Sample

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Transforms for taking samples of the elements in a collection, or samples of the values associated with each key in a collection of key-value pairs.

Examples

In the following example, we create a pipeline with a PCollection. Then, we get a random sample of elements in different ways.

Example 1: Sample elements from a PCollection

We use Sample.FixedSizeGlobally() to get a fixed-size random sample of elements from the entire PCollection.

import apache_beam as beam

with beam.Pipeline() as pipeline:
  sample = (
      pipeline
      | 'Create produce' >> beam.Create([
          '🍓 Strawberry',
          '🥕 Carrot',
          '🍆 Eggplant',
          '🍅 Tomato',
          '🥔 Potato',
      ])
      | 'Sample N elements' >> beam.combiners.Sample.FixedSizeGlobally(3)
      | beam.Map(print))

Output:

['🥕 Carrot', '🍆 Eggplant', '🍅 Tomato']

Example 2: Sample elements for each key

We use Sample.FixedSizePerKey() to get fixed-size random samples for each unique key in a PCollection of key-values.

import apache_beam as beam

with beam.Pipeline() as pipeline:
  samples_per_key = (
      pipeline
      | 'Create produce' >> beam.Create([
          ('spring', '🍓'),
          ('spring', '🥕'),
          ('spring', '🍆'),
          ('spring', '🍅'),
          ('summer', '🥕'),
          ('summer', '🍅'),
          ('summer', '🌽'),
          ('fall', '🥕'),
          ('fall', '🍅'),
          ('winter', '🍆'),
      ])
      | 'Samples per key' >> beam.combiners.Sample.FixedSizePerKey(3)
      | beam.Map(print))

Output:

('spring', ['🍓', '🥕', '🍆'])
('summer', ['🥕', '🍅', '🌽'])
('fall', ['🥕', '🍅'])
('winter', ['🍆'])
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