small changes.

doc/dask.rst

@@ -87,10 +87,8 @@ for the full disclaimer). By default, :py:meth:`~xarray.open_mfdataset` will chu
 netCDF file into a single Dask array; again, supply the ``chunks`` argument to
 control the size of the resulting Dask arrays. In more complex cases, you can
 open each file individually using :py:meth:`~xarray.open_dataset` and merge the result, as
-described in :ref:`combining data`. If you have a distributed cluster running,
-passing the keyword argument ``parallel=True`` to :py:meth:`~xarray.open_mfdataset`
-will speed up the reading of large multi-file datasets by executing those read tasks
-in parallel using ``dask.delayed``.
+described in :ref:`combining data`. Passing the keyword argument ``parallel=True`` to :py:meth:`~xarray.open_mfdataset` will speed up the reading of large multi-file datasets by
+executing those read tasks in parallel using ``dask.delayed``.
 
 You'll notice that printing a dataset still shows a preview of array values,
 even if they are actually Dask arrays. We can do this quickly with Dask because
@@ -157,6 +155,12 @@ explicit conversion step. One notable exception is indexing operations: to
 enable label based indexing, xarray will automatically load coordinate labels
 into memory.
 
+.. tip::
+
+   By default, dask uses its multi-threaded scheduler, which distributes work across
+   multiple cores and allows for processing some datasets that do not fit into memory.
+   For running across a cluster, `setup the distributed scheduler <https://docs.dask.org/en/latest/setup.html>`_.
+
 The easiest way to convert an xarray data structure from lazy Dask arrays into
 *eager*, in-memory NumPy arrays is to use the :py:meth:`~xarray.Dataset.load` method:
 
@@ -417,7 +421,3 @@ With analysis pipelines involving both spatial subsetting and temporal resamplin
 
 6. The dask `diagnostics <https://docs.dask.org/en/latest/understanding-performance.html>`_ can be
    useful in identifying performance bottlenecks.
-
-7. Installing the optional `bottleneck <https://github.com/kwgoodman/bottleneck>`_ library
-   will result in greatly reduced memory usage when using :py:meth:`~xarray.Dataset.rolling`
-   on dask arrays,

doc/index.rst

@@ -11,11 +11,11 @@ intuitive, more concise, and less error-prone developer experience.
 The package includes a large and growing library of domain-agnostic functions
 for advanced analytics and visualization with these data structures.
 
-Xarray is particularly tailored to working with netCDF_ files, which were the
+Xarray is inspired by and borrows heavily from pandas_, the popular data
+analysis package focused on labelled tabular data.
+It is particularly tailored to working with netCDF_ files, which were the
 source of xarray's data model, and integrates tightly with dask_ for parallel
 computing.
-It is inspired by and borrows heavily from pandas_, the popular data
-analysis package focused on labelled tabular data.
 
 .. _NumPy: http://www.numpy.org
 .. _pandas: http://pandas.pydata.org

xarray/core/dataset.py

@@ -613,7 +613,7 @@ def sizes(self) -> Mapping[Hashable, int]:
         """
         return self.dims
 
-    def load(self: T, **kwargs) -> T:
+    def load(self, **kwargs) -> "Dataset":
         """Manually trigger loading and/or computation of this dataset's data
         from disk or a remote source into memory and return this dataset.
         Unlike compute, the original dataset is modified and returned.
@@ -771,7 +771,7 @@ def _dask_postpersist(dsk, info, *args):
 
         return Dataset._construct_direct(variables, *args)
 
-    def compute(self: T, **kwargs) -> T:
+    def compute(self, **kwargs) -> "Dataset":
         """Manually trigger loading and/or computation of this dataset's data
         from disk or a remote source into memory and return a new dataset.
         Unlike load, the original dataset is left unaltered.