CombineGlobally

Pydoc Pydoc




Combines all elements in a collection.

See more information in the Beam Programming Guide.

Examples

In the following examples, we create a pipeline with a PCollection of produce. Then, we apply CombineGlobally in multiple ways to combine all the elements in the PCollection.

CombineGlobally accepts a function that takes an iterable of elements as an input, and combines them to return a single element.

Example 1: Combining with a function

We define a function get_common_items which takes an iterable of sets as an input, and calculates the intersection (common items) of those sets.

import apache_beam as beam

def get_common_items(sets):
  # set.intersection() takes multiple sets as separete arguments.
  # We unpack the `sets` list into multiple arguments with the * operator.
  # The combine transform might give us an empty list of `sets`,
  # so we use a list with an empty set as a default value.
  return set.intersection(*(sets or [set()]))

with beam.Pipeline() as pipeline:
  common_items = (
      pipeline
      | 'Create produce' >> beam.Create([
          {'🍓', '🥕', '🍌', '🍅', '🌶️'},
          {'🍇', '🥕', '🥝', '🍅', '🥔'},
          {'🍉', '🥕', '🍆', '🍅', '🍍'},
          {'🥑', '🥕', '🌽', '🍅', '🥥'},
      ])
      | 'Get common items' >> beam.CombineGlobally(get_common_items)
      | beam.Map(print))

Output:

{'🍅', '🥕'}

Example 2: Combining with a lambda function

We can also use lambda functions to simplify Example 1.

import apache_beam as beam

with beam.Pipeline() as pipeline:
  common_items = (
      pipeline
      | 'Create produce' >> beam.Create([
          {'🍓', '🥕', '🍌', '🍅', '🌶️'},
          {'🍇', '🥕', '🥝', '🍅', '🥔'},
          {'🍉', '🥕', '🍆', '🍅', '🍍'},
          {'🥑', '🥕', '🌽', '🍅', '🥥'},
      ])
      | 'Get common items' >>
      beam.CombineGlobally(lambda sets: set.intersection(*(sets or [set()])))
      | beam.Map(print))

Output:

{'🍅', '🥕'}

Example 3: Combining with multiple arguments

You can pass functions with multiple arguments to CombineGlobally. They are passed as additional positional arguments or keyword arguments to the function.

In this example, the lambda function takes sets and exclude as arguments.

import apache_beam as beam

with beam.Pipeline() as pipeline:
  common_items_with_exceptions = (
      pipeline
      | 'Create produce' >> beam.Create([
          {'🍓', '🥕', '🍌', '🍅', '🌶️'},
          {'🍇', '🥕', '🥝', '🍅', '🥔'},
          {'🍉', '🥕', '🍆', '🍅', '🍍'},
          {'🥑', '🥕', '🌽', '🍅', '🥥'},
      ])
      | 'Get common items with exceptions' >> beam.CombineGlobally(
          lambda sets, exclude: \
              set.intersection(*(sets or [set()])) - exclude,
          exclude={'🥕'})
      | beam.Map(print)
  )

Output:

{'🍅'}

Example 4: Combining with a CombineFn

The more general way to combine elements, and the most flexible, is with a class that inherits from CombineFn.

import apache_beam as beam

class PercentagesFn(beam.CombineFn):
  def create_accumulator(self):
    return {}

  def add_input(self, accumulator, input):
    # accumulator == {}
    # input == '🥕'
    if input not in accumulator:
      accumulator[input] = 0  # {'🥕': 0}
    accumulator[input] += 1  # {'🥕': 1}
    return accumulator

  def merge_accumulators(self, accumulators):
    # accumulators == [
    #     {'🥕': 1, '🍅': 2},
    #     {'🥕': 1, '🍅': 1, '🍆': 1},
    #     {'🥕': 1, '🍅': 3},
    # ]
    merged = {}
    for accum in accumulators:
      for item, count in accum.items():
        if item not in merged:
          merged[item] = 0
        merged[item] += count
    # merged == {'🥕': 3, '🍅': 6, '🍆': 1}
    return merged

  def extract_output(self, accumulator):
    # accumulator == {'🥕': 3, '🍅': 6, '🍆': 1}
    total = sum(accumulator.values())  # 10
    percentages = {item: count / total for item, count in accumulator.items()}
    # percentages == {'🥕': 0.3, '🍅': 0.6, '🍆': 0.1}
    return percentages

with beam.Pipeline() as pipeline:
  percentages = (
      pipeline
      | 'Create produce' >> beam.Create(
          ['🥕', '🍅', '🍅', '🥕', '🍆', '🍅', '🍅', '🍅', '🥕', '🍅'])
      | 'Get percentages' >> beam.CombineGlobally(PercentagesFn())
      | beam.Map(print))

Output:

{'🥕': 0.3, '🍅': 0.6, '🍆': 0.1}

You can use the following combiner transforms:

Pydoc Pydoc