Chunked array docs

doc/conf.py

@@ -323,6 +323,7 @@
     "dask": ("https://docs.dask.org/en/latest", None),
     "cftime": ("https://unidata.github.io/cftime", None),
     "sparse": ("https://sparse.pydata.org/en/latest/", None),
+    "cubed": ("https://tom-e-white.com/cubed/", None),
 }
 
 

doc/internals/chunked-arrays.rst

@@ -0,0 +1,101 @@
+.. currentmodule:: xarray
+
+.. _internals.chunkedarrays:
+
+Alternative chunked array types
+===============================
+
+.. warning::
+
+    This is a *highly* experimental feature. Please report any bugs or other difficulties on `xarray's issue tracker <https://github.com/pydata/xarray/issues>`_.
+    In particular see discussion on `xarray issue #6807 <https://github.com/pydata/xarray/issues/6807>`_
+
+Xarray can wrap chunked dask arrays (see :ref:`dask`), but can also wrap any other chunked array type that exposes the correct interface.
+This allows us to support using other frameworks for distributed and out-of-core processing, with user code still written as xarray commands.
+In particular xarray can now also supports wrapping :py:class:`cubed.Array` objects
+(see `Cubed's documentation <https://tom-e-white.com/cubed/>`_ and the `cubed-xarray package <https://github.com/xarray-contrib/cubed-xarray>`_).
+
+The basic idea is that by wrapping an array that has an explicit notion of ``.chunks``, xarray can expose control over
+the choice of chunking scheme to users via methods like :py:meth:`DataArray.chunk` whilst the wrapped array actually
+implements the handling of processing all of the chunks.
+
+Chunked array methods and "core operations"
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A chunked array needs to meet all the :ref:`requirements for normal duck arrays <internals.duckarrays.requirements>`, but must also
+implement additional features.
+
+Chunked arrays have additional attributes and methods, such as ``.chunks`` and ``.rechunk``.
+Furthermore, Xarray dispatches chunk-aware computations across one or more chunked arrays using special functions known
+as "core operations". Examples include ``map_blocks``, ``blockwise``, and ``apply_gufunc``.
+
+The core operations are generalizations of functions first implemented in :py:module:`dask.array`.
+The implementation of these functions is specific to the type of arrays passed to them. For example, when applying the
+``map_blocks`` core operation, :py:class:`dask.array.Array` objects must be processed by :py:func:`dask.array.map_blocks`,
+whereas :py:class:`cubed.Array` objects must be processed by :py:func:`cubed.map_blocks`.
+
+In order to use the correct implementation of a core operation for the array type encountered, xarray dispatches to the
+corresponding subclass of :py:class:`~xarray.core.parallelcompat.ChunkManagerEntrypoint`,
+also known as a "Chunk Manager". Therefore **a full list of the operations that need to be defined is set by the
+API of the** :py:class:`~xarray.core.parallelcompat.ChunkManagerEntrypoint` **abstract base class**. Note that chunked array
+methods are also currently dispatched using this class.
+
+Chunked array creation is also handled by this class. As chunked array objects have a one-to-one correspondence with
+in-memory numpy arrays, it should be possible to create a chunked array from a numpy array by passing the desired
+chunking pattern to an implementation of :py:class:`~xarray.core.parallelcompat.ChunkManagerEntrypoint.from_array``.
+
+.. note::
+
+    The :py:class:`~xarray.core.parallelcompat.ChunkManagerEntrypoint` abstract base class is mostly just acting as a
+    namespace for containing the chunked-aware function primitives. Ideally in the future we would have an API standard
+    for chunked array types which codified this structure, making the entrypoint system unnecessary.
+
+.. currentmodule:: xarray.core.parallelcompat
+
+.. autoclass:: xarray.core.parallelcompat.ChunkManagerEntrypoint
+   :members:
+
+Registering a new ChunkManagerEntrypoint subclass
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Rather than hard-coding various chunk managers to deal with specific chunked array implementations, xarray uses an
+entrypoint system to allow developers of new chunked array implementations to register their corresponding subclass of
+:py:class:`~xarray.core.parallelcompat.ChunkManagerEntrypoint`.
+
+
+To register a new entrypoint you need to add an entry to the ``setup.cfg`` like this::
+
+    [options.entry_points]
+    xarray.chunkmanagers =
+        dask = xarray.core.daskmanager:DaskManager
+
+See also `cubed-xarray <https://github.com/xarray-contrib/cubed-xarray>`_ for another example.
+
+To check that the entrypoint has worked correctly, you may find it useful to display the available chunkmanagers using
+the internal function :py:func:`~xarray.core.parallelcompat.list_chunkmanagers`.
+
+.. autofunction:: list_chunkmanagers
+
+
+User interface
+~~~~~~~~~~~~~~
+
+Once the chunkmanager subclass has been registered, xarray objects wrapping the desired array type can be created in 3 ways:
+
+#. By manually passing the array type to the :py:class:`DataArray` constructor, see the examples for :ref:`numpy-like arrays <userguide.duckarrays>`,
+
+#. Calling :py:meth:`DataArray.chunk`, passing the keyword arguments ``chunked_array_type`` and ``from_array_kwargs``,
+
+#. Calling :py:func:`open_dataset`, passing the keyword arguments ``chunked_array_type`` and ``from_array_kwargs``.
+
+The latter two methods ultimately call the chunkmanager's implementation of ``.from_array``, to which they pass the ``from_array_kwargs`` dict.
+The ``chunked_array_type`` kwarg selects which registered chunkmanager subclass to dispatch to. It defaults to ``'dask'`` if found,
+otherwise to whichever chunkmanager is registered if only one is registered, else it will raise an error.
+
+Parallel processing without chunks
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To use a parallel array type that does not expose a concept of chunks explicitly, none of the information on this page
+is theoretically required. Such an array type (e.g. `Ramba <https://github.com/Python-for-HPC/ramba>`_ or
+`Arkouda <https://github.com/Bears-R-Us/arkouda>`_) could be wrapped using xarray's existing support for
+:ref:`numpy-like "duck" arrays <userguide.duckarrays>`.

doc/internals/duck-arrays-integration.rst

@@ -11,6 +11,8 @@ Integrating with duck arrays
 Xarray can wrap custom numpy-like arrays (":term:`duck array`\s") - see the :ref:`user guide documentation <userguide.duckarrays>`.
 This page is intended for developers who are interested in wrapping a new custom array type with xarray.
 
+.. _internals.duckarrays.requirements:
+
 Duck array requirements
 ~~~~~~~~~~~~~~~~~~~~~~~
 

doc/internals/index.rst

@@ -21,6 +21,7 @@ The pages in this section are intended for:
 
    variable-objects
    duck-arrays-integration
+   chunked-arrays
    extending-xarray
    zarr-encoding-spec
    how-to-add-new-backend

doc/user-guide/duckarrays.rst

@@ -27,7 +27,7 @@ Some numpy-like array types that xarray already has some support for:
 
     For information on wrapping dask arrays see :ref:`dask`. Whilst xarray wraps dask arrays in a similar way to that
     described on this page, chunked array types like :py:class:`dask.array.Array` implement additional methods that require
-    slightly different user code (e.g. calling ``.chunk`` or ``.compute``).
+    slightly different user code (e.g. calling ``.chunk`` or ``.compute``). See the docs on :ref:`wrapping chunked arrays <internals.chunkedarrays>`.
 
 Why "duck"?
 -----------

doc/whats-new.rst

@@ -38,6 +38,8 @@ Bug fixes
 Documentation
 ~~~~~~~~~~~~~
 
+- Added page on wrapping chunked numpy-like arrays as alternatives to dask arrays.
+  (:pull:`7951`) By `Tom Nicholas <https://github.com/TomNicholas>`_.
 - Expanded the page on wrapping numpy-like "duck" arrays.
   (:pull:`7911`) By `Tom Nicholas <https://github.com/TomNicholas>`_.
 - Added examples to docstrings of :py:meth:`Dataset.isel`, :py:meth:`Dataset.reduce`, :py:meth:`Dataset.argmin`,

xarray/core/dataset.py

@@ -8648,7 +8648,7 @@ def argmax(self: T_Dataset, dim: Hashable | None = None, **kwargs) -> T_Dataset:
         ... )
 
         # Indices of the maximum values along the 'student' dimension are calculated
-        
+
         >>> argmax_indices = dataset.argmax(dim="test")
 
         >>> argmax_indices