Compatibility with dask 2021.02.0 (pydata#4884)

* Compatibility with dask 2021.02.0

* Rework postpersist and postcompute

ci/requirements/environment-windows.yml

@@ -8,7 +8,7 @@ dependencies:
   # - cdms2  # Not available on Windows
   # - cfgrib  # Causes Python interpreter crash on Windows: https://github.com/pydata/xarray/pull/3340
   - cftime
-  - dask<2021.02.0
+  - dask
   - distributed
   - h5netcdf
   - h5py=2

ci/requirements/environment.yml

@@ -9,7 +9,7 @@ dependencies:
   - cdms2
   - cfgrib
   - cftime
-  - dask<2021.02.0
+  - dask
   - distributed
   - h5netcdf
   - h5py=2

xarray/core/dataset.py

@@ -866,79 +866,69 @@ def __dask_postcompute__(self):
         import dask
 
         info = [
-            (True, k, v.__dask_postcompute__())
+            (k, None) + v.__dask_postcompute__()
             if dask.is_dask_collection(v)
-            else (False, k, v)
+            else (k, v, None, None)
             for k, v in self._variables.items()
         ]
-        args = (
-            info,
+        construct_direct_args = (
             self._coord_names,
             self._dims,
             self._attrs,
             self._indexes,
             self._encoding,
             self._close,
         )
-        return self._dask_postcompute, args
+        return self._dask_postcompute, (info, construct_direct_args)
 
     def __dask_postpersist__(self):
         import dask
 
         info = [
-            (True, k, v.__dask_postpersist__())
+            (k, None, v.__dask_keys__()) + v.__dask_postpersist__()
             if dask.is_dask_collection(v)
-            else (False, k, v)
+            else (k, v, None, None, None)
             for k, v in self._variables.items()
         ]
-        args = (
-            info,
+        construct_direct_args = (
             self._coord_names,
             self._dims,
             self._attrs,
             self._indexes,
             self._encoding,
             self._close,
         )
-        return self._dask_postpersist, args
+        return self._dask_postpersist, (info, construct_direct_args)
 
     @staticmethod
-    def _dask_postcompute(results, info, *args):
+    def _dask_postcompute(results, info, construct_direct_args):
         variables = {}
-        results2 = list(results[::-1])
-        for is_dask, k, v in info:
-            if is_dask:
-                func, args2 = v
-                r = results2.pop()
-                result = func(r, *args2)
+        results_iter = iter(results)
+        for k, v, rebuild, rebuild_args in info:
+            if v is None:
+                variables[k] = rebuild(next(results_iter), *rebuild_args)
             else:
-                result = v
-            variables[k] = result
+                variables[k] = v
 
-        final = Dataset._construct_direct(variables, *args)
+        final = Dataset._construct_direct(variables, *construct_direct_args)
         return final
 
     @staticmethod
-    def _dask_postpersist(dsk, info, *args):
+    def _dask_postpersist(dsk, info, construct_direct_args):
+        from dask.optimization import cull
+
         variables = {}
         # postpersist is called in both dask.optimize and dask.persist
         # When persisting, we want to filter out unrelated keys for
         # each Variable's task graph.
-        is_persist = len(dsk) == len(info)
-        for is_dask, k, v in info:
-            if is_dask:
-                func, args2 = v
-                if is_persist:
-                    name = args2[1][0]
-                    dsk2 = {k: v for k, v in dsk.items() if k[0] == name}
-                else:
-                    dsk2 = dsk
-                result = func(dsk2, *args2)
+        for k, v, dask_keys, rebuild, rebuild_args in info:
+            if v is None:
+                dsk2, _ = cull(dsk, dask_keys)
+                variables[k] = rebuild(dsk2, *rebuild_args)
             else:
-                result = v
-            variables[k] = result
+                variables[k] = v
 
-        return Dataset._construct_direct(variables, *args)
+        return Dataset._construct_direct(variables, *construct_direct_args)
 
     def compute(self, **kwargs) -> "Dataset":
         """Manually trigger loading and/or computation of this dataset's data