Source code for apache_beam.dataframe.partitionings

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from __future__ import absolute_import

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)


[docs]class Partitioning(object): """A class representing a (consistent) partitioning of dataframe objects. """
[docs] 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
[docs] 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
[docs] def test_partition_fn(self, df): return self.partition_fn(df)
[docs]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() 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. """ _INDEX_PARTITIONS = 10 def __init__(self, levels=None): self._levels = levels def __eq__(self, other): return type(self) == type(other) and self._levels == other._levels def __ne__(self, other): return not self == other def __hash__(self): if self._levels: return hash(tuple(sorted(self._levels))) else: return hash(type(self))
[docs] def is_subpartitioning_of(self, other): if isinstance(other, Nothing): 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 other._levels for level in self._levels) else: return False
[docs] def partition_fn(self, df): if self._levels is None: levels = list(range(df.index.nlevels)) else: levels = self._levels hashes = sum( pd.util.hash_array(df.index.get_level_values(level)) for level in levels) for key in range(self._INDEX_PARTITIONS): yield key, df[hashes % self._INDEX_PARTITIONS == key]
[docs]class Singleton(Partitioning): """A partitioning of all the data into a single partition. """ def __eq__(self, other): return type(self) == type(other) def __ne__(self, other): return not self == other def __hash__(self): return hash(type(self))
[docs] def is_subpartitioning_of(self, other): return True
[docs] def partition_fn(self, df): yield None, df
[docs]class Nothing(Partitioning): """A partitioning imposing no constraints on the actual partitioning. """ def __eq__(self, other): return type(self) == type(other) def __ne__(self, other): return not self == other def __hash__(self): return hash(type(self))
[docs] def is_subpartitioning_of(self, other): return isinstance(other, Nothing)
[docs] def test_partition_fn(self, df): num_partitions = max(df.size, 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]