Support Dask interface

doc/whats-new.rst

@@ -15,9 +15,16 @@ What's New
 
 .. _whats-new.0.10.0:
 
+
+v0.10.0 (unreleased)
+--------------------
+
 Changes since v0.10.0 rc1 (Unreleased)
 --------------------------------------
 
+- Experimental support for the Dask collection interface (:issue:`1674`).
+  By `Matthew Rocklin <https://github.com/mrocklin>`_.
+
 Bug fixes
 ~~~~~~~~~
 

xarray/core/dataarray.py

@@ -576,6 +576,35 @@ def reset_coords(self, names=None, drop=False, inplace=False):
             dataset[self.name] = self.variable
             return dataset
 
+    def __dask_graph__(self):
+        return self._to_temp_dataset().__dask_graph__()
+
+    def __dask_keys__(self):
+        return self._to_temp_dataset().__dask_keys__()
+
+    @property
+    def __dask_optimize__(self):
+        return self._to_temp_dataset().__dask_optimize__
+
+    @property
+    def __dask_scheduler__(self):
+        return self._to_temp_dataset().__dask_optimize__
+
+    def __dask_postcompute__(self):
+        func, args = self._to_temp_dataset().__dask_postcompute__()
+        return self._dask_finalize, (func, args, self.name)
+
+    def __dask_postpersist__(self):
+        func, args = self._to_temp_dataset().__dask_postpersist__()
+        return self._dask_finalize, (func, args, self.name)
+
+    @staticmethod
+    def _dask_finalize(results, func, args, name):
+        ds = func(results, *args)
+        variable = ds._variables.pop(_THIS_ARRAY)
+        coords = ds._variables
+        return DataArray(variable, coords, name=name, fastpath=True)
+
     def load(self, **kwargs):
         """Manually trigger loading of this array's data from disk or a
         remote source into memory and return this array.

xarray/core/dataset.py

@@ -493,6 +493,77 @@ def load(self, **kwargs):
 
         return self
 
+    def __dask_graph__(self):
+        graphs = {k: v.__dask_graph__() for k, v in self.variables.items()}
+        graphs = {k: v for k, v in graphs.items() if v is not None}
+        if not graphs:
+            return None
+        else:
+            from dask import sharedict
+            return sharedict.merge(*graphs.values())
+
+    def __dask_keys__(self):
+        import dask
+        return [v.__dask_keys__() for v in self.variables.values()
+                if dask.is_dask_collection(v)]
+
+    @property
+    def __dask_optimize__(self):
+        import dask.array as da
+        return da.Array.__dask_optimize__
+
+    @property
+    def __dask_scheduler__(self):
+        import dask.array as da
+        return da.Array.__dask_scheduler__
+
+    def __dask_postcompute__(self):
+        import dask
+        info = [(True, k, v.__dask_postcompute__())
+                if dask.is_dask_collection(v) else
+                (False, k, v) for k, v in self._variables.items()]
+        return self._dask_postcompute, (info, self._coord_names, self._dims,
+                                        self._attrs, self._file_obj,
+                                        self._encoding)
+
+    def __dask_postpersist__(self):
+        import dask
+        info = [(True, k, v.__dask_postpersist__())
+                if dask.is_dask_collection(v) else
+                (False, k, v) for k, v in self._variables.items()]
+        return self._dask_postpersist, (info, self._coord_names, self._dims,
+                                        self._attrs, self._file_obj,
+                                        self._encoding)
+
+    @staticmethod
+    def _dask_postcompute(results, info, *args):
+        variables = OrderedDict()
+        results2 = list(results[::-1])
+        for is_dask, k, v in info:
+            if is_dask:
+                func, args2 = v
+                r = results2.pop()
+                result = func(r, *args2)
+            else:
+                result = v
+            variables[k] = result
+
+        final = Dataset._construct_direct(variables, *args)
+        return final
+
+    @staticmethod
+    def _dask_postpersist(dsk, info, *args):
+        variables = OrderedDict()
+        for is_dask, k, v in info:
+            if is_dask:
+                func, args2 = v
+                result = func(dsk, *args2)
+            else:
+                result = v
+            variables[k] = result
+
+        return Dataset._construct_direct(variables, *args)
+
     def compute(self, **kwargs):
         """Manually trigger loading of this dataset's data from disk or a
         remote source into memory and return a new dataset. The original is

xarray/core/variable.py

@@ -355,6 +355,41 @@ def compute(self, **kwargs):
         new = self.copy(deep=False)
         return new.load(**kwargs)
 
+    def __dask_graph__(self):
+        if isinstance(self._data, dask_array_type):
+            return self._data.__dask_graph__()
+        else:
+            return None
+
+    def __dask_keys__(self):
+        return self._data.__dask_keys__()
+
+    @property
+    def __dask_optimize__(self):
+        return self._data.__dask_optimize__
+
+    @property
+    def __dask_scheduler__(self):
+        return self._data.__dask_scheduler__
+
+    def __dask_postcompute__(self):
+        array_func, array_args = self._data.__dask_postcompute__()
+        return self._dask_finalize, (array_func, array_args, self._dims,
+                                     self._attrs, self._encoding)
+
+    def __dask_postpersist__(self):
+        array_func, array_args = self._data.__dask_postpersist__()
+        return self._dask_finalize, (array_func, array_args, self._dims,
+                                     self._attrs, self._encoding)
+
+    @staticmethod
+    def _dask_finalize(results, array_func, array_args, dims, attrs, encoding):
+        if isinstance(results, dict):  # persist case
+            name = array_args[0]
+            results = {k: v for k, v in results.items() if k[0] == name}  # cull
+        data = array_func(results, *array_args)
+        return Variable(dims, data, attrs=attrs, encoding=encoding)
+
     @property
     def values(self):
         """The variable's data as a numpy.ndarray"""

xarray/tests/test_dask.py

@@ -206,6 +206,34 @@ def test_bivariate_ufunc(self):
         self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(v, 0))
         self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(0, v))
 
+    @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                        reason='Need dask 0.16 for new interface')
+    def test_compute(self):
+        u = self.eager_var
+        v = self.lazy_var
+
+        assert dask.is_dask_collection(v)
+        (v2,) = dask.compute(v + 1)
+        assert not dask.is_dask_collection(v2)
+
+        assert ((u + 1).data == v2.data).all()
+
+    @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                        reason='Need dask 0.16 for new interface')
+    def test_persist(self):
+        u = self.eager_var
+        v = self.lazy_var + 1
+
+        (v2,) = dask.persist(v)
+        assert v is not v2
+        assert len(v2.__dask_graph__()) < len(v.__dask_graph__())
+        assert v2.__dask_keys__() == v.__dask_keys__()
+        assert dask.is_dask_collection(v)
+        assert dask.is_dask_collection(v2)
+
+        self.assertLazyAndAllClose(u + 1, v)
+        self.assertLazyAndAllClose(u + 1, v2)
+
 
 class TestDataArrayAndDataset(DaskTestCase):
     def assertLazyAndIdentical(self, expected, actual):
@@ -251,6 +279,34 @@ def test_lazy_array(self):
         actual = xr.concat([v[:2], v[2:]], 'x')
         self.assertLazyAndAllClose(u, actual)
 
+    @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                        reason='Need dask 0.16 for new interface')
+    def test_compute(self):
+        u = self.eager_array
+        v = self.lazy_array
+
+        assert dask.is_dask_collection(v)
+        (v2,) = dask.compute(v + 1)
+        assert not dask.is_dask_collection(v2)
+
+        assert ((u + 1).data == v2.data).all()
+
+    @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                        reason='Need dask 0.16 for new interface')
+    def test_persist(self):
+        u = self.eager_array
+        v = self.lazy_array + 1
+
+        (v2,) = dask.persist(v)
+        assert v is not v2
+        assert len(v2.__dask_graph__()) < len(v.__dask_graph__())
+        assert v2.__dask_keys__() == v.__dask_keys__()
+        assert dask.is_dask_collection(v)
+        assert dask.is_dask_collection(v2)
+
+        self.assertLazyAndAllClose(u + 1, v)
+        self.assertLazyAndAllClose(u + 1, v2)
+
     def test_concat_loads_variables(self):
         # Test that concat() computes not-in-memory variables at most once
         # and loads them in the output, while leaving the input unaltered.
@@ -402,28 +458,6 @@ def counting_get(*args, **kwargs):
             ds.load()
         self.assertEqual(count[0], 1)
 
-    def test_persist_Dataset(self):
-        ds = Dataset({'foo': ('x', range(5)),
-                      'bar': ('x', range(5))}).chunk()
-        ds = ds + 1
-        n = len(ds.foo.data.dask)
-
-        ds2 = ds.persist()
-
-        assert len(ds2.foo.data.dask) == 1
-        assert len(ds.foo.data.dask) == n  # doesn't mutate in place
-
-    def test_persist_DataArray(self):
-        x = da.arange(10, chunks=(5,))
-        y = DataArray(x)
-        z = y + 1
-        n = len(z.data.dask)
-
-        zz = z.persist()
-
-        assert len(z.data.dask) == n
-        assert len(zz.data.dask) == zz.data.npartitions
-
     def test_stack(self):
         data = da.random.normal(size=(2, 3, 4), chunks=(1, 3, 4))
         arr = DataArray(data, dims=('w', 'x', 'y'))
@@ -737,3 +771,62 @@ def build_dask_array(name):
     return dask.array.Array(
         dask={(name, 0): (kernel, name)}, name=name,
         chunks=((1,),), dtype=np.int64)
+
+
+# test both the perist method and the dask.persist function
+# the dask.persist function requires a new version of dask
+@pytest.mark.parametrize('persist', [
+    lambda x: x.persist(),
+    pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                       lambda x: dask.persist(x)[0],
+                       reason='Need Dask 0.16')
+])
+def test_persist_Dataset(persist):
+    ds = Dataset({'foo': ('x', range(5)),
+                  'bar': ('x', range(5))}).chunk()
+    ds = ds + 1
+    n = len(ds.foo.data.dask)
+
+    ds2 = persist(ds)
+
+    assert len(ds2.foo.data.dask) == 1
+    assert len(ds.foo.data.dask) == n  # doesn't mutate in place
+
+
+@pytest.mark.parametrize('persist', [
+    lambda x: x.persist(),
+    pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                       lambda x: dask.persist(x)[0],
+                       reason='Need Dask 0.16')
+])
+def test_persist_DataArray(persist):
+    x = da.arange(10, chunks=(5,))
+    y = DataArray(x)
+    z = y + 1
+    n = len(z.data.dask)
+
+    zz = persist(z)
+
+    assert len(z.data.dask) == n
+    assert len(zz.data.dask) == zz.data.npartitions
+
+
+@pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
+                    reason='Need dask 0.16 for new interface')
+def test_dataarray_with_dask_coords():
+    import toolz
+    x = xr.Variable('x', da.arange(8, chunks=(4,)))
+    y = xr.Variable('y', da.arange(8, chunks=(4,)) * 2)
+    data = da.random.random((8, 8), chunks=(4, 4)) + 1
+    array = xr.DataArray(data, dims=['x', 'y'])
+    array.coords['xx'] = x
+    array.coords['yy'] = y
+
+    assert dict(array.__dask_graph__()) == toolz.merge(data.__dask_graph__(),
+                                                       x.__dask_graph__(),
+                                                       y.__dask_graph__())
+
+    (array2,) = dask.compute(array)
+    assert not dask.is_dask_collection(array2)
+
+    assert all(isinstance(v._variable.data, np.ndarray) for v in array2.coords.values())

xarray/tests/test_distributed.py

@@ -4,7 +4,9 @@
 
 distributed = pytest.importorskip('distributed')
 da = pytest.importorskip('dask.array')
-from distributed.utils_test import cluster, loop
+import dask
+from distributed.utils_test import cluster, loop, gen_cluster
+from distributed.client import futures_of, wait
 
 from xarray.tests.test_backends import create_tmp_file, ON_WINDOWS
 from xarray.tests.test_dataset import create_test_data
@@ -32,3 +34,33 @@ def test_dask_distributed_integration_test(loop, engine):
                     assert isinstance(restored.var1.data, da.Array)
                     computed = restored.compute()
                     assert_allclose(original, computed)
+
+
+@pytest.mark.skipif(distributed.__version__ <= '1.19.3',
+                    reason='Need recent distributed version to clean up get')
+@gen_cluster(client=True, timeout=None)
+def test_async(c, s, a, b):
+    x = create_test_data()
+    assert not dask.is_dask_collection(x)
+    y = x.chunk({'dim2': 4}) + 10
+    assert dask.is_dask_collection(y)
+    assert dask.is_dask_collection(y.var1)
+    assert dask.is_dask_collection(y.var2)
+
+    z = y.persist()
+    assert str(z)
+
+    assert dask.is_dask_collection(z)
+    assert dask.is_dask_collection(z.var1)
+    assert dask.is_dask_collection(z.var2)
+    assert len(y.__dask_graph__()) > len(z.__dask_graph__())
+
+    assert not futures_of(y)
+    assert futures_of(z)
+
+    future = c.compute(z)
+    w = yield future
+    assert not dask.is_dask_collection(w)
+    assert_allclose(x + 10, w)
+
+    assert s.task_state