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https://issues.apache.org/jira/browse/BEAM-11881?focusedWorklogId=564154&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-564154
]
ASF GitHub Bot logged work on BEAM-11881:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 10/Mar/21 23:36
Start Date: 10/Mar/21 23:36
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on a change in pull request
#14135:
URL: https://github.com/apache/beam/pull/14135#discussion_r591956347
##########
File path: sdks/python/apache_beam/dataframe/transforms.py
##########
@@ -282,30 +282,38 @@ def __repr__(self, indent=0):
self.outputs))
# First define some helper functions.
- def output_is_partitioned_by(expr, stage, partitioning):
- if partitioning == partitionings.Nothing():
- # Always satisfied.
- return True
- elif stage.partitioning == partitionings.Singleton():
- # Within a stage, the singleton partitioning is trivially preserved.
- return True
- elif expr in stage.inputs:
+ def output_partitioning_in_stage(expr, stage):
+ """ Return the output partitioning of expr when computed in stage,
+ or returns None if the expression cannot be computed in this stage.
+ """
+ if expr in stage.inputs or expr in inputs:
# Inputs are all partitioned by stage.partitioning.
- return stage.partitioning.is_subpartitioning_of(partitioning)
- elif expr.preserves_partition_by().is_subpartitioning_of(partitioning):
- # Here expr preserves at least the requested partitioning; its outputs
- # will also have this partitioning iff its inputs do.
- if expr.requires_partition_by().is_subpartitioning_of(partitioning):
- # If expr requires at least this partitioning, we will arrange such
- # that its inputs satisfy this.
- return True
- else:
- # Otherwise, recursively check all the inputs.
- return all(
- output_is_partitioned_by(arg, stage, partitioning)
- for arg in expr.args())
- else:
- return False
+ return stage.partitioning
+
+ # Anything that's not an input must have arguments
+ assert len(expr.args())
+
+ arg_partitionings = set(
+ output_partitioning_in_stage(arg, stage) for arg in expr.args()
+ if not is_scalar(arg))
+
+ # TODO: what does it mean for all args to be 0? only scalar arguments?
the
Review comment:
No we didn't run into this case. I think this makes sense based on our
discussion yesterday - there's currently no way to create an expression that
operates on only scalar inputs. The only thing you can do with a
`DeferredScalar` is use it in arithmetic with a `DeferredFrame`. If/when we
support arithmetic on DeferredScalars I think we'd hit this. Probably we should
just return expr.preserves_partition_by() in this case.
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Issue Time Tracking
-------------------
Worklog Id: (was: 564154)
Time Spent: 3h 10m (was: 3h)
> DataFrame subpartitioning order is incorrect
> --------------------------------------------
>
> Key: BEAM-11881
> URL: https://issues.apache.org/jira/browse/BEAM-11881
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-core
> Reporter: Brian Hulette
> Priority: P2
> Labels: dataframe-api
> Time Spent: 3h 10m
> Remaining Estimate: 0h
>
> Currently we've defined
> Nothing() < Index([i]) < Index([i,j]) < .. < Index() < Singleton()
> s.t. Singleton is a subpartitoning of Index, is a subpartitioning of
> Index([i,j]), but this is incorrect. The order should be
> Singleton() < Index([i]) < Index([i,j]) < .. < Index() < Nothing()
> s.t. every other partitioning is a subpartitioning of Singleton. This is
> logical, since Singleton will collect the largest amount of data on a single
> node, partitioning by a single index will be alittle more distributed, and
> partitioning by the full Index() will be the most distribtued.
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