Using `with_outputs` and `with_output_types` in Apache Beam (Python SDK)

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What will you learn?

In this tutorial, you will learn how to effectively utilize with_outputs and with_output_types features in Apache Beam using Python SDK. These features allow for directing multiple output elements and specifying data types explicitly within your Apache Beam pipeline.

Introduction to the Problem and Solution

In Apache Beam, when processing a single input element that results in multiple output elements, the with_outputs feature comes into play. This feature enables the segregation of different outputs from processing logic into separate PCollections for further processing.

Moreover, with the use of with_output_types, you can explicitly define the data types of the outputs. This practice promotes type safety and contributes to maintaining a structured code base within your Apache Beam pipeline.

Code

import apache_beam as beam

class MyDoFn(beam.DoFn):
    def process(self, element):
        if condition:
            yield beam.pvalue.TaggedOutput('output_tag', output_data)
        else:
            yield output_data

# Usage in Pipeline
output_collection = my_input | beam.ParDo(MyDoFn()).with_outputs('output_tag', main='main_collection', 'int')

# Define Data Types for Output Collections
typed_collections = output_collection | 'Type Cast' >> beam.TypeOptions(int)

# Copyright PHD

Explanation: – A custom DoFn class is created with conditional logic to emit outputs through either the main collection or a tagged collection. – The .with_outputs() method directs different outputs based on provided conditions. – By specifying ‘int’, it ensures that the tagged collection contains elements of integer data type.

Explanation

The solution involves creating a custom ParDo function where elements are directed into different collections using tags specified with TaggedOutput. These tags are then used with .with_outputs() method to manage multiple types of outputs within an Apache Beam pipeline. Additionally, by utilizing TypeOptions, specific data types are enforced for PCollection elements.

    How does with_outputs differ from regular ParDo operations?

    With regular ParDos, all elements are emitted into one main PCollection. However, with with_outputs, specific elements can be directed into separate PCollections based on conditions specified within our DoFn implementation.

    Can I have multiple tagged collections using TaggedOutput?

    Yes, multiple tagged collections can be created based on requirements. Each tag represents a distinct PCollection where specific elements satisfying certain conditions are directed.

    Is specifying output types necessary with every tag created?

    While not mandatory, specifying output types for each tag using TaggedOutput is beneficial for ensuring type consistency across your pipeline and avoiding potential errors related to data type mismatches during execution.

    How do I access these tagged collections later in my pipeline?

    Tagged collections can be accessed by referencing their respective tags when needed downstream in pipeline transformations or analysis tasks.

    Can I apply additional transformations directly on these tagged PCollections?

    Yes, transformations such as mapping functions or filters can be applied directly on these tagged PCollections generated through with_outputs operation within your pipeline.

    What happens if no tag matches during processing inside my DoFn class?

    If none of the conditions match resulting in emitting an element via TaggedOutput method while executing .process() function; those elements typically flow out through the default main collection defined alongside tags during .add_tags() call without any tagging information associated with them.

    Conclusion

    Mastering with_ouputs and with_output_types functionalities is essential for designing intricate pipelines in Apache Beam Python SDK. Efficiently directing outputs into distinct PCollections and enforcing explicit data types enhances maintainability and readability of your codebase.

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