Apache Beam & Dataflow
PCollections, transforms (ParDo, GroupByKey), windowing, triggers, watermarks, Dataflow runner, autoscaling, templates
1What is a PCollection in Apache Beam?
What is a PCollection in Apache Beam?
Answer
A PCollection is the primary data abstraction in Apache Beam. It represents a distributed, potentially unbounded dataset that can be processed in parallel. Unlike regular collections, a PCollection is immutable, meaning each transform creates a new PCollection rather than modifying the original.
2What is the main difference between a bounded and unbounded PCollection?
What is the main difference between a bounded and unbounded PCollection?
Answer
A bounded PCollection has a finite, known size (like a file or table), while an unbounded one represents a potentially infinite data stream (like streaming events). This distinction affects how Beam processes data: bounded uses classic batch processing, while unbounded requires windowing and triggers to handle the continuous flow.
3What is the role of the ParDo transform in Apache Beam?
What is the role of the ParDo transform in Apache Beam?
Answer
ParDo (Parallel Do) is the most flexible transform in Apache Beam. It applies a user-defined function (DoFn) to each element of a PCollection in parallel. ParDo can produce zero, one, or multiple output elements for each input element, making it suitable for filtering, mapping, and flat-mapping.
How to use side inputs in a ParDo transform?
What is the difference between GroupByKey and CoGroupByKey in Apache Beam?
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