* storage of the tensor.

- For example, consider a tensor with::
shape = [10, 20, 30] dim_names = [x, y, z] permutations = [2, 0, 1]

This means the logical tensor has names [z, x, y] and shape [30, 10, 20].

Note

Values inside each **data** tensor element are stored in row-major/C-contiguous
order according to the corresponding **shape**.

## Community Extension Types#

In addition to the canonical extension types listed above, there exist Arrow extension types that have been established as standards within specific domain areas. These have not been officially designated as canonical through a discussion and vote on the Arrow development mailing list but are well known within subcommunities of Arrow developers.

### GeoArrow#

GeoArrow defines a collection of Arrow extension types for representing vector geometries. It is well known within the Arrow geospatial subcommunity. The GeoArrow specification is not yet finalized.