TheNeuralBit commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms URL: https://github.com/apache/beam/pull/10760#discussion_r394630321
########## File path: sdks/python/apache_beam/dataframe/transforms.py ########## @@ -0,0 +1,244 @@ +# +# 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 + +import pandas as pd + +import apache_beam as beam +from apache_beam import transforms +from apache_beam.dataframe import expressions +from apache_beam.dataframe import frame_base + + +class DataframeTransform(transforms.PTransform): + """A PTransform for applying function that takes and returns dataframes + to one or more PCollections. + + For example, if pcoll is a PCollection of dataframes, one could write:: + + pcoll | DataframeTransform(lambda df: df.group_by('key').sum()) + + To pass multiple PCollections, pass a tuple of PCollections wich will be + passed to the callable as positional arguments, or a dictionary of + PCollections, in which case they will be passed as keyword arguments. + """ + def __init__(self, func, proxy): + self._func = func + self._proxy = proxy + + def expand(self, input_pcolls): + def wrap_as_dict(values): + if isinstance(values, dict): + return dict + elif isinstance(values, tuple): + return dict(enumerate(values)) + else: + return {None: values} + + # TODO: Infer the proxy from the input schema. + def proxy(key): + if key is None: + return self._proxy + else: + return self._proxy[key] + + # The input can be a dictionary, tuple, or plain PCollection. + # Wrap as a dict for homogeneity. + # TODO: Possibly inject batching here. + input_dict = wrap_as_dict(input_pcolls) + placeholders = { + key: frame_base.DeferredFrame.wrap( + expressions.PlaceholderExpression(proxy(key))) + for key in input_dict.keys() + } + + # The calling convention of the user-supplied func varies according to the + # type of the input. + if isinstance(input_pcolls, dict): + result_frames = self._func(**placeholders) + elif isinstance(input_pcolls, tuple): + self._func(*(value for _, value in sorted(placeholders.items()))) + else: + result_frames = self._func(placeholders[None]) + + # Likewise the output may be a dict, tuple, or raw (deferred) Dataframe. + result_dict = wrap_as_dict(result_frames) + + result_pcolls = self._apply_deferred_ops( + {placeholders[key]._expr: pcoll + for key, pcoll in input_dict.items()}, + {key: df._expr + for key, df in result_dict.items()}) + + # Convert the result back into a set of PCollections. + if isinstance(result_frames, dict): + return result_pcolls + elif isinstance(result_frames, tuple): + return tuple(*(value for _, value in sorted(result_pcolls.items()))) + else: + return result_pcolls[None] + + def _apply_deferred_ops( + self, + inputs, # type: Dict[PlaceholderExpr, PCollection] + outputs, # type: Dict[Any, Expression] + ): # -> Dict[Any, PCollection] + """Construct a Beam graph that evaluates a set of expressions on a set of + input PCollections. + + :param inputs: A mapping of placeholder expressions to PCollections. + :param outputs: A mapping of keys to expressions defined in terms of the + placeholders of inputs. + + Returns a dictionary whose keys are those of outputs, and whose values are + PCollections corresponding to the values of outputs evaluated at the + values of inputs. + + Logically, `_apply_deferred_ops({x: a, y: b}, {f: F(x, y), g: G(x, y)})` + returns `{f: F(a, b), g: G(a, b)}`. + """ + class ComputeStage(beam.PTransform): + """A helper transform that computes a single stage of operations. + """ + def __init__(self, stage): + self.stage = stage + + def default_label(self): + return '%s:%s' % (self.stage.ops, id(self)) + + def expand(self, pcolls): + if self.stage.is_grouping: + # Arrange such that partitioned_pcoll is properly partitioned. + input_pcolls = { + k: pcoll | 'Flat%s' % k >> beam.FlatMap(_partition_by_index) + for k, + pcoll in pcolls.items() + } + partitioned_pcoll = input_pcolls | beam.CoGroupByKey( + ) | beam.MapTuple( + lambda _, inputs: {k: pd.concat(vs) + for k, vs in inputs.items()}) + else: + # Already partitioned, or no partitioning needed. + (k, pcoll), = pcolls.items() + partitioned_pcoll = pcoll | beam.Map(lambda df: {k: df}) + + # Actually evaluate the expressions. + def evaluate(partition, stage=self.stage): + session = expressions.Session( + {expr: partition[expr._id] + for expr in stage.inputs}) + for expr in stage.outputs: + yield beam.pvalue.TaggedOutput(expr._id, expr.evaluate_at(session)) + + return partitioned_pcoll | beam.FlatMap(evaluate).with_outputs() + + class Stage(object): + """Used to build up a set of operations that can be fused together. + """ + def __init__(self, inputs, is_grouping): + self.inputs = set(inputs) + self.is_grouping = is_grouping or len(self.inputs) > 1 + self.ops = [] + self.outputs = set() + + # First define some helper functions. + def output_is_partitioned_by_index(expr, stage): + if expr in stage.inputs: + return stage.is_grouping + elif expr.preserves_partition_by_index(): + if expr.requires_partition_by_index(): + return True + else: + return all( + output_is_partitioned_by_index(arg, stage) for arg in expr.args()) + else: + return False + + def _partition_by_index(df, levels=None, parts=10): Review comment: nit: none of the other functions defined here have an underscore prefix, can you drop this one? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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