# How to add a new backend Adding a new backend for read support to Xarray does not require one to integrate any code in Xarray; all you need to do is: - Create a class that inherits from Xarray [`BackendEntrypoint`](../generated/xarray.backends.BackendEntrypoint.html.md#xarray.backends.BackendEntrypoint) and implements the method `open_dataset` see [BackendEntrypoint subclassing](#rst-backend-entrypoint) - Declare this class as an external plugin in your project configuration, see [How to register a backend](#rst-backend-registration) If you also want to support lazy loading and dask see [How to support lazy loading](#rst-lazy-loading). Note that the new interface for backends is available from Xarray version >= 0.18 onwards. You can see what backends are currently available in your working environment with [`list_engines()`](../generated/xarray.backends.list_engines.html.md#xarray.backends.list_engines). ## BackendEntrypoint subclassing Your `BackendEntrypoint` sub-class is the primary interface with Xarray, and it should implement the following attributes and methods: - the `open_dataset` method (mandatory) - the `open_dataset_parameters` attribute (optional) - the `guess_can_open` method (optional) - the `description` attribute (optional) - the `url` attribute (optional). This is what a `BackendEntrypoint` subclass should look like: ```python from xarray.backends import BackendEntrypoint class MyBackendEntrypoint(BackendEntrypoint): def open_dataset( self, filename_or_obj, *, drop_variables=None, # other backend specific keyword arguments # `chunks` and `cache` DO NOT go here, they are handled by xarray ): return my_open_dataset(filename_or_obj, drop_variables=drop_variables) open_dataset_parameters = ["filename_or_obj", "drop_variables"] def guess_can_open(self, filename_or_obj): try: _, ext = os.path.splitext(filename_or_obj) except TypeError: return False return ext in {".my_format", ".my_fmt"} description = "Use .my_format files in Xarray" url = "https://link_to/your_backend/documentation" ``` `BackendEntrypoint` subclass methods and attributes are detailed in the following. ### open_dataset The backend `open_dataset` shall implement reading from file, the variables decoding and it shall instantiate the output Xarray class [`Dataset`](../generated/xarray.Dataset.html.md#xarray.Dataset). The following is an example of the high level processing steps: ```python def open_dataset( self, filename_or_obj, *, drop_variables=None, decode_times=True, decode_timedelta=True, decode_coords=True, my_backend_option=None, ): vars, attrs, coords = my_reader( filename_or_obj, drop_variables=drop_variables, my_backend_option=my_backend_option, ) vars, attrs, coords = my_decode_variables( vars, attrs, decode_times, decode_timedelta, decode_coords ) # see also conventions.decode_cf_variables ds = xr.Dataset(vars, attrs=attrs, coords=coords) ds.set_close(my_close_method) return ds ``` The output [`Dataset`](../generated/xarray.Dataset.html.md#xarray.Dataset) shall implement the additional custom method `close`, used by Xarray to ensure the related files are eventually closed. This method shall be set by using [`set_close()`](../generated/xarray.Dataset.set_close.html.md#xarray.Dataset.set_close). The input of `open_dataset` method are one argument (`filename_or_obj`) and one keyword argument (`drop_variables`): - `filename_or_obj`: can be any object but usually it is a string containing a path or an instance of [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html#pathlib.Path). - `drop_variables`: can be `None` or an iterable containing the variable names to be dropped when reading the data. If it makes sense for your backend, your `open_dataset` method should implement in its interface the following boolean keyword arguments, called **decoders**, which default to `None`: - `mask_and_scale` - `decode_times` - `decode_timedelta` - `use_cftime` - `concat_characters` - `decode_coords` Note: all the supported decoders shall be declared explicitly in backend `open_dataset` signature and adding a `**kwargs` is not allowed. These keyword arguments are explicitly defined in Xarray [`open_dataset()`](../generated/xarray.open_dataset.html.md#xarray.open_dataset) signature. Xarray will pass them to the backend only if the User explicitly sets a value different from `None`. For more details on decoders see [Decoders](#rst-decoders). Your backend can also take as input a set of backend-specific keyword arguments. All these keyword arguments can be passed to [`open_dataset()`](../generated/xarray.open_dataset.html.md#xarray.open_dataset) grouped either via the `backend_kwargs` parameter or explicitly using the syntax `**kwargs`. If you don’t want to support the lazy loading, then the [`Dataset`](../generated/xarray.Dataset.html.md#xarray.Dataset) shall contain values as a [`numpy.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) and your work is almost done. ### open_dataset_parameters `open_dataset_parameters` is the list of backend `open_dataset` parameters. It is not a mandatory parameter, and if the backend does not provide it explicitly, Xarray creates a list of them automatically by inspecting the backend signature. If `open_dataset_parameters` is not defined, but `**kwargs` and `*args` are in the backend `open_dataset` signature, Xarray raises an error. On the other hand, if the backend provides the `open_dataset_parameters`, then `**kwargs` and `*args` can be used in the signature. However, this practice is discouraged unless there is a good reasons for using `**kwargs` or `*args`. ### guess_can_open `guess_can_open` is used to identify the proper engine to open your data file automatically in case the engine is not specified explicitly. If you are not interested in supporting this feature, you can skip this step since [`BackendEntrypoint`](../generated/xarray.backends.BackendEntrypoint.html.md#xarray.backends.BackendEntrypoint) already provides a default [`guess_can_open()`](../generated/xarray.backends.BackendEntrypoint.guess_can_open.html.md#xarray.backends.BackendEntrypoint.guess_can_open) that always returns `False`. Backend `guess_can_open` takes as input the `filename_or_obj` parameter of Xarray [`open_dataset()`](../generated/xarray.open_dataset.html.md#xarray.open_dataset), and returns a boolean. ### description and url `description` is used to provide a short text description of the backend. `url` is used to include a link to the backend’s documentation or code. These attributes are surfaced when a user prints [`BackendEntrypoint`](../generated/xarray.backends.BackendEntrypoint.html.md#xarray.backends.BackendEntrypoint). If `description` or `url` are not defined, an empty string is returned. ### Decoders The decoders implement specific operations to transform data from on-disk representation to Xarray representation. A classic example is the “time” variable decoding operation. In NetCDF, the elements of the “time” variable are stored as integers, and the unit contains an origin (for example: “seconds since 1970-1-1”). In this case, Xarray transforms the pair integer-unit in a [`numpy.datetime64`](https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64). The standard coders implemented in Xarray are: - [`xarray.coding.strings.CharacterArrayCoder()`](../generated/xarray.coding.strings.CharacterArrayCoder.html.md#xarray.coding.strings.CharacterArrayCoder) - [`xarray.coding.strings.EncodedStringCoder()`](../generated/xarray.coding.strings.EncodedStringCoder.html.md#xarray.coding.strings.EncodedStringCoder) - `xarray.coding.variables.UnsignedIntegerCoder()` - [`xarray.coding.variables.CFMaskCoder()`](../generated/xarray.coding.variables.CFMaskCoder.html.md#xarray.coding.variables.CFMaskCoder) - [`xarray.coding.variables.CFScaleOffsetCoder()`](../generated/xarray.coding.variables.CFScaleOffsetCoder.html.md#xarray.coding.variables.CFScaleOffsetCoder) - [`xarray.coding.times.CFTimedeltaCoder()`](../generated/xarray.coding.times.CFTimedeltaCoder.html.md#xarray.coding.times.CFTimedeltaCoder) - [`xarray.coding.times.CFDatetimeCoder()`](../generated/xarray.coding.times.CFDatetimeCoder.html.md#xarray.coding.times.CFDatetimeCoder) Xarray coders all have the same interface. They have two methods: `decode` and `encode`. The method `decode` takes a `Variable` in on-disk format and returns a `Variable` in Xarray format. Variable attributes no more applicable after the decoding, are dropped and stored in the `Variable.encoding` to make them available to the `encode` method, which performs the inverse transformation. In the following an example on how to use the coders `decode` method: ```ipython3 var = xr.Variable( dims=("x",), data=np.arange(10.0), attrs={"scale_factor": 10, "add_offset": 2} ) var ``` ```ipython3 coder = xr.coding.variables.CFScaleOffsetCoder() decoded_var = coder.decode(var) decoded_var ``` ```ipython3 decoded_var.encoding ``` Some of the transformations can be common to more backends, so before implementing a new decoder, be sure Xarray does not already implement that one. The backends can reuse Xarray’s decoders, either instantiating the coders and using the method `decode` directly or using the higher-level function [`decode_cf_variables()`](../generated/xarray.conventions.decode_cf_variables.html.md#xarray.conventions.decode_cf_variables) that groups Xarray decoders. In some cases, the transformation to apply strongly depends on the on-disk data format. Therefore, you may need to implement your own decoder. An example of such a case is when you have to deal with the time format of a grib file. grib format is very different from the NetCDF one: in grib, the time is stored in two attributes dataDate and dataTime as strings. Therefore, it is not possible to reuse the Xarray time decoder, and implementing a new one is mandatory. Decoders can be activated or deactivated using the boolean keywords of Xarray [`open_dataset()`](../generated/xarray.open_dataset.html.md#xarray.open_dataset) signature: `mask_and_scale`, `decode_times`, `decode_timedelta`, `use_cftime`, `concat_characters`, `decode_coords`. Such keywords are passed to the backend only if the User sets a value different from `None`. Note that the backend does not necessarily have to implement all the decoders, but it shall declare in its `open_dataset` interface only the boolean keywords related to the supported decoders. ## How to register a backend Define a new entrypoint in your `pyproject.toml` (or `setup.cfg/setup.py` for older configurations), with: - group: `xarray.backends` - name: the name to be passed to [`open_dataset()`](../generated/xarray.open_dataset.html.md#xarray.open_dataset) as `engine` - object reference: the reference of the class that you have implemented. You can declare the entrypoint in your project configuration like so: ```toml [project.entry-points."xarray.backends"] my_engine = "my_package.my_module:MyBackendEntrypoint" ``` ```toml [tool.poetry.plugins."xarray.backends"] my_engine = "my_package.my_module:MyBackendEntrypoint" ``` ```cfg [options.entry_points] xarray.backends = my_engine = my_package.my_module:MyBackendEntrypoint ``` ```default setuptools.setup( entry_points={ "xarray.backends": [ "my_engine=my_package.my_module:MyBackendEntrypoint" ], }, ) ``` See the [Python Packaging User Guide](https://packaging.python.org/specifications/entry-points/#data-model) for more information on entrypoints and details of the syntax. If you’re using Poetry, note that table name in `pyproject.toml` is slightly different. See [the Poetry docs](https://python-poetry.org/docs/pyproject/#plugins) for more information on plugins. ## How to support lazy loading If you want to make your backend effective with big datasets, then you should take advantage of xarray’s support for lazy loading and indexing. Basically, when your backend constructs the `Variable` objects, you need to replace the [`numpy.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) inside the variables with a custom [`BackendArray`](../generated/xarray.backends.BackendArray.html.md#xarray.backends.BackendArray) subclass that supports lazy loading and indexing. See the example below: ```python backend_array = MyBackendArray() data = indexing.LazilyIndexedArray(backend_array) var = xr.Variable(dims, data, attrs=attrs, encoding=encoding) ``` Where: - [`LazilyIndexedArray`](../generated/xarray.core.indexing.LazilyIndexedArray.html.md#xarray.core.indexing.LazilyIndexedArray) is a wrapper class provided by Xarray that manages the lazy loading and indexing. - `MyBackendArray` should be implemented by the backend and must inherit from [`BackendArray`](../generated/xarray.backends.BackendArray.html.md#xarray.backends.BackendArray). ### BackendArray subclassing The BackendArray subclass must implement the following method and attributes: - the `__getitem__` method that takes an index as an input and returns a [NumPy](https://numpy.org/) array, - the `shape` attribute, - the `dtype` attribute. It may also optionally implement an additional `async_getitem` method. Xarray supports different types of [Indexing and selecting data](../user-guide/indexing.html.md), that can be grouped in three types of indexes: [`BasicIndexer`](../generated/xarray.core.indexing.BasicIndexer.html.md#xarray.core.indexing.BasicIndexer), [`OuterIndexer`](../generated/xarray.core.indexing.OuterIndexer.html.md#xarray.core.indexing.OuterIndexer), and [`VectorizedIndexer`](../generated/xarray.core.indexing.VectorizedIndexer.html.md#xarray.core.indexing.VectorizedIndexer). This implies that the implementation of the method `__getitem__` can be tricky. In order to simplify this task, Xarray provides a helper function, [`explicit_indexing_adapter()`](../generated/xarray.core.indexing.explicit_indexing_adapter.html.md#xarray.core.indexing.explicit_indexing_adapter), that transforms all the input indexer types (basic, outer, vectorized) in a tuple which is interpreted correctly by your backend. This is an example `BackendArray` subclass implementation: ```python from xarray.backends import BackendArray class MyBackendArray(BackendArray): def __init__( self, shape, dtype, lock, # other backend specific keyword arguments ): self.shape = shape self.dtype = dtype self.lock = lock def __getitem__( self, key: xarray.core.indexing.ExplicitIndexer ) -> np.typing.ArrayLike: return indexing.explicit_indexing_adapter( key, self.shape, indexing.IndexingSupport.BASIC, self._raw_indexing_method, ) def _raw_indexing_method(self, key: tuple) -> np.typing.ArrayLike: # thread safe method that access to data on disk with self.lock: ... return item ``` Note that `BackendArray.__getitem__` must be thread safe to support multi-thread processing. The [`explicit_indexing_adapter()`](../generated/xarray.core.indexing.explicit_indexing_adapter.html.md#xarray.core.indexing.explicit_indexing_adapter) method takes in input the `key`, the array `shape` and the following parameters: - `indexing_support`: the type of index supported by `raw_indexing_method` - `raw_indexing_method`: a method that shall take in input a key in the form of a tuple and return an indexed [`numpy.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray). For more details see [`IndexingSupport`](../generated/xarray.core.indexing.IndexingSupport.html.md#xarray.core.indexing.IndexingSupport) and [Indexing examples](#rst-indexing). ### Async support Backends can also optionally support loading data asynchronously via xarray’s asynchronous loading methods (e.g. `~xarray.Dataset.load_async`). To support async loading the `BackendArray` subclass must additionally implement the `BackendArray.async_getitem` method. Note that implementing this method is only necessary if you want to be able to load data from different xarray objects concurrently. Even without this method your `BackendArray` implementation is still free to concurrently load chunks of data for a single `Variable` itself, so long as it does so behind the synchronous `__getitem__` interface. ### Dask support In order to support [Dask Distributed](https://distributed.dask.org/) and [`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing), the `BackendArray` subclass should be serializable either with [Pickling](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-pickle) or [cloudpickle](https://github.com/cloudpipe/cloudpickle). That implies that all the reference to open files should be dropped. For opening files, we therefore suggest to use the helper class provided by Xarray [`CachingFileManager`](../generated/xarray.backends.CachingFileManager.html.md#xarray.backends.CachingFileManager). ### Indexing examples **BASIC** In the `BASIC` indexing support, numbers and slices are supported. Example: ```python # () shall return the full array backend_array._raw_indexing_method(()) ``` ```default array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]) ``` ```python # shall support integers backend_array._raw_indexing_method((1, 1)) ``` ```default 5 ``` ```python # shall support slices backend_array._raw_indexing_method((slice(0, 3), slice(2, 4))) ``` ```default array([[2, 3], [6, 7], [10, 11]]) ``` **OUTER** The `OUTER` indexing shall support number, slices and in addition it shall support also lists of integers. The outer indexing is equivalent to combining multiple input list with `itertools.product()`: ```python backend_array._raw_indexing_method(([0, 1], [0, 1, 2])) ``` ```default array([[0, 1, 2], [4, 5, 6]]) ``` ```python # shall support integers backend_array._raw_indexing_method((1, 1)) ``` ```default 5 ``` **OUTER_1VECTOR** The `OUTER_1VECTOR` indexing shall supports number, slices and at most one list. The behaviour with the list shall be the same as `OUTER` indexing. If you support more complex indexing as explicit indexing or numpy indexing, you can have a look to the implementation of Zarr backend and Scipy backend, currently available in `backends` module. ### Preferred chunk sizes To potentially improve performance with lazy loading, the backend may define for each variable the chunk sizes that it prefers—that is, sizes that align with how the variable is stored. (Note that the backend is not directly involved in [Dask](https://dask.org/) chunking, because Xarray internally manages chunking.) To define the preferred chunk sizes, store a mapping within the variable’s encoding under the key `"preferred_chunks"` (that is, `var.encoding["preferred_chunks"]`). The mapping’s keys shall be the names of dimensions with preferred chunk sizes, and each value shall be the corresponding dimension’s preferred chunk sizes expressed as either an integer (such as `{"dim1": 1000, "dim2": 2000}`) or a tuple of integers (such as `{"dim1": (1000, 100), "dim2": (2000, 2000, 2000)}`). Xarray uses the preferred chunk sizes in some special cases of the `chunks` argument of the [`open_dataset()`](../generated/xarray.open_dataset.html.md#xarray.open_dataset) and [`open_mfdataset()`](../generated/xarray.open_mfdataset.html.md#xarray.open_mfdataset) functions. If `chunks` is a `dict`, then for any dimensions missing from the keys or whose value is `None`, Xarray sets the chunk sizes to the preferred sizes. If `chunks` equals `"auto"`, then Xarray seeks ideal chunk sizes informed by the preferred chunk sizes. Specifically, it determines the chunk sizes using [`dask.array.core.normalize_chunks()`](https://docs.dask.org/en/latest/generated/dask.array.core.normalize_chunks.html#dask.array.core.normalize_chunks) with the `previous_chunks` argument set according to the preferred chunk sizes.