PCollection
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Cheatsheet: PCollection
Recommended reading: 3 mins
Core Description
Understand the core data container, its properties, and state representations.
beam.Create()
returns: PCollectionPurpose
Generates a PCollection from an in-memory iterable (lists, sets, dictionary lists).
Syntax Signature
beam.Create(iterable)Usage Example
import apache_beam as beam
with beam.Pipeline() as p:
elements = p | "Create Elements" >> beam.Create([10, 20, 30])
elements | beam.Map(print)Expected Stdout / Output
10
20
30Time Complexity
O(N) where N is the number of elements in the iterable
Used In
Testing, loading configurations, and quick sandbox runs.
Related Methods
ReadFromText(), ReadFromPubSub()
Remember:
Avoid creating very large PCollections using beam.Create since the entire list is held in driver memory.
PCollection Attributes
returns: Data RepresentationPurpose
Understanding core immutability, distributed elements, and schemas.
Syntax Signature
PCollection Characteristics (Immutable, Distributed, Bounded/Unbounded)Usage Example
import apache_beam as beam
# PCollections are immutable; transforms return new collections
inputs = p | beam.Create([1, 2, 3, 4])
evens = inputs | "FilterEvens" >> beam.Filter(lambda x: x % 2 == 0)Used In
Architecting streaming or batch pipelines.
Common Pitfall
Attempting to modify elements in-place inside user code processes.
Remember:
PCollections do not support index lookups or random access. All access must flow through transforms.
More Free Data Engineering Cheatsheets (DataPlayArena)Interactive syntax references