ParDo
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Cheatsheet: ParDo
Recommended reading: 3 mins
Core Description
Master the fundamental transform for general-purpose parallel data processing.
beam.ParDo()
returns: PCollectionPurpose
Applies a user-defined DoFn processing class concurrently across all PCollection elements.
Syntax Signature
beam.ParDo(dofn, *args, **kwargs)Usage Example
import apache_beam as beam
class MultiplyFn(beam.DoFn):
def process(self, element):
yield element * 10
with beam.Pipeline() as p:
(p
| beam.Create([1, 2])
| beam.ParDo(MultiplyFn())
| beam.Map(print))Expected Stdout / Output
10
20Time Complexity
O(N) parallel processing across worker threads
Used In
Filtering data, structural formatting, side input lookup, and schema evolution.
Related Methods
beam.Map(), beam.Filter(), beam.FlatMap()
Common Pitfall
Forgetting that process() must yield or return iterable elements.
Remember:
Keep the processing class stateless unless you explicitly utilize stateful processing APIs.
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