sdks/python/apache_beam/dataframe/partitionings.py

#
# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.

import random
from typing import Any
from typing import Iterable
from typing import Tuple
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 __repr__(self):
    return self.__class__.__name__

  def is_subpartitioning_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 __lt__(self, other):
    return self != other and self <= other

  def __le__(self, other):
    return not self.is_subpartitioning_of(other)

  def partition_fn(self, df, num_partitions):
    # type: (Frame, int) -> 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

  def test_partition_fn(self, df):
    return self.partition_fn(df, 5)


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

      Singleton() < Index([i]) < Index([i, j]) < ... < Index() < Arbitrary()

  The ordering is implemented via the is_subpartitioning_of method, where the
  examples on the right are subpartitionings of the examples on the left above.
  """
  def __init__(self, levels=None):
    self._levels = levels

  def __repr__(self):
    if self._levels:
      return 'Index%s' % self._levels
    else:
      return 'Index'

  def __eq__(self, other):
    return type(self) == type(other) and self._levels == other._levels

  def __hash__(self):
    if self._levels:
      return hash(tuple(sorted(self._levels)))
    else:
      return hash(type(self))

  def is_subpartitioning_of(self, other):
    if isinstance(other, Singleton):
      return True
    elif isinstance(other, Index):
      if self._levels is None:
        return True
      elif other._levels is None:
        return False
      else:
        return all(level in self._levels for level in other._levels)
    elif isinstance(other, Arbitrary):
      return False
    else:
      raise ValueError(f"Encountered unknown type {other!r}")

  def _hash_index(self, df):
    if self._levels is None:
      levels = list(range(df.index.nlevels))
    else:
      levels = self._levels
    return sum(
        pd.util.hash_array(df.index.get_level_values(level))
        for level in levels)

  def partition_fn(self, df, num_partitions):
    hashes = self._hash_index(df)
    for key in range(num_partitions):
      yield key, df[hashes % num_partitions == key]

  def check(self, dfs):
    # Drop empty DataFrames
    dfs = [df for df in dfs if len(df)]

    if not len(dfs):
      return True

    def apply_consistent_order(dfs):
      # Apply consistent order between dataframes by using sum of the index's
      # hash.
      # Apply consistent order within dataframe with sort_index()
      # Also drops any empty dataframes.
      return sorted((df.sort_index() for df in dfs if len(df)),
                    key=lambda df: sum(self._hash_index(df)))

    dfs = apply_consistent_order(dfs)
    repartitioned_dfs = apply_consistent_order(
        df for _, df in self.test_partition_fn(pd.concat(dfs)))

    # Assert that each index is identical
    for df, repartitioned_df in zip(dfs, repartitioned_dfs):
      if not df.index.equals(repartitioned_df.index):
        return False

    return True


class Singleton(Partitioning):
  """A partitioning of all the data into a single partition.
  """
  def __eq__(self, other):
    return type(self) == type(other)

  def __hash__(self):
    return hash(type(self))

  def is_subpartitioning_of(self, other):
    return isinstance(other, Singleton)

  def partition_fn(self, df, num_partitions):
    yield None, df

  def check(self, dfs):
    return len(dfs) <= 1


class Arbitrary(Partitioning):
  """A partitioning imposing no constraints on the actual partitioning.
  """
  def __eq__(self, other):
    return type(self) == type(other)

  def __hash__(self):
    return hash(type(self))

  def is_subpartitioning_of(self, other):
    return True

  def test_partition_fn(self, df):
    num_partitions = 10

    def shuffled(seq):
      seq = list(seq)
      random.shuffle(seq)
      return seq

    # pylint: disable=range-builtin-not-iterating
    part = pd.Series(shuffled(range(len(df))), index=df.index) % num_partitions
    for k in range(num_partitions):
      yield k, df[part == k]

  def check(self, dfs):
    return True