Deciding Between Side Inputs and Constructor Arguments for Static DoFn Parameters

What will you learn?

In this comprehensive guide, you will delve into the best practices for managing static parameters within Apache Beam DoFns using Python. By exploring the distinctions between side inputs and constructor arguments, you will gain insights into when to appropriately utilize each approach.

Introduction to the Problem and Solution

When developing Apache Beam pipelines in Python, it’s common to encounter scenarios where DoFn operations necessitate additional static or predetermined parameters. This leads us to a pivotal question: Should these static parameters be passed as side inputs or defined through constructor arguments?

To tackle this challenge effectively, we will dissect both methodologies�side inputs and constructor arguments�shedding light on their advantages and suitable implementation scenarios. By grasping the intricacies of each technique, you can make well-informed decisions that elevate your pipeline’s readability, maintainability, and overall performance.


# Using Constructor Argument
class MyDoFn(beam.DoFn):
    def __init__(self, static_param):
        self.static_param = static_param

    def process(self, element):
        # Incorporate self.static_param in your processing logic

# Implementation:
static_value = 'some_static_value'
pipeline | beam.ParDo(MyDoFn(static_value))

# Using Side Input
class MySideInputDoFn(beam.DoFn):
    def process(self, element, static_param):
        # Directly leverage static_param in your processing logic

# Implementation:
static_value = 'some_static_value'
pipeline | beam.ParDo(MySideInputDoFn(), static_param=beam.pvalue.AsSingleton(static_value))

# Copyright PHD


The choice between employing constructor arguments or side inputs predominantly hinges on your data’s nature and its intended usage within the DoFn.

  • Constructor Arguments: Suited for scenarios where your parameter remains truly constant�unchanging throughout the pipeline’s lifespan�and when encapsulating your operation is desirable. It facilitates seamless unit testing by enabling instantiation of your DoFn with diverse parameters directly.

  • Side Inputs: Ideal when your parameter may stem from another pipeline segment or exhibit variability across windows or bundles in streaming tasks. While side inputs offer flexibility, excessive reliance on them for genuinely static data can introduce complexity.

Each method serves a distinct purpose based on context. For unvarying values known during pipeline construction and invariant across elements/windows/bundles, constructor arguments offer simplicity and clarity. Conversely, if dynamic adjustments based on other PCollection outputs are essential�even with sporadic changes�side inputs become indispensable.

    What are side inputs?

    Side inputs enable transmitting supplementary data to a ParDo transform�s DoFn, often sourced from another pipeline section.

    When should I use constructor arguments instead of side inputs?

    Opt for constructor arguments for constants that remain fixed during execution without reliance on any other computations within the pipeline.

    Can I use both side inputs and constructor arguments simultaneously?

    Yes! Appropriately combining both methods within a single DoFn is entirely valid based on varying requirements.

    How do I test a DoFN utilizing constructor arguments?

    During unit tests, instantiate your class with specific values akin to initializing any other object in Python.

    Are there performance implications associated with choosing one over the other?

    Primarily no; however, opting for overly intricate patterns unnecessarily can potentially lead to readability challenges rather than performance issues per se.


    Navigating between passing parameters via constructors or through side input hinges on comprehending the stability of those parameters during job execution. For scenarios involving constants known prior to runtime initiation, prioritize simplicity by leveraging constructor arguments; reserve side input utilization for dynamic situations necessitating interaction with other dataset components processed by Apache Beam.

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