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https://issues.apache.org/jira/browse/BEAM-10036?focusedWorklogId=439798&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-439798
]
ASF GitHub Bot logged work on BEAM-10036:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 01/Jun/20 23:34
Start Date: 01/Jun/20 23:34
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on a change in pull request
#11766:
URL: https://github.com/apache/beam/pull/11766#discussion_r433537957
##########
File path: sdks/python/apache_beam/dataframe/partitionings.py
##########
@@ -0,0 +1,133 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+from typing import Any
+from typing import Iterable
+from typing import TypeVar
+
+import pandas as pd
+
+Frame = TypeVar('Frame', bound=pd.core.generic.NDFrame)
+
+
+class Partitioning(object):
+ """A class representing a (consistent) partitioning of dataframe objects.
+ """
+ def is_subpartition_of(self, other):
+ # type: (Partitioning) -> bool
+
+ """Returns whether self is a sub-partition of other.
+
+ Specifically, returns whether something partitioned by self is necissarily
+ also partitioned by other.
+ """
+ raise NotImplementedError
+
+ def partition_fn(self, df):
+ # type: (Frame) -> Iterable[Tuple[Any, Frame]]
+
+ """A callable that actually performs the partitioning of a Frame df.
+
+ This will be invoked via a FlatMap in conjunction with a GroupKey to
+ achieve the desired partitioning.
+ """
+ raise NotImplementedError
+
+
+class Index(Partitioning):
+ """A partitioning by index (either fully or partially).
+
+ If the set of "levels" of the index to consider is not specified, the entire
+ index is used.
+
+ These form a partial order, given by
+
+ Nothing() < Index([i]) < Index([i, j]) < ... < Index() < Singleton()
Review comment:
This ordering is determined by `is_subpartition_of` correct? I wonder if
there's a way to clearly say that in this docstring?
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -23,6 +23,7 @@
from apache_beam.dataframe import expressions
from apache_beam.dataframe import frame_base
+from apache_beam.dataframe import frames # pylint: disable=unused-import
Review comment:
What is this for?
##########
File path: sdks/python/apache_beam/dataframe/partitionings.py
##########
@@ -0,0 +1,133 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+from typing import Any
+from typing import Iterable
+from typing import TypeVar
+
+import pandas as pd
+
+Frame = TypeVar('Frame', bound=pd.core.generic.NDFrame)
+
+
+class Partitioning(object):
+ """A class representing a (consistent) partitioning of dataframe objects.
+ """
+ def is_subpartition_of(self, other):
+ # type: (Partitioning) -> bool
+
+ """Returns whether self is a sub-partition of other.
+
+ Specifically, returns whether something partitioned by self is necissarily
+ also partitioned by other.
+ """
+ raise NotImplementedError
+
+ def partition_fn(self, df):
+ # type: (Frame) -> Iterable[Tuple[Any, Frame]]
+
+ """A callable that actually performs the partitioning of a Frame df.
+
+ This will be invoked via a FlatMap in conjunction with a GroupKey to
+ achieve the desired partitioning.
+ """
+ raise NotImplementedError
+
+
+class Index(Partitioning):
+ """A partitioning by index (either fully or partially).
+
+ If the set of "levels" of the index to consider is not specified, the entire
+ index is used.
+
+ These form a partial order, given by
+
+ Nothing() < Index([i]) < Index([i, j]) < ... < Index() < Singleton()
+ """
+
+ _INDEX_PARTITIONS = 100
Review comment:
Previously this was 10 right (in `partitioned_by_index`)? Assuming this
intentional, but I just wanted to double-check its not a typo.
##########
File path: sdks/python/apache_beam/dataframe/partitionings.py
##########
@@ -0,0 +1,133 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+from typing import Any
+from typing import Iterable
+from typing import TypeVar
+
+import pandas as pd
+
+Frame = TypeVar('Frame', bound=pd.core.generic.NDFrame)
+
+
+class Partitioning(object):
+ """A class representing a (consistent) partitioning of dataframe objects.
+ """
+ def is_subpartition_of(self, other):
Review comment:
nit: I think I'd prefer `is_subpartitioning_of`
##########
File path: sdks/python/apache_beam/dataframe/expressions.py
##########
@@ -85,16 +87,10 @@ def evaluate_at(self, session): # type: (Session) -> T
"""Returns the result of self with the bindings given in session."""
raise NotImplementedError(type(self))
- def requires_partition_by_index(self): # type: () -> bool
- """Whether this expression requires its argument(s) to be partitioned
- by index."""
- # TODO: It might be necessary to support partitioning by part of the index,
- # for some args, which would require returning more than a boolean here.
+ def requires_partition_by(self): # type: () -> Partitioning
raise NotImplementedError(type(self))
- def preserves_partition_by_index(self): # type: () -> bool
- """Whether the result of this expression will be partitioned by index
- whenever all of its inputs are partitioned by index."""
+ def preserves_partition_by(self): # type: () -> Partitioning
Review comment:
The meaning of this function is a little confusing now since it implies
some connection to the input partitioning, but it also has it's own
partitioning. Would renaming it to `outputs_..` or `produces_..` still be
accurate, or is the output partitioning actually a function of both "preserves"
and the input?
I also think we should consider changing `.._partition_by` to
`.._partitioning` for clarity.
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Issue Time Tracking
-------------------
Worklog Id: (was: 439798)
Time Spent: 20m (was: 10m)
> More flexible dataframes partitioning.
> --------------------------------------
>
> Key: BEAM-10036
> URL: https://issues.apache.org/jira/browse/BEAM-10036
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Robert Bradshaw
> Assignee: Robert Bradshaw
> Priority: P2
> Time Spent: 20m
> Remaining Estimate: 0h
>
> Currently we only track a boolean of whether a dataframe is partitioned by
> the (full) index.
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